med-transformer-glas-dataset.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.10","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":2170465,"sourceType":"datasetVersion","datasetId":1165452},{"sourceId":2822666,"sourceType":"datasetVersion","datasetId":1726202},{"sourceId":2962429,"sourceType":"datasetVersion","datasetId":1726065}],"dockerImageVersionId":30146,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"%load_ext autoreload\n%autoreload 2","metadata":{"id":"TW3LOZmIqIkH","execution":{"iopub.status.busy":"2023-12-01T15:35:32.690436Z","iopub.execute_input":"2023-12-01T15:35:32.690878Z","iopub.status.idle":"2023-12-01T15:35:32.747712Z","shell.execute_reply.started":"2023-12-01T15:35:32.690781Z","shell.execute_reply":"2023-12-01T15:35:32.747174Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import pdb\nimport math\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport matplotlib.pyplot as plt\nimport random","metadata":{"id":"gse-kUY0X07u","execution":{"iopub.status.busy":"2023-12-01T15:35:36.711939Z","iopub.execute_input":"2023-12-01T15:35:36.712214Z","iopub.status.idle":"2023-12-01T15:35:38.170262Z","shell.execute_reply.started":"2023-12-01T15:35:36.712183Z","shell.execute_reply":"2023-12-01T15:35:38.169648Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def reset_seed():\n  torch.manual_seed(42)\n  random.seed(42)\n  torch.cuda.manual_seed(42)","metadata":{"id":"9h1iopcJieWm","execution":{"iopub.status.busy":"2023-12-01T15:35:43.792500Z","iopub.execute_input":"2023-12-01T15:35:43.793229Z","iopub.status.idle":"2023-12-01T15:35:43.832439Z","shell.execute_reply.started":"2023-12-01T15:35:43.793192Z","shell.execute_reply":"2023-12-01T15:35:43.831737Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Custom Dataset","metadata":{"id":"tA-fDGW2gyQp"}},{"cell_type":"code","source":"cd \"../input/check-point\"","metadata":{"id":"_CXevDqPiVIj","outputId":"acb6ef93-da2f-456e-f4a0-e441df3a292c","execution":{"iopub.status.busy":"2023-12-01T15:36:46.097338Z","iopub.execute_input":"2023-12-01T15:36:46.098150Z","iopub.status.idle":"2023-12-01T15:36:46.142079Z","shell.execute_reply.started":"2023-12-01T15:36:46.098109Z","shell.execute_reply":"2023-12-01T15:36:46.141270Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"cd \"working/\"","metadata":{"execution":{"iopub.status.busy":"2023-12-01T15:36:48.608373Z","iopub.execute_input":"2023-12-01T15:36:48.608666Z","iopub.status.idle":"2023-12-01T15:36:48.647874Z","shell.execute_reply.started":"2023-12-01T15:36:48.608635Z","shell.execute_reply":"2023-12-01T15:36:48.646989Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from QU_Dataset import *","metadata":{"execution":{"iopub.status.busy":"2023-12-01T15:36:59.104250Z","iopub.execute_input":"2023-12-01T15:36:59.104914Z","iopub.status.idle":"2023-12-01T15:37:01.675736Z","shell.execute_reply.started":"2023-12-01T15:36:59.104876Z","shell.execute_reply":"2023-12-01T15:37:01.674846Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"TRAIN_DIR = \"/kaggle/input/qu-data-my/Train Image\"\nTRAIN_MASK = \"/kaggle/input/qu-data-my/Train Mask\"\nTEST_DIR = \"/kaggle/input/qu-data-my/Test Image\"\nTEST_MASK = \"/kaggle/input/qu-data-my/Test Mask\" \nprint(TRAIN_DIR)","metadata":{"id":"WqomQ2N5hKfd","execution":{"iopub.status.busy":"2023-12-01T15:45:09.947150Z","iopub.execute_input":"2023-12-01T15:45:09.947778Z","iopub.status.idle":"2023-12-01T15:45:09.999336Z","shell.execute_reply.started":"2023-12-01T15:45:09.947737Z","shell.execute_reply":"2023-12-01T15:45:09.998596Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import os\nprint(os.getcwd())","metadata":{"execution":{"iopub.status.busy":"2023-12-01T15:45:12.519378Z","iopub.execute_input":"2023-12-01T15:45:12.519658Z","iopub.status.idle":"2023-12-01T15:45:12.567682Z","shell.execute_reply.started":"2023-12-01T15:45:12.519630Z","shell.execute_reply":"2023-12-01T15:45:12.566890Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%cd /kaggle/input/qu-data-my","metadata":{"execution":{"iopub.status.busy":"2023-12-01T15:45:14.777316Z","iopub.execute_input":"2023-12-01T15:45:14.777612Z","iopub.status.idle":"2023-12-01T15:45:14.827657Z","shell.execute_reply.started":"2023-12-01T15:45:14.777579Z","shell.execute_reply":"2023-12-01T15:45:14.826879Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import torch.nn as nn\nfrom torch.autograd import Variable\nfrom torchvision import transforms\nfrom torch.utils.data.dataset import Dataset\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport torchvision\nfrom torch.utils.data import DataLoader\nfrom PIL import Image\nimport os\nimport albumentations as A\nfrom albumentations.pytorch import ToTensorV2\nimport numpy as np\n\nclass QU_Dataset(Dataset):\n  def __init__(self, image_dir, mask_dir, transform=None, state=None):\n    self.image_dir = image_dir\n    self.mask_dir = mask_dir\n    self.transform = transform\n    self.images = os.listdir(image_dir)\n\n  def __len__(self):\n    return len(self.images)\n\n  def __getitem__(self, index):\n    img_path = os.path.join(self.image_dir, self.images[index])\n    mask_path = os.path.join(self.mask_dir, self.images[index]).replace(\".bmp\",\"_anno.bmp\")\n    image = np.array(Image.open(img_path).convert(\"RGB\"))\n    mask = np.array(Image.open(mask_path))\n    mask[mask >= 1] = 1.0\n\n    if self.transform is not None:\n      augmentations = self.transform(image=image, mask=mask)\n      image = augmentations[\"image\"]\n      mask = augmentations[\"mask\"]\n    return image, mask\n\nIMAGE_HEIGHT = 128\nIMAGE_WIDTH = 128\n\ntrain_transform = A.Compose(\n    [\n        A.Resize(height=IMAGE_HEIGHT, width=IMAGE_WIDTH),\n        A.Rotate(limit=35, p=1.0),\n        A.HorizontalFlip(p=0.5),\n        A.VerticalFlip(p=0.1),\n        A.Normalize(\n            mean = [0.0, 0.0, 0.0],\n            std = [1.0, 1.0, 1.0],\n            max_pixel_value = 255.0\n        ),\n        ToTensorV2(),\n    ])\nval_transform = A.Compose(\n    [\n        A.Resize(height=IMAGE_HEIGHT, width=IMAGE_WIDTH),\n        A.Normalize(\n            mean = [0.0, 0.0, 0.0],\n            std = [1.0, 1.0, 1.0],\n            max_pixel_value = 255.0\n        ),\n        ToTensorV2(),\n    ])\n# Notebook\n# Data\n\n# Add Data\n","metadata":{"execution":{"iopub.status.busy":"2023-12-01T15:45:16.796529Z","iopub.execute_input":"2023-12-01T15:45:16.797128Z","iopub.status.idle":"2023-12-01T15:45:16.857011Z","shell.execute_reply.started":"2023-12-01T15:45:16.797089Z","shell.execute_reply":"2023-12-01T15:45:16.856230Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_ds1 = QU_Dataset(TRAIN_DIR, TRAIN_MASK, transform=None)\ntrain_ds, val_ds = torch.utils.data.random_split(train_ds1,[76, 9], generator=torch.Generator().manual_seed(42))\ntest_ds = QU_Dataset(TEST_DIR, TEST_MASK, transform=val_transform)","metadata":{"id":"nXYFa8-VhhSo","execution":{"iopub.status.busy":"2023-12-01T15:45:20.512765Z","iopub.execute_input":"2023-12-01T15:45:20.513056Z","iopub.status.idle":"2023-12-01T15:45:20.598899Z","shell.execute_reply.started":"2023-12-01T15:45:20.513022Z","shell.execute_reply":"2023-12-01T15:45:20.598194Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"reset_seed()\nBATCH_SIZE = 4\nNUM_WORKERS = 2\nIMAGE_HEIGHT = 128\nIMAGE_WIDTH = 128\nPIN_MEMORY = True\ntrain_loader = DataLoader(\n    train_ds,\n    batch_size = BATCH_SIZE,\n    num_workers = NUM_WORKERS,\n    pin_memory = PIN_MEMORY,\n    shuffle=True,\n)\nval_loader = DataLoader(\n    val_ds,\n    batch_size=BATCH_SIZE,\n    num_workers = NUM_WORKERS,\n    pin_memory = PIN_MEMORY,\n    shuffle=False\n)\ntest_loader = DataLoader(\n    test_ds,\n    batch_size=BATCH_SIZE,\n    num_workers = NUM_WORKERS,\n    pin_memory = PIN_MEMORY,\n    shuffle=False\n)","metadata":{"id":"75sO4L-zj-S4","execution":{"iopub.status.busy":"2023-12-01T15:45:23.215310Z","iopub.execute_input":"2023-12-01T15:45:23.215624Z","iopub.status.idle":"2023-12-01T15:45:23.266521Z","shell.execute_reply.started":"2023-12-01T15:45:23.215587Z","shell.execute_reply":"2023-12-01T15:45:23.265804Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"for X, y in train_ds:\n  print(X.shape)\n  print(y.shape)\n  break","metadata":{"id":"x5liTz2bqlsH","outputId":"3d91c67e-2a1e-49fd-a7ad-ee412f83421a","execution":{"iopub.status.busy":"2023-12-01T15:45:27.045161Z","iopub.execute_input":"2023-12-01T15:45:27.045936Z","iopub.status.idle":"2023-12-01T15:45:27.243831Z","shell.execute_reply.started":"2023-12-01T15:45:27.045878Z","shell.execute_reply":"2023-12-01T15:45:27.243048Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.imshow(X.transpose(0,2).transpose(0,1).numpy())","metadata":{"id":"Bp1lzU-JqqU2","outputId":"c88162ac-3eb7-4a9c-b01b-c2e6b8c61f96","execution":{"iopub.status.busy":"2023-12-01T15:45:30.854135Z","iopub.execute_input":"2023-12-01T15:45:30.854674Z","iopub.status.idle":"2023-12-01T15:45:30.916458Z","shell.execute_reply.started":"2023-12-01T15:45:30.854636Z","shell.execute_reply":"2023-12-01T15:45:30.915324Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.imshow(y)","metadata":{"execution":{"iopub.status.busy":"2023-12-01T15:45:47.196425Z","iopub.execute_input":"2023-12-01T15:45:47.197233Z","iopub.status.idle":"2023-12-01T15:45:47.548282Z","shell.execute_reply.started":"2023-12-01T15:45:47.197187Z","shell.execute_reply":"2023-12-01T15:45:47.547557Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Axial Attention","metadata":{"id":"RJTRPKetYVNm"}},{"cell_type":"code","source":"def conv1x1(in_planes, out_planes, stride=1):\n  return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)\n\nclass qkv_transform(nn.Conv1d):\n  \"\"\"Conv1d for qkv_transform\"\"\"","metadata":{"id":"mJxhdk-8YUIR","execution":{"iopub.status.busy":"2023-12-01T15:48:18.598412Z","iopub.execute_input":"2023-12-01T15:48:18.599063Z","iopub.status.idle":"2023-12-01T15:48:18.652763Z","shell.execute_reply.started":"2023-12-01T15:48:18.599024Z","shell.execute_reply":"2023-12-01T15:48:18.651928Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"![image.png](data:image/png;base64,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)","metadata":{"id":"LypIoL_XLoT3"}},{"cell_type":"code","source":"class AxialAttention(nn.Module):\n  \n  def __init__(self,\n               in_planes,\n               out_planes,\n               groups=8,\n               kernel_size=56,\n               stride=1,\n               bias=False,\n               width=False):\n    \"\"\"\n    args :\n      in_planes : d_q\n      out_planes : d_out\n      groups : number heads \n      kernel_size : size of memory block\n    \"\"\"\n    super().