VT_ADL_Tumor_No_Tumor.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"accelerator":"GPU","colab":{"name":"VT_ADL_Tumor_No_Tumor.ipynb","provenance":[{"file_id":"1C2xN04tdQo34FCe9cGOlDesT91mCtrWC","timestamp":1623010520112}],"collapsed_sections":[],"machine_shape":"hm","mount_file_id":"1c9c8Koxp1_UAYEk8wIw3RmivOiRZEygO","authorship_tag":"ABX9TyPzDOi1SN4rw5RIqgG3Iu60"},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","metadata":{"id":"gYZjZru8zwRC"},"source":["# VT-ADL\n","From https://github.com/pankajmishra000/VT-ADL.git \n","## Environment"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"bzRS3cwLUiQZ","executionInfo":{"status":"ok","timestamp":1624029950398,"user_tz":-120,"elapsed":554,"user":{"displayName":"émilie Mathian","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghj8h3hShtS9NvuPJyEia0EjyB-YjKDSE5l8HU71w=s64","userId":"16359843418835537267"}},"outputId":"660a750a-3dba-4477-c15f-82127b02084e"},"source":["from google.colab import drive\n","drive.mount('/content/gdrive')\n","import os\n","\n","# your workspace in your drive\n","workspace = 'VTADL_LNEN'\n","\n","try:\n","  os.chdir(os.path.join('/content/gdrive/My Drive/', workspace))\n","except:\n","  os.mkdir(os.path.join('/content/gdrive/My Drive/', workspace))\n","  os.chdir(os.path.join('/content/gdrive/My Drive/', workspace))"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"81n6gxqm_Vaw","executionInfo":{"status":"ok","timestamp":1624029954222,"user_tz":-120,"elapsed":3083,"user":{"displayName":"émilie Mathian","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghj8h3hShtS9NvuPJyEia0EjyB-YjKDSE5l8HU71w=s64","userId":"16359843418835537267"}},"outputId":"bbd23032-5e0f-44fd-be38-7fbd898cee64"},"source":["! pip install einops"],"execution_count":2,"outputs":[{"output_type":"stream","text":["Requirement already satisfied: einops in /usr/local/lib/python3.7/dist-packages (0.3.0)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"uK6aiYTd8Pn9","executionInfo":{"status":"ok","timestamp":1624029954224,"user_tz":-120,"elapsed":12,"user":{"displayName":"émilie Mathian","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghj8h3hShtS9NvuPJyEia0EjyB-YjKDSE5l8HU71w=s64","userId":"16359843418835537267"}},"outputId":"9f45a523-d062-4cd2-84f8-3b174e4ead83"},"source":["os.listdir()"],"execution_count":3,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['TilesNormalVTADL',\n"," 'runs',\n"," 'pytorch_Ssim',\n"," '__pycache__',\n"," 'BT_dataset.py',\n"," 'config.py',\n"," 'mdn1.py',\n"," 'student_transformer.py',\n"," 'test.py',\n"," 'spatial.py',\n"," 'utility_fun.py',\n"," 'model_res18.py',\n"," 'train.py',\n"," 'saved_model_NormalTumor1',\n"," 'VTADL',\n"," 'VT_AE.py',\n"," 'mvtech.py',\n"," 'VT_ADL_Tumor_No_Tumor.ipynb']"]},"metadata":{"tags":[]},"execution_count":3}]},{"cell_type":"code","metadata":{"id":"9fbqoynm9nkP","executionInfo":{"status":"ok","timestamp":1624029956554,"user_tz":-120,"elapsed":2338,"user":{"displayName":"émilie Mathian","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghj8h3hShtS9NvuPJyEia0EjyB-YjKDSE5l8HU71w=s64","userId":"16359843418835537267"}}},"source":["import model_res18 as M"],"execution_count":4,"outputs":[]},{"cell_type":"code","metadata":{"id":"Jl1-kQde-gpb","executionInfo":{"status":"ok","timestamp":1624029956556,"user_tz":-120,"elapsed":16,"user":{"displayName":"émilie Mathian","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghj8h3hShtS9NvuPJyEia0EjyB-YjKDSE5l8HU71w=s64","userId":"16359843418835537267"}}},"source":["import spatial"],"execution_count":5,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"gygiQvWs0CEn"},"source":["## Training"]},{"cell_type":"code","metadata":{"id":"NUsOAPlpr2r6","executionInfo":{"status":"aborted","timestamp":1624036860830,"user_tz":-120,"elapsed":511,"user":{"displayName":"émilie Mathian","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghj8h3hShtS9NvuPJyEia0EjyB-YjKDSE5l8HU71w=s64","userId":"16359843418835537267"}}},"source":["# -*- coding: utf-8 -*-\n","\"\"\"\n","@author: Pankaj Mishra\n","\"\"\"\n","\n","import torch\n","import torch.nn as nn\n","from student_transformer import ViT\n","import model_res18 as M\n","from einops import rearrange\n","import spatial as S\n","from torch.optim.lr_scheduler import StepLR\n","import torch\n","import mvtech\n","import torchvision.utils as utils\n","import matplotlib.pyplot as plt\n","from torch.optim import Adam\n","import torch.nn.functional as F\n","from torch.utils.tensorboard import SummaryWriter\n","from torch.optim.lr_scheduler import StepLR\n","import mdn1 \n","import os\n","import numpy as np\n","import pytorch_Ssim\n","\n","#from VT_AE import VT_AE as ae\n","import argparse\n","\n","#### NETWORK DECLARATION ####\n","# torch.autograd.set_detect_anomaly(True) # this is to check any problem in the network by backtracking\n","\n","class VT_AE(nn.Module):\n","    def __init__(self, image_size = 512,\n","                    patch_size = 64,\n","                    num_classes = 1,\n","                    dim = 512,\n","                    depth = 6,\n","                    heads = 8,\n","                    mlp_dim = 1024,\n","                    train= True,\n","                    pretrained_path = False):\n","\n","        super(VT_AE, self).__init__()\n","        self.vt = ViT(\n","            image_size = image_size,\n","            patch_size = patch_size,\n","            num_classes = num_classes,\n","            dim = dim,\n","            depth = depth,\n","            heads = heads,\n","            mlp_dim = mlp_dim )\n","        \n","     \n","        self.decoder = M.decoder2(8)\n","        # self.G_estimate= mdn1.MDN() # Trained in modular fashion\n","        self.Digcap = S.DigitCaps(in_num_caps=((image_size//patch_size)**2)*8*8, in_dim_caps=8)\n","        self.mask = torch.ones(1, image_size//patch_size, image_size//patch_size).bool().cuda()\n","        self.Train = train\n","        self.pretrained_path = pretrained_path\n","        if self.Train:\n","            if pretrained_path == False:\n","                print(\"\\nInitializing network weights.........