{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.9","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":24800,"databundleVersionId":1831594,"sourceType":"competition"},{"sourceId":1923646,"sourceType":"datasetVersion","datasetId":1147272}],"dockerImageVersionId":30056,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"from matplotlib import pyplot as plt\nimport pandas as pd\nimport os\nimport torchvision.transforms as tr\nimport numpy as np\nimport torch\nfrom torch import nn\nfrom torch import optim\nimport torch.nn.functional as F\nimport matplotlib.pyplot as plt\nimport pydicom\nimport glob\nimport collections\nfrom datetime import datetime\nfrom skimage import measure\nfrom skimage.measure import block_reduce\nfrom matplotlib import pyplot as plt\nfrom mpl_toolkits.mplot3d.art3d import Poly3DCollection\nfrom skimage.transform import resize\nimport torchvision.models as models\nfrom torch.utils.data import Dataset, DataLoader\nfrom torch.autograd import Variable\nimport seaborn as sns\nfrom collections import defaultdict","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-01-19T20:29:12.032762Z","iopub.execute_input":"2023-01-19T20:29:12.033792Z","iopub.status.idle":"2023-01-19T20:29:14.959597Z","shell.execute_reply.started":"2023-01-19T20:29:12.033675Z","shell.execute_reply":"2023-01-19T20:29:14.95833Z"},"trusted":true},"outputs":[],"execution_count":1},{"cell_type":"code","source":"pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 torchaudio==0.11.0 --extra-index-url https://download.pytorch.org/whl/cu113","metadata":{"execution":{"iopub.status.busy":"2023-01-19T20:29:14.961525Z","iopub.execute_input":"2023-01-19T20:29:14.961895Z","iopub.status.idle":"2023-01-19T20:33:03.955353Z","shell.execute_reply.started":"2023-01-19T20:29:14.961852Z","shell.execute_reply":"2023-01-19T20:33:03.953773Z"},"trusted":true},"outputs":[{"name":"stdout","text":"Looking in indexes: https://pypi.org/simple, https://download.pytorch.org/whl/cu113\nCollecting torch==1.11.0+cu113\n  Downloading https://download.pytorch.org/whl/cu113/torch-1.11.0%2Bcu113-cp37-cp37m-linux_x86_64.whl (1637.0 MB)\n\u001b[K     |████████████████████████████████| 1637.0 MB 3.8 kB/s  eta 0:00:01   |▍                               | 21.5 MB 13.3 MB/s eta 0:02:02     |██▎                             | 119.2 MB 8.7 MB/s eta 0:02:54     |███▉                            | 197.6 MB 9.6 MB/s eta 0:02:31     |████                            | 201.5 MB 9.6 MB/s eta 0:02:31     |██████▏                         | 316.7 MB 18.3 MB/s eta 0:01:13     |██████▎                         | 323.2 MB 19.5 MB/s eta 0:01:08     |███████▊                        | 396.0 MB 12.4 MB/s eta 0:01:41     |██████████████▎                 | 730.3 MB 12.5 MB/s eta 0:01:13     |██████████████▊                 | 754.6 MB 10.6 MB/s eta 0:01:24     |██████████████████▏             | 931.0 MB 10.7 MB/s eta 0:01:07     |████████████████████▉           | 1065.6 MB 13.1 MB/s eta 0:00:44     |███████████████████████▎        | 1188.0 MB 9.4 MB/s eta 0:00:48     |█████████████████████████▊      | 1314.3 MB 13.0 MB/s eta 0:00:25     |███████████████████████████▌    | 1404.1 MB 12.3 MB/s eta 0:00:19     |███████████████████████████████▍| 1605.2 MB 13.6 MB/s eta 0:00:03     |███████████████████████████████▋| 1615.7 MB 8.2 MB/s eta 0:00:03\n\u001b[?25hCollecting torchvision==0.12.0+cu113\n  Downloading https://download.pytorch.org/whl/cu113/torchvision-0.12.0%2Bcu113-cp37-cp37m-linux_x86_64.whl (22.3 MB)\n\u001b[K     |████████████████████████████████| 22.3 MB 9.3 MB/s eta 0:00:01    |█████████▉                      | 6.9 MB 8.4 MB/s eta 0:00:02\n\u001b[?25hCollecting torchaudio==0.11.0\n  Downloading https://download.pytorch.org/whl/cu113/torchaudio-0.11.0%2Bcu113-cp37-cp37m-linux_x86_64.whl (2.9 MB)\n\u001b[K     |████████████████████████████████| 2.9 MB 12.3 MB/s eta 0:00:01\n\u001b[?25hRequirement already satisfied: typing-extensions in /opt/conda/lib/python3.7/site-packages (from torch==1.11.0+cu113) (3.7.4.3)\nRequirement already satisfied: pillow!=8.3.*,>=5.3.0 in /opt/conda/lib/python3.7/site-packages (from torchvision==0.12.0+cu113) (7.2.0)\nRequirement already satisfied: numpy in /opt/conda/lib/python3.7/site-packages (from torchvision==0.12.0+cu113) (1.19.5)\nRequirement already satisfied: requests in /opt/conda/lib/python3.7/site-packages (from torchvision==0.12.0+cu113) (2.25.1)\nRequirement already satisfied: idna<3,>=2.5 in /opt/conda/lib/python3.7/site-packages (from requests->torchvision==0.12.0+cu113) (2.10)\nRequirement already satisfied: chardet<5,>=3.0.2 in /opt/conda/lib/python3.7/site-packages (from requests->torchvision==0.12.0+cu113) (3.0.4)\nRequirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.7/site-packages (from requests->torchvision==0.12.0+cu113) (1.26.2)\nRequirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.7/site-packages (from requests->torchvision==0.12.0+cu113) (2020.12.5)\nInstalling collected packages: torch, torchvision, torchaudio\n  Attempting uninstall: torch\n    Found existing installation: torch 1.7.0\n    Uninstalling torch-1.7.0:\n      Successfully uninstalled torch-1.7.0\n  Attempting uninstall: torchvision\n    Found existing installation: torchvision 0.8.1\n    Uninstalling torchvision-0.8.1:\n      Successfully uninstalled torchvision-0.8.1\n  Attempting uninstall: torchaudio\n    Found existing installation: torchaudio 0.7.0a0+ac17b64\n    Uninstalling torchaudio-0.7.0a0+ac17b64:\n      Successfully uninstalled torchaudio-0.7.0a0+ac17b64\n\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\nfastai 2.2.5 requires torch<1.8,>=1.7.0, but you have torch 1.11.0+cu113 which is incompatible.