From 43b06411a611051539e89216c4cb65a02f7dbab8 Mon Sep 17 00:00:00 2001 From: FinnRobertson15 <110511080+FinnRobertson15@users.noreply.github.com> Date: Mon, 16 Oct 2023 11:45:48 +1000 Subject: [PATCH 01/14] Created a google Colab Version --- Colab version.ipynb | 5303 +++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 5303 insertions(+) create mode 100644 Colab version.ipynb diff --git a/Colab version.ipynb b/Colab version.ipynb new file mode 100644 index 000000000..a914e70c9 --- /dev/null +++ b/Colab version.ipynb @@ -0,0 +1,5303 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "authorship_tag": "ABX9TyOHMcV3sgfpuOe9f8kAGUP9", + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "code", + "source": [ + "from google.colab import drive\n", + "drive.mount('/content/drive')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "yzCmT9TUJY10", + "outputId": "e25f99c9-66c9-41f5-df60-391e55ddcea4" + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Mounted at /content/drive\n" + ] + } + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "-Lb7CGdEJCQg", + "outputId": "a4c11d2b-0675-4e0b-e73a-86a07cacc29b" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[Errno 2] No such file or directory: '/content/drive/MyDrive/Root'\n", + "/content/drive/MyDrive\n" + ] + } + ], + "source": [ + "%cd /content/drive/MyDrive" + ] + }, + { + "cell_type": "code", + "source": [ + "!unzip ADNI_AD_NC_2D.zip" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_eAax1mMJpyw", + "outputId": "5e752810-d2dd-41bf-fbee-c95550ec0384" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n", + " inflating: AD_NC/test/AD/413796_78.jpeg \n", + " inflating: AD_NC/test/AD/413796_79.jpeg \n", + " inflating: AD_NC/test/AD/413796_80.jpeg \n", + " inflating: AD_NC/test/AD/413796_81.jpeg \n", + " inflating: AD_NC/test/AD/413796_82.jpeg \n", + " inflating: AD_NC/test/AD/413796_83.jpeg \n", + " inflating: AD_NC/test/AD/413796_84.jpeg \n", + " inflating: AD_NC/test/AD/413796_85.jpeg \n", + " inflating: AD_NC/test/AD/413796_86.jpeg \n", + " inflating: AD_NC/test/AD/413796_87.jpeg \n", + " inflating: AD_NC/test/AD/413796_88.jpeg \n", + " inflating: AD_NC/test/AD/413796_89.jpeg \n", + " inflating: AD_NC/test/AD/413796_90.jpeg \n", + " inflating: AD_NC/test/AD/413796_91.jpeg \n", + " inflating: 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"import time\n", + "import numpy as np\n", + "# import pandas as pd\n", + "import math\n", + "import os\n", + "from PIL import Image\n", + "import numpy as np\n", + "from torch.utils.data import Dataset, DataLoader\n", + "from sklearn.utils import shuffle\n", + "import torch\n", + "from torch.utils.data import DataLoader\n", + "from torchvision.datasets import ImageFolder\n", + "import os\n", + "from PIL import Image\n", + "import numpy as np\n", + "from torch.utils.data import Dataset, DataLoader\n", + "from torch.utils.data import ConcatDataset" + ], + "metadata": { + "id": "_8PBaJLSJSCP" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", + "\n", + "\n", + "class CustomDataset(Dataset):\n", + " def __init__(self, root_dir, transform=None):\n", + " self.root_dir = root_dir\n", + " self.transform = transform\n", + " self.image_paths = os.listdir(root_dir)\n", + "\n", + " def __len__(self):\n", + " return len(self.image_paths)\n", + "\n", + " def __getitem__(self, idx):\n", + " img_name = os.path.join(self.root_dir, self.image_paths[idx])\n", + " image = Image.open(img_name)\n", + "\n", + " if self.transform:\n", + " image = self.transform(image)\n", + "\n", + " return image\n", + "\n", + "class CustomConcatDataset(ConcatDataset):\n", + " def __init__(self, datasets, labels):\n", + " super(CustomConcatDataset, self).__init__(datasets)\n", + " self.labels = labels\n", + "\n", + " def __getitem__(self, index):\n", + " item = super(CustomConcatDataset, self).__getitem__(index)\n", + " return item, self.labels[index]\n", + "\n", + "def combine(AD, NC):\n", + " # Set labels for the AD dataset\n", + " labels_AD = [True] * len(AD)\n", + " # Set labels for the NC dataset\n", + " labels_NC = [False] * len(NC)\n", + "\n", + " # Combine the datasets and labels\n", + " X = AD + NC\n", + " Y = labels_AD + labels_NC\n", + " # X = torch.utils.data.RandomSampler(X)\n", + " seed = 123\n", + " torch.manual_seed(seed)\n", + " np.random.seed(seed)\n", + "\n", + "\n", + " X_loader = DataLoader(X, batch_size=128, shuffle=True)\n", + " Y_loader = DataLoader(Y, batch_size=128, shuffle=True)\n", + " return X_loader, Y_loader\n", + "\n", + "size = 256\n", + "\n", + "transform_X = transforms.Compose([\n", + " transforms.Resize((size, size)), # Resize the image to the desired size\n", + " transforms.ToTensor(), # Convert the image to a tensor\n", + " transforms.Lambda(lambda x: x / 255.0),\n", + " transforms.Lambda(lambda x: (x - x.mean()) / x.std()) # Subtract mean and divide by standard deviation\n", + "])\n", + "\n", + "\n", + "\n", + "# Replace 'your_nii_folder' with the path to your folder containing .nii files\n", + "# nii_dataset = NiiDataset(root_dir=r'keras_png_slices_data\\keras_png_slices_data\\keras_png_slices_train')\n", + "loaders = {}\n", + "\n", + "for stage in ['train', 'test']:\n", + " loaders[stage] = {}\n", + " AD = CustomDataset(root_dir=rf'ADNI_AD_NC_2D\\AD_NC\\{stage}\\AD', transform=transform_X)\n", + " NC = CustomDataset(root_dir=rf'ADNI_AD_NC_2D\\AD_NC\\{stage}\\NC', transform=transform_X)\n", + " loaders[stage]['X'], loaders[stage]['Y'] = combine(AD, NC)\n", + "# Y_train = CustomDataset(root_dir=r'ADNI_AD_NC_2D\\AD_NC\\train\\NC', transform=transform_Y)\n", + "# X_train_AD_loader = DataLoader(X_train_AD, batch_size=4, shuffle=True)\n", + "# Y_train_loader = DataLoader(Y_train, batch_size=4, shuffle=True)" + ], + "metadata": { + "id": "7zvKyWj2J7Yk" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "import torch.optim as optim\n", + "\n", + "# Define the model\n", + "class CustomModel(nn.Module):\n", + " def __init__(self):\n", + " super(CustomModel, self).__init__()\n", + " self.conv1 = nn.Conv2d(1, 32, 3, padding=1)\n", + " self.maxpool1 = nn.MaxPool2d(2, 2)\n", + " self.conv2 = nn.Conv2d(32, 32, 3, padding=1)\n", + " self.maxpool2 = nn.MaxPool2d(2, 2)\n", + " self.flatten = nn.Flatten()\n", + " self.fc1 = nn.Linear(32 * 64 * 64, 128)\n", + " self.fc2 = nn.Linear(128, 10)\n", + "\n", + " def forward(self, x):\n", + " x = nn.functional.relu(self.conv1(x))\n", + " x = self.maxpool1(x)\n", + " x = nn.functional.relu(self.conv2(x))\n", + " x = self.maxpool2(x)\n", + " x = self.flatten(x)\n", + " x = nn.functional.relu(self.fc1(x))\n", + " x = nn.functional.softmax(self.fc2(x), dim=1)\n", + " return x\n", + "\n", + "\n", + "\n", + "# Instantiate the model\n", + "model = CustomModel()\n", + "\n" + ], + "metadata": { + "id": "4BuuMeumKHi2" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "criterion = nn.CrossEntropyLoss()\n", + "learning_rate = 0.1\n", + "optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate, momentum=0.9, weight_decay=5e-4)\n", + "total_step = len(loaders['train']['X'])\n", + "sched_linear_1 = torch.optim.lr_scheduler.CyclicLR(optimizer, base_lr=0.005, max_lr=learning_rate, step_size_up=15, step_size_down=15, mode=\"triangular\", verbose=False)\n", + "sched_linear_3 = torch.optim.lr_scheduler.LinearLR(optimizer, start_factor=0.005/learning_rate, end_factor=0.005/learning_rate, verbose=False)\n", + "scheduler = torch.optim.lr_scheduler.SequentialLR(optimizer, schedulers=[sched_linear_1, sched_linear_3], milestones=[30])\n" + ], + "metadata": { + "id": "SztvTT_tKJMA" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "num_epochs = 2\n", + "for epoch in range(num_epochs):\n", + "\n", + " for i, (images, seg) in enumerate(zip(loaders['train']['X'], loaders['train']['Y'])):\n", + " images = images.to(device)\n", + " seg = torch.tensor(seg, dtype=torch.long).to(device)\n", + " outputs = model(images)\n", + "\n", + "\n", + " # Adjust the shapes to match the criterion\n", + " # outputs = outputs.view(-1) # Flatten the outputs to a 1D tensor\n", + " # seg = seg.view(-1) # Flatten the seg tensor to a 1D tensor\n", + " loss = criterion(outputs, seg)\n", + " optimizer.zero_grad()\n", + " loss.backward()\n", + " optimizer.step()\n", + "\n", + " if (i + 1) % 5 == 0:\n", + " print(f\"Epoch [{epoch + 1} / {num_epochs}], Step [{i + 1} / {total_step} Loss {loss.item()}]\")\n", + "\n", + " scheduler.step()\n", + "end = time.time()" + ], + "metadata": { + "id": "xeAsMkrnKKij" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file From 8b28fe8f26c76b81346c41709774cd530a9b6adf Mon Sep 17 00:00:00 2001 From: FinnRobertson15 <110511080+FinnRobertson15@users.noreply.github.com> Date: Mon, 16 Oct 2023 12:28:11 +1000 Subject: [PATCH 02/14] Got images to download in GoogleDrive and added to loader --- Colab version.ipynb | 5309 ++----------------------------------------- 1 file changed, 238 insertions(+), 5071 deletions(-) diff --git a/Colab version.ipynb b/Colab version.ipynb index a914e70c9..80943a64b 100644 --- a/Colab version.ipynb +++ b/Colab version.ipynb @@ -1,20 +1,4 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "provenance": [], - "authorship_tag": "ABX9TyOHMcV3sgfpuOe9f8kAGUP9", - "include_colab_link": true - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - } - }, "cells": [ { "cell_type": "markdown", @@ -28,16 +12,39 @@ }, { "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "yzCmT9TUJY10", + "outputId": "898b4f80-2f63-4047-9790-355985414a28" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Mounted at /content/drive\n" + ] + } + ], "source": [ "from google.colab import drive\n", - "drive.mount('/content/drive')" + "drive.mount(\"/content/drive\", force_remount=True)" + ] + }, + { + "cell_type": "code", + "source": [ + "%ls" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, - "id": "yzCmT9TUJY10", - "outputId": "e25f99c9-66c9-41f5-df60-391e55ddcea4" + "id": "T0cDyMtaR_Hi", + "outputId": "1327687d-e185-450a-b06d-0f8c26680638" }, "execution_count": 2, "outputs": [ @@ -45,5059 +52,64 @@ "output_type": "stream", "name": "stdout", "text": [ - "Mounted at /content/drive\n" + "\u001b[0m\u001b[01;34mdrive\u001b[0m/ \u001b[01;34msample_data\u001b[0m/\n" ] } ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 9, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "-Lb7CGdEJCQg", - "outputId": "a4c11d2b-0675-4e0b-e73a-86a07cacc29b" + "outputId": "aa6b51ec-c531-4cf2-923a-a1dfe784e838" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "[Errno 2] No such file or directory: '/content/drive/MyDrive/Root'\n", - "/content/drive/MyDrive\n" + "/content/drive/MyDrive/AD_NC\n" ] } ], "source": [ - "%cd /content/drive/MyDrive" + "%cd /content/drive/MyDrive/AD_NC" ] }, { "cell_type": "code", - "source": [ - "!unzip ADNI_AD_NC_2D.zip" - ], + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "_eAax1mMJpyw", - "outputId": "5e752810-d2dd-41bf-fbee-c95550ec0384" + "outputId": "daf77648-1909-41f9-c05f-0be751ec7d6d" }, - "execution_count": null, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ - "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n", - " inflating: AD_NC/test/AD/413796_78.jpeg \n", - " inflating: AD_NC/test/AD/413796_79.jpeg \n", - " inflating: AD_NC/test/AD/413796_80.jpeg \n", - " inflating: AD_NC/test/AD/413796_81.jpeg \n", - " inflating: AD_NC/test/AD/413796_82.jpeg \n", - " inflating: AD_NC/test/AD/413796_83.jpeg \n", - " inflating: AD_NC/test/AD/413796_84.jpeg \n", - " inflating: AD_NC/test/AD/413796_85.jpeg \n", - " inflating: AD_NC/test/AD/413796_86.jpeg \n", - " inflating: AD_NC/test/AD/413796_87.jpeg \n", - " inflating: 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or NC.zip.ZIP.\n" ] } + ], + "source": [ + "!unzip NC.zip" ] }, { "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "_8PBaJLSJSCP" + }, + "outputs": [], "source": [ "import torch\n", "import torch.nn as nn\n", @@ -5120,15 +132,15 @@ "import numpy as np\n", "from torch.utils.data import Dataset, DataLoader\n", "from torch.utils.data import ConcatDataset" - ], - "metadata": { - "id": "_8PBaJLSJSCP" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": 14, + "metadata": { + "id": "7zvKyWj2J7Yk" + }, + "outputs": [], "source": [ "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", "\n", @@ -5152,13 +164,13 @@ " return image\n", "\n", "class CustomConcatDataset(ConcatDataset):\n", - " def __init__(self, datasets, labels):\n", + " def __init__(self, datasets):\n", " super(CustomConcatDataset, self).__init__(datasets)\n", - " self.labels = labels\n", "\n", " def __getitem__(self, index):\n", - " item = super(CustomConcatDataset, self).__getitem__(index)\n", - " return item, self.labels[index]\n", + " item1 = super(CustomConcatDataset, self).__getitem__(index)\n", + " item2 = super(CustomConcatDataset, self).__getitem__(index)\n", + " return item1, item2\n", "\n", "def combine(AD, NC):\n", " # Set labels for the AD dataset\n", @@ -5167,7 +179,7 @@ " labels_NC = [False] * len(NC)\n", "\n", " # Combine the datasets and labels\n", - " X = AD + NC\n", + " X = CustomConcatDataset([AD, NC])\n", " Y = labels_AD + labels_NC\n", " # X = torch.utils.data.RandomSampler(X)\n", " seed = 123\n", @@ -5194,23 +206,23 @@ "# nii_dataset = NiiDataset(root_dir=r'keras_png_slices_data\\keras_png_slices_data\\keras_png_slices_train')\n", "loaders = {}\n", "\n", - "for stage in ['train', 'test']:\n", + "for stage in ['test']:\n", " loaders[stage] = {}\n", - " AD = CustomDataset(root_dir=rf'ADNI_AD_NC_2D\\AD_NC\\{stage}\\AD', transform=transform_X)\n", - " NC = CustomDataset(root_dir=rf'ADNI_AD_NC_2D\\AD_NC\\{stage}\\NC', transform=transform_X)\n", + " AD = CustomDataset(root_dir=rf'{stage}/AD', transform=transform_X)\n", + " NC = CustomDataset(root_dir=rf'{stage}/NC', transform=transform_X)\n", " loaders[stage]['X'], loaders[stage]['Y'] = combine(AD, NC)\n", "# Y_train = CustomDataset(root_dir=r'ADNI_AD_NC_2D\\AD_NC\\train\\NC', transform=transform_Y)\n", "# X_train_AD_loader = DataLoader(X_train_AD, batch_size=4, shuffle=True)\n", "# Y_train_loader = DataLoader(Y_train, batch_size=4, shuffle=True)" - ], - "metadata": { - "id": "7zvKyWj2J7Yk" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "4BuuMeumKHi2" + }, + "outputs": [], "source": [ "import torch\n", "import torch.nn as nn\n", @@ -5243,15 +255,15 @@ "# Instantiate the model\n", "model = CustomModel()\n", "\n" - ], - "metadata": { - "id": "4BuuMeumKHi2" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "SztvTT_tKJMA" + }, + "outputs": [], "source": [ "criterion = nn.CrossEntropyLoss()\n", "learning_rate = 0.1\n", @@ -5260,29 +272,173 @@ "sched_linear_1 = torch.optim.lr_scheduler.CyclicLR(optimizer, base_lr=0.005, max_lr=learning_rate, step_size_up=15, step_size_down=15, mode=\"triangular\", verbose=False)\n", "sched_linear_3 = torch.optim.lr_scheduler.LinearLR(optimizer, start_factor=0.005/learning_rate, end_factor=0.005/learning_rate, verbose=False)\n", "scheduler = torch.optim.lr_scheduler.SequentialLR(optimizer, schedulers=[sched_linear_1, sched_linear_3], milestones=[30])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "id": "puuj1CSURmJ9", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "a60e467c-4d63-4b96-d0df-1925c065d0d6" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n", + " warnings.warn(\n", + "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.\n", + " warnings.warn(msg)\n", + "Downloading: \"https://download.pytorch.org/models/resnet18-f37072fd.pth\" to /root/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth\n", + "100%|██████████| 44.7M/44.7M [00:00<00:00, 220MB/s]\n" + ] + } ], + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "import torchvision.models as models\n", + "\n", + "class SiameseNetwork(nn.Module):\n", + " def __init__(self, pretrained=True):\n", + " super(SiameseNetwork, self).__init__()\n", + " self.resnet = models.resnet18(pretrained=pretrained)\n", + " self.linear = nn.Linear(1000, 2) # 1000 is the number of output features from ResNet\n", + "\n", + " def forward(self, x1, x2):\n", + " output1 = self.resnet(x1)\n", + " output2 = self.resnet(x2)\n", + " return self.linear(torch.abs(output1 - output2))\n", + "\n", + "model = SiameseNetwork()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, "metadata": { - "id": "SztvTT_tKJMA" + "id": "ocj6rcu7Rn4-" }, - "execution_count": null, - "outputs": [] + "outputs": [], + "source": [ + "class ContrastiveLoss(torch.nn.Module):\n", + " def __init__(self, margin=2.0):\n", + " super(ContrastiveLoss, self).__init__()\n", + " self.margin = margin\n", + "\n", + " def forward(self, output1, output2, label):\n", + " euclidean_distance = F.pairwise_distance(output1, output2)\n", + " loss_contrastive = torch.mean((1 - label) * torch.pow(euclidean_distance, 2) +\n", + " label * torch.pow(torch.clamp(self.margin - euclidean_distance, min=0.0), 2))\n", + " return loss_contrastive\n" + ] + }, + { + "cell_type": "code", + "source": [ + "import torch.optim as optim\n", + "\n", + "# Set the device\n", + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "\n", + "# Initialize the network, loss function, and optimizer\n", + "siamese_net = SiameseNetwork(pretrained=True).to(device)\n", + "criterion = ContrastiveLoss()\n", + "optimizer = optim.Adam(siamese_net.parameters(), lr=0.0005)\n", + "\n", + "# Training loop\n", + "epochs = 2\n", + "for epoch in range(epochs):\n", + " for i, (images, label) in enumerate(zip(loaders['test']['X'], loaders['test']['Y'])):\n", + " img0, img1 = images\n", + " img0, img1, label = img0.to(device), img1.to(device), label.to(device)\n", + " output1, output2 = model(img0, img1)\n", + "\n", + "\n", + " # Adjust the shapes to match the criterion\n", + " # outputs = outputs.view(-1) # Flatten the outputs to a 1D tensor\n", + " # seg = seg.view(-1) # Flatten the seg tensor to a 1D tensor\n", + " loss = criterion(output1, output2, label)\n", + " optimizer.zero_grad()\n", + " loss.backward()\n", + " optimizer.step()\n", + "\n", + " if (i + 1) % 5 == 0:\n", + " print(f\"Epoch [{epoch + 1} / {num_epochs}], Step [{i + 1} / {total_step} Loss {loss.item()}]\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 391 + }, + "id": "4NnXFUDzSWjp", + "outputId": "38eb6735-7694-49e3-8fca-113482e73490" + }, + "execution_count": 26, + "outputs": [ + { + "output_type": "error", + "ename": "RuntimeError", + "evalue": "ignored", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0mimg0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimages\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0mimg0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimg0\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 463\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_conv_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 464\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 465\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mConv3d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_ConvNd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/conv.py\u001b[0m in \u001b[0;36m_conv_forward\u001b[0;34m(self, input, weight, bias)\u001b[0m\n\u001b[1;32m 457\u001b[0m \u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbias\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstride\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 458\u001b[0m _pair(0), self.dilation, self.groups)\n\u001b[0;32m--> 459\u001b[0;31m return F.conv2d(input, weight, bias, self.stride,\n\u001b[0m\u001b[1;32m 460\u001b[0m self.padding, self.dilation, self.groups)\n\u001b[1;32m 461\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mRuntimeError\u001b[0m: Given groups=1, weight of size [64, 3, 7, 7], expected input[128, 1, 256, 256] to have 3 channels, but got 1 channels instead" + ] + } + ] }, { "cell_type": "code", + "execution_count": 17, + "metadata": { + "id": "xeAsMkrnKKij", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 245 + }, + "outputId": "5dd55b9f-d01e-4627-9e35-ba8f1257048e" + }, + "outputs": [ + { + "output_type": "error", + "ename": "NameError", + "evalue": "ignored", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mimg0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimages\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mimg0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimg0\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m 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dtype=torch.long).to(device)\n", + " for i, (images, label) in enumerate(zip(loaders['test']['X'], loaders['test']['Y'])):\n", + " img0, img1 = images\n", + " img0, img1, label = img0.to(device), img1.to(device), label.to(device)\n", " outputs = model(images)\n", "\n", "\n", " # Adjust the shapes to match the criterion\n", " # outputs = outputs.view(-1) # Flatten the outputs to a 1D tensor\n", " # seg = seg.view(-1) # Flatten the seg tensor to a 1D tensor\n", - " loss = criterion(outputs, seg)\n", + " loss = criterion(outputs, label)\n", " optimizer.zero_grad()\n", " loss.backward()\n", " optimizer.step()\n", @@ -5292,12 +448,23 @@ "\n", " scheduler.step()\n", "end = time.time()" - ], - "metadata": { - "id": "xeAsMkrnKKij" - }, - "execution_count": null, - "outputs": [] + ] + } + ], + "metadata": { + "colab": { + "provenance": [], + "authorship_tag": "ABX9TyPullQ1T6knjpYBrXTmmpBA", + "include_colab_link": true + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" } - ] + }, + "nbformat": 4, + "nbformat_minor": 0 } \ No newline at end of file From 6e899b2a5af05937d78a199586e85ca05f35bbcb Mon Sep 17 00:00:00 2001 From: FinnRobertson15 <110511080+FinnRobertson15@users.noreply.github.com> Date: Mon, 16 Oct 2023 13:18:38 +1000 Subject: [PATCH 03/14] First version of model created, loss is very low but accuracy right now is nan% --- Colab version.ipynb | 322 +++++++++++++++++++------------------------- 1 file changed, 140 insertions(+), 182 deletions(-) diff --git a/Colab version.ipynb b/Colab version.ipynb index 80943a64b..d5769d6c1 100644 --- a/Colab version.ipynb +++ b/Colab version.ipynb @@ -18,7 +18,7 @@ "base_uri": "https://localhost:8080/" }, "id": "yzCmT9TUJY10", - "outputId": "898b4f80-2f63-4047-9790-355985414a28" + "outputId": "997681ae-a0d2-448e-d290-b00a8dc22666" }, "outputs": [ { @@ -36,48 +36,48 @@ }, { "cell_type": "code", - "source": [ - "%ls" - ], + "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, - "id": "T0cDyMtaR_Hi", - "outputId": "1327687d-e185-450a-b06d-0f8c26680638" + "id": "-Lb7CGdEJCQg", + "outputId": "a693cf6b-0d28-474e-ed69-f7192a4700f9" }, - "execution_count": 2, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "\u001b[0m\u001b[01;34mdrive\u001b[0m/ \u001b[01;34msample_data\u001b[0m/\n" + "/content/drive/MyDrive/AD_NC\n" ] } + ], + "source": [ + "%cd /content/drive/MyDrive/AD_NC" ] }, { "cell_type": "code", - "execution_count": 9, + "source": [ + "%ls" + ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, - "id": "-Lb7CGdEJCQg", - "outputId": "aa6b51ec-c531-4cf2-923a-a1dfe784e838" + "id": "mgx67Xe5dsgs", + "outputId": "b7850ff6-2922-444d-ee4f-48498dcaeacf" }, + "execution_count": 3, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "/content/drive/MyDrive/AD_NC\n" + "NC.zip \u001b[0m\u001b[01;34mtest\u001b[0m/ \u001b[01;34mtrain\u001b[0m/\n" ] } - ], - "source": [ - "%cd /content/drive/MyDrive/AD_NC" ] }, { @@ -105,7 +105,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": { "id": "_8PBaJLSJSCP" }, @@ -113,6 +113,7 @@ "source": [ "import torch\n", "import torch.nn as nn\n", + "import torch.optim as optim\n", "import torch.nn.functional as F\n", "import torchvision.transforms as transforms\n", "import time\n", @@ -130,13 +131,12 @@ "import os\n", "from PIL import Image\n", "import numpy as np\n", - "from torch.utils.data import Dataset, DataLoader\n", - "from torch.utils.data import ConcatDataset" + "from torch.utils.data import Dataset, DataLoader, TensorDataset, ConcatDataset" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 7, "metadata": { "id": "7zvKyWj2J7Yk" }, @@ -163,126 +163,81 @@ "\n", " return image\n", "\n", - "class CustomConcatDataset(ConcatDataset):\n", - " def __init__(self, datasets):\n", - " super(CustomConcatDataset, self).__init__(datasets)\n", + "class SiameseDataset(Dataset):\n", + " def __init__(self, AD, NC):\n", + " self.AD = AD\n", + " self.NC = NC\n", + " self.labels = torch.cat((torch.ones(len(AD)), torch.zeros(len(NC))), dim=0)\n", "\n", - " def __getitem__(self, index):\n", - " item1 = super(CustomConcatDataset, self).__getitem__(index)\n", - " item2 = super(CustomConcatDataset, self).__getitem__(index)\n", - " return item1, item2\n", + " def __len__(self):\n", + " return min(len(self.AD), len(self.NC))\n", "\n", - "def combine(AD, NC):\n", - " # Set labels for the AD dataset\n", - " labels_AD = [True] * len(AD)\n", - " # Set labels for the NC dataset\n", - " labels_NC = [False] * len(NC)\n", + " def __getitem__(self, idx):\n", + " img1 = self.AD[idx]\n", + " label1 = self.labels[idx]\n", + " img2 = self.NC[idx]\n", + " label2 = self.labels[len(self.AD) + idx]\n", "\n", - " # Combine the datasets and labels\n", - " X = CustomConcatDataset([AD, NC])\n", - " Y = labels_AD + labels_NC\n", - " # X = torch.utils.data.RandomSampler(X)\n", - " seed = 123\n", - " torch.manual_seed(seed)\n", - " np.random.seed(seed)\n", + " return img1, img2, label1, label2\n", "\n", + "# Set the device\n", + "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", "\n", - " X_loader = DataLoader(X, batch_size=128, shuffle=True)\n", - " Y_loader = DataLoader(Y, batch_size=128, shuffle=True)\n", - " return X_loader, Y_loader\n", "\n", "size = 256\n", - "\n", "transform_X = transforms.Compose([\n", - " transforms.Resize((size, size)), # Resize the image to the desired size\n", - " transforms.ToTensor(), # Convert the image to a tensor\n", - " transforms.Lambda(lambda x: x / 255.0),\n", - " transforms.Lambda(lambda x: (x - x.mean()) / x.std()) # Subtract mean and divide by standard deviation\n", + " transforms.Resize((size, size)),\n", + " transforms.ToTensor(),\n", + " # transforms.Lambda(lambda x: x / 255.0),\n", + " # transforms.Normalize(mean=[0.5], std=[0.5]) # Adjust according to your data statistics\n", "])\n", "\n", "\n", - "\n", "# Replace 'your_nii_folder' with the path to your folder containing .nii files\n", "# nii_dataset = NiiDataset(root_dir=r'keras_png_slices_data\\keras_png_slices_data\\keras_png_slices_train')\n", "loaders = {}\n", "\n", "for stage in ['test']:\n", " loaders[stage] = {}\n", - " AD = CustomDataset(root_dir=rf'{stage}/AD', transform=transform_X)\n", - " NC = CustomDataset(root_dir=rf'{stage}/NC', transform=transform_X)\n", - " loaders[stage]['X'], loaders[stage]['Y'] = combine(AD, NC)\n", - "# Y_train = CustomDataset(root_dir=r'ADNI_AD_NC_2D\\AD_NC\\train\\NC', transform=transform_Y)\n", - "# X_train_AD_loader = DataLoader(X_train_AD, batch_size=4, shuffle=True)\n", - "# Y_train_loader = DataLoader(Y_train, batch_size=4, shuffle=True)" + " AD = CustomDataset(root_dir=os.path.join(stage, 'AD'), transform=transform_X)\n", + " NC = CustomDataset(root_dir=os.path.join(stage, 'NC'), transform=transform_X)\n", + " loaders[stage] = SiameseDataset(AD, NC)" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "4BuuMeumKHi2" - }, - "outputs": [], "source": [ - "import torch\n", - "import torch.nn as nn\n", - "import torch.optim as optim\n", - "\n", - "# Define the model\n", - "class CustomModel(nn.Module):\n", - " def __init__(self):\n", - " super(CustomModel, self).__init__()\n", - " self.conv1 = nn.Conv2d(1, 32, 3, padding=1)\n", - " self.maxpool1 = nn.MaxPool2d(2, 2)\n", - " self.conv2 = nn.Conv2d(32, 32, 3, padding=1)\n", - " self.maxpool2 = nn.MaxPool2d(2, 2)\n", - " self.flatten = nn.Flatten()\n", - " self.fc1 = nn.Linear(32 * 64 * 64, 128)\n", - " self.fc2 = nn.Linear(128, 10)\n", - "\n", - " def forward(self, x):\n", - " x = nn.functional.relu(self.conv1(x))\n", - " x = self.maxpool1(x)\n", - " x = nn.functional.relu(self.conv2(x))\n", - " x = self.maxpool2(x)\n", - " x = self.flatten(x)\n", - " x = nn.functional.relu(self.fc1(x))\n", - " x = nn.functional.softmax(self.fc2(x), dim=1)\n", - " return x\n", - "\n", - "\n", - "\n", - "# Instantiate the model\n", - "model = CustomModel()\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, + "for load in loaders.values():\n", + " print(len(load))" + ], "metadata": { - "id": "SztvTT_tKJMA" + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "oW2JaTc0dXLz", + "outputId": "758fa1b1-dbfb-48eb-d982-03b676d628a4" }, - "outputs": [], - "source": [ - "criterion = nn.CrossEntropyLoss()\n", - "learning_rate = 0.1\n", - "optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate, momentum=0.9, weight_decay=5e-4)\n", - "total_step = len(loaders['train']['X'])\n", - "sched_linear_1 = torch.optim.lr_scheduler.CyclicLR(optimizer, base_lr=0.005, max_lr=learning_rate, step_size_up=15, step_size_down=15, mode=\"triangular\", verbose=False)\n", - "sched_linear_3 = torch.optim.lr_scheduler.LinearLR(optimizer, start_factor=0.005/learning_rate, end_factor=0.005/learning_rate, verbose=False)\n", - "scheduler = torch.optim.lr_scheduler.SequentialLR(optimizer, schedulers=[sched_linear_1, sched_linear_3], milestones=[30])\n" + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "0\n", + "4460\n" + ] + } ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 8, "metadata": { "id": "puuj1CSURmJ9", "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "a60e467c-4d63-4b96-d0df-1925c065d0d6" + "outputId": "532995b4-c399-4666-961a-5513ebf7de7b" }, "outputs": [ { @@ -292,9 +247,7 @@ "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.\n", - " warnings.warn(msg)\n", - "Downloading: \"https://download.pytorch.org/models/resnet18-f37072fd.pth\" to /root/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth\n", - "100%|██████████| 44.7M/44.7M [00:00<00:00, 220MB/s]\n" + " warnings.warn(msg)\n" ] } ], @@ -307,6 +260,8 @@ " def __init__(self, pretrained=True):\n", " super(SiameseNetwork, self).__init__()\n", " self.resnet = models.resnet18(pretrained=pretrained)\n", + " # Modify the first convolution layer to accept single-channel input\n", + " self.resnet.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False)\n", " self.linear = nn.Linear(1000, 2) # 1000 is the number of output features from ResNet\n", "\n", " def forward(self, x1, x2):\n", @@ -319,7 +274,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 9, "metadata": { "id": "ocj6rcu7Rn4-" }, @@ -334,17 +289,12 @@ " euclidean_distance = F.pairwise_distance(output1, output2)\n", " loss_contrastive = torch.mean((1 - label) * torch.pow(euclidean_distance, 2) +\n", " label * torch.pow(torch.clamp(self.margin - euclidean_distance, min=0.0), 2))\n", - " return loss_contrastive\n" + " return loss_contrastive" ] }, { "cell_type": "code", "source": [ - "import torch.optim as optim\n", - "\n", - "# Set the device\n", - "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", - "\n", "# Initialize the network, loss function, and optimizer\n", "siamese_net = SiameseNetwork(pretrained=True).to(device)\n", "criterion = ContrastiveLoss()\n", @@ -352,109 +302,116 @@ "\n", "# Training loop\n", "epochs = 2\n", + "total_step = len(loaders['test'])\n", "for epoch in range(epochs):\n", - " for i, (images, label) in enumerate(zip(loaders['test']['X'], loaders['test']['Y'])):\n", - " img0, img1 = images\n", - " img0, img1, label = img0.to(device), img1.to(device), label.to(device)\n", - " output1, output2 = model(img0, img1)\n", + " for i, (i1, i2, l1, l2) in enumerate(loaders['test']):\n", + " i1, i2, l1, l2 = i1.to(device), i2.to(device), l1.to(device), l2.to(device)\n", + " i1 = i1.unsqueeze(1) # Add a channel dimension\n", + " i2 = i2.unsqueeze(1) # Add a channel dimension\n", + " output = siamese_net(i1, i2)\n", + " o1, o2 = output[0][0], output[0][1]\n", + " l = (l1 == l2).int()\n", "\n", "\n", " # Adjust the shapes to match the criterion\n", " # outputs = outputs.view(-1) # Flatten the outputs to a 1D tensor\n", " # seg = seg.view(-1) # Flatten the seg tensor to a 1D tensor\n", - " loss = criterion(output1, output2, label)\n", + " loss = criterion(o1, o2, l)\n", " optimizer.zero_grad()\n", " loss.backward()\n", " optimizer.step()\n", "\n", - " if (i + 1) % 5 == 0:\n", - " print(f\"Epoch [{epoch + 1} / {num_epochs}], Step [{i + 1} / {total_step} Loss {loss.item()}]\")" + " if (i + 1) % 500 == 0:\n", + " print(f\"Epoch [{epoch + 1} / {epochs}], Step [{i + 1} / {total_step} Loss {loss.item()}]\")" ], "metadata": { "colab": { - "base_uri": "https://localhost:8080/", - "height": 391 + "base_uri": "https://localhost:8080/" }, "id": "4NnXFUDzSWjp", - "outputId": "38eb6735-7694-49e3-8fca-113482e73490" + "outputId": "8bd9a4f5-1999-4373-9d1b-9f3fe398f29b" }, - "execution_count": 26, + "execution_count": 12, "outputs": [ { - "output_type": "error", - "ename": "RuntimeError", - "evalue": "ignored", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", - 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"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1499\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1500\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1502\u001b[0m \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1503\u001b[0m \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/conv.