__init__()\n    self.in_planes = in_planes\n    self.out_planes = out_planes\n    self.groups = groups\n    self.group_planes = out_planes // groups\n    self.kernel_size = kernel_size\n    self.stride = stride\n    self.bias = bias\n    self.width = width\n\n    # Multi-head self attention\n    # d_q = out_planes // 2, d_out = out_planes (out_planes = 16) => number of \n    self.qkv_transform = qkv_transform(in_planes, out_planes*2, kernel_size=1, stride=1,\n                                       padding=0, bias=False)\n    self.bn_qkv = nn.BatchNorm1d(out_planes * 2) # shape : q,k : (out_planes, d_q)\n    self.bn_similarity = nn.BatchNorm2d(groups * 3)\n    self.bn_output = nn.BatchNorm1d(out_planes * 2)\n\n    # position embedding\n    # (2 * kernel_size - 1) position : -(kernel_size-1),...,0,...,(kernel_size-1)\n    # number of channels = channels of r_q + channels of r_k + channels of r_v\n    # group_planes * 2   =  group_planes//2 * 2 + group_planes (dim value + dim query + dim key)\n    # => dim of embedding position : group_planes * 2 * (kernel_size * 2 - 1)\n    self.relative = nn.Parameter(torch.randn(self.group_planes * 2, kernel_size * 2 - 1), requires_grad=True)\n    query_index = torch.arange(kernel_size).unsqueeze(0)\n    key_index = torch.arange(kernel_size).unsqueeze(1)\n    relative_index = key_index - query_index + kernel_size - 1\n    self.register_buffer('flatten_index', relative_index.view(-1))\n    if stride > 1:\n      self.pooling = nn.AvgPool2d(stride, stride=stride)\n    \n    self.reset_paremeters()\n  \n  def forward(self, x):\n    # axial attention width-axis \n    if self.width:\n      x = x.permute(0, 2, 1, 3) # N, H, C, W\n    else:\n      x = x.permute(0, 3, 1, 2) # N, W, C, H\n    N, W, C, H = x.shape\n    x = x.contiguous().view(N*W, C, H)\n    # Transformations\n    qkv = self.bn_qkv(self.qkv_transform(x))\n    #print(x.shape)\n    #print(self.qkv_transform(x).shape)\n    q, k, v = torch.split(qkv.reshape(N*W, self.groups, self.group_planes * 2, H), \n                          [self.group_planes // 2, self.group_planes // 2, self.group_planes], dim=2)\n    \n    # Calculate position embedding\n    # self.flatten_index : shape (kernel_size * kernel_size)\n    all_embedding = torch.index_select(self.relative, 1, self.flatten_index).view(self.group_planes * 2, self.kernel_size, self.kernel_size)\n    q_embedding, k_embedding, v_embedding = torch.split(all_embedding, [self.group_planes // 2, self.group_planes // 2, self.group_planes], dim=0)\n    qqn = q_embedding[0].detach().cpu().numpy()\n    #plt.imshow(qqn)\n    kqn = k_embedding[0].detach().cpu().numpy()\n    #plt.imshow(kqn)\n    # q : shape (N*W, number heads, self.group_planes // 2 = d_q, H)\n    # k : shape (N*W, number heads, self.group_planes // 2 = d_q, H)\n    # v : shape (N*W, number heads, self.group_planes = 2 * d_q, H)\n    # q_embedding : shape (self.group_planes // 2, kernel_size, kernel_size)\n    # k_embedding : shape (self.group_planes // 2, kernel_size, kernel_size)\n    # v_embedding : shape (self.group_planes, kernel_size, kernel_size)\n    # qr : shape (N*W, number heads, kernel_size, kernel_size)\n    # kr : shape (N*W, number heads, kernel_size, kernel_size)\n    # why transpose(2, 3) -> because index of equal (2) Medical-Transform\n    # qk : shape\n    qr = torch.einsum('bgci,cij->bgij', q, q_embedding)\n    kr = torch.einsum('bgci,cij->bgij', k, k_embedding).transpose(2, 3)\n    qk = torch.einsum('bgci,bgcj->bgij', q, k)\n    # batchnorm each qr, qk, kr before sum\n    stacked_similarity = torch.cat([qk, qr, kr], dim=1)\n    stacked_similarity = self.bn_similarity(stacked_similarity).view(N*W, 3, self.groups, H, H).sum(dim=1)\n    # stacked_similarity = self.bn_qr(qr) + self.bn_kr(kr) + self.bn_qk(qk)\n    # (N*W, groups, H, H)\n    similarity = F.softmax(stacked_similarity, dim=3)\n    sv = torch.einsum('bgij,bgcj->bgci', similarity, v)\n    sve = torch.einsum('bgij,cij->bgci', similarity, v_embedding)\n    stacked_output = torch.cat([sv, sve], dim=-1).view(N*W, self.out_planes*2, H)\n    output = self.bn_output(stacked_output).view(N, W, self.out_planes, 2, H).sum(dim=-2)\n\n    if self.width:\n      output = output.permute(0, 2, 1, 3)\n    else:\n      output = output.permute(0, 2, 3, 1)\n    \n    if self.stride > 1:\n      output = self.pooling(output)\n    return output\n\n  def reset_paremeters(self):\n    self.qkv_transform.weight.data.normal_(0, math.sqrt(1. / self.in_planes))\n    nn.init.normal_(self.relative, 0, math.sqrt(1. / self.group_planes))\n","metadata":{"id":"6OtmkIvHYl0H","execution":{"iopub.status.busy":"2023-12-01T15:48:22.962862Z","iopub.execute_input":"2023-12-01T15:48:22.963381Z","iopub.status.idle":"2023-12-01T15:48:23.051273Z","shell.execute_reply.started":"2023-12-01T15:48:22.963347Z","shell.execute_reply":"2023-12-01T15:48:23.050435Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\"\"\"\nx_test : shape \n\"\"\"\nx_test = torch.randn(1, 128, 9, 9)\ngate = AxialAttention(in_planes=128,out_planes= 128,groups = 8,kernel_size= 9)\nb = gate(x_test)\nprint(x_test.shape)\nprint(b.shape)\n","metadata":{"id":"UWQl-KleO4Ke","outputId":"bdf91b5b-346e-4a42-96ae-08dde3f14377","execution":{"iopub.status.busy":"2023-12-01T15:48:28.191873Z","iopub.execute_input":"2023-12-01T15:48:28.192139Z","iopub.status.idle":"2023-12-01T15:48:28.387951Z","shell.execute_reply.started":"2023-12-01T15:48:28.192111Z","shell.execute_reply":"2023-12-01T15:48:28.387149Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"kernel_size = 5\nque_index = torch.arange(5).unsqueeze(0)\nkey_index = torch.arange(5).unsqueeze(1)\na = key_index - que_index + kernel_size - 1\nprint(a)\nb = a.view(-1)\na = torch.randn(4, kernel_size * 2 - 1)\nprint(f'a:{a}')\nprint(a.shape)\nprint(f'b:{b}')","metadata":{"id":"f4maZI3Yb0E_","outputId":"1598c556-d5fe-48f2-e0af-7bf21b3da515","execution":{"iopub.status.busy":"2023-12-01T15:48:30.846024Z","iopub.execute_input":"2023-12-01T15:48:30.846663Z","iopub.status.idle":"2023-12-01T15:48:30.912865Z","shell.execute_reply.started":"2023-12-01T15:48:30.846622Z","shell.execute_reply":"2023-12-01T15:48:30.912112Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"c = torch.index_select(a, 1, b).reshape(4, kernel_size, kernel_size)\n\"\"\"\nq, k, v = torch.split(c, [1, 1, 2], dim=0)\nprint(q, q.shape)\nprint(k, k.shape)\nprint(v, v.shape)\n\"\"\"\nc.shape","metadata":{"id":"DhEcaIOBEMPW","outputId":"a9e5376a-66a2-4295-8d65-92dd678f156e","execution":{"iopub.status.busy":"2023-12-01T15:48:33.604160Z","iopub.execute_input":"2023-12-01T15:48:33.604464Z","iopub.status.idle":"2023-12-01T15:48:33.654379Z","shell.execute_reply.started":"2023-12-01T15:48:33.604415Z","shell.execute_reply":"2023-12-01T15:48:33.653468Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"id":"3qy8ps0YaCve"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Gated-position axial attention","metadata":{"id":"QDsD0n8yaGrV"}},{"cell_type":"code","source":"class AxialAttention_dynamic(nn.Module):\n  def __init__(self, \n               in_planes, \n               out_planes, \n               groups=8, \n               kernel_size=56,\n               stride=1, \n               bias=False, \n               width=False):\n      assert (in_planes % groups == 0) and (out_planes % groups == 0)\n      super(AxialAttention_dynamic, self).__init__()\n      self.in_planes = in_planes\n      self.out_planes = out_planes\n      self.groups = groups\n      self.group_planes = out_planes // groups\n      self.kernel_size = kernel_size\n      self.stride = stride\n      self.bias = bias\n      self.width = width\n\n      # Multi-head self attention\n      self.qkv_transform = qkv_transform(in_planes, out_planes * 2, kernel_size=1, stride=1,\n                                          padding=0, bias=False)\n      self.bn_qkv = nn.BatchNorm1d(out_planes * 2)\n      self.bn_similarity = nn.BatchNorm2d(groups * 3)\n      self.bn_output = nn.BatchNorm1d(out_planes * 2)\n\n      # Priority on encoding\n\n      ## Initial values \n\n      self.f_qr = nn.Parameter(torch.tensor(0.1),  requires_grad=False) \n      self.f_kr = nn.Parameter(torch.tensor(0.1),  requires_grad=False)\n      self.f_sve = nn.Parameter(torch.tensor(0.1),  requires_grad=False)\n      self.f_sv = nn.Parameter(torch.tensor(1.0),  requires_grad=False)\n\n\n      # Position embedding\n      self.relative = nn.Parameter(torch.randn(self.group_planes * 2, kernel_size * 2 - 1), requires_grad=True)\n      query_index = torch.arange(kernel_size).unsqueeze(0)\n      key_index = torch.arange(kernel_size).unsqueeze(1)\n      relative_index = key_index - query_index + kernel_size - 1\n      self.register_buffer('flatten_index', relative_index.view(-1))\n      if stride > 1:\n          self.pooling = nn.AvgPool2d(stride, stride=stride)\n\n      self.reset_parameters()\n      # self.print_para()\n\n  def forward(self, x):\n      if self.width:\n          x = x.permute(0, 2, 1, 3)\n      else:\n          x = x.permute(0, 3, 1, 2)  # N, W, C, H\n      N, W, C, H = x.shape\n      x = x.contiguous().view(N * W, C, H)\n\n      # Transformations\n      qkv = self.bn_qkv(self.qkv_transform(x))\n      q, k, v = torch.split(qkv.reshape(N * W, self.groups, self.group_planes * 2, H), [self.group_planes // 2, self.group_planes // 2, self.group_planes], dim=2)\n\n      # Calculate position embedding\n      all_embeddings = torch.index_select(self.relative, 1, self.flatten_index).view(self.group_planes * 2, self.kernel_size, self.kernel_size)\n      q_embedding, k_embedding, v_embedding = torch.split(all_embeddings, [self.group_planes // 2, self.group_planes // 2, self.group_planes], dim=0)\n      qr = torch.einsum('bgci,cij->bgij', q, q_embedding)\n      kr = torch.einsum('bgci,cij->bgij', k, k_embedding).transpose(2, 3)\n      qk = torch.einsum('bgci, bgcj->bgij', q, k)\n\n\n      # multiply by factors\n      qr = torch.mul(qr, self.f_qr)\n      kr = torch.mul(kr, self.f_kr)\n\n      stacked_similarity = torch.cat([qk, qr, kr], dim=1)\n      stacked_similarity = self.bn_similarity(stacked_similarity).view(N * W, 3, self.groups, H, H).sum(dim=1)\n      #stacked_similarity = self.bn_qr(qr) + self.bn_kr(kr) + self.bn_qk(qk)\n      # (N, groups, H, H, W)\n      similarity = F.softmax(stacked_similarity, dim=3)\n      