\")\n","                initialize_weights(self.vt, self.decoder)\n","           \n","    def forward(self,x):\n","        b = x.size(0)\n","        encoded = self.vt(x, self.mask)\n","        if self.Train:\n","            encoded = add_noise(encoded)\n","        encoded1, vectors = self.Digcap(encoded.view(b,encoded.size(1)*8*8,-1))\n","        recons = self.decoder(encoded1.view(b,-1,8,8))\n","        # pi, mu, sigma = self.G_estimate(encoded)       \n","        # return encoded, pi, sigma, mu, recons\n","            \n","        return encoded, recons\n","\n","# Initialize weight function\n","def initialize_weights(*models):\n","    for model in models:\n","        for module in model.modules():\n","            if isinstance(module, nn.Conv2d) or isinstance(module, nn.Linear):\n","                nn.init.kaiming_normal_(module.weight)\n","                if module.bias is not None:\n","                    module.bias.data.zero_()\n","            elif isinstance(module, nn.BatchNorm2d):\n","                module.weight.data.fill_(1)\n","                module.bias.data.zero_()\n","                \n","##### Adding Noise ############\n","\n","def add_noise(latent, noise_type=\"gaussian\", sd=0.2):\n","    \"\"\"Here we add noise to the latent features concatenated from the 4 autoencoders.\n","    Arguements:\n","    'gaussian' (string): Gaussian-distributed additive noise.\n","    'speckle' (string) : Multiplicative noise using out = image + n*image, where n is uniform noise with specified mean & variance.\n","    'sd' (integer) : standard deviation used for geenrating noise\n","\n","    Input :\n","        latent : numpy array or cuda tensor.\n","\n","    Output:\n","        Array: Noise added input, can be np array or cuda tnesor.\n","    \"\"\"\n","    assert sd >= 0.0\n","    if noise_type == \"gaussian\":\n","        mean = 0.\n","\n","        n = torch.distributions.Normal(torch.tensor([mean]), torch.tensor([sd]))\n","        noise = n.sample(latent.size()).squeeze(-1).cuda()\n","        latent = latent + noise\n","        return latent\n","\n","    if noise_type == \"speckle\":\n","        noise = torch.randn(latent.size()).cuda()\n","        latent = latent + latent * noise\n","        return latent\n","\n","# if __name__ == \"__main__\":\n","#     from torchsummary import summary\n","\n","#     mod = VT_AE().cuda()\n","#     print(mod)\n","#     summary(mod, (3,512,512))\n","\n","\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"SxLPFUxaveP6","executionInfo":{"status":"error","timestamp":1624036861125,"user_tz":-120,"elapsed":6903997,"user":{"displayName":"émilie Mathian","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghj8h3hShtS9NvuPJyEia0EjyB-YjKDSE5l8HU71w=s64","userId":"16359843418835537267"}},"outputId":"048cd69e-7fd3-4652-a08e-4dac4f87ffb8"},"source":[" \n","\n","## Argparse declaration ##\n","\n","# ap = argparse.ArgumentParser()\n","# ap.add_argument(\"-p\", \"--product\", required=True,default = 'hazelnut',help=\"product from the dataset MvTec or BTAD\")\n","# ap.add_argument(\"-e\", \"--epochs\", required=False, default= 400, help=\"Number of epochs to train\")\n","# ap.add_argument(\"-lr\", \"--learning_rate\", required=False, default= 0.0001, help=\"learning rate\")\n","# ap.add_argument(\"-ps\",\"--patch_size\", required=False, default=64, help=\"Patch size of the images\")\n","# ap.add_argument(\"-b\", \"--batch_size\", required=False, default=8, help= \"batch size\")\n","\n","product = 'Normal'\n","epochs = 4000\n","learning_rate = 0.0001\n","patch_size = 64\n","batch_size = 8\n","MHA = 16\n","\n","# args = vars(ap.parse_args())\n","\n","writer = SummaryWriter()\n","\n","prdt = product # args[\"product\"]\n","\n","epoch =epochs # args[\"epochs\"]\n","minloss = 1e10\n","ep =0\n","ssim_loss = pytorch_Ssim.SSIM() # SSIM Loss\n","\n","#Dataset\n","data = mvtech.Mvtec(batch_size,product=prdt)\n","\n","# Model declaration\n","model = VT_AE(patch_size=patch_size,train=True, pretrained_path = True, heads = MHA).cuda()\n","model.load_state_dict(torch.load(f'VTADL/saved_model_lowlr/VT_AE_Mvtech_{prdt}'+'.pt'))\n","G_estimate= mdn1.MDN().cuda()\n","G_estimate.load_state_dict(torch.load(f'VTADL/saved_model_lowlr/G_estimate_Mvtech_{prdt}'+'.pt'))\n","\n","\n","#model.load_state_dict(torch.load(f'/saved_model_NormalTumor1/VT_AE_LNEN_{prdt}'+'.pt'))\n","\n","print('HERE')\n","### put model to train ## \n","#(The two models are trained as a separate module so that it would be easy to use as an independent module in different scenarios)\n","model.train()\n","G_estimate.train()\n","\n","#Optimiser Declaration\n","Optimiser = Adam(list(model.parameters())+list(G_estimate.parameters()), lr=learning_rate, weight_decay=0.0001)\n","\n","scheduler = StepLR(Optimiser, step_size=500, gamma=0.1)\n","\n","############## TRAIN #####################\n","# torch.autograd.set_detect_anomaly(True) #uncomment if you want to track an error\n","Continue = True\n","print('\\nNetwork training started.....')\n","for i in range(epoch):\n","    print('i   ', i)\n","    if Continue:\n","        t_loss = []\n","        # Decay Learning Rate\n","        scheduler.step()\n","        print('Epoch:', i,'LR:', scheduler.get_lr())\n","        for j, m in data.train_loader:\n","            model.zero_grad()\n","            \n","            # vector,pi, mu, sigma, reconstructions = model(j.cuda())\n","            vector, reconstructions = model(j.cuda())\n","            pi, mu, sigma = G_estimate(vector)\n","            \n","            #Loss calculations\n","            loss1 = F.mse_loss(reconstructions,j.cuda(), reduction='mean') #Rec Loss\n","            loss2 = -ssim_loss(j.cuda(), reconstructions) #SSIM loss for structural similarity\n","            loss3 = mdn1.mdn_loss_function(vector,mu,sigma,pi) #MDN loss for gaussian approximation\n","            \n","            print(f' loss3  : {loss3.item()}')\n","            loss = 5*loss1 + 0.5*loss2 + loss3       #Total loss\n","            print(f' tot loss  : {loss}')\n","            t_loss.append(loss.item())   #storing all batch losses to calculate mean epoch loss\n","            \n","            # Tensorboard definitions\n","        \n","            writer.add_scalar('recon-loss', loss1.item(), i)\n","            writer.add_scalar('ssim loss', loss2.item(), i)\n","            writer.add_scalar('Gaussian loss', loss3.item(), i)\n","            writer.add_histogram('Vectors', vector)\n","        \n","            ## Uncomment below to store the distributions of pi, var and mean ##        \n","            writer.add_histogram('Pi', pi)\n","            writer.add_histogram('Variance', sigma)\n","            writer.add_histogram('Mean', mu)\n","\n","            #Optimiser step\n","            loss.backward()\n","            torch.nn.utils.clip_grad_norm(model.parameters(), 0.5)\n","            Optimiser.step()\n","        \n","        # #Tensorboard definitions for the mean epoch values\n","        # writer.add_image('Reconstructed Image',utils.make_grid(reconstructions),i,dataformats = 'CHW')\n","        # writer.add_scalar('Mean Epoch loss', np.mean(t_loss), i)\n","        # print(f'Mean Epoch {i} loss: {np.mean(t_loss)}')\n","        print(f'Min loss epoch: {ep} with min loss: {minloss}')\n","            \n","        # writer.close()\n","        \n","        # Saving the best model\n","        if np.mean(t_loss) <= minloss:\n","            print('np.mean(t_loss) <= minloss  ', np.mean(t_loss) <= minloss)\n","            minloss = np.mean(t_loss)\n","            ep = i\n","            os.makedirs('VTADL/saved_model_lowlr', exist_ok=True)\n","            torch.save(model.state_dict(), f'VTADL/saved_model_lowlr/VT_AE_Mvtech_{prdt}'+'.pt')\n","            torch.save(G_estimate.state_dict(), f'VTADL/saved_model_lowlr/G_estimate_Mvtech_{prdt}'+'.pt')\n","        if i -  ep > 100:\n","            Continue = False\n","            break\n","    else:\n","        break\n","            \n"],"execution_count":7,"outputs":[{"output_type":"stream","text":["['train', 'test']\n","total train images of Normal Normal are: 2415\n","['train', 'test']\n","the good images for test images of Normal Normal is not included in the test anomolous data\n","total test images of Tumor Normal are: 480\n","['train', 'test']\n","total test images of Normal Normal are: 757\n"," --Size of Normal train loader: torch.Size([2415, 3, 512, 512])--\n"," --Size of Normal test anomaly loader: torch.Size([480, 3, 512, 512])--\n"," --Size of Normal test normal loader: torch.Size([757, 3, 512, 512])--\n"," --Total Image in Normal Validation loader: 20--\n","HERE\n","\n","Network training started.....\n","i    0\n","Epoch: 0 LR: [0.0001]\n"],"name":"stdout"},{"output_type":"stream","text":["/usr/local/lib/python3.7/dist-packages/torch/optim/lr_scheduler.py:134: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n","  \"https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\", UserWarning)\n","/usr/local/lib/python3.7/dist-packages/torch/optim/lr_scheduler.py:370: UserWarning: To get the last learning rate computed by the scheduler, please use `get_last_lr()`.\n","  \"please use `get_last_lr()`.\", UserWarning)\n"],"name":"stderr"},{"output_type":"stream","text":[" loss3  : 3092.4853515625\n"," tot loss  : 3092.511962890625\n"],"name":"stdout"},{"output_type":"stream","text":["/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:95: UserWarning: torch.nn.utils.clip_grad_norm is now deprecated in favor of torch.nn.utils.clip_grad_norm_.\n"],"name":"stderr"},{"output_type":"stream","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n"," loss3  : 1128.723876953125\n"," tot loss  : 1128.9207763671875\n"," loss3  : 993.774658203125\n"," tot loss  : 993.8704223632812\n"," loss3  : 1093.3968505859375\n"," tot loss  : 1093.48779296875\n"," loss3  : 1217.698974609375\n"," tot loss  : 1217.8582763671875\n"," loss3  : 993.7186279296875\n"," tot loss  : 993.7880859375\n"," loss3  : 980.7203979492188\n"," tot loss  : 980.8631591796875\n"," loss3  : 981.1780395507812\n"," tot loss  : 981.3377685546875\n"," loss3  : 1009.4786376953125\n"," tot loss  : 1009.6541137695312\n"," loss3  : 1043.0692138671875\n"," tot loss  : 1043.2562255859375\n"," loss3  : 927.7667236328125\n"," tot loss  : 927.9154052734375\n"," loss3  : 878.3583984375\n"," tot loss  : 878.56298828125\n"," loss3  : 983.91064453125\n"," tot loss  : 984.0806274414062\n"," loss3  : 994.0332641601562\n"," tot loss  : 994.1064453125\n"," loss3  : 1043.190673828125\n"," tot loss  : 1043.318603515625\n"," loss3  : 993.0489501953125\n"," tot loss  : 993.1566162109375\n"," loss3  : 1021.1660766601562\n"," tot loss  : 1021.3032836914062\n"," loss3  : 968.2913818359375\n"," tot loss  : 968.369384765625\n"," loss3  : 1409.9310302734375\n"," tot loss  : 1410.0128173828125\n"," loss3  : 1504.977294921875\n"," tot loss  : 1505.107666015625\n"," loss3  : 1319.091552734375\n"," tot loss  : 1319.196044921875\n"," loss3  : 1038.807861328125\n"," tot loss  : 1038.961669921875\n"," loss3  : 1113.1004638671875\n"," tot loss  : 1113.244140625\n"," loss3  : 1050.8822021484375\n"," tot loss  : 1051.024169921875\n"," loss3  : 1197.816162109375\n"," tot loss  : 1197.892578125\n"," loss3  : 1340.7607421875\n"," tot loss  : 1340.8824462890625\n"," loss3  : 1094.8349609375\n"," tot loss  : 1095.029052734375\n"," loss3  : 1073.194091796875\n"," tot loss  : 1073.2547607421875\n"," loss3  : 1239.557861328125\n"," tot loss  : 1239.67138671875\n"," loss3  : 1032.3763427734375\n"," tot loss  : 1032.5428466796875\n"," loss3  : 1218.409912109375\n"," tot loss  : 1218.544189453125\n"," loss3  : 1279.139892578125\n"," tot loss  : 1279.2291259765625\n"," loss3  : 1109.29296875\n"," tot loss  : 1109.502685546875\n"," loss3  : 1149.7808837890625\n"," tot loss  : 1149.9488525390625\n"," loss3  : 1372.56640625\n"," tot loss  : 1372.716796875\n"," loss3  : 1574.032470703125\n"," tot loss  : 1574.191650390625\n"," loss3  : 1695.3250732421875\n"," tot loss  : 1695.52490234375\n"," loss3  : 1198.9896240234375\n"," tot loss  : 1199.1151123046875\n"," loss3  : 1065.2322998046875\n"," tot loss  : 