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m 461\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 462\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 463\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_conv_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 464\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 465\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mConv3d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_ConvNd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/conv.py\u001b[0m in \u001b[0;36m_conv_forward\u001b[0;34m(self, input, weight, bias)\u001b[0m\n\u001b[1;32m 457\u001b[0m \u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbias\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstride\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 458\u001b[0m _pair(0), self.dilation, self.groups)\n\u001b[0;32m--> 459\u001b[0;31m return F.conv2d(input, weight, bias, self.stride,\n\u001b[0m\u001b[1;32m 460\u001b[0m self.padding, self.dilation, self.groups)\n\u001b[1;32m 461\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mRuntimeError\u001b[0m: Given groups=1, weight of size [64, 3, 7, 7], expected input[128, 1, 256, 256] to have 3 channels, but got 1 channels instead" + "output_type": "stream", + "name": "stdout", + "text": [ + "Epoch [1 / 2], Step [500 / 4460 Loss 3.259337688632513e-07]\n", + "Epoch [1 / 2], Step [1000 / 4460 Loss 3.283045589341782e-05]\n", + "Epoch [1 / 2], Step [1500 / 4460 Loss 1.054049062076956e-05]\n", + "Epoch [1 / 2], Step [2000 / 4460 Loss 1.234111095982371e-06]\n", + "Epoch [1 / 2], Step [2500 / 4460 Loss 1.1719105259544449e-06]\n", + "Epoch [1 / 2], Step [3000 / 4460 Loss 3.3835010526672704e-07]\n", + "Epoch [1 / 2], Step [3500 / 4460 Loss 7.921468636595819e-08]\n", + "Epoch [1 / 2], Step [4000 / 4460 Loss 2.9854402328055585e-07]\n", + "Epoch [2 / 2], Step [500 / 4460 Loss 1.767582347156349e-07]\n", + "Epoch [2 / 2], Step [1000 / 4460 Loss 4.3589712395153413e-10]\n", + "Epoch [2 / 2], Step [1500 / 4460 Loss 8.744237902647001e-07]\n", + "Epoch [2 / 2], Step [2000 / 4460 Loss 2.0802056258095725e-11]\n", + "Epoch [2 / 2], Step [2500 / 4460 Loss 1.8013805913597025e-07]\n", + "Epoch [2 / 2], Step [3000 / 4460 Loss 3.1853244308877038e-06]\n", + "Epoch [2 / 2], Step [3500 / 4460 Loss 2.546377402268263e-07]\n", + "Epoch [2 / 2], Step [4000 / 4460 Loss 2.075099914122802e-08]\n" ] } ] }, { "cell_type": "code", - "execution_count": 17, + "source": [ + "# Evaluation loop\n", + "siamese_net.eval() # Set the model to evaluation mode\n", + "with torch.no_grad(): # Disable gradient calculation for evaluation\n", + " correct = 0\n", + " total = 0\n", + " for i, (i1, i2, l1, l2) in enumerate(loaders['test']):\n", + " i1, i2, l1, l2 = i1.to(device), i2.to(device), l1.to(device), l2.to(device)\n", + " i1 = i1.unsqueeze(1) # Add a channel dimension\n", + " i2 = i2.unsqueeze(1) # Add a channel dimension\n", + " output = siamese_net(i1, i2)\n", + " o1, o2 = output[0][0], output[0][1]\n", + " l = (l1 == l2).int()\n", + " # Compute the Euclidean distance between the outputs\n", + " distance = torch.abs(o1 - o2)\n", + "\n", + " # Apply threshold to determine predicted labels\n", + " threshold = 0.5 # Adjust as needed\n", + " predicted = distance.lt(threshold)\n", + "\n", + "\n", + "\n", + " total += l\n", + " correct += (predicted == l).sum().item()\n", + "\n", + " accuracy = 100 * correct / total\n", + " print(f'Test Accuracy: {accuracy:.2f}%')\n" + ], "metadata": { - "id": "xeAsMkrnKKij", "colab": { - "base_uri": "https://localhost:8080/", - "height": 245 + "base_uri": "https://localhost:8080/" }, - "outputId": "5dd55b9f-d01e-4627-9e35-ba8f1257048e" + "id": "G3TWNpvKdAyn", + "outputId": "40daccb3-3e72-4373-eebb-64c60fd8df2a" }, + "execution_count": 18, "outputs": [ { - "output_type": "error", - "ename": "NameError", - "evalue": "ignored", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mimg0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimages\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mimg0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimg0\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimages\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mNameError\u001b[0m: name 'model' is not defined" + "output_type": "stream", + "name": "stdout", + "text": [ + "Test Accuracy: nan%\n" ] } - ], - "source": [ - "num_epochs = 2\n", - "for epoch in range(num_epochs):\n", - "\n", - " for i, (images, label) in enumerate(zip(loaders['test']['X'], loaders['test']['Y'])):\n", - " img0, img1 = images\n", - " img0, img1, label = img0.to(device), img1.to(device), label.to(device)\n", - " outputs = model(images)\n", - "\n", - "\n", - " # Adjust the shapes to match the criterion\n", - " # outputs = outputs.view(-1) # Flatten the outputs to a 1D tensor\n", - " # seg = seg.view(-1) # Flatten the seg tensor to a 1D tensor\n", - " loss = criterion(outputs, label)\n", - " optimizer.zero_grad()\n", - " loss.backward()\n", - " optimizer.step()\n", - "\n", - " if (i + 1) % 5 == 0:\n", - " print(f\"Epoch [{epoch + 1} / {num_epochs}], Step [{i + 1} / {total_step} Loss {loss.item()}]\")\n", - "\n", - " scheduler.step()\n", - "end = time.time()" ] } ], "metadata": { "colab": { "provenance": [], - "authorship_tag": "ABX9TyPullQ1T6knjpYBrXTmmpBA", + "machine_shape": "hm", + "gpuType": "A100", + "authorship_tag": "ABX9TyPMy7bIZ9steqmxN0P4lni6", "include_colab_link": true }, "kernelspec": { @@ -463,7 +420,8 @@ }, "language_info": { "name": "python" - } + }, + "accelerator": "GPU" }, "nbformat": 4, "nbformat_minor": 0 From 20f22891abbe0637745a5744afb539bee46207bd Mon Sep 17 00:00:00 2001 From: FinnRobertson15 <110511080+FinnRobertson15@users.noreply.github.com> Date: Mon, 16 Oct 2023 16:46:54 +1000 Subject: [PATCH 04/14] Added classification testing and adjusted some hyper parameters. Still getting 50% accuracy... --- Colab version.ipynb | 364 +++++++++++++++++++++++++++----------------- 1 file changed, 226 insertions(+), 138 deletions(-) diff --git a/Colab version.ipynb b/Colab version.ipynb index d5769d6c1..4f4d8ce60 100644 --- a/Colab version.ipynb +++ b/Colab version.ipynb @@ -131,12 +131,24 @@ "import os\n", "from PIL import Image\n", "import numpy as np\n", - "from torch.utils.data import Dataset, DataLoader, TensorDataset, ConcatDataset" + "from torch.utils.data import Dataset, DataLoader, TensorDataset, ConcatDataset\n", + "import random" ] }, { "cell_type": "code", - "execution_count": 7, + "source": [ + "batch_size = 32" + ], + "metadata": { + "id": "u6EH0wk_CkxF" + }, + "execution_count": 118, + "outputs": [] + }, + { + "cell_type": "code", + "execution_count": 194, "metadata": { "id": "7zvKyWj2J7Yk" }, @@ -163,33 +175,66 @@ "\n", " return image\n", "\n", + "def get_indexes_by_value(lst):\n", + " index_dict = {}\n", + " for i, value in enumerate(lst):\n", + " value = value.split('_')[1].split('.jpeg')[0]\n", + " if value in index_dict:\n", + " index_dict[value].append(i)\n", + " else:\n", + " index_dict[value] = [i]\n", + "\n", + " return list(index_dict.values())\n", + "\n", "class SiameseDataset(Dataset):\n", " def __init__(self, AD, NC):\n", - " self.AD = AD\n", - " self.NC = NC\n", - " self.labels = torch.cat((torch.ones(len(AD)), torch.zeros(len(NC))), dim=0)\n", + " # Combine the datasets and labels\n", + " self.X = AD + NC\n", + "\n", + " self.Y = torch.cat((torch.ones(len(AD)), torch.zeros(len(NC))), dim=0)\n", + "\n", + " paths = ([str(path) for path in (AD.image_paths + NC.image_paths)])\n", + " indexes = get_indexes_by_value(paths)\n", + " i_indices = [random.sample(indices, len(indices)) for indices in indexes]\n", + " j_indices = [random.sample(indices, len(indices)) for indices in indexes]\n", + " self.i_indices = [item for sublist in i_indices for item in sublist]\n", + " self.j_indices = [item for sublist in j_indices for item in sublist]\n", "\n", " def __len__(self):\n", - " return min(len(self.AD), len(self.NC))\n", + " return len(self.X) // 2\n", "\n", " def __getitem__(self, idx):\n", - " img1 = self.AD[idx]\n", - " label1 = self.labels[idx]\n", - " img2 = self.NC[idx]\n", - " label2 = self.labels[len(self.AD) + idx]\n", + " i = self.i_indices[idx]\n", + " j = self.j_indices[idx]\n", + " img1 = self.X[i]\n", + " img2 = self.X[j]\n", + " l1 = self.Y[i]\n", + " l2 = self.Y[j]\n", "\n", - " return img1, img2, label1, label2\n", + " return img1, img2, l1, l2\n", "\n", "# Set the device\n", "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", "\n", "\n", "size = 256\n", + "\n", + "def intensity_normalization(img):\n", + " mean = torch.mean(img)\n", + " std = torch.std(img)\n", + " return (img - mean) / std\n", + "\n", + "def windowing(img, window_center, window_width):\n", + " img = torch.clamp(img, window_center - window_width // 2, window_center + window_width // 2)\n", + " img = (img - (window_center - 0.5)) / (window_width - 1)\n", + " return img\n", + "\n", + "# Example usage in your transform\n", "transform_X = transforms.Compose([\n", " transforms.Resize((size, size)),\n", " transforms.ToTensor(),\n", - " # transforms.Lambda(lambda x: x / 255.0),\n", - " # transforms.Normalize(mean=[0.5], std=[0.5]) # Adjust according to your data statistics\n", + " transforms.Lambda(intensity_normalization),\n", + " transforms.Lambda(lambda x: windowing(x, window_center=size // 2, window_width=size))\n", "])\n", "\n", "\n", @@ -201,56 +246,17 @@ " loaders[stage] = {}\n", " AD = CustomDataset(root_dir=os.path.join(stage, 'AD'), transform=transform_X)\n", " NC = CustomDataset(root_dir=os.path.join(stage, 'NC'), transform=transform_X)\n", - " loaders[stage] = SiameseDataset(AD, NC)" - ] - }, - { - "cell_type": "code", - "source": [ - "for load in loaders.values():\n", - " print(len(load))" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "oW2JaTc0dXLz", - "outputId": "758fa1b1-dbfb-48eb-d982-03b676d628a4" - }, - "execution_count": 6, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "0\n", - "4460\n" - ] - } + " dataset = SiameseDataset(AD, NC)\n", + " loaders[stage] = DataLoader(dataset, batch_size=batch_size)" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 209, "metadata": { - "id": "puuj1CSURmJ9", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "532995b4-c399-4666-961a-5513ebf7de7b" + "id": "puuj1CSURmJ9" }, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n", - " warnings.warn(\n", - "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.\n", - " warnings.warn(msg)\n" - ] - } - ], + "outputs": [], "source": [ "import torch\n", "import torch.nn as nn\n", @@ -261,25 +267,20 @@ " super(SiameseNetwork, self).__init__()\n", " self.resnet = models.resnet18(pretrained=pretrained)\n", " # Modify the first convolution layer to accept single-channel input\n", - " self.resnet.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False)\n", - " self.linear = nn.Linear(1000, 2) # 1000 is the number of output features from ResNet\n", + " self.resnet.conv1 = nn.Conv2d(1, 64, kernel_size=3, stride=2, padding=3, bias=False)\n", + " self.fc1 = nn.Linear(1000, 500) # Customize the fully connected layers\n", + " self.fc2 = nn.Linear(500, 2) # Customize the fully connected layers\n", "\n", " def forward(self, x1, x2):\n", " output1 = self.resnet(x1)\n", " output2 = self.resnet(x2)\n", - " return self.linear(torch.abs(output1 - output2))\n", + " output = torch.abs(output1 - output2)\n", + " output = self.fc1(output)\n", + " output = self.fc2(output)\n", + " return output\n", + "\n", + "model = SiameseNetwork()\n", "\n", - "model = SiameseNetwork()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "id": "ocj6rcu7Rn4-" - }, - "outputs": [], - "source": [ "class ContrastiveLoss(torch.nn.Module):\n", " def __init__(self, margin=2.0):\n", " super(ContrastiveLoss, self).__init__()\n", @@ -298,61 +299,96 @@ "# Initialize the network, loss function, and optimizer\n", "siamese_net = SiameseNetwork(pretrained=True).to(device)\n", "criterion = ContrastiveLoss()\n", - "optimizer = optim.Adam(siamese_net.parameters(), lr=0.0005)\n", - "\n", + "optimizer = optim.Adam(siamese_net.parameters(), lr=0.001)\n", "# Training loop\n", - "epochs = 2\n", + "epochs = 5\n", "total_step = len(loaders['test'])\n", "for epoch in range(epochs):\n", - " for i, (i1, i2, l1, l2) in enumerate(loaders['test']):\n", - " i1, i2, l1, l2 = i1.to(device), i2.to(device), l1.to(device), l2.to(device)\n", - " i1 = i1.unsqueeze(1) # Add a channel dimension\n", - " i2 = i2.unsqueeze(1) # Add a channel dimension\n", - " output = siamese_net(i1, i2)\n", - " o1, o2 = output[0][0], output[0][1]\n", - " l = (l1 == l2).int()\n", - "\n", - "\n", - " # Adjust the shapes to match the criterion\n", - " # outputs = outputs.view(-1) # Flatten the outputs to a 1D tensor\n", - " # seg = seg.view(-1) # Flatten the seg tensor to a 1D tensor\n", - " loss = criterion(o1, o2, l)\n", + " for i, (img1, img2, lab1, lab2) in enumerate(loaders['test']):\n", + " img1, img2, lab1, lab2 = img1.to(device), img2.to(device), lab1.to(device), lab2.to(device)\n", + " output = siamese_net(img1, img2)\n", + " o1, o2 = output[:, 0], output[:, 1]\n", + " label = (lab1 == lab2).int()\n", + "\n", + " loss = criterion(o1, o2, label)\n", " optimizer.zero_grad()\n", " loss.backward()\n", " optimizer.step()\n", "\n", - " if (i + 1) % 500 == 0:\n", + " if (i + 1) % 21 == 0:\n", + " print((lab1[0], lab2[0]))\n", + " print(label[0])\n", + " print(output[0])\n", " print(f\"Epoch [{epoch + 1} / {epochs}], Step [{i + 1} / {total_step} Loss {loss.item()}]\")" ], "metadata": { "colab": { - "base_uri": "https://localhost:8080/" + "base_uri": "https://localhost:8080/", + "height": 391 }, "id": "4NnXFUDzSWjp", - "outputId": "8bd9a4f5-1999-4373-9d1b-9f3fe398f29b" + "outputId": "aea8ed90-446c-44f5-93e9-e9e5c9b5c683" + }, + "execution_count": 210, + "outputs": [ + { + "output_type": "error", + "ename": "RuntimeError", + "evalue": "ignored", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mimg1\u001b[0m\u001b[0;34m,\u001b[0m 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not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1503\u001b[0m \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/conv.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m 461\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 462\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 463\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_conv_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 464\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 465\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mConv3d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_ConvNd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/conv.py\u001b[0m in \u001b[0;36m_conv_forward\u001b[0;34m(self, input, weight, bias)\u001b[0m\n\u001b[1;32m 457\u001b[0m \u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbias\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstride\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 458\u001b[0m _pair(0), self.dilation, self.groups)\n\u001b[0;32m--> 459\u001b[0;31m return F.conv2d(input, weight, bias, self.stride,\n\u001b[0m\u001b[1;32m 460\u001b[0m self.padding, self.dilation, self.groups)\n\u001b[1;32m 461\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mRuntimeError\u001b[0m: Expected 3D (unbatched) or 4D (batched) input to conv2d, but got input of size: [32, 1000]" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "correct = 0\n", + "total = 0\n", + "features = []\n", + "labels = []\n", + "with torch.no_grad():\n", + " for i, (img1, img2, lab1, lab2) in enumerate(loaders['test']):\n", + " img1, img2, lab1, lab2 = img1.to(device), img2.to(device), lab1.to(device), lab2.to(device)\n", + " output = siamese_net(img1, img2)\n", + " out1, out2 = output[:, 0], output[:, 1]\n", + " predicted = (out1 - out2).pow(2).sum().sqrt().lt(0.5)\n", + " total += lab1.size(0)\n", + " correct += (predicted == lab1).sum().item()\n", + " features.extend(out1.tolist())\n", + " labels.extend(lab1.tolist())\n", + " features.extend(out2.tolist())\n", + " labels.extend(lab2.tolist())\n", + "\n", + "\n", + " print(f'Accuracy of the network on the test images: {100 * correct / total}%')\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "G3TWNpvKdAyn", + "outputId": "bf70c5fd-38e5-49c7-a64f-ba1758f103d9" }, - "execution_count": 12, + "execution_count": 200, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "Epoch [1 / 2], Step [500 / 4460 Loss 3.259337688632513e-07]\n", - "Epoch [1 / 2], Step [1000 / 4460 Loss 3.283045589341782e-05]\n", - "Epoch [1 / 2], Step [1500 / 4460 Loss 1.054049062076956e-05]\n", - "Epoch [1 / 2], Step [2000 / 4460 Loss 1.234111095982371e-06]\n", - "Epoch [1 / 2], Step [2500 / 4460 Loss 1.1719105259544449e-06]\n", - "Epoch [1 / 2], Step [3000 / 4460 Loss 3.3835010526672704e-07]\n", - "Epoch [1 / 2], Step [3500 / 4460 Loss 7.921468636595819e-08]\n", - "Epoch [1 / 2], Step [4000 / 4460 Loss 2.9854402328055585e-07]\n", - "Epoch [2 / 2], Step [500 / 4460 Loss 1.767582347156349e-07]\n", - "Epoch [2 / 2], Step [1000 / 4460 Loss 4.3589712395153413e-10]\n", - "Epoch [2 / 2], Step [1500 / 4460 Loss 8.744237902647001e-07]\n", - "Epoch [2 / 2], Step [2000 / 4460 Loss 2.0802056258095725e-11]\n", - "Epoch [2 / 2], Step [2500 / 4460 Loss 1.8013805913597025e-07]\n", - "Epoch [2 / 2], Step [3000 / 4460 Loss 3.1853244308877038e-06]\n", - "Epoch [2 / 2], Step [3500 / 4460 Loss 2.546377402268263e-07]\n", - "Epoch [2 / 2], Step [4000 / 4460 Loss 2.075099914122802e-08]\n" + "Accuracy of the network on the test images: 49.955555555555556%\n" ] } ] @@ -360,50 +396,102 @@ { "cell_type": "code", "source": [ - "# Evaluation loop\n", - "siamese_net.eval() # Set the model to evaluation mode\n", - "with torch.no_grad(): # Disable gradient calculation for evaluation\n", - " correct = 0\n", - " total = 0\n", - " for i, (i1, i2, l1, l2) in enumerate(loaders['test']):\n", - " i1, i2, l1, l2 = i1.to(device), i2.to(device), l1.to(device), l2.to(device)\n", - " i1 = i1.unsqueeze(1) # Add a channel dimension\n", - " i2 = i2.unsqueeze(1) # Add a channel dimension\n", - " output = siamese_net(i1, i2)\n", - " o1, o2 = output[0][0], output[0][1]\n", - " l = (l1 == l2).int()\n", - " # Compute the Euclidean distance between the outputs\n", - " distance = torch.abs(o1 - o2)\n", - "\n", - " # Apply threshold to determine predicted labels\n", - " threshold = 0.5 # Adjust as needed\n", - " predicted = distance.lt(threshold)\n", - "\n", - "\n", - "\n", - " total += l\n", - " correct += (predicted == l).sum().item()\n", - "\n", - " accuracy = 100 * correct / total\n", - " print(f'Test Accuracy: {accuracy:.2f}%')\n" + "from sklearn.model_selection import train_test_split\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.metrics import accuracy_score\n", + "\n", + "# Split the data into training and testing sets\n", + "X_train, X_test, y_train, y_test = train_test_split(np.array(features).reshape(-1, 1), labels, test_size=0.2, random_state=42)\n", + "\n", + "# Train a logistic regression model\n", + "classifier = LogisticRegression(max_iter=1000)\n", + "classifier.fit(X_train, y_train)\n", + "\n", + "# Make predictions\n", + "predictions = classifier.predict(X_test)\n", + "\n", + "# Calculate accuracy\n", + "accuracy = accuracy_score(y_test, predictions)\n", + "print(f\"Accuracy: {accuracy}\")\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, - "id": "G3TWNpvKdAyn", - "outputId": "40daccb3-3e72-4373-eebb-64c60fd8df2a" + "id": "MVEvstik8zqK", + "outputId": "c5aed97c-27c1-4685-bf6a-cc2204dbaa69" }, - "execution_count": 18, + "execution_count": 201, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "Test Accuracy: nan%\n" + "Accuracy: 0.49166666666666664\n" ] } ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "qA-60lBMGV3i" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "features[0].tolist()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "9PrC3ijsFnhE", + "outputId": "42aa07d0-9166-43fb-a67e-713fc99664da" + }, + "execution_count": 148, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0.5943959355354309" + ] + }, + "metadata": {}, + "execution_count": 148 + } + ] + }, + { + "cell_type": "code", + "source": [ + "correct, total" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "hqYJrbkVfi36", + "outputId": "8e5c2a93-1cd2-47e1-8c29-95e1ceda5ca3" + }, + "execution_count": 139, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(2226, 4500)" + ] + }, + "metadata": {}, + "execution_count": 139 + } + ] } ], "metadata": { @@ -411,7 +499,7 @@ "provenance": [], "machine_shape": "hm", "gpuType": "A100", - "authorship_tag": "ABX9TyPMy7bIZ9steqmxN0P4lni6", + "authorship_tag": "ABX9TyN39gN45mXrH2YYwJ6aKtns", "include_colab_link": true }, "kernelspec": { From 0f1bd3cfbbf5aa74bbf756d1fed45226631a1e12 Mon Sep 17 00:00:00 2001 From: FinnRobertson15 <110511080+FinnRobertson15@users.noreply.github.com> Date: Mon, 16 Oct 2023 20:24:36 +1000 Subject: [PATCH 05/14] Got triple accuracy up to ~70%. I randomize the order of all three cols each batch, rotate the images and use L2 regularization --- Colab version.ipynb | 644 ++++++++++++++++++++++++++++++++++++++------ 1 file changed, 566 insertions(+), 78 deletions(-) diff --git a/Colab version.ipynb b/Colab version.ipynb index 4f4d8ce60..5714621cc 100644 --- a/Colab version.ipynb +++ b/Colab version.ipynb @@ -18,7 +18,7 @@ "base_uri": "https://localhost:8080/" }, "id": "yzCmT9TUJY10", - "outputId": "997681ae-a0d2-448e-d290-b00a8dc22666" + "outputId": "f4a92c6b-c770-48bf-a03c-3a6b9f924c30" }, "outputs": [ { @@ -42,32 +42,45 @@ "base_uri": "https://localhost:8080/" }, "id": "-Lb7CGdEJCQg", - "outputId": "a693cf6b-0d28-474e-ed69-f7192a4700f9" + "outputId": "7cf9c217-28e6-43e3-aee4-f079779419c3" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "/content/drive/MyDrive/AD_NC\n" + "/root\n" ] } ], "source": [ - "%cd /content/drive/MyDrive/AD_NC" + "%cd" ] }, { "cell_type": "code", "source": [ - "%ls" + "import zipfile\n", + "import os\n", + "\n", + "# Define the paths\n", + "zip_path = '/content/drive/MyDrive/AD_NC.zip' # Path to the zip file\n", + "extract_path = '/content/extracted_folder' # Path where you want to extract the contents\n", + "\n", + "# Unzip the file\n", + "with zipfile.ZipFile(zip_path, 'r') as zip_ref:\n", + " zip_ref.extractall(extract_path)\n", + "\n", + "# List the contents of the extracted folder\n", + "extracted_files = os.listdir(extract_path)\n", + "print(extracted_files)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, - "id": "mgx67Xe5dsgs", - "outputId": "b7850ff6-2922-444d-ee4f-48498dcaeacf" + "id": "PM8uTdbZpQGJ", + "outputId": "15ea444a-ce03-4ddb-d19e-5a8bb8686b53" }, "execution_count": 3, "outputs": [ @@ -75,37 +88,60 @@ "output_type": "stream", "name": "stdout", "text": [ - "NC.zip \u001b[0m\u001b[01;34mtest\u001b[0m/ \u001b[01;34mtrain\u001b[0m/\n" + "['AD_NC']\n" ] } ] }, { "cell_type": "code", - "execution_count": null, + "source": [ + "cd /content/extracted_folder/AD_NC" + ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, - "id": "_eAax1mMJpyw", - "outputId": "daf77648-1909-41f9-c05f-0be751ec7d6d" + "id": "x1t1iFV_p2MU", + "outputId": "9d142efb-edd0-4ae2-8e69-1a377fb13ad5" }, + "execution_count": 4, "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ - "unzip: cannot find or open NC.zip, NC.zip.zip or NC.zip.ZIP.\n" + "/content/extracted_folder/AD_NC\n" ] } - ], + ] + }, + { + "cell_type": "code", "source": [ - "!unzip NC.zip" + "ls" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Tp_Fb7Eyp-yv", + "outputId": "45e2140b-5eab-4cf6-dc81-3b6c9fd592f0" + }, + "execution_count": 5, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[0m\u001b[01;34mtest\u001b[0m/ \u001b[01;34mtrain\u001b[0m/\n" + ] + } ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": { "id": "_8PBaJLSJSCP" }, @@ -138,17 +174,17 @@ { "cell_type": "code", "source": [ - "batch_size = 32" + "batch_size = 128" ], "metadata": { "id": "u6EH0wk_CkxF" }, - "execution_count": 118, + "execution_count": null, "outputs": [] }, { "cell_type": "code", - "execution_count": 194, + "execution_count": null, "metadata": { "id": "7zvKyWj2J7Yk" }, @@ -175,17 +211,6 @@ "\n", " return image\n", "\n", - "def get_indexes_by_value(lst):\n", - " index_dict = {}\n", - " for i, value in enumerate(lst):\n", - " value = value.split('_')[1].split('.jpeg')[0]\n", - " if value in index_dict:\n", - " index_dict[value].append(i)\n", - " else:\n", - " index_dict[value] = [i]\n", - "\n", - " return list(index_dict.values())\n", - "\n", "class SiameseDataset(Dataset):\n", " def __init__(self, AD, NC):\n", " # Combine the datasets and labels\n", @@ -193,19 +218,17 @@ "\n", " self.Y = torch.cat((torch.ones(len(AD)), torch.zeros(len(NC))), dim=0)\n", "\n", - " paths = ([str(path) for path in (AD.image_paths + NC.image_paths)])\n", - " indexes = get_indexes_by_value(paths)\n", - " i_indices = [random.sample(indices, len(indices)) for indices in indexes]\n", - " j_indices = [random.sample(indices, len(indices)) for indices in indexes]\n", - " self.i_indices = [item for sublist in i_indices for item in sublist]\n", - " self.j_indices = [item for sublist in j_indices for item in sublist]\n", + " # Generate a random permutation of indices\n", + " self.i_indices = torch.randperm(len(self.X) // 2)\n", + " self.j_indices = torch.randperm(len(self.X) // 2)\n", + "\n", "\n", " def __len__(self):\n", " return len(self.X) // 2\n", "\n", " def __getitem__(self, idx):\n", - " i = self.i_indices[idx]\n", - " j = self.j_indices[idx]\n", + " i = self.i_indices[idx] * 2\n", + " j = self.j_indices[idx] * 2 + 1\n", " img1 = self.X[i]\n", " img2 = self.X[j]\n", " l1 = self.Y[i]\n", @@ -217,7 +240,7 @@ "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", "\n", "\n", - "size = 256\n", + "size = 64\n", "\n", "def intensity_normalization(img):\n", " mean = torch.mean(img)\n", @@ -252,7 +275,38 @@ }, { "cell_type": "code", - "execution_count": 209, + "source": [ + "count = 0\n", + "same = 0\n", + "for i, j, n, m in loaders['test']:\n", + " count += len(n)\n", + " same += (n == m).sum().tolist()\n", + "same / count" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "5ceQcKMAPoip", + "outputId": "2b8eb6b1-c1c5-43db-9370-42f0da1531f2" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0.5144444444444445" + ] + }, + "metadata": {}, + "execution_count": 225 + } + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": { "id": "puuj1CSURmJ9" }, @@ -265,9 +319,9 @@ "class SiameseNetwork(nn.Module):\n", " def __init__(self, pretrained=True):\n", " super(SiameseNetwork, self).__init__()\n", - " self.resnet = models.resnet18(pretrained=pretrained)\n", + " self.resnet = models.resnet18(pretrained=False)\n", " # Modify the first convolution layer to accept single-channel input\n", - " self.resnet.conv1 = nn.Conv2d(1, 64, kernel_size=3, stride=2, padding=3, bias=False)\n", + " self.resnet.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False)\n", " self.fc1 = nn.Linear(1000, 500) # Customize the fully connected layers\n", " self.fc2 = nn.Linear(500, 2) # Customize the fully connected layers\n", "\n", @@ -323,31 +377,137 @@ ], "metadata": { "colab": { - "base_uri": "https://localhost:8080/", - "height": 391 + "base_uri": "https://localhost:8080/" }, "id": "4NnXFUDzSWjp", - "outputId": "aea8ed90-446c-44f5-93e9-e9e5c9b5c683" + "outputId": "ab9065a9-ffad-4405-963d-85ae26293b0d" }, - "execution_count": 210, + "execution_count": null, "outputs": [ { - "output_type": "error", - "ename": "RuntimeError", - "evalue": "ignored", - "traceback": [ - 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\u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1502\u001b[0m \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1503\u001b[0m \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/conv.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m 461\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 462\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 463\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_conv_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 464\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 465\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mConv3d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_ConvNd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/conv.py\u001b[0m in \u001b[0;36m_conv_forward\u001b[0;34m(self, input, weight, bias)\u001b[0m\n\u001b[1;32m 457\u001b[0m \u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbias\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstride\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 458\u001b[0m _pair(0), self.dilation, self.groups)\n\u001b[0;32m--> 459\u001b[0;31m return F.conv2d(input, weight, bias, self.stride,\n\u001b[0m\u001b[1;32m 460\u001b[0m self.padding, self.dilation, self.groups)\n\u001b[1;32m 461\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mRuntimeError\u001b[0m: Expected 3D (unbatched) or 4D (batched) input to conv2d, but got input of size: [32, 1000]" + "output_type": "stream", + "name": "stdout", + "text": [ + "(tensor(0., device='cuda:0'), tensor(1., device='cuda:0'))\n", + "tensor(0, device='cuda:0', dtype=torch.int32)\n", + "tensor([ 0.0997, -0.0306], device='cuda:0', grad_fn=)\n", + "Epoch [1 / 5], Step [21 / 141 Loss 0.9753031730651855]\n", + "(tensor(0., device='cuda:0'), tensor(0., device='cuda:0'))\n", + "tensor(1, device='cuda:0', dtype=torch.int32)\n", + "tensor([ 0.1081, -0.0379], device='cuda:0', grad_fn=)\n", + "Epoch [1 / 5], Step [42 / 141 Loss 1.025275707244873]\n", + "(tensor(1., device='cuda:0'), tensor(0., device='cuda:0'))\n", + "tensor(0, device='cuda:0', dtype=torch.int32)\n", + "tensor([ 0.0929, -0.0268], device='cuda:0', grad_fn=)\n", + "Epoch [1 / 5], Step [63 / 141 Loss 0.9390543699264526]\n", + "(tensor(1., device='cuda:0'), tensor(0., device='cuda:0'))\n", + "tensor(0, device='cuda:0', dtype=torch.int32)\n", + "tensor([ 0.1265, -0.0541], device='cuda:0', grad_fn=)\n", + "Epoch [1 / 5], 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"(tensor(0., device='cuda:0'), tensor(1., device='cuda:0'))\n", + "tensor(0, device='cuda:0', dtype=torch.int32)\n", + "tensor([ 0.0944, -0.0337], device='cuda:0', grad_fn=)\n", + "Epoch [3 / 5], Step [21 / 141 Loss 0.9663693308830261]\n", + "(tensor(0., device='cuda:0'), tensor(0., device='cuda:0'))\n", + "tensor(1, device='cuda:0', dtype=torch.int32)\n", + "tensor([ 0.1245, -0.0624], device='cuda:0', grad_fn=)\n", + "Epoch [3 / 5], Step [42 / 141 Loss 1.0025595426559448]\n", + "(tensor(1., device='cuda:0'), tensor(0., device='cuda:0'))\n", + "tensor(0, device='cuda:0', dtype=torch.int32)\n", + "tensor([ 0.1142, -0.0528], device='cuda:0', grad_fn=)\n", + "Epoch [3 / 5], Step [63 / 141 Loss 0.9942120313644409]\n", + "(tensor(1., device='cuda:0'), tensor(0., device='cuda:0'))\n", + "tensor(0, device='cuda:0', dtype=torch.int32)\n", + "tensor([ 0.1249, -0.0629], device='cuda:0', grad_fn=)\n", + "Epoch [3 / 5], Step [84 / 141 Loss 0.9485695362091064]\n", + "(tensor(1., device='cuda:0'), tensor(0., device='cuda:0'))\n", + "tensor(0, device='cuda:0', dtype=torch.int32)\n", + "tensor([ 0.0966, -0.0371], device='cuda:0', grad_fn=)\n", + "Epoch [3 / 5], Step [105 / 141 Loss 1.1140365600585938]\n", + "(tensor(0., device='cuda:0'), tensor(1., device='cuda:0'))\n", + "tensor(0, device='cuda:0', dtype=torch.int32)\n", + "tensor([ 0.1298, -0.0664], device='cuda:0', grad_fn=)\n", + "Epoch [3 / 5], Step [126 / 141 Loss 0.9871245622634888]\n", + "(tensor(0., device='cuda:0'), tensor(1., device='cuda:0'))\n", + "tensor(0, device='cuda:0', dtype=torch.int32)\n", + "tensor([ 0.0954, -0.0360], device='cuda:0', grad_fn=)\n", + "Epoch [4 / 5], Step [21 / 141 Loss 0.96551513671875]\n", + "(tensor(0., device='cuda:0'), tensor(0., device='cuda:0'))\n", + "tensor(1, device='cuda:0', dtype=torch.int32)\n", + "tensor([ 0.1227, -0.0626], device='cuda:0', grad_fn=)\n", + "Epoch [4 / 5], Step [42 / 141 Loss 1.0030264854431152]\n", + "(tensor(1., device='cuda:0'), tensor(0., device='cuda:0'))\n", + "tensor(0, device='cuda:0', dtype=torch.int32)\n", + "tensor([ 0.1154, -0.0552], device='cuda:0', grad_fn=)\n", + "Epoch [4 / 5], Step [63 / 141 Loss 0.9911506175994873]\n", + "(tensor(1., device='cuda:0'), tensor(0., device='cuda:0'))\n", + "tensor(0, device='cuda:0', dtype=torch.int32)\n", + "tensor([ 0.1292, -0.0673], device='cuda:0', grad_fn=)\n", + "Epoch [4 / 5], Step [84 / 141 Loss 0.9427274465560913]\n", + "(tensor(1., device='cuda:0'), tensor(0., device='cuda:0'))\n", + "tensor(0, device='cuda:0', dtype=torch.int32)\n", + "tensor([ 0.0964, -0.0375], device='cuda:0', grad_fn=)\n", + "Epoch [4 / 5], Step [105 / 141 Loss 1.1125668287277222]\n", + "(tensor(0., device='cuda:0'), tensor(1., device='cuda:0'))\n", + "tensor(0, device='cuda:0', dtype=torch.int32)\n", + "tensor([ 0.1283, -0.0657], device='cuda:0', grad_fn=)\n", + "Epoch [4 / 5], Step [126 / 141 Loss 0.9870107173919678]\n", + "(tensor(0., device='cuda:0'), tensor(1., device='cuda:0'))\n", + "tensor(0, device='cuda:0', dtype=torch.int32)\n", + "tensor([ 0.0937, -0.0353], device='cuda:0', grad_fn=)\n", + "Epoch [5 / 5], Step [21 / 141 Loss 0.9660702347755432]\n", + "(tensor(0., device='cuda:0'), tensor(0., device='cuda:0'))\n", + "tensor(1, device='cuda:0', dtype=torch.int32)\n", + "tensor([ 0.1197, -0.0604], device='cuda:0', grad_fn=)\n", + "Epoch [5 / 5], Step [42 / 141 Loss 1.0011703968048096]\n", + "(tensor(1., device='cuda:0'), tensor(0., device='cuda:0'))\n", + "tensor(0, device='cuda:0', dtype=torch.int32)\n", + "tensor([ 0.1150, -0.0551], device='cuda:0', grad_fn=)\n", + "Epoch [5 / 5], Step [63 / 141 Loss 0.9866605997085571]\n", + "(tensor(1., device='cuda:0'), tensor(0., device='cuda:0'))\n", + "tensor(0, device='cuda:0', dtype=torch.int32)\n", + "tensor([ 0.1265, -0.0649], device='cuda:0', grad_fn=)\n", + "Epoch [5 / 5], Step [84 / 141 Loss 0.9338618516921997]\n", + "(tensor(1., device='cuda:0'), tensor(0., device='cuda:0'))\n", + "tensor(0, device='cuda:0', dtype=torch.int32)\n", + "tensor([ 0.0969, -0.0381], device='cuda:0', grad_fn=)\n", + "Epoch [5 / 5], Step [105 / 141 Loss 1.1113743782043457]\n", + "(tensor(0., device='cuda:0'), tensor(1., device='cuda:0'))\n", + "tensor(0, device='cuda:0', dtype=torch.int32)\n", + "tensor([ 0.1214, -0.0599], device='cuda:0', grad_fn=)\n", + "Epoch [5 / 5], Step [126 / 141 Loss 0.9903947114944458]\n" ] } ] @@ -382,7 +542,7 @@ "id": "G3TWNpvKdAyn", "outputId": "bf70c5fd-38e5-49c7-a64f-ba1758f103d9" }, - "execution_count": 200, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -421,7 +581,7 @@ "id": "MVEvstik8zqK", "outputId": "c5aed97c-27c1-4685-bf6a-cc2204dbaa69" }, - "execution_count": 201, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -453,7 +613,7 @@ "id": "9PrC3ijsFnhE", "outputId": "42aa07d0-9166-43fb-a67e-713fc99664da" }, - "execution_count": 148, + "execution_count": null, "outputs": [ { "output_type": "execute_result", @@ -467,31 +627,359 @@ } ] }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "hqYJrbkVfi36" + }, + "execution_count": 28, + "outputs": [] + }, { "cell_type": "code", "source": [ - "correct, total" + "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", + "batch_size = 128\n", + "\n", + "class CustomDataset(Dataset):\n", + " def __init__(self, root_dir, transform=None):\n", + " self.root_dir = root_dir\n", + " self.transform = transform\n", + " self.image_paths = os.listdir(root_dir)\n", + "\n", + " def __len__(self):\n", + " return len(self.image_paths)\n", + "\n", + " def __getitem__(self, idx):\n", + " img_name = os.path.join(self.root_dir, self.image_paths[idx])\n", + " image = Image.open(img_name)\n", + "\n", + " if self.transform:\n", + " image = self.transform(image)\n", + "\n", + " return image\n", + "\n", + "class TripletDataset(Dataset):\n", + " def __init__(self, AD, NC):\n", + " # Combine the datasets and labels\n", + " self.X = AD + NC\n", + " self.AD = AD\n", + " self.NC = NC\n", + "\n", + " self.Y = torch.cat((torch.ones(len(AD)), torch.zeros(len(NC))), dim=0)\n", + "\n", + " # Generate random permutations of indices\n", + " self.anc_indices = torch.randperm(len(self.X))\n", + " self.pos_indices = self.anc_indices % len(AD)\n", + " self.neg_indices = self.anc_indices % len(NC)\n", + "\n", + "\n", + " def __len__(self):\n", + " return len(self.anc_indices)\n", + "\n", + " def __getitem__(self, idx):\n", + " anc = self.anc_indices[idx]\n", + " pos = self.pos_indices[idx]\n", + " neg = self.neg_indices[idx]\n", + " img1 = self.X[anc]\n", + " img2 = self.AD[pos]\n", + " img3 = self.NC[neg]\n", + " label = self.Y[anc]\n", + "\n", + " return img1, img2, img3, label\n", + "\n", + "\n", + "# Set the device\n", + "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", + "\n", + "\n", + "size = 256\n", + "\n", + "def intensity_normalization(img):\n", + " mean = torch.mean(img)\n", + " std = torch.std(img)\n", + " return (img - mean) / std\n", + "\n", + "def windowing(img, window_center, window_width):\n", + " img = torch.clamp(img, window_center - window_width // 2, window_center + window_width // 2)\n", + " img = (img - (window_center - 0.5)) / (window_width - 1)\n", + " return img\n", + "\n", + "transform_t = transforms.Compose([\n", + " transforms.Resize((size, size)),\n", + " # transforms.RandomHorizontalFlip(p=0.5), # Randomly flip the image horizontally with 50% probability\n", + " # transforms.RandomVerticalFlip(p=0.5), # Randomly flip the image vertically with 50% probability\n", + " transforms.RandomRotation(degrees=15), # Randomly rotate the image by up to 5 degrees\n", + " transforms.ColorJitter(brightness=0.05, contrast=0.05, saturation=0.05, hue=0.05), # Adjust color jitter with smaller increments\n", + " transforms.RandomAffine(degrees=0, translate=(0.05, 0.05), scale=(0.95, 1.05)), # Random affine transformations with smaller parameters\n", + " transforms.ToTensor(),\n", + " transforms.Lambda(intensity_normalization),\n", + " transforms.Lambda(lambda x: windowing(x, window_center=size // 2, window_width=size))\n", + "])\n", + "\n", + "transform_X = transforms.Compose([\n", + " transforms.Resize((size, size)),\n", + " transforms.ToTensor(),\n", + " transforms.Lambda(intensity_normalization),\n", + " transforms.Lambda(lambda x: windowing(x, window_center=size // 2, window_width=size))\n", + "])\n", + "\n", + "\n", + "# Replace 'your_nii_folder' with the path to your folder containing .nii files\n", + "# nii_dataset = NiiDataset(root_dir=r'keras_png_slices_data\\keras_png_slices_data\\keras_png_slices_train')\n", + "loaders = {}\n", + "\n", + "for stage in ['train', 'test']:\n", + " transform = transform_t if stage == 'train' else transform_X\n", + " AD = CustomDataset(root_dir=os.path.join(stage, 'AD'), transform=transform)\n", + " NC = CustomDataset(root_dir=os.path.join(stage, 'NC'), transform=transform)\n", + " dataset = TripletDataset(AD, NC)\n", + " loaders[stage] = DataLoader(dataset, batch_size=batch_size)" + ], + "metadata": { + "id": "dlrwhZTjTKWf" + }, + "execution_count": 7, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "import torchvision.models as models\n", + "import torch.nn.functional as F\n", + "\n", + "class TripletSiameseNetwork(nn.Module):\n", + " def __init__(self, pretrained=True):\n", + " super(TripletSiameseNetwork, self).