{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\n        \nimport torch\nimport torch.nn as nn\nfrom torch.utils import data\nfrom torchvision.models import vgg19\nfrom torchvision import transforms\nfrom torchvision import datasets\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport cv2\n\n# You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"transform = transforms.Compose([\n            transforms.Resize((224, 224)), \n            transforms.ToTensor(),\n            transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])\n\n\n\ndataset = datasets.ImageFolder(root=\"../input/siim-isic-melanoma-classification/jpeg\", transform=transform)\n\n# define the dataloader to load that single image\ndataloader = data.DataLoader(dataset=dataset, shuffle=False, batch_size=1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"vgg = torch.load(\"../input/vgg16modelpath/vgg16_model.pth\")\nvgg.eval()\n\n# get the image from the dataloader\nimg, _ = next(iter(dataloader))\n\n# get the most likely prediction of the model\npred = vgg(img).argmax(dim=1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"\n\n# pull the gradients out of the model\ngradients = vgg.get_activations_gradient()\nprint(gradients)\n\n# pool the gradients across the channels\npooled_gradients = torch.mean(gradients, dim=[0, 2, 3])\n\n# get the activations of the last convolutional layer\nactivations = vgg.get_activations(img).detach()\n\n# weight the channels by corresponding gradients\nfor i in range(512):\n    activations[:, i, :, :] *= pooled_gradients[i]\n    \n# average the channels of the activations\nheatmap = torch.mean(activations, dim=1).squeeze()\n\n# relu on top of the heatmap\n# expression (2) in https://arxiv.org/pdf/1610.02391.pdf\nheatmap = np.maximum(heatmap, 0)\n\n# normalize the heatmap\nheatmap /= torch.max(heatmap)\n\n# draw the heatmap\nplt.matshow(heatmap.squeeze())","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":4}