1065.3857421875\n"," loss3  : 3959.640380859375\n"," tot loss  : 3959.774169921875\n"," loss3  : 6298.4169921875\n"," tot loss  : 6298.59130859375\n"," loss3  : 1412.9481201171875\n"," tot loss  : 1413.0806884765625\n"," loss3  : 1226.1517333984375\n"," tot loss  : 1226.28173828125\n"," loss3  : 1151.746826171875\n"," tot loss  : 1151.8197021484375\n"," loss3  : 1332.121826171875\n"," tot loss  : 1332.2652587890625\n"," loss3  : 1261.4437255859375\n"," tot loss  : 1261.6011962890625\n"," loss3  : 1089.0\n"," tot loss  : 1089.208740234375\n"," loss3  : 1210.635009765625\n"," tot loss  : 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1161.0008544921875\n"," loss3  : 1090.837158203125\n"," tot loss  : 1091.0677490234375\n"," loss3  : 1076.2313232421875\n"," tot loss  : 1076.4459228515625\n"," loss3  : 1190.51416015625\n"," tot loss  : 1190.7652587890625\n"," loss3  : 1158.13720703125\n"," tot loss  : 1158.374267578125\n"," loss3  : 1045.83740234375\n"," tot loss  : 1046.108642578125\n"," loss3  : 1058.503662109375\n"," tot loss  : 1058.6551513671875\n"," loss3  : 1246.325927734375\n"," tot loss  : 1246.579345703125\n"," loss3  : 1309.6881103515625\n"," tot loss  : 1309.931640625\n"," loss3  : 1085.51123046875\n"," tot loss  : 1085.6473388671875\n"," loss3  : 1034.8004150390625\n"," tot loss  : 1035.024658203125\n"," loss3  : 1069.3851318359375\n"," tot loss  : 1069.6103515625\n"," loss3  : 1070.1383056640625\n"," tot loss  : 1070.3961181640625\n"," loss3  : 937.75244140625\n"," tot loss  : 937.9514770507812\n"," loss3  : 1020.295166015625\n"," tot loss  : 1020.4723510742188\n"," loss3  : 1226.37158203125\n"," tot loss  : 1226.58154296875\n"," loss3  : 1312.78564453125\n"," tot loss  : 1312.903564453125\n"," loss3  : 1144.5302734375\n"," tot loss  : 1144.7049560546875\n"," loss3  : 1073.070068359375\n"," tot loss  : 1073.271728515625\n"," loss3  : 1294.506103515625\n"," tot loss  : 1294.6396484375\n"," loss3  : 1281.494384765625\n"," tot loss  : 1281.634521484375\n"," loss3  : 1146.36767578125\n"," tot loss  : 1146.5645751953125\n"," loss3  : 998.658203125\n"," tot loss  : 998.8366088867188\n"," loss3  : 2544.48046875\n"," tot loss  : 2544.717041015625\n"," loss3  : 1704.3831787109375\n"," tot loss  : 1704.5958251953125\n"," loss3  : 2165.74853515625\n"," tot loss  : 2165.936767578125\n"," loss3  : 2408.749267578125\n"," tot loss  : 2408.997314453125\n"," loss3  : 2115.643798828125\n"," tot loss  : 2115.897216796875\n"," loss3  : 1411.8404541015625\n"," tot loss  : 1412.103271484375\n"," loss3  : 1227.6336669921875\n"," tot loss  : 1227.8397216796875\n"," loss3  : 4300.34619140625\n"," tot loss  : 4300.58642578125\n"," loss3  : 5900.9853515625\n"," tot loss  : 5901.22119140625\n"," loss3  : 2544.8330078125\n"," tot loss  : 2545.065673828125\n"," loss3  : 2141.2080078125\n"," tot loss  : 2141.414794921875\n"," loss3  : 1553.699462890625\n"," tot loss  : 1553.8621826171875\n"," loss3  : 1348.474365234375\n"," tot loss  : 1348.811279296875\n"," loss3  : 1292.864501953125\n"," tot loss  : 1293.134765625\n"," loss3  : 1430.2470703125\n"," tot loss  : nan\n"," loss3  : nan\n"," tot loss  : nan\n"],"name":"stdout"},{"output_type":"error","ename":"ValueError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)","\u001b[0;32m<ipython-input-7-872e769be632>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     84\u001b[0m            \u001b[0mwriter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_scalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'ssim loss'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloss2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     85\u001b[0m            \u001b[0mwriter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_scalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Gaussian loss'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloss3\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 86\u001b[0;31m            \u001b[0mwriter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_histogram\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Vectors'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvector\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     87\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     88\u001b[0m            \u001b[0;31m## Uncomment below to store the distributions of pi, var and mean ##\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/python3.7/dist-packages/torch/utils/tensorboard/writer.py\u001b[0m in \u001b[0;36madd_histogram\u001b[0;34m(self, tag, values, global_step, bins, walltime, max_bins)\u001b[0m\n\u001b[1;32m    427\u001b[0m             \u001b[0mbins\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdefault_bins\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    428\u001b[0m         self._get_file_writer().add_summary(\n\u001b[0;32m--> 429\u001b[0;31m             histogram(tag, values, bins, max_bins=max_bins), global_step, walltime)\n\u001b[0m\u001b[1;32m    430\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    431\u001b[0m     def add_histogram_raw(self, tag, min, max, num, sum, sum_squares,\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/utils/tensorboard/summary.py\u001b[0m in \u001b[0;36mhistogram\u001b[0;34m(name, values, bins, max_bins)\u001b[0m\n\u001b[1;32m    298\u001b[0m     \"\"\"\n\u001b[1;32m    299\u001b[0m     \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmake_np\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 300\u001b[0;31m     \u001b[0mhist\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmake_histogram\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbins\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_bins\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    301\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mSummary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mSummary\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mValue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtag\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhisto\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhist\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    302\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/utils/tensorboard/summary.py\u001b[0m in \u001b[0;36mmake_histogram\u001b[0;34m(values, bins, max_bins)\u001b[0m\n\u001b[1;32m    336\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    337\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcounts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mlimits\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[0;32m--> 338\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'The histogram is empty, please file a bug report.'