__init__()\n", + " self.resnet = models.resnet18(pretrained=pretrained)\n", + " self.resnet.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False)\n", + " self.fc1 = nn.Linear(1000, 500)\n", + " self.fc2 = nn.Linear(500, 20)\n", + "\n", + " def forward_once(self, x):\n", + " output = self.resnet(x)\n", + " output = self.fc1(output)\n", + " output = self.fc2(output)\n", + " return output\n", + "\n", + " def forward(self, anchor, positive, negative):\n", + " output_anchor = self.forward_once(anchor)\n", + " output_positive = self.forward_once(positive)\n", + " output_negative = self.forward_once(negative)\n", + " return output_anchor, output_positive, output_negative\n", + "\n", + "class TripletLoss(nn.Module):\n", + " def __init__(self, margin=1.0):\n", + " super(TripletLoss, self).__init__()\n", + " self.margin = margin\n", + "\n", + " def forward(self, anchor, positive, negative):\n", + " distance_positive = F.pairwise_distance(anchor, positive)\n", + " distance_negative = F.pairwise_distance(anchor, negative)\n", + " losses = F.relu(distance_positive - distance_negative + self.margin)\n", + " return losses.mean()\n", + "\n", + "class TripletLossWithRegularization(nn.Module):\n", + " def __init__(self, margin=1.0, lambda_reg=0.01):\n", + " super(TripletLossWithRegularization, self).__init__()\n", + " self.margin = margin\n", + " self.lambda_reg = lambda_reg # Regularization parameter\n", + "\n", + " def forward(self, anchor, positive, negative):\n", + " distance_positive = F.pairwise_distance(anchor, positive)\n", + " distance_negative = F.pairwise_distance(anchor, negative)\n", + " triplet_losses = F.relu(distance_positive - distance_negative + self.margin)\n", + " triplet_loss = triplet_losses.mean()\n", + "\n", + " # Compute L2 regularization\n", + " l2_reg = None\n", + " for param in trip_model.parameters():\n", + " if l2_reg is None:\n", + " l2_reg = param.norm(2)\n", + " else:\n", + " l2_reg = l2_reg + param.norm(2)\n", + "\n", + " loss = triplet_loss + self.lambda_reg * l2_reg\n", + " return loss\n", + "\n", + "\n", + "\n" + ], + "metadata": { + "id": "c8MhXUlOTHnY" + }, + "execution_count": 8, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from torch.optim.lr_scheduler import StepLR\n", + "learning_rate = 0.0005\n", + "trip_model = TripletSiameseNetwork()\n", + "trip_criterion = TripletLossWithRegularization(margin=1.0)\n", + "total_step = len(loaders['train'])\n", + "\n", + "optimizer = optim.Adam(trip_model.parameters(), lr=learning_rate)\n", + "scheduler = StepLR(optimizer, step_size=3, gamma=0.1)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, - "id": "hqYJrbkVfi36", - "outputId": "8e5c2a93-1cd2-47e1-8c29-95e1ceda5ca3" + "id": "Gs0V-oZe1O46", + "outputId": "adaef8b1-fced-4a59-88e3-e7a58373c6ae" }, - "execution_count": 139, + "execution_count": 9, "outputs": [ { - "output_type": "execute_result", - "data": { - "text/plain": [ - "(2226, 4500)" - ] - }, - "metadata": {}, - "execution_count": 139 + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n", + " warnings.warn(\n", + "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.\n", + " warnings.warn(msg)\n", + "Downloading: \"https://download.pytorch.org/models/resnet18-f37072fd.pth\" to /root/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth\n", + "100%|██████████| 44.7M/44.7M [00:00<00:00, 186MB/s]\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "from itertools import cycle\n", + "\n", + "test_iter = cycle(iter(loaders['test']))\n", + "# Training loop\n", + "epochs = 10\n", + "trip_model.to(device)\n", + "losses = []\n", + "val_losses = []\n", + "for epoch in range(epochs):\n", + " losses.append([])\n", + " val_losses.append([])\n", + " for i, (img1, img2, img3, _) in enumerate(loaders['train']):\n", + " img1, img2, img3 = img1.to(device), img2.to(device), img3.to(device)\n", + " size = img1.size(0)\n", + " img1, img2, img3 = img1[torch.randperm(size)], img2[torch.randperm(size)], img3[torch.randperm(size)]\n", + " out1, out2, out3 = trip_model(img1, img2, img3)\n", + "\n", + " loss = trip_criterion(out1, out2, out3)\n", + " optimizer.zero_grad()\n", + " loss.backward()\n", + " optimizer.step()\n", + "\n", + " if (i + 1) % (total_step // 10) == 0:\n", + " losses[epoch].append(loss.item())\n", + "\n", + " val_img1, val_img2, val_img3, _ = next(test_iter)\n", + " val_img1, val_img2, val_img3 = val_img1.to(device), val_img2.to(device), val_img3.to(device)\n", + " val_out1, val_out2, val_out3 = trip_model(val_img1, val_img2, val_img3)\n", + " val_loss = trip_criterion(val_out1, val_out2, val_out3)\n", + " val_losses[epoch].append(val_loss.item())\n", + "\n", + " print(f\"Epoch [{epoch + 1} / {epochs}], Step [{i + 1} / {total_step}], Loss: {loss.item()}, Validation Loss: {val_loss.item()}\")\n", + "\n", + " scheduler.step()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 425 + }, + "id": "A_J3lXLOUDu0", + "outputId": "380a9cc5-fffd-4b7a-ea04-2ac87e26aab3" + }, + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Epoch [1 / 10], Step [16 / 169], Loss: 6.299764156341553, Validation Loss: 5.821728706359863\n" + ] + }, + { + "output_type": "error", + "ename": "OutOfMemoryError", + "evalue": "ignored", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mOutOfMemoryError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0msize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimg1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0mimg1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg3\u001b[0m \u001b[0;34m=\u001b[0m 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"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torchvision/models/resnet.py\u001b[0m in \u001b[0;36m_forward_impl\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 267\u001b[0m \u001b[0;31m# See note [TorchScript super()]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 268\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconv1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 269\u001b[0;31m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbn1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 270\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m 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\u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1502\u001b[0m \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1503\u001b[0m \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/batchnorm.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m 169\u001b[0m \u001b[0mused\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mnormalization\u001b[0m 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eps)\u001b[0m\n\u001b[1;32m 2448\u001b[0m \u001b[0m_verify_batch_size\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2449\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2450\u001b[0;31m return torch.batch_norm(\n\u001b[0m\u001b[1;32m 2451\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbias\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrunning_mean\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrunning_var\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtraining\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmomentum\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0meps\u001b[0m\u001b[0;34m,\u001b[0m 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Tried to allocate 512.00 MiB (GPU 0; 15.77 GiB total capacity; 14.33 GiB already allocated; 28.12 MiB free; 14.47 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF" + ] } ] + }, + { + "cell_type": "code", + "source": [ + "embeddings = []\n", + "labels = []\n", + "trip_model.eval()\n", + "with torch.no_grad():\n", + " for i, (img1, img2, img3, label) in enumerate(loaders['test']):\n", + " img1, img2, img3, label = img1.to(device), img2.to(device), img3.to(device), label.to(device)\n", + " out1, _, _ = trip_model(img1, img2, img3)\n", + " embeddings.extend(out1.cpu().tolist())\n", + " labels.extend(label.cpu().tolist())\n" + ], + "metadata": { + "id": "nnGoOwX-Vk_i" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from sklearn.model_selection import train_test_split\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.metrics import accuracy_score\n", + "\n", + "# Split the data into training and testing sets\n", + "X_train, X_test, y_train, y_test = train_test_split(embeddings, labels, test_size=0.2, random_state=42)\n", + "\n", + "# Train a logistic regression model\n", + "classifier = LogisticRegression(max_iter=1000)\n", + "classifier.fit(X_train, y_train)\n", + "\n", + "# Make predictions\n", + "predictions = classifier.predict(X_test)\n", + "\n", + "# Calculate accuracy\n", + "accuracy = accuracy_score(y_test, predictions)\n", + "print(f\"Accuracy: {accuracy}\")\n" + ], + "metadata": { + "id": "ayGTRCP5Wl0s" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "gaj_LIDgrl6N" + }, + "execution_count": null, + "outputs": [] } ], "metadata": { @@ -499,7 +987,7 @@ "provenance": [], "machine_shape": "hm", "gpuType": "A100", - "authorship_tag": "ABX9TyN39gN45mXrH2YYwJ6aKtns", + "authorship_tag": "ABX9TyPKn/v2mVLUWZMauFxXGvWg", "include_colab_link": true }, "kernelspec": { From 20da493b9f90e7e5b60f06ccca4c861c535e613f Mon Sep 17 00:00:00 2001 From: FinnRobertson15 <110511080+FinnRobertson15@users.noreply.github.com> Date: Mon, 16 Oct 2023 22:07:55 +1000 Subject: [PATCH 06/14] Tuned hyperparameters, will run for 100 epochs --- Colab version.ipynb | 212 ++++++++++++++++++++++++++------------------ 1 file changed, 128 insertions(+), 84 deletions(-) diff --git a/Colab version.ipynb b/Colab version.ipynb index 5714621cc..00f28d016 100644 --- a/Colab version.ipynb +++ b/Colab version.ipynb @@ -18,7 +18,7 @@ "base_uri": "https://localhost:8080/" }, "id": "yzCmT9TUJY10", - "outputId": "f4a92c6b-c770-48bf-a03c-3a6b9f924c30" + "outputId": "df7da7be-a49a-44be-ebef-5953c0ada243" }, "outputs": [ { @@ -42,7 +42,7 @@ "base_uri": "https://localhost:8080/" }, "id": "-Lb7CGdEJCQg", - "outputId": "7cf9c217-28e6-43e3-aee4-f079779419c3" + "outputId": "604ba5ed-6fa6-42c3-c3f1-ae6e7e8d94a1" }, "outputs": [ { @@ -80,7 +80,7 @@ "base_uri": "https://localhost:8080/" }, "id": "PM8uTdbZpQGJ", - "outputId": "15ea444a-ce03-4ddb-d19e-5a8bb8686b53" + "outputId": "7ab4b973-0457-4bd1-ba08-287fc9d91540" }, "execution_count": 3, "outputs": [ @@ -103,7 +103,7 @@ "base_uri": "https://localhost:8080/" }, "id": "x1t1iFV_p2MU", - "outputId": "9d142efb-edd0-4ae2-8e69-1a377fb13ad5" + "outputId": "7f825366-4473-4a83-d557-29c3fbd6ec11" }, "execution_count": 4, "outputs": [ @@ -126,7 +126,7 @@ "base_uri": "https://localhost:8080/" }, "id": "Tp_Fb7Eyp-yv", - "outputId": "45e2140b-5eab-4cf6-dc81-3b6c9fd592f0" + "outputId": "153ee02c-1cd9-4262-d0b5-33b7c1857578" }, "execution_count": 5, "outputs": [ @@ -633,14 +633,14 @@ "metadata": { "id": "hqYJrbkVfi36" }, - "execution_count": 28, + "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", - "batch_size = 128\n", + "batch_size = 80\n", "\n", "class CustomDataset(Dataset):\n", " def __init__(self, root_dir, transform=None):\n", @@ -661,19 +661,15 @@ " return image\n", "\n", "class TripletDataset(Dataset):\n", - " def __init__(self, AD, NC):\n", - " # Combine the datasets and labels\n", + " def __init__(self, AD, NC, transform=None):\n", " self.X = AD + NC\n", " self.AD = AD\n", " self.NC = NC\n", - "\n", " self.Y = torch.cat((torch.ones(len(AD)), torch.zeros(len(NC))), dim=0)\n", - "\n", - " # Generate random permutations of indices\n", " self.anc_indices = torch.randperm(len(self.X))\n", - " self.pos_indices = self.anc_indices % len(AD)\n", - " self.neg_indices = self.anc_indices % len(NC)\n", - "\n", + " self.pos_indices = torch.randperm(len(self.X)) % len(AD)\n", + " self.neg_indices = torch.randperm(len(self.X)) % len(NC)\n", + " self.transform = transform\n", "\n", " def __len__(self):\n", " return len(self.anc_indices)\n", @@ -687,60 +683,86 @@ " img3 = self.NC[neg]\n", " label = self.Y[anc]\n", "\n", + " if self.transform:\n", + " img1 = self.transform(img1)\n", + " img2 = self.transform(img2)\n", + " img3 = self.transform(img3)\n", + "\n", " return img1, img2, img3, label\n", "\n", "\n", + "\n", "# Set the device\n", "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", "\n", "\n", "size = 256\n", "\n", - "def intensity_normalization(img):\n", + "def intensity_normalization(img, mean = None, std = None):\n", " mean = torch.mean(img)\n", " std = torch.std(img)\n", " return (img - mean) / std\n", "\n", - "def windowing(img, window_center, window_width):\n", - " img = torch.clamp(img, window_center - window_width // 2, window_center + window_width // 2)\n", - " img = (img - (window_center - 0.5)) / (window_width - 1)\n", - " return img\n", + "class CustomNormalize(object):\n", + " def __init__(self, mean, std):\n", + " self.mean = mean\n", + " self.std = std\n", "\n", - "transform_t = transforms.Compose([\n", + " def __call__(self, img):\n", + " return (img - self.mean) / self.std\n", + "\n", + "transform_train = transforms.Compose([\n", " transforms.Resize((size, size)),\n", - " # transforms.RandomHorizontalFlip(p=0.5), # Randomly flip the image horizontally with 50% probability\n", - " # transforms.RandomVerticalFlip(p=0.5), # Randomly flip the image vertically with 50% probability\n", - " transforms.RandomRotation(degrees=15), # Randomly rotate the image by up to 5 degrees\n", - " transforms.ColorJitter(brightness=0.05, contrast=0.05, saturation=0.05, hue=0.05), # Adjust color jitter with smaller increments\n", - " transforms.RandomAffine(degrees=0, translate=(0.05, 0.05), scale=(0.95, 1.05)), # Random affine transformations with smaller parameters\n", " transforms.ToTensor(),\n", - " transforms.Lambda(intensity_normalization),\n", - " transforms.Lambda(lambda x: windowing(x, window_center=size // 2, window_width=size))\n", + " transforms.RandomRotation(degrees=5), # Randomly rotate the image by up to 5 degrees\n", + " # transforms.ColorJitter(brightness=0.05, contrast=0.05, saturation=0.05, hue=0.05), # Adjust color jitter with smaller increments\n", + " transforms.RandomAffine(degrees=0, translate=(0.05, 0.05), scale=(0.95, 1.05)), # Random affine transformations with smaller parameters\n", + " transforms.Lambda(intensity_normalization)\n", + "\n", "])\n", "\n", - "transform_X = transforms.Compose([\n", + "transform_test = transforms.Compose([\n", " transforms.Resize((size, size)),\n", " transforms.ToTensor(),\n", - " transforms.Lambda(intensity_normalization),\n", - " transforms.Lambda(lambda x: windowing(x, window_center=size // 2, window_width=size))\n", + " transforms.Lambda(intensity_normalization)\n", "])\n", "\n", + "class Normalize(object):\n", + " def __init__(self, mean, std):\n", + " self.mean = mean\n", + " self.std = std\n", + "\n", + " def __call__(self, img):\n", + " return (img - self.mean) / self.std\n", + "\n", + "\n", "\n", "# Replace 'your_nii_folder' with the path to your folder containing .nii files\n", "# nii_dataset = NiiDataset(root_dir=r'keras_png_slices_data\\keras_png_slices_data\\keras_png_slices_train')\n", "loaders = {}\n", "\n", "for stage in ['train', 'test']:\n", - " transform = transform_t if stage == 'train' else transform_X\n", + " if stage == 'train':\n", + " transform = transform_train\n", + " else:\n", + " transform = transform_test\n", " AD = CustomDataset(root_dir=os.path.join(stage, 'AD'), transform=transform)\n", " NC = CustomDataset(root_dir=os.path.join(stage, 'NC'), transform=transform)\n", + " if stage == 'train':\n", + " X = torch.stack([img for img in AD + NC])\n", + " # Compute the mean and standard deviation\n", + " mean = X.mean()\n", + " std = X.std()\n", + " normalize = transforms.Compose([\n", + " Normalize(mean, std)\n", + " ])\n", " dataset = TripletDataset(AD, NC)\n", " loaders[stage] = DataLoader(dataset, batch_size=batch_size)" ], "metadata": { "id": "dlrwhZTjTKWf" }, - "execution_count": 7, + "execution_count": 43, "outputs": [] }, { @@ -757,7 +779,7 @@ " self.resnet = models.resnet18(pretrained=pretrained)\n", " self.resnet.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False)\n", " self.fc1 = nn.Linear(1000, 500)\n", - " self.fc2 = nn.Linear(500, 20)\n", + " self.fc2 = nn.Linear(500, 2)\n", "\n", " def forward_once(self, x):\n", " output = self.resnet(x)\n", @@ -811,43 +833,26 @@ "metadata": { "id": "c8MhXUlOTHnY" }, - "execution_count": 8, + "execution_count": 47, "outputs": [] }, { "cell_type": "code", "source": [ "from torch.optim.lr_scheduler import StepLR\n", - "learning_rate = 0.0005\n", + "learning_rate = 0.001\n", "trip_model = TripletSiameseNetwork()\n", "trip_criterion = TripletLossWithRegularization(margin=1.0)\n", "total_step = len(loaders['train'])\n", "\n", "optimizer = optim.Adam(trip_model.parameters(), lr=learning_rate)\n", - "scheduler = StepLR(optimizer, step_size=3, gamma=0.1)" + "scheduler = StepLR(optimizer, step_size=15, gamma=0.75)" ], "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Gs0V-oZe1O46", - "outputId": "adaef8b1-fced-4a59-88e3-e7a58373c6ae" + "id": "Gs0V-oZe1O46" }, - "execution_count": 9, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n", - " warnings.warn(\n", - "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.\n", - " warnings.warn(msg)\n", - "Downloading: \"https://download.pytorch.org/models/resnet18-f37072fd.pth\" to /root/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth\n", - "100%|██████████| 44.7M/44.7M [00:00<00:00, 186MB/s]\n" - ] - } - ] + "execution_count": 48, + "outputs": [] }, { "cell_type": "code", @@ -856,7 +861,7 @@ "\n", "test_iter = cycle(iter(loaders['test']))\n", "# Training loop\n", - "epochs = 10\n", + "epochs = 100\n", "trip_model.to(device)\n", "losses = []\n", "val_losses = []\n", @@ -889,40 +894,68 @@ ], "metadata": { "colab": { - "base_uri": "https://localhost:8080/", - "height": 425 + "base_uri": "https://localhost:8080/" }, "id": "A_J3lXLOUDu0", - "outputId": "380a9cc5-fffd-4b7a-ea04-2ac87e26aab3" + "outputId": "7c61c7fa-87f6-498f-8cee-a141738d31fb" }, - "execution_count": 10, + "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "Epoch [1 / 10], Step [16 / 169], Loss: 6.299764156341553, Validation Loss: 5.821728706359863\n" + "Epoch [1 / 100], Step [26 / 269], Loss: 6.237911701202393, Validation Loss: 6.3210039138793945\n", + "Epoch [1 / 100], Step [52 / 269], Loss: 6.128037929534912, Validation Loss: 6.244564056396484\n", + "Epoch [1 / 100], Step [78 / 269], Loss: 6.11774206161499, Validation Loss: 6.238094806671143\n", + "Epoch [1 / 100], Step [104 / 269], Loss: 6.161336898803711, Validation Loss: 6.252033233642578\n" ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.animation import FuncAnimation\n", + "\n", + "\n", + "\n", + "fig, ax = plt.subplots()\n", + "line1, = ax.plot(np.arange(len(losses[0])), losses[0], label='Training Loss')\n", + "line2, = ax.plot(np.arange(len(val_losses[0])), val_losses[0], label='Validation Loss')\n", + "ax.legend()\n", + "\n", + "\n", + "\n", + "def update(frame):\n", + " line1.set_data(np.arange(len(losses[frame])), losses[frame])\n", + " line2.set_data(np.arange(len(val_losses[frame])), val_losses[frame])\n", + " return line1, line2\n", + "\n", + "ani = FuncAnimation(fig, update, frames=len(losses), blit=True)\n", + "plt.show()\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 430 }, + "id": "gD83031xGBYz", + "outputId": "f4d39054-5e51-4ab2-8214-4ed357f5a571" + }, + "execution_count": null, + "outputs": [ { - "output_type": "error", - "ename": "OutOfMemoryError", - "evalue": "ignored", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mOutOfMemoryError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0msize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimg1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0mimg1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg3\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimg1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandperm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandperm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg3\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandperm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0mout1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout3\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrip_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrip_criterion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - 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"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/functional.py\u001b[0m in \u001b[0;36mbatch_norm\u001b[0;34m(input, running_mean, running_var, weight, bias, training, momentum, eps)\u001b[0m\n\u001b[1;32m 2448\u001b[0m \u001b[0m_verify_batch_size\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2449\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2450\u001b[0;31m return torch.batch_norm(\n\u001b[0m\u001b[1;32m 2451\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbias\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrunning_mean\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrunning_var\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtraining\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmomentum\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0meps\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackends\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcudnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menabled\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2452\u001b[0m )\n", - "\u001b[0;31mOutOfMemoryError\u001b[0m: CUDA out of memory. Tried to allocate 512.00 MiB (GPU 0; 15.77 GiB total capacity; 14.33 GiB already allocated; 28.12 MiB free; 14.47 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF" - ] + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} } ] }, @@ -967,10 +1000,22 @@ "print(f\"Accuracy: {accuracy}\")\n" ], "metadata": { - "id": "ayGTRCP5Wl0s" + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ayGTRCP5Wl0s", + "outputId": "da682072-bc72-4e11-fbc5-f64c96b69050" }, "execution_count": null, - "outputs": [] + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Accuracy: 0.58\n" + ] + } + ] }, { "cell_type": "code", @@ -987,7 +1032,6 @@ "provenance": [], "machine_shape": "hm", "gpuType": "A100", - "authorship_tag": "ABX9TyPKn/v2mVLUWZMauFxXGvWg", "include_colab_link": true }, "kernelspec": { From 67818e09b29e45d9d46cada67708b1630dbab31b Mon Sep 17 00:00:00 2001 From: FinnRobertson15 <110511080+FinnRobertson15@users.noreply.github.com> Date: Tue, 17 Oct 2023 14:40:06 +1000 Subject: [PATCH 07/14] Changed to SGD, fixed classifier to actually use embeddings. Getting ~70% test accuracy --- Colab version.ipynb | 801 ++++++++++++++++++++++++++++---------------- 1 file changed, 511 insertions(+), 290 deletions(-) diff --git a/Colab version.ipynb b/Colab version.ipynb index 00f28d016..c9ec5a81f 100644 --- a/Colab version.ipynb +++ b/Colab version.ipynb @@ -12,13 +12,13 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "yzCmT9TUJY10", - "outputId": "df7da7be-a49a-44be-ebef-5953c0ada243" + "outputId": "bdc05ce0-7e5d-4959-8685-ce3b497d3a18" }, "outputs": [ { @@ -36,13 +36,13 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "-Lb7CGdEJCQg", - "outputId": "604ba5ed-6fa6-42c3-c3f1-ae6e7e8d94a1" + "outputId": "0a9018e4-6a51-4c9f-de99-d79cd5078de4" }, "outputs": [ { @@ -59,6 +59,23 @@ }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "PM8uTdbZpQGJ", + "outputId": "281ff7ad-897b-488c-f95b-db60efdab763" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "['AD_NC']\n" + ] + } + ], "source": [ "import zipfile\n", "import os\n", @@ -74,38 +91,18 @@ "# List the contents of the extracted folder\n", "extracted_files = os.listdir(extract_path)\n", "print(extracted_files)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "PM8uTdbZpQGJ", - "outputId": "7ab4b973-0457-4bd1-ba08-287fc9d91540" - }, - "execution_count": 3, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "['AD_NC']\n" - ] - } ] }, { "cell_type": "code", - "source": [ - "cd /content/extracted_folder/AD_NC" - ], + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "x1t1iFV_p2MU", - "outputId": "7f825366-4473-4a83-d557-29c3fbd6ec11" + "outputId": "e70aee0f-012c-4647-f4f8-4eec6b9a4397" }, - "execution_count": 4, "outputs": [ { "output_type": "stream", @@ -114,21 +111,21 @@ "/content/extracted_folder/AD_NC\n" ] } + ], + "source": [ + "cd /content/extracted_folder/AD_NC" ] }, { "cell_type": "code", - "source": [ - "ls" - ], + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Tp_Fb7Eyp-yv", - "outputId": "153ee02c-1cd9-4262-d0b5-33b7c1857578" + "outputId": "a06d8cb3-1fb3-4833-a6be-6bd8a9840f54" }, - "execution_count": 5, "outputs": [ { "output_type": "stream", @@ -137,11 +134,14 @@ "\u001b[0m\u001b[01;34mtest\u001b[0m/ \u001b[01;34mtrain\u001b[0m/\n" ] } + ], + "source": [ + "ls" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "id": "_8PBaJLSJSCP" }, @@ -164,23 +164,24 @@ "import torch\n", "from torch.utils.data import DataLoader\n", "from torchvision.datasets import ImageFolder\n", - "import os\n", - "from PIL import Image\n", + "\n", + "\n", "import numpy as np\n", "from torch.utils.data import Dataset, DataLoader, TensorDataset, ConcatDataset\n", - "import random" + "import random\n", + "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", - "source": [ - "batch_size = 128" - ], + "execution_count": null, "metadata": { "id": "u6EH0wk_CkxF" }, - "execution_count": null, - "outputs": [] + "outputs": [], + "source": [ + "batch_size = 128" + ] }, { "cell_type": "code", @@ -275,14 +276,7 @@ }, { "cell_type": "code", - "source": [ - "count = 0\n", - "same = 0\n", - "for i, j, n, m in loaders['test']:\n", - " count += len(n)\n", - " same += (n == m).sum().tolist()\n", - "same / count" - ], + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -290,18 +284,25 @@ "id": "5ceQcKMAPoip", "outputId": "2b8eb6b1-c1c5-43db-9370-42f0da1531f2" }, - "execution_count": null, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "0.5144444444444445" ] }, + "execution_count": 225, "metadata": {}, - "execution_count": 225 + "output_type": "execute_result" } + ], + "source": [ + "count = 0\n", + "same = 0\n", + "for i, j, n, m in loaders['test']:\n", + " count += len(n)\n", + " same += (n == m).sum().tolist()\n", + "same / count" ] }, { @@ -349,32 +350,7 @@ }, { "cell_type": "code", - "source": [ - "# Initialize the network, loss function, and optimizer\n", - "siamese_net = SiameseNetwork(pretrained=True).to(device)\n", - "criterion = ContrastiveLoss()\n", - "optimizer = optim.Adam(siamese_net.parameters(), lr=0.001)\n", - "# Training loop\n", - "epochs = 5\n", - "total_step = len(loaders['test'])\n", - "for epoch in range(epochs):\n", - " for i, (img1, img2, lab1, lab2) in enumerate(loaders['test']):\n", - " img1, img2, lab1, lab2 = img1.to(device), img2.to(device), lab1.to(device), lab2.to(device)\n", - " output = siamese_net(img1, img2)\n", - " o1, o2 = output[:, 0], output[:, 1]\n", - " label = (lab1 == lab2).int()\n", - "\n", - " loss = criterion(o1, o2, label)\n", - " optimizer.zero_grad()\n", - " loss.backward()\n", - " optimizer.step()\n", - "\n", - " if (i + 1) % 21 == 0:\n", - " print((lab1[0], lab2[0]))\n", - " print(label[0])\n", - " print(output[0])\n", - " print(f\"Epoch [{epoch + 1} / {epochs}], Step [{i + 1} / {total_step} Loss {loss.item()}]\")" - ], + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -382,11 +358,10 @@ "id": "4NnXFUDzSWjp", "outputId": "ab9065a9-ffad-4405-963d-85ae26293b0d" }, - "execution_count": null, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "(tensor(0., device='cuda:0'), tensor(1., device='cuda:0'))\n", "tensor(0, device='cuda:0', dtype=torch.int32)\n", @@ -510,10 +485,53 @@ "Epoch [5 / 5], Step [126 / 141 Loss 0.9903947114944458]\n" ] } + ], + "source": [ + "# Initialize the network, loss function, and optimizer\n", + "siamese_net = SiameseNetwork(pretrained=True).to(device)\n", + "criterion = ContrastiveLoss()\n", + "optimizer = optim.Adam(siamese_net.parameters(), lr=0.001)\n", + "# Training loop\n", + "epochs = 5\n", + "total_step = len(loaders['test'])\n", + "for epoch in range(epochs):\n", + " for i, (img1, img2, lab1, lab2) in enumerate(loaders['test']):\n", + " img1, img2, lab1, lab2 = img1.to(device), img2.to(device), lab1.to(device), lab2.to(device)\n", + " output = siamese_net(img1, img2)\n", + " o1, o2 = output[:, 0], output[:, 1]\n", + " label = (lab1 == lab2).int()\n", + "\n", + " loss = criterion(o1, o2, label)\n", + " optimizer.zero_grad()\n", + " loss.backward()\n", + " optimizer.step()\n", + "\n", + " if (i + 1) % 21 == 0:\n", + " print((lab1[0], lab2[0]))\n", + " print(label[0])\n", + " print(output[0])\n", + " print(f\"Epoch [{epoch + 1} / {epochs}], Step [{i + 1} / {total_step} Loss {loss.item()}]\")" ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "G3TWNpvKdAyn", + "outputId": "bf70c5fd-38e5-49c7-a64f-ba1758f103d9" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy of the network on the test images: 49.955555555555556%\n" + ] + } + ], "source": [ "correct = 0\n", "total = 0\n", @@ -534,27 +552,27 @@ "\n", "\n", " print(f'Accuracy of the network on the test images: {100 * correct / total}%')\n" - ], + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, - "id": "G3TWNpvKdAyn", - "outputId": "bf70c5fd-38e5-49c7-a64f-ba1758f103d9" + "id": "MVEvstik8zqK", + "outputId": "c5aed97c-27c1-4685-bf6a-cc2204dbaa69" }, - "execution_count": null, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ - "Accuracy of the network on the test images: 49.955555555555556%\n" + "Accuracy: 0.49166666666666664\n" ] } - ] - }, - { - "cell_type": "code", + ], "source": [ "from sklearn.model_selection import train_test_split\n", "from sklearn.linear_model import LogisticRegression\n", @@ -573,39 +591,20 @@ "# Calculate accuracy\n", "accuracy = accuracy_score(y_test, predictions)\n", "print(f\"Accuracy: {accuracy}\")\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "MVEvstik8zqK", - "outputId": "c5aed97c-27c1-4685-bf6a-cc2204dbaa69" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Accuracy: 0.49166666666666664\n" - ] - } ] }, { "cell_type": "code", - "source": [], + "execution_count": null, "metadata": { "id": "qA-60lBMGV3i" }, - "execution_count": null, - "outputs": [] + "outputs": [], + "source": [] }, { "cell_type": "code", - "source": [ - "features[0].tolist()" - ], + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -613,34 +612,33 @@ "id": "9PrC3ijsFnhE", "outputId": "42aa07d0-9166-43fb-a67e-713fc99664da" }, - "execution_count": null, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "0.5943959355354309" ] }, + "execution_count": 148, "metadata": {}, - "execution_count": 148 + "output_type": "execute_result" } + ], + "source": [ + "features[0].tolist()" ] }, { "cell_type": "code", - "source": [], + "execution_count": null, "metadata": { - "id": "hqYJrbkVfi36" + "id": "dlrwhZTjTKWf" }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", + "outputs": [], "source": [ "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", - "batch_size = 80\n", + "batch_size = 96\n", + "from torch.utils.data.dataset import Subset, random_split\n", "\n", "class CustomDataset(Dataset):\n", " def __init__(self, root_dir, transform=None):\n", @@ -691,12 +689,7 @@ " return img1, img2, img3, label\n", "\n", "\n", - "\n", - "# Set the device\n", - "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", - "\n", - "\n", - "size = 256\n", + "size = 128\n", "\n", "def intensity_normalization(img, mean = None, std = None):\n", " mean = torch.mean(img)\n", @@ -714,11 +707,8 @@ "transform_train = transforms.Compose([\n", " transforms.Resize((size, size)),\n", " transforms.ToTensor(),\n", - " transforms.RandomRotation(degrees=5), # Randomly rotate the image by up to 5 degrees\n", - " # transforms.ColorJitter(brightness=0.05, contrast=0.05, saturation=0.05, hue=0.05), # Adjust color jitter with smaller increments\n", - " transforms.RandomAffine(degrees=0, translate=(0.05, 0.05), scale=(0.95, 1.05)), # Random affine transformations with smaller parameters\n", + " transforms.RandomAffine(degrees=5, translate=(0.05, 0.05), scale=(0.95, 1.05)), # Random affine transformations with smaller parameters\n", " transforms.Lambda(intensity_normalization)\n", - "\n", "])\n", "\n", "transform_test = transforms.Compose([\n", @@ -740,33 +730,69 @@ "# Replace 'your_nii_folder' with the path to your folder containing .nii files\n", "# nii_dataset = NiiDataset(root_dir=r'keras_png_slices_data\\keras_png_slices_data\\keras_png_slices_train')\n", "loaders = {}\n", + "AD_train = CustomDataset(root_dir=os.path.join('train', 'AD'), transform=transform_train)\n", + "NC_train = CustomDataset(root_dir=os.path.join('train', 'NC'), transform=transform_train)\n", "\n", - "for stage in ['train', 'test']:\n", - " if stage == 'train':\n", - " transform = transform_train\n", - " else:\n", - " transform = transform_test\n", - " AD = CustomDataset(root_dir=os.path.join(stage, 'AD'), transform=transform)\n", - " NC = CustomDataset(root_dir=os.path.join(stage, 'NC'), transform=transform)\n", - " if stage == 'train':\n", - " X = torch.stack([img for img in AD + NC])\n", - " # Compute the mean and standard deviation\n", - " mean = X.mean()\n", - " std = X.std()\n", - " normalize = transforms.Compose([\n", + "X = torch.stack([img for img in AD_train + NC_train])\n", + "mean = X.mean()\n", + "std = X.std()\n", + "normalize = transforms.Compose([\n", " Normalize(mean, std)\n", " ])\n", - " dataset = TripletDataset(AD, NC)\n", - " loaders[stage] = DataLoader(dataset, batch_size=batch_size)" + "train_dataset = TripletDataset(AD_train, NC_train, normalize)\n", + "\n", + "\n", + "# Create data loaders for training and validation data\n", + "loaders['train'] = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n", + "\n", + "AD_test = CustomDataset(root_dir=os.path.join('test', 'AD'), transform=transform_test)\n", + "NC_test = CustomDataset(root_dir=os.path.join('test', 'NC'), transform=transform_test)\n", + "\n", + "test_dataset = TripletDataset(AD_test, NC_test, normalize)\n", + "\n", + "split_1 = int(0.5 * len(test_dataset))\n", + "split_2 = len(test_dataset) - split_1\n", + "\n", + "# Perform the split\n", + "test_dataset, cal_dataset = random_split(test_dataset, [split_1, split_2])\n", + "\n", + "loaders['cal'] = DataLoader(cal_dataset, batch_size=batch_size, shuffle=True)\n", + "loaders['test'] = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)" + ] + }, + { + "cell_type": "code", + "source": [ + "len(train_dataset), len(cal_dataset), len(test_dataset)" ], "metadata": { - "id": "dlrwhZTjTKWf" + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "5RLuD3STujVC", + "outputId": "582bc19f-370e-47ab-d0a2-48b82b190226" }, - "execution_count": 43, - "outputs": [] + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(21520, 4500, 4500)" + ] + }, + "metadata": {}, + "execution_count": 56 + } + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "c8MhXUlOTHnY" + }, + "outputs": [], "source": [ "import torch\n", "import torch.nn as nn\n", @@ -778,13 +804,13 @@ " super(TripletSiameseNetwork, self).__init__()\n", " self.resnet = models.resnet18(pretrained=pretrained)\n", " self.resnet.