\nfastai 2.2.5 requires torchvision<0.9,>=0.8, but you have torchvision 0.12.0+cu113 which is incompatible.\nallennlp 1.3.0 requires torch<1.8.0,>=1.6.0, but you have torch 1.11.0+cu113 which is incompatible.\u001b[0m\nSuccessfully installed torch-1.11.0+cu113 torchaudio-0.11.0+cu113 torchvision-0.12.0+cu113\n\u001b[33mWARNING: You are using pip version 21.0; however, version 22.3.1 is available.\nYou should consider upgrading via the '/opt/conda/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\nNote: you may need to restart the kernel to use updated packages.\n","output_type":"stream"}],"execution_count":2},{"cell_type":"code","source":"import warnings\nwarnings.filterwarnings('ignore')","metadata":{"execution":{"iopub.status.busy":"2023-01-19T20:33:03.95742Z","iopub.execute_input":"2023-01-19T20:33:03.957808Z","iopub.status.idle":"2023-01-19T20:33:03.962226Z","shell.execute_reply.started":"2023-01-19T20:33:03.957769Z","shell.execute_reply":"2023-01-19T20:33:03.961345Z"},"trusted":true},"outputs":[],"execution_count":3},{"cell_type":"code","source":"df = pd.read_csv('../input/vinbigdata-chest-xray-abnormalities-detection/train.csv')\ndf.head(10)","metadata":{"execution":{"iopub.status.busy":"2023-01-19T20:33:03.963812Z","iopub.execute_input":"2023-01-19T20:33:03.964123Z","iopub.status.idle":"2023-01-19T20:33:04.209349Z","shell.execute_reply.started":"2023-01-19T20:33:03.964092Z","shell.execute_reply":"2023-01-19T20:33:04.208371Z"},"trusted":true},"outputs":[{"execution_count":4,"output_type":"execute_result","data":{"text/plain":"                           image_id          class_name  class_id rad_id  \\\n0  50a418190bc3fb1ef1633bf9678929b3          No finding        14    R11   \n1  21a10246a5ec7af151081d0cd6d65dc9          No finding        14     R7   \n2  9a5094b2563a1ef3ff50dc5c7ff71345        Cardiomegaly         3    R10   \n3  051132a778e61a86eb147c7c6f564dfe  Aortic enlargement         0    R10   \n4  063319de25ce7edb9b1c6b8881290140          No finding        14    R10   \n5  1c32170b4af4ce1a3030eb8167753b06  Pleural thickening        11     R9   \n6  0c7a38f293d5f5e4846aa4ca6db4daf1                 ILD         5    R17   \n7  47ed17dcb2cbeec15182ed335a8b5a9e         Nodule/Mass         8     R9   \n8  d3637a1935a905b3c326af31389cb846  Aortic enlargement         0    R10   \n9  afb6230703512afc370f236e8fe98806  Pulmonary fibrosis        13     R9   \n\n    x_min   y_min   x_max   y_max  \n0     NaN     NaN     NaN     NaN  \n1     NaN     NaN     NaN     NaN  \n2   691.0  1375.0  1653.0  1831.0  \n3  1264.0   743.0  1611.0  1019.0  \n4     NaN     NaN     NaN     NaN  \n5   627.0   357.0   947.0   433.0  \n6  1347.0   245.0  2188.0  2169.0  \n7   557.0  2352.0   675.0  2484.0  \n8  1329.0   743.0  1521.0   958.0  \n9  1857.0  1607.0  2126.0  2036.0  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>image_id</th>\n      <th>class_name</th>\n      <th>class_id</th>\n      <th>rad_id</th>\n      <th>x_min</th>\n      <th>y_min</th>\n      <th>x_max</th>\n      <th>y_max</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>50a418190bc3fb1ef1633bf9678929b3</td>\n      <td>No finding</td>\n      <td>14</td>\n      <td>R11</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>21a10246a5ec7af151081d0cd6d65dc9</td>\n      <td>No finding</td>\n      <td>14</td>\n      <td>R7</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>9a5094b2563a1ef3ff50dc5c7ff71345</td>\n      <td>Cardiomegaly</td>\n      <td>3</td>\n      <td>R10</td>\n      <td>691.0</td>\n      <td>1375.0</td>\n      <td>1653.0</td>\n      <td>1831.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>051132a778e61a86eb147c7c6f564dfe</td>\n      <td>Aortic enlargement</td>\n      <td>0</td>\n      <td>R10</td>\n      <td>1264.0</td>\n      <td>743.0</td>\n      <td>1611.0</td>\n      <td>1019.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>063319de25ce7edb9b1c6b8881290140</td>\n      <td>No finding</td>\n      <td>14</td>\n      <td>R10</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>1c32170b4af4ce1a3030eb8167753b06</td>\n      <td>Pleural thickening</td>\n      <td>11</td>\n      <td>R9</td>\n      <td>627.0</td>\n      <td>357.0</td>\n      <td>947.0</td>\n      <td>433.0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>0c7a38f293d5f5e4846aa4ca6db4daf1</td>\n      <td>ILD</td>\n      <td>5</td>\n      <td>R17</td>\n      <td>1347.0</td>\n      <td>245.0</td>\n      <td>2188.0</td>\n      <td>2169.0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>47ed17dcb2cbeec15182ed335a8b5a9e</td>\n      <td>Nodule/Mass</td>\n      <td>8</td>\n      <td>R9</td>\n      <td>557.0</td>\n      <td>2352.0</td>\n      <td>675.0</td>\n      <td>2484.0</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>d3637a1935a905b3c326af31389cb846</td>\n      <td>Aortic enlargement</td>\n      <td>0</td>\n      <td>R10</td>\n      <td>1329.0</td>\n      <td>743.0</td>\n      <td>1521.0</td>\n      <td>958.0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>afb6230703512afc370f236e8fe98806</td>\n      <td>Pulmonary fibrosis</td>\n      <td>13</td>\n      <td>R9</td>\n      <td>1857.0</td>\n      <td>1607.0</td>\n      <td>2126.0</td>\n      <td>2036.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":4},{"cell_type":"code","source":"input_file = os.listdir('../input/vinbigdata-chest-xray-abnormalities-detection/train')\ninput_files = []\nfor ip in input_file:\n    input_files.append(ip.split('.')