\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    339\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    340\u001b[0m     \u001b[0msum_sq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mValueError\u001b[0m: The histogram is empty, please file a bug report."]}]},{"cell_type":"code","metadata":{"id":"M85oRKoIMmQ4","executionInfo":{"status":"aborted","timestamp":1624036860825,"user_tz":-120,"elapsed":513,"user":{"displayName":"émilie Mathian","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghj8h3hShtS9NvuPJyEia0EjyB-YjKDSE5l8HU71w=s64","userId":"16359843418835537267"}}},"source":["import os\n","import shutil\n","import random\n","l_tumor = os.listdir('TilesNormalVTADL/Normal/test/Tumor')\n","for ele in l_tumor:\n","    if random.random() < 0.5:\n","        os.remove(os.path.join('TilesNormalVTADL/Normal/test/Tumor', ele))"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1zL9mWGFb6Br","executionInfo":{"elapsed":209,"status":"ok","timestamp":1623857280172,"user":{"displayName":"émilie Mathian","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghj8h3hShtS9NvuPJyEia0EjyB-YjKDSE5l8HU71w=s64","userId":"16359843418835537267"},"user_tz":-120},"outputId":"45b44212-592d-44be-88fa-32d89ba9dfb0"},"source":["print(len(os.listdir('TilesNormalVTADL/Normal/test/Tumor')))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["480\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"-gudxnXKcHYN"},"source":["import os\n","import shutil\n","l_tumor = os.listdir('TilesNormalVTADL/Normal/test/Normal')\n","for ele in l_tumor:\n","    if ele.find('TNE1084') != -1:\n","        os.remove(os.path.join('TilesNormalVTADL/Normal/test/Normal', ele))\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QqLZIUX-U1fH","executionInfo":{"elapsed":209,"status":"ok","timestamp":1623855494556,"user":{"displayName":"émilie Mathian","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghj8h3hShtS9NvuPJyEia0EjyB-YjKDSE5l8HU71w=s64","userId":"16359843418835537267"},"user_tz":-120},"outputId":"11f529f3-22af-4f5a-ebff-c2775822b54d"},"source":["len(os.listdir('TilesNormalVTADL/Normal/test/Normal')\n",") + len(os.listdir('TilesNormalVTADL/Normal/test/Tumor')\n",")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["757"]},"metadata":{"tags":[]},"execution_count":8}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ieljNXi5U-sk","executionInfo":{"elapsed":1425,"status":"ok","timestamp":1623855469521,"user":{"displayName":"émilie Mathian","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghj8h3hShtS9NvuPJyEia0EjyB-YjKDSE5l8HU71w=s64","userId":"16359843418835537267"},"user_tz":-120},"outputId":"083303b3-97f4-4dea-927e-c7275dae1fb4"},"source":["len(os.listdir('TilesNormalVTADL/Normal/train/Normal')\n",") "],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["3458"]},"metadata":{"tags":[]},"execution_count":6}]},{"cell_type":"code","metadata":{"id":"QjIqx-KeBcRy"},"source":["import os\n","import shutil\n","l_normal = os.listdir('TilesNormalVTADL/Normal/train/Normal')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5YfARKFdBeta","executionInfo":{"elapsed":5,"status":"ok","timestamp":1623854534166,"user":{"displayName":"émilie Mathian","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghj8h3hShtS9NvuPJyEia0EjyB-YjKDSE5l8HU71w=s64","userId":"16359843418835537267"},"user_tz":-120},"outputId":"8d45e2f1-6940-4a3d-97a1-e9a2d72a8602"},"source":["print(len(l_normal))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["5115\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"id":"r5hmqSn-PawQ","executionInfo":{"elapsed":204,"status":"ok","timestamp":1623854007790,"user":{"displayName":"émilie Mathian","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghj8h3hShtS9NvuPJyEia0EjyB-YjKDSE5l8HU71w=s64","userId":"16359843418835537267"},"user_tz":-120},"outputId":"17973926-14b4-426d-9632-cd5853fbf320"},"source":["l_normal[0]"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"},"text/plain":["'NL-97_31417_9241.jpg'"]},"metadata":{"tags":[]},"execution_count":6}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"id":"k0qaghR_Pc1A","executionInfo":{"elapsed":216,"status":"ok","timestamp":1623854031173,"user":{"displayName":"émilie Mathian","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghj8h3hShtS9NvuPJyEia0EjyB-YjKDSE5l8HU71w=s64","userId":"16359843418835537267"},"user_tz":-120},"outputId":"a15c9034-d886-43d9-ab69-48b751f9aa5a"},"source":["l_normal[0].split('_')[0]"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"},"text/plain":["'NL-97'"]},"metadata":{"tags":[]},"execution_count":8}]},{"cell_type":"code","metadata":{"id":"ZLt4HDkNPk2p"},"source":["import os\n","import shutil\n","import random\n","l_normal = os.listdir('TilesNormalVTADL/Normal/train/Normal')\n","sample_normal = []\n","for ele in l_normal:\n","    sc = ele.split('_')[0]\n","    if sc not in sample_normal:\n","        sample_normal.append(sc)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"ih5kWx2VPlef"},"source":["random.shuffle(sample_normal)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Qqv7jhC4Pllk"},"source":["import os\n","import shutil\n","import random\n","l_normal = os.listdir('TilesNormalVTADL/Normal/train/Normal')\n","for ele in l_normal:\n","    #sc = ele.split('_')[0]\n","    if random.random()<.3:\n","        os.remove(os.path.join('TilesNormalVTADL/Normal/train/Normal', ele))\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"viYScuChPlrB","executionInfo":{"elapsed":207,"status":"ok","timestamp":1623857753866,"user":{"displayName":"émilie Mathian","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghj8h3hShtS9NvuPJyEia0EjyB-YjKDSE5l8HU71w=s64","userId":"16359843418835537267"},"user_tz":-120},"outputId":"e37bce67-0cad-49db-e2ee-322ca327ec9f"},"source":["len(os.listdir('TilesNormalVTADL/Normal/train/Normal'))"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["2415"]},"metadata":{"tags":[]},"execution_count":5}]},{"cell_type":"code","metadata":{"id":"0l9ivvviPluw"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"sFIjv6WwPly_"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"UBeg2Mq-Pl2w"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"wWc1x9d5LwyT"},"source":["### Comment:\n","- I decrease the default learning rate by a factor 10 to limite the instability."]