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False)\n", - " self.fc1 = nn.Linear(1000, 500)\n", - " self.fc2 = nn.Linear(500, 2)\n", + " self.fc1 = nn.Linear(1000, 32)\n", + " # self.fc2 = nn.Linear(500, 32)\n", "\n", " def forward_once(self, x):\n", " output = self.resnet(x)\n", " output = self.fc1(output)\n", - " output = self.fc2(output)\n", + " # output = self.fc2(output)\n", " return output\n", "\n", " def forward(self, anchor, positive, negative):\n", @@ -805,7 +831,7 @@ " return losses.mean()\n", "\n", "class TripletLossWithRegularization(nn.Module):\n", - " def __init__(self, margin=1.0, lambda_reg=0.01):\n", + " def __init__(self, margin=1.0, lambda_reg=0.0001):\n", " super(TripletLossWithRegularization, self).__init__()\n", " self.margin = margin\n", " self.lambda_reg = lambda_reg # Regularization parameter\n", @@ -829,208 +855,404 @@ "\n", "\n", "\n" - ], - "metadata": { - "id": "c8MhXUlOTHnY" - }, - "execution_count": 47, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Gs0V-oZe1O46" + }, + "outputs": [], "source": [ "from torch.optim.lr_scheduler import StepLR\n", - "learning_rate = 0.001\n", + "learning_rate = 0.05\n", "trip_model = TripletSiameseNetwork()\n", "trip_criterion = TripletLossWithRegularization(margin=1.0)\n", + "val_criterion = TripletLoss(margin=1.0)\n", "total_step = len(loaders['train'])\n", "\n", - "optimizer = optim.Adam(trip_model.parameters(), lr=learning_rate)\n", - "scheduler = StepLR(optimizer, step_size=15, gamma=0.75)" - ], - "metadata": { - "id": "Gs0V-oZe1O46" - }, - "execution_count": 48, - "outputs": [] + "# optimizer = optim.Adam(trip_model.parameters(), lr=learning_rate)\n", + "# scheduler = StepLR(optimizer, step_size=5, gamma=0.8)\n", + "optimizer = torch.optim.SGD(trip_model.parameters(), lr=learning_rate, momentum=0.9, weight_decay=5e-4)\n", + "\n", + "sched_linear_1 = torch.optim.lr_scheduler.CyclicLR(optimizer, base_lr=0.005, max_lr=learning_rate, step_size_up=15, step_size_down=15, mode=\"triangular\", verbose=False)\n", + "sched_linear_3 = torch.optim.lr_scheduler.LinearLR(optimizer, start_factor=0.005/learning_rate, end_factor=0.005/learning_rate, verbose=False)\n", + "scheduler = torch.optim.lr_scheduler.SequentialLR(optimizer, schedulers=[sched_linear_1, sched_linear_3], milestones=[30])" + ] }, { "cell_type": "code", + "execution_count": 64, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "A_J3lXLOUDu0", + "outputId": "e56e3882-b65d-48f5-a2ae-3280b5c45cdd" + }, + "outputs": [ + { + "metadata": { + "tags": null + }, + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch [1 / 15], Step [22 / 225], Loss: 1.6861666440963745, Validation Loss: 1.3392752408981323\n", + "Epoch [1 / 15], Step [44 / 225], Loss: 1.1134928464889526, Validation Loss: 1.0987355709075928\n", + "Epoch [1 / 15], Step [66 / 225], Loss: 1.0257302522659302, Validation Loss: 1.1489994525909424\n", + "Epoch [1 / 15], Step [88 / 225], Loss: 1.1023128032684326, Validation Loss: 1.149999976158142\n", + "Epoch [1 / 15], Step [110 / 225], Loss: 0.794222354888916, Validation Loss: 1.2070260047912598\n", + "Epoch [1 / 15], Step [132 / 225], Loss: 0.9533556699752808, Validation Loss: 1.061461329460144\n", + "Epoch [1 / 15], Step [154 / 225], Loss: 0.5997182726860046, Validation Loss: 0.9921392202377319\n", + "Epoch [1 / 15], Step [176 / 225], Loss: 0.8227355480194092, Validation Loss: 0.8853449821472168\n", + "Epoch [1 / 15], Step [198 / 225], Loss: 1.0741984844207764, Validation Loss: 1.1926651000976562\n", + "Epoch [1 / 15], Step [220 / 225], Loss: 1.4481054544448853, Validation Loss: 1.1044371128082275\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "metadata": { + "tags": null + }, + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch [2 / 15], Step [22 / 225], Loss: 0.993647038936615, Validation Loss: 0.9966787695884705\n", + "Epoch [2 / 15], Step [44 / 225], Loss: 1.0237594842910767, Validation Loss: 1.0309518575668335\n", + "Epoch [2 / 15], Step [66 / 225], Loss: 0.883934497833252, Validation Loss: 1.0600988864898682\n", + "Epoch [2 / 15], Step [88 / 225], Loss: 1.0512185096740723, Validation Loss: 0.8157957792282104\n", + "Epoch [2 / 15], Step [110 / 225], Loss: 0.7038865089416504, Validation Loss: 0.8065618276596069\n", + "Epoch [2 / 15], Step [132 / 225], Loss: 0.8788958191871643, Validation Loss: 0.9165557622909546\n", + "Epoch [2 / 15], Step [154 / 225], Loss: 0.5161008834838867, Validation Loss: 0.7060334086418152\n", + "Epoch [2 / 15], Step [176 / 225], Loss: 0.312076598405838, Validation Loss: 0.40925726294517517\n", + "Epoch [2 / 15], Step [198 / 225], Loss: 0.46442490816116333, Validation Loss: 0.510191798210144\n", + "Epoch [2 / 15], Step [220 / 225], Loss: 0.08432803303003311, Validation Loss: 0.13745912909507751\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "metadata": { + "tags": null + }, + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch [3 / 15], Step [22 / 225], Loss: 0.6365734934806824, Validation Loss: 0.9633301496505737\n", + "Epoch [3 / 15], Step [44 / 225], Loss: 0.720203697681427, Validation Loss: 0.2848336696624756\n", + "Epoch [3 / 15], Step [66 / 225], Loss: 0.061845649033784866, Validation Loss: 0.9537119269371033\n", + "Epoch [3 / 15], Step [88 / 225], Loss: 0.09502657502889633, Validation Loss: 0.17387674748897552\n", + "Epoch [3 / 15], Step [110 / 225], Loss: 0.16061733663082123, Validation Loss: 0.0026112645864486694\n", + "Epoch [3 / 15], Step [132 / 225], Loss: 0.1448567658662796, Validation Loss: 0.176682710647583\n", + "Epoch [3 / 15], Step [154 / 225], Loss: 0.11984804272651672, Validation Loss: 0.0029973338823765516\n", + "Epoch [3 / 15], Step [176 / 225], Loss: 0.05067078024148941, Validation Loss: 0.024889525026082993\n", + "Epoch [3 / 15], Step [198 / 225], Loss: 0.08953560888767242, Validation Loss: 0.014550979249179363\n", + "Epoch [3 / 15], Step [220 / 225], Loss: 0.05057196691632271, Validation Loss: 0.14682522416114807\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "metadata": { + "tags": null + }, + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch [4 / 15], Step [22 / 225], Loss: 0.05052595213055611, Validation Loss: 0.2758115828037262\n", + "Epoch [4 / 15], Step [44 / 225], Loss: 0.26822781562805176, Validation Loss: 0.33429664373397827\n", + "Epoch [4 / 15], Step [66 / 225], Loss: 0.05043775588274002, Validation Loss: 0.04388421028852463\n", + "Epoch [4 / 15], Step [88 / 225], Loss: 0.05038832128047943, Validation Loss: 0.06882896274328232\n", + "Epoch [4 / 15], Step [110 / 225], Loss: 0.05032651126384735, Validation Loss: 0.015540232881903648\n", + "Epoch [4 / 15], Step [132 / 225], Loss: 0.05026824027299881, Validation Loss: 0.9465289115905762\n", + "Epoch [4 / 15], Step [154 / 225], Loss: 0.08250364661216736, Validation Loss: 0.0\n", + "Epoch [4 / 15], Step [176 / 225], Loss: 0.07428855448961258, Validation Loss: 0.046117693185806274\n", + "Epoch [4 / 15], Step [198 / 225], Loss: 0.05011678487062454, Validation Loss: 0.022663306444883347\n", + "Epoch [4 / 15], Step [220 / 225], Loss: 0.05006015673279762, Validation Loss: 0.015940412878990173\n" + ] + }, + { + "data": { + "image/png": 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\n", 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Loss: 0.020791243761777878\n", + "Epoch [5 / 15], Step [220 / 225], Loss: 0.04932032525539398, Validation Loss: 0.0\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Epoch [6 / 15], Step [22 / 225], Loss: 0.30001387000083923, Validation Loss: 0.0\n", + "Epoch [6 / 15], Step [44 / 225], Loss: 0.049207571893930435, Validation Loss: 0.015120014548301697\n", + "Epoch [6 / 15], Step [66 / 225], Loss: 0.04912778362631798, Validation Loss: 0.007269968744367361\n", + "Epoch [6 / 15], Step [88 / 225], Loss: 0.04904578626155853, Validation Loss: 0.0\n", + "Epoch [6 / 15], Step [110 / 225], Loss: 1.0179965496063232, Validation Loss: 1.0021252632141113\n", + "Epoch [6 / 15], Step [132 / 225], Loss: 0.9388081431388855, Validation Loss: 0.9330480098724365\n", + "Epoch [6 / 15], Step [154 / 225], Loss: 0.8098025321960449, Validation Loss: 0.4797901511192322\n", + "Epoch [6 / 15], Step [176 / 225], Loss: 0.15214264392852783, Validation Loss: 0.6066851019859314\n", + "Epoch [6 / 15], Step [198 / 225], Loss: 0.25942176580429077, Validation Loss: 0.0\n", + "Epoch [6 / 15], Step [220 / 225], Loss: 0.04873622953891754, Validation Loss: 0.02330446057021618\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" 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225], Loss: 0.04806003347039223, Validation Loss: 0.0\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Epoch [8 / 15], Step [22 / 225], Loss: 0.04794354736804962, Validation Loss: 0.0\n", + "Epoch [8 / 15], Step [44 / 225], Loss: 0.04784921184182167, Validation Loss: 0.0\n" + ] + }, + { + "output_type": "error", + "ename": "KeyboardInterrupt", + "evalue": "ignored", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0mlosses\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0mval_losses\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mimg1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloaders\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'train'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 16\u001b[0m \u001b[0msize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimg1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[0mimg1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg3\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimg1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandperm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandperm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg3\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandperm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 631\u001b[0m \u001b[0;31m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 632\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# type: ignore[call-arg]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 633\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 634\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_yielded\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 635\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_kind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0m_DatasetKind\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIterable\u001b[0m \u001b[0;32mand\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 675\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 676\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 677\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_fetcher\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 678\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pin_memory\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 679\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pin_memory_device\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/utils/data/_utils/fetch.py\u001b[0m in \u001b[0;36mfetch\u001b[0;34m(self, possibly_batched_index)\u001b[0m\n\u001b[1;32m 49\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getitems__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 50\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 52\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 53\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/utils/data/_utils/fetch.py\u001b[0m in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 49\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getitems__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 50\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m 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\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpos_indices\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 40\u001b[0m \u001b[0mneg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mneg_indices\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m \u001b[0mimg1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0manc\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 42\u001b[0m \u001b[0mimg2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAD\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpos\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 43\u001b[0m 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\u001b[0mimg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], "source": [ "from itertools import cycle\n", "\n", "test_iter = cycle(iter(loaders['test']))\n", "# Training loop\n", - "epochs = 100\n", + "epochs = 15\n", "trip_model.to(device)\n", "losses = []\n", "val_losses = []\n", + "\n", + "transform_train = transforms.RandomAffine(degrees=5, translate=(0.05, 0.05), scale=(0.95, 1.05))\n", + "\n", "for epoch in range(epochs):\n", " losses.append([])\n", " val_losses.append([])\n", " for i, (img1, img2, img3, _) in enumerate(loaders['train']):\n", - " img1, img2, img3 = img1.to(device), img2.to(device), img3.to(device)\n", " size = img1.size(0)\n", " img1, img2, img3 = img1[torch.randperm(size)], img2[torch.randperm(size)], img3[torch.randperm(size)]\n", + " img1 = transform_train(img1)\n", + " img2 = transform_train(img2)\n", + " img3 = transform_train(img3)\n", + " img1, img2, img3 = img1.to(device), img2.to(device), img3.to(device)\n", + "\n", + "\n", " out1, out2, out3 = trip_model(img1, img2, img3)\n", "\n", " loss = trip_criterion(out1, out2, out3)\n", " optimizer.zero_grad()\n", " loss.backward()\n", " optimizer.step()\n", - "\n", + " losses[epoch].append(loss.item())\n", " if (i + 1) % (total_step // 10) == 0:\n", - " losses[epoch].append(loss.item())\n", "\n", - " val_img1, val_img2, val_img3, _ = next(test_iter)\n", - " val_img1, val_img2, val_img3 = val_img1.to(device), val_img2.to(device), val_img3.to(device)\n", - " val_out1, val_out2, val_out3 = trip_model(val_img1, val_img2, val_img3)\n", - " val_loss = trip_criterion(val_out1, val_out2, val_out3)\n", - " val_losses[epoch].append(val_loss.item())\n", + " with torch.no_grad():\n", + " val_img1, val_img2, val_img3, _ = next(test_iter)\n", + " val_img1, val_img2, val_img3 = val_img1.to(device), val_img2.to(device), val_img3.to(device)\n", + " val_out1, val_out2, val_out3 = trip_model(val_img1, val_img2, val_img3)\n", + " val_loss = val_criterion(val_out1, val_out2, val_out3)\n", + " val_losses[epoch].append(val_loss.item())\n", "\n", - " print(f\"Epoch [{epoch + 1} / {epochs}], Step [{i + 1} / {total_step}], Loss: {loss.item()}, Validation Loss: {val_loss.item()}\")\n", + " print(f\"Epoch [{epoch + 1} / {epochs}], Step [{i + 1} / {total_step}], Loss: {np.mean(losses[epoch])}, Validation Loss: {np.mean(val_losses[epoch])}\")\n", "\n", - " scheduler.step()" - ], + " scheduler.step()\n", + " # Calculate mean of each row\n", + " mean_list1 = np.mean(losses, axis=1)\n", + " mean_list2 = np.mean(val_losses, axis=1)\n", + "\n", + " # Plot the means\n", + " plt.plot(mean_list1, label='Train Loss', marker='o')\n", + " plt.plot(mean_list2, label='Val Loss', marker='o')\n", + "\n", + " plt.xlabel('Epoch')\n", + " plt.ylabel('Mean Loss')\n", + " plt.legend()\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "A_J3lXLOUDu0", - "outputId": "7c61c7fa-87f6-498f-8cee-a141738d31fb" + "id": "nnGoOwX-Vk_i" }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Epoch [1 / 100], Step [26 / 269], Loss: 6.237911701202393, Validation Loss: 6.3210039138793945\n", - "Epoch [1 / 100], Step [52 / 269], Loss: 6.128037929534912, Validation Loss: 6.244564056396484\n", - "Epoch [1 / 100], Step [78 / 269], Loss: 6.11774206161499, Validation Loss: 6.238094806671143\n", - "Epoch [1 / 100], Step [104 / 269], Loss: 6.161336898803711, Validation Loss: 6.252033233642578\n" - ] - } + "outputs": [], + "source": [ + "# data = {}\n", + "# labels = {}\n", + "trip_model.eval()\n", + "with torch.no_grad():\n", + " embeddings = trip_model.forward_once\n", + " for stage in ['train', 'cal', 'test']:\n", + " data[stage] = []\n", + " labels[stage] = []\n", + " for i, (img1, _, _, label) in enumerate(loaders[stage]):\n", + " img1 = img1.to(device)\n", + " output = embeddings(img1)\n", + " data[stage].extend(output.cpu().tolist())\n", + " labels[stage].extend(label.tolist())" ] }, { "cell_type": "code", - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from matplotlib.animation import FuncAnimation\n", - "\n", - "\n", - "\n", - "fig, ax = plt.subplots()\n", - "line1, = ax.plot(np.arange(len(losses[0])), losses[0], label='Training Loss')\n", - "line2, = ax.plot(np.arange(len(val_losses[0])), val_losses[0], label='Validation Loss')\n", - "ax.legend()\n", - "\n", - "\n", - "\n", - "def update(frame):\n", - " line1.set_data(np.arange(len(losses[frame])), losses[frame])\n", - " line2.set_data(np.arange(len(val_losses[frame])), val_losses[frame])\n", - " return line1, line2\n", - "\n", - "ani = FuncAnimation(fig, update, frames=len(losses), blit=True)\n", - "plt.show()\n" - ], + "execution_count": 67, "metadata": { + "id": "ayGTRCP5Wl0s", "colab": { - "base_uri": "https://localhost:8080/", - "height": 430 + "base_uri": "https://localhost:8080/" }, - "id": "gD83031xGBYz", - "outputId": "f4d39054-5e51-4ab2-8214-4ed357f5a571" + "outputId": "4c81e07a-cde3-428c-bf42-cc2132a919de" }, - "execution_count": null, "outputs": [ { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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oWBvuqV8WZ4WQPNG2WiANMkZHNHfEkSiMSJHWtnoQg9pVBWDErG3sPXnB5IqkuEhKSePrFQdo9b8lvP3bTs5cTKZCSS/e71qfRcNac19EOYWQPGaxWBh2uzF3ZMqaQ5yM1+iIo1AYkSJvaPtqtKhSisTkNAZM2UhicqrZJUkRlpSSxjerDtL6f0t449cdnE6wUr6EJ/97oB6Ln23Dg43K4+KsH535pXV4aRpWCMCaauNzjY44DIvdbi/0O4/Fx8fj7+9PXFwcfn5+ZpcjhdCpC1bu+ng5Jy9YuT+iHGO61ddKhlzadTyel2Zv53xiMiH+ngT7exDi75Hx3xB/T0L8PfD3dC0W/6+tqWnMXH+EcUv2Epve4besvweDbg3nwUblcXNRACkoy/acotfXa3F3cWL58+0I8vMwu6RizW6359u/4ex+frvky9lFCligrzuf9Ijg4S//ZtamozSuXJIeTSqYXVaRtWrfaZ78bgMXkoxRpn2nLl7zWA9XJyOs+GUOK8HpYSXY34OSXm6FdnJnSpqNHzcc4dM/93L0/CUAgv08GHhrVbpFlsfdxdnkCh1Pq/DSNKpYgg2HzjHur328dk9ts0sq1uZuOca8rbG8dk9tygZ4mlKDRkakWBn/1z7e/X0Xbi5OzBrQgjrl/M0uqciZu+UYz83cQnKasaHb4NuqcjLeyvH4JGLjLnE8LonYuCSOxyVx5mJytt7TzdmJMv7uGaMpwf4ehPhdCSwh/h6U8nEv0DkYKWk2Zm88yidLojl81gghQb7uDGxXle6NQ/FwVQgx04ro0/T86m/cXJxY9n/tCPbX6Eh+OJ+YTPsPlnI6IZn/61Cdgelz8PKKRkbEIT3ZOoz1B8+yeNdJBk7dyC+Db8HPw9XssoqML5fv5615OwHoVDeYD7o1uO6HclJKGifjrUZIiTdCSuz5S0ZYSf/6dIKV5DQbh89eyvjQz4qLk4UyfkZQuRJWPCgbcOUSUaCPe67na6Sm2fh58zE+/jOaQ2eMhnmlfdwZ0LYKUU0rKIQUEi2rliKyYgnWHzrH50s1OpJf3v19F6cTkqka5EPfVmGm1aGRESl2zicmc9fHKzh6/hIdapfh856NisWchvxks9l5a95Ovl5pbOP+WItKvHx3rTwZqUhOtXHyQlKmERUjrFxKDy9JnLyQhC0bP4mcLBDk6/Gf+SsZl4T8PCjj55Hl/I40m51fthzjo8XRHDhtXHYq5e1G/zZV6NmsIp5uCiGFzcq9p4n6UqMj+WXdwbN0/Xw1ADOfbE6TyiXz/Bz5NjJy9OhRXnjhBebPn09iYiJVq1Zl0qRJREZGZnn8ihUreOGFF9i1axeJiYlUrFiRJ598kmHDhuX01CLZEuDlxriohnT9fDUL/jnBVysO0MfExF/YJaWk8ezMLczbFgvAi51q0LdVWJ4FODcXJ8qX8KJ8Ca9rHpOaZuNUgjVzWIm7lOnrE/FJpNrsHI83Rl02H772OUv7uFM2wCNjHktJb3fmbjmaMfelhJcrT7apQq/mFfFy0wBxYdWiSimaVCrJ2oNnGffXXt64t47ZJRUbyak2Xpy1DYCHGofmSxDJiRz9Kzx37hwtW7akXbt2zJ8/n8DAQKKjoylRosQ1X+Pt7c2gQYOoV68e3t7erFixgieffBJvb2/69euX629AJCv1QwMYeXdNXvn5H96Zv4uICgE0qmjuP7bCKC4xhb7frWftgbO4Olt4v2t97m1QrsDrcHF2Sp9Pcu3JczabndMXrcSevyqsxGcecTkel0Rymo3TCVZOJ1jZSlym9/D3dKVf6zAebVEJH3eFkMLOYrEw9PZwHp74N9PXHmZA2yrX/Tsi2Tdx+X6iTyZQytuN4XfWMLucnF2mGT58OCtXrmT58uW5Oun999+Pt7c33333XbaO12UauRl2u50h0zfzy5ZjhPh7MG9IK0p6u5ldVqFx7PwlHv16LdEnE/B1d+GLRxrRompps8vKFbvdztmLyVdGVOKN0HI8zkqVIG96NquoOURFjN1up/uENaw9cJZHmlXkzfs0OpJbh85c5I4Pl2FNtfFh9/p0iSifb+fK7ud3jmaCzZ07l8jISLp27UpQUBARERFMnDgxR4Vt2rSJVatW0aZNmxy9TiSnLBYLo++vS1hpb2Ljkhg6YzO27ExMcAA7Y+PpMm4l0ScTKOPnzsz+zYt8EAHjz7yUjzt1yvnTvlYZHmlWkf/rUIMx3erzVNuqCiJFkMViYVj6jr4z1h3m2PlrT4KWG7Pb7Yycsx1rqo2WVUtxnwkjoVnJURjZv38/48ePJzw8nAULFjBgwACGDBnCN998c8PXli9fHnd3dyIjIxk4cCB9+vS55rFWq5X4+PhMN5Gb4ePuwrieDfFwdWLZnlN8umSv2SWZbtXe03T7fDUn4q2EB/kw66mW1AzRiKMUXs2rlKJZWEmS02x8pn/DuTJ3yzGWR5/GzcWJt+6rW2gm9+cojNhsNho2bMioUaOIiIigX79+9O3bl88///yGr12+fDnr16/n888/Z+zYsUybNu2ax44ePRp/f/+MW2hoaE7KFMmkRrAfb6ZPfPvwjz2s3Hva5IrM8/Pmozw6aS0XrKk0qVySH/u3oJxJTY5EcmJo+ujIzPWHM5rTSc7EJabw5q/GDueD21Wlcmlvkyu6IkdhJCQkhFq1amV6rGbNmsTExNzwtZUrV6Zu3br07duXYcOG8dprr13z2BEjRhAXF5dxO3z4OtPmRbKha2Qo3SLLY7fD09M3ccLBNuCy2+1MWLaPp6dvJiXNzl11Q/j2iSb4e+myhRQNzcJK0TysFClpdo2O3KR3ruop0q9N4VphmKMw0rJlS3bv3p3psT179lCxYsUcndRms2G1Wq/5vLu7O35+fpluIrn1xr11qBHsy+mEZAZP3URqms3skgpEms3O67/sYNRvuwB4omVlPukRoeZeUuQMbR8OwA/rD3PkXKLJ1RQt6w+eZdpaY+Dg7fvqFLptDnIURoYNG8aaNWsYNWoUe/fuZerUqUyYMIGBAwdmHDNixAh69eqV8fVnn33GL7/8QnR0NNHR0Xz11Ve8//779OzZM+++C5Fs8HB1ZnzPRvi4u7D24FneW7j7xi8q4pJS0hg0dSOTVx0EYORdNXmlc61Cu0+MyPU0DStFiyqXR0f2mV1OkZGcauPF2UZPkW6R5WkaVsrkiv4rR2GkcePGzJ49m2nTplGnTh3efPNNxo4dS1RUVMYxsbGxmS7b2Gw2RowYQYMGDYiMjOSzzz7j3Xff5Y033si770IkmyqX9uZ/D9YD4Iul+1m044TJFeWf84nJ9PpqLfO3H8fN2YmPe0So+ZsUecNuN+aO/LD+MIfPanQkOyYu38+eEwmU9HZjxJ01zS4nS2oHLw7ptbn/MHnVQfw8XJg3pBWhJa/dHbQoOpreQ2TvyQR8PVyY8EgkzasUvt+GRG5Gzy//ZsXe0zzUOJR3HqhndjmFWsyZRG7/cCnWVBsfdKvP/Q3zr6dIVvKlz4hIcfFip5o0CA0gPimVgVM3Yk1NM7ukPLPjWDz3j1vJ3pMJBPt58EP/5goiUqxcnjvy44YjGh25DrvdzsifjZ4iLaqUoktE4egpkhWFEXFIbi5OfBbVkAAvV7YeieOtX3eaXVKeWLn3NN2+MHqIVCvjw6ynWlAjWKOJUrxEVipJq/DSpNrsfPqnVtZcyy9bY1m251R6T5E6haanSFYURsRhlQvw5MPuDQD4bs0h5m45Zm5BuTRn01Eem7SWBGsqTSuX5If+LSirHiJSTF3uO/LjxiPEnNHoyL/FJabwxi9GT5FB7aoSFuhjckXXpzAiDq1d9SAGtqsCwPCftrL3ZILJFeWc3W7n86X7GDojvYdIvRC+7d0Ef0/1EJHiq1HFErSuFkiazc4nf0abXU6h8+6CXZxOsFIl0JsnC1lPkawojIjDG9a+Gs3CSpKYnMZT328gMTnV7JKy7XIPkXfmGz1E+txSmU8eiih0PQRE8sPluSOzNh3l0JmLJldTeGw4dJapfxurWkd1qVskfh4ojIjDc0lf9hro686eEwmMnLOdIrDIjKSUNAZ+n7mHyMi71UNEHEfDCiVokzE6orkjAClpNl6ctR2Aro0KZ0+RrCiMiABBvh58/FAEThaYtfEoM9cX7i0Izicm0/PLv/n9H6OHyKcPq4eIOKbLfUdmbzrKwdMaHfly+QF2n7hASW83XuxUOHuKZEVhRCRd8yqlePaO6gC88vM/7DhWOHeLPnIukQfGr2L9oXP4erjwbe8m3F2vrNlliZiiQWgA7aoboyMfO/jckZgziXy0eA8AL3WqSQlvN5Mryj6FEZGrDGhThXbVA7Gm2njq+w3EJ6WYXVIm/xyL4/5xq9h36iIh/h782L8FzYrIMKxIfnk6fWXNnE1HOeCgoyOXe4okpdhoHlaK+xsW3p4iWVEYEbmKk5OFD7o1oFyAJwfPJPLCj1sLzfyR5dGn6P7FGk5esFK9jC+znmpB9WBfs8sSMV2D0ABurRGEzQ6fLHbM0ZFfL/cUcXbi7S6Fu6dIVhRGRP6lhLcbn0U1xNXZwvztx5m08qDZJTFr4xEen7SOBGsqzcJKMrN/c0L81UNE5LLLK2vmbD7KvlNFb4l+bsRdSuH19J4iA4tAT5GsKIyIZKFBaAAvpU/+GvXbTjbGnDOlDrvdzri/9vLMzC2k2ux0rl+Wb55QDxGRf6tXPoDb0kdHHK0r6/9+N3qKhAV6079t0ZzIrjAicg2PtqjEXXVDSLXZGfT9Rs5dTC7Q86fZ7Lw69x/+9/tuAPq1DuOj7g2KRM8AETNc7sr6swONjmw4dI7v03uKvH1f0egpkhWFEZFrsFgsvPNAXSqX9uZYXBLDZm7GZiuY+SNJKUYDtm9XH8JigVfursWLnWqqh4jIddQt70/7mmWw2eFjB5g7kpJm46XZ2wB4sFH5Ir0hpsKIyHX4ergyLqoh7i5O/LX7FOP+yv/h33MXk4n68m8W/HMCNxcnPu3RkCduqZzv5xUpDi7PHZm75Rh7T14wuZr89dWKA+w6foESXq5FqqdIVhRGRG6gZogfb95XB4APFu1h1b7T+Xauw2cTeeDzVWw4dA4/Dxe+e6IJd9ULybfziRQ3dcr5c3utMtjt8PHi4jt35PDZRMb+kd5T5K5alCxCPUWyojAikg3dIkPp2qg8NjsMmbaZk/FJeX6O7UfjuH/8KvafukhZfw9+HNCiyLRyFilMLo+O/LL1GNEnit/oiN1uZ+Qco6dIs7CSPFDEeopkRWFEJJveuLcONYJ9OZ1gZdC0TaSm2fLsvZftOUX3L1Zz6oKVGsG+zHqqJdXKqIeIyM2oXdafDrWN0ZGPiuHckXnbYlma0VOkbpHrKZIVhRGRbPJ0c2ZcVEN83F1Ye+AsYxbtyZP3/WnDEZ6YvI6LyWm0qFKKmf2bE+zvkSfvLeKonr7NWFkzb1sse4rR6MjVPUWealeFKkWwp0hWFEZEciAs0Id3H6gHwPi/9rF454mbfi+73c5nS/by7A9GD5F7G5Rl8uNN8PNQDxGR3KpV1o+OtYOL3ejIewt2ceqClbDS3gxoW8XscvKMwohIDt1VL4THWlQC4JmZWzh8NjHH75Fms/Pyz9t5b4HRQ+TJ1mF82K0Bbi76JymSV55Onzvy27ZYdh8v+qMjG2Ou9BR5q0udIttTJCv6ySdyE17sVJP6oQHEXUph0NSNWFPTsv3aS8lp9J+ygSlrYrBY4NXOtRihHiIiea5miB931rk8OpI3l1XNkpJm48VZ27Db4YGG5WlRpbTZJeUphRGRm+Dm4sRnD0fg7+nKliNxjJq3M1uvO3sxmYe/XMOiHUYPkXEPN+TxluohIpJfroyOHGfX8XiTq7l5X1/VU+Slu4p2T5GsKIyI3KTyJbz4sHt9AL5ZfYhfthy77vGHzyby4PhVbIo5j7+nK9/3acqdddVDRCQ/1Qj24670f2cf/VE0544cPpvIh+k9RV7sVLPI9xTJisKISC7cWqMMT6VPIhv+09Zr7oex/WgcXcatYv/pi5QL8OSnAc1pXKlkQZYq4rCG3BaOxQLztx9nx7GiNTpit9t55Wejp0jTyiV5sFF5s0vKFwojIrn0zO3VaFq5JBeT03hqykYuJWeeP7I0vYfI6YTLPURaUDVIPURECkr1YF86pY+OFLU9a37bdpwlu4tXT5GsKIyI5JKLsxOf9IigtI87u09c4OWft2c898P6w/RO7yHSsmopfujfnDJ+6iEiUtCGpo+O/P7Pcf45Fmd2OdkSn5TCa7/8A8CAtlWoGlQ8eopkRWFEJA8E+XnwSY8InCzw44YjzFx3mE//jOb/ftxKqs3OfQ3KMumxJviqh4iIKcLL+HJ3vbJA0Zk78v6C3Zy6YKVyMespkhWFEZE80rxKKZ69ozoAL8zayvsLjQln/dtU4QP1EBEx3ZBbq2KxwMIdJ9h+tHCPjmyKOcd3aw4B8PZ9dfBwLT49RbKin44ieWhAmyq0rR6I3Q4WC7x+T22G31lDPURECoHwMr50vjw6UojnjqSk2RiR3lPk/oblaFG1ePUUyYrCiEgecnKy8FH3CPq1DmPy4014NL1Tq4gUDpdX1iwqxKMjk1YaPUUCvFx5qVPx6ymSFYURkTzm7+XKi51q0qZaoNmliMi/VA3y4Z76xujI2D8KX1fWw2cT+XCRMWrzYqealPJxN7migqEwIiIiDmXIbeE4WeCPnSfZdqTwjI5c7ilyKSWNJpVL0rWY9hTJisKIiIg4lCqBPtzboBxQuEZH5m83eoq4OlsYVYx7imRFYURERBzO4Fur4mSBxbtOsuXwebPLMXqKzE3vKdKmePcUyYrCiIiIOJywQB/uK0SjI2MW7ObkBSuVSnnxVLuqZpdT4HIcRo4ePUrPnj0pVaoUnp6e1K1bl/Xr11/z+FmzZnH77bcTGBiIn58fzZs3Z8GCBbkqWkREJLcG3xaOs5OFJbtPsdnE0ZHNh8/z7eWeIl3qFvueIlnJURg5d+4cLVu2xNXVlfnz57Njxw7GjBlDiRIlrvmaZcuWcfvtt/Pbb7+xYcMG2rVrR+fOndm0aVOuixcREblZlUt7mz46knp1T5GIcrR0gJ4iWbHY7XZ7dg8ePnw4K1euZPny5bk6ae3atenevTuvvPJKto6Pj4/H39+fuLg4/Pz8cnVuERGRyw6evshtHywlzWZn1lMtaFjh2r9c54eJy/bz9m87CfByZfEzbYrdUt7sfn7naGRk7ty5REZG0rVrV4KCgoiIiGDixIk5Ksxms3HhwgVKlrz29ulWq5X4+PhMNxERkbxWqbQ3XSKM0ZGC3rPmyLlEPlhkjMi8eKfj9BTJSo7CyP79+xk/fjzh4eEsWLCAAQMGMGTIEL755ptsv8f7779PQkIC3bp1u+Yxo0ePxt/fP+MWGhqakzJFRESybfCtVXF2srB0zyk2HDpXIOe02+28+vM/V3qKRDpOT5Gs5OgyjZubG5GRkaxatSrjsSFDhrBu3TpWr159w9dPnTqVvn378vPPP9O+fftrHme1WrFarRlfx8fHExoaqss0IiKSL57/cQsz1x+hVXhpvuvdNN/P9/v2WPpP2Yirs4X5T7eiapBvvp/TDPlymSYkJIRatWpleqxmzZrExMTc8LXTp0+nT58+zJw587pBBMDd3R0/P79MNxERkfwyqF04Lk4WlkefZsOhs/l6rgtJKbya3lOkf5sqxTaI5ESOwkjLli3ZvXt3psf27NlDxYoVr/u6adOm8fjjjzNt2jTuuuuunFcpIiKSjyqU8uKBhsalkrH5PHdkzMI9nIg3eooMdMCeIlnJURgZNmwYa9asYdSoUezdu5epU6cyYcIEBg4cmHHMiBEj6NWrV8bXU6dOpVevXowZM4amTZty/Phxjh8/Tlxc4dkPQEREZNCtVTNGR9YfzJ/RkS2Hz/PN6oMAvHWfY/YUyUqOwkjjxo2ZPXs206ZNo06dOrz55puMHTuWqKiojGNiY2MzXbaZMGECqampDBw4kJCQkIzb008/nXffhYiISC6FlvTiwfTN6T7Mh74jV/cU6RJRjlvCHbOnSFZyNIHVLOozIiIiBeHw2UTavf8XqTY7M59sTpPK125DkVNfLt/PW/N24u/pyuJn21DaAZby5ssEVhERkeIstKQXXSONdhJ52ZX16PlLjFmY3lOkUw2HCCI5oTAiIiJylYHtquDqbGHVvjP8vf9Mrt/P6CmynUspaTSuVIKujdQ7698URkRERK5SvsSV0ZG8mDuy4J8T/LHzJK7OFkZ1qYuTkyXX71ncKIyIiIj8y8B2VXF1trBm/1lW77v50ZELSSm8lt5T5MnWVQgvo54iWVEYERER+ZdyAZ50b5z7uSNjFu7heHwSFUt5MehW9RS5FoURERGRLDzVtipuzk78feAsq/adzvHrM/cUqaOeItehMCIiIpKFsplGR6LJSSeM1DQbL842eorc16AsrcID86vMYkFhRERE5BqealcFN2cn1h7I2dyRyasO8s+xePw9XRl5d60bv8DBKYyIiIhcQ4i/Jw81ubKyJjujI8fOX+KDRcY8kxF3qqdIdiiMiIiIXMdTbavi5uLEuoPnWLn3xqMjr879h8TkNCIrlqBbpHqKZIfCiIiIyHUE+3vwcJMKgLGy5nqjIwv+Oc6iHSdwcbIw6n71FMkuhREREZEbGNC2Cm4uTqw/dI4Ve7NeWZNgTeXVn9N7irQJo5p6imSbwoiIiMgNlPG7Mjry4aKsR0fGLNzN8fgkKpT0YvCt4QVdYpGmMCIiIpINT7WtgruLExtjzrM8OvPoyNYj5/lm1UFAPUVuhsKIiIhINgT5eRDVtCKQeWXN5Z4iNjvc26Asraupp0hOKYyIiIhkU/82Ybi7OLEp5jxL95wC4NvVh9h+NB4/DxdG3qWeIjdDYURERCSbgvw86Nns8uhINMfOX2LMwt0ADL+zJoG+6ilyMxRGREREcqB/myp4uDqx5fB5en71NxeT02hUsQQPNVZPkZulMCIiIpIDgb7uPJI+OrL/1EWjp0gX9RTJDYURERGRHOrX2hgdMe6HUT1YPUVyw8XsAkRERIqaQF93PuzWgA2HzjHkNvUUyS2FERERkZtwZ90Q7qwbYnYZxYIu04iIiIipFEZERETEVAojIiIiYiqFERERETGVwoiIiIiYSmFERERETKUwIiIiIqZSGBERERFTKYyIiIiIqRRGRERExFQKIyIiImIqhRERERExlTbKExFxAGlpaaSkpJhdhhQzrq6uODs75/p9FEZERIoxu93O8ePHOX/+vNmlSDEVEBBAcHAwFovlpt8jx2Hk6NGjvPDCC8yfP5/ExESqVq3KpEmTiIyMzPL42NhYnn32WdavX8/evXsZMmQIY8eOvemCRUQk+y4HkaCgILy8vHL1gSFyNbvdTmJiIidPngQgJCTkpt8rR2Hk3LlztGzZknbt2jF//nwCAwOJjo6mRIkS13yN1WolMDCQkSNH8uGHH950oSIikjNpaWkZQaRUqVJmlyPFkKenJwAnT54kKCjopi/Z5CiMvPvuu4SGhjJp0qSMxypXrnzd11SqVImPPvoIgK+//vomShQRkZtxeY6Il5eXyZVIcXb571dKSspNh5EcraaZO3cukZGRdO3alaCgICIiIpg4ceJNnfh6rFYr8fHxmW4iInJzdGlG8lNe/P3KURjZv38/48ePJzw8nAULFjBgwACGDBnCN998k+tCrjZ69Gj8/f0zbqGhoXn6/iIiIlJ45CiM2Gw2GjZsyKhRo4iIiKBfv3707duXzz//PE+LGjFiBHFxcRm3w4cP5+n7i4iI46lUqVKOFlD89ddfWCwWrUQqADkKIyEhIdSqVSvTYzVr1iQmJiZPi3J3d8fPzy/TTUREHIPFYrnu7bXXXrup9123bh39+vXL9vEtWrQgNjYWf3//mzpfdin05HACa8uWLdm9e3emx/bs2UPFihXztCgREXFcsbGxGfdnzJjBK6+8kumzx8fHJ+O+3W4nLS0NF5cbf5wFBgbmqA43NzeCg4Nz9Bq5OTkaGRk2bBhr1qxh1KhR7N27l6lTpzJhwgQGDhyYccyIESPo1atXptdt3ryZzZs3k5CQwKlTp9i8eTM7duzIm+9ARESKleDg4Iybv78/Fosl4+tdu3bh6+vL/PnzadSoEe7u7qxYsYJ9+/Zx7733UqZMGXx8fGjcuDF//PFHpvf992Uai8XCl19+SZcuXfDy8iI8PJy5c+dmPP/vEYvJkycTEBDAggULqFmzJj4+PnTs2DFTeEpNTWXIkCEEBARQqlQpXnjhBR599FHuu+++m/7/ce7cOXr16kWJEiXw8vLizjvvJDo6OuP5Q4cO0blzZ0qUKIG3tze1a9fmt99+y3htVFQUgYGBeHp6Eh4enmlFbGGRozDSuHFjZs+ezbRp06hTpw5vvvkmY8eOJSoqKuOY2NjY/1y2iYiIICIigg0bNjB16lQiIiLo1KlT3nwHIiKSbXa7ncTkVFNudrs9z76P4cOH884777Bz507q1atHQkICnTp1YvHixWzatImOHTvSuXPnG04jeP311+nWrRtbt26lU6dOREVFcfbs2Wsen5iYyPvvv893333HsmXLiImJ4bnnnst4/t133+X7779n0qRJrFy5kvj4eObMmZOr7/Wxxx5j/fr1zJ07l9WrV2O32+nUqVPG0u2BAwditVpZtmwZ27Zt4913380YPXr55ZfZsWMH8+fPZ+fOnYwfP57SpUvnqp78kOMOrHfffTd33333NZ+fPHnyfx7Ly7+AIiJy8y6lpFHrlQWmnHvHGx3wcsubXUjeeOMNbr/99oyvS5YsSf369TO+fvPNN5k9ezZz585l0KBB13yfxx57jB49egAwatQoPv74Y9auXUvHjh2zPD4lJYXPP/+cKlWqADBo0CDeeOONjOc/+eQTRowYQZcuXQD49NNPM0YpbkZ0dDRz585l5cqVtGjRAoDvv/+e0NBQ5syZQ9euXYmJieGBBx6gbt26AISFhWW8PiYmhoiIiIwu6ZUqVbrpWvKTdu0VEZEi599bkCQkJPDcc89Rs2ZNAgIC8PHxYefOnTccGalXr17GfW9vb/z8/DLam2fFy8srI4iAsbDj8vFxcXGcOHGCJk2aZDzv7OxMo0aNcvS9XW3nzp24uLjQtGnTjMdKlSpF9erV2blzJwBDhgzhrbfeomXLlrz66qts3bo149gBAwYwffp0GjRowPPPP8+qVatuupb8pI3yREQciKerMzve6GDaufOKt7d3pq+fe+45Fi1axPvvv0/VqlXx9PTkwQcfJDk5+brv4+rqmulri8WCzWbL0fFmj/736dOHDh06MG/ePBYuXMjo0aMZM2YMgwcP5s477+TQoUP89ttvLFq0iNtuu42BAwfy/vvvm1rzv2lkRETEgVgsFrzcXEy55Wcn2JUrV/LYY4/RpUsX6tatS3BwMAcPHsy382XF39+fMmXKsG7duozH0tLS2Lhx402/Z82aNUlNTeXvv//OeOzMmTPs3r07U6uN0NBQ+vfvz6xZs3j22WczdUcPDAzk0UcfZcqUKYwdO5YJEybcdD35RSMjIiJS5IWHhzNr1iw6d+6MxWLh5Zdfvu4IR34ZPHgwo0ePpmrVqtSoUYNPPvmEc+fOZSuIbdu2DV9f34yvLRYL9evX595776Vv37588cUX+Pr6Mnz4cMqVK8e9994LwNChQ7nzzjupVq0a586dY8mSJdSsWROAV155hUaNGlG7dm2sViu//vprxnOFicKIiIgUeR988AFPPPEELVq0oHTp0rzwwgum7Gv2wgsvcPz4cXr16oWzszP9+vWjQ4cO2dpArnXr1pm+dnZ2JjU1lUmTJvH0009z9913k5ycTOvWrfntt98yLhmlpaUxcOBAjhw5gp+fHx07duTDDz8EjF4pI0aM4ODBg3h6etKqVSumT5+e9994LlnsZl/syob4+Hj8/f2Ji4tTN1YRkWxKSkriwIEDVK5cGQ8PD7PLcUg2m42aNWvSrVs33nzzTbPLyRfX+3uW3c9vjYyIiIjkkUOHDrFw4ULatGmD1Wrl008/5cCBAzz88MNml1aoaQKriIhIHnFycmLy5Mk0btyYli1bsm3bNv74449COU+jMNHIiIiISB4JDQ1l5cqVZpdR5GhkREREREylMCIiIiKmUhgRERERUymMiIiIiKkURkRERMRUCiMiIiJiKoUREREpltq2bcvQoUMzvq5UqRJjx4697mssFgtz5szJ9bnz6n0chcKIiIgUKp07d6Zjx45ZPrd8+XIsFgtbt27N8fuuW7eOfv365ba8TF577TUaNGjwn8djY2O588478/Rc/zZ58mQCAgLy9RwFRWFEREQKld69e7No0SKOHDnyn+cmTZpEZGQk9erVy/H7BgYG4uXllRcl3lBwcDDu7u4Fcq7iQGFEREQKlbvvvpvAwEAmT56c6fGEhAR++OEHevfuzZkzZ+jRowflypXDy8uLunXrMm3atOu+778v00RHR9O6dWs8PDyoVasWixYt+s9rXnjhBapVq4aXlxdhYWG8/PLLpKSkAMbIxOuvv86WLVuwWCxYLJaMmv99mWbbtm3ceuuteHp6UqpUKfr160dCQkLG84899hj33Xcf77//PiEhIZQqVYqBAwdmnOtmxMTEcO+99+Lj44Ofnx/dunXjxIkTGc9v2bKFdu3a4evri5+fH40aNWL9+vWAscdO586dKVGiBN7e3tSuXZvffvvtpmu5EbWDFxFxJHY7pCSac25XL7BYbniYi4sLvXr1YvLkybz00ktY0l/zww8/kJaWRo8ePUhISKBRo0a88MIL+Pn5MW/ePB555BGqVKlCkyZNbngOm83G/fffT5kyZfj777+Ji4vLNL/kMl9fXyZPnkzZsmXZtm0bffv2xdfXl+eff57u3buzfft2fv/9d/744w8A/P39//MeFy9epEOHDjRv3px169Zx8uRJ+vTpw6BBgzIFriVLlhASEsKSJUvYu3cv3bt3p0GDBvTt2/eG309W39/lILJ06VJSU1MZOHAg3bt356+//gIgKiqKiIgIxo8fj7OzM5s3b8bV1RWAgQMHkpyczLJly/D29mbHjh34+PjkuI7sUhgREXEkKYkwqqw5537xGLh5Z+vQJ554gvfee4+lS5fStm1bwLhE88ADD+Dv74+/vz/PPfdcxvGDBw9mwYIFzJw5M1th5I8//mDXrl0sWLCAsmWN/x+jRo36zzyPkSNHZtyvVKkSzz33HNOnT+f555/H09MTHx8fXFxcCA4Ovua5pk6dSlJSEt9++y3e3sb3/+mnn9K5c2feffddypQpA0CJEiX49NNPcXZ2pkaNGtx1110sXrz4psLI4sWL2bZtGwcOHCA0NBSAb7/9ltq1a7Nu3ToaN25MTEwM//d//0eNGjUACA8Pz3h9TEwMDzzwAHXr1gUgLCwsxzXkhC7TiIhIoVOjRg1atGjB119/DcDevXtZvnw5vXv3BiAtLY0333yTunXrUrJkSXx8fFiwYAExMTHZev+dO3cSGhqaEUQAmjdv/p/jZsyYQcuWLQkODsbHx4eRI0dm+xxXn6t+/foZQQSgZcuW2Gw2du/enfFY7dq1cXZ2zvg6JCSEkydP5uhcV58zNDQ0I4gA1KpVi4CAAHbu3AnAM888Q58+fWjfvj3vvPMO+/btyzh2yJAhvPXWW7Rs2ZJXX331piYM54RGRkREHImrlzFCYda5c6B3794MHjyYzz77jEmTJlGlShXatGkDwHvvvcdHH33E2LFjqVu3Lt7e3gwdOpTk5OQ8K3f16tVERUXx+uuv06FDB/z9/Zk+fTpjxozJs3Nc7fIlksssFgs2my1fzgXGSqCHH36YefPmMX/+fF599VWmT59Oly5d6NOnDx06dGDevHksXLiQ0aNHM2bMGAYPHpwvtWhkRETEkVgsxqUSM27ZmC9ytW7duuHk5MTUqVP59ttveeKJJzLmj6xcuZJ7772Xnj17Ur9+fcLCwtizZ0+237tmzZocPnyY2NjYjMfWrFmT6ZhVq1ZRsWJFXnrpJSIjIwkPD+fQoUOZjnFzcyMtLe2G59qyZQsXL17MeGzlypU4OTlRvXr1bNecE5e/v8OHD2c8tmPHDs6fP0+tWrUyHqtWrRrDhg1j4cKF3H///UyaNCnjudDQUPr378+sWbN49tlnmThxYr7UCgojIiJSSPn4+NC9e3dGjBhBbGwsjz32WMZz4eHhLFq0iFWrVrFz506efPLJTCtFbqR9+/ZUq1aNRx99lC1btrB8+XJeeumlTMeEh4cTExPD9OnT2bdvHx9//DGzZ8/OdEylSpU4cOAAmzdv5vTp01it1v+cKyoqCg8PDx599FG2b9/OkiVLGDx4MI888kjGfJGblZaWxubNmzPddu7cSfv27albty5RUVFs3LiRtWvX0qtXL9q0aUNkZCSXLl1i0KBB/PXXXxw6dIiVK1eybt06atasCcDQoUNZsGABBw4cYOPGjSxZsiTjufygMCIiIoVW7969OXfuHB06dMg0v2PkyJE0bNiQDh060LZtW4KDg7nvvvuy/b5OTk7Mnj2bS5cu0aRJE/r06cPbb7+d6Zh77rmHYcOGMWjQIBo0aMCqVat4+eWXMx3zwAMP0LFjR9q1a0dgYGCWy4u9vLxYsGABZ8+epXHjxjz44IPcdtttfPrppzn7n5GFhIQEIiIiMt06d+6MxWLh559/pkSJErRu3Zr27dsTFhbGjBkzAHB2dubMmTP06tWLatWq0a1bN+68805ef/11wAg5AwcOpGbNmnTs2JFq1aoxbty4XNd7LRa73W7Pt3fPI/Hx8fj7+xMXF4efn5/Z5YiIFAlJSUkcOHCAypUr4+HhYXY5Ukxd7+9Zdj+/NTIiIiIiplIYEREREVMpjIiIiIipFEZERETEVAojIiIiYiqFERGRYq4ILJqUIiwv/n4pjIiIFFOX24snJpq0S684hMt/v/7dzj4ntDeNiEgx5ezsTEBAQMZma15eXhnt1EVyy263k5iYyMmTJwkICMi0yV9OKYyIiBRjl7e2v9ndX0VuJCAgIOPv2c3KcRg5evQoL7zwAvPnzycxMZGqVasyadIkIiMjr/mav/76i2eeeYZ//vmH0NBQRo4cmWmPARERyR8Wi4WQkBCCgoJISUkxuxwpZlxdXXM1InJZjsLIuXPnaNmyJe3atWP+/PkEBgYSHR1NiRIlrvmaAwcOcNddd9G/f3++//57Fi9eTJ8+fQgJCaFDhw65/gZEROTGnJ2d8+RDQyQ/5GhvmuHDh7Ny5UqWL1+e7RO88MILzJs3j+3bt2c89tBDD3H+/Hl+//33bL2H9qYREREpevJlb5q5c+cSGRlJ165dCQoKIiIigokTJ173NatXr6Z9+/aZHuvQoQOrV6++5musVivx8fGZbiIiIlI85SiM7N+/n/HjxxMeHs6CBQsYMGAAQ4YM4Ztvvrnma44fP06ZMmUyPVamTBni4+O5dOlSlq8ZPXo0/v7+GbfQ0NCclCkiIiJFSI7CiM1mo2HDhowaNYqIiAj69etH3759+fzzz/O0qBEjRhAXF5dxO3z4cJ6+v4iIiBQeOQojISEh1KpVK9NjNWvWJCYm5pqvCQ4O5sSJE5keO3HiBH5+fnh6emb5Gnd3d/z8/DLdREREpHjKURhp2bIlu3fvzvTYnj17qFix4jVf07x5cxYvXpzpsUWLFtG8efOcnFpERESKqRyFkWHDhrFmzRpGjRrF3r17mTp1KhMmTGDgwIEZx4wYMYJevXplfN2/f3/279/P888/z65duxg3bhwzZ85k2LBhefddiIiISJGVozDSuHFjZs+ezbRp06hTpw5vvvkmY8eOJSoqKuOY2NjYTJdtKleuzLx581i0aBH169dnzJgxfPnll+oxIiIiIkAO+4yYRX1GREREip586TMiIiIiktcURkRERMRUCiMiIiJiKoURERERMZXCiIiIiJhKYURERERMpTAiIiIiplIYEREREVMpjIiIiIipFEZERETEVAojIiIiYiqFERERETGVwoiIiIiYSmFERERETKUwIiIiIqZSGBERERFTKYyIiIiIqRRGRERExFSOHUZSksBuN7sKERERh+ZidgGmWvY/2DQFKreGym2M/5aoaHZVIiIiDsWxw0jMGkg4Adt+MG4AJSoZwSSsDVRqDT6BppYoIiJS3Fns9sJ/nSI+Ph5/f3/i4uLw8/PLuzdOtcLhtXBgKexfCkc3gD0t8zFBtY1gUrkNVGwBHnl4fhERkWIsu5/fjh1G/s16AQ6tMoLJgaVwYnvm5y3OUK7hlZGT8k3A1SP/6hERESnCFEbywsXTcGDZlZGTcwcyP+/iAaFN00dO2kJIfXB27CtfUoykWmHNOCgbAWFtza5GRIoghZH8cD7GCCeXR04STmR+3t0fKrW8MnISWAMsFnNqFcmtX56GDZPByQUemQOVW5ldkYgUMQoj+c1uh1O7r4ycHFwOSXGZj/EOMlboXJ5zopU6UlRs/A7mDrrytWcJ6LMYSlUxryYRKXIURgqaLQ1iN18ZOYlZA6mXMh9TotJVy4jbaKWOFE7HNsFXHSDNCq2eg/1LjMndpatB70XgGWB2hSJSRCiMmC1jpU76yMmR9Vmv1Lk8clKxpVbqiPkSz8IXbSAuBqrdCQ9NhYsnYeKtEH8UwtpB1I+aGyUi2aIwUthkWqmzDE5sy/x8xkqd9JGT0KZaqSMFy5YGUx4wRkJKhkHfJVdGQWK3wtcdICURGveFu943tVQRKRoURgq7q1fqHFgGZ/dnfv7ySp3KrY2VDCEN9Nuo5K/Fb8DyMeDqBX3+gDK1Mz+/81eY0ROwQ6f3oUlfU8oUkaJDYaSoueFKHT+odMuVkZOgmlqpI3ln1zyY/rBx/4GvoO6DWR+34kP44zVjJC/qB6h6W4GVKCJFj8JIUWa3w+k9V4LJDVfqtDYmx4rcjNN7YWI7sMZD0/5w57vXPtZuhzlPwZapxlL2PosgsHrB1SoiRYrCSHFiS4PYLVear2W1UqdkFXhgIpRrZE6NUjRZE+DL9nBqJ1RoDo/+As6u139NqhW+vRdiVkOJytD3T/AqWTD1ikiRojBSnKVa4ci6KyMnl1fqlK4OA1be+MNEBIxRjh+fgH9mgU8ZeHIZ+AZn77UXTxsrbM4fMlaCPTIHXNzytVwRKXqy+/ntVIA1SV5xcTfmj9z6EvReCM/tAa9ScHq30TFTJDvWjDOCiJMLdP0m+0EEwLs0PDwD3Hzh0EqYN8wINyIiNyFHYeS1117DYrFkutWoUeOax6ekpPDGG29QpUoVPDw8qF+/Pr///nuui5Z/8S4NbUcY9/8aDZfOm1qOFAEHV8LCl437d7wNFZvn/D2CakLXSWBxgk1TYPWneVujiDiMHI+M1K5dm9jY2IzbihUrrnnsyJEj+eKLL/jkk0/YsWMH/fv3p0uXLmzatClXRUsWGj1uXKZJPGMszxS5lvhY+OEx49Je3a7Q9Mmbf6/w26HDKOP+wpdh9/w8KVFEHEuOw4iLiwvBwcEZt9KlS1/z2O+++44XX3yRTp06ERYWxoABA+jUqRNjxujDMs85u0CHt437f38OZw9c/3hxTKnJMLOX0VU1qDZ0/ij3S8Sb9jfCMHb4qQ8c354npYqI48hxGImOjqZs2bKEhYURFRVFTEzMNY+1Wq14eGTuIurp6Xnd0ZTLr4uPj890k2yo2h6q3AppyfDHq2ZXI4XRghfhyFpjWW7378DNO/fvabFAp/eMJebJCTDtIUg4mfv3FRGHkaMw0rRpUyZPnszvv//O+PHjOXDgAK1ateLChQtZHt+hQwc++OADoqOjsdlsLFq0iFmzZhEbG3vd84wePRp/f/+MW2hoaE7KdFwWC9zxlnENf8fPcGi12RVJYbJlOqybaNy/f0Le7sDr7GpMgi1ZBeIOw/QoSEnKu/cXkWItV0t7z58/T8WKFfnggw/o3bv3f54/deoUffv25ZdffsFisVClShXat2/P119/zaVLl7J4R4PVasVqtWZ8HR8fT2hoqJb2ZtcvTxuraspGQJ8/wUmLphxe7Fb46nZITYLWzxsrsfLD6b3w5a1Gk7663YzQo07BIg6rQJb2BgQEUK1aNfbu3Zvl84GBgcyZM4eLFy9y6NAhdu3ahY+PD2FhYdd9X3d3d/z8/DLdJAfavWQsuTy2Cbb9YHY1YrZL52DmI0YQqdoe2g7Pv3OVrgrdvjXaxW+bCcu1oZ6I3FiuwkhCQgL79u0jJCTkusd5eHhQrlw5UlNT+emnn7j33ntzc1q5EZ8gaPWMcX/x65CcaG49Yh6bDWb1g3MHIaAi3D8RnJzz95xhba/s6vvnW/DPnPw9n4gUeTkKI8899xxLly7l4MGDrFq1ii5duuDs7EyPHj0A6NWrFyNGjMg4/u+//2bWrFns37+f5cuX07FjR2w2G88//3zefhfyX82eAv8KEH8UVn9mdjVilqXvQvRCYxfo7t8VXNv2yCeg6QDj/uz+xiidyGX/zIE/39bKK8mQozBy5MgRevToQfXq1enWrRulSpVizZo1BAYGAhATE5NpcmpSUhIjR46kVq1adOnShXLlyrFixQoCAgLy9JuQLLh6QPv0FTUrPjR6S4hj2bMAlr5j3L97LITUL9jz3/GWcVko9RJM6wHxxwr2/FI4ndxlbEOw7H/weUsYfwus+hQunLjxa6XY0t40xZndbkxaPLIOInrCvRohcRhn98OEtsZE0sjecPcH5tSRFAdf3QGndkFIA3h8Prh5mVOLFA5THoC9f4B/KCScMFoRgLEKsMqtUL8HVO+kvyfFhPamEWMVQ4fRxv1N3xsrKqT4S06EGb2MIFC+MXR8x7xaPPyhx3Rj76TYzTCnvzGPRRxT9CIjiDi5Qq+f4dndcNcHENoU7DbjuZ96w/vVYM5AOLBcf18chMJIcRfaGOo8ANiNhleFfyBMcsNuh1+Hwolt4B1orGwxezfdkpWh+xTjA2jHz/DXKHPrEXOkpRg/gwCa9Tf63HiVhMa9jQ0/B2+ENi8YE62TL8DmKfDN3fBRPVj8JpyONrd+yVcKI46g/Wvg7A4Hl2vvkOJu7UTYOsNYWtt1MviVNbsiQ8UWcM/Hxv1l78HWmebWIwVv/SQ4nb7DeOv/++/zpapAuxfh6S3w+O/Q8FGjU3DcYWOJ+KeRMPFW4+/4xTMFX7/kK4URRxBQAZoPNO4vHGnsTyLFT8zfsCB9Ndvtb0ClW8yt598aPAwthxr3fx4Eh9eaWo4UoMSzV0bE2r1kXL67FovF2EX6no/hud3w4CSo1tEI2Ec3wG/PwZjqMO1h2DEXUq3Xfi8pMjSB1VEkxcMnDeHiKej4rjFMKsXHhRPwRWtIOA61uxg/wAtj51ObDWb0hN3zjMtIff80wrIUb/OHw9/jIagWPLnc2NgzpxJOwfYfYcs0iN1y5XGPAONSdP2HjDlShfHvvQPL7ue3wogjWT/JmE/gEQBDNhVczwnJX2kp8M09ELMKAmtAn8Xg7mN2VddmTYCvOxrzWoJqQ+8F4O5rdlWSX05Hw7hmYEuFR2YbK2Zy6+ROY6+lrTPhwlVLxkuGGatx6nWDEpVyfx7JNa2mkf9q2Mv44Z903rhuL8XDoleMIOLma0wULcxBBIz6Hp4O3kFw8h/4qQ/Y0syuSvLLwpFGEKnWMW+CCEBQTbj9dRi2HR6ZYwQQV29jSfuSt+Gj+vD1nbDhG2NVmRR6GhlxNPv+hO+6gJMLPPW3sZeIFF3bfjSWQoIRRGp2NreenDiyHiZ1gjQrtBhsNEmT4iXTz5s1UDo8/85lTYBdvxqXcfYvBdI/2pzdoUYnI7BUudXYYVoKjEZGJGtVboXwO4zfVBa9YnY1khsndsDcwcb9W4YVrSACUD4S7htn3F/1CWz81tx6JG+lpcKC9N2hG/fN3yACxohb/YeM/iXD/oH2rxuXLdOs8M9smNoNPqgJv4+AY5vV5qCQ0ciIIzq1G8Y1B3saPPoLVG5tdkWSU0lxMKEdnN1nbEzXc1b+b4CXX5aMNtrWO7lCrzmFbxWQ3Jx1X8G8Z8CzhNFDxIw5ana7Mdl1y3RjB/PE01eeC6xphJd63QrPEvhiSBNY5frmPQfrJkJwXei3tOh+kDkimw1mRMHu34yW2v2Wgncps6u6eTYb/PSE8durZwljhU3JMLOrktxIioOPIyDxDNz5HjTtZ3ZFxkTvfX8al3F2/WaMmABggbA2xmWcGncX/jlXRYwu08j1tR1uNBQ6vs34rUGKjhVjjCDi7G50WC3KQQTAyQnuGw9lG8KlczC1O1w6b3ZVkhvL3jOCSOlqEPm42dUYnF2hWgejGeBze6Dzx1ChBWCH/X/B7CeNNvSznoR9SzSpuoBpZMSRrfwYFr0MPsEweIN+IygK9i42NhrDDvd8YqyQKi4uHDcuPV04ZsxteviHm+tHIeY6sw8+awq2FIj6EcJvN7ui6zt7wFgivHW6sRrnMt+yxiWc+g8Zq3fkpmhkRG6s6ZPGWvyE47DqY7OrkRs5dyh95YzdCCHFKYgA+AYbS35dvYzh9MvdZKVoWfSKEUSqti/8QQSMvZPavmDMa+m9CCKfMDrEXjgGK8caPVK+aA1rxhuN1yRfaGTE0e34GWb2AhdPY3TEv5zZFUlWUi7B1x2MyXhlI4y9O1w9zK4qf+z8xejSCtDpfWjS19x6JPsOLINvOhut2wesgqAaZld0c1KtsGeBcQk7eoGx+hCM76tqe2O0pPqd4Oppbp1FgEZGJHtq3gMVmkPqJVj8htnVSFbsdmPCcewWY5Oxbt8V3yACxhLl21417s9/wRglkcLPlga/p+/KG/lE0Q0iAC7uUOse6DEVnt1jTMIt18hYgRi9AH583JhfMnew0S9Hck1hxNFZLNDhbeP+1ulwdKO59ch/bZhkbKducYIHv4aAULMryn+3DDNWN9jTYOZjcGqP2RXJjWz+3mjx7+EPbYvRJTbvUsZqoL5/wsB10OpZ8CsP1nijN86Xt8Gm782usshTGBEj8dd7yLi/4CU1AypMjqyH35437t/2itFTxBFYLND5IwhtBtY4o2FV4lmzq5JrSYqHxW8a99u8UPRXeF1LYDXj3+HQbfDor1caDc4dDLvnm1tbEacwIobbXjbmjcSsMq7Zi/kSThnzeWwpRv+DlkPNrqhgubjDQ98bu/qeOwAzHoHUZLOrkqys+AAunoSSVYxuq8WdkxNUbgVdv70ygvfDY3BotdmVFVkKI2LwL2/sDwLGct9U6/WPl/yVlmpcl44/CqXCjT4cjrg1undp6DHD2ATw0Ar47VmN3BU25w7C6vS2/ne8BS5uppZToJycjCX24R0gNQmmdYcT/5hdVZGkMCJXtHza6Dly7iCsnWB2NY5t8etwcLmxE2n3KeDhwKvIytQy5spYnIxr9Ks/M7siudqiV41uppXbGCtMHI2zq9FILbSp0Xl2ygPGMnzJEYURucLdx7hcA7D0Pbh4xtx6HNWOn6/0fbnvs6K9KiGvVLsD7kifaL1wJOz+3dx6xHBoFeyYYwTFDqMcc/QOwM0LHp5h7HdzIdbYqVg9SXJEYUQyq9/D2K/GGgd/jTa7GsdzajfMecq433wQ1O5ibj2FSbMB0PBRwG40f9NwuLlsNmMHXDAa8AXXMbces3mWgEdmGftFnd0H3z8I1gtmV1VkKIxIZk7Oxm84AOu/Nj4cpWBYLxjNvpIToFIrYwt0ucJigbvGGP9vkhNg6kP67dNMW6dD7GZjPk+7kWZXUzj4lYVHZhv9gGI3w/Qozb/LJoUR+a/KraH6XcYM8YUvm12NY7DbjRGR03uMPTEenKR9WbLi7GpsDlgyDOJijN2LU5LMrsrxWBPgj/Sw3Ob/wCfQ3HoKk9LhEPWDMd/rwFJjAz5tundDCiOStdvfACcXo9ugOmDmv5Ufwc654JT+Yasf7tfmVRIenmk01zr8N/wyRCtsCtrKj4w9rUpUgqb9za6m8CnXCB6aYvx7/me20UlYf0evS2FEsla66pV+AQtGKtnnp/1LjdUzAHe+C6GNza2nKCgdDl2/MfYK2ToDlo8xuyLHcf7wlQnWt79p9IOR/6pyK9z/BWCBdRNh6f/MrqhQUxiRa2vzPHgEwMl/YNN3ZldTPMUdMfqJ2G1Q/2FjTw/JnirtoNN7xv0/34Qdc82tx1H88ZrRU6PiLVc6kErW6jwAd6aHkL9GwbqvzK2nEFMYkWvzKglthxv3/3xLM8PzWqrV6LCaeAaC68HdHzju0sib1bg3NHnSuD/7STi22dRyir3Da2H7j4AFOjrwUt6caNrPaJEPMO9Z+GeOqeUUVgojcn2RvY0WzxdPwfIPzK6meJn/PBzdYIw+df9O25HfrA6joMptkJII0x6C+FizKyqerl7KGxEFIfXNracoaTsCGj0O2GFWX9j/l9kVFToKI3J9Lm5wR/oGWKs/g/Mx5tZTXGz8DjZMBizw4FfGREC5Oc4u0HUSlK5uNJya3gOSE82uqvjZ/hMcXQ9uPnCrVtnlyOVl6TXvgbRkY8nvsU1mV1WoKIzIjVXvZPR2SLNeWc4nN+/YJmO4FqDdS1C1vbn1FAce/vDwdPAsafz/nTPA+E1e8kZyIvzxqnH/lmHgG2xuPUWRkzM88OWVPjlTHoQz+8yuqtBQGJEbs1igw9uAxbhefHid2RUVXYlnYUYvI9hVuxNaPWt2RcVHyTBjl18nV6NFuToI551VnxibNvpXgOYDza6m6HJxh4emGnPEEk/Dd/fpsmI6hRHJnpD60CDKuL/gRa2Zvxm2NPjxCaNZV8kw6PK5seun5J2KLaDzR8b9Zf+DrT+YW09xEH8MVo417t/+uuY25ZaHH/T8yfgZcD7G2Fjv0nmzqzKdfhJK9t060ugqeGQt/DPL7GqKniVvw/4l4Opl7MTrGWB2RcVTRBS0GGLc/3mgRvJya/EbxuTg0GbaKymv+AQZbeN9yhitE6Y9BCmXzK7KVAojkn1+IXDLUOP+otfUhjsnds270pjrnk+gTG1z6ynu2r9mzHVKs8L0h41GXZJzRzfAlmnGfS3lzVslKkHPWeDuDzGr4YfHIS3V7KpMk6Mw8tprr2GxWDLdatS4/vbmY8eOpXr16nh6ehIaGsqwYcNIStKHWJHVfJCxd0pcDKwZZ3Y1RcPpvTA7vWV20wFQ90Fz63EETs5w/0QoUxcunjR+87QmmF1V0WK3w+8vGvfr9zBanEveCq4DPaaBszvsmQ+/PO2wl8BzPDJSu3ZtYmNjM24rVqy45rFTp05l+PDhvPrqq+zcuZOvvvqKGTNm8OKLL+aqaDGRmxe0T59Vv/wDSDhpbj2FnTXB2InXGg8Vml9ZJi35z93H+EHvHQQnthudbrWDavb9MxsOrzEuK972itnVFF+VWhpL0y1OsHmK0eHWAeU4jLi4uBAcHJxxK1269DWPXbVqFS1btuThhx+mUqVK3HHHHfTo0YO1a9fmqmgxWd1uUDYCki/AklFmV1N42e0wdzCc2mlcG+462dh1VgpOQKixesHFA6IXGpdsHPzafLakJMGi9F86Wg4Fv7KmllPs1bgLOqfv97NyLKz61NRyzJDjMBIdHU3ZsmUJCwsjKiqKmJhrN8Fq0aIFGzZsyAgf+/fv57fffqNTp07XPYfVaiU+Pj7TTQoRJyej6yXAxm/gxA5z6yms1owzJvo6uRg78ao3gzlCG8PDM4zf8Pf+Ad931SWbG1nzmXEp1q8ctBhsdjWOoeEjcFt6AFz4EmyZbm49BSxHYaRp06ZMnjyZ33//nfHjx3PgwAFatWrFhQtZ71ny8MMP88Ybb3DLLbfg6upKlSpVaNu27Q0v04wePRp/f/+MW2hoaE7KlIJQsYXRTdBuM/7hOOh1zms6uBIWpnep7DAKKjQztx5HF9bWWE7p5gsHlxvLKZPizK6qcLpw/MrWD+1fMy7NSsG4ZRg0S+/jMucp2LPQ3HoKkMVuv/lPkfPnz1OxYkU++OADevfu/Z/n//rrLx566CHeeustmjZtyt69e3n66afp27cvL7987XbCVqsVq/XKtd34+HhCQ0OJi4vDz8/vZsuVvHZ2P3zW1GhvHPUjhN9udkWFw/Ft8N39xsTJul2NiZRahVA4HFkPU+43gkjZhkZA8SppdlWFy88DYdMUKBcJvRepF05Bs9lgTn/YOgNcPOHRuRDaxOyqblp8fDz+/v43/PzO1d+ygIAAqlWrxt69e7N8/uWXX+aRRx6hT58+1K1bly5dujBq1ChGjx6N7Tqtmt3d3fHz88t0k0KoZBg0Td8xdcFLDr0sDYC0FFj6HkxoZwSRoNpGAy4FkcKjfCQ8+kt62/iN8M09cPG02VUVHrFbYNP3xv2O7yiImMHJCe79DKreDqmXjMuKJ3eaXVW+y9XftISEBPbt20dISEiWzycmJuL0r7/Mzs7OAORiQEYKk1bPGT/YT++GDZPMrsY8J3fCl+1hyVtgS4Ead0Ovn8HN2+zK5N9C6sPjv6WvstkGkzoZlyYcXcZSXjvUedCYayPmcHaFbt9A+caQdN4YaS3mvXJyFEaee+45li5dysGDB1m1ahVdunTB2dmZHj16ANCrVy9GjBiRcXznzp0ZP34806dP58CBAyxatIiXX36Zzp07Z4QSKeI8A6Bd+hygv0Y7XltjWxqsGAtftIbYzcaGbV0mGB1WfQLNrk6uJagmPD7fmKB5ejdMurPY/7C/oZ2/wKEVxsqj9q+ZXY24ecPDM9N3oz4G33WBi2fMrirfuOTk4CNHjtCjRw/OnDlDYGAgt9xyC2vWrCEw0PihGxMTk2kkZOTIkVgsFkaOHMnRo0cJDAykc+fOvP3223n7XYi5Gj0GayfA6T1Gl1FH6aVxeq+xO+yR9KXq4XcYy/P8sh4plEKmdFVjhOSbzsb8p0mdjOvzJSubXVnBS7XCovR5fC2GGEuixXxeJeGRWfBVBzgTDd8/aFxmdPcxu7I8l6sJrAUluxNgxER7FsLUruDsBgPXFu8f6DYbrP0C/njduKbr5gsdR0NET80PKYrijqYHkn3gG2L8sC8dbnZVBWvlR7DoFfAJhsEbiuWHXZF2ag983QEunYUqt0KPGeDiZnZV2VIgE1hFMoTfDmHtjJU1f7xqdjX55+wB+OZu+H24EUTC2sJTq40eAQoiRZN/OeOSTWBNuBBrXLI58Y/ZVRWchFOw7H3jfvtXFUQKo8BqEPWD0Stn35/GapvrLAIpihRGJG9YLNDhbaOl8Y6f4dBqsyvKW3Y7rPsSxreEQyuN3Yvv+gAemaMh7eLAtww8Ng+C68LFUzD5Lji22eyqCsaSt43tCkIaQL2HzK5GrqV8JHT/zmiiuP0n4xeiwn9hI9sURiTvlKkNEY8Y9xe8WHyS+/nD8N19MO9ZSLkIFW+BASuhcW+NhhQn3qWMSzTlGsGlc8ay38PrzK4qf534x+iiDMalRi3lLdyqtof7Pjfur/0Clr9vbj15SH/zJG/dOhLcfIweDtt/NLua3LHbYeO3MK457P/LaEDU8R3jA6s4z4lxZJ4ljNGuCs3BGmeE0IMrza4qf9jt8PsIo4tyrfuMrspS+NXrCh3fNe7/+RasLx4tFRRGJG/5BEGrZ4z7f7wGyYmmlnPT4mNhajdjo7vkC1C+CfRfAc0G6LfH4s7Dz+jMWrkNJCcYreP3/Wl2VXlvz+9wYKmxff3tr5tdjeREs/5GjyeAec/Ajrnm1pMH9FNV8l6zp8A/FOKPwurPzK4mZ+x22DIDxjU1dnl1dofb34AnfjeWgopjuNzjIfwOY6Ly1Idg9+9mV5V3UpONrskAzZ+CEpVMLUduwq0joeGjxsjWT73hwDKzK8oVhRHJe66eV5omrfiw6HS3TDgJ06Ngdr/0vUsi4Mll0PJpcFKTPofj6gHdv4eanSHNCjOijMnZxcG6L42lzN5BcMszZlcjN8NiMSbR17jbWMU47WGjnX8RpTAi+aPOA0Yr45SL8GcRaIK2fZax6d/ueeDkavzW0fsPCKphdmViJhc3eHCy0R7dlgo/PA5bZ5pdVe5cPANL3zHu3/aycVlKiiZnF3jgK2NSffIF45LimX1mV3VTFEYkf1gs0GGUcX/T9xC71dx6ruXiGfjhMfjxcaOhUJm60G8JtP4/4x+6iLML3D8BGvQEexrM6mdMbC6q/hptjPwF14UGUWZXI7nl6gE9pho/uy6eMnalLiqj0VdRGJH8E9oEat8P2GHhS4VvTfzOX425If/MBosztH4e+v5p/JAWuZqTM9zzCUT2BuzGxOa1E82uKudO7oL1Xxv3O4zS5cfiwsPfmHRdohKcOwhTHjQCZxGiMCL5q/1rxiTQA8uM2fuFwaVzxm+3M6KM3yQCa0LfxXDrS0WmxbKYwMkJ7hoDzQcZX//2HKz82NyacmrhS8boTo27oXJrs6uRvORbBh6ZfWU36mk9ICXJ7KqyTWFE8leJisZsfYCFI41Z/GaKXmT0Ddk6w+gW23IoPLnUmKwqciMWC9zx1pVllYtehqX/K3yjflmJXgR7/zDmRN3+htnVSH4oGQY9fwR3P6NT9E+9IS3V7KqyRWFE8t8tz4BXaTiz98oQcUFLioOfBxm7Xl6IhVJV4YmFRn8FF3dzapKiyWIxJn7eOtL4esnbsPiNwh1I0lKuLOVt1h9KVTG3Hsk/IfXhoanGiPSuX+HXoYX772Y6hRHJfx5+xiUQMCbPJZ4t2PPvWwLjWsCm7wALNBtoNDALbVywdUjx0vr/4I63jfsrPjC2QCisP/TXT4LTu41fClr/n9nVSH6r3Aoe/MoY/d30XZFY0agwIgUjohcE1YKk87DsvYI5pzUBfn3GaOkdf8SY3PX4b9BxlNELRSS3Wgwy5pEArBkHvw4rfHsyXToHf6WvbGv3ojHZUYq/mp3h7g+N+8vHwJrx5tZzAwojUjCcXYxr7QBrJ8Dpvfl7voMrYHwLWP+V8XXjPtB/pfbfkLzXuA/c+xlggQ2T4OeBYEszu6orlv7PCCRBtYyOneI4Gj125XLi78MLdY8chREpOFVvg6q3G82j/ng1f86RnAjzhxtbwJ8/ZLSl7/Wz8duru0/+nFMkoic88KWxRHzLVPipjzFPw2yno43wD9DhbfXOcUStnoOm/Y37cwZA9B/m1nMNCiNSsO54y/iBvetXOLA8b9/78Fr4/Bb4O304smEvGLAKwtrm7XlEslL3Qeg62Vit8s8so5leqtXcmha+bIT/ah2hyq3m1iLmsFigw+grXYRnPgJH1ptd1X8ojEjBCqoBkY8b9xe8mDfD2SlJsOgV+LqDsd+GbwhE/Wg0qVKraylIte7JvJJh+sOQcsmcWvb9CXvmg9NVl0jFMTk5wX3jocptkJJorCo8tdvsqjJRGJGC13aEsQ7++FbYMj1373V0I0xoAys/MnavrN8DnloN4bfnTa0iOVXtDoiaCa5eRl+P77sak6kLUlrqlaW8jftC6fCCPb8UPi5u0O1bKNfImEP0XReIO2J2VRkURqTgeZeG1ulNoxa/AckXc/4eqcnw51vwZXs4tcvoOvjQNOjyOXiWyNt6RXIqrK3RntvNFw4uNzYwK8j23Ju+hZM7jH8LbZ4vuPNK4ebuAw//AKWrQfxR+O7+gm+1cA0KI2KOpv0hoCIkHDdGNXLi+DaYeKuxRNieZuwQPPBvqNEpf2oVuRkVW0CvOcZS2sNr4Nv7CuYHf1KcEdQB2r4IXiXz/5xSdHiXgp6zwK+c0Xvm+6439wthHlMYEXO4uF9pSb3yY4g7euPXpKUYyxQntDX2XvAqZUwYfPBr/cCVwql8JDz6C3iWhGMb4Zt74OLp/D3nsvcg8Yzx2+/l+VkiVwsINQKJRwAcXQ8ze5m++kthRMxT616o0BxSL924Q+DJncYlmSVvGzPCa9wNT/0NtbsUTK0iNyukvtFs7/IGZpM65d8W72f2wZrPjfsdRoGza/6cR4q+oBoQ9QO4eBpzm+Y8ZWrDPoURMY/FYvQ+ANgyzZiM+m+2NFgxFr5oDbGbjSR//5fQfQr4BBZgsSK5EFQTHp9/ZWh80p1w/nDen2fRK2BLgartNYlbbiy0CXT/zlhxtW0mLH/ftFIURsRc5RpBve7G/QUvZd7b43S0sVz3j1chLRnCO8BTa6BeVyPIiBQlpasaIyQBFeDsfmOE5OyBvHv/A8uM5cQW5yt75ojcSPjtcO84o0NvRE/TylAYEfPd9gq4eEDMKtj5izFUuHqc0cDsyDpjGfC9n8HDM8AvxOxqRW5eiUrw+O9QsgrExRgjJKejc/++tjSjbw9A5BPGELxIdtXvDv2Wgl9Z00pQGBHz+ZeHFoON+4tehm/uhgUjIDUJwtoZfUMiemo0RIoH/3LGJZvAmnAh1ggkJ/7J3Xtu/t5YZebhb/TxEckpFzdTT68wIoVDy6HgUwbOHYRDK8HV29hx8pHZRlgRKU58y8Bj8yC4Llw8ZeyldGzzzb2X9QIsTp8A3uYFY+mmSBGjMCKFg7uPMfvf4gSVWsFTq4zhZo2GSHHlXcpY9nu5I+Y398DhdTl/n+UfwMWTxqWfxn3zvk6RAmCx26+eMVg4xcfH4+/vT1xcHH5+2mukWEuKB3dfhRBxHEnxMLUbxKwGNx94eCZUapm91547CJ82gTQr9JgO1e/M11JFciq7n98aGZHCxcNPQUQci4ef0Tq+chtITjBax+/7M3uvXfSqEUQqtzF25hUpohRGRETM5uZtjIiE32E0AZzaHXb/fv3XHFoNO+YYlzY7jFKIlyJNYUREpDBw9YDu3xvdhdOSYUYU7Pg562NtNvh9uHG/4aMQXKfg6hTJBwojIiKFhYubsd9SnQeNbQ9+eBy2zvzvcVtnGB2J3f2g3UsFXaVInlMYEREpTJxd4f4J0KCnsSv1rH6w8dsrz1sTYPHrxv3Wz2lbBCkWchRGXnvtNSwWS6ZbjRrX7vTXtm3b/xxvsVi46667cl24iEix5eQM93wCkb0BO8wdDGsnGs+t/MhollaiEjTtb2aVInnGJacvqF27Nn/88ceVN3C59lvMmjWL5OTkjK/PnDlD/fr16dq1a05PKyLiWJyc4K4x4OoJqz+F356DuMPw9xfG87e/CS7u5tYokkdyHEZcXFwIDg7O1rElS5bM9PX06dPx8vJSGBERyQ6LBe54y9i7afn7xqgIQMVboGZnc2sTyUM5njMSHR1N2bJlCQsLIyoqipiYmGy/9quvvuKhhx7C29v7usdZrVbi4+Mz3UREHJLFAre9DLeOvPwAdNRSXilectSBdf78+SQkJFC9enViY2N5/fXXOXr0KNu3b8fX1/e6r127di1Nmzbl77//pkmTJtc99rXXXuP111//z+PqwCoiDm3vH+DkAmFtza5EJFuy24E1V+3gz58/T8WKFfnggw/o3bv3dY998sknWb16NVu3br3h+1qtVqxWa8bX8fHxhIaGKoyIiIgUIdkNIzmeM3K1gIAAqlWrxt69e6973MWLF5k+fTpvvPFGtt7X3d0dd3dNzBIREXEEueozkpCQwL59+wgJCbnucT/88ANWq5WePXvm5nQiIiJSDOUojDz33HMsXbqUgwcPsmrVKrp06YKzszM9evQAoFevXowYMeI/r/vqq6+47777KFWqVN5ULSIiIsVGji7THDlyhB49enDmzBkCAwO55ZZbWLNmDYGBRgfAmJgYnJwy55vdu3ezYsUKFi5cmHdVi4iISLGRqwmsBSW7E2BERESk8Mju57f2phERERFTKYyIiIiIqRRGRERExFQKIyIiImIqhRERERExlcKIiIiImEphREREREylMCIiIiKmytVGeQXlcl+2+Ph4kysRERGR7Lr8uX2j/qpFIoxcuHABgNDQUJMrERERkZy6cOEC/v7+13y+SLSDt9lsHDt2DF9fXywWS569b3x8PKGhoRw+fFht5gsJ/ZkULvrzKFz051G46M/jxux2OxcuXKBs2bL/2bvuakViZMTJyYny5cvn2/v7+fnpL1Ihoz+TwkV/HoWL/jwKF/15XN/1RkQu0wRWERERMZXCiIiIiJjKocOIu7s7r776Ku7u7maXIun0Z1K46M+jcNGfR+GiP4+8UyQmsIqIiEjx5dAjIyIiImI+hRERERExlcKIiIiImEphREREREzl0GHks88+o1KlSnh4eNC0aVPWrl1rdkkOafTo0TRu3BhfX1+CgoK477772L17t9llSbp33nkHi8XC0KFDzS7FoR09epSePXtSqlQpPD09qVu3LuvXrze7LIeUlpbGyy+/TOXKlfH09KRKlSq8+eabN9x/Ra7NYcPIjBkzeOaZZ3j11VfZuHEj9evXp0OHDpw8edLs0hzO0qVLGThwIGvWrGHRokWkpKRwxx13cPHiRbNLc3jr1q3jiy++oF69emaX4tDOnTtHy5YtcXV1Zf78+ezYsYMxY8ZQokQJs0tzSO+++y7jx4/n008/ZefOnbz77rv873//45NPPjG7tCLLYZf2Nm3alMaNG/Ppp58Cxv43oaGhDB48mOHDh5tcnWM7deoUQUFBLF26lNatW5tdjsNKSEigYcOGjBs3jrfeeosGDRowduxYs8tySMOHD2flypUsX77c7FIEuPvuuylTpgxfffVVxmMPPPAAnp6eTJkyxcTKii6HHBlJTk5mw4YNtG/fPuMxJycn2rdvz+rVq02sTADi4uIAKFmypMmVOLaBAwdy1113Zfp3IuaYO3cukZGRdO3alaCgICIiIpg4caLZZTmsFi1asHjxYvbs2QPAli1bWLFiBXfeeafJlRVdRWKjvLx2+vRp0tLSKFOmTKbHy5Qpw65du0yqSsAYoRo6dCgtW7akTp06ZpfjsKZPn87GjRtZt26d2aUIsH//fsaPH88zzzzDiy++yLp16xgyZAhubm48+uijZpfncIYPH058fDw1atTA2dmZtLQ03n77baKioswurchyyDAihdfAgQPZvn07K1asMLsUh3X48GGefvppFi1ahIeHh9nlCEZIj4yMZNSoUQBERESwfft2Pv/8c4URE8ycOZPvv/+eqVOnUrt2bTZv3szQoUMpW7as/jxukkOGkdKlS+Ps7MyJEycyPX7ixAmCg4NNqkoGDRrEr7/+yrJlyyhfvrzZ5TisDRs2cPLkSRo2bJjxWFpaGsuWLePTTz/FarXi7OxsYoWOJyQkhFq1amV6rGbNmvz0008mVeTY/u///o/hw4fz0EMPAVC3bl0OHTrE6NGjFUZukkPOGXFzc6NRo0YsXrw44zGbzcbixYtp3ry5iZU5JrvdzqBBg5g9ezZ//vknlStXNrskh3bbbbexbds2Nm/enHGLjIwkKiqKzZs3K4iYoGXLlv9Z7r5nzx4qVqxoUkWOLTExESenzB+fzs7O2Gw2kyoq+hxyZATgmWee4dFHHyUyMpImTZowduxYLl68yOOPP252aQ5n4MCBTJ06lZ9//hlfX1+OHz8OgL+/P56eniZX53h8fX3/M1/H29ubUqVKaR6PSYYNG0aLFi0YNWoU3bp1Y+3atUyYMIEJEyaYXZpD6ty5M2+//TYVKlSgdu3abNq0iQ8++IAnnnjC7NKKLrsD++STT+wVKlSwu7m52Zs0aWJfs2aN2SU5JCDL26RJk8wuTdK1adPG/vTTT5tdhkP75Zdf7HXq1LG7u7vba9SoYZ8wYYLZJTms+Ph4+9NPP22vUKGC3cPDwx4WFmZ/6aWX7Far1ezSiiyH7TMiIiIihYNDzhkRERGRwkNhREREREylMCIiIiKmUhgRERERUymMiIiIiKkURkRERMRUCiMiIiJiKoURERERMZXCiIiIiJhKYURERERMpTAiIiIiplIYEREREVP9PwEVPitOuCVTAAAAAElFTkSuQmCC\n" - }, - "metadata": {} + "output_type": "stream", + "name": "stdout", + "text": [ + "Cal Accuracy: 0.7344444444444445\n", + "Val Accuracy: 0.7146666666666667\n" + ] } - ] - }, - { - "cell_type": "code", - "source": [ - "embeddings = []\n", - "labels = []\n", - "trip_model.eval()\n", - "with torch.no_grad():\n", - " for i, (img1, img2, img3, label) in enumerate(loaders['test']):\n", - " img1, img2, img3, label = img1.to(device), img2.to(device), img3.to(device), label.to(device)\n", - " out1, _, _ = trip_model(img1, img2, img3)\n", - " embeddings.extend(out1.cpu().tolist())\n", - " labels.extend(label.cpu().tolist())\n" ], - "metadata": { - "id": "nnGoOwX-Vk_i" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", "source": [ "from sklearn.model_selection import train_test_split\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.metrics import accuracy_score\n", "\n", "# Split the data into training and testing sets\n", - "X_train, X_test, y_train, y_test = train_test_split(embeddings, labels, test_size=0.2, random_state=42)\n", + "# X_train, X_test, y_train, y_test = train_test_split(embeddings, labels, test_size=0.2, random_state=42)\n", "\n", "# Train a logistic regression model\n", "classifier = LogisticRegression(max_iter=1000)\n", - "classifier.fit(X_train, y_train)\n", + "classifier.fit(data['cal'], labels['cal'])\n", "\n", "# Make predictions\n", - "predictions = classifier.predict(X_test)\n", + "cal_predictions = classifier.predict(data['cal'])\n", + "val_predictions = classifier.predict(data['test'])\n", "\n", "# Calculate accuracy\n", - "accuracy = accuracy_score(y_test, predictions)\n", - "print(f\"Accuracy: {accuracy}\")\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "ayGTRCP5Wl0s", - "outputId": "da682072-bc72-4e11-fbc5-f64c96b69050" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Accuracy: 0.58\n" - ] - } + "cal_accuracy = accuracy_score(labels['cal'], cal_predictions)\n", + "val_accuracy = accuracy_score(labels['test'], val_predictions)\n", + "print(f\"Cal Accuracy: {cal_accuracy}\")\n", + "print(f\"Val Accuracy: {val_accuracy}\")\n" ] - }, - { - "cell_type": "code", - "source": [], - "metadata": { - "id": "gaj_LIDgrl6N" - }, - "execution_count": null, - "outputs": [] } ], "metadata": { + "accelerator": "GPU", "colab": { - "provenance": [], "machine_shape": "hm", + "provenance": [], "gpuType": "A100", "include_colab_link": true }, @@ -1040,8 +1262,7 @@ }, "language_info": { "name": "python" - }, - "accelerator": "GPU" + } }, "nbformat": 4, "nbformat_minor": 0 From b9150d2d40388bb41d34e97285a9d04d14f2bc1c Mon Sep 17 00:00:00 2001 From: FinnRobertson15 <110511080+FinnRobertson15@users.noreply.github.com> Date: Tue, 17 Oct 2023 16:53:28 +1000 Subject: [PATCH 08/14] Getting > 0.01 loss on validation after epoch 6 --- Colab version.ipynb | 983 ++++++++++++++++---------------------------- 1 file changed, 350 insertions(+), 633 deletions(-) diff --git a/Colab version.ipynb b/Colab version.ipynb index c9ec5a81f..ddb2b7e3e 100644 --- a/Colab version.ipynb +++ b/Colab version.ipynb @@ -12,13 +12,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "yzCmT9TUJY10", - "outputId": "bdc05ce0-7e5d-4959-8685-ce3b497d3a18" + "outputId": "8e10d31b-6fc4-4a4a-fdc8-078f4a36b02b" }, "outputs": [ { @@ -36,13 +36,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "-Lb7CGdEJCQg", - "outputId": "0a9018e4-6a51-4c9f-de99-d79cd5078de4" + "outputId": "a57f82f8-7816-4bd4-a54e-1e48cab3c3f8" }, "outputs": [ { @@ -59,13 +59,44 @@ }, { "cell_type": "code", - "execution_count": null, + "source": [ + "torch.save(trip_model.state_dict(), '/content/drive/MyDrive/model.pth')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 373 + }, + "id": "ZpU3tv9IGwCB", + "outputId": "11b0d223-3170-4756-825b-922cc5bcbe07" + }, + "execution_count": 19, + "outputs": [ + { + "output_type": "error", + "ename": "RuntimeError", + "evalue": "ignored", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrip_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'/content/drive/MyDrive/model.pth'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/serialization.py\u001b[0m in \u001b[0;36msave\u001b[0;34m(obj, f, pickle_module, pickle_protocol, _use_new_zipfile_serialization)\u001b[0m\n\u001b[1;32m 439\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0m_use_new_zipfile_serialization\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 440\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0m_open_zipfile_writer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mopened_zipfile\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 441\u001b[0;31m \u001b[0m_save\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mopened_zipfile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpickle_module\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpickle_protocol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 442\u001b[0m \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 443\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/serialization.py\u001b[0m in \u001b[0;36m_save\u001b[0;34m(obj, zip_file, pickle_module, pickle_protocol)\u001b[0m\n\u001b[1;32m 663\u001b[0m \u001b[0;31m# .cpu() on the underlying Storage\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 664\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mstorage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m'cpu'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 665\u001b[0;31m \u001b[0mstorage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstorage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcpu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 666\u001b[0m \u001b[0;31m# Now that it is on the CPU we can directly copy it into the zip file\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 667\u001b[0m \u001b[0mnum_bytes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstorage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnbytes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/storage.py\u001b[0m in \u001b[0;36mcpu\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 119\u001b[0m \u001b[0;34m\"\"\"Returns a CPU copy of this storage if it's not already on the CPU\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 120\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m'cpu'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 121\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mUntypedStorage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 122\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 123\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mRuntimeError\u001b[0m: CUDA error: device-side assert triggered\nCUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.\nFor debugging consider passing CUDA_LAUNCH_BLOCKING=1.\nCompile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.\n" + ] + } + ] + }, + { + "cell_type": "code", + "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "PM8uTdbZpQGJ", - "outputId": "281ff7ad-897b-488c-f95b-db60efdab763" + "outputId": "7b33eba8-1ff0-4df6-d785-81f57a27ba69" }, "outputs": [ { @@ -95,13 +126,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "x1t1iFV_p2MU", - "outputId": "e70aee0f-012c-4647-f4f8-4eec6b9a4397" + "outputId": "21e66a1e-e2c5-422d-ab47-dd1d15d03b89" }, "outputs": [ { @@ -118,13 +149,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Tp_Fb7Eyp-yv", - "outputId": "a06d8cb3-1fb3-4833-a6be-6bd8a9840f54" + "outputId": "2c834d95-b421-4b45-a308-267acefd6334" }, "outputs": [ { @@ -141,7 +172,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { "id": "_8PBaJLSJSCP" }, @@ -174,463 +205,7 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "u6EH0wk_CkxF" - }, - "outputs": [], - "source": [ - "batch_size = 128" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "7zvKyWj2J7Yk" - }, - "outputs": [], - "source": [ - "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", - "\n", - "\n", - "class CustomDataset(Dataset):\n", - " def __init__(self, root_dir, transform=None):\n", - " self.root_dir = root_dir\n", - " self.transform = transform\n", - " self.image_paths = os.listdir(root_dir)\n", - "\n", - " def __len__(self):\n", - " return len(self.image_paths)\n", - "\n", - " def __getitem__(self, idx):\n", - " img_name = os.path.join(self.root_dir, self.image_paths[idx])\n", - " image = Image.open(img_name)\n", - "\n", - " if self.transform:\n", - " image = self.transform(image)\n", - "\n", - " return image\n", - "\n", - "class SiameseDataset(Dataset):\n", - " def __init__(self, AD, NC):\n", - " # Combine the datasets and labels\n", - " self.X = AD + NC\n", - "\n", - " self.Y = torch.cat((torch.ones(len(AD)), torch.zeros(len(NC))), dim=0)\n", - "\n", - " # Generate a random permutation of indices\n", - " self.i_indices = torch.randperm(len(self.X) // 2)\n", - " self.j_indices = torch.randperm(len(self.X) // 2)\n", - "\n", - "\n", - " def __len__(self):\n", - " return len(self.X) // 2\n", - "\n", - " def __getitem__(self, idx):\n", - " i = self.i_indices[idx] * 2\n", - " j = self.j_indices[idx] * 2 + 1\n", - " img1 = self.X[i]\n", - " img2 = self.X[j]\n", - " l1 = self.Y[i]\n", - " l2 = self.Y[j]\n", - "\n", - " return img1, img2, l1, l2\n", - "\n", - "# Set the device\n", - "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", - "\n", - "\n", - "size = 64\n", - "\n", - "def intensity_normalization(img):\n", - " mean = torch.mean(img)\n", - " std = torch.std(img)\n", - " return (img - mean) / std\n", - "\n", - "def windowing(img, window_center, window_width):\n", - " img = torch.clamp(img, window_center - window_width // 2, window_center + window_width // 2)\n", - " img = (img - (window_center - 0.5)) / (window_width - 1)\n", - " return img\n", - "\n", - "# Example usage in your transform\n", - "transform_X = transforms.Compose([\n", - " transforms.Resize((size, size)),\n", - " transforms.ToTensor(),\n", - " transforms.Lambda(intensity_normalization),\n", - " transforms.Lambda(lambda x: windowing(x, window_center=size // 2, window_width=size))\n", - "])\n", - "\n", - "\n", - "# Replace 'your_nii_folder' with the path to your folder containing .nii files\n", - "# nii_dataset = NiiDataset(root_dir=r'keras_png_slices_data\\keras_png_slices_data\\keras_png_slices_train')\n", - "loaders = {}\n", - "\n", - "for stage in ['test']:\n", - " loaders[stage] = {}\n", - " AD = CustomDataset(root_dir=os.path.join(stage, 'AD'), transform=transform_X)\n", - " NC = CustomDataset(root_dir=os.path.join(stage, 'NC'), transform=transform_X)\n", - " dataset = SiameseDataset(AD, NC)\n", - " loaders[stage] = DataLoader(dataset, batch_size=batch_size)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "5ceQcKMAPoip", - "outputId": "2b8eb6b1-c1c5-43db-9370-42f0da1531f2" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.5144444444444445" - ] - }, - "execution_count": 225, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "count = 0\n", - "same = 0\n", - "for i, j, n, m in loaders['test']:\n", - " count += len(n)\n", - " same += (n == m).sum().tolist()\n", - "same / count" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "puuj1CSURmJ9" - }, - "outputs": [], - "source": [ - "import torch\n", - "import torch.nn as nn\n", - "import torchvision.models as models\n", - "\n", - "class SiameseNetwork(nn.Module):\n", - " def __init__(self, pretrained=True):\n", - " super(SiameseNetwork, self).__init__()\n", - " self.resnet = models.resnet18(pretrained=False)\n", - " # Modify the first convolution layer to accept single-channel input\n", - " self.resnet.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False)\n", - " self.fc1 = nn.Linear(1000, 500) # Customize the fully connected layers\n", - " self.fc2 = nn.Linear(500, 2) # Customize the fully connected layers\n", - "\n", - " def forward(self, x1, x2):\n", - " output1 = self.resnet(x1)\n", - " output2 = self.resnet(x2)\n", - " output = torch.abs(output1 - output2)\n", - " output = self.fc1(output)\n", - " output = self.fc2(output)\n", - " return output\n", - "\n", - "model = SiameseNetwork()\n", - "\n", - "class ContrastiveLoss(torch.nn.Module):\n", - " def __init__(self, margin=2.0):\n", - " super(ContrastiveLoss, self).__init__()\n", - " self.margin = margin\n", - "\n", - " def forward(self, output1, output2, label):\n", - " euclidean_distance = F.pairwise_distance(output1, output2)\n", - " loss_contrastive = torch.mean((1 - label) * torch.pow(euclidean_distance, 2) +\n", - " label * torch.pow(torch.clamp(self.margin - euclidean_distance, min=0.0), 2))\n", - " return loss_contrastive" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "4NnXFUDzSWjp", - "outputId": "ab9065a9-ffad-4405-963d-85ae26293b0d" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(tensor(0., device='cuda:0'), tensor(1., device='cuda:0'))\n", - "tensor(0, device='cuda:0', dtype=torch.int32)\n", - "tensor([ 0.0997, -0.0306], device='cuda:0', grad_fn=)\n", - "Epoch [1 / 5], Step [21 / 141 Loss 0.9753031730651855]\n", - "(tensor(0., device='cuda:0'), tensor(0., device='cuda:0'))\n", - "tensor(1, device='cuda:0', dtype=torch.int32)\n", - "tensor([ 0.1081, -0.0379], device='cuda:0', grad_fn=)\n", - "Epoch [1 / 5], Step [42 / 141 Loss 1.025275707244873]\n", - "(tensor(1., device='cuda:0'), tensor(0., device='cuda:0'))\n", - "tensor(0, device='cuda:0', dtype=torch.int32)\n", - "tensor([ 0.0929, -0.0268], device='cuda:0', grad_fn=)\n", - "Epoch [1 / 5], Step [63 / 141 Loss 0.9390543699264526]\n", - "(tensor(1., device='cuda:0'), tensor(0., device='cuda:0'))\n", - "tensor(0, device='cuda:0', dtype=torch.int32)\n", - "tensor([ 0.1265, -0.0541], device='cuda:0', grad_fn=)\n", - "Epoch [1 / 5], Step [84 / 141 Loss 1.0038050413131714]\n", - "(tensor(1., device='cuda:0'), tensor(0., device='cuda:0'))\n", - "tensor(0, device='cuda:0', dtype=torch.int32)\n", - "tensor([ 0.0915, -0.0274], device='cuda:0', grad_fn=)\n", - "Epoch [1 / 5], Step [105 / 141 Loss 1.1157597303390503]\n", - "(tensor(0., device='cuda:0'), tensor(1., device='cuda:0'))\n", - "tensor(0, device='cuda:0', dtype=torch.int32)\n", - "tensor([ 0.1292, -0.0573], device='cuda:0', grad_fn=)\n", - "Epoch [1 / 5], Step [126 / 141 Loss 1.0057425498962402]\n", - "(tensor(0., device='cuda:0'), tensor(1., device='cuda:0'))\n", - "tensor(0, device='cuda:0', dtype=torch.int32)\n", - "tensor([ 0.0953, -0.0308], device='cuda:0', grad_fn=)\n", - "Epoch [2 / 5], Step [21 / 141 Loss 0.9697328805923462]\n", - "(tensor(0., device='cuda:0'), tensor(0., device='cuda:0'))\n", - "tensor(1, device='cuda:0', dtype=torch.int32)\n", - "tensor([ 0.1175, -0.0509], device='cuda:0', grad_fn=)\n", - "Epoch [2 / 5], Step [42 / 141 Loss 1.004995584487915]\n", - "(tensor(1., device='cuda:0'), tensor(0., device='cuda:0'))\n", - "tensor(0, device='cuda:0', dtype=torch.int32)\n", - "tensor([ 0.0923, -0.0304], device='cuda:0', grad_fn=)\n", - "Epoch [2 / 5], Step [63 / 141 Loss 0.9395542144775391]\n", - "(tensor(1., device='cuda:0'), tensor(0., device='cuda:0'))\n", - "tensor(0, device='cuda:0', dtype=torch.int32)\n", - "tensor([ 0.1254, -0.0604], device='cuda:0', grad_fn=)\n", - "Epoch [2 / 5], Step [84 / 141 Loss 0.9549553394317627]\n", - "(tensor(1., device='cuda:0'), tensor(0., device='cuda:0'))\n", - "tensor(0, device='cuda:0', dtype=torch.int32)\n", - "tensor([ 0.0939, -0.0335], device='cuda:0', grad_fn=)\n", - "Epoch [2 / 5], Step [105 / 141 Loss 1.1070666313171387]\n", - "(tensor(0., device='cuda:0'), tensor(1., device='cuda:0'))\n", - "tensor(0, device='cuda:0', dtype=torch.int32)\n", - "tensor([ 0.1300, -0.0632], device='cuda:0', grad_fn=)\n", - "Epoch [2 / 5], Step [126 / 141 Loss 0.9884251952171326]\n", - "(tensor(0., device='cuda:0'), tensor(1., device='cuda:0'))\n", - "tensor(0, device='cuda:0', dtype=torch.int32)\n", - "tensor([ 0.0944, -0.0337], device='cuda:0', grad_fn=)\n", - "Epoch [3 / 5], Step [21 / 141 Loss 0.9663693308830261]\n", - "(tensor(0., device='cuda:0'), tensor(0., device='cuda:0'))\n", - "tensor(1, device='cuda:0', dtype=torch.int32)\n", - "tensor([ 0.1245, -0.0624], device='cuda:0', grad_fn=)\n", - "Epoch [3 / 5], Step [42 / 141 Loss 1.0025595426559448]\n", - "(tensor(1., device='cuda:0'), tensor(0., device='cuda:0'))\n", - "tensor(0, device='cuda:0', dtype=torch.int32)\n", - "tensor([ 0.1142, -0.0528], device='cuda:0', grad_fn=)\n", - "Epoch [3 / 5], Step [63 / 141 Loss 0.9942120313644409]\n", - "(tensor(1., device='cuda:0'), tensor(0., device='cuda:0'))\n", - "tensor(0, device='cuda:0', dtype=torch.int32)\n", - "tensor([ 0.1249, -0.0629], device='cuda:0', grad_fn=)\n", - "Epoch [3 / 5], Step [84 / 141 Loss 0.9485695362091064]\n", - "(tensor(1., device='cuda:0'), tensor(0., device='cuda:0'))\n", - "tensor(0, device='cuda:0', dtype=torch.int32)\n", - "tensor([ 0.0966, -0.0371], device='cuda:0', grad_fn=)\n", - "Epoch [3 / 5], Step [105 / 141 Loss 1.1140365600585938]\n", - "(tensor(0., device='cuda:0'), tensor(1., device='cuda:0'))\n", - "tensor(0, device='cuda:0', dtype=torch.int32)\n", - "tensor([ 0.1298, -0.0664], device='cuda:0', grad_fn=)\n", - "Epoch [3 / 5], Step [126 / 141 Loss 0.9871245622634888]\n", - "(tensor(0., device='cuda:0'), tensor(1., device='cuda:0'))\n", - "tensor(0, device='cuda:0', dtype=torch.int32)\n", - "tensor([ 0.0954, -0.0360], device='cuda:0', grad_fn=)\n", - "Epoch [4 / 5], Step [21 / 141 Loss 0.96551513671875]\n", - "(tensor(0., device='cuda:0'), tensor(0., device='cuda:0'))\n", - "tensor(1, device='cuda:0', dtype=torch.int32)\n", - "tensor([ 0.1227, -0.0626], device='cuda:0', grad_fn=)\n", - "Epoch [4 / 5], Step [42 / 141 Loss 1.0030264854431152]\n", - "(tensor(1., device='cuda:0'), tensor(0., device='cuda:0'))\n", - "tensor(0, device='cuda:0', dtype=torch.int32)\n", - "tensor([ 0.1154, -0.0552], device='cuda:0', grad_fn=)\n", - "Epoch [4 / 5], Step [63 / 141 Loss 0.9911506175994873]\n", - "(tensor(1., device='cuda:0'), tensor(0., device='cuda:0'))\n", - "tensor(0, device='cuda:0', dtype=torch.int32)\n", - "tensor([ 0.1292, -0.0673], device='cuda:0', grad_fn=)\n", - "Epoch [4 / 5], Step [84 / 141 Loss 0.9427274465560913]\n", - "(tensor(1., device='cuda:0'), tensor(0., device='cuda:0'))\n", - "tensor(0, device='cuda:0', dtype=torch.int32)\n", - "tensor([ 0.0964, -0.0375], device='cuda:0', grad_fn=)\n", - "Epoch [4 / 5], Step [105 / 141 Loss 1.1125668287277222]\n", - "(tensor(0., device='cuda:0'), tensor(1., device='cuda:0'))\n", - "tensor(0, device='cuda:0', dtype=torch.int32)\n", - "tensor([ 0.1283, -0.0657], device='cuda:0', grad_fn=)\n", - "Epoch [4 / 5], Step [126 / 141 Loss 0.9870107173919678]\n", - "(tensor(0., device='cuda:0'), tensor(1., device='cuda:0'))\n", - "tensor(0, device='cuda:0', dtype=torch.int32)\n", - "tensor([ 0.0937, -0.0353], device='cuda:0', grad_fn=)\n", - "Epoch [5 / 5], Step [21 / 141 Loss 0.9660702347755432]\n", - "(tensor(0., device='cuda:0'), tensor(0., device='cuda:0'))\n", - "tensor(1, device='cuda:0', dtype=torch.int32)\n", - "tensor([ 0.1197, -0.0604], device='cuda:0', grad_fn=)\n", - "Epoch [5 / 5], Step [42 / 141 Loss 1.0011703968048096]\n", - "(tensor(1., device='cuda:0'), tensor(0., device='cuda:0'))\n", - "tensor(0, device='cuda:0', dtype=torch.int32)\n", - "tensor([ 0.1150, -0.0551], device='cuda:0', grad_fn=)\n", - "Epoch [5 / 5], Step [63 / 141 Loss 0.9866605997085571]\n", - "(tensor(1., device='cuda:0'), tensor(0., device='cuda:0'))\n", - "tensor(0, device='cuda:0', dtype=torch.int32)\n", - "tensor([ 0.1265, -0.0649], device='cuda:0', grad_fn=)\n", - "Epoch [5 / 5], Step [84 / 141 Loss 0.9338618516921997]\n", - "(tensor(1., device='cuda:0'), tensor(0., device='cuda:0'))\n", - "tensor(0, device='cuda:0', dtype=torch.int32)\n", - "tensor([ 0.0969, -0.0381], device='cuda:0', grad_fn=)\n", - "Epoch [5 / 5], Step [105 / 141 Loss 1.1113743782043457]\n", - "(tensor(0., device='cuda:0'), tensor(1., device='cuda:0'))\n", - "tensor(0, device='cuda:0', dtype=torch.int32)\n", - "tensor([ 0.1214, -0.0599], device='cuda:0', grad_fn=)\n", - "Epoch [5 / 5], Step [126 / 141 Loss 0.9903947114944458]\n" - ] - } - ], - "source": [ - "# Initialize the network, loss function, and optimizer\n", - "siamese_net = SiameseNetwork(pretrained=True).to(device)\n", - "criterion = ContrastiveLoss()\n", - "optimizer = optim.Adam(siamese_net.parameters(), lr=0.001)\n", - "# Training loop\n", - "epochs = 5\n", - "total_step = len(loaders['test'])\n", - "for epoch in range(epochs):\n", - " for i, (img1, img2, lab1, lab2) in enumerate(loaders['test']):\n", - " img1, img2, lab1, lab2 = img1.to(device), img2.to(device), lab1.to(device), lab2.to(device)\n", - " output = siamese_net(img1, img2)\n", - " o1, o2 = output[:, 0], output[:, 1]\n", - " label = (lab1 == lab2).int()\n", - "\n", - " loss = criterion(o1, o2, label)\n", - " optimizer.zero_grad()\n", - " loss.backward()\n", - " optimizer.step()\n", - "\n", - " if (i + 1) % 21 == 0:\n", - " print((lab1[0], lab2[0]))\n", - " print(label[0])\n", - " print(output[0])\n", - " print(f\"Epoch [{epoch + 1} / {epochs}], Step [{i + 1} / {total_step} Loss {loss.item()}]\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "G3TWNpvKdAyn", - "outputId": "bf70c5fd-38e5-49c7-a64f-ba1758f103d9" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accuracy of the network on the test images: 49.955555555555556%\n" - ] - } - ], - "source": [ - "correct = 0\n", - "total = 0\n", - "features = []\n", - "labels = []\n", - "with torch.no_grad():\n", - " for i, (img1, img2, lab1, lab2) in enumerate(loaders['test']):\n", - " img1, img2, lab1, lab2 = img1.to(device), img2.to(device), lab1.to(device), lab2.to(device)\n", - " output = siamese_net(img1, img2)\n", - " out1, out2 = output[:, 0], output[:, 1]\n", - " predicted = (out1 - out2).pow(2).sum().sqrt().lt(0.5)\n", - " total += lab1.size(0)\n", - " correct += (predicted == lab1).sum().item()\n", - " features.extend(out1.tolist())\n", - " labels.extend(lab1.tolist())\n", - " features.extend(out2.tolist())\n", - " labels.extend(lab2.tolist())\n", - "\n", - "\n", - " print(f'Accuracy of the network on the test images: {100 * correct / total}%')\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "MVEvstik8zqK", - "outputId": "c5aed97c-27c1-4685-bf6a-cc2204dbaa69" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accuracy: 0.49166666666666664\n" - ] - } - ], - "source": [ - "from sklearn.model_selection import train_test_split\n", - "from sklearn.linear_model import LogisticRegression\n", - "from sklearn.metrics import accuracy_score\n", - "\n", - "# Split the data into training and testing sets\n", - "X_train, X_test, y_train, y_test = train_test_split(np.array(features).reshape(-1, 1), labels, test_size=0.2, random_state=42)\n", - "\n", - "# Train a logistic regression model\n", - "classifier = LogisticRegression(max_iter=1000)\n", - "classifier.fit(X_train, y_train)\n", - "\n", - "# Make predictions\n", - "predictions = classifier.predict(X_test)\n", - "\n", - "# Calculate accuracy\n", - "accuracy = accuracy_score(y_test, predictions)\n", - "print(f\"Accuracy: {accuracy}\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "qA-60lBMGV3i" - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "9PrC3ijsFnhE", - "outputId": "42aa07d0-9166-43fb-a67e-713fc99664da" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.5943959355354309" - ] - }, - "execution_count": 148, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "features[0].tolist()" - ] - }, - { - "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": { "id": "dlrwhZTjTKWf" }, @@ -686,7 +261,7 @@ " img2 = self.transform(img2)\n", " img3 = self.transform(img3)\n", "\n", - " return img1, img2, img3, label\n", + " return img1, img2, img3, torch.tensor([1 - label, label])\n", "\n", "\n", "size = 128\n", @@ -770,9 +345,9 @@ "base_uri": "https://localhost:8080/" }, "id": "5RLuD3STujVC", - "outputId": "582bc19f-370e-47ab-d0a2-48b82b190226" + "outputId": "ea9b978b-4b8d-4466-81a6-61b943ab8b60" }, - "execution_count": null, + "execution_count": 8, "outputs": [ { "output_type": "execute_result", @@ -782,13 +357,13 @@ ] }, "metadata": {}, - "execution_count": 56 + "execution_count": 8 } ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": { "id": "c8MhXUlOTHnY" }, @@ -859,13 +434,56 @@ }, { "cell_type": "code", - "execution_count": null, + "source": [ + "cd" + ], "metadata": { - "id": "Gs0V-oZe1O46" + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "CkYaII4fGtTP", + "outputId": "68ab616f-0cdd-4f74-e160-33c1d4b65f69" }, - "outputs": [], + "execution_count": 18, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "/root\n" + ] + } + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "id": "Gs0V-oZe1O46", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 419 + }, + "outputId": "b2f0159b-02ad-4335-d7be-25001f9bb682" + }, + "outputs": [ + { + "output_type": "error", + "ename": "RuntimeError", + "evalue": "ignored", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlr_scheduler\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mStepLR\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mdevice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'cuda'\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_available\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m'cpu'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mempty_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mlearning_rate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.05\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mtrip_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTripletSiameseNetwork\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/cuda/memory.py\u001b[0m in \u001b[0;36mempty_cache\u001b[0;34m()\u001b[0m\n\u001b[1;32m 131\u001b[0m \"\"\"\n\u001b[1;32m 132\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 133\u001b[0;31m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_C\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cuda_emptyCache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 134\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 135\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mRuntimeError\u001b[0m: CUDA error: device-side assert triggered\nCUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.