[0])","metadata":{"execution":{"iopub.status.busy":"2023-01-19T20:33:04.212481Z","iopub.execute_input":"2023-01-19T20:33:04.213112Z","iopub.status.idle":"2023-01-19T20:33:05.616519Z","shell.execute_reply.started":"2023-01-19T20:33:04.213069Z","shell.execute_reply":"2023-01-19T20:33:05.615375Z"},"trusted":true},"outputs":[],"execution_count":5},{"cell_type":"code","source":"len(input_files)","metadata":{"execution":{"iopub.status.busy":"2023-01-19T20:33:05.61814Z","iopub.execute_input":"2023-01-19T20:33:05.618545Z","iopub.status.idle":"2023-01-19T20:33:05.625264Z","shell.execute_reply.started":"2023-01-19T20:33:05.618508Z","shell.execute_reply":"2023-01-19T20:33:05.624081Z"},"trusted":true},"outputs":[{"execution_count":6,"output_type":"execute_result","data":{"text/plain":"15000"},"metadata":{}}],"execution_count":6},{"cell_type":"code","source":"df[df['image_id']==input_files[3]].sort_values(by=['class_id'])","metadata":{"execution":{"iopub.status.busy":"2023-01-19T20:33:05.626742Z","iopub.execute_input":"2023-01-19T20:33:05.627075Z","iopub.status.idle":"2023-01-19T20:33:05.659399Z","shell.execute_reply.started":"2023-01-19T20:33:05.627042Z","shell.execute_reply":"2023-01-19T20:33:05.658034Z"},"trusted":true},"outputs":[{"execution_count":7,"output_type":"execute_result","data":{"text/plain":"                               image_id          class_name  class_id rad_id  \\\n13623  7ecd6f67f649f26c05805c8359f9e528  Pleural thickening        11     R9   \n13961  7ecd6f67f649f26c05805c8359f9e528  Pulmonary fibrosis        13    R10   \n38156  7ecd6f67f649f26c05805c8359f9e528  Pulmonary fibrosis        13     R9   \n47023  7ecd6f67f649f26c05805c8359f9e528  Pulmonary fibrosis        13     R8   \n\n        x_min  y_min   x_max  y_max  \n13623  1769.0  396.0  2071.0  551.0  \n13961   748.0  557.0  1099.0  911.0  \n38156   714.0  597.0  1014.0  830.0  \n47023   810.0  612.0  1170.0  958.0  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>image_id</th>\n      <th>class_name</th>\n      <th>class_id</th>\n      <th>rad_id</th>\n      <th>x_min</th>\n      <th>y_min</th>\n      <th>x_max</th>\n      <th>y_max</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>13623</th>\n      <td>7ecd6f67f649f26c05805c8359f9e528</td>\n      <td>Pleural thickening</td>\n      <td>11</td>\n      <td>R9</td>\n      <td>1769.0</td>\n      <td>396.0</td>\n      <td>2071.0</td>\n      <td>551.0</td>\n    </tr>\n    <tr>\n      <th>13961</th>\n      <td>7ecd6f67f649f26c05805c8359f9e528</td>\n      <td>Pulmonary fibrosis</td>\n      <td>13</td>\n      <td>R10</td>\n      <td>748.0</td>\n      <td>557.0</td>\n      <td>1099.0</td>\n      <td>911.0</td>\n    </tr>\n    <tr>\n      <th>38156</th>\n      <td>7ecd6f67f649f26c05805c8359f9e528</td>\n      <td>Pulmonary fibrosis</td>\n      <td>13</td>\n      <td>R9</td>\n      <td>714.0</td>\n      <td>597.0</td>\n      <td>1014.0</td>\n      <td>830.0</td>\n    </tr>\n    <tr>\n      <th>47023</th>\n      <td>7ecd6f67f649f26c05805c8359f9e528</td>\n      <td>Pulmonary fibrosis</td>\n      <td>13</td>\n      <td>R8</td>\n      <td>810.0</td>\n      <td>612.0</td>\n      <td>1170.0</td>\n      <td>958.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":7},{"cell_type":"code","source":"class traindataset(torch.utils.data.Dataset):\n\n    def __init__(self, df, file_list, transform):\n        super().__init__()\n        \n        self.file_list = file_list\n        self.df = df\n    \n    def __len__(self) :\n        return len(self.file_list)\n\n    def __getitem__(self, idx):\n        \n        img_id = self.file_list[idx]\n        df = self.df\n        s = 224\n        N = 15\n        d_f = df[df['image_id']==img_id]\n        dff = d_f.sort_values(by=['class_id'])\n        class_id = dff['class_id'].values.tolist()\n        img_pxl = pydicom.read_file('../input/vinbigdata-chest-xray-abnormalities-detection/train/'+img_id+'.dicom').pixel_array\n        img_res = resize(img_pxl,(s,s),anti_aliasing=True)\n        img_np = img_res.astype(np.float32())\n        img_tr = torch.from_numpy(img_np)\n        x_ = s/img_pxl.shape[1]\n        y_ = s/img_pxl.shape[0]\n        xmin = [x*x_ for x in dff['x_min'].values.tolist()]\n        ymin = [y*y_ for y in dff['y_min'].values.tolist()]\n        xmax = [x1*x_ for x1 in dff['x_max'].values.tolist()]\n        ymax = [y1*y_ for y1 in dff['y_max'].values.tolist()]\n        #bbox = []\n        #for z in range(len(xmin)):\n        #    bbox.append([xmin[z],ymin[z],xmax[z],ymax[z]])\n        mask = np.zeros((N,s,s))\n        for k,m in enumerate(class_id):\n            if m != 14:\n                x1,x2,y1,y2 = int(xmin[k]),int(xmax[k]),int(ymin[k]),int(ymax[k])\n                mask[m,y1:y2,x1:x2] = 1\n        mask_numpy = mask.astype(np.float32())\n        mask_tensor = torch.from_numpy(mask_numpy)\n        #mask_tensor = np.transpose(mask_tensor, (2,0,1))\n        return img_tr,mask_tensor","metadata":{"execution":{"iopub.status.busy":"2023-01-19T20:33:05.661025Z","iopub.execute_input":"2023-01-19T20:33:05.661492Z","iopub.status.idle":"2023-01-19T20:33:05.676766Z","shell.execute_reply.started":"2023-01-19T20:33:05.661452Z","shell.execute_reply":"2023-01-19T20:33:05.675114Z"},"trusted":true},"outputs":[],"execution_count":8},{"cell_type":"code","source":"traindata = traindataset(file_list = input_files,df =df,transform = None)\ndata_loader = torch.utils.data.DataLoader(traindata, batch_size=1, shuffle=True, num_workers=1)","metadata":{"execution":{"iopub.status.busy":"2023-01-19T20:33:05.67858Z","iopub.execute_input":"2023-01-19T20:33:05.67895Z","iopub.status.idle":"2023-01-19T20:33:05.691358Z","shell.execute_reply.started":"2023-01-19T20:33:05.678913Z","shell.execute_reply":"2023-01-19T20:33:05.690085Z"},"trusted":true},"outputs":[],"execution_count":9},{"cell_type":"code","source":"from matplotlib.patches import Rectangle\nfor k,kk in enumerate(traindata):\n    plt.imshow(kk[0])\n    for org in kk[1][3]:\n        