},{"cell_type":"markdown","metadata":{"id":"1d7JW9ayL0Ji"},"source":["## Test\n"]},{"cell_type":"markdown","metadata":{"id":"hZGVsiLz0erE"},"source":["\n","### Get attention maps"]},{"cell_type":"code","metadata":{"id":"nj0b3rmhEjYl"},"source":["# -*- coding: utf-8 -*-\n","\"\"\"\n","@author: Pankaj Mishra\n","\"\"\"\n","\n","import torch\n","import mvtech\n","import torch.nn.functional as F\n","import os\n","import numpy as np\n","import pytorch_Ssim\n","from einops import rearrange\n","from sklearn.metrics import roc_auc_score\n","from sklearn.metrics import roc_curve, auc\n","from sklearn.metrics import precision_recall_curve\n","import mdn1\n","from VT_AE import VT_AE as ae\n","from utility_fun import *\n","\n","prdt = \"front\"\n","patch_size = 64\n","\n","ssim_loss = pytorch_Ssim.SSIM() # SSIM Loss\n","\n","#Dataset\n","data = mvtech.Mvtec(1,product=prdt)\n","\n","# Model declaration\n","model = ae(train=False).cuda()\n","G_estimate= mdn1.MDN().cuda()\n","\n","# Loading weights\n","model.load_state_dict(torch.load(f'VTADL/saved_model_lowlr/VT_AE_Mvtech_{prdt}'+'.pt'))\n","G_estimate.load_state_dict(torch.load(f'VTADL/saved_model_lowlr/G_estimate_Mvtech_{prdt}'+'.pt'))\n","\n","#put model to eval\n","model.eval()\n","G_estimate.eval()\n","\n","\n","#### testing #####\n","loader = [data.train_loader,data.test_norm_loader,data.test_anom_loader]\n","\n","t_loss_norm =[]\n","t_loss_anom =[]\n","\n","def Thresholding(data_load = loader[1:], upsample = 1, thres_type = 0, fpr_thres = 0.3):\n","    '''\n","    Parameters\n","    ----------\n","    data : TYPE, optional\n","        DESCRIPTION. The default is data.train_loader.\n","    upsample : INT, optional\n","        DESCRIPTION. 0 - NearestUpsample2d; 1- BilinearUpsampling.\n","    thres_type : INT, optional\n","        DESCRIPTION. 0 - 30% of fpr reached; 1 - thresholding using best F1 score\n","    fpr_thres : FLOAT, Optional\n","        DESCRIPTION. False Positive Rate threshold value. Default is 0.3\n","\n","    Returns\n","    -------\n","    Threshold: Threshold value\n","\n","    '''\n","    norm_loss_t = []\n","    normalised_score_t = []\n","    mask_score_t = []\n","\n","    for data in data_load:\n","        for i, j in data:\n","            vector, reconstructions = model(i.cuda())\n","            pi, mu, sigma = G_estimate(vector)\n","            \n","            #Loss calculations\n","            loss1 = F.mse_loss(reconstructions,i.cuda(), reduction='mean') #Rec Loss\n","            loss2 = -ssim_loss(i.cuda(), reconstructions) #SSIM loss for structural similarity\n","            loss3 = mdn1.mdn_loss_function(vector,mu,sigma,pi, test= True) #MDN loss for gaussian approximation\n","            loss = loss1 + loss2 + loss3.sum()       #Total loss\n","            norm_loss_t.append(loss3.detach().cpu().numpy())\n","                \n","            if upsample==0 :\n","                #Mask patch\n","                mask_patch = rearrange(j.squeeze(0).squeeze(0), '(h p1) (w p2) -> (h w) p1 p2', p1 = patch_size, p2 = patch_size)\n","                mask_patch_score = Binarization(mask_patch.sum(1).sum(1),0.)\n","                mask_score_t.append(mask_patch_score) # Storing all masks\n","                norm_score = norm_loss_t[-1]\n","                normalised_score_t.append(norm_score)# Storing all patch scores\n","            elif upsample == 1:\n","                mask_score_t.append(j.squeeze(0).squeeze(0).cpu().numpy()) # Storing all masks\n","                m = torch.nn.UpsamplingBilinear2d((512,512))\n","                norm_score = norm_loss_t[-1].reshape(-1,1,512//patch_size,512//patch_size)\n","                score_map = m(torch.tensor(norm_score))\n","                score_map = Filter(score_map , type =0) # add normalization here for the testing\n","                normalised_score_t.append(score_map) # Storing all score maps               \n","                \n","                \n","    scores = np.asarray(normalised_score_t).flatten()\n","    masks = np.asarray(mask_score_t).flatten()\n","    \n","    if thres_type == 0 :\n","        fpr, tpr, _ = roc_curve(masks, scores)\n","        fp3 = np.where(fpr<=fpr_thres)\n","        threshold = _[fp3[-1][-1]]\n","    elif thres_type == 1:\n","        precision, recall, thresholds = precision_recall_curve(masks, scores)\n","        a = 2 * precision * recall\n","        b = precision + recall\n","        f1 = np.divide(a, b, out=np.zeros_like(a), where=b != 0)\n","        threshold = thresholds[np.argmax(f1)] \n","    return threshold\n","    \n","\n","def Patch_Overlap_Score(data_load = loader[1:], threshold = 0, upsample =1):\n","    \n","    norm_loss_t = []\n","    normalised_score_t = []\n","    mask_score_t = []\n","    loss1_tn = []\n","    loss2_tn = []\n","    loss3_tn = []\n","    loss1_ta = []\n","    loss2_ta = []\n","    loss3_ta = []\n","    \n","    score_tn = []\n","    score_ta = []\n","    \n","\n","    for n,data in enumerate(data_load):\n","        total_loss_all = []\n","        for c,(i, j) in enumerate(data):\n","            vector, reconstructions = model(i.cuda())\n","            pi, mu, sigma = G_estimate(vector)\n","           \n","            #Loss calculations\n","            loss1 = F.mse_loss(reconstructions,i.cuda(), reduction='mean') #Rec Loss\n","            loss2 = -ssim_loss(i.cuda(), reconstructions) #SSIM loss for structural similarity\n","            loss3 = mdn1.mdn_loss_function(vector,mu,sigma,pi, test= True) #MDN loss for gaussian approximation\n","            loss = loss1 -loss2 + loss3.max()       #Total loss\n","            norm_loss_t.append(loss3.detach().cpu().numpy())\n","            total_loss_all.append(loss.detach().cpu().numpy())\n","            \n","            if n == 0 :\n","                loss1_tn.append(loss1.detach().cpu().numpy())\n","                loss2_tn.append(loss2.detach().cpu().numpy())\n","                loss3_tn.append(loss3.sum().detach().cpu().numpy())\n","            if n == 1:\n","                loss1_ta.append(loss1.detach().cpu().numpy())\n","                loss2_ta.append(loss2.detach().cpu().numpy())\n","                loss3_ta.append(loss3.sum().detach().cpu().numpy())\n","                \n","            if upsample==0 :\n","                #Mask patch\n","                mask_patch = rearrange(j.squeeze(0).squeeze(0), '(h p1) (w p2) -> (h w) p1 p2', p1 = patch_size, p2 = patch_size)\n","                mask_patch_score = Binarization(mask_patch.sum(1).sum(1),0.)\n","                mask_score_t.append(mask_patch_score) # Storing all masks\n","                norm_score = Binarization(norm_loss_t[-1], threshold)\n","                m = torch.nn.UpsamplingNearest2d((512,512))\n","                score_map = m(torch.tensor(norm_score.reshape(-1,1,512//patch_size,512//patch_size)))\n","               \n","                \n","                normalised_score_t.append(norm_score)# Storing all patch scores\n","            elif upsample == 1:\n","                mask_score_t.append(j.squeeze(0).squeeze(0).cpu().numpy()) # Storing all masks\n","                \n","                m = torch.nn.UpsamplingBilinear2d((512,512))\n","                norm_score = norm_loss_t[-1].reshape(-1,1,512//patch_size,512//patch_size)\n","                score_map = m(torch.tensor(norm_score))\n","                score_map = Filter(score_map , type =0) \n","\n","                   \n","                normalised_score_t.append(score_map) # Storing all score maps\n","                \n","            ## Plotting\n","            if c%5 == 0:\n","                plot(i,j,score_map[0][0])\n","            if n == 0:\n","                score_tn.append(score_map.max())\n","            if n ==1:\n","                score_ta.append(score_map.max())\n","                \n","                \n","        if n == 0 :\n","            t_loss_all_normal = total_loss_all\n","        if n == 1:\n","            t_loss_all_anomaly = total_loss_all\n","        \n","    ## PRO Score            \n","    scores = np.asarray(normalised_score_t).flatten()\n","    masks = np.asarray(mask_score_t).flatten()\n","    PRO_score = roc_auc_score(masks, scores)\n","    \n","    ## Image Anomaly Classification Score (AUC)\n","    roc_data = np.concatenate((t_loss_all_normal, t_loss_all_anomaly))\n","    roc_targets = np.concatenate((np.zeros(len(t_loss_all_normal)), np.ones(len(t_loss_all_anomaly))))\n","    AUC_Score_total = roc_auc_score(roc_targets, roc_data)\n","    \n","    # AUC Precision Recall Curve\n","    precision, recall, thres = precision_recall_curve(roc_targets, roc_data)\n","    AUC_PR = auc(recall, precision)\n","\n","    \n","    return PRO_score, AUC_Score_total, AUC_PR\n","\n","# if __name__==\"__main__\":\n","    \n","thres = Thresholding()\n","PRO, AUC, AUC_PR = Patch_Overlap_Score()\n","\n","print(f'PRO Score: {PRO} \\nAUC Total: {AUC} \\nPR_AUC Total: {AUC_PR}')\n","\n"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"HiD61uuK0pND"},"source":["### Adaptation of the inference module without masks"]},{"cell_type":"code","metadata":{"id":"KHr-sQUwNQpO"},"source":["# -*- coding: utf-8 -*-\n","\"\"\"\n","@author: Pankaj Mishra\n","\"\"\"\n","\n","import torch\n","import mvtech\n","import torch.nn.functional as F\n","import os\n","import numpy as np\n","import pytorch_Ssim\n","from einops import rearrange\n","from sklearn.metrics import roc_auc_score\n","from sklearn.metrics import roc_curve, auc\n","from sklearn.metrics import precision_recall_curve\n","import mdn1\n","from VT_AE import VT_AE as ae\n","from utility_fun import *\n","\n","prdt = \"front\"\n","patch_size = 64\n","\n","ssim_loss = pytorch_Ssim.SSIM() # SSIM Loss\n","\n","#Dataset\n","data = mvtech.Mvtec(1,product=prdt)\n","\n","# Model declaration\n","model = ae(train=False).cuda()\n","G_estimate= mdn1.MDN().cuda()\n","\n","# Loading weights\n","model.load_state_dict(torch.load(f'VTADL/saved_model_lowlr/VT_AE_Mvtech_{prdt}'+'.pt'))\n","G_estimate.load_state_dict(torch.load(f'VTADL/saved_model_lowlr/G_estimate_Mvtech_{prdt}'+'.pt'))\n","\n","#put model to eval\n","model.eval()\n","G_estimate.eval()\n","\n","\n","#### testing #####\n","loader = [data.train_loader,data.test_norm_loader,data.test_anom_loader]\n","\n","t_loss_norm =[]\n","t_loss_anom =[]\n","\n","def Thresholding(data_load = loader[1:], upsample = 1, thres_type = 0, fpr_thres = 0.3):\n","    '''\n","    Parameters\n","    ----------\n","    data : TYPE, optional\n","        DESCRIPTION. The default is data.train_loader.\n","    upsample : INT, optional\n","        DESCRIPTION. 0 - NearestUpsample2d; 1- BilinearUpsampling.\n","    thres_type : INT, optional\n","        DESCRIPTION. 