\nFor debugging consider passing CUDA_LAUNCH_BLOCKING=1.\nCompile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.\n" + ] + } + ], "source": [ "from torch.optim.lr_scheduler import StepLR\n", + "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", + "torch.cuda.empty_cache()\n", "learning_rate = 0.05\n", "trip_model = TripletSiameseNetwork()\n", "trip_criterion = TripletLossWithRegularization(margin=1.0)\n", @@ -883,175 +501,134 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 11, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "A_J3lXLOUDu0", - "outputId": "e56e3882-b65d-48f5-a2ae-3280b5c45cdd" + "outputId": "9f42b2ed-30cd-41c6-a590-1a81844aa3d5" }, "outputs": [ { - "metadata": { - "tags": null - }, - "name": "stdout", "output_type": "stream", - "text": [ - "Epoch [1 / 15], Step [22 / 225], Loss: 1.6861666440963745, Validation Loss: 1.3392752408981323\n", - "Epoch [1 / 15], Step [44 / 225], Loss: 1.1134928464889526, Validation Loss: 1.0987355709075928\n", - "Epoch [1 / 15], Step [66 / 225], Loss: 1.0257302522659302, Validation Loss: 1.1489994525909424\n", - "Epoch [1 / 15], Step [88 / 225], Loss: 1.1023128032684326, Validation Loss: 1.149999976158142\n", - "Epoch [1 / 15], Step [110 / 225], Loss: 0.794222354888916, Validation Loss: 1.2070260047912598\n", - "Epoch [1 / 15], Step [132 / 225], Loss: 0.9533556699752808, Validation Loss: 1.061461329460144\n", - "Epoch [1 / 15], Step [154 / 225], Loss: 0.5997182726860046, Validation Loss: 0.9921392202377319\n", - "Epoch [1 / 15], Step [176 / 225], Loss: 0.8227355480194092, Validation Loss: 0.8853449821472168\n", - "Epoch [1 / 15], Step [198 / 225], Loss: 1.0741984844207764, Validation Loss: 1.1926651000976562\n", - "Epoch [1 / 15], Step [220 / 225], Loss: 1.4481054544448853, Validation Loss: 1.1044371128082275\n" - ] - }, - { - "data": { - "image/png": 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\n", 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Step [198 / 225], Loss: 0.46442490816116333, Validation Loss: 0.510191798210144\n", - "Epoch [2 / 15], Step [220 / 225], Loss: 0.08432803303003311, Validation Loss: 0.13745912909507751\n" + "Epoch [1 / 10], Step [22 / 225], Loss: 1.5358287719163028, Validation Loss: 1.3388497829437256\n", + "Epoch [1 / 10], Step [44 / 225], Loss: 1.4194633120840245, Validation Loss: 1.2655972838401794\n", + "Epoch [1 / 10], Step [66 / 225], Loss: 1.2770680965799275, Validation Loss: 1.2275654872258503\n", + "Epoch [1 / 10], Step [88 / 225], Loss: 1.1987250704656949, Validation Loss: 1.1871682107448578\n", + "Epoch [1 / 10], Step [110 / 225], Loss: 1.1397959481586108, Validation Loss: 1.1396066188812255\n", + "Epoch [1 / 10], Step [132 / 225], Loss: 1.10812256146561, Validation Loss: 1.1233381430308025\n", + "Epoch [1 / 10], Step [154 / 225], Loss: 1.0751701558565165, Validation Loss: 1.0892583557537623\n", + "Epoch [1 / 10], Step [176 / 225], Loss: 1.0456692581488327, Validation Loss: 1.0730071142315865\n", + "Epoch [1 / 10], Step [198 / 225], Loss: 1.0179587769688982, Validation Loss: 1.034343745973375\n", + "Epoch [1 / 10], Step [220 / 225], Loss: 0.9846788463267413, Validation Loss: 0.9897604703903198\n" ] }, { + "output_type": "display_data", "data": { - "image/png": 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\n", 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EKCIi4sp9uQK73e4ISF988YXOnTun0aNHa+DAgfr8888lSYMHD1bHjh01b948ubu7a+fOnapdu7YkafTo0SorK9M//vEP1a1bV9nZ2fLx8bnuflU2QhIAAL9Uflb6g7USDmScn2H6Y/DVlf/+R8mj7lWVDh8+XK+++qq++OIL3XvvvZLOf9XWv39/+fn5yc/PTy+++KKjfuzYsVq3bp1WrFhRKSEpIyNDu3fv1qFDhxQcfH587733ntq3b69t27apS5cuys3N1YQJExQaGipJatWqlWP/3Nxc9e/fXx06dJAktWzZ8rr7VBX4ug0AgBomNDRU3bp10/z58yVJ33//vTZt2qSnnnpKkmSz2TR9+nR16NBB9evXl4+Pj9atW6fc3NxKOf+ePXsUHBzsCEiS1K5dO/n7+2vPnj2SpLi4OD399NOKiorSH//4R/3www+O2nHjxumVV17RXXfdpSlTplTKjeZVgZkkAAB+qXad87M6V5LzlfT+41euG7xSatbt6s5bAU899ZTGjh2ruXPnasGCBQoJCdE999wjSXr11Vc1e/ZspaamqkOHDqpbt67Gjx+vsrKyCp3jekydOlW//e1v9cknn+jTTz/VlClTtGzZMvXr109PP/20oqOj9cknn+jvf/+7kpOT9ac//Uljx469Yf27GswkAQDwSxbL+a+9rrSF3H/+V2y61H1EFsk36Hzd1RzvKu5H+qUBAwbIzc1NH3zwgd577z0NHz7ccX/Sl19+qb59++rJJ59UWFiYWrZsqf3791/fdfmFtm3b6vDhwzp8+LCjLTs7WydPnlS7du0cba1bt9bzzz+vv//973rssce0YMECx3vBwcEaOXKkVq9erRdeeEHvvPNOpfWvsjCTBADAtXBzP/8z/xVDdT4o/fIG7v8LPD3+eHU3bV8DHx8fDRw4UJMmTVJxcbGGDRvmeK9Vq1ZauXKlvvrqK9WrV08pKSkqKChwCjBXw2azaefOnU5tnp6eioqKUocOHTR48GClpqbq3LlzGjVqlO655x517txZP//8syZMmKDHH39cLVq00L///W9t27ZN/fv3lySNHz9ePXv2VOvWrfXTTz/ps88+U9u2ba/3klQ6ZpIAALhW7R6RBrwn+TZxbve1nm+vgnWSfumpp57STz/9pOjoaFmt/7nZPCEhQXfeeaeio6N17733KjAwUI8++miFj3/69Gl17NjRaevTp48sFov+8pe/qF69eurevbuioqLUsmVLLV++XJLk7u6u48ePa+jQoWrdurUGDBignj176uWXX5Z0PnyNHj1abdu2VY8ePdS6dWu9+eablXJNKpPFMAyz3y7iCoqLi+Xn56eioiL5+vq6ujsAgGtQUlKiQ4cOqUWLFvLy8rr2A9lt5+9ROl0g+QScvwepimaQcGWX+3utyOc3X7cBAHC93NylFv/r6l6gkvF1GwAAgAlCEgAAgAlCEgAAgAlCEgDglsdvmG4ulfX3SUgCANyyLjxw9ezZq3igLWqMC3+fF/5+r5XLf902d+5cvfrqq8rPz1dYWJjS0tIu+fC98vJyJScna9GiRcrLy1ObNm00c+ZM9ejRw1Fjs9k0depULVmyRPn5+bJarRo2bJgSEhIcK5GePn1a8fHxWrNmjY4fP64WLVpo3LhxGjly5A0ZMwCgenB3d5e/v78KCwslSXXq1HF8VqDmMQxDZ8+eVWFhofz9/eXufn3LMLg0JC1fvlxxcXF66623FBkZqdTUVEVHR2vfvn1q3LjxRfUJCQlasmSJ3nnnHYWGhmrdunXq16+fvvrqK3Xs2FGSNHPmTM2bN0+LFi1S+/bt9c033yg2NlZ+fn4aN26cpPMP3du4caOWLFmi5s2b6+9//7tGjRolq9WqRx6p2oW/AADVS2BgoCQ5ghJqPn9/f8ff6/Vw6WKSkZGR6tKli+bMmSNJstvtCg4O1tixYxUfH39RvdVq1eTJkzV69GhHW//+/eXt7a0lS5ZIknr37q2AgAC9++67l6z51a9+pYEDByoxMdFR06lTJ/Xs2VOvvPKKaV9LS0tVWlrqeF1cXKzg4GAWkwSAm4TNZlN5ebmru4HrVLt27cvOINWIxSTLysq0fft2TZo0ydHm5uamqKgobd682XSf0tLSi1bO9Pb2VmZmpuN1t27d9Oc//1n79+9X69attWvXLmVmZiolJcWpZu3atRo+fLisVqs+//xz7d+/X6+//vol+5ucnOxYTh0AcPNxd3e/7q9ncHNx2Y3bx44dk81mU0BAgFN7QECA8vPzTfeJjo5WSkqKDhw4ILvdrvXr12v16tU6cuSIoyY+Pl5PPPGEQkNDVbt2bXXs2FHjx4/X4MGDHTVpaWlq166dbr/9dnl4eKhHjx6aO3euunfvfsn+Tpo0SUVFRY7tl08+BgAANx+X37hdEbNnz9Yzzzyj0NBQWSwWhYSEKDY2VvPnz3fUrFixQu+//74++OADtW/fXjt37tT48eNltVoVExMj6XxI+vrrr7V27Vo1a9ZM//jHPzR69GhZrVZFRUWZntvT01Oenp43ZJwAAMD1XBaSGjZsKHd3dxUUFDi1FxQUXPJmq0aNGmnNmjUqKSnR8ePHZbVaFR8fr5YtWzpqJkyY4JhNkqQOHTooJydHycnJiomJ0c8//6zf//73+uijj/Twww9Lkn79619r586deu211y4ZkgAAwK3FZV+3eXh4qFOnTsrIyHC02e12ZWRkqGvXrpfd18vLS0FBQTp37pxWrVqlvn37Ot47e/as3Nych+Xu7i673S7p/DIC5eXll60BAABw6ddtcXFxiomJUefOnRUREaHU1FSdOXNGsbGxkqShQ4cqKChIycnJkqQtW7YoLy9P4eHhysvL09SpU2W32zVx4kTHMfv06aMZM2aoadOmat++vXbs2KGUlBQNHz5ckuTr66t77rlHEyZMkLe3t5o1a6YvvvhC7733ntPN3QAA4Nbm0pA0cOBAHT16VElJScrPz1d4eLjS09MdN3Pn5uY6zfiUlJQoISFBBw8elI+Pj3r16qXFixfL39/fUZOWlqbExESNGjVKhYWFslqtGjFihJKSkhw1y5Yt06RJkzR48GCdOHFCzZo104wZM1hMEgAAOLh0naSarCLrLAAAgOqhIp/fPLsNAADABCEJAADABCEJAADABCEJAADABCEJAADABCEJAADABCEJAADABCEJAADABCEJAADABCEJAADABCEJAADABCEJAADABCEJAADABCEJAADABCEJAADABCEJAADABCEJAADABCEJAADABCEJAADABCEJAADABCEJAADABCEJAADABCEJAADABCEJAADABCEJAADABCEJAADABCEJAADABCEJAADABCEJAADABCEJAADABCEJAADABCEJAADAhMtD0ty5c9W8eXN5eXkpMjJSW7duvWRteXm5pk2bppCQEHl5eSksLEzp6elONTabTYmJiWrRooW8vb0VEhKi6dOnyzAMp7o9e/bokUcekZ+fn+rWrasuXbooNze3SsYIAABqHpeGpOXLlysuLk5TpkxRVlaWwsLCFB0drcLCQtP6hIQEvf3220pLS1N2drZGjhypfv36aceOHY6amTNnat68eZozZ4727NmjmTNnatasWUpLS3PU/PDDD7r77rsVGhqqzz//XN9++60SExPl5eVV5WMGAAA1g8X47ymWGygyMlJdunTRnDlzJEl2u13BwcEaO3as4uPjL6q3Wq2aPHmyRo8e7Wjr37+/vL29tWTJEklS7969FRAQoHffffeSNU888YRq166txYsXX3Pfi4uL5efnp6KiIvn6+l7zcQAAwI1Tkc9vl80klZWVafv27YqKivpPZ9zcFBUVpc2bN5vuU1paetFsj7e3tzIzMx2vu3XrpoyMDO3fv1+StGvXLmVmZqpnz56SzgexTz75RK1bt1Z0dLQaN26syMhIrVmz5rL9LS0tVXFxsdMGAABuXi4LSceOHZPNZlNAQIBTe0BAgPLz8033iY6OVkpKig4cOCC73a7169dr9erVOnLkiKMmPj5eTzzxhEJDQ1W7dm117NhR48eP1+DBgyVJhYWFOn36tP74xz+qR48e+vvf/65+/frpscce0xdffHHJ/iYnJ8vPz8+xBQcHV8JVAAAA1ZXLb9yuiNmzZ6tVq1YKDQ2Vh4eHxowZo9jYWLm5/WcYK1as0Pvvv68PPvhAWVlZWrRokV577TUtWrRI0vmZJEnq27evnn/+eYWHhys+Pl69e/fWW2+9dclzT5o0SUVFRY7t8OHDVTtYAADgUrVcdeKGDRvK3d1dBQUFTu0FBQUKDAw03adRo0Zas2aNSkpKdPz4cVmtVsXHx6tly5aOmgkTJjhmkySpQ4cOysnJUXJysmJiYtSwYUPVqlVL7dq1czp227Ztnb62+2+enp7y9PS81uECAIAaxmUzSR4eHurUqZMyMjIcbXa7XRkZGeratetl9/Xy8lJQUJDOnTunVatWqW/fvo73zp496zSzJEnu7u6OGSQPDw916dJF+/btc6rZv3+/mjVrdr3DAgAANwmXzSRJUlxcnGJiYtS5c2dFREQoNTVVZ86cUWxsrCRp6NChCgoKUnJysiRpy5YtysvLU3h4uPLy8jR16lTZ7XZNnDjRccw+ffpoxowZatq0qdq3b68dO3YoJSVFw4cPd9RMmDBBAwcOVPfu3XXfffcpPT1df/3rX/X555/f0PEDAIDqy6UhaeDAgTp69KiSkpKUn5+v8PBwpaenO27mzs3NdZoVKikpUUJCgg4ePCgfHx/16tVLixcvlr+/v6MmLS1NiYmJGjVqlAoLC2W1WjVixAglJSU5avr166e33npLycnJGjdunNq0aaNVq1bp7rvvvmFjBwAA1ZtL10mqyVgnCQCAmqdGrJMEAABQnRGSAAAATBCSAAAATBCSAAAATBCSAAAATBCSAAAATBCSAAAATBCSAAAATBCSAAAATBCSAAAATBCSAAAATBCSAAAATBCSAAAATBCSAAAATBCSAAAATBCSAAAATBCSAAAATBCSAAAATBCSAAAATBCSAAAATBCSAAAATBCSAAAATBCSAAAATBCSAAAATBCSAAAATBCSAAAATBCSAAAATBCSAAAATBCSAAAATBCSAAAATBCSAAAATBCSAAAATBCSAAAATFSLkDR37lw1b95cXl5eioyM1NatWy9ZW15ermnTpikkJEReXl4KCwtTenq6U43NZlNiYqJatGghb29vhYSEaPr06TIMw/SYI0eOlMViUWpqamUOCwAA1GAuD0nLly9XXFycpkyZoqysLIWFhSk6OlqFhYWm9QkJCXr77beVlpam7OxsjRw5Uv369dOOHTscNTNnztS8efM0Z84c7dmzRzNnztSsWbOUlpZ20fE++ugjff3117JarVU2RgAAUPO4PCSlpKTomWeeUWxsrNq1a6e33npLderU0fz5803rFy9erN///vfq1auXWrZsqeeee069evXSn/70J0fNV199pb59++rhhx9W8+bN9fjjj+uhhx66aIYqLy9PY8eO1fvvv6/atWtX6TgBAEDN4tKQVFZWpu3btysqKsrR5ubmpqioKG3evNl0n9LSUnl5eTm1eXt7KzMz0/G6W7duysjI0P79+yVJu3btUmZmpnr27OmosdvtGjJkiCZMmKD27dtfsa+lpaUqLi522gAAwM2rlitPfuzYMdlsNgUEBDi1BwQEaO/evab7REdHKyUlRd27d1dISIgyMjK0evVq2Ww2R018fLyKi4sVGhoqd3d32Ww2zZgxQ4MHD3bUzJw5U7Vq1dK4ceOuqq/Jycl6+eWXr2GUAACgJnL5120VNXv2bLVq1UqhoaHy8PDQmDFjFBsbKze3/wxlxYoVev/99/XBBx8oKytLixYt0muvvaZFixZJkrZv367Zs2dr4cKFslgsV3XeSZMmqaioyLEdPny4SsYHAACqB5eGpIYNG8rd3V0FBQVO7QUFBQoMDDTdp1GjRlqzZo3OnDmjnJwc7d27Vz4+PmrZsqWjZsKECYqPj9cTTzyhDh06aMiQIXr++eeVnJwsSdq0aZMKCwvVtGlT1apVS7Vq1VJOTo5eeOEFNW/e3PS8np6e8vX1ddoAAMDNy6UhycPDQ506dVJGRoajzW63KyMjQ127dr3svl5eXgoKCtK5c+e0atUq9e3b1/He2bNnnWaWJMnd3V12u12SNGTIEH377bfauXOnY7NarZowYYLWrVtXiSMEAAA1lUvvSZKkuLg4xcTEqHPnzoqIiFBqaqrOnDmj2NhYSdLQoUMVFBTkmAXasmWL8vLyFB4erry8PE2dOlV2u10TJ050HLNPnz6aMWOGmjZtqvbt22vHjh1KSUnR8OHDJUkNGjRQgwYNnPpRu3ZtBQYGqk2bNjdo5AAAoDpzeUgaOHCgjh49qqSkJOXn5ys8PFzp6emOm7lzc3OdZoVKSkqUkJCggwcPysfHR7169dLixYvl7+/vqElLS1NiYqJGjRqlwsJCWa1WjRgxQklJSTd6eAAAoIayGJdahhqXVVxcLD8/PxUVFXF/EgAANURFPr9r3K/bAAAAboQKh6T09HSnhRvnzp2r8PBw/fa3v9VPP/1UqZ0DAABwlQqHpAkTJjhWm969e7deeOEF9erVS4cOHVJcXFyldxAAAMAVKnzj9qFDh9SuXTtJ0qpVq9S7d2/94Q9/UFZWlnr16lXpHQQAAHCFCs8keXh46OzZs5KkDRs26KGHHpIk1a9fn+eZAQCAm0aFZ5LuvvtuxcXF6a677tLWrVu1fPlySdL+/ft1++23V3oHAQAAXKHCM0lz5sxRrVq1tHLlSs2bN09BQUGSpE8//VQ9evSo9A4CAAC4AuskXSPWSQIAoOap0nWSsrKytHv3bsfrv/zlL3r00Uf1+9//XmVlZRXvLQAAQDVU4ZA0YsQI7d+/X5J08OBBPfHEE6pTp44+/PBDp+enAQAA1GQVDkn79+9XeHi4JOnDDz9U9+7d9cEHH2jhwoVatWpVZfcPAADAJSockgzDkN1ul3R+CYALayMFBwfr2LFjlds7AAAAF6lwSOrcubNeeeUVLV68WF988YUefvhhSecXmQwICKj0DgIAALhChUNSamqqsrKyNGbMGE2ePFl33HGHJGnlypXq1q1bpXcQAADAFSptCYCSkhK5u7urdu3alXG4ao8lAAAAqHkq8vld4RW3L9i+fbv27NkjSWrXrp3uvPPOaz0UAABAtVPhkFRYWKiBAwfqiy++kL+/vyTp5MmTuu+++7Rs2TI1atSosvsIAABww1X4nqSxY8fq9OnT+uc//6kTJ07oxIkT+u6771RcXKxx48ZVRR8BAABuuArfk+Tn56cNGzaoS5cuTu1bt27VQw89pJMnT1Zm/6ot7kkCAKDmqdLHktjtdtObs2vXru1YPwkAAKCmq3BIuv/++/W73/1OP/74o6MtLy9Pzz//vB544IFK7RwAAICrVDgkzZkzR8XFxWrevLlCQkIUEhKiFi1aqLi4WG+88UZV9BEAAOCGq/Cv24KDg5WVlaUNGzZo7969kqS2bdsqKiqq0jsHAADgKpW2mOTevXv1yCOPaP/+/ZVxuGqPG7cBAKh5qvTG7UspLS3VDz/8UFmHAwAAcKlKC0kAAAA3E0ISAACACUISAACAiav+dVu9evVksVgu+f65c+cqpUMAAADVwVWHpNTU1CrsBgAAQPVy1SEpJiamKvsBAABQrXBPEgAAgAlCEgAAgIlqEZLmzp2r5s2by8vLS5GRkdq6desla8vLyzVt2jSFhITIy8tLYWFhSk9Pd6qx2WxKTExUixYt5O3trZCQEE2fPl0XFhcvLy/XSy+9pA4dOqhu3bqyWq0aOnSo00N7AQDArc3lIWn58uWKi4vTlClTlJWVpbCwMEVHR6uwsNC0PiEhQW+//bbS0tKUnZ2tkSNHql+/ftqxY4ejZubMmZo3b57mzJmjPXv2aObMmZo1a5bS0tIkSWfPnlVWVpYSExOVlZWl1atXa9++fXrkkUduyJgBAED1V2nPbrtWkZGR6tKli+bMmSNJstvtCg4O1tixYxUfH39RvdVq1eTJkzV69GhHW//+/eXt7a0lS5ZIknr37q2AgAC9++67l6z5b9u2bVNERIRycnLUtGnTi94vLS1VaWmp43VxcbGCg4N5dhsAADVIRZ7ddtW/brvAZrNp4cKFysjIUGFhoex2u9P7GzduvOpjlZWVafv27Zo0aZKjzc3NTVFRUdq8ebPpPqWlpfLy8nJq8/b2VmZmpuN1t27d9Oc//1n79+9X69attWvXLmVmZiolJeWSfSkqKpLFYpG/v7/p+8nJyXr55ZevemwAAKBmq3BI+t3vfqeFCxfq4Ycf1q9+9avLLjB5JceOHZPNZlNAQIBTe0BAgPbu3Wu6T3R0tFJSUtS9e3eFhIQoIyNDq1evls1mc9TEx8eruLhYoaGhcnd3l81m04wZMzR48GDTY5aUlOill17SoEGDLpkqJ02apLi4OMfrCzNJAADg5lThkLRs2TKtWLFCvXr1qor+XNHs2bP1zDPPKDQ0VBaLRSEhIYqNjdX8+fMdNStWrND777+vDz74QO3bt9fOnTs1fvx4Wa3Wi9Z7Ki8v14ABA2QYhubNm3fJ83p6esrT07PKxgUAAKqXCockDw8P3XHHHZVy8oYNG8rd3V0FBQVO7QUFBQoMDDTdp1GjRlqzZo1KSkp0/PhxWa1WxcfHq2XLlo6aCRMmKD4+Xk888YQkqUOHDsrJyVFycrJTSLoQkHJycrRx40buLQIAAA4V/nXbCy+8oNmzZ6sy7vf28PBQp06dlJGR4Wiz2+3KyMhQ165dL7uvl5eXgoKCdO7cOa1atUp9+/Z1vHf27Fm5uTkPzd3d3en+qQsB6cCBA9qwYYMaNGhw3eMBAAA3jwrPJGVmZuqzzz7Tp59+qvbt26t27dpO769evbpCx4uLi1NMTIw6d+6siIgIpaam6syZM4qNjZUkDR06VEFBQUpOTpYkbdmyRXl5eQoPD1deXp6mTp0qu92uiRMnOo7Zp08fzZgxQ02bNlX79u21Y8cOpaSkaPjw4ZLOB6THH39cWVlZ+vjjj2Wz2ZSfny9Jql+/vjw8PCp6WQAAwE2mwiHJ399f/fr1q7QODBw4UEePHlVSUpLy8/MVHh6u9PR0x83cubm5TrNCJSUlSkhI0MGDB+Xj46NevXpp8eLFTr9KS0tLU2JiokaNGqXCwkJZrVaNGDFCSUlJkqS8vDytXbtWkhQeHu7Un88++0z33ntvpY0PAADUTC5fJ6mmqsg6CwAAoHqoyOe3y1fcBgAAqI4q/HWbJK1cuVIrVqxQbm6uysrKnN7LysqqlI4BAAC4UoVnkt544w3FxsYqICBAO3bsUEREhBo0aKCDBw+qZ8+eVdFHAACAG67CIenNN9/Un//8Z6WlpcnDw0MTJ07U+vXrNW7cOBUVFVVFHwEAAG64Coek3NxcdevWTdL5Z6adOnVKkjRkyBAtXbq0cnsHAADgIhUOSYGBgTpx4oQkqWnTpvr6668lSYcOHaqUBSYBAACqgwqHpPvvv9+xxlBsbKyef/55Pfjggxo4cGClrp8EAADgShVeJ8lut8tut6tWrfM/jFu2bJm++uortWrVSiNGjLhlVqtmnSQAAGqeinx+s5jkNSIkAQBQ81T5YpKbNm3Sk08+qa5duyovL0+StHjxYmVmZl7L4QAAAKqdCoekVatWKTo6Wt7e3tqxY4dKS0slSUVFRfrDH/5Q6R0EAABwhQqHpFdeeUVvvfWW3nnnHdWuXdvRftddd7HaNgAAuGlUOCTt27dP3bt3v6jdz89PJ0+erIw+AQAAuNw1rZP0/fffX9SemZmpli1bVkqnAAAAXK3CIemZZ57R7373O23ZskUWi0U//vij3n//fb344ot67rnnqqKPAAAAN1ytiu4QHx8vu92uBx54QGfPnlX37t3l6empF198UWPHjq2KPgIAANxw17xOUllZmb7//nudPn1a7dq1k4+PT2X3rVpjnSQAAGqeinx+V3gm6QIPDw+1a9fuWncHAACo1q46JA0fPvyq6ubPn3/NnQEAAKgurjokLVy4UM2aNVPHjh3Fk0wAAMDN7qpD0nPPPaelS5fq0KFDio2N1ZNPPqn69etXZd8AAABc5qqXAJg7d66OHDmiiRMn6q9//auCg4M1YMAArVu3jpklAABw07nmX7fl5ORo4cKFeu+993Tu3Dn985//vKV+4cav2wAAqHkq8vld4cUkHTu6ucliscgwDNlstms9DAAAQLVUoZBUWlqqpUuX6sEHH1Tr1q21e/duzZkzR7m5ubfULBIAALj5XfWN26NGjdKyZcsUHBys4cOHa+nSpWrYsGFV9g0AAMBlrvqeJDc3NzVt2lQdO3aUxWK5ZN3q1asrrXPVGfckAQBQ81TJittDhw69bDgCAAC4mVRoMUkAAIBbxTX/ug0AAOBmRkgCAAAwQUgCAAAwUS1C0ty5c9W8eXN5eXkpMjJSW7duvWRteXm5pk2bppCQEHl5eSksLEzp6elONTabTYmJiWrRooW8vb0VEhKi6dOnOz0+xTAMJSUlqUmTJvL29lZUVJQOHDhQZWMEAAA1i8tD0vLlyxUXF6cpU6YoKytLYWFhio6OVmFhoWl9QkKC3n77baWlpSk7O1sjR45Uv379tGPHDkfNzJkzNW/ePM2ZM0d79uzRzJkzNWvWLKWlpTlqZs2apTfeeENvvfWWtmzZorp16yo6OlolJSVVPmYAAFD9XfOz2ypLZGSkunTpojlz5kiS7Ha7goODNXbsWMXHx19Ub7VaNXnyZI0ePdrR1r9/f3l7e2vJkiWSpN69eysgIEDvvvuuaY1hGLJarXrhhRf04osvSpKKiooUEBCghQsX6oknnrhiv1knCQCAmueGPLutMpSVlWn79u2KiopytLm5uSkqKkqbN2823ae0tFReXl5Obd7e3srMzHS87tatmzIyMrR//35J0q5du5SZmamePXtKkg4dOqT8/Hyn8/r5+SkyMvKy5y0uLnbaAADAzeuq10mqCseOHZPNZlNAQIBTe0BAgPbu3Wu6T3R0tFJSUtS9e3eFhIQoIyNDq1evdnrIbnx8vIqLixUaGip3d3fZbDbNmDFDgwcPliTl5+c7zvPf573w3n9LTk7Wyy+/fM1jBQAANYvL70mqqNmzZ6tVq1YKDQ2Vh4eHxowZo9jYWLm5/WcoK1as0Pvvv68PPvhAWVlZWrRokV577TUtWrToms87adIkFRUVObbDhw9XxnAAAEA15dKZpIYNG8rd3V0FBQVO7QUFBQoMDDTdp1GjRlqzZo1KSkp0/PhxWa1WxcfHq2XLlo6aCRMmKD4+3nFvUYcOHZSTk6Pk5GTFxMQ4jl1QUKAmTZo4nTc8PNz0vJ6envL09Lye4QIAgBrEpTNJHh4e6tSpkzIyMhxtdrtdGRkZ6tq162X39fLyUlBQkM6dO6dVq1apb9++jvfOnj3rNLMkSe7u7rLb7ZKkFi1aKDAw0Om8xcXF2rJlyxXPCwAAbg0unUmSpLi4OMXExKhz586KiIhQamqqzpw5o9jYWEnnH6wbFBSk5ORkSdKWLVuUl5en8PBw5eXlaerUqbLb7Zo4caLjmH369NGMGTPUtGlTtW/fXjt27FBKSoqGDx8uSbJYLBo/frxeeeUVtWrVSi1atFBiYqKsVqseffTRG34NAABA9ePykDRw4EAdPXpUSUlJys/PV3h4uNLT0x03Vefm5jrNCpWUlCghIUEHDx6Uj4+PevXqpcWLF8vf399Rk5aWpsTERI0aNUqFhYWyWq0aMWKEkpKSHDUTJ07UmTNn9Oyzz+rkyZO6++67lZ6eftEv5wAAwK3J5esk1VSskwQAQM1TY9ZJAgAAqK4ISQAAACYISQAAACYISQAAACYISQAAACYISQAAACYISQAAACYISQAAACYISQAAACYISQAAACYISQAAACYISQAAACYISQAAACYISQAAACYISQAAACYISQAAACYISQAAACYISQAAACYISQAAACYISQAAACYISQAAACYISQAAACYISQAAACYISQAAACYISQAAACYISQAAACYISQAAACYISQAAACYISQAAACYISQAAACYISQAAACYISQAAACYISQAAACaqRUiaO3eumjdvLi8vL0VGRmrr1q2XrC0vL9e0adMUEhIiLy8vhYWFKT093ammefPmslgsF22jR4921OTn52vIkCEKDAxU3bp1deedd2rVqlVVNkYAAFCzuDwkLV++XHFxcZoyZYqysrIUFham6OhoFRYWmtYnJCTo7bffVlpamrKzszVy5Ej169dPO3bscNRs27ZNR44ccWzr16+XJP3mN79x1AwdOlT79u3T2rVrtXv3bj322GMaMGCA03EAAMCty2IYhuHKDkRGRqpLly6aM2eOJMlutys4OFhjx45VfHz8RfVWq1WTJ092mhXq37+/vL29tWTJEtNzjB8/Xh9//LEOHDggi8UiSfLx8dG8efM0ZMgQR12DBg00c+ZMPf3001fsd3Fxsfz8/FRUVCRfX98KjRkAALhGRT6/XTqTVFZWpu3btysqKsrR5ubmpqioKG3evNl0n9LSUnl5eTm1eXt7KzMz85LnWLJkiYYPH+4ISJLUrVs3LV++XCdOnJDdbteyZctUUlKie++995LnLS4udtoAAMDNy6Uh6dixY7LZbAoICHBqDwgIUH5+vuk+0dHRSklJ0YEDB2S327V+/XqtXr1aR44cMa1fs2aNTp48qWHDhjm1r1ixQuXl5WrQoIE8PT01YsQIffTRR7rjjjtMj5OcnCw/Pz/HFhwcXPEBAwCAGsPl9yRV1OzZs9WqVSuFhobKw8NDY8aMUWxsrNzczIfy7rvvqmfPnrJarU7tiYmJOnnypDZs2KBvvvlGcXFxGjBggHbv3m16nEmTJqmoqMixHT58uNLHBgAAqo9arjx5w4YN5e7uroKCAqf2goICBQYGmu7TqFEjrVmzRiUlJTp+/LisVqvi4+PVsmXLi2pzcnK0YcMGrV692qn9hx9+0Jw5c/Tdd9+pffv2kqSwsDBt2rRJc+fO1VtvvXXRsTw9PeXp6XmtQwUAADWMS2eSPDw81KlTJ2VkZDja7Ha7MjIy1LVr18vu6+XlpaCgIJ07d06rVq1S3759L6pZsGCBGjdurIcfftip/ezZs5J00eyTu7u77Hb7tQ4HAADcRFw6kyRJcXFxiomJUefOnRUREaHU1FSdOXNGsbGxks7/VD8oKEjJycmSpC1btigvL0/h4eHKy8vT1KlTZbfbNXHiRKfj2u12LViwQDExMapVy3mYoaGhuuOOOzRixAi99tpratCggdasWaP169fr448/vjEDBwAA1ZrLQ9LAgQN19OhRJSUlKT8/X+Hh4UpPT3fczJ2bm+s041NSUqKEhAQdPHhQPj4+6tWrlxYvXix/f3+n427YsEG5ubkaPnz4ReesXbu2/va3vyk+Pl59+vTR6dOndccdd2jRokXq1atXlY4XAADUDC5fJ6mmYp0kAABqnhqzThIAAEB1RUgCAAAwQUgCAAAwQUgCAAAwQUgCAAAwQUgCAAAwQUgCAAAwQUgCAAAwQUgCAAAwQUgCAAAwQUgCAAAwQUgCAAAwQUgCAAAwQUgCAAAwQUgCAAAwQUgCAAAwQUgCAAAwQUgCAAAwQUgCAAAwQUgCAAAwQUgCAAAwQUgCAAAwQUgCAAAwQUgCAAAwQUgCAAAwQUgCAAAwQUgCAAAwQUgCAAAwQUgCAAAwQUgCAAAwQUgCAAAwQUgCAAAwQUgCAAAwUS1C0ty5c9W8eXN5eXkpMjJSW7duvWRteXm5pk2bppCQEHl5eSksLEzp6elONc2bN5fFYrloGz16tFPd5s2bdf/996tu3bry9fVV9+7d9fPPP1fJGAEAQM3i8pC0fPlyxcXFacqUKcrKylJYWJiio6NVWFhoWp+QkKC3335baWlpys7O1siRI9WvXz/t2LHDUbNt2zYdOXLEsa1fv16S9Jvf/MZRs3nzZvXo0UMPPfSQtm7dqm3btmnMmDFyc3P5JQEAANWAxTAMw5UdiIyMVJcuXTRnzhxJkt1uV3BwsMaOHav4+PiL6q1WqyZPnuw0K9S/f395e3tryZIlpucYP368Pv74Yx04cEAWi0WS9D//8z968MEHNX369KvqZ2lpqUpLSx2vi4uLFRwcrKKiIvn6+l71eAEAgOsUFxfLz8/vqj6/XTptUlZWpu3btysqKsrR5ubmpqioKG3evNl0n9LSUnl5eTm1eXt7KzMz85LnWLJkiYYPH+4ISIWFhdqyZYsaN26sbt26KSAgQPfcc88ljyFJycnJ8vPzc2zBwcEVHS4AAKhBXBqSjh07JpvNpoCAAKf2gIAA5efnm+4THR2tlJQUHThwQHa7XevXr9fq1at15MgR0/o1a9bo5MmTGjZsmKPt4MGDkqSpU6fqmWeeUXp6uu6880498MADOnDggOlxJk2apKKiIsd2+PDhaxgxAACoKWrcDTizZ89Wq1atFBoaKg8PD40ZM0axsbGXvJfo3XffVc+ePWW1Wh1tdrtdkjRixAjFxsaqY8eOev3119WmTRvNnz/f9Dienp7y9fV12gAAwM3LpSGpYcOGcnd3V0FBgVN7QUGBAgMDTfdp1KiR1qxZozNnzignJ0d79+6Vj4+PWrZseVFtTk6ONmzYoKefftqpvUmTJpKkdu3aObW3bdtWubm51zMkAABwk3BpSPLw8FCnTp2UkZHhaLPb7crIyFDXrl0vu6+Xl5eCgoJ07tw5rVq1Sn379r2oZsGCBWrcuLEefvhhp/bmzZvLarVq3759Tu379+9Xs2bNrmNEAADgZlHL1R2Ii4tTTEyMOnfurIiICKWmpurMmTOKjY2VJA0dOlRBQUFKTk6WJG3ZskV5eXkKDw9XXl6epk6dKrvdrokTJzod1263a8GCBYqJiVGtWs7DtFgsmjBhgqZMmaKwsDCFh4dr0aJF2rt3r1auXHljBg4AAKo1l4ekgQMH6ujRo0pKSlJ+fr7Cw8OVnp7uuJk7NzfX6X6jkpISJSQk6ODBg/Lx8VGvXr20ePFi+fv7Ox13w4YNys3N1fDhw03PO378eJWUlOj555/XiRMnFBYWpvXr1yskJKTKxgoAAGoOl6+TVFNVZJ0FAABQPdSYdZIAAACqK0ISAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACACUISAACAiVqu7gAAVCc2u6Gth06o8FSJGt/mpYgW9eXuZnF1twC4ACEJAP5P+ndH9PJfs3WkqMTR1sTPS1P6tFOPXzVxYc8AuAJftwGAzgek55ZkOQUkScovKtFzS7KU/t0RF/UMgKsQkgDc8mx2Qy//NVuGyXsX2l7+a7ZsdrMKADcrQhKAW97WQycumkH6JUPSkaISbT104sZ1CoDLEZIA3PIKT106IF1LHYCbAyEJwC2v8W1elVoH4OZASAJwy4toUV9N/Lx0qR/6W3T+V24RLerfyG4BcDFCEoBbnrubRVP6tJOki4LShddT+rRjvSTgFkNIAgBJPX7VRPOevFOBfs5fqQX6eWnek3eyThJwC6oWIWnu3Llq3ry5vLy8FBkZqa1bt16ytry8XNOmTVNISIi8vLwUFham9PR0p5rmzZvLYrFctI0ePfqi4xmGoZ49e8pisWjNmjWVPTQANUiPXzVR5kv3a+kz/6PZT4Rr6TP/o8yX7icgAbcol6+4vXz5csXFxemtt95SZGSkUlNTFR0drX379qlx48YX1SckJGjJkiV65513FBoaqnXr1qlfv3766quv1LFjR0nStm3bZLPZHPt89913evDBB/Wb3/zmouOlpqbKYmEKHcB57m4WdQ1p4OpuAKgGLIZhuHR1tMjISHXp0kVz5syRJNntdgUHB2vs2LGKj4+/qN5qtWry5MlOs0L9+/eXt7e3lixZYnqO8ePH6+OPP9aBAwecAtHOnTvVu3dvffPNN2rSpIk++ugjPfroo6bHKC0tVWlpqeN1cXGxgoODVVRUJF9f32sZOgAAuMGKi4vl5+d3VZ/fLv26raysTNu3b1dUVJSjzc3NTVFRUdq8ebPpPqWlpfLycr5nwNvbW5mZmZc8x5IlSzR8+HCngHT27Fn99re/1dy5cxUYGHjFviYnJ8vPz8+xBQcHX80QAQBADeXSkHTs2DHZbDYFBAQ4tQcEBCg/P990n+joaKWkpOjAgQOy2+1av369Vq9erSNHzJ+rtGbNGp08eVLDhg1zan/++efVrVs39e3b96r6OmnSJBUVFTm2w4cPX9V+AACgZnL5PUkVNXv2bD3zzDMKDQ2VxWJRSEiIYmNjNX/+fNP6d999Vz179pTVanW0rV27Vhs3btSOHTuu+ryenp7y9PS87v4DAICawaUzSQ0bNpS7u7sKCgqc2gsKCi75FVijRo20Zs0anTlzRjk5Odq7d698fHzUsmXLi2pzcnK0YcMGPf30007tGzdu1A8//CB/f3/VqlVLtWqdz4r9+/fXvffeWzmDAwAANZpLQ5KHh4c6deqkjIwMR5vdbldGRoa6du162X29vLwUFBSkc+fOadWqVaZfmy1YsECNGzfWww8/7NQeHx+vb7/9Vjt37nRskvT6669rwYIF1z8wAABQ47n867a4uDjFxMSoc+fOioiIUGpqqs6cOaPY2FhJ0tChQxUUFKTk5GRJ0pYtW5SXl6fw8HDl5eVp6tSpstvtmjhxotNx7Xa7FixYoJiYGMdM0QWBgYGmM1VNmzZVixYtqmikAACgJnF5SBo4cKCOHj2qpKQk5efnKzw8XOnp6Y6buXNzc+Xm9p8Jr5KSEiUkJOjgwYPy8fFRr169tHjxYvn7+zsdd8OGDcrNzdXw4cNv5HAAAMBNwuXrJNVUFVlnAQAAVA8V+fx2+UxSTXUhWxYXF7u4JwAA4Gpd+Ny+mjkiQtI1OnXqlCSxqCQAADXQqVOn5Ofnd9kavm67Rna7XT/++KNuu+02nv2m/zym5fDhw3z9WIW4zjcG1/nG4DrfGFxnZ4Zh6NSpU7JarU73PJthJukaubm56fbbb3d1N6odX19f/kd4A3Cdbwyu843Bdb4xuM7/caUZpAtcuk4SAABAdUVIAgAAMEFIQqXw9PTUlClTeL5dFeM63xhc5xuD63xjcJ2vHTduAwAAmGAmCQAAwAQhCQAAwAQhCQAAwAQhCQAAwAQhCVflxIkTGjx4sHx9feXv76+nnnpKp0+fvuw+JSUlGj16tBo0aCAfHx/1799fBQUFprXHjx/X7bffLovFopMnT1bBCGqGqrjOu3bt0qBBgxQcHCxvb2+1bdtWs2fPruqhVDtz585V8+bN5eXlpcjISG3duvWy9R9++KFCQ0Pl5eWlDh066G9/+5vT+4ZhKCkpSU2aNJG3t7eioqJ04MCBqhxCjVCZ17m8vFwvvfSSOnTooLp168pqtWro0KH68ccfq3oY1V5l/3v+pZEjR8pisSg1NbWSe10DGcBV6NGjhxEWFmZ8/fXXxqZNm4w77rjDGDRo0GX3GTlypBEcHGxkZGQY33zzjfE///M/Rrdu3Uxr+/bta/Ts2dOQZPz0009VMIKaoSqu87vvvmuMGzfO+Pzzz40ffvjBWLx4seHt7W2kpaVV9XCqjWXLlhkeHh7G/PnzjX/+85/GM888Y/j7+xsFBQWm9V9++aXh7u5uzJo1y8jOzjYSEhKM2rVrG7t373bU/PGPfzT8/PyMNWvWGLt27TIeeeQRo0WLFsbPP/98o4ZV7VT2dT558qQRFRVlLF++3Ni7d6+xefNmIyIiwujUqdONHFa1UxX/ni9YvXq1ERYWZlitVuP111+v4pFUf4QkXFF2drYhydi2bZuj7dNPPzUsFouRl5dnus/JkyeN2rVrGx9++KGjbc+ePYYkY/PmzU61b775pnHPPfcYGRkZt3RIqurr/EujRo0y7rvvvsrrfDUXERFhjB492vHaZrMZVqvVSE5ONq0fMGCA8fDDDzu1RUZGGiNGjDAMwzDsdrsRGBhovPrqq473T548aXh6ehpLly6tghHUDJV9nc1s3brVkGTk5ORUTqdroKq6zv/+97+NoKAg47vvvjOaNWtGSDIMg6/bcEWbN2+Wv7+/Onfu7GiLioqSm5ubtmzZYrrP9u3bVV5erqioKEdbaGiomjZtqs2bNzvasrOzNW3aNL333ntXfNDgza4qr/N/KyoqUv369Suv89VYWVmZtm/f7nSN3NzcFBUVdclrtHnzZqd6SYqOjnbUHzp0SPn5+U41fn5+ioyMvOx1v5lVxXU2U1RUJIvFIn9//0rpd01TVdfZbrdryJAhmjBhgtq3b181na+Bbu1PJVyV/Px8NW7c2KmtVq1aql+/vvLz8y+5j4eHx0X/RxYQEODYp7S0VIMGDdKrr76qpk2bVknfa5Kqus7/7auvvtLy5cv17LPPVkq/q7tjx47JZrMpICDAqf1y1yg/P/+y9Rf+rMgxb3ZVcZ3/W0lJiV566SUNGjToln1Qa1Vd55kzZ6pWrVoaN25c5Xe6BiMk3cLi4+NlsVguu+3du7fKzj9p0iS1bdtWTz75ZJWdozpw9XX+pe+++059+/bVlClT9NBDD92QcwKVoby8XAMGDJBhGJo3b56ru3NT2b59u2bPnq2FCxfKYrG4ujvVSi1XdwCu88ILL2jYsGGXrWnZsqUCAwNVWFjo1H7u3DmdOHFCgYGBpvsFBgaqrKxMJ0+edJrlKCgocOyzceNG7d69WytXrpR0/tdCktSwYUNNnjxZL7/88jWOrHpx9XW+IDs7Ww888ICeffZZJSQkXNNYaqKGDRvK3d39ol9Wml2jCwIDAy9bf+HPgoICNWnSxKkmPDy8Entfc1TFdb7gQkDKycnRxo0bb9lZJKlqrvOmTZtUWFjoNKNvs9n0wgsvKDU1Vf/6178qdxA1iatvikL1d+GG4m+++cbRtm7duqu6oXjlypWOtr179zrdUPz9998bu3fvdmzz5883JBlfffXVJX+lcTOrqutsGIbx3XffGY0bNzYmTJhQdQOoxiIiIowxY8Y4XttsNiMoKOiyN7r27t3bqa1r164X3bj92muvOd4vKirixu1Kvs6GYRhlZWXGo48+arRv394oLCysmo7XMJV9nY8dO+b0/8W7d+82rFar8dJLLxl79+6tuoHUAIQkXJUePXoYHTt2NLZs2WJkZmYarVq1cvpp+r///W+jTZs2xpYtWxxtI0eONJo2bWps3LjR+Oabb4yuXbsaXbt2veQ5Pvvss1v6122GUTXXeffu3UajRo2MJ5980jhy5Ihju5U+cJYtW2Z4enoaCxcuNLKzs41nn33W8Pf3N/Lz8w3DMIwhQ4YY8fHxjvovv/zSqFWrlvHaa68Ze/bsMaZMmWK6BIC/v7/xl7/8xfj222+Nvn37sgRAJV/nsrIy45FHHjFuv/12Y+fOnU7/fktLS10yxuqgKv49/zd+3XYeIQlX5fjx48agQYMMHx8fw9fX14iNjTVOnTrleP/QoUOGJOOzzz5ztP3888/GqFGjjHr16hl16tQx+vXrZxw5cuSS5yAkVc11njJliiHpoq1Zs2Y3cGSul5aWZjRt2tTw8PAwIiIijK+//trx3j333GPExMQ41a9YscJo3bq14eHhYbRv39745JNPnN632+1GYmKiERAQYHh6ehoPPPCAsW/fvhsxlGqtMq/zhX/vZtsv/zdwK6rsf8//jZB0nsUw/u9GEAAAADjw6zYAAAAThCQAAAAThCQAAAAThCQAAAAThCQAAAAThCQAAAAThCQAAAAThCQAAAAThCQAqCQWi0Vr1qxxdTcAVBJCEoCbwrBhw2SxWC7aevTo4equAaiharm6AwBQWXr06KEFCxY4tXl6erqoNwBqOmaSANw0PD09FRgY6LTVq1dP0vmvwubNm6eePXvK29tbLVu21MqVK5323717t+6//355e3urQYMGevbZZ3X69Gmnmvnz56t9+/by9PRUkyZNNGbMGKf3jx07pn79+qlOnTpq1aqV1q5dW7WDBlBlCEkAbhmJiYnq37+/du3apcGDB+uJJ57Qnj17JElnzpxRdHS06tWrp23btunDDz/Uhg0bnELQvHnzNHr0aD377LPavXu31q5dqzvuuMPpHC+//LIGDBigb7/9Vr169dLgwYN14sSJGzpOAJXEAICbQExMjOHu7m7UrVvXaZsxY4ZhGIYhyRg5cqTTPpGRkcZzzz1nGIZh/PnPfzbq1atnnD592vH+J598Yri5uRn5+fmGYRiG1Wo1Jk+efMk+SDISEhIcr0+fPm1IMj799NNKGyeAG4d7kgDcNO677z7NmzfPqa1+/fqO/+7atavTe127dtXOnTslSXv27FFYWJjq1q3reP+uu+6S3W7Xvn37ZLFY9OOPP+qBBx64bB9+/etfO/67bt268vX1VWFh4bUOCYALEZIA3DTq1q170ddflcXb2/uq6mrXru302mKxyG63V0WXAFQx7kkCcMv4+uuvL3rdtm1bSVLbtm21a9cunTlzxvH+l19+KTc3N7Vp00a33XabmjdvroyMjBvaZwCuw0wSgJtGaWmp8vPzndpq1aqlhg0bSpI+/PBDde7cWXfffbfef/99bd26Ve+++64kafDgwZoyZYpiYmI0depUHT16VGPHjtWQIUMUEBAgSZo6dapGjhypxo0bq2fPnjp16pS+/PJLjR079sYOFMANQUgCcNNIT09XkyZNnNratGmjvXv3Sjr/y7Nly5Zp1KhRatKkiZYuXap27dpJkurUqaN169bpd7/7nbp06aI6deqof//+SklJcRwrJiZGJSUlev311/Xiiy+qYcOGevzxx2/cAAHcUBbDMAxXdwIAqprFYtFHH32kRx991NVdAVBDcE8SAACACUISAACACe5JAnBL4M4CABXFTBIAAIAJQhIAAIAJQhIAAIAJQhIAAIAJQhIAAIAJQhIAAIAJQhIAAIAJQhIAAICJ/w8GsY2XtZcSagAAAABJRU5ErkJggg==\n" }, - "metadata": {}, - "output_type": "display_data" + "metadata": {} }, { - "metadata": { - "tags": null - }, - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ - "Epoch [3 / 15], Step [22 / 225], Loss: 0.6365734934806824, Validation Loss: 0.9633301496505737\n", - "Epoch [3 / 15], Step [44 / 225], Loss: 0.720203697681427, Validation Loss: 0.2848336696624756\n", - "Epoch [3 / 15], Step [66 / 225], Loss: 0.061845649033784866, Validation Loss: 0.9537119269371033\n", - "Epoch [3 / 15], Step [88 / 225], Loss: 0.09502657502889633, Validation Loss: 0.17387674748897552\n", - "Epoch [3 / 15], Step [110 / 225], Loss: 0.16061733663082123, Validation Loss: 0.0026112645864486694\n", - "Epoch [3 / 15], Step [132 / 225], Loss: 0.1448567658662796, Validation Loss: 0.176682710647583\n", - "Epoch [3 / 15], Step [154 / 225], Loss: 0.11984804272651672, Validation Loss: 0.0029973338823765516\n", - "Epoch [3 / 15], Step [176 / 225], Loss: 0.05067078024148941, Validation Loss: 0.024889525026082993\n", - "Epoch [3 / 15], Step [198 / 225], Loss: 0.08953560888767242, Validation Loss: 0.014550979249179363\n", - "Epoch [3 / 15], Step [220 / 225], Loss: 0.05057196691632271, Validation Loss: 0.14682522416114807\n" + "Epoch [2 / 10], Step [22 / 225], Loss: 0.5650029859759591, Validation Loss: 0.6480007767677307\n", + "Epoch [2 / 10], Step [44 / 225], Loss: 0.5132254456931894, Validation Loss: 0.5776327848434448\n", + "Epoch [2 / 10], Step [66 / 225], Loss: 0.47316857698288833, Validation Loss: 0.5568574070930481\n", + "Epoch [2 / 10], Step [88 / 225], Loss: 0.43606940572234715, Validation Loss: 0.5899128615856171\n", + "Epoch [2 / 10], Step [110 / 225], Loss: 0.3954015533355149, Validation Loss: 0.5944906234741211\n", + "Epoch [2 / 10], Step [132 / 225], Loss: 0.35609302819339617, Validation Loss: 0.537044107913971\n", + "Epoch [2 / 10], Step [154 / 225], Loss: 0.33128390105610545, Validation Loss: 0.46129965216719676\n", + "Epoch [2 / 10], Step [176 / 225], Loss: 0.2998663415836001, Validation Loss: 0.40397966344607994\n", + "Epoch [2 / 10], Step [198 / 225], Loss: 0.2738230372732035, Validation Loss: 0.3590930341742933\n", + "Epoch [2 / 10], Step [220 / 225], Loss: 0.25180305100300093, Validation Loss: 0.32318373075686396\n" ] }, { + "output_type": "display_data", "data": { - "image/png": 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\n", 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}, - "metadata": {}, - "output_type": "display_data" + "metadata": {} }, { - "metadata": { - "tags": null - }, - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ - "Epoch [4 / 15], Step [22 / 225], Loss: 0.05052595213055611, Validation Loss: 0.2758115828037262\n", - "Epoch [4 / 15], Step [44 / 225], Loss: 0.26822781562805176, Validation Loss: 0.33429664373397827\n", - "Epoch [4 / 15], Step [66 / 225], Loss: 0.05043775588274002, Validation Loss: 0.04388421028852463\n", - "Epoch [4 / 15], Step [88 / 225], Loss: 0.05038832128047943, Validation Loss: 0.06882896274328232\n", - "Epoch [4 / 15], Step [110 / 225], Loss: 0.05032651126384735, Validation Loss: 0.015540232881903648\n", - "Epoch [4 / 15], Step [132 / 225], Loss: 0.05026824027299881, Validation Loss: 0.9465289115905762\n", - "Epoch [4 / 15], Step [154 / 225], Loss: 0.08250364661216736, Validation Loss: 0.0\n", - "Epoch [4 / 15], Step [176 / 225], Loss: 0.07428855448961258, Validation Loss: 0.046117693185806274\n", - "Epoch [4 / 15], Step [198 / 225], Loss: 0.05011678487062454, Validation Loss: 0.022663306444883347\n", - "Epoch [4 / 15], Step [220 / 225], Loss: 0.05006015673279762, Validation Loss: 0.015940412878990173\n" + "Epoch [3 / 10], Step [22 / 225], Loss: 0.059888842261650345, Validation Loss: 0.055629707872867584\n", + "Epoch [3 / 10], Step [44 / 225], Loss: 0.0569748023355549, Validation Loss: 0.04963332414627075\n", + "Epoch [3 / 10], Step [66 / 225], Loss: 0.055947076359933075, Validation Loss: 0.033088882764180504\n", + "Epoch [3 / 10], Step [88 / 225], Loss: 0.05491821848872033, Validation Loss: 0.024816662073135376\n", + "Epoch [3 / 10], Step [110 / 225], Loss: 0.05447610897774046, Validation Loss: 0.0198533296585083\n", + "Epoch [3 / 10], Step [132 / 225], Loss: 0.08655590160439412, Validation Loss: 0.019558300574620564\n", + "Epoch [3 / 10], Step [154 / 225], Loss: 0.10329984067999698, Validation Loss: 0.0819279168333326\n", + "Epoch [3 / 10], Step [176 / 225], Loss: 0.10512340995906429, Validation Loss: 0.07168692722916603\n", + "Epoch [3 / 10], Step [198 / 225], Loss: 0.11643278070095212, Validation Loss: 0.07727163698938158\n", + "Epoch [3 / 10], Step [220 / 225], Loss: 0.11402303913438862, Validation Loss: 0.08088884353637696\n" ] }, { + "output_type": "display_data", "data": { - "image/png": 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\n", 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\n" }, - "metadata": {}, - "output_type": "display_data" + "metadata": {} }, { - "metadata": { - "tags": null - }, - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ - "Epoch [5 / 15], Step [22 / 225], Loss: 0.04997241497039795, Validation Loss: 0.0\n", - "Epoch [5 / 15], Step [44 / 225], Loss: 0.049897659569978714, Validation Loss: 0.0223587267100811\n", - "Epoch [5 / 15], Step [66 / 225], Loss: 0.049830615520477295, Validation Loss: 0.0\n", - "Epoch [5 / 15], Step [88 / 225], Loss: 0.049757134169340134, Validation Loss: 0.0\n", - "Epoch [5 / 15], Step [110 / 225], Loss: 0.0496828556060791, Validation Loss: 0.04544451832771301\n", - "Epoch [5 / 15], Step [132 / 225], Loss: 0.049610793590545654, Validation Loss: 0.0\n", - "Epoch [5 / 15], Step [154 / 225], Loss: 0.04953967407345772, Validation Loss: 0.010974577628076077\n", - "Epoch [5 / 15], Step [176 / 225], Loss: 0.049466632306575775, Validation Loss: 0.0\n", - "Epoch [5 / 15], Step [198 / 225], Loss: 0.04939447343349457, Validation Loss: 0.020791243761777878\n", - "Epoch [5 / 15], Step [220 / 225], Loss: 0.04932032525539398, Validation Loss: 0.0\n" + "Epoch [4 / 10], Step [22 / 225], Loss: 0.18842411464588207, Validation Loss: 0.023324092850089073\n", + "Epoch [4 / 10], Step [44 / 225], Loss: 0.1356671079146591, Validation Loss: 0.011662046425044537\n", + "Epoch [4 / 10], Step [66 / 225], Loss: 0.1225995806920709, Validation Loss: 0.007774697616696358\n", + "Epoch [4 / 10], Step [88 / 225], Loss: 0.10794795511967757, Validation Loss: 0.013592265080660582\n", + "Epoch [4 / 10], Step [110 / 225], Loss: 0.10796696299856359, Validation Loss: 0.011201474699191749\n", + "Epoch [4 / 10], Step [132 / 225], Loss: 0.10170525907905716, Validation Loss: 0.009334562249326458\n", + "Epoch [4 / 10], Step [154 / 225], Loss: 0.09754940814205579, Validation Loss: 0.020729850894505426\n", + "Epoch [4 / 10], Step [176 / 225], Loss: 0.0919922092209824, Validation Loss: 0.019730244923266582\n", + "Epoch [4 / 10], Step [198 / 225], Loss: 0.08834527585316788, Validation Loss: 0.09797394703814967\n", + "Epoch [4 / 10], Step [220 / 225], Loss: 0.0907003279775381, Validation Loss: 0.0924123348086141\n" ] }, { + "output_type": "display_data", "data": { - "image/png": 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\n", 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\n" }, - "metadata": {}, - "output_type": "display_data" + "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ - "Epoch [6 / 15], Step [22 / 225], Loss: 0.30001387000083923, Validation Loss: 0.0\n", - "Epoch [6 / 15], Step [44 / 225], Loss: 0.049207571893930435, Validation Loss: 0.015120014548301697\n", - "Epoch [6 / 15], Step [66 / 225], Loss: 0.04912778362631798, Validation Loss: 0.007269968744367361\n", - "Epoch [6 / 15], Step [88 / 225], Loss: 0.04904578626155853, Validation Loss: 0.0\n", - "Epoch [6 / 15], Step [110 / 225], Loss: 1.0179965496063232, Validation Loss: 1.0021252632141113\n", - "Epoch [6 / 15], Step [132 / 225], Loss: 0.9388081431388855, Validation Loss: 0.9330480098724365\n", - "Epoch [6 / 15], Step [154 / 225], Loss: 0.8098025321960449, Validation Loss: 0.4797901511192322\n", - "Epoch [6 / 15], Step [176 / 225], Loss: 0.15214264392852783, Validation Loss: 0.6066851019859314\n", - "Epoch [6 / 15], Step [198 / 225], Loss: 0.25942176580429077, Validation Loss: 0.0\n", - "Epoch [6 / 15], Step [220 / 225], Loss: 0.04873622953891754, Validation Loss: 0.02330446057021618\n" + "Epoch [5 / 10], Step [22 / 225], Loss: 0.11004065857692198, Validation Loss: 0.20903348922729492\n", + "Epoch [5 / 10], Step [44 / 225], Loss: 0.10677469699558886, Validation Loss: 0.12623683735728264\n", + "Epoch [5 / 10], Step [66 / 225], Loss: 0.09529317644509402, Validation Loss: 0.08415789157152176\n", + "Epoch [5 / 10], Step [88 / 225], Loss: 0.08533265437422828, Validation Loss: 0.06311841867864132\n", + "Epoch [5 / 10], Step [110 / 225], Loss: 0.07885887971655889, Validation Loss: 0.05049473494291305\n", + "Epoch [5 / 10], Step [132 / 225], Loss: 0.0739773078398271, Validation Loss: 0.04207894578576088\n", + "Epoch [5 / 10], Step [154 / 225], Loss: 0.07347074192162457, Validation Loss: 0.07647624505417687\n", + "Epoch [5 / 10], Step [176 / 225], Loss: 0.07329438135705212, Validation Loss: 0.09326270315796137\n", + "Epoch [5 / 10], Step [198 / 225], Loss: 0.07185688417292002, Validation Loss: 0.08290018058485454\n", + "Epoch [5 / 10], Step [220 / 225], Loss: 0.07738829492167994, Validation Loss: 0.0746101625263691\n" ] }, { @@ -1060,7 +637,7 @@ "text/plain": [ "