plt.gca().add_patch(Rectangle((org[0], org[1]), (org[2]-org[0]), (org[3]-org[1]),linewidth=1,edgecolor='b',facecolor='none'))\n    plt.show()\n    \n    if k ==3:\n        break","metadata":{"execution":{"iopub.status.busy":"2023-01-19T20:33:05.69285Z","iopub.execute_input":"2023-01-19T20:33:05.693206Z","iopub.status.idle":"2023-01-19T20:33:17.559414Z","shell.execute_reply.started":"2023-01-19T20:33:05.693158Z","shell.execute_reply":"2023-01-19T20:33:17.558211Z"},"trusted":true},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{"needs_background":"light"}}],"execution_count":10},{"cell_type":"code","source":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport numpy as np\nimport re\n\n\nnfnet_params = {\n    'F0': {\n        'width': [256, 512, 1536, 1536], 'depth': [1, 2, 6, 3], 'drop_rate': 0.2},\n    'F1': {\n        'width': [256, 512, 1536, 1536], 'depth': [2, 4, 12, 6], 'drop_rate': 0.3},\n    'F2': {\n        'width': [256, 512, 1536, 1536], 'depth': [3, 6, 18, 9], 'drop_rate': 0.4},\n    'F3': {\n        'width': [256, 512, 1536, 1536], 'depth': [4, 8, 24, 12], 'drop_rate': 0.4},\n    'F4': {\n        'width': [256, 512, 1536, 1536], 'depth': [5, 10, 30, 15], 'drop_rate': 0.5},\n    'F5': {\n        'width': [256, 512, 1536, 1536], 'depth': [6, 12, 36, 18], 'drop_rate': 0.5},\n    'F6': {\n        'width': [256, 512, 1536, 1536], 'depth': [7, 14, 42, 21], 'drop_rate': 0.5},\n    'F7': {\n        'width': [256, 512, 1536, 1536], 'depth': [8, 16, 48, 24], 'drop_rate': 0.5},\n}\n\n# These extra constant values ensure that the activations\n# are variance preserving\nclass VPGELU(nn.Module):\n    def forward(self, input: torch.Tensor) -> torch.Tensor:\n        return F.gelu(input) * 1.7015043497085571\n\nclass VPReLU(nn.Module):\n    def forward(self, input: torch.Tensor) -> torch.Tensor:\n        return F.relu(input, inplace=True) * 1.7139588594436646\n\nactivations_dict = {\n    'gelu': VPGELU(),\n    'relu': VPReLU()\n}\n\nclass NFNet(nn.Module):\n    def __init__(self, num_classes:int, variant:str='F0', stochdepth_rate:float=None, \n        alpha:float=0.2, se_ratio:float=0.5, activation:str='gelu'):\n        super(NFNet, self).__init__()\n\n        if not variant in nfnet_params:\n            raise RuntimeError(f\"Variant {variant} does not exist and could not be loaded.\")\n\n        block_params = nfnet_params[variant]\n\n        self.activation = activations_dict[activation]\n        self.drop_rate = block_params['drop_rate']\n        self.num_classes = num_classes\n\n        self.stem = Stem(activation=activation)\n\n        num_blocks, index = sum(block_params['depth']), 0\n\n        blocks = []\n        expected_std = 1.0\n        in_channels = block_params['width'][0] // 2\n\n        block_args = zip(\n            block_params['width'],\n            block_params['depth'],\n            [0.5] * 4, # bottleneck pattern\n            [128] * 4, # group pattern. Original groups [128] * 4\n            [1, 2, 2, 2] # stride pattern\n        )\n\n        for (block_width, stage_depth, expand_ratio, group_size, stride) in block_args:\n            for block_index in range(stage_depth):\n                beta = 1. / expected_std\n\n                block_sd_rate = stochdepth_rate * index / num_blocks\n                out_channels = block_width\n\n                blocks.append(NFBlock(\n                    in_channels=in_channels, \n                    out_channels=out_channels,\n                    stride=stride if block_index == 0 else 1,\n                    alpha=alpha,\n                    beta=beta,\n                    se_ratio=se_ratio,\n                    group_size=group_size,\n                    stochdepth_rate=block_sd_rate,\n                    activation=activation))\n\n                in_channels = out_channels\n                index += 1\n\n                if block_index == 0:\n                    expected_std = 1.0\n                \n                expected_std = (expected_std **2 + alpha**2)**0.5\n\n        self.body = nn.Sequential(*blocks)\n\n        final_conv_channels = 2*in_channels\n        self.final_conv = WSConv2D(in_channels=out_channels, out_channels=final_conv_channels, kernel_size=1)\n        self.pool = nn.AvgPool2d(1)\n        \n        if self.drop_rate > 0.:\n            self.dropout = nn.Dropout(self.drop_rate)\n\n        self.linear = nn.Linear(final_conv_channels, self.num_classes)\n        nn.init.normal_(self.linear.weight, 0, 0.01)\n\n    def forward(self, x):\n        out = self.stem(x)\n        out = self.body(out)\n        out = self.activation(self.final_conv(out))\n        pool = torch.mean(out, dim=(2,3))\n\n        if self.training and self.drop_rate > 0.:\n            pool = self.dropout(pool)\n\n        return self.linear(pool)\n\n    def exclude_from_weight_decay(self, name:str) -> bool:\n        # Regex to find layer names like\n        # \"stem.6.bias\", \"stem.6.gain\", \"body.0.skip_gain\", \n        # \"body.0.conv0.bias\", \"body.0.conv0.gain\"\n        regex = re.compile('stem.*(bias|gain)|conv.*(bias|gain)|skip_gain')\n        return len(regex.findall(name)) > 0\n\n    def exclude_from_clipping(self, name: str) -> bool:\n        # Last layer should not be clipped\n        return name.startswith('linear')\n\nclass Stem(nn.Module):\n    def __init__(self, activation:str='gelu'):\n        super(Stem, self).