{"cells":[{"metadata":{"trusted":true},"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport pathlib, sys, os, random, time\nimport numba, cv2, gc\n\nimport matplotlib.pyplot as plt\n%matplotlib inline\n\nimport warnings\nwarnings.filterwarnings('ignore')\n\nfrom tqdm.notebook import tqdm","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import rasterio\nfrom rasterio.windows import Window","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.utils.data as D\n\nimport torchvision\nfrom torchvision import transforms as T","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import sys\nsys.path.append(\"../input/segmodelspytorch013/seg.pytorch\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"!mkdir -p /root/.cache/torch/hub/checkpoints/\n# !cp ../input/pytorch-pretrained-models/resnet34-333f7ec4.pth /root/.cache/torch/hub/checkpoints/\n!cp ../input/gen-efficientnet-pretrained/tf_efficientnet_b3_ns-9d44bf68.pth /root/.cache/torch/hub/checkpoints/","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"import segmentation_models_pytorch as smp\n\nmodel = smp.Unet(\n    encoder_name=\"timm-efficientnet-b3\",        # choose encoder, e.g. mobilenet_v2 or efficientnet-b7\n    encoder_weights=\"noisy-student\",     # use `imagenet` pretreined weights for encoder initialization\n    in_channels=3,                  # model input channels (1 for grayscale images, 3 for RGB, etc.)\n    classes=1,                      # model output channels (number of classes in your dataset)\n)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(model)","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}