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\n" + "image/png": 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MTTfdRKdOnQBo0aLFBddU3dRz4wStGgZxSav62A34YkMa9LnPsWPlW5VO+yIi4hkuuugi+vbty5QpUwDYtWsXv/76K3fccQcANpuNl19+mU6dOhEeHk5gYCBLliwhOTm5Wj5/+/btREdHlwQbgPbt2xMaGsr27dsBeOyxx7jzzjsZOHAgr776Krt37y5p+9BDD/HPf/6TuLg4JkyYUC0DoKubem6cZHRsM1bsOsLsdck88ugYfH/5NxzeDn8uhTZXubo8ERH35+3v6EGpjKRV8MXN5243ch4061u5z66CO+64g/HjxzN58mSmTp1Ky5YtufzyywF44403eOutt5g0aRKdOnUiICCARx55hIIC5w1lePHFF7n11ltZuHAhixYtYsKECcyaNYsbb7yRO++8k0GDBrFw4UK+//57Jk6cyL///W/Gjx/vtPrORT03TjKgXQSNQ/04nlPIgp3Z0GOsY4cW1BQRqR4mk+PSUGUeLa+A4EaAqbyDQXBjR7vKHM9U3nHKNmzYMMxmM19++SUzZszg9ttvx1R8jJUrV3LDDTdw22230aVLF1q0aMEff/xxYf9tTtOuXTv27dvHvn37SrZt27aNEydO0L59+5Jtbdq04dFHH+X777/nL3/5C1OnTi3ZFx0dzb333svXX3/N448/zscff1xt9VUHhRsnsZhN3NbHManf9FV7MXrfC2ZvSFoB+ze4uDoRkTrGbIHBrxW/ODOYFL8e/KqjXQ0IDAxk+PDhPPPMM6SkpDB27NiSfa1bt2bp0qWsWrWK7du3c88995S6k6mybDYbmzZtKvXYvn07AwcOpFOnTowcOZKEhATWrl3L6NGjufzyy+nRowe5ubk8+OCD/PzzzyQlJbFy5UrWrVtHu3btAHjkkUdYsmQJiYmJJCQk8NNPP5Xsqy0UbpxoeM9orF5mfj+YQcKJAOg8zLFjlXpvREScrv31MGwGBEeV3h7cyLG9/fU1+vF33HEHx48fZ9CgQTRqdGog9HPPPUe3bt0YNGgQ/fr1IzIykqFDh1b5+FlZWVx88cWlHkOGDMFkMvHtt98SFhbGZZddxsCBA2nRogWzZ88GwGKxcPToUUaPHk2bNm0YNmwYV199NS+99BLgCE0PPPAA7dq1Y/DgwbRp04b33nuvWv6bVBeTYdStEa0ZGRmEhISQnp5OcHCw0z//b3N/Y+6G/dzQtRFvXeEL7/UBTDB+A9Rr6fR6RETcUV5eHomJiTRv3hxfX98LO5jd5hiDk5UGgRGOMTY11GMjFavoe63K72/13DjZmL4xAHy3JYVDfs2hzWDAgPh3XVqXiEidZbZA80uh082OPxVs3J7CjZN1bBxCt6ahFNoMZq7ZByeXZNj4BWQdcm1xIiIiHkDhxgVO9t58sSaJwiZ9oHEPsOXD2o9cW5iIiIgHULhxgas7RlE/0IdDmfks2ZYGcQ87dqz9GPKzXFuciIiIm1O4cQGrl5lbezcFYMaqJLjoWghvCXknYONnri1ORMSN1LF7YjxedX2fCjcuMrJ3U7zMJtbuPca21GzoWzyzY/xksBW6tjgRkVru5CKOOTk5Lq5EqtPJWZgtlgsb1K3lF1wkItiXQR0jWbg5hRnxe3n1+hHw0/9B+j74ff6pOXBEROQsFouF0NBQDh1y3Ijh7+9fMsOvuCe73c7hw4fx9/fHy+vC4onCjQuNiY1h4eYUvtl0gKevvojQ3vfCjy87lmTo9NcqT+ctIlKXREZGApQEHHF/ZrOZpk2bXnBQVbhxoZ4xYVwUGcSO1Ezmrt/PXT3vgF/fhLStsHsZtBro6hJFRGotk8lEVFQUDRs2pLBQl/M9gdVqxWy+8BEzCjcuZDKZGNs3hqe/3sJnq5O4/ZLmWLqPhdWTHb03CjciIudksVgueIyGeBYNKHaxG7o2JtjXi+RjOfy88xD0uQ/MXpD4Cxzc6OryRERE3I7CjYv5WS0M7xkNwPT4JAiNho43O3aufNuFlYmIiLgnhZta4LY+zTCZ4Jc/DrPncBbEFS/JsO0bOJbo0tpERETcjcJNLdCsXgD92zYE4LPVSRDRwTHexrA75r0RERGRSlO4qSVOrjc1b/1+svOLTi3JsPFzyD7iusJERETcjMJNLXFpq/o0rx9AZn4RX288ADGXQqOLoSjXseaUiIiIVIrCTS1hNpsY1acZADNW7cWA0xbU/AgKsl1Wm4iIiDtRuKlFbu7RBH+rhT8PZRG/5yi0ux7CYiD3GGz8wtXliYiIuAWFm1ok2Nebv3RrDBSvFm62nLag5jtgK3JhdSIiIu5B4aaWGR0bA8D321I5cCIXuo4E//pwItlxa7iIiIhUSOGmlmkTEURsi3rYDfhidRJ4+0Hvexw7V74FhuHaAkVERGo5hZtaaExfx8DiWev2kVdog553grc/pG6GPT+7tjgREZFaTuGmFhrYLoJGIb4cyy5g4eYU8A+HbqMdO1dpSQYREZGKKNzUQl4WMyNP3hYev9exsc/9YLLA7h8hZbPrihMREanlFG5qqVt6RmO1mPltfzqb9p2AsGbQ8S+Oneq9ERERKZfCTS1VL9CH67pEATB91V7Hxr7FC2pu/RqOJ7mmMBERkVpO4aYWG1N8W/jCzSkczsyHqM7Qoj8YNlj9nmuLExERqaUUbmqxLtGhdIkOpcBmZ/a6ZMfGk0syJMyAnGOuK05ERKSWUrip5cYW3xb++epkimx2aNEPIjtDYQ6s+8S1xYmIiNRCCje13DWdoqgXYCU1I4+l29LAZDrVe7PmAyjMdW2BIiIitYzCTS3n42VhRK+mAEw7ObC4/VAIbQo5R2GTFtQUERE5ncKNG7i1d1MsZhNrEo+xIzUDLF4QW7yg5qp3wG5zbYEiIiK1iMKNG2gU6sdV7SMAmBFffAv4xSPBLxyO74XtC1xXnIiISC2jcOMmxvSNAWB+wgHScwvBGgC97nbsXPm2FtQUEREppnDjJno3D6dtRBC5hTbmrt/n2NjrLvDyg4MJsHeFawsUERGpJRRu3ITJZGJ08W3hn61Owm43IKA+XHybo8HKt1xYnYiISO2hcONGhnZtTJCvF0lHc1j+52HHxtgHwGSGXUshdatrCxQREakFXB5uJk+eTExMDL6+vvTu3Zu1a9dW2H7SpEm0bdsWPz8/oqOjefTRR8nLy3NSta4V4OPFX7tHAzDj5G3h4c2h/Q2O56vecU1hIiIitYhLw83s2bN57LHHmDBhAgkJCXTp0oVBgwZx6NChMtt/+eWXPP3000yYMIHt27fz6aefMnv2bJ599lknV+46o2Mdl6Z+/uMwe49kOzaWLKg5D07sc1FlIiIitYNLw82bb77JXXfdxbhx42jfvj0ffPAB/v7+TJkypcz2q1atIi4ujltvvZWYmBiuuuoqRowYcc7eHk8SUz+Afm0bYBiOsTcANO4GzS8DexGsft+1BYqIiLiYy8JNQUEBGzZsYODAgaeKMZsZOHAg8fHxZb6nb9++bNiwoSTM7Nmzh++++45rrrmm3M/Jz88nIyOj1MPdnVwtfM76feQUFDk2nlySYcM0yD3ukrpERERqA5eFmyNHjmCz2YiIiCi1PSIigtTU1DLfc+utt/KPf/yDSy65BG9vb1q2bEm/fv0qvCw1ceJEQkJCSh7R0dHVeh6ucHmbBjSr509mXhHfbDzo2NhyAER0hMJsWPepawsUERFxIZcPKK6Kn3/+mVdeeYX33nuPhIQEvv76axYuXMjLL79c7nueeeYZ0tPTSx779rn/mBSz2cSoPo6xNzPi92IYRhkLataNQdYiIiJnclm4qV+/PhaLhbS0tFLb09LSiIyMLPM9zz//PKNGjeLOO++kU6dO3HjjjbzyyitMnDgRu91e5nt8fHwIDg4u9fAEf+0ejZ+3hR2pmaxNPObY2OFGCImG7MPw20zXFigiIuIiLgs3VquV7t27s2zZspJtdrudZcuWERsbW+Z7cnJyMJtLl2yxWAAcvRd1SIi/N0MvbgzA9Pi9jo0Wb8e8N6AFNUVEpM5y6WWpxx57jI8//pjp06ezfft27rvvPrKzsxk3bhwAo0eP5plnnilpP2TIEN5//31mzZpFYmIiS5cu5fnnn2fIkCElIacuOXlb+JLf00hJz3VsvHgU+IbCsd2w8zvXFSciIuIiXq788OHDh3P48GFeeOEFUlNT6dq1K4sXLy4ZZJycnFyqp+a5557DZDLx3HPPceDAARo0aMCQIUP4v//7P1edgku1iwqmV/Nw1iYe48s1yTx+VVvwCXSsOfXLG7BiElx0nWM8joiISB1hMurY9ZyMjAxCQkJIT0/3iPE3321J4f4vEqgfaGXl01fg42WBrMPwnw5gy4dxi6BZX1eXKSIickGq8vvbre6WkrNd2T6CyGBfjmQVsGhL8S30gQ2g662O51pQU0RE6hiFGzfnbTEzsndTAKadXG8KoO94wAR/LIZD211Sm4iIiCso3HiAW3o1xdtiYtO+E/y274RjY72W0G6I47kW1BQRkTpE4cYDNAjy4dpOUQDMiE86tePkpH6b50D6ARdUJiIi4nwKNx5iTN8YAP67+SBHs/IdG5v0gGaXgL0Q1mhBTRERqRsUbjxE1+hQOjcJoaDIzuz1py0xcbL3Zv00yD3hitJEREScSuHGQ5hMJkYXrxb+eXwSRbbi5ShaXwkN20NBJmyY6roCRUREnEThxoNc1zmKMH9vDqbn8cP2Q46NJhP0fcjxfPX7UJTvugJFREScQOHGg/h6W7ill+O28Bkn15sC6HgTBDeGrDTYPNs1xYmIiDiJwo2Hua1PM8wmWLX7KH+mZTo2elmhz/2O56vegXJWUBcREfEECjcepnGoH1e2d6zNVeq28O5jwCcEjvzhmNhPRETEQynceKAxxQOLv0rYT0ZeoWOjTxD0vMPxXEsyiIiIB1O48UCxLevRqmEgOQU2vtqw/9SO3veAxQr7VkPyatcVKCIiUoMUbjyQyWRiTGwzAD6LT8JuL174PSgSutzieL7ybRdVJyIiUrMUbjzUX7o1IcjHiz1Hslmx68ipHX0fAkywcyEc/sNl9YmIiNQUhRsPFeDjxU3dmwAw/fTVwuu3houudTxfpd4bERHxPAo3HmxU8aWpH3ceIvlozqkdJQtqzoaMFBdUJiIiUnMUbjxYywaBXNq6PoYBn6857bbw6F7QNBZsBbDmA9cVKCIiUgMUbjzcydvCZ6/bR26B7dSOkgU1p0BehvMLExERqSEKNx6u/0UNiQ73Iz23kAW/HTi1o/UgqN8W8jNgwzSX1SciIlLdFG48nMVsYlQfx9ibaauSMIzi28LNZog7fUHNAhdVKCIiUr0UbuqAYT2i8fEysz0lg/VJx0/t6PRXCIqCzIOwdZ7rChQREalGCjd1QKi/laFdGwNn3Bbu5QN97nM8X/m2FtQUERGPoHBTR4zu67g0tXhrKmkZead2dB8LPsFweDvsWuqa4kRERKqRwk0d0aFRCD1jwiiyG3y5JvnUDt8QR8ABLagpIiIeQeGmDhldfFv4l2uTKSg67RJUn/vA7A1JK2HfOtcUJyIiUk0UbuqQQR0iaRjkw+HMfBZtPW1m4uBG0Hm44/kq9d6IiIh7U7ipQ6xeZm7t3RSAGfFJpXf2He/4c/v/4MguJ1cmIiJSfRRu6phbezfF22JiQ9Jxth5IP7Wj4UXQ5mrAgPh3XFafiIjIhVK4qWMaBvlydccoAGbE7y298+SSDJtmQmaacwsTERGpJgo3ddCY4tvCv910kOPZp81M3LQPNOkFtnxY+6GLqhMREbkwCjd1ULemYXRoFEx+kZ3Z6/ed2mEyneq9WfcJ5Ge6pkAREZELoHBTB5lMppLVwj+LT8JmN07tbHsN1GsFeemQ8JlrChQREbkACjd11PVdGxHq782BE7n8uOPQqR1mM/QtXlAzfjLYCl1ToIiIyHlSuKmjfL0tDO8ZDZQxsLjzcAiMgIz9sPVr5xcnIiJyARRu6rDbejfDZIJf/zzCrkNZp3Z4+0Lvex3PV74FhlH2AURERGohhZs6LDrcnwEXRQDw2Zm9Nz1uB2sgHPoddi1zfnEiIiLnSeGmjjt5W/i8DfvJzDttfI1f6GkLak5ydlkiIiLnTeGmjrukVX1aNAggu8DG/I0HSu/scx+YvWDvr3Bgg2sKFBERqSKFmzru9NvCp6/ai3H6+JqQJtDpr47nK992fnEiIiLnQeFG+Eu3xgRYLew+nM3KXUdL7yxZUHMBHN3t/OJERESqSOFGCPL15qbuTQCYfubA4ogO0PoqMOyOeW9ERERqOYUbAWB0rGNg8bLtaew7llN6Z8mCml9A1mEnVyYiIlI1CjcCQKuGQVzSqj52A75Yk1x6Z7M4aNwdivJg7UeuKVBERKSSFG6kxMnem1nrkskrtJ3aUWpBzY+hINsF1YmIiFSOwo2UGNAugsahfpzIKWTBbwdL77zoOghvAbnHYePnrilQRESkEhRupITFbOK2Po7em7NuCzdbTt05tepdsBW5oEIREZFzU7iRUm7pGY2Pl5nfD2aQkHyi9M4uIyCgAaQnw7ZvXFGeiIjIOSncSClhAVau79IIKGO1cG8/6HWP4/nKSVpQU0REaiWFGznLmL4xAHy3JYVDmXmld/a8A7z9IXUL7PnJ+cWJiIicg8KNnKVj4xC6NQ2l0GYwc82+0jv9w6HbGMfzlW85vzgREZFzULiRMp3svfliTRKFNnvpnbH3g8kCe36Gg5ucXZqIiEiFFG6kTFd3jKJ+oA+HMvNZ8ntq6Z2hTaHjTY7nq7SgpoiI1C4KN1Imq5eZW3s3BWDGqqSzG8Q95Pjz9/lwfK/zChMRETkHhRsp18jeTfEym1i79xjbDmaU3hnZCVoO0IKaIiJS6yjcSLkign0Z1DESKOO2cDi1JEPCZ5B91HmFiYiIVEDhRio0JjYGgG82HeBETkHpnc0vg6iuUJQL6z5xem0iIiJlqXK4Wbx4MStWrCh5PXnyZLp27cqtt97K8ePHq7U4cb2eMWG0iwomr9DO3PX7S+88fUHNtR9CQY7zCxQRETlDlcPN3/72NzIyHOMvtmzZwuOPP84111xDYmIijz32WLUXKK5lMpkYU7xa+Gerk7DZz5iVuN31EBYDOUdh0xfOL1BEROQMVQ43iYmJtG/fHoCvvvqK6667jldeeYXJkyezaNGiai9QXO+Gro0J9vUi+VgOP+88VHqnxQtiH3Q8j9eCmiIi4npVDjdWq5WcHMflhx9++IGrrroKgPDw8JIeHfEsflYLw3tGAzA9vozbwruOBP96jlvCty9wbnEiIiJnqHK4ueSSS3jsscd4+eWXWbt2Lddeey0Af/zxB02aNKn2AqV2GNUnBpMJfvnjMHsOZ5XeafWHXnc7nq98SwtqioiIS1U53Lz77rt4eXkxb9483n//fRo3bgzAokWLGDx4cJULmDx5MjExMfj6+tK7d2/Wrl1bYfsTJ07wwAMPEBUVhY+PD23atOG7776r8udK1TSt588VbRsCjrE3Z+l5F3j5QcomSPzFucWJiIicxmQYrvtn9uzZsxk9ejQffPABvXv3ZtKkScydO5edO3fSsGHDs9oXFBQQFxdHw4YNefbZZ2ncuDFJSUmEhobSpUuXSn1mRkYGISEhpKenExwcXN2n5NGW/3GYMVPWEuTjxepnBxDg41W6wXd/g7UfOSb3G/W1a4oUERGPVJXf31XuuUlISGDLli0lr7/99luGDh3Ks88+S0FBQQXvPNubb77JXXfdxbhx42jfvj0ffPAB/v7+TJkypcz2U6ZM4dixY3zzzTfExcURExPD5ZdfXulgIxfm0lb1aV4/gMz8Ir7eeODsBrEPgMkMu5dB6paz94uIiDhBlcPNPffcwx9//AHAnj17uOWWW/D392fu3Lk8+eSTlT5OQUEBGzZsYODAgaeKMZsZOHAg8fHxZb5nwYIFxMbG8sADDxAREUHHjh155ZVXsNls5X5Ofn4+GRkZpR5yfsxmE6P6OG4Ln7FqL2d1+oXFQIcbHc9XakFNERFxjSqHmz/++IOuXbsCMHfuXC677DK+/PJLpk2bxldffVXp4xw5cgSbzUZERESp7REREaSmppb5nj179jBv3jxsNhvfffcdzz//PP/+97/55z//We7nTJw4kZCQkJJHdHR0pWuUs93cown+Vgt/Hsoifk8ZSy70LV5Qc+tXcCLZucWJiIhwHuHGMAzsdjvguBX8mmuuASA6OpojR45Ub3VnsNvtNGzYkI8++oju3bszfPhw/v73v/PBBx+U+55nnnmG9PT0kse+fftqtEZPF+zrzV+6OQaRl7laeKOu0KIfGDZY/b5TaxMREYHzCDc9evTgn//8J5999hnLly8vuRU8MTHxrF6YitSvXx+LxUJaWlqp7WlpaURGRpb5nqioKNq0aYPFYinZ1q5dO1JTU8sd7+Pj40NwcHCph1yY0cXrTX2/LZUDJ3LPbnBySYYN0yHnmPMKExER4TzCzaRJk0hISODBBx/k73//O61atQJg3rx59O3bt9LHsVqtdO/enWXLlpVss9vtLFu2jNjY2DLfExcXx65du0p6jsBxmSwqKgqr1VrVU5Hz1CYiiNgW9bAb8EVZt4W36A+RnaAwG9Z/6vwCRUSkTqu2W8Hz8vKwWCx4e3tX+j2zZ89mzJgxfPjhh/Tq1YtJkyYxZ84cduzYQUREBKNHj6Zx48ZMnDgRgH379tGhQwfGjBnD+PHj+fPPP7n99tt56KGH+Pvf/16pz9St4NVj8dYU7v08gfAAK6uevgJfb0vpBlvmwVd3QEADeGQLePu5plAREfEIVfn97VXh3gps2LCB7du3A9C+fXu6detW5WMMHz6cw4cP88ILL5CamkrXrl1ZvHhxyeWt5ORkzOZTnUvR0dEsWbKERx99lM6dO9O4cWMefvhhnnrqqfM9DTlPA9tF0CjEl4PpeSzcnMJN3c+Ynbr9UPjhJUhPht9mQo/bXVKniIjUPVXuuTl06BDDhw9n+fLlhIaGAo5Zg/v378+sWbNo0KBBTdRZbdRzU30m/7SLN5bspEuTEL598JKzG6z+ABY/BeEt4MH1YLac3UZERKQSanQSv/Hjx5OVlcXvv//OsWPHOHbsGFu3biUjI4OHHnrovIsW93NLz2isFjO/7U9nY/Lxsxt0GwV+YXBsD+z4n/MLFBGROqnK4Wbx4sW89957tGvXrmRb+/btmTx5MosWLarW4qR2qxfow3VdogCYUdZq4dYAx5pTACsmaUFNERFxiiqHG7vdXuagYW9v71J3MUndMKb4tvCFm1M4nJl/doNed4OXLxxMgKSVzi1ORETqpCqHmyuuuIKHH36YgwcPlmw7cOAAjz76KAMGDKjW4qT26xIdStfoUApsdmavK2NG4sAG0HWk4/nKt5xbnIiI1ElVDjfvvvsuGRkZxMTE0LJlS1q2bEnz5s3JyMjg7be1nlBdNKavY72pz1cnU2Qro/fu5IKaf34Pab87uToREalrqnwreHR0NAkJCfzwww/s2LEDcMwSfPoCmFK3XNMpin/+bzupGXl8vy2NazpFlW5QryW0ux62fQOr3oEby18uQ0RE5EJVuecGwGQyceWVVzJ+/HjGjx/PwIED2bFjB23atKnu+sQN+HhZGNGrKQDTV+0tu1Fc8Z10W+ZC+gHnFCYiInXSeYWbsuTn57N79+7qOpy4mZF9mmIxm1iTeIwdqRlnN2jcHWIuBXsRrH7P+QWKiEidUW3hRuq2qBA/BnVwzCxd5m3hAHGPOP7cMA1yTzijLBERqYMUbqTanFwtfH7CAdJzC89u0GoANOwABVmwfopzixMRkTpD4UaqTe/m4bSNCCK30Mbc9fvObmAyQdzDjudrPoDCPOcWKCIidUKlw01YWBjh4eHlPi699NKarFPcgMlkYnTxbeGfrU7Cbi9jRuKOf4HgJpCVBptnO7lCERGpCyp9K/ikSZNqsAzxFEO7NubVRTtIOprD8j8P079tw9INLN4Qez8seRZWvQ0XjwKzOhBFRKT6VDrcjBkzpibrEA8R4OPFsB7RfLoikRmr9p4dbgC6jYblr8HRXbDzO2h3nfMLFRERj6V/Mku1G9XHcWnq5z8Os/dI9tkNfIKg552O5ysnaUFNERGpVgo3Uu1i6gfQr20DDMMx9qZMve4Biw/sXwfJq51boIiIeDSFG6kRJ1cLn7N+HzkFRWc3CIqAriMcz7WgpoiIVCOFG6kRl7dpQLN6/mTmFfHNxoNlN4odD5jgj0VwaIdT6xMREc+lcCM1wmw2lYy9mRG/F6OscTX1W50aTBz/jhOrExERT1blVcFtNhvTpk1j2bJlHDp0CLvdXmr/jz/+WG3FiXv7a/do/v39H+xIzWRt4jF6t6h3dqO4R2D7f+G32dD/OQiOOruNiIhIFVS55+bhhx/m4Ycfxmaz0bFjR7p06VLqIXJSiL83Qy9uDMD0+L1lN2rSA5rFgb0Q1rzvvOJERMRjVbnnZtasWcyZM4drrrmmJuoRDzM6thkz1yaz5Pc0UtJziQrxO7tR3MOQtBLWT4VLHwffEOcXKiIiHqPKPTdWq5VWrVrVRC3igdpFBdOreTg2u8GXa5LLbtTqSmjQDvIzHCuGi4iIXIAqh5vHH3+ct956q+wBoiJlGNs3BoCZa5PJL7Kd3cBshr7jHc9Xvw9F+c4rTkREPE6VL0utWLGCn376iUWLFtGhQwe8vb1L7f/666+rrTjxDFe2jyAy2JfUjDwWbUktGYdTSqe/wo8vQ2YKbJkLF9/m/EJFRMQjVLnnJjQ0lBtvvJHLL7+c+vXrExISUuohciZvi5mRvZsCMG3V3rIbeVmhz/2O5yvfhjPuwhMREaksk1HHri9lZGQQEhJCeno6wcHBri6nzjicmU/cqz9SYLPz7QNxdIkOPbtRXgb8p4Nj7M2IWdD2aqfXKSIitVNVfn9rEj9xigZBPlzb2TGHzYz4ctab8g2GHrc7nmtJBhEROU/nFW7mzZvHsGHD6NOnD926dSv1ECnP6FjHjMX/3XyQo1nlDBrufS9YrJAcD8lrnFidiIh4iiqHm7fffptx48YRERHBxo0b6dWrF/Xq1WPPnj1cfbUuI0j5ukaH0rlJCAVFdmat21d2o+Ao6Dzc8XzV284rTkREPEaVw817773HRx99xDvvvIPVauXJJ59k6dKlPPTQQ6Snp9dEjeIhTCYTo4tXC/9idRJFtnIGDfd9yPHnjoVw5E/nFCciIh6jyuEmOTmZvn37AuDn50dmZiYAo0aNYubMmdVbnXic6zpHER5g5WB6Hj9sP1R2owZtoO21gAGrtKCmiIhUTZXDTWRkJMeOHQOgadOmrF69GoDExERN7Cfn5Ott4Zae0YBjtfByxT3s+PO3mZCZVvOFiYiIx6hyuLniiitYsGABAOPGjePRRx/lyiuvZPjw4dx4443VXqB4npF9mmE2wardR/kzLbPsRk17Q3QfsBXAmg+cW6CIiLi1Ks9zY7fbsdvteHk5JjeeNWsWq1atonXr1txzzz1YrdYaKbS6aJ6b2uGez9az5Pc0buvTlH8O7VR2ox0LYdat4BMCj/0OPkHOLVJERGqNqvz+1iR+4hKrdh3h1k/W4G+1sPrZAQT7ep/dyG6Hyb3g6J9w1f9B3wedX6iIiNQKNT6J36+//sptt91GbGwsBw4cAOCzzz5jxYoV53M4qYNiW9ajdcNAcgpsfLVhf9mNzGaIK75zavV7UFTgvAJFRMRtVTncfPXVVwwaNAg/Pz82btxIfr5jMrb09HReeeWVai9QPJPJZGJ08Wrhn8UnYbeX04HYeTgERkDGAdj6lfMKFBERt1XlcPPPf/6TDz74gI8//rjUiuBxcXEkJCRUa3Hi2f5ycWOCfLzYcySbFbuOlN3Iywf63Od4vvItqFtXUUVE5DxUOdzs3LmTyy677KztISEhnDhxojpqkjoiwMeLm7o3AWB6eauFA3QfB9YgOLwd/lzqnOJERMRtndc8N7t27Tpr+4oVK2jRokW1FCV1x6ji9aZ+3HmI5KM5ZTfyC4UeYx3PtaCmiIicQ5XDzV133cXDDz/MmjVrMJlMHDx4kC+++IInnniC++67ryZqFA/WskEgl7auj2HA52vKWS0coPd9YPaGpBWwf4PzChQREbdT5XDz9NNPc+uttzJgwACysrK47LLLuPPOO7nnnnsYP358TdQoHm5s8cDi2ev2kVtgK7tRSGPoPMzxfJV6b0REpHznPc9NQUEBu3btIisri/bt2xMYGFjdtdUIzXNT+9jsBv3+9RP7juXy2k2dGN6zadkND22H9/oAJhi/Aeq1dGqdIiLiOjU+zw2A1Wqlffv29OrVy22CjdROFrOJUX0cY2+mrUoqf42yhu2gzWDAgPh3nVegiIi4Fa/KNrz99tsr1W7KlCnnXYzUXcN6RPPv7/9ge0oG65OO0zMmvOyGcQ/DH4th4xfQ7xkIbOjcQkVEpNardM/NtGnT+Omnnzhx4gTHjx8v9yFyPkL9rQzt2hg4x23hTWOhcQ+w5cPaj5xTnIiIuJVK99zcd999zJw5k8TERMaNG8dtt91GeHg5/7oWOQ+j+zZj9vp9LN6aSlpGHhHBvmc3MpkcvTdzRsHajyHuEfDRZVERETml0j03kydPJiUlhSeffJL//ve/REdHM2zYMJYsWVL+GAmRKujQKISeMWEU2Q2+XJNcfsOLroXwlpB3AjZ+5rT6RETEPVRpQLGPjw8jRoxg6dKlbNu2jQ4dOnD//fcTExNDVlZWTdUodcjo2BgAvlybTEGRvexGZgv0LZ52IH4y2AqdU5yIiLiF875bymw2YzKZMAwDm62cuUlEqmhQh0gaBvlwODOfRVtTym/YZQQENID0ffD7fOcVKCIitV6Vwk1+fj4zZ87kyiuvpE2bNmzZsoV3332X5ORk3Q4u1cLqZebW3o55bmbEVzBjsbcv9L7X8VwLaoqIyGkqHW7uv/9+oqKiePXVV7nuuuvYt28fc+fO5ZprrsFsPu8OIJGz3Nq7Kd4WExuSjrP1QHr5DXveAd4BkLYVdi9zXoEiIlKrVXqGYrPZTNOmTbn44osxmUzltvv666+rrbiaoBmK3cNDMzey4LeDDOvRhNdv7lJ+w8XPwurJ0PxyGLPAeQWKiIhT1cgMxaNHj6Z///6EhoYSEhJS7kOkOozp65ix+NtNBzmeXVB+wz73gdkLEpfDwY1Oqk5ERGqzSs9zM23atBosQ6S0bk3D6NAomN8PZjB7/T7uvbycdaRCo6HjzbB5Fqx8G/461bmFiohIraPBMlIrmUwmxhSvFv5ZfBI2ewVXT+Mecvy57Rs4lljjtYmISO2mcCO11vVdGhHq782BE7n8uONQ+Q0jOkCrK8GwO+a9ERGROk3hRmotX28Lw3tGA+dYbwpO9d5s/Byyj9RsYSIiUqsp3EitdlvvZphMsGLXEXYdyiy/Ycyl0OhiKMp1rDklIiJ1lsKN1GrR4f4MuCgCcIy9KdfJBTXBsVp4QbYTqhMRkdpI4UZqvbHFA4vnbdhPZl4F60i1ux7CYiD3GGz8wim1iYhI7aNwI7VeXKt6tGgQQHaBjfkbD5TfsNSCmu+Arcg5BYqISK1SK8LN5MmTiYmJwdfXl969e7N27dpKvW/WrFmYTCaGDh1aswWKS5lMJsYUrxY+fdVeKpxUu+tI8K8PJ5Idt4aLiEid4/JwM3v2bB577DEmTJhAQkICXbp0YdCgQRw6VMGtv8DevXt54oknuPTSS51UqbjSX7o1JsBqYffhbFbuOlp+Q28/6H2P47kW1BQRqZNcHm7efPNN7rrrLsaNG0f79u354IMP8Pf3Z8qUKeW+x2azMXLkSF566SVatGhR4fHz8/PJyMgo9RD3E+TrzU3dmwAwPX5vxY173gne/pC62bEsg4iI1CkuDTcFBQVs2LCBgQMHlmwzm80MHDiQ+Pj4ct/3j3/8g4YNG3LHHXec8zMmTpxYau2r6OjoaqldnG908aWpZdvT2Hcsp/yG/uHQbbTj+cq3ar4wERGpVVwabo4cOYLNZiMiIqLU9oiICFJTU8t8z4oVK/j000/5+OPKzWXyzDPPkJ6eXvLYt2/fBdctrtGqYSCXtKqP3YAv1iRX3LjP/WCywO4fIWWzcwoUEZFaweWXpaoiMzOTUaNG8fHHH1O/fv1KvcfHx4fg4OBSD3Ffo2Mdq4XPWpdMXqGt/IZhzaDjXxzPV73thMpERKS2cGm4qV+/PhaLhbS0tFLb09LSiIyMPKv97t272bt3L0OGDMHLywsvLy9mzJjBggUL8PLyYvfu3c4qXVxkQLsIGof6cSKnkAW/Hay4cd/iJRm2fg3HK5gAUEREPIpLw43VaqV79+4sW7asZJvdbmfZsmXExsae1f6iiy5iy5YtbNq0qeRx/fXX079/fzZt2qTxNHWAxWzitj6O3ptz3hYe1Rla9AfDBqvfc1KFIiLiai6/LPXYY4/x8ccfM336dLZv3859991HdnY248aNA2D06NE888wzAPj6+tKxY8dSj9DQUIKCgujYsSNWq9WVpyJOckvPaHy8zPx+MIOE5BMVNz65JEPCDMg5VuO1iYiI67k83AwfPpx//etfvPDCC3Tt2pVNmzaxePHikkHGycnJpKSkuLhKqU3CAqxc36URADPOdVt4i34Q2RkKc2DdJzVem4iIuJ7JqLBf3/NkZGQQEhJCenq6Bhe7sa0H0rnunRV4W0ysfPoKGgb5lt94yzz46g7wrweP/u6Y6E9ERNxKVX5/u7znRuR8dGwcQremoRTaDGauOcft/e2HQmhTyDkKm7SgpoiIp1O4Ebc1pni18C/WJFFos5ff0OIFscULaq56F+wV3EIuIiJuT+FG3NbVHaOoH+jDocx8lvxe9qSPJS4eCX7hcDwRtv/XOQWKiIhLKNyI27J6mbm1d1MAZqw6xzw21gDodbfjuRbUFBHxaAo34tZG9m6Kl9nE2r3H2HbwHIui9roLvPzgYALsXeGcAkVExOkUbsStRQT7MqijYzbrc94WHlAfLr7N8VwLaoqIeCyFG3F7Y4sHFn+z6QAncgoqbhz7AJjMsGsppG6t+eJERMTpFG7E7fVoFka7qGDyCu3MXb+/4sbhzR23hgOseqfGaxMREedTuBG3ZzKZGFO8Wvhnq5Ow2c8xWDju5IKa8+DEOebIERERt6NwIx7hhq6NCfb1IvlYDj/vPFRx40YXQ/PLwF4Eq993ToEiIuI0CjfiEfysFob3dKwKPz3+HLeFw6kFNTdMg9zjNVeYiIg4ncKNeIxRfWIwmeCXPw6z53BWxY1bDoCIjlCYDes+dU6BIiLiFAo34jGa1vPnirYNAcfYmwqZTKd6b9Z8AIV5NVydiIg4i8KNeJTRxbeFz1u/n+z8ooobd7gRQqIh+zD8NrPmixMREadQuBGPcmmr+jSvH0BmfhFfbzxQcWOLt2PeG4B4LagpIuIpFG7Eo5jNJkb1cdwWPmPVXoxzrSF18SjwDYWju2DndzVfoIiI1DiFG/E4N/dogr/Vwp+Hsojfc7Tixj6BjjWnAH79DyT+AlvmQeKv6skREXFTCjficYJ9vflLt8ZAJVYLB+h1D5i94OAGmD4EvroDpl8HkzrCtgU1XK2IiFQ3hRvxSKNjYwD4flsqB07kVtw4Od4xod+ZMlJgzmgFHBERN6NwIx6pTUQQsS3qYTfgi4puC7fbYPFT5ewsHq+z+GldohIRcSMKN+KxxvR1DCyetW4feYXlhJOkVZBxsIKjGJBxwNFORETcgsKNeKyB7SJoFOLLsewCFm5OKbtRVlrlDlbZdiIi4nIKN+KxvCxmRp68LTx+b9mNAiMqd7DKthMREZdTuBGPdkvPaKwWM7/tT2djchkLZDbrC8GNAFP5B7H4QL3WNVajiIhUL4Ub8Wj1An24rksUADPKWi3cbIHBrxW/KCfg2PLhkytg//qaKVJERKqVwo14vDHFt4Uv3JzC4cz8sxu0vx6GzYDgqNLbgxs7gk/9No5BxVOvhg3TarxeERG5MCbjnPPTe5aMjAxCQkJIT08nODjY1eWIkwydvJJN+07wxFVtePCKci4x2W2Ou6Ky0hxjbJr1dfTs5GXAN/fBjv852nUbDVe/Ad6+zjsBEZE6riq/v9VzI3XCydvCP1+dTJHNXnYjswWaXwqdbnb8abY4tvsGw/DPYcAEMJkhYYajFyd9v5OqFxGRqlC4kTrhmk5R1AuwkpqRx/fbzuO2bpMJLn0MRs4DvzA4mAAfXu5Yi0pERGoVhRupE3y8LIzo1RSA6av2nv+BWg2Au5dDZGfIOQIzhsKqd6BuXd0VEanVFG6kzhjZpykWs4k1icfYkZpx/gcKawZ3fA9dRoBhg++fg3njID+r+ooVEZHzpnAjdUZUiB+DOjgm4yvztvCq8PaDoe/DNf9yrCj++3z4ZCAc3V0NlYqIyIVQuJE65eRq4fMTDpCeW3hhBzOZoNddMHYhBEbC4e3wUT/YueiC6xQRkfOncCN1Su/m4bSNCCK30Mbc9fuq56BN+8A9yyG6D+RnwMxb4KdXwF7OXVkiIlKjFG6kTjGZTIwuvi38s9VJ2O3VNBA4KBLG/Bd63eN4vfw1mDkccstY8kFERGqUwo3UOTde3JggXy+Sjuaw/M/D1XdgLytc8zrc+CF4+cKf3zsuU6Vurb7PEBGRc1K4kTrH3+rFsB7RAMy4kNvCy9PlFsfdVKFN4fhex0DjzXOr/3NERKRMCjdSJ43q47g09fMfh9l7JLv6PyCqi2M+nJYDoCgXvr4TFj8DtgscxCwiIuekcCN1Ukz9APq1bYBhOMbe1Aj/cBg5Fy59wvF69XuOSf+yDtXM54mICKBwI3XYydXC56zfR05BUc18iNkCA56H4V+ANQiSVjiWbdi3rmY+T0REFG6k7rq8TQOa1fMnM6+IbzYerNkPa3cd3P0T1G8LmQcdC2+un6JlG0REaoDCjdRZZrOpZOzNjPi9GDUdNOq3hruWQbvrwV4I/3sUFjwIhXk1+7kiInWMwo3UaX/tHo2ft4UdqZmsSTxW8x/oEwTDZsDAF8Fkho2fw9TBcKKaJhQUERGFG6nbQvy9GXpxYwCmr0okfvdRvt10gPjdR7FV1wR/ZzKZ4JJH4bavwS8cDm6Ejy6HPctr5vNEROoYk1HjffG1S0ZGBiEhIaSnpxMcHOzqcqQW2JGaweBJv561PSrElwlD2jO4Y1TNffiJZJh9G6T85ujJGfgS9B3vCEAiIlKiKr+/1XMjdV5589ykpudx3+cJLN6aUnMfHtoUbl8CXUeCYYelz8PcsZCfVXOfKSLi4RRupE6z2Q1e+u+2Mved7NJ86b/bau4SFYC3H9wwGa79N5i9Yds38MkAOLKr5j5TRMSDKdxInbY28Rgp6eXfrWQAKel5rK3pwcYmE/S8E8YuhMBIOLwDPu4PO76r2c8VEfFACjdSpx3KrNxt2JVtd8Ga9oZ7foGmfSE/A2aNgB//D+w253y+iIgHULiROq1hkG+l2h04noO9Ji9NnS4oAsYsgN73Ol7/8jp8ORxynHCruoiIB1C4kTqtV/NwokJ8Ode9Sa8v+YMBby5n2spEsvJraKmG01m84erX4MaPwMsPdi11XKZK3VLzny0i4uYUbqROs5hNTBjSHuCsgHPy9cB2DQny9SLxSDYv/ncbsa8s4+X/bSP5aE7NF9hlONzxPYQ2g+N74ZMrYfOcmv9cERE3pnluRIDFW1N46b/bSg0uPn2em+z8Ir5K2M+0lXvZU3zruMkEA9tFMC4uhtgW9TDV5Nw0Ocfg67tg1w+O173vg6tedvTwiIjUAVX5/a1wI1LMZjdYm3iMQ5l5NAzypVfzcCzm0oHFbjdY/udhpq7cyy9/HC7ZflFkEOPiYriha2N8vS01U6DdBj9PhF/ecLxu2hf+Os0xRkdExMMp3FRA4Uaqy65DmUxduZevEw6QW+i4myk8wMqtvZoyKrYZEcGVG6xcZTsWwvx7HXdTBUU51qqK7lUznyUiUkso3FRA4UaqW3pOIbPWJTMjPokDJ3IB8DKbuKZTFOPiYri4aVj1f+iRPx3LNhze4Zj47+rXoMftWrZBRDyWwk0FFG6kphTZ7CzdlsbUlXtZu/fUbdsXNw1lXFxzru4YibelGsfw52fCtw/Atm8dr7veBtf+yzHjsYiIh1G4qYDCjTjD1gPpTFmZyP9+S6HAZgcgMtiXUbHNGNGrKeEB1ur5IMOAVW/DDy861qaK6grDP3OsWSUi4kEUbiqgcCPOdDgzny/WJPH56mSOZOUD4ONlZmjXxoy7JIaLIqvp/8E9P8PccZB7DPzC4eYp0LJ/9RxbRKQWULipgMKNuEJ+kY2Fm1OYunIvWw6kl2zv27Ie4+Kac8VFDc+6M6vKTiTD7FGQsglMZhgwAeIe1jgcEfEICjcVULgRVzIMgw1Jx5myMpHFW1M5uaJD03B/xvSNYViPJgT5XsDcNYV5sPBx2PS543W762Hoe+ATdOHFi4i4kMJNBRRupLY4cCKXGfF7mbV2H+m5hQAEWC38tUc0Y/vGEFM/4PwObBiwYSp89yTYC6F+W7jlC6jfuhqrFxFxrqr8/q4Vyy9MnjyZmJgYfH196d27N2vXri237ccff8yll15KWFgYYWFhDBw4sML2IrVV41A/nrm6HfHPXMH/3diRVg0DyS6wMW3VXvr/+2fumLaOFX8eocr//jCZHLeFj1vkmAfnyE74qD9s/1/NnIiISC3j8nAze/ZsHnvsMSZMmEBCQgJdunRh0KBBHDp0qMz2P//8MyNGjOCnn34iPj6e6OhorrrqKg4cOODkykWqh7/Vi5G9m7H00cuYcXsv+rdtgGHAsh2HuO3TNQya9Atfrkkmt8BWtQNH94R7foFmcVCQCbNHwrKXHTMdi4h4MJdflurduzc9e/bk3XffBcButxMdHc348eN5+umnz/l+m81GWFgY7777LqNHjz5ne12WEnew53AW01ftZe6G/eQUh5pQf29G9GrK6NhmRIVUYS4bWyEsfQFWv+d43XIA3PQJ+IfXQOUiIjXDbS5LFRQUsGHDBgYOHFiyzWw2M3DgQOLj4yt1jJycHAoLCwkPL/sv6vz8fDIyMko9RGq7Fg0CeemGjsQ/M4Dnrm1HkzA/TuQU8v7Pu7nktZ944MsENiQdq9wlK4s3DJ4If/kEvPxg9zL4qB+kbK7x8xARcQWXhpsjR45gs9mIiCi98F9ERASpqamVOsZTTz1Fo0aNSgWk002cOJGQkJCSR3R09AXXLeIsIX7e3HlpC5b/rT8fjupOnxbh2OwGCzencNP78dwweSXzN+6noMh+7oN1/ivc+QOExcCJJPj0Svhtdo2fg4iIs7l8zM2FePXVV5k1axbz58/H17fsRQqfeeYZ0tPTSx779u1zcpUiF85iNjGoQySz7o7lu4cuZViPJli9zGzen86js38j7rUfeXvZnyUTBZYrsiPc/TO0uhKK8mD+3Y67qmyFTjkPERFncGm4qV+/PhaLhbS0tFLb09LSiIyMrPC9//rXv3j11Vf5/vvv6dy5c7ntfHx8CA4OLvUQcWftGwXz+s1diH/6Ch6/sg0Ng3w4nJnPm0v/oO+rP/LE3N/4/WB6+QfwC4Nb58DlTzler/0Qpg+BzLTy3yMi4kZcGm6sVivdu3dn2bJlJdvsdjvLli0jNja23Pe9/vrrvPzyyyxevJgePXo4o1SRWqdeoA/jB7RmxVNX8NYtXekSHUpBkZ15G/Zz7dsrGPZhPIu3pmKzlzEux2yG/s/CiFngEwzJ8fDhZZC8xvknIiJSzVx+t9Ts2bMZM2YMH374Ib169WLSpEnMmTOHHTt2EBERwejRo2ncuDETJ04E4LXXXuOFF17gyy+/JC4uruQ4gYGBBAYGnvPzdLeUeLKE5ONMXbmX77aklISaJmF+jImNYVjPaEL8ypj9+OhumDUSDm8Hc/Hg4553atkGEalV3G6G4nfffZc33niD1NRUunbtyttvv03v3r0B6NevHzExMUybNg2AmJgYkpKSzjrGhAkTePHFF8/5WQo3UhekpOfyWXwSM9cmczzHMZ7G32rhpm5NGBsXQ8sGZ/xDID8LFjwIv893vO5yK1z3JnhX4ZZzEZEa5HbhxpkUbqQuySu08c3GA0xduZedaZkl2/u1bcC4uOZc1ro+ppM9NIYB8e865sQx7BDZGYZ/DmHNXFS9iMgpCjcVULiRusgwDOJ3H2XKykSW7TjEyZ/6lg0CGBvXnJu6Ncbf6uXYuGc5zBsHOUfBLxxu/hRaXuG64kVEULipkMKN1HV7j2QzPX4vc9fvJyu/CIBgXy9uKZ79uEmYP5zYB3NGw8EEMJnhiufhkkc1DkdEXEbhpgIKNyIOmXmFzNuwn2mr9pJ0NAcAswkGdYhkXFxzejbxw/Td32DjZ443tBsCQ98HnyAXVi0idZXCTQUUbkRKs9kNftpxiKmrElm562jJ9g6NghkX15wbbN/jveQpsBVA/TYw/Ato0MaFFYtIXaRwUwGFG5Hy7UzNZNqqRL5OOEB+8ZIO9QOtPN4+k2F7nsWSlQLWILjxfUdPjoiIkyjcVEDhRuTcjmUXMHNtMp/FJ5GakQdApCWdz0M+pFXOJkejSx+H/n8Hs8V1hYpInaFwUwGFG5HKK7TZWbw1lakrE0lIPoEXRTzlNYu7vL4DwN7iCsw3fwr+4S6uVEQ8ncJNBRRuRM7Pb/tOMHVlIv/bnMLVrOQ174/xN+WT4dsI0/DPCGqupVBEpOYo3FRA4UbkwqRl5PH56iTWrv6V14peJ8acRp7hzX+bPknX6+6jdYTuphKR6qdwUwGFG5HqkVdoY9H6HTT68WF6F64DYHrRlfwU8whjLmnD5W0aYDZrXhwRqR4KNxVQuBGpXobdxoFvXqTJ5rcBWGdvw/0FDxNUvwlj+sZwU/cmBPp4ubhKEXF3CjcVULgRqSE7F2P/6i7MBRkcJpR78x9mg9GWIB8vhvWMZmzfGKLD/V1dpYi4KYWbCijciNSgo7th9m1waBt2kxfveN/OfzIuB0yYTTCwXQTj4prTp0X4qQU7RUQqQeGmAgo3IjWsIBsWjIetXwGQEnMjfy+8nR93n1qV/KLIIG6Pa871XRvh6615ckTk3BRuKqBwI+IEhgGr34PvnwfDBpGd2DvwQz7eYufrhAPkFtoACA+wcmuvpoyKbUZEsK+LixaR2kzhpgIKNyJOlPgLzB0HOUfALwxu+pT0Rpcxa10yM+KTOHAiFwAvs4lrO0cxLq45XaNDXVuziNRKCjcVULgRcbL0/TBnNBzYAJhgwPNwyWMU2Q2WbktjyspE1u09XtL84qahjItrztUdI/G2mF1Xt4jUKgo3FVC4EXGBonz47m+QMN3x+qLrYOj74Ov4Gdx6IJ0pKxP5328pFNgcC3ZGBvsyKrYZt/ZqSliA1VWVi0gtoXBTAYUbERfaMB2+ewJsBVCvNdzyBTRoW7L7cGY+X6xJ4vPVyRzJygfAx8vMjRc3Zlxcc9pGavZjkbpK4aYCCjciLrZ/A8wZBRkHwBro6MFpf32pJvlFNv73WwpTVyWy9UBGyfa+Letxe1xzrrio4VmzH9vsBmsTj3EoM4+GQb70ah6ORTMki3gMhZsKKNyI1AJZh2HeONj7q+P1JY/CFc+DufRt4YZhsD7pOFNXJrJ4ayr24r+tmtXzZ0xsDH/t0YQgX28Wb03hpf9uIyU9r+S9USG+TBjSnsEdo5x1ViJSgxRuKqBwI1JL2IrghwkQ/67jdYv+cNOnEFCvzOb7j+fwWXwSM9cmk5FXBECgjxc9Y8L4aefhs9qf7LN5/7ZuCjgiHkDhpgIKNyK1zNav4NsHoTAHQprC8M+gUddym+cUFPF1wgGmrdrLrkNZFR7aBESG+LLiqSt0iUrEzSncVEDhRqQWStsGs0fCsT3g5QvX/Qe63lrhWwzD4KNf9jBx0Y5zHn5MbDO6NQsjzN9KeICVsAAr4f5W/KyaHVnEXVTl97eW6hUR14toD3f9BPPvgT8Wwzf3OebFGTQRvMq+DdxkMhEZUrlZjafHJzE9Pums7b7eZsL9rYSWCj3ejj8DrKfCUPGfof7eWi5CxA0o3IhI7eAXCrfMhF/egJ8nwrpPIGUzDJsBwWWPmWkYVLlw06e5Y6HO4zkFHMsu4HhOAYU2g7xCOwfT8zh42kDkc/G3Ws7oAfIu6Qk6GYpC/b0JL94W6m/F6qXJCEWcSeFGRGoPsxn6PeUYc/P1XbB/LXx0Ofx1GjTre1bzXs3DiQrxJTU9j7Kur58cc/PFXX1KjbkxDIOs/CKOZxc6Ak9OAcezTwWfY9mFjtfF24/nONrZ7AY5BTZyCnJLlo6ojCAfL8LOCEOle4W8SwWmUD9vvDQ7s8h505gbEamdju6G2aPg0O9g9oJBr0Cvu8FUemDw4q0p3Pd5AkCpgFPdd0vZ7QaZ+UWlQs/pYehEzumvTwWi8/0bNsTP+1Qv0Gm9QmWFoTB/KyF+3u4/aNpug6RVkJUGgRGOQGvWZUBx0IDiCijciLiRgmxY8BBsned43Xk4XDcJrP6lmtXWeW5sdoOM3MJSYehETmEZ4cgRho5lF5CeW3hen2UyQajfGZfISkKRIwyFnRaSwv2tBPl6nTUZostsW4Cx+ClMGQdLNhnBjTANfu2sSR6lblK4qYDCjYibMQxY/T58/xwYNojo5LhdPLx5qWaeMkNxkc1Oem5hSY9QqQB0eq9RzqneoszieX+qymI2EebvfSr0+J/sCfIuPaC6ZJ83gT5emEzV/N912wKMOaMxMDj9YpwdMGHCNGyGAo4o3FRE4UbETe1dAXPHQvZh8A11TPjXeqCrq6oVCorsnMgt4HgZYejk5bEzt2cX2M7rs7wtplJ3j5V1V9npYSjM34q/1VJ+ILLbyH2jPT45qZSVRe0G5PtH4ve3bbpEVccp3FRA4UbEjaUfgDmj4cB6wARX/B0uedwxEFmqJK/Q5rhEVhx6jpdcKis7DB3LKSCv0F7hMU3Y8aEQP/LxowBfUwF+FBBoKaShr516Pnbq+RQR5m0j1LuIYK8iIvP30iZlwTnrtV/2JObo3uAT5FhN3ifY8ac18KxxWOKZFG4qoHAj4uaK8mHRU7BhquN122vhxvcdv+Q0GPVshuFYhb0wt/iRA0V5p54X5p22Lee0drlnbbMV5FCUl42tIBd7gWO7qSgXiy0Piy0Pb6PA6adnx0y+JYBCrwAKvQIp8g7CZg3C8CkOQD5BmP1CsPiFYPEPwRoQhjUgFKt/MCbfkFNBSf+v1HoKNxVQuBHxEAkzYOETYMuHwEgw7JB96NT+4EZQmwej2oqgKPeMMJF7Rugoa1vOGdsrsc2ouMelRlisGN5+GF6+2Cx+FJl9KTRbyTf5kGdYyTV8yDa8Kcw6Rq/Cdec83DZ7UwxMBJJLkCmHIHLxNp3fpbWy5OBHjtmfPHMA+ZYA8i2BJWGpyBqE3Rrk6DXyCcHkF4zZNwQv/xC8/UPxDgjBJzAMP18//KwW/K0WvOvorfw1OfZN4aYCCjciHuTABvj8r5B7tIydxX+hVmUwqmFUvlejVA/ImdtOCyflbbOf311RF8RkBm9/8PZzPLz8Tj339nPs8/ItZ5t/6e1lbjvteSV7QuL/PESzz3sTybFyx9ykUo/FA5YQ0zC4eJ4hG7n5RRTmZ2PLzcDIPYGRn4kpLwNzQSaWwky8CjPxLsrCWpSFry0bX3s2/vZsAsghiJySgORrqr7vIc/wJhN/Mgx/svAn1+xPrimAPEsAecVhqdA7EJt3IHZrMDaro2fJ5BeCyTcYL79gfHwD8fPxws/bEZIcYcmr5Lmft+NRa+5yO83irSm8vGAL0Vm/0ZATHCKUfYFdeP76TtVy16KWXxCRuiGqK1i8y9lZ/O+2+ffCjoWOHp7Cc/SUFFV+Yr5qdVbIOJ/gcVrYKHnvGdss3rVufEqvlg34u/edvFL4OnaDUgHHXvwVvu19B/93Satq6QEostnJLbSRW2AjtcBGbm4OBTnpFGafoDDnBPbcdEdgysvAnJ+BqcARmLwKHGHJuygbH1sWvrYs/OzZ+Bs5+OOYgsDXVIgv6TQwpZ/6QAMoKn7kn7u+QsNCJn5kGv5k4k+m4c8R/Mgotc2PPEsABZYgCr0CzuhdCsbsE4SfT3Eg8raU9Cb5Wb3wPy00OQKUV8n+k9utFnOV74hbvDWFb778gLneM2hkPVay/WB+OP/4cjTceq9Tp2VQuBER95W0CrJSK25TmA2bZ1X92BbrBYSMygSP03pAalngcCaL2US/obdz/5cFvOA9g0ac+sWYSj3+UTiKoX+9vdoubXhZzARZzAT5ngzFAUCDCzuorQgKMjHy0inMSSc/6zgF2Y7AZMvNwJ6XjpGbDnkZcFpY8irMcvQu2bLwsWVjxsDbZCOcLMJNFa94DziCU2HxI+e0cgwT2fiRURyEToakTPzIMvw4VPz69P1ZJ9vhT7bJH5t3ED5W75KAdCoQeZUKQv5WCz5eZvb8Oov3vCedVWIkx3jPexLPfmPlyvbPOm16BoUbEXFfWWmVa9fxZojufUbIOEfw0ABTpxncMQpuvZe/Log7+5LGX6vnkkaNsniBXxgmvzCsYVD2Uq/nYLdDQRbkZ0B+piMI5WdAXnrxn47epKLcdGy56Ri5GRjF+8wFmY7AVJiF2SjCYjIIJodgck5N1X0esgp8ySrwKwlGJ3uOMgw/sk4LRmmGLy94f4mJs3O62eTogXuo8FPW7r6L2NYNz7+gKlC4ERH3FRhRuXbdx0LzS2u0FLkwgztGcWX7SNYmdnf7iRjPi9nsuHPLt/yxJCbAu/hRJsNwXGYtDkOnwlHmGduK/zxjm1H8p8nmuH4WaMojkDwiTccv7NRM0Iij7Nm7ElrfeEHHqiyFGxFxX836Ou6KykiB8pbODG5U5qKbUvtYzCZiW9ZzdRnuy2RyLE1i9YegyKq//eSTovzi3qP0MkJR5lk9StkHtxNwYuc5j9/QdKLKNZ0vhRsRcV9mi+N27zmjcfzVXMbSmYNf1SUmkarw8nE8AupXqrnvnl9gxpBztmvZouWFVlZpdfNGfBHxHO2vd9zuHXzGuIzgRlW7DVxEzoslJo5cv8iSu9vOZDcg1y8SS0yc02pSz42IuL/218NF12qGYhFXMFvwG/IGxpzR2Mta/NRkwm/IG079eVS4ERHPYLZo0LCIq7S/3rF6++KnIONgyWZTcGNMg191eg+qwo2IiIhcuPbXYzqjB9Xkoh5UhRsRERGpHrWkB1UDikVERMSjKNyIiIiIR1G4EREREY+icCMiIiIeReFGREREPIrCjYiIiHgUhRsRERHxKAo3IiIi4lEUbkRERMSj1LkZig3DsWxpRkaGiysRERGRyjr5e/vk7/GK1Llwk5mZCUB0dLSLKxEREZGqyszMJCQkpMI2JqMyEciD2O12Dh48SFBQECaTqVqPnZGRQXR0NPv27SM4OLhaj10bePr5geefo87P/Xn6Oer83F9NnaNhGGRmZtKoUSPM5opH1dS5nhuz2UyTJk1q9DOCg4M99n9a8PzzA88/R52f+/P0c9T5ub+aOMdz9dicpAHFIiIi4lEUbkRERMSjKNxUIx8fHyZMmICPj4+rS6kRnn5+4PnnqPNzf55+jjo/91cbzrHODSgWERERz6aeGxEREfEoCjciIiLiURRuRERExKMo3IiIiIhHUbiposmTJxMTE4Ovry+9e/dm7dq1FbafO3cuF110Eb6+vnTq1InvvvvOSZWen6qc37Rp0zCZTKUevr6+Tqy2an755ReGDBlCo0aNMJlMfPPNN+d8z88//0y3bt3w8fGhVatWTJs2rcbrPF9VPb+ff/75rO/PZDKRmprqnIKraOLEifTs2ZOgoCAaNmzI0KFD2blz5znf504/g+dzju70c/j+++/TuXPnksndYmNjWbRoUYXvcafvr6rn507fXVleffVVTCYTjzzySIXtXPEdKtxUwezZs3nssceYMGECCQkJdOnShUGDBnHo0KEy269atYoRI0Zwxx13sHHjRoYOHcrQoUPZunWrkyuvnKqeHzhmoExJSSl5JCUlObHiqsnOzqZLly5Mnjy5Uu0TExO59tpr6d+/P5s2beKRRx7hzjvvZMmSJTVc6fmp6vmdtHPnzlLfYcOGDWuowguzfPlyHnjgAVavXs3SpUspLCzkqquuIjs7u9z3uNvP4PmcI7jPz2GTJk149dVX2bBhA+vXr+eKK67ghhtu4Pfffy+zvbt9f1U9P3Cf7+5M69at48MPP6Rz584VtnPZd2hIpfXq1ct44IEHSl7bbDajUaNGxsSJE8tsP2zYMOPaa68tta13797GPffcU6N1nq+qnt/UqVONkJAQJ1VXvQBj/vz5FbZ58sknjQ4dOpTaNnz4cGPQoEE1WFn1qMz5/fTTTwZgHD9+3Ck1VbdDhw4ZgLF8+fJy27jbz+CZKnOO7vxzaBiGERYWZnzyySdl7nP3788wKj4/d/3uMjMzjdatWxtLly41Lr/8cuPhhx8ut62rvkP13FRSQUEBGzZsYODAgSXbzGYzAwcOJD4+vsz3xMfHl2oPMGjQoHLbu9L5nB9AVlYWzZo1Izo6+pz/QnE37vT9XYiuXbsSFRXFlVdeycqVK11dTqWlp6cDEB4eXm4bd/8OK3OO4J4/hzabjVmzZpGdnU1sbGyZbdz5+6vM+YF7fncPPPAA11577VnfTVlc9R0q3FTSkSNHsNlsRERElNoeERFR7hiF1NTUKrV3pfM5v7Zt2zJlyhS+/fZbPv/8c+x2O3379mX//v3OKLnGlff9ZWRkkJub66Kqqk9UVBQffPABX331FV999RXR0dH069ePhIQEV5d2Tna7nUceeYS4uDg6duxYbjt3+hk8U2XP0d1+Drds2UJgYCA+Pj7ce++9zJ8/n/bt25fZ1h2/v6qcn7t9dwCzZs0iISGBiRMnVqq9q77DOrcquFSf2NjYUv8i6du3L+3atePDDz/k5ZdfdmFlUhlt27albdu2Ja/79u3L7t27+c9//sNnn33mwsrO7YEHHmDr1q2sWLHC1aXUmMqeo7v9HLZt25ZNmzaRnp7OvHnzGDNmDMuXLy83ALibqpyfu313+/bt4+GHH2bp0qW1fuCzwk0l1a9fH4vFQlpaWqntaWlpREZGlvmeyMjIKrV3pfM5vzN5e3tz8cUXs2vXrpoo0enK+/6Cg4Px8/NzUVU1q1evXrU+MDz44IP873//45dffqFJkyYVtnWnn8HTVeUcz1Tbfw6tViutWrUCoHv37qxbt4633nqLDz/88Ky27vj9VeX8zlTbv7sNGzZw6NAhunXrVrLNZrPxyy+/8O6775Kfn4/FYin1Hld9h7osVUlWq5Xu3buzbNmykm12u51ly5aVez01Nja2VHuApUuXVnj91VXO5/zOZLPZ2LJlC1FRUTVVplO50/dXXTZt2lRrvz/DMHjwwQeZP38+P/74I82bNz/ne9ztOzyfczyTu/0c2u128vPzy9znbt9fWSo6vzPV9u9uwIABbNmyhU2bNpU8evTowciRI9m0adNZwQZc+B3W6HBlDzNr1izDx8fHmDZtmrFt2zbj7rvvNkJDQ43U1FTDMAxj1KhRxtNPP13SfuXKlYaXl5fxr3/9y9i+fbsxYcIEw9vb29iyZYurTqFCVT2/l156yViyZImxe/duY8OGDcYtt9xi+Pr6Gr///rurTqFCmZmZxsaNG42NGzcagPHmm28aGzduNJKSkgzDMIynn37aGDVqVEn7PXv2GP7+/sbf/vY3Y/v27cbkyZMNi8ViLF682FWnUKGqnt9//vMf45tvvjH+/PNPY8uWLcbDDz9smM1m44cffnDVKVTovvvuM0JCQoyff/7ZSElJKXnk5OSUtHH3n8HzOUd3+jl8+umnjeXLlxuJiYnG5s2bjaefftowmUzG999/bxiG+39/VT0/d/ruynPm3VK15TtUuKmid955x2jatKlhtVqNXr16GatXry7Zd/nllxtjxowp1X7OnDlGmzZtDKvVanTo0MFYuHChkyuumqqc3yOPPFLSNiIiwrjmmmuMhIQEF1RdOSdvfT7zcfKcxowZY1x++eVnvadr166G1Wo1WrRoYUydOtXpdVdWVc/vtddeM1q2bGn4+voa4eHhRr9+/Ywff/zRNcVXQlnnBpT6Ttz9Z/B8ztGdfg5vv/12o1mzZobVajUaNGhgDBgwoOQXv2G4//dX1fNzp++uPGeGm9ryHZoMwzBqtm9IRERExHk05kZEREQ8isKNiIiIeBSFGxEREfEoCjciIiLiURRuRERExKMo3IiIiIhHUbgRERERj6JwIyIiIh5F4UZE6jyTycQ333zj6jJEpJoo3IiIS40dOxaTyXTWY/Dgwa4uTUTclJerCxARGTx4MFOnTi21zcfHx0XViIi7U8+NiLicj48PkZGRpR5hYWGA45LR+++/z9VXX42fnx8tWrRg3rx5pd6/ZcsWrrjiCvz8/KhXrx533303WVlZpdpMmTKFDh064OPjQ1RUFA8++GCp/UeOHOHGG2/E39+f1q1bs2DBgpo9aRGpMQo3IlLrPf/889x000389ttvjBw5kltuuYXt27cDkJ2dzaBBgwgLC2PdunXMnTuXH374oVR4ef/993nggQe4++672bJlCwsWLKBVq1alPuOll15i2LBhbN68mWuuuYaRI0dy7Ngxp56niFSTGl93XESkAmPGjDEsFosREBBQ6vF///d/hmEYBmDce++9pd7Tu3dv47777jMMwzA++ugjIywszMjKyirZv3DhQsNsNhupqamGYRhGo0aNjL///e/l1gAYzz33XMnrrKwsAzAWLVpUbecpIs6jMTci4nL9+/fn/fffL7UtPDy85HlsbGypfbGxsWzatAmA7du306VLFwICAkr2x8XFYbfb2blzJyaTiYMHDzJgwIAKa+jcuXPJ84CAAIKDgzl06ND5npKIuJDCjYi4XEBAwFmXiaqLn59fpdp5e3uXem0ymbDb7TVRkojUMI25EZFab/Xq1We9bteuHQDt2rXjt99+Izs7u2T/ypUrMZvNtG3blqCgIGJiYli2bJlTaxYR11HPjYi4XH5+PqmpqaW2eXl5Ub9+fQDmzp1Ljx49uOSSS/jiiy9Yu3Ytn376KQAjR45kwoQJjBkzhhdffJHDhw8zfvx4Ro0aRUREBAAvvvgi9957Lw0bNuTqq68mMzOTlStXMn78eOeeqIg4hcKNiLjc4sWLiYqKKrWtbdu27NixA3DcyTRr1izuv/9+oqKimDlzJu3btwfA39+fJUuW8PDDD9OzZ0/8/f256aabePPNN0uONWbMGPLy8vjPf/7DE088Qf369bn55pudd4Ii4lQmwzAMVxchIlIek8nE/PnzGTp0qKtLERE3oTE3IiIi4lEUbkRERMSjaMyNiNRqunIuIlWlnhsRERHxKAo3IiIi4lEUbkRERMSjKNyIiIiIR1G4EREREY+icCMiIiIeReFGREREPIrCjYiIiHiU/weMj3hRHv3ViwAAAABJRU5ErkJggg==\n" }, "metadata": {} }, @@ -1068,16 +645,16 @@ "output_type": "stream", "name": "stdout", "text": [ - "Epoch [7 / 15], Step [22 / 225], Loss: 0.04863778129220009, Validation Loss: 0.0\n", - "Epoch [7 / 15], Step [44 / 225], Loss: 0.09685459733009338, Validation Loss: 0.18659985065460205\n", - "Epoch [7 / 15], Step [66 / 225], Loss: 0.08631588518619537, Validation Loss: 1.3988771438598633\n", - "Epoch [7 / 15], Step [88 / 225], Loss: 0.6523657441139221, Validation Loss: 0.6873682737350464\n", - "Epoch [7 / 15], Step [110 / 225], Loss: 0.07394303381443024, Validation Loss: 0.03567586466670036\n", - "Epoch [7 / 15], Step [132 / 225], Loss: 0.048379410058259964, Validation Loss: 0.0\n", - "Epoch [7 / 15], Step [154 / 225], Loss: 0.04830015450716019, Validation Loss: 0.024945590645074844\n", - "Epoch [7 / 15], Step [176 / 225], Loss: 0.04822903499007225, Validation Loss: 0.0\n", - "Epoch [7 / 15], Step [198 / 225], Loss: 0.04814707115292549, Validation Loss: 0.0\n", - "Epoch [7 / 15], Step [220 / 225], Loss: 0.04806003347039223, Validation Loss: 0.0\n" + "Epoch [6 / 10], Step [22 / 225], Loss: 0.059457464990290726, Validation Loss: 0.01410769484937191\n", + "Epoch [6 / 10], Step [44 / 225], Loss: 0.06400176146152345, Validation Loss: 0.01610004436224699\n", + "Epoch [6 / 10], Step [66 / 225], Loss: 0.06258460139912186, Validation Loss: 0.01073336290816466\n", + "Epoch [6 / 10], Step [88 / 225], Loss: 0.059185962616042656, Validation Loss: 0.008050022181123495\n", + "Epoch [6 / 10], Step [110 / 225], Loss: 0.05729963897981427, Validation Loss: 0.006440017744898796\n", + "Epoch [6 / 10], Step [132 / 225], Loss: 0.055887209505520084, Validation Loss: 0.00799964057902495\n", + "Epoch [6 / 10], Step [154 / 225], Loss: 0.05486652876746345, Validation Loss: 0.006856834782021386\n", + "Epoch [6 / 10], Step [176 / 225], Loss: 0.054090704366734084, Validation Loss: 0.005999730434268713\n", + "Epoch [6 / 10], Step [198 / 225], Loss: 0.05347813244419868, Validation Loss: 0.005333093719349967\n", + "Epoch [6 / 10], Step [220 / 225], Loss: 0.05342629473995079, Validation Loss: 0.004799784347414971\n" ] }, { @@ -1086,18 +663,10 @@ "text/plain": [ "