__init__()\n        \n        self.activation = activations_dict[activation]\n        self.conv0 = WSConv2D(in_channels=3, out_channels=16, kernel_size=3, stride=2)\n        self.conv1 = WSConv2D(in_channels=16, out_channels=32, kernel_size=3, stride=1)\n        self.conv2 = WSConv2D(in_channels=32, out_channels=64, kernel_size=3, stride=1)\n        self.conv3 = WSConv2D(in_channels=64, out_channels=128, kernel_size=3, stride=2)\n    \n    def forward(self, x):\n        out = self.activation(self.conv0(x))\n        out = self.activation(self.conv1(out))\n        out = self.activation(self.conv2(out))\n        out = self.conv3(out)\n        return out\n\nclass NFBlock(nn.Module):\n    def __init__(self, in_channels:int, out_channels:int, expansion:float=0.5, \n        se_ratio:float=0.5, stride:int=1, beta:float=1.0, alpha:float=0.2, \n        group_size:int=1, stochdepth_rate:float=None, activation:str='gelu'):\n\n        super(NFBlock, self).__init__()\n\n        self.in_channels = in_channels\n        self.out_channels = out_channels\n        self.expansion = expansion\n        self.se_ratio = se_ratio\n        self.activation = activations_dict[activation]\n        self.beta, self.alpha = beta, alpha\n        self.group_size = group_size\n        \n        width = int(self.out_channels * expansion)\n        self.groups = width // group_size\n        self.width = group_size * self.groups\n        self.stride = stride\n\n        self.conv0 = WSConv2D(in_channels=self.in_channels, out_channels=self.width, kernel_size=1)\n        self.conv1 = WSConv2D(in_channels=self.width, out_channels=self.width, kernel_size=3, stride=stride, padding=1, groups=self.groups)\n        self.conv1b = WSConv2D(in_channels=self.width, out_channels=self.width, kernel_size=3, stride=1, padding=1, groups=self.groups)\n        self.conv2 = WSConv2D(in_channels=self.width, out_channels=self.out_channels, kernel_size=1)\n        \n        self.use_projection = self.stride > 1 or self.in_channels != self.out_channels\n        if self.use_projection:\n            if stride > 1:\n                self.shortcut_avg_pool = nn.AvgPool2d(kernel_size=2, stride=2, padding=0 if self.in_channels==1536 else 1)\n            self.conv_shortcut = WSConv2D(self.in_channels, self.out_channels, kernel_size=1)\n            \n        self.squeeze_excite = SqueezeExcite(self.out_channels, self.out_channels, se_ratio=self.se_ratio, activation=activation)\n        self.skip_gain = nn.Parameter(torch.zeros(()))\n\n        self.use_stochdepth = stochdepth_rate is not None and stochdepth_rate > 0. and stochdepth_rate < 1.\n        if self.use_stochdepth:\n            self.stoch_depth = StochDepth(stochdepth_rate)\n\n    def forward(self, x):\n        out = self.activation(x) * self.beta\n\n        if self.stride > 1:\n            shortcut = self.shortcut_avg_pool(out)\n            shortcut = self.conv_shortcut(shortcut)\n        elif self.use_projection:\n            shortcut = self.conv_shortcut(out)\n        else:\n            shortcut = x\n\n        out = self.activation(self.conv0(out))\n        out = self.activation(self.conv1(out))\n        out = self.activation(self.conv1b(out))\n        out = self.conv2(out)\n        out = (self.squeeze_excite(out)*2) * out\n\n        if self.use_stochdepth:\n            out = self.stoch_depth(out)\n\n        return out * self.alpha * self.skip_gain + shortcut\n\n# Implementation mostly from https://arxiv.org/abs/2101.08692\n# Implemented changes from https://arxiv.org/abs/2102.06171 and\n#  https://github.com/deepmind/deepmind-research/tree/master/nfnets\nclass WSConv2D(nn.Conv2d):\n    def __init__(self, in_channels: int, out_channels: int, kernel_size, stride = 1, padding = 0,\n        dilation = 1, groups: int = 1, bias: bool = True, padding_mode: str = 'zeros'):\n\n        super(WSConv2D, self).__init__(in_channels, out_channels, kernel_size, stride, padding, dilation, groups, bias, padding_mode)\n        \n        nn.init.xavier_normal_(self.weight)\n        self.gain = nn.Parameter(torch.ones(self.out_channels, 1, 1, 1))\n        self.register_buffer('eps', torch.tensor(1e-4, requires_grad=False), persistent=False)\n        self.register_buffer('fan_in', torch.tensor(np.prod(self.weight.shape[1:]), requires_grad=False).type_as(self.weight), persistent=False)\n\n    def standardized_weights(self):\n        # Original code: HWCN\n        mean = torch.mean(self.weight, axis=[1,2,3], keepdims=True)\n        var = torch.var(self.weight, axis=[1,2,3], keepdims=True)\n        scale = torch.rsqrt(torch.maximum(var * self.fan_in, self.eps))\n        return (self.weight - mean) * scale * self.gain\n        \n    def forward(self, x):\n        return F.conv2d(\n            input=x,\n            weight=self.standardized_weights(),\n            bias=self.bias,\n            stride=self.stride,\n            padding=self.padding,\n            dilation=self.dilation,\n            groups=self.groups\n        )\n\nclass SqueezeExcite(nn.Module):\n    def __init__(self, in_channels:int, out_channels:int, se_ratio:float=0.5, activation:str='gelu'):\n        super(SqueezeExcite, self).__init__()\n\n        self.in_channels = in_channels\n        self.out_channels = out_channels\n        self.se_ratio = se_ratio\n\n        self.hidden_channels = max(1, int(self.in_channels * self.se_ratio))\n        \n        self.activation = activations_dict[activation]\n        self.linear = nn.Linear(self.in_channels, self.hidden_channels)\n        self.linear_1 = nn.Linear(self.hidden_channels, self.out_channels)\n        self.sigmoid = nn.Sigmoid()\n\n    def forward(self, x):\n        out = torch.mean(x, (2,3))\n        out = self.linear_1(self.activation(self.linear(out)))\n        out = self.sigmoid(out)\n\n        b,c,_,_ = x.size()\n        return out.view(b,c,1,1).expand_as(x)\n\nclass StochDepth(nn.Module):\n    def __init__(self, stochdepth_rate:float):\n        super(StochDepth, self).