sv = torch.einsum('bgij,bgcj->bgci', similarity, v)\n      sve = torch.einsum('bgij,cij->bgci', similarity, v_embedding)\n\n      # multiply by factors\n      sv = torch.mul(sv, self.f_sv)\n      sve = torch.mul(sve, self.f_sve)\n\n      stacked_output = torch.cat([sv, sve], dim=-1).view(N * W, self.out_planes * 2, H)\n      output = self.bn_output(stacked_output).view(N, W, self.out_planes, 2, H).sum(dim=-2)\n\n      if self.width:\n          output = output.permute(0, 2, 1, 3)\n      else:\n          output = output.permute(0, 2, 3, 1)\n\n      if self.stride > 1:\n          output = self.pooling(output)\n\n      return output\n  def reset_parameters(self):\n      self.qkv_transform.weight.data.normal_(0, math.sqrt(1. / self.in_planes))\n      #nn.init.uniform_(self.relative, -0.1, 0.1)\n      nn.init.normal_(self.relative, 0., math.sqrt(1. / self.group_planes))","metadata":{"id":"kiofQUBSaNm-","execution":{"iopub.status.busy":"2023-12-01T15:48:37.722785Z","iopub.execute_input":"2023-12-01T15:48:37.723084Z","iopub.status.idle":"2023-12-01T15:48:37.799365Z","shell.execute_reply.started":"2023-12-01T15:48:37.723051Z","shell.execute_reply":"2023-12-01T15:48:37.798577Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"x_test = torch.randn(1, 128, 9, 9)\ngate = AxialAttention_dynamic(in_planes=128,out_planes= 128,groups = 8,kernel_size= 9)\nb = gate(x_test)\nprint(x_test.shape)\nprint(b.shape)","metadata":{"id":"naRg02z6dgdA","outputId":"7d9ea888-2f6e-4654-e354-ab5bfe723a4c","execution":{"iopub.status.busy":"2023-12-01T15:48:43.433288Z","iopub.execute_input":"2023-12-01T15:48:43.433914Z","iopub.status.idle":"2023-12-01T15:48:43.490051Z","shell.execute_reply.started":"2023-12-01T15:48:43.433866Z","shell.execute_reply":"2023-12-01T15:48:43.489230Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Axial Attention without position","metadata":{"id":"53G-Y9LLeHqs"}},{"cell_type":"code","source":"class AxialAttention_wopos(nn.Module):\n  def __init__(self, in_planes, out_planes, groups=8, kernel_size=56,\n                stride=1, bias=False, width=False):\n      assert (in_planes % groups == 0) and (out_planes % groups == 0)\n      super(AxialAttention_wopos, self).__init__()\n      self.in_planes = in_planes\n      self.out_planes = out_planes\n      self.groups = groups\n      self.group_planes = out_planes // groups\n      self.kernel_size = kernel_size\n      self.stride = stride\n      self.bias = bias\n      self.width = width\n\n      # Multi-head self attention\n      self.qkv_transform = qkv_transform(in_planes, out_planes * 2, kernel_size=1, stride=1,\n                                          padding=0, bias=False)\n      self.bn_qkv = nn.BatchNorm1d(out_planes * 2)\n      self.bn_similarity = nn.BatchNorm2d(groups )\n\n      self.bn_output = nn.BatchNorm1d(out_planes * 1)\n\n      if stride > 1:\n          self.pooling = nn.AvgPool2d(stride, stride=stride)\n\n      self.reset_parameters()\n\n  def forward(self, x):\n      if self.width:\n          x = x.permute(0, 2, 1, 3)\n      else:\n          x = x.permute(0, 3, 1, 2)  # N, W, C, H\n      N, W, C, H = x.shape\n      x = x.contiguous().view(N * W, C, H)\n\n      # Transformations\n      qkv = self.bn_qkv(self.qkv_transform(x))\n      q, k, v = torch.split(qkv.reshape(N * W, self.groups, self.group_planes * 2, H), [self.group_planes // 2, self.group_planes // 2, self.group_planes], dim=2)\n\n      qk = torch.einsum('bgci, bgcj->bgij', q, k)\n\n      stacked_similarity = self.bn_similarity(qk).reshape(N * W, 1, self.groups, H, H).sum(dim=1).contiguous()\n\n      similarity = F.softmax(stacked_similarity, dim=3)\n      sv = torch.einsum('bgij,bgcj->bgci', similarity, v)\n\n      sv = sv.reshape(N*W,self.out_planes * 1, H).contiguous()\n      output = self.bn_output(sv).reshape(N, W, self.out_planes, 1, H).sum(dim=-2).contiguous()\n\n\n      if self.width:\n          output = output.permute(0, 2, 1, 3)\n      else:\n          output = output.permute(0, 2, 3, 1)\n\n      if self.stride > 1:\n          output = self.pooling(output)\n\n      return output\n\n  def reset_parameters(self):\n      self.qkv_transform.weight.data.normal_(0, math.sqrt(1. / self.in_planes))\n      #nn.init.uniform_(self.relative, -0.1, 0.1)\n      # nn.init.normal_(self.relative, 0., math.sqrt(1. / self.group_planes))","metadata":{"id":"aEP6wS8teNE1","execution":{"iopub.status.busy":"2023-12-01T15:48:46.305761Z","iopub.execute_input":"2023-12-01T15:48:46.306053Z","iopub.status.idle":"2023-12-01T15:48:46.367777Z","shell.execute_reply.started":"2023-12-01T15:48:46.306019Z","shell.execute_reply":"2023-12-01T15:48:46.366996Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"x_test = torch.randn(1, 128, 64, 64)\ngate = AxialAttention_wopos(in_planes=128,out_planes= 128,stride=2,groups = 8,kernel_size= 64)\nb = gate(x_test)\nprint(x_test.shape)\nprint(b.shape)","metadata":{"id":"RLUrT_zNeYY1","outputId":"c380f354-a93b-4551-f93c-a54d2e5ca0b5","execution":{"iopub.status.busy":"2023-12-01T15:48:50.030558Z","iopub.execute_input":"2023-12-01T15:48:50.031148Z","iopub.status.idle":"2023-12-01T15:48:50.130751Z","shell.execute_reply.started":"2023-12-01T15:48:50.031109Z","shell.execute_reply":"2023-12-01T15:48:50.129875Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Mix model","metadata":{"id":"pnbeXqB7eblt"}},{"cell_type":"markdown","source":"![image.png](data:image/png;base64,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)","metadata":{"id":"deWxYVs-ewET"}},{"cell_type":"code","source":"class AxialBlock(nn.Module):\n  expansion = 2\n\n  def __init__(self, \n               inplanes, \n               planes, \n               stride=1, \n               downsample=None, \n               groups=1,\n               base_width=64,\n               dilation=1,\n               norm_layer=None,\n               kernel_size=56):\n    super().