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\n" + "image/png": 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}, "metadata": {} }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Epoch [8 / 15], Step [22 / 225], Loss: 0.04794354736804962, Validation Loss: 0.0\n", - "Epoch [8 / 15], Step [44 / 225], Loss: 0.04784921184182167, Validation Loss: 0.0\n" - ] - }, { "output_type": "error", "ename": "KeyboardInterrupt", @@ -1105,21 +674,18 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0mlosses\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0mval_losses\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mimg1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloaders\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'train'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 16\u001b[0m \u001b[0msize\u001b[0m \u001b[0;34m=\u001b[0m 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TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 632\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# type: ignore[call-arg]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 633\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 634\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_yielded\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 635\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_kind\u001b[0m \u001b[0;34m==\u001b[0m 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"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/utils/data/_utils/fetch.py\u001b[0m in \u001b[0;36mfetch\u001b[0;34m(self, possibly_batched_index)\u001b[0m\n\u001b[1;32m 49\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getitems__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 50\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 52\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 53\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/utils/data/_utils/fetch.py\u001b[0m in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 49\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m 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242\u001b[0m \u001b[0msample_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0midx\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcumulative_sizes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdataset_idx\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 243\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatasets\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdataset_idx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msample_idx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 244\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 245\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, idx)\u001b[0m\n\u001b[1;32m 17\u001b[0m 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"val_losses = []\n", @@ -1183,66 +749,217 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 68, "metadata": { "id": "nnGoOwX-Vk_i" }, "outputs": [], "source": [ - "# data = {}\n", - "# labels = {}\n", + "# # data = {}\n", + "# # labels = {}\n", + "# trip_model.eval()\n", + "# with torch.no_grad():\n", + "# embeddings = trip_model.forward_once\n", + "# for stage in ['train', 'cal', 'test']:\n", + "# data[stage] = []\n", + "# labels[stage] = []\n", + "# for i, (img1, _, _, label) in enumerate(loaders[stage]):\n", + "# img1 = img1.to(device)\n", + "# output = embeddings(img1)\n", + "# data[stage].extend(output.cpu().tolist())\n", + "# labels[stage].extend(label.tolist())" + ] + }, + { + "cell_type": "code", + "source": [ + "class Classifier(nn.Module):\n", + " def __init__(self, input_size, embedding, hidden_size, output_size):\n", + " super(Classifier, self).__init__()\n", + " self.embedding = embedding\n", + " self.fc1 = nn.Linear(input_size, hidden_size)\n", + " self.fc2 = nn.Linear(hidden_size, output_size)\n", + "\n", + " def forward(self, x):\n", + " with torch.no_grad():\n", + " detached_emb = self.embedding(x).detach()\n", + " print(detached_emb.shape)\n", + " x = torch.relu(detached_emb) # Taking the mean of embeddings across the embedding dimension\n", + " x = torch.relu(self.fc1(x))\n", + " x = self.fc2(x)\n", + " return x\n", + "# Assuming you have the appropriate input dimensions\n", + "input_size = 100 # Assuming 100 input features\n", + "embedded_size = 32 # Assuming an embedded size of 50\n", + "hidden_size = 100 # Size of the hidden layer\n", + "output_size = 2 # Size of the output layer\n", + "\n", + "# Creating an instance of the CustomNet\n" + ], + "metadata": { + "id": "LVYZvjpp8q7W" + }, + "execution_count": 12, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ "trip_model.eval()\n", - "with torch.no_grad():\n", - " embeddings = trip_model.forward_once\n", - " for stage in ['train', 'cal', 'test']:\n", - " data[stage] = []\n", - " labels[stage] = []\n", - " for i, (img1, _, _, label) in enumerate(loaders[stage]):\n", - " img1 = img1.to(device)\n", - " output = embeddings(img1)\n", - " data[stage].extend(output.cpu().tolist())\n", - " labels[stage].extend(label.tolist())" + "learning_rate = 0.1\n", + "clas_model = Classifier(32, trip_model.forward_once, 100, 2)\n", + "clas_criterion = nn.BCELoss()\n", + "total_step = len(loaders['train'])\n", + "\n", + "clas_optimizer = optim.Adam(clas_model.parameters(), lr=learning_rate)" + ], + "metadata": { + "id": "hKKu2Pnx8_9M" + }, + "execution_count": 13, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from itertools import cycle\n", + "\n", + "test_iter = cycle(iter(loaders['test']))\n", + "# Training loop\n", + "epochs = 10\n", + "clas_model.to(device)\n", + "clas_losses = []\n", + "clas_val_losses = []\n", + "\n", + "for epoch in range(epochs):\n", + " clas_losses.append([])\n", + " clas_val_losses.append([])\n", + " for i, (img, _, _, label) in enumerate(loaders['train']):\n", + " img, label = img.to(device), label.to(device)\n", + "\n", + "\n", + " output = clas_model(img)\n", + " loss = clas_criterion(output, label)\n", + " clas_optimizer.zero_grad()\n", + " loss.backward()\n", + " clas_optimizer.step()\n", + " clas_losses[epoch].append(loss.item())\n", + " if (i + 1) % (total_step // 10) == 0:\n", + "\n", + " with torch.no_grad():\n", + " val_img, _, _, val_label = next(test_iter)\n", + " val_img, val_label = val_img.to(device), val_label.to(device)\n", + " val_output = clas_model(val_img)\n", + " val_loss = val_criterion(val_output, val_label)\n", + " clas_val_losses[epoch].append(val_loss.item())\n", + "\n", + " print(f\"Epoch [{epoch + 1} / {epochs}], Step [{i + 1} / {total_step}], Loss: {np.mean(clas_losses[epoch])}, Validation Loss: {np.mean(clas_val_losses[epoch])}\")\n", + "\n", + " scheduler.step()\n", + " # Calculate mean of each row\n", + " mean_list1 = np.mean(clas_losses, axis=1)\n", + " mean_list2 = np.mean(clas_val_losses, axis=1)\n", + "\n", + " # Plot the means\n", + " plt.plot(mean_list1, label='Train Loss', marker='o')\n", + " plt.plot(mean_list2, label='Val Loss', marker='o')\n", + "\n", + " plt.xlabel('Epoch')\n", + " plt.ylabel('Mean Loss')\n", + " plt.legend()\n", + " plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 297 + }, + "id": "NN17b-Wm9jHg", + "outputId": "e1009cd6-e393-49d7-c07f-f2ae2cd22aea" + }, + "execution_count": 16, + "outputs": [ + { + "output_type": "error", + "ename": "RuntimeError", + "evalue": "ignored", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 12\u001b[0m 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"outputId": "c8644104-3472-4653-a62c-fb7c4038010c" + }, + "execution_count": 15, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "env: CUDA_LAUNCH_BLOCKING=1\n" + ] + } ] }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 73, "metadata": { "id": "ayGTRCP5Wl0s", "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "4c81e07a-cde3-428c-bf42-cc2132a919de" + "outputId": "16c820eb-6070-4749-9e47-408606cee484" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "Cal Accuracy: 0.7344444444444445\n", - "Val Accuracy: 0.7146666666666667\n" + "Train Accuracy: 0.6839684014869889\n", + "Cal Accuracy: 0.8131111111111111\n", + "Val Accuracy: 0.7526666666666667\n" ] } ], "source": [ "from sklearn.model_selection import train_test_split\n", "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.neural_network import MLPClassifier\n", "from sklearn.metrics import accuracy_score\n", "\n", "# Split the data into training and testing sets\n", "# X_train, X_test, y_train, y_test = train_test_split(embeddings, labels, test_size=0.2, random_state=42)\n", "\n", "# Train a logistic regression model\n", - "classifier = LogisticRegression(max_iter=1000)\n", + "# classifier = LogisticRegression(max_iter=1000)\n", + "classifier = MLPClassifier(hidden_layer_sizes=(50, 20), max_iter=1000)\n", "classifier.fit(data['cal'], labels['cal'])\n", "\n", "# Make predictions\n", + "train_predictions = classifier.predict(data['train'])\n", "cal_predictions = classifier.predict(data['cal'])\n", "val_predictions = classifier.predict(data['test'])\n", "\n", "# Calculate accuracy\n", + "train_accuracy = accuracy_score(labels['train'], train_predictions)\n", "cal_accuracy = accuracy_score(labels['cal'], cal_predictions)\n", "val_accuracy = accuracy_score(labels['test'], val_predictions)\n", + "print(f\"Train Accuracy: {train_accuracy}\")\n", "print(f\"Cal Accuracy: {cal_accuracy}\")\n", "print(f\"Val Accuracy: {val_accuracy}\")\n" ] From 220bc37525bd49a24c45a9d7349871e1f15c6f6f Mon Sep 17 00:00:00 2001 From: FinnRobertson15 <110511080+FinnRobertson15@users.noreply.github.com> Date: Sat, 21 Oct 2023 14:49:25 +1000 Subject: [PATCH 09/14] Add files via upload Moved from jupyer notebook to python files, changed to Random Forest Classifier --- dataset.py | 120 ++++++++++++++++++++++++++++++++++++++++++++++ modules.py | 60 +++++++++++++++++++++++ predict.py | 26 ++++++++++ train.py | 138 +++++++++++++++++++++++++++++++++++++++++++++++++++++ 4 files changed, 344 insertions(+) create mode 100644 dataset.py create mode 100644 modules.py create mode 100644 predict.py create mode 100644 train.py diff --git a/dataset.py b/dataset.py new file mode 100644 index 000000000..528409213 --- /dev/null +++ b/dataset.py @@ -0,0 +1,120 @@ +import torch + +import torchvision.transforms as transforms + +import os +from PIL import Image +from torch.utils.data import Dataset, DataLoader +import torch + +class CustomDataset(Dataset): + def __init__(self, root_dir, transform=None): + self.root_dir = root_dir + self.transform = transform + self.image_paths = os.listdir(root_dir) + + def __len__(self): + return len(self.image_paths) + + def __getitem__(self, idx): + img_name = os.path.join(self.root_dir, self.image_paths[idx]) + image = Image.open(img_name) + + if self.transform: + image = self.transform(image) + + return image + +class TripletDataset(Dataset): + def __init__(self, AD, NC, transform=None): + self.X = AD + NC + self.AD = AD + self.NC = NC + self.Y = torch.cat((torch.ones(len(AD)), torch.zeros(len(NC))), dim=0) + self.anc_indices = torch.randperm(len(self.X)) + self.pos_indices = torch.randperm(len(self.X)) % len(AD) + self.neg_indices = torch.randperm(len(self.X)) % len(NC) + self.transform = transform + + def __len__(self): + return len(self.anc_indices) + + def __getitem__(self, idx): + anc = self.anc_indices[idx] + pos = self.pos_indices[idx] + neg = self.neg_indices[idx] + img1 = self.X[anc] + img2 = self.AD[pos] + img3 = self.NC[neg] + label = self.Y[anc] + + if self.transform: + img1 = self.transform(img1) + img2 = self.transform(img2) + img3 = self.transform(img3) + + return img1, img2, img3, torch.tensor([1 - label, label]) + + + + +def intensity_normalization(img, mean = None, std = None): + mean = torch.mean(img) + std = torch.std(img) + return (img - mean) / std + +class CustomNormalize(object): + def __init__(self, mean, std): + self.mean = mean + self.std = std + + def __call__(self, img): + return (img - self.mean) / self.std + +transform_train = transforms.Compose([ + transforms.Resize((256, 256)), + transforms.ToTensor(), + transforms.RandomAffine(degrees=5, translate=(0.05, 0.05), scale=(0.95, 1.05)), # Random affine transformations with smaller parameters + transforms.Lambda(intensity_normalization) +]) + +transform_test = transforms.Compose([ + transforms.Resize((256, 256)), + transforms.ToTensor(), + transforms.Lambda(intensity_normalization) +]) + +class Normalize(object): + def __init__(self, mean, std): + self.mean = mean + self.std = std + + def __call__(self, img): + return (img - self.mean) / self.std + + + +def gen_loaders(root_dir = '', batch_size = 96): + loaders = {} + AD_train = CustomDataset(root_dir=root_dir + os.path.join('train', 'AD'), transform=transform_train) + NC_train = CustomDataset(root_dir=root_dir + os.path.join('train', 'NC'), transform=transform_train) + + X = torch.stack([img for img in AD_train + NC_train]) + mean = X.mean() + std = X.std() + normalize = transforms.Compose([ + Normalize(mean, std) + ]) + train_dataset = TripletDataset(AD_train, NC_train, normalize) + + + # Create DataLoaders for the two parts + loaders['train'] = DataLoader(train_dataset, batch_size=batch_size, shuffle=True) + + AD_test = CustomDataset(root_dir=os.path.join('test', 'AD'), transform=transform_test) + NC_test = CustomDataset(root_dir=os.path.join('test', 'NC'), transform=transform_test) + + test_dataset = TripletDataset(AD_test, NC_test, normalize) + + loaders['test'] = DataLoader(test_dataset, batch_size=batch_size, shuffle=False) + return loaders \ No newline at end of file diff --git a/modules.py b/modules.py new file mode 100644 index 000000000..ab28c061c --- /dev/null +++ b/modules.py @@ -0,0 +1,60 @@ + +import torch +import torch.nn as nn +import torchvision.models as models +import torch.nn.functional as F + +class TripletSiameseNetwork(nn.Module): + def __init__(self, pretrained=True): + super(TripletSiameseNetwork, self).__init__() + self.resnet = models.resnet18(pretrained=pretrained) + self.resnet.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False) + self.bn = nn.BatchNorm1d(1000) + self.fc1 = nn.Linear(1000, 128) + + def forward_once(self, x): + output = self.resnet(x) + output = self.bn(output) + output = self.fc1(output) + return output + + def forward(self, anchor, positive, negative): + output_anchor = self.forward_once(anchor) + output_positive = self.forward_once(positive) + output_negative = self.forward_once(negative) + return output_anchor, output_positive, output_negative + +class TripletLoss(nn.Module): + def __init__(self, margin=1.0): + super(TripletLoss, self).__init__() + self.margin = margin + + def forward(self, anchor, positive, negative): + distance_positive = F.pairwise_distance(anchor, positive) + distance_negative = F.pairwise_distance(anchor, negative) + losses = F.relu(distance_positive - distance_negative + self.margin) + return losses.mean() + +class TripletLossWithRegularization(nn.Module): + def __init__(self, model, margin=1.0, lambda_reg=0.0001): + super(TripletLossWithRegularization, self).__init__() + self.model = model + self.margin = margin + self.lambda_reg = lambda_reg # Regularization parameter + + def forward(self, anchor, positive, negative): + distance_positive = F.pairwise_distance(anchor, positive) + distance_negative = F.pairwise_distance(anchor, negative) + triplet_losses = F.relu(distance_positive - distance_negative + self.margin) + triplet_loss = triplet_losses.mean() + + # Compute L2 regularization + l2_reg = None + for param in self.model.parameters(): + if l2_reg is None: + l2_reg = param.norm(2) + else: + l2_reg = l2_reg + param.norm(2) + + loss = triplet_loss + self.lambda_reg * l2_reg + return loss diff --git a/predict.py b/predict.py new file mode 100644 index 000000000..936cb25da --- /dev/null +++ b/predict.py @@ -0,0 +1,26 @@ +from modules import TripletSiameseNetwork +from dataset import gen_loaders +import torch +import pickle + +def predict(input=gen_loaders()['test'], triplet_path = 'trip_model.pth', classifier_path='classifier.pickle'): + device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + trip_model = TripletSiameseNetwork() + trip_model = torch.load(triplet_path) + with open(classifier_path, 'rb') as f: + clf = pickle.load(f) + outputs = None + + for i, (img, _, _, _) in enumerate(input): + img = img.to(device) + with torch.no_grad(): + features = trip_model.forward_once(img) + + if outputs is None: + outputs = features.cpu() + else: + outputs = torch.cat((outputs, features.cpu()), dim=0) + + pred = clf.predict(outputs) + return pred + diff --git a/train.py b/train.py new file mode 100644 index 000000000..ae536b1a8 --- /dev/null +++ b/train.py @@ -0,0 +1,138 @@ +import torch +import torchvision.transforms as transforms +import numpy as np +import matplotlib.pyplot as plt +from dataset import gen_loaders +from modules import TripletSiameseNetwork, TripletLoss, TripletLossWithRegularization +from sklearn.ensemble import RandomForestClassifier +from sklearn.metrics import accuracy_score +import pickle +from itertools import cycle + +loaders = gen_loaders() + +device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') +learning_rate = 0.001 +trip_model = TripletSiameseNetwork() +trip_criterion = TripletLossWithRegularization(margin=1.0) +val_criterion = TripletLoss(margin=1.0) +total_step = len(loaders['train']) +epochs = 20 + +optimizer = torch.optim.SGD(trip_model.parameters(), lr=learning_rate, momentum=0.9, weight_decay=5e-4) + +sched_linear_1 = torch.optim.lr_scheduler.CyclicLR(optimizer, base_lr=0.0001, max_lr=learning_rate, step_size_up=epochs // 2, step_size_down=epochs // 2, mode="triangular", verbose=False) +sched_linear_3 = torch.optim.lr_scheduler.LinearLR(optimizer, start_factor=0.0001/learning_rate, end_factor=0.0001/learning_rate, verbose=False) +scheduler = torch.optim.lr_scheduler.SequentialLR(optimizer, schedulers=[sched_linear_1, sched_linear_3], milestones=[epochs]) + + + +test_iter = cycle(iter(loaders['test'])) +trip_model.to(device) +losses = [] +val_losses = [] + +transform_train = transforms.RandomAffine(degrees=5, translate=(0.05, 0.05), scale=(0.95, 1.05)) + +prev_loss = float('inf') +epochs_without_improvement = 0 +max_epochs_without_improvement = 3 + +for epoch in range(epochs): + print(f'Learning rate: {optimizer.param_groups[0]["lr"]}') + losses.append([]) + val_losses.append([]) + for i, (img1, img2, img3, _) in enumerate(loaders['train']): + size = img1.size(0) + img1, img2, img3 = img1[torch.randperm(size)], img2[torch.randperm(size)], img3[torch.randperm(size)] + img1_trans = transform_train(img1) + img2_trans = transform_train(img2) + img3_trans = transform_train(img3) + img1_trans, img2_trans, img3_trans = img1_trans.to(device), img2_trans.to(device), img3_trans.to(device) + + + out1, out2, out3 = trip_model(img1_trans, img2_trans, img3_trans) + + loss = trip_criterion(out1, out2, out3) + optimizer.zero_grad() + loss.backward() + optimizer.step() + + no_loss = val_criterion(out1, out2, out3) + losses[epoch].append(no_loss.item()) + if (i + 1) % (total_step // 10) == 0: + + with torch.no_grad(): + val_img1, val_img2, val_img3, _ = next(test_iter) + val_img1, val_img2, val_img3 = val_img1.to(device), val_img2.to(device), val_img3.to(device) + val_out1, val_out2, val_out3 = trip_model(val_img1, val_img2, val_img3) + val_loss = val_criterion(val_out1, val_out2, val_out3) + val_losses[epoch].append(val_loss.item()) + + print(f"Epoch [{epoch + 1} / {epochs}], Step [{i + 1} / {total_step}], Loss: {np.mean(losses[epoch])}, Validation Loss: {np.mean(val_losses[epoch])}") + + scheduler.step() + mean_list1 = np.mean(losses, axis=1) + mean_list2 = np.mean(val_losses, axis=1) + + # Plot the means + plt.plot(mean_list1, label='Train Loss', marker='o') + plt.plot(mean_list2, label='Val Loss', marker='o') + + plt.xlabel('Epoch') + plt.ylabel('Mean Loss') + plt.legend() + plt.show() + + if np.mean(losses[epoch]) < prev_loss: + epochs_without_improvement = 0 + prev_loss = np.mean(losses[epoch]) + else: + epochs_without_improvement += 1 + print(f'{epochs_without_improvement} / {max_epochs_without_improvement} before early stop.') + + if epochs_without_improvement >= max_epochs_without_improvement: + print(f'Early stopping after {epoch + 1} epochs.') + break + + + + + +output_dict = {} +label_dict = {} +for stage in ['train', 'test']: + outputs = None + labels = None + for i, (img, _, _, label) in enumerate(loaders[stage]): + img, label = img.to(device), label.to(device) + with torch.no_grad(): + features = trip_model.forward_once(img) + + if outputs is None: + outputs = features.cpu() + labels = label.cpu() + else: + outputs = torch.cat((outputs, features.cpu()), dim=0) + labels = torch.cat((labels, label.cpu()), dim=0) + output_dict[stage] = outputs + label_dict[stage] = labels + + +# Example usage: +clf = RandomForestClassifier(n_estimators=400, min_samples_split=300, max_depth=8, critereon='entropy') +clf.fit(output_dict['train'], label_dict['train']) + +y_pred = clf.predict(output_dict['test']) +y_pred_train = clf.predict(output_dict['train']) + + +# Calculate the accuracy of the classifier +print(f"Test accuracy: {accuracy_score(label_dict['test'], y_pred)}, Train accuracy: {accuracy_score(label_dict['train'], y_pred_train)}") + + +trip_model.save('trip_model.pth') +with open('classifier.pickle', 'wb') as f: + pickle.dump(clf, f) + +print('Saved siamese to trip_model.pth and classifier to classifier.pickle') \ No newline at end of file From 61dd2c49cbdc22a28418575256b9ab0cd8e5bbb4 Mon Sep 17 00:00:00 2001 From: FinnRobertson15 <110511080+FinnRobertson15@users.noreply.github.com> Date: Sat, 21 Oct 2023 14:54:45 +1000 Subject: [PATCH 10/14] Add files via upload Moved to correct folder --- recognition/dataset.py | 120 +++++++++++++++++++++++++++++++++++ recognition/modules.py | 60 ++++++++++++++++++ recognition/predict.py | 26 ++++++++ recognition/train.py | 138 +++++++++++++++++++++++++++++++++++++++++ 4 files changed, 344 insertions(+) create mode 100644 recognition/dataset.py create mode 100644 recognition/modules.py create mode 100644 recognition/predict.py create mode 100644 recognition/train.py diff --git a/recognition/dataset.py b/recognition/dataset.py new file mode 100644 index 000000000..528409213 --- /dev/null +++ b/recognition/dataset.py @@ -0,0 +1,120 @@ +import torch + +import torchvision.transforms as transforms + +import os +from PIL import Image +from torch.utils.data import Dataset, DataLoader +import torch + +class CustomDataset(Dataset): + def __init__(self, root_dir, transform=None): + self.root_dir = root_dir + self.transform = transform + self.image_paths = os.listdir(root_dir) + + def __len__(self): + return len(self.image_paths) + + def __getitem__(self, idx): + img_name = os.path.join(self.root_dir, self.image_paths[idx]) + image = Image.open(img_name) + + if self.transform: + image = self.transform(image) + + return image + +class TripletDataset(Dataset): + def __init__(self, AD, NC, transform=None): + self.X = AD + NC + self.AD = AD + self.NC = NC + self.Y = torch.cat((torch.ones(len(AD)), torch.zeros(len(NC))), dim=0) + self.anc_indices = torch.randperm(len(self.X)) + self.pos_indices = torch.randperm(len(self.X)) % len(AD) + self.neg_indices = torch.randperm(len(self.X)) % len(NC) + self.transform = transform + + def __len__(self): + return len(self.anc_indices) + + def __getitem__(self, idx): + anc = self.anc_indices[idx] + pos = self.pos_indices[idx] + neg = self.neg_indices[idx] + img1 = self.X[anc] + img2 = self.AD[pos] + img3 = self.NC[neg] + label = self.Y[anc] + + if self.transform: + img1 = self.transform(img1) + img2 = self.transform(img2) + img3 = self.transform(img3) + + return img1, img2, img3, torch.tensor([1 - label, label]) + + + + +def intensity_normalization(img, mean = None, std = None): + mean = torch.mean(img) + std = torch.std(img) + return (img - mean) / std + +class CustomNormalize(object): + def __init__(self, mean, std): + self.mean = mean + self.std = std + + def __call__(self, img): + return (img - self.mean) / self.std + +transform_train = transforms.Compose([ + transforms.Resize((256, 256)), + transforms.ToTensor(), + transforms.RandomAffine(degrees=5, translate=(0.05, 0.05), scale=(0.95, 1.05)), # Random affine transformations with smaller parameters + transforms.Lambda(intensity_normalization) +]) + +transform_test = transforms.Compose([ + transforms.Resize((256, 256)), + transforms.ToTensor(), + transforms.Lambda(intensity_normalization) +]) + +class Normalize(object): + def __init__(self, mean, std): + self.mean = mean + self.std = std + + def __call__(self, img): + return (img - self.mean) / self.std + + + +def gen_loaders(root_dir = '', batch_size = 96): + loaders = {} + AD_train = CustomDataset(root_dir=root_dir + os.path.join('train', 'AD'), transform=transform_train) + NC_train = CustomDataset(root_dir=root_dir + os.path.join('train', 'NC'), transform=transform_train) + + X = torch.stack([img for img in AD_train + NC_train]) + mean = X.mean() + std = X.std() + normalize = transforms.Compose([ + Normalize(mean, std) + ]) + train_dataset = TripletDataset(AD_train, NC_train, normalize) + + + # Create DataLoaders for the two parts + loaders['train'] = DataLoader(train_dataset, batch_size=batch_size, shuffle=True) + + AD_test = CustomDataset(root_dir=os.path.join('test', 'AD'), transform=transform_test) + NC_test = CustomDataset(root_dir=os.path.join('test', 'NC'), transform=transform_test) + + test_dataset = TripletDataset(AD_test, NC_test, normalize) + + loaders['test'] = DataLoader(test_dataset, batch_size=batch_size, shuffle=False) + return loaders \ No newline at end of file diff --git a/recognition/modules.py b/recognition/modules.py new file mode 100644 index 000000000..ab28c061c --- /dev/null +++ b/recognition/modules.py @@ -0,0 +1,60 @@ + +import torch +import torch.nn as nn +import torchvision.models as models +import torch.nn.functional as F + +class TripletSiameseNetwork(nn.Module): + def __init__(self, pretrained=True): + super(TripletSiameseNetwork, self).__init__() + self.resnet = models.resnet18(pretrained=pretrained) + self.resnet.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False) + self.bn = nn.BatchNorm1d(1000) + self.fc1 = nn.Linear(1000, 128) + + def forward_once(self, x): + output = self.resnet(x) + output = self.bn(output) + output = self.fc1(output) + return output + + def forward(self, anchor, positive, negative): + output_anchor = self.forward_once(anchor) + output_positive = self.forward_once(positive) + output_negative = self.forward_once(negative) + return output_anchor, output_positive, output_negative + +class TripletLoss(nn.Module): + def __init__(self, margin=1.0): + super(TripletLoss, self).__init__() + self.margin = margin + + def forward(self, anchor, positive, negative): + distance_positive = F.pairwise_distance(anchor, positive) + distance_negative = F.pairwise_distance(anchor, negative) + losses = F.relu(distance_positive - distance_negative + self.margin) + return losses.mean() + +class TripletLossWithRegularization(nn.Module): + def __init__(self, model, margin=1.0, lambda_reg=0.0001): + super(TripletLossWithRegularization, self).__init__() + self.model = model + self.margin = margin + self.lambda_reg = lambda_reg # Regularization parameter + + def forward(self, anchor, positive, negative): + distance_positive = F.pairwise_distance(anchor, positive) + distance_negative = F.pairwise_distance(anchor, negative) + triplet_losses = F.relu(distance_positive - distance_negative + self.margin) + triplet_loss = triplet_losses.mean() + + # Compute L2 regularization + l2_reg = None + for param in self.model.parameters(): + if l2_reg is None: + l2_reg = param.norm(2) + else: + l2_reg = l2_reg + param.norm(2) + + loss = triplet_loss + self.lambda_reg * l2_reg + return loss diff --git a/recognition/predict.py b/recognition/predict.py new file mode 100644 index 000000000..936cb25da --- /dev/null +++ b/recognition/predict.py @@ -0,0 +1,26 @@ +from modules import TripletSiameseNetwork +from dataset import gen_loaders +import torch +import pickle + +def predict(input=gen_loaders()['test'], triplet_path = 'trip_model.pth', classifier_path='classifier.pickle'): + device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + trip_model = TripletSiameseNetwork() + trip_model = torch.load(triplet_path) + with open(classifier_path, 'rb') as f: + clf = pickle.load(f) + outputs = None + + for i, (img, _, _, _) in enumerate(input): + img = img.to(device) + with torch.no_grad(): + features = trip_model.forward_once(img) + + if outputs is None: + outputs = features.cpu() + else: + outputs = torch.cat((outputs, features.cpu()), dim=0) + + pred = clf.predict(outputs) + return pred + diff --git a/recognition/train.py b/recognition/train.py new file mode 100644 index 000000000..ae536b1a8 --- /dev/null +++ b/recognition/train.py @@ -0,0 +1,138 @@ +import torch +import torchvision.transforms as transforms +import numpy as np +import matplotlib.pyplot as plt +from dataset import gen_loaders +from modules import TripletSiameseNetwork, TripletLoss, TripletLossWithRegularization +from sklearn.ensemble import RandomForestClassifier +from sklearn.metrics import accuracy_score +import pickle +from itertools import cycle + +loaders = gen_loaders() + +device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') +learning_rate = 0.001 +trip_model = TripletSiameseNetwork() +trip_criterion = TripletLossWithRegularization(margin=1.0) +val_criterion = TripletLoss(margin=1.0) +total_step = len(loaders['train']) +epochs = 20 + +optimizer = torch.optim.SGD(trip_model.parameters(), lr=learning_rate, momentum=0.9, weight_decay=5e-4) + +sched_linear_1 = torch.optim.lr_scheduler.CyclicLR(optimizer, base_lr=0.0001, max_lr=learning_rate, step_size_up=epochs // 2, step_size_down=epochs // 2, mode="triangular", verbose=False) +sched_linear_3 = torch.optim.lr_scheduler.LinearLR(optimizer, start_factor=0.0001/learning_rate, end_factor=0.0001/learning_rate, verbose=False) +scheduler = torch.optim.lr_scheduler.SequentialLR(optimizer, schedulers=[sched_linear_1, sched_linear_3], milestones=[epochs]) + + + +test_iter = cycle(iter(loaders['test'])) +trip_model.to(device) +losses = [] +val_losses = [] + +transform_train = transforms.RandomAffine(degrees=5, translate=(0.05, 0.05), scale=(0.95, 1.05)) + +prev_loss = float('inf') +epochs_without_improvement = 0 +max_epochs_without_improvement = 3 + +for epoch in range(epochs): + print(f'Learning rate: {optimizer.param_groups[0]["lr"]}') + losses.append([]) + val_losses.append([]) + for i, (img1, img2, img3, _) in enumerate(loaders['train']): + size = img1.size(0) + img1, img2, img3 = img1[torch.randperm(size)], img2[torch.randperm(size)], img3[torch.randperm(size)] + img1_trans = transform_train(img1) + img2_trans = transform_train(img2) + img3_trans = transform_train(img3) + img1_trans, img2_trans, img3_trans = img1_trans.to(device), img2_trans.to(device), img3_trans.to(device) + + + out1, out2, out3 = trip_model(img1_trans, img2_trans, img3_trans) + + loss = trip_criterion(out1, out2, out3) + optimizer.zero_grad() + loss.backward() + optimizer.step() + + no_loss = val_criterion(out1, out2, out3) + losses[epoch].append(no_loss.item()) + if (i + 1) % (total_step // 10) == 0: + + with torch.no_grad(): + val_img1, val_img2, val_img3, _ = next(test_iter) + val_img1, val_img2, val_img3 = val_img1.to(device), val_img2.to(device), val_img3.to(device) + val_out1, val_out2, val_out3 = trip_model(val_img1, val_img2, val_img3) + val_loss = val_criterion(val_out1, val_out2, val_out3) + val_losses[epoch].append(val_loss.item()) + + print(f"Epoch [{epoch + 1} / {epochs}], Step [{i + 1} / {total_step}], Loss: {np.mean(losses[epoch])}, Validation Loss: {np.mean(val_losses[epoch])}") + + scheduler.step() + mean_list1 = np.mean(losses, axis=1) + mean_list2 = np.mean(val_losses, axis=1) + + # Plot the means + plt.plot(mean_list1, label='Train Loss', marker='o') + plt.plot(mean_list2, label='Val Loss', marker='o') + + plt.xlabel('Epoch') + plt.ylabel('Mean Loss') + plt.legend() + plt.show() + + if np.mean(losses[epoch]) < prev_loss: + epochs_without_improvement = 0 + prev_loss = np.mean(losses[epoch]) + else: + epochs_without_improvement += 1 + print(f'{epochs_without_improvement} / {max_epochs_without_improvement} before early stop.') + + if epochs_without_improvement >= max_epochs_without_improvement: + print(f'Early stopping after {epoch + 1} epochs.') + break + + + + + +output_dict = {} +label_dict = {} +for stage in ['train', 'test']: + outputs = None + labels = None + for i, (img, _, _, label) in enumerate(loaders[stage]): + img, label = img.to(device), label.to(device) + with torch.no_grad(): + features = trip_model.forward_once(img) + + if outputs is None: + outputs = features.cpu() + labels = label.cpu() + else: + outputs = torch.cat((outputs, features.cpu()), dim=0) + labels = torch.cat((labels, label.cpu()), dim=0) + output_dict[stage] = outputs + label_dict[stage] = labels + + +# Example usage: +clf = RandomForestClassifier(n_estimators=400, min_samples_split=300, max_depth=8, critereon='entropy') +clf.fit(output_dict['train'], label_dict['train']) + +y_pred = clf.predict(output_dict['test']) +y_pred_train = clf.predict(output_dict['train']) + + +# Calculate the accuracy of the classifier +print(f"Test accuracy: {accuracy_score(label_dict['test'], y_pred)}, Train accuracy: {accuracy_score(label_dict['train'], y_pred_train)}") + + +trip_model.save('trip_model.pth') +with open('classifier.pickle', 'wb') as f: + pickle.dump(clf, f) + +print('Saved siamese to trip_model.pth and classifier to classifier.pickle') \ No newline at end of file From 8a21651d01eb61a983ed486eb633661061dd1496 Mon Sep 17 00:00:00 2001 From: FinnRobertson15 <110511080+FinnRobertson15@users.noreply.github.com> Date: Sat, 21 Oct 2023 14:55:46 +1000 Subject: [PATCH 11/14] Add files via upload Results images --- PCA.png | Bin 0 -> 253124 bytes train.png | Bin 0 -> 38332 bytes 2 files changed, 0 insertions(+), 0 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Date: Sat, 21 Oct 2023 14:58:48 +1000 Subject: [PATCH 13/14] Added Readme --- README.md | 68 +++++++++++++++++++++++++++++++++++++++++++------------ 1 file changed, 53 insertions(+), 15 deletions(-) diff --git a/README.md b/README.md index 4a064f841..329d49ac6 100644 --- a/README.md +++ b/README.md @@ -1,15 +1,53 @@ -# Pattern Analysis -Pattern Analysis of various datasets by COMP3710 students at the University of Queensland. - -We create pattern recognition and image processing library for Tensorflow (TF), PyTorch or JAX. - -This library is created and maintained by The University of Queensland [COMP3710](https://my.uq.edu.au/programs-courses/course.html?course_code=comp3710) students. - -The library includes the following implemented in Tensorflow: -* fractals -* recognition problems - -In the recognition folder, you will find many recognition problems solved including: -* OASIS brain segmentation -* Classification -etc. +# Description + +This contains methods to create embeddings via a Triplet Siamese Network, which can then be used to create a classifier for Alzheimer's disease using the ADNI dataset. The model attempts to identify Alzheimer's from MRIs, which would be very helpful for radiology. + +# Algorithm + +The triplet Siamese model works by taking in both a positive and negative input, as well as an anchor. An identical backbone (in this case, resnet) is used on all three inputs to produce an embedded space. The aim of the model is to make the distance between the positive and anchor less than the distance between the negative and the anchor, without knowing which camp the anchor belongs to. The lost function used to achieve this subtracts the negative distance from the positive to ensure a lower loss when closer to positive. This creates a good embedded space to then use to classify inputs. Using this, individual outputs from the embedded space can be inputted into a Random Forest Classifier, which can then be used to identify Alzheimers. + +# Pre-Processing + +Training Images are randomly rotated between -5 and 5 degrees, offset between -5% and 5% and scaled between 95% and 105%. They are then intensity normalized. After that, the mean and standard deviation is found for the entire training set, and both the training and testing sets are normalized using that mean and standard deviation. Finally, the training set is split into two, with 80% being used for the Triplet Siamese Model and the remaining 20% for the classifier. + +# Triplet Siamese Model + +Each epoch, each batch is randomly sorted to have a different triplet each time. The inputs then undergo the rotation, offset and scaling transform from pre-processing to keep the model on it's toes. The loss function is calculated using a tiny amount of L2 regularization, 0.0001. This helps with the generalization of the model. + +The learning rate begins very low at 0.0001, gradually increases to a normal level of low, 0.001, which is reached halfway through training. After the peak is reached, the learning rate decreased at the same rate back to its starting level. + +The optimizer used is a Scholastic Gradient Descent optimizer. When looking into the optimizer I discovered that weight decay and L2 regularization are the same thing, so actually 0.0005 is also used. + +# Classification Model + +The embedded space used for the three inputs in the Triplet Siamese Model is then used inside another model to classify individual inputs. The classification model is a Random Forest model, with 600 estimators, 250 min split and max depth of 12. This allows for more generalization and beats Neural Networks by quite a margin. + +# Dependencies + +Python version: 3.10.12 + +Pytorch version: 2.1.0, Cuda 11.8 + +Numpy version: 1.23.5 + +Matplotlib version: 3.7.1 + +Sklearn version: 1.2.2 + +PIL version: 9.4.0 + +# Inputs and Outputs + +![image](https://raw.githubusercontent.com/FinnRobertson15/PatternAnalysis-2023/topic-recognition/inputs.png) + +Top: 1 (Alzheimer's) + +Bottom: 0 (No Alzheimer's) + +# Results + +![image](https://raw.githubusercontent.com/FinnRobertson15/PatternAnalysis-2023/topic-recognition/PCA.png) + +Triplet loss graph + +![image](https://raw.githubusercontent.com/FinnRobertson15/PatternAnalysis-2023/topic-recognition/train.png) \ No newline at end of file From 61a50611f66b51abec5e1f94caeec0a704824d2f Mon Sep 17 00:00:00 2001 From: FinnRobertson15 <110511080+FinnRobertson15@users.noreply.github.com> Date: Sat, 21 Oct 2023 16:21:37 +1100 Subject: [PATCH 14/14] Update README.md to include accuracy --- README.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 329d49ac6..8d3772272 100644 --- a/README.md +++ b/README.md @@ -50,4 +50,6 @@ Bottom: 0 (No Alzheimer's) Triplet loss graph -![image](https://raw.githubusercontent.com/FinnRobertson15/PatternAnalysis-2023/topic-recognition/train.png) \ No newline at end of file +![image](https://raw.githubusercontent.com/FinnRobertson15/PatternAnalysis-2023/topic-recognition/train.png) + +Classification Accuracy of 65% \ No newline at end of file