__init__()\n\n        self.drop_rate = stochdepth_rate\n\n    def forward(self, x):\n        if not self.training:\n            return x\n\n        batch_size = x.shape[0]\n        rand_tensor = torch.rand(batch_size, 1, 1, 1).type_as(x).to(x.device)\n        keep_prob = 1 - self.drop_rate\n        binary_tensor = torch.floor(rand_tensor + keep_prob)\n        \n        return x * binary_tensor","metadata":{"execution":{"iopub.status.busy":"2023-01-19T20:33:17.56112Z","iopub.execute_input":"2023-01-19T20:33:17.561565Z","iopub.status.idle":"2023-01-19T20:33:17.81365Z","shell.execute_reply.started":"2023-01-19T20:33:17.561528Z","shell.execute_reply":"2023-01-19T20:33:17.812319Z"},"trusted":true},"outputs":[],"execution_count":11},{"cell_type":"code","source":"import torch\nfrom torch.optim import Optimizer\n\n\n# Compute norm depending on the shape of x\ndef unitwise_norm(x):\n    if (len(torch.squeeze(x).shape)) <= 1: # Scalars, vectors\n        axis = 0\n        keepdims = False\n    elif len(x.shape) in [2,3]: # Linear layers\n        # Original code: IO\n        # Pytorch: OI\n        axis = 1\n        keepdims = True\n    elif len(x.shape) == 4: # Conv kernels\n        # Original code: HWIO\n        # Pytorch: OIHW\n        axis = [1, 2, 3]\n        keepdims = True\n    else:\n        raise ValueError(f'Got a parameter with len(shape) not in [1, 2, 3, 4]! {x}')\n\n    return torch.sqrt(torch.sum(torch.square(x), axis=axis, keepdim=keepdims))\n\n\n# This is a copy of the pytorch SGD implementation\n# enhanced with gradient clipping\nclass SGD_AGC(Optimizer):\n    def __init__(self, named_params, lr:float, momentum=0, dampening=0,\n                 weight_decay=0, nesterov=False, clipping:float=None, eps:float=1e-3):\n        if lr < 0.0:\n            raise ValueError(\"Invalid learning rate: {}\".format(lr))\n        if momentum < 0.0:\n            raise ValueError(\"Invalid momentum value: {}\".format(momentum))\n        if weight_decay < 0.0:\n            raise ValueError(\"Invalid weight_decay value: {}\".format(weight_decay))\n\n        defaults = dict(lr=lr, momentum=momentum, dampening=dampening,\n                        weight_decay=weight_decay, nesterov=nesterov,\n                        # Extra defaults\n                        clipping=clipping,\n                        eps=eps\n                        )\n\n        if nesterov and (momentum <= 0 or dampening != 0):\n            raise ValueError(\"Nesterov momentum requires a momentum and zero dampening\")\n\n        # Put params in list so each one gets its own group\n        params = []\n        for name, param in named_params:\n            params.append({'params': param, 'name': name})\n\n        super(SGD_AGC, self).__init__(params, defaults)\n\n    def __setstate__(self, state):\n        super(SGD_AGC, self).__setstate__(state)\n        for group in self.param_groups:\n            group.setdefault('nesterov', False)\n\n    @torch.no_grad()\n    def step(self, closure=None):\n        loss = None\n        if closure is not None:\n            with torch.enable_grad():\n                loss = closure()\n\n        for group in self.param_groups:\n            weight_decay = group['weight_decay']\n            momentum = group['momentum']\n            dampening = group['dampening']\n            nesterov = group['nesterov']\n\n            # Extra values for clipping\n            clipping = group['clipping']\n            eps = group['eps']\n\n            for p in group['params']:\n                if p.grad is None:\n                    continue\n                d_p = p.grad\n\n                # =========================\n                # Gradient clipping\n                if clipping is not None:\n                    param_norm = torch.maximum(unitwise_norm(p), torch.tensor(eps).to(p.device))\n                    grad_norm = unitwise_norm(d_p)\n                    max_norm = param_norm * group['clipping']\n\n                    trigger_mask = grad_norm > max_norm\n                    clipped_grad = p.grad * (max_norm / torch.maximum(grad_norm, torch.tensor(1e-6).to(p.device)))\n                    d_p = torch.where(trigger_mask, clipped_grad, d_p)\n                # =========================\n\n                if weight_decay != 0:\n                    d_p = d_p.add(p, alpha=weight_decay)\n                if momentum != 0:\n                    param_state = self.state[p]\n                    if 'momentum_buffer' not in param_state:\n                        buf = param_state['momentum_buffer'] = torch.clone(d_p).detach()\n                    else:\n                        buf = param_state['momentum_buffer']\n                        buf.mul_(momentum).add_(d_p, alpha=1 - dampening)\n                    if nesterov:\n                        d_p = d_p.add(buf, alpha=momentum)\n                    else:\n                        d_p = buf\n\n                p.add_(d_p, alpha=-group['lr'])\n\n        return loss","metadata":{"execution":{"iopub.status.busy":"2023-01-19T20:33:17.815503Z","iopub.execute_input":"2023-01-19T20:33:17.815958Z","iopub.status.idle":"2023-01-19T20:33:17.83761Z","shell.execute_reply.started":"2023-01-19T20:33:17.815908Z","shell.execute_reply":"2023-01-19T20:33:17.836555Z"},"trusted":true},"outputs":[],"execution_count":12},{"cell_type":"code","source":"import torch\nimport torch.nn as nn\nimport torchvision\nimport torchvision.transforms as transforms","metadata":{"execution":{"iopub.status.busy":"2023-01-19T20:33:17.839029Z","iopub.execute_input":"2023-01-19T20:33:17.839664Z","iopub.status.idle":"2023-01-19T20:33:17.856104Z","shell.execute_reply.started":"2023-01-19T20:33:17.83962Z","shell.execute_reply":"2023-01-19T20:33:17.854253Z"},"trusted":true},"outputs":[],"execution_count":13},{"cell_type":"code","source":"model = NFNet(num_classes=15, variant='F1', stochdepth_rate=0.25, alpha=0.2, se_ratio=0.5, activation='gelu').cuda()\n\n# Loss