__init__()\n    if norm_layer is None:\n      norm_layer = nn.BatchNorm2d\n    width = int(planes * (base_width / 64.))\n    # Both self.conv2 and self.downsample layers downsample the input when stride != 1\n    self.groups = groups\n    self.conv_down = conv1x1(inplanes, width)\n    self.bn1 = norm_layer(width)\n    self.hight_block = AxialAttention(width, width, groups=groups, kernel_size=kernel_size)\n    self.width_block = AxialAttention(width, width, groups=groups, kernel_size=kernel_size, width=True)\n    self.conv_up = conv1x1(width, planes*self.expansion)\n    self.bn2 = norm_layer(planes * self.expansion)\n    self.relu = nn.ReLU(inplace=True)\n    self.downsample = downsample\n    self.stride = stride\n\n  def forward(self, x):\n    identity = x\n\n    out = self.conv_down(x)\n    out = self.bn1(out)\n    out = self.relu(out)\n    \n    out = self.hight_block(out)\n    out = self.width_block(out)\n    out = self.relu(out)\n\n    out = self.conv_up(out)\n    out = self.bn2(out)\n\n    if self.downsample is not None:\n      identity = self.downsample(x)\n    \n    out += identity\n    out = self.relu(out)\n\n    return out","metadata":{"id":"1qI_Ll8OexXO","execution":{"iopub.status.busy":"2023-12-01T15:48:53.281428Z","iopub.execute_input":"2023-12-01T15:48:53.282207Z","iopub.status.idle":"2023-12-01T15:48:53.337054Z","shell.execute_reply.started":"2023-12-01T15:48:53.282165Z","shell.execute_reply":"2023-12-01T15:48:53.336296Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"x_test = torch.randn(1, 256, 9, 9)\ngate = AxialBlock(inplanes=256,planes= 128,groups = 8,kernel_size= 9)\nb = gate(x_test)\nprint(x_test.shape)\nprint(gate.groups)","metadata":{"id":"kC2CFacBgvid","outputId":"3eec64be-2877-4d96-99e6-d2ea675361e1","execution":{"iopub.status.busy":"2023-12-01T15:48:56.401096Z","iopub.execute_input":"2023-12-01T15:48:56.401373Z","iopub.status.idle":"2023-12-01T15:48:56.471290Z","shell.execute_reply.started":"2023-12-01T15:48:56.401340Z","shell.execute_reply":"2023-12-01T15:48:56.470558Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"class AxialBlock_dynamic(nn.Module):\n  expansion = 2\n\n  def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,\n                base_width=64, dilation=1, norm_layer=None, kernel_size=56):\n      super(AxialBlock_dynamic, self).__init__()\n      if norm_layer is None:\n          norm_layer = nn.BatchNorm2d\n      width = int(planes * (base_width / 64.))\n      # Both self.conv2 and self.downsample layers downsample the input when stride != 1\n      self.conv_down = conv1x1(inplanes, width)\n      self.bn1 = norm_layer(width)\n      self.hight_block = AxialAttention_dynamic(width, width, groups=groups, kernel_size=kernel_size)\n      self.width_block = AxialAttention_dynamic(width, width, groups=groups, kernel_size=kernel_size, stride=stride, width=True)\n      self.conv_up = conv1x1(width, planes * self.expansion)\n      self.bn2 = norm_layer(planes * self.expansion)\n      self.relu = nn.ReLU(inplace=True)\n      self.downsample = downsample\n      self.stride = stride\n\n  def forward(self, x):\n      identity = x\n\n      out = self.conv_down(x)\n      out = self.bn1(out)\n      out = self.relu(out)\n\n      out = self.hight_block(out)\n      out = self.width_block(out)\n      out = self.relu(out)\n\n      out = self.conv_up(out)\n      out = self.bn2(out)\n\n      if self.downsample is not None:\n          identity = self.downsample(x)\n      #print(out.shape)\n      #print(identity.shape)\n      out += identity\n      out = self.relu(out)\n\n      return out","metadata":{"id":"sfxghBPNhhu1","execution":{"iopub.status.busy":"2023-12-01T15:48:58.692844Z","iopub.execute_input":"2023-12-01T15:48:58.693127Z","iopub.status.idle":"2023-12-01T15:48:58.750780Z","shell.execute_reply.started":"2023-12-01T15:48:58.693095Z","shell.execute_reply":"2023-12-01T15:48:58.749916Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"x_test = torch.randn(1, 256, 9, 9)\ngate = AxialBlock_dynamic(inplanes=256,planes= 128,groups = 8,kernel_size= 9)\nb = gate(x_test)\nprint(x_test.shape)\nprint(b.shape)","metadata":{"id":"7-GvUM3GjEa1","outputId":"5d8610e2-bd9a-476c-cb32-406d2bc042c0","execution":{"iopub.status.busy":"2023-12-01T15:49:02.152104Z","iopub.execute_input":"2023-12-01T15:49:02.152380Z","iopub.status.idle":"2023-12-01T15:49:02.213370Z","shell.execute_reply.started":"2023-12-01T15:49:02.152347Z","shell.execute_reply":"2023-12-01T15:49:02.212624Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"class AxialBlock_wopos(nn.Module):\n  expansion = 2\n\n  def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,\n                base_width=64, dilation=1, norm_layer=None, kernel_size=56):\n      super(AxialBlock_wopos, self).