0 - 30% of fpr reached; 1 - thresholding using best F1 score\n","    fpr_thres : FLOAT, Optional\n","        DESCRIPTION. False Positive Rate threshold value. Default is 0.3\n","\n","    Returns\n","    -------\n","    Threshold: Threshold value\n","\n","    '''\n","    norm_loss_t = []\n","    normalised_score_t = []\n","    mask_score_t = []\n","    loss1_t = []\n","    loss2_t = []\n","    for i, j in data_load:\n","        #print('i.size(),j.size()  ',i.size(),j.size())\n","        vector, reconstructions = model(i.cuda())\n","        pi, mu, sigma = G_estimate(vector)\n","        \n","        #Loss calculations\n","        loss1 = F.mse_loss(reconstructions,i.cuda(), reduction='mean') #Rec Loss~\n","        loss2 = -ssim_loss(i.cuda(), reconstructions) #SSIM loss for structural similarity\n","        loss3 = mdn1.mdn_loss_function(vector,mu,sigma,pi, test= True) #MDN loss for gaussian approximation\n","        loss = loss1 + loss2 + loss3.sum()       #Total loss\n","        norm_loss_t.append(loss3.sum().detach().cpu().numpy())\n","        loss1_t.append(loss1.detach().cpu().numpy())\n","        loss2_t.append(loss2.detach().cpu().numpy())\n","        if upsample==0 :\n","            norm_score = norm_loss_t[-1]\n","            normalised_score_t.append(norm_score)# Storing all patch scores\n","        elif upsample == 1:\n","            mask_score_t.append(j.squeeze(0).squeeze(0).cpu().numpy()) # Storing all masks\n","            m = torch.nn.UpsamplingBilinear2d((512,512))\n","            norm_score = norm_loss_t[-1].reshape(-1,1,512//patch_size,512//patch_size)\n","            score_map = m(torch.tensor(norm_score))\n","            score_map = Filter(score_map , type =0) # add normalization here for the testing\n","            normalised_score_t.append(score_map) # Storing all score maps               \n","            \n","                \n","    scores = np.asarray(normalised_score_t).flatten()\n","    loss1 =  np.asarray(loss1_t).flatten()\n","    loss2 =  np.asarray(loss2_t).flatten()\n","    masks = np.asarray(mask_score_t).flatten()\n","\n","    return scores, loss1, loss2\n","\n","    \n","scores_n, loss1_n, loss2_n = Thresholding(data_load = loader[1], upsample =  0)\n","scores_d, loss1_d, loss2_d = Thresholding(data_load = loader[2], upsample =  0)\n"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"7zRtIFtT0_ei"},"source":["#### Losses distribution"]},{"cell_type":"code","metadata":{"id":"FXle7vPdU1JU"},"source":["import pandas as pd\n","ValDf =  pd.DataFrame({ 'Type' : ['0'] * len(list(scores_n)) + ['1'] * len(list(scores_d)),\n","                        'Scores_l3': list(scores_n) + list(scores_d),\n","                        'Scores_l1': list(loss1_n) + list(loss1_d),\n","                       'Scores_l2': list(loss2_n) + list(loss2_d)\n","\n","                       },)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"jp15NXppZvYe"},"source":["ValDf"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"XvV3nL8JbARJ"},"source":["import seaborn as sns\n","sns.displot(data = ValDf, x = 'Scores_l3', hue = 'Type')\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"-Csk-ZMJbPxS"},"source":["sns.displot(data = ValDf, x = 'Scores_l2', hue = 'Type')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"KiikL2CBbapl"},"source":["sns.displot(data = ValDf, x = 'Scores_l1', hue = 'Type')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"imkrZmqffAkf"},"source":["ValDf['SumLoss']  = ValDf.iloc[:,1:].sum(axis=1)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"QTboaf6SfovL"},"source":["sns.displot(data = ValDf, x = 'SumLoss', hue = 'Type')"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"wvG16o4s1Qtj"},"source":["#### ROC curve\n","The ROC curve has been calculated acoording to the Gaussian mixture model density network loss. "]},{"cell_type":"code","metadata":{"id":"9A4Ww_mjjDcS"},"source":["ValDf = ValDf.sort_values(by='Scores_l3', ascending=False)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"sJGYfbnKjQLQ"},"source":["ValDf['Type'] = ValDf['Type'].astype('int32')\n","#count all classes\n","class_count = ValDf['Type'].value_counts()\n","pos_count = class_count[1]\n","neg_count = class_count[0]\n","pos_count\n","\n","#store results here\n","coords = [(0,0)]\n","#FPR as horizontal x axis    \n","fp=0\n","#TPR as vertical y axis\n","tp=0\n","\n","#for each truthful prediction\n","for truth in ValDf['Type']:\n","    if truth == 1:\n","        tp +=1\n","    else:\n","        fp +=1\n","    #update result\n","    coords.append((fp, tp))\n","#get tally as separate lists\n","fp, tp = map(list, zip(*coords))\n","#calculate tpr and fpr\n","tpr = tp/pos_count\n","fpr = fp/neg_count\n","# print(fpr)\n","# print(tpr)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"z2gWiC7aj0JC"},"source":["plt.plot(fpr, tpr)\n","plt.plot([0, 1], [0, 1], 'r-')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"XWIZFNoZkTgA"},"source":["from sklearn import metrics\n","Fpr, Tpr, thresholds = metrics.roc_curve(ValDf['Type'], ValDf['Scores_l3'], pos_label=1)\n","metrics.auc(Fpr, Tpr)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"mX90EKrs4MGD"},"source":["## (Git)"]},{"cell_type":"code","metadata":{"id":"ttgQNf5DAG-V"},"source":["! rm -r .git"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"kJKfblyl-m-t"},"source":["! git config --global user.email \"mathian.emilie@gmail.com\"\n","! git config --global user.name \"emathian\"\n","! git init\n","! git add README.md\n","! git commit -m \"first commit\"\n","! git branch -M main\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"908TP6EUCsDZ"},"source":["! git remote add origin https://emathian:EmyAury9490@github.com/emathian/AnomalyDetectionHw8.git"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"0zfR5nOP-5KZ"},"source":["! git push -u origin main"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"fKwljulX3tfj"},"source":["! git add --all"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Eakd7bzO4LLa"},"source":["! git commit -am \"ball baerings models\""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"pdz71nrLIdXS"},"source":["! git branch --set-upstream-to=origin/main"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"VV9F0tXWIhTY"},"source":["! git commit -am 'ap'"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"cscZR01P50pU"},"source":["! git push https://emathian:EmyAury9490@github.com/emathian/AnomalyDetectionHw8.git"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"F6a3GheR9xS1"},"source":["! git branch"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"1p7oEfH7I9jV"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"3ATY-FsvPij8"},"source":["bb"],"execution_count":null,"outputs":[]}]}