and optimizer\ncriterion = nn.CrossEntropyLoss()\noptimizer = SGD_AGC(named_params=model.named_parameters(), lr=0.001, momentum=0.9, clipping=0.1, weight_decay=5e-4, nesterov=True)\nscheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=200)\ncalc = nn.MSELoss()\n","metadata":{"execution":{"iopub.status.busy":"2023-01-19T20:33:17.857816Z","iopub.execute_input":"2023-01-19T20:33:17.858253Z","iopub.status.idle":"2023-01-19T20:33:20.187994Z","shell.execute_reply.started":"2023-01-19T20:33:17.858204Z","shell.execute_reply":"2023-01-19T20:33:20.185209Z"},"trusted":true},"outputs":[{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)","\u001b[0;32m<ipython-input-14-ef4d0dee7b2b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNFNet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_classes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m15\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvariant\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'F1'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstochdepth_rate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.25\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mse_ratio\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mactivation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'gelu'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\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      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m# Loss and optimizer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mcriterion\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCrossEntropyLoss\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      5\u001b[0m \u001b[0moptimizer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSGD_AGC\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnamed_params\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnamed_parameters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.001\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmomentum\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclipping\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweight_decay\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5e-4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnesterov\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36mcuda\u001b[0;34m(self, device)\u001b[0m\n\u001b[1;32m    461\u001b[0m                 raise AttributeError(\"`\" + item + \"` is not \"\n\u001b[1;32m    462\u001b[0m                                      \"an nn.Module\")\n\u001b[0;32m--> 463\u001b[0;31m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    464\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mmod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    465\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_apply\u001b[0;34m(self, fn)\u001b[0m\n\u001b[1;32m    357\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparam\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mParameter\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    358\u001b[0m             raise TypeError(\"cannot assign '{}' object to parameter '{}' \"\n\u001b[0;32m--> 359\u001b[0;31m                             \u001b[0;34m\"(torch.nn.Parameter or None required)\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    360\u001b[0m                             .format(torch.typename(param), name))\n\u001b[1;32m    361\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mparam\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrad_fn\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_apply\u001b[0;34m(self, fn)\u001b[0m\n\u001b[1;32m    357\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparam\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mParameter\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    358\u001b[0m             raise TypeError(\"cannot assign '{}' object to parameter '{}' \"\n\u001b[0;32m--> 359\u001b[0;31m                             \u001b[0;34m\"(torch.nn.Parameter or None required)\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    360\u001b[0m                             .format(torch.typename(param), name))\n\u001b[1;32m    361\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mparam\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrad_fn\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_apply\u001b[0;34m(self, fn)\u001b[0m\n\u001b[1;32m    379\u001b[0m         \"\"\"\n\u001b[1;32m    380\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodule\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mModule\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 381\u001b[0;31m             raise TypeError(\"{} is not a Module subclass\".format(\n\u001b[0m\u001b[1;32m    382\u001b[0m                 torch.typename(module)))\n\u001b[1;32m    383\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_six\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstring_classes\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/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(t)\u001b[0m\n\u001b[1;32m    461\u001b[0m                 raise AttributeError(\"`\" + item + \"` is not \"\n\u001b[1;32m    462\u001b[0m                                      \"an nn.Module\")\n\u001b[0;32m--> 463\u001b[0;31m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    464\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mmod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    465\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.7/site-packages/torch/cuda/__init__.py\u001b[0m in \u001b[0;36m_lazy_init\u001b[0;34m()\u001b[0m\n\u001b[1;32m    164\u001b[0m             \u001b[0m_lazy_seed_tracker\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mqueue_seed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcallable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtraceback\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat_stack\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    165\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--> 166\u001b[0;31m             \u001b[0;31m# Don't store the actual traceback to avoid memory cycle\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    167\u001b[0m             \u001b[0m_queued_calls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcallable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtraceback\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat_stack\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    168\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mAssertionError\u001b[0m: Torch not compiled with CUDA enabled"],"ename":"AssertionError","evalue":"Torch not compiled with CUDA enabled","output_type":"error"}],"execution_count":14},{"cell_type":"code","source":"def dice_loss(pred, target, smooth = 1.):\n    pred = pred.contiguous()\n    target = target.contiguous()    \n\n    intersection = (pred * target).sum(dim=2).sum(dim=2)\n    \n    loss = (1 - ((2. * intersection + smooth) / (pred.sum(dim=2).sum(dim=2) + target.sum(dim=2).sum(dim=2) + smooth)))\n    \n    return loss.mean()","metadata":{"execution":{"iopub.status.busy":"2023-01-19T20:33:20.189558Z","iopub.status.idle":"2023-01-19T20:33:20.190107Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def calc_loss(pred, target, metrics, bce_weight=0.5):\n    bce = F.binary_cross_entropy_with_logits(pred, target)\n        \n    pred = F.sigmoid(pred)\n    dice = dice_loss(pred, target)\n    \n    loss = bce * bce_weight + dice * (1 - bce_weight)\n    \n    metrics['bce'] += bce.data.cpu().numpy() * target.size(0)\n    metrics['dice'] += dice.data.cpu().numpy() * target.size(0)\n    metrics['loss'] += loss.data.cpu().numpy() * target.size(0)\n    \n    return loss","metadata":{"execution":{"iopub.status.busy":"2023-01-19T20:33:20.191753Z","iopub.status.idle":"2023-01-19T20:33:20.192511Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"metrics = defaultdict(float)","metadata":{"execution":{"iopub.status.busy":"2023-01-19T20:33:20.19428Z","iopub.status.idle":"2023-01-19T20:33:20.194984Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"epochs = 1\nsteps = 0\nprint_every = 750\ntrain_losses, train_accuracy = [], []\n#model.load_state_dict(torch.load('./final_model.pth'))\nfor epoch in range(epochs):\n    model.train()\n    size = 0\n    running_loss = 0\n    acc = 0\n    for a,(image_train, y_train) in enumerate(data_loader):\n        steps += 1\n        image_train, y_train = image_train.unsqueeze(0).cuda(), y_train.cuda()\n        image_train = Variable(image_train,requires_grad=True)\n        image_train=  image_train.detach().cpu().numpy()\n        image_train = np.dstack([image_train]*3)\n        image_train = image_train.reshape(-1,3,224,224)\n        image_train = torch.from_numpy(image_train).cuda()\n        y_train = y_train.detach().cpu().numpy()[0,:,1,1]\n        y_train =torch.from_numpy(y_train).cuda()\n        optimizer.zero_grad()\n        y_predtrain = model.forward(image_train)\n        #y_train=y_train.type(torch.LongTensor)\n        #loss = calc_loss(y_predtrain, y_train,metrics)\n        loss = calc(y_predtrain, y_train)\n        loss.backward()\n        optimizer.step()\n        running_loss += loss.item()\n        ps = torch.exp(y_predtrain)\n        top_p = torch.max(y_predtrain, 1)\n        top_class = torch.argmax(y_predtrain,dim = 1)\n        equals = top_class == y_train\n        print(torch.mean(equals.type(torch.FloatTensor)).item())\n        acc += torch.mean(equals.type(torch.FloatTensor)).item()\n        size += image_train.shape[0]\n        model.eval()\n        print(f\"Epoch {epoch+1}/{epochs}.. \"\n              f\"Train loss: {running_loss/print_every:.3f}.. \"\n              f\"Train accuracy: {acc/len(data_loader):.3f}\")\n    #torch.save(model.state_dict(),'./'+str(epoch)+'model.pth')\n    #print('model saved')\n    torch.save(model.state_dict(),'./'+str(epoch)+'unet_model.pth')\n    train_losses.append(float(running_loss)/float(size))\n    train_accuracy.append(float(acc)/float(size))\n    print('train_losses',epoch,train_losses)\n    print('train_accuracy',epoch,train_accuracy)\ntorch.save(model.state_dict(),'./final_unet_model.pth')\nprint('model saved')\nprint('train_losses',epoch,train_losses)\nprint('train_accuracy',epoch,train_accuracy)","metadata":{"execution":{"iopub.status.busy":"2023-01-19T20:33:20.196459Z","iopub.status.idle":"2023-01-19T20:33:20.197195Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"img_pxl = pydicom.read_file('../input/vinbigdata-chest-xray-abnormalities-detection/train/000d68e42b71d3eac10ccc077aba07c1.dicom').pixel_array\nimg_res = resize(img_pxl,(1024,1024),anti_aliasing=True)\nimg_np = img_res.astype(np.float32())\nimg_tr = torch.from_numpy(img_np).unsqueeze(0).unsqueeze(0).cuda()\n\nimg_tr = Variable(img_tr,requires_grad=True)\nimg_tr=  img_tr.detach().cpu().numpy()\nimg_tr = np.dstack([img_tr]*3)\nimg_tr = img_tr.reshape(-1,3,1024,1024)\nimg_tr = torch.from_numpy(img_tr).cuda()\noutput = model(img_tr)","metadata":{"execution":{"iopub.status.busy":"2023-01-19T20:33:20.198743Z","iopub.status.idle":"2023-01-19T20:33:20.199469Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"predict = output.cpu().detach().squeeze()\nprint(predict.shape)","metadata":{"execution":{"iopub.status.busy":"2023-01-19T20:33:20.201287Z","iopub.status.idle":"2023-01-19T20:33:20.202093Z"},"trusted":true},"outputs":[],"execution_count":null}]}