__init__()\n      if norm_layer is None:\n          norm_layer = nn.BatchNorm2d\n      # print(kernel_size)\n      width = int(planes * (base_width / 64.))\n      # Both self.conv2 and self.downsample layers downsample the input when stride != 1\n      self.conv_down = conv1x1(inplanes, width)\n      self.conv1 = nn.Conv2d(width, width, kernel_size = 1)\n      self.bn1 = norm_layer(width)\n      self.hight_block = AxialAttention_wopos(width, width, groups=groups, kernel_size=kernel_size)\n      self.width_block = AxialAttention_wopos(width, width, groups=groups, kernel_size=kernel_size, stride=stride, width=True)\n      self.conv_up = conv1x1(width, planes * self.expansion)\n      self.bn2 = norm_layer(planes * self.expansion)\n      self.relu = nn.ReLU(inplace=True)\n      self.downsample = downsample\n      self.stride = stride\n\n  def forward(self, x):\n      identity = x\n\n      # pdb.set_trace()\n\n      out = self.conv_down(x)\n      out = self.bn1(out)\n      out = self.relu(out)\n      # print(out.shape)\n      out = self.hight_block(out)\n      out = self.width_block(out)\n\n      out = self.relu(out)\n\n      out = self.conv_up(out)\n      out = self.bn2(out)\n\n      if self.downsample is not None:\n          identity = self.downsample(x)\n\n      out += identity\n      out = self.relu(out)\n\n      return out","metadata":{"id":"UcIG9HmRjJ0k","execution":{"iopub.status.busy":"2023-12-01T15:49:04.631867Z","iopub.execute_input":"2023-12-01T15:49:04.632678Z","iopub.status.idle":"2023-12-01T15:49:04.688030Z","shell.execute_reply.started":"2023-12-01T15:49:04.632640Z","shell.execute_reply":"2023-12-01T15:49:04.687328Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"x_test = torch.randn(1, 256, 64, 64)\ngate = AxialBlock_dynamic(inplanes=256,planes= 128,stride=1,groups = 8,kernel_size= 64)\nb = gate(x_test)\nprint(x_test.shape)\nprint(b.shape)","metadata":{"id":"u_Pj2CorjLts","outputId":"f6ab7651-227f-4d2c-eedd-8e182d8b9f25","execution":{"iopub.status.busy":"2023-12-01T15:49:07.907776Z","iopub.execute_input":"2023-12-01T15:49:07.908510Z","iopub.status.idle":"2023-12-01T15:49:08.130754Z","shell.execute_reply.started":"2023-12-01T15:49:07.908476Z","shell.execute_reply":"2023-12-01T15:49:08.129890Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"![image.png](data:image/png;base64,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)","metadata":{"id":"PCu5-5EAIcR1"}},{"cell_type":"code","source":"class ResAxialAttentionUNet(nn.Module):\n\n  def __init__(self, block, layers, num_classes=2, zero_init_residual=True,\n                groups=8, width_per_group=64, replace_stride_with_dilation=None,\n                norm_layer=None, s=0.125, img_size = 128,imgchan = 3):\n      super(ResAxialAttentionUNet, self).__init__()\n      if norm_layer is None:\n          norm_layer = nn.BatchNorm2d\n      self._norm_layer = norm_layer\n\n      self.inplanes = int(64 * s)\n      self.dilation = 1\n      if replace_stride_with_dilation is None:\n          replace_stride_with_dilation = [False, False, False]\n      if len(replace_stride_with_dilation) != 3:\n          raise ValueError(\"replace_stride_with_dilation should be None \"\n                            \"or a 3-element tuple, got {}\".format(replace_stride_with_dilation))\n      self.groups = groups\n      self.base_width = width_per_group\n      self.conv1 = nn.Conv2d(imgchan, self.inplanes, kernel_size=7, stride=2, padding=3,\n                              bias=False)\n      self.conv2 = nn.Conv2d(self.inplanes, 128, kernel_size=3, stride=1, padding=1, bias=False)\n      self.conv3 = nn.Conv2d(128, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False)\n      self.bn1 = norm_layer(self.inplanes)\n      self.bn2 = norm_layer(128)\n      self.bn3 = norm_layer(self.inplanes)\n      self.relu = nn.ReLU(inplace=True)\n      # self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n      self.layer1 = self._make_layer(block, int(128 * s), layers[0], kernel_size= (img_size//2))\n      self.layer2 = self._make_layer(block, int(256 * s), layers[1], stride=2, kernel_size=(img_size//2),\n                                      dilate=replace_stride_with_dilation[0])\n      self.layer3 = self._make_layer(block, int(512 * s), layers[2], stride=2, kernel_size=(img_size//4),\n                                      dilate=replace_stride_with_dilation[1])\n      self.layer4 = self._make_layer(block, int(1024 * s), layers[3], stride=2, kernel_size=(img_size//8),\n                                      dilate=replace_stride_with_dilation[2])\n      \n      # Decoder\n      self.decoder1 = nn.Conv2d(int(1024 *2*s)      ,        int(1024*2*s), kernel_size=3, stride=2, padding=1)\n      self.decoder2 = nn.Conv2d(int(1024  *2*s)     , int(1024*s), kernel_size=3, stride=1, padding=1)\n      self.decoder3 = nn.Conv2d(int(1024*s),  int(512*s), kernel_size=3, stride=1, padding=1)\n      self.decoder4 = nn.Conv2d(int(512*s) ,  int(256*s), kernel_size=3, stride=1, padding=1)\n      self.decoder5 = nn.Conv2d(int(256*s) , int(128*s) , kernel_size=3, stride=1, padding=1)\n      self.adjust   = nn.Conv2d(int(128*s) , num_classes, kernel_size=1, stride=1, padding=0)\n      self.soft     = nn.Softmax(dim=1)\n\n\n  def _make_layer(self, block, planes, blocks, kernel_size=56, stride=1, dilate=False):\n      norm_layer = self._norm_layer\n      downsample = None\n      previous_dilation = self.dilation\n      if dilate:\n          self.dilation *= stride\n          stride = 1\n      if stride != 1 or self.inplanes != planes * block.expansion:\n          downsample = nn.Sequential(\n              conv1x1(self.inplanes, planes * block.expansion, stride),\n              norm_layer(planes * block.expansion),\n          )\n\n      layers = []\n      layers.append(block(self.inplanes, planes, stride, downsample, groups=self.groups,\n                          base_width=self.base_width, dilation=previous_dilation, \n                          norm_layer=norm_layer, kernel_size=kernel_size))\n      self.inplanes = planes * block.expansion\n      if stride != 1:\n          kernel_size = kernel_size // 2\n\n      for _ in range(1, blocks):\n          layers.append(block(self.inplanes, planes, groups=self.groups,\n                              base_width=self.base_width, dilation=self.dilation,\n                              norm_layer=norm_layer, kernel_size=kernel_size))\n\n      return nn.Sequential(*layers)\n\n  def _forward_impl(self, x):\n      \n      # AxialAttention Encoder\n      # pdb.set_trace()\n      x = self.conv1(x)\n      x = self.bn1(x)\n      x = self.relu(x)\n      x = self.conv2(x)\n      x = self.bn2(x)\n      x = self.relu(x)\n      x = self.conv3(x)\n      x = self.bn3(x)\n      x = self.relu(x)\n      #print(x.shape)\n      x1 = self.layer1(x)\n      #print(x.shape)\n      x2 = self.layer2(x1)\n      # print(x2.shape)\n      x3 = self.layer3(x2)\n      # print(x3.shape)\n      x4 = self.layer4(x3)\n\n      x = F.relu(F.interpolate(self.decoder1(x4), scale_factor=(2,2), mode ='bilinear'))\n      x = torch.add(x, x4)\n      x = F.relu(F.interpolate(self.decoder2(x) , scale_factor=(2,2), mode ='bilinear'))\n      x = torch.add(x, x3)\n      x = F.relu(F.interpolate(self.decoder3(x) , scale_factor=(2,2), mode ='bilinear'))\n      x = torch.add(x, x2)\n      x = F.relu(F.interpolate(self.decoder4(x) , scale_factor=(2,2), mode ='bilinear'))\n      x = torch.add(x, x1)\n      x = F.relu(F.interpolate(self.decoder5(x) , scale_factor=(2,2), mode ='bilinear'))\n      x = self.adjust(F.relu(x))\n      # pdb.set_trace()\n      return x\n\n  def forward(self, x):\n      return self._forward_impl(x)","metadata":{"id":"7t9_jqHOVnzw","execution":{"iopub.status.busy":"2023-12-01T15:49:11.632790Z","iopub.execute_input":"2023-12-01T15:49:11.633419Z","iopub.status.idle":"2023-12-01T15:49:11.710330Z","shell.execute_reply.started":"2023-12-01T15:49:11.633382Z","shell.execute_reply":"2023-12-01T15:49:11.709622Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def gated(pretrained=False, **kwargs):\n  model = ResAxialAttentionUNet(AxialBlock_dynamic, [1, 2, 4, 1], s= 0.125, **kwargs)\n  return model","metadata":{"id":"sQT85XFD3sfW","execution":{"iopub.status.busy":"2023-12-01T15:49:17.165323Z","iopub.execute_input":"2023-12-01T15:49:17.165617Z","iopub.status.idle":"2023-12-01T15:49:17.213741Z","shell.execute_reply.started":"2023-12-01T15:49:17.165584Z","shell.execute_reply":"2023-12-01T15:49:17.213018Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"net = gated()\nx_test = torch.rand(1, 3, 128, 128)\nnet(x_test);","metadata":{"id":"hYvsTMBgNB7e","outputId":"206488d8-92b3-4653-8d97-df59183e03d4","execution":{"iopub.status.busy":"2023-12-01T15:49:19.577160Z","iopub.execute_input":"2023-12-01T15:49:19.577456Z","iopub.status.idle":"2023-12-01T15:49:19.928556Z","shell.execute_reply.started":"2023-12-01T15:49:19.577406Z","shell.execute_reply":"2023-12-01T15:49:19.927760Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"class medt_net(nn.Module):\n\n  def __init__(self, block, block_2, layers, num_classes=2, zero_init_residual=True,\n              groups=8, width_per_group=64, replace_stride_with_dilation=None,\n              norm_layer=None, s=0.125, img_size = 128,imgchan = 3):\n    super(medt_net, self).__init__()\n    if norm_layer is None:\n        norm_layer = nn.BatchNorm2d\n    self._norm_layer = norm_layer\n\n    self.inplanes = int(64 * s)\n    self.dilation = 1\n    if replace_stride_with_dilation is None:\n        replace_stride_with_dilation = [False, False, False]\n    if len(replace_stride_with_dilation) != 3:\n        raise ValueError(\"replace_stride_with_dilation should be None \"\n                          \"or a 3-element tuple, got {}\".format(replace_stride_with_dilation))\n    self.groups = groups\n    self.base_width = width_per_group\n    self.conv1 = nn.Conv2d(imgchan, self.inplanes, kernel_size=7, stride=2, padding=3,\n                            bias=False)\n    self.conv2 = nn.Conv2d(self.inplanes, 128, kernel_size=3, stride=1, padding=1, bias=False)\n    self.conv3 = nn.Conv2d(128, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False)\n    self.bn1 = norm_layer(self.inplanes)\n    self.bn2 = norm_layer(128)\n    self.bn3 = norm_layer(self.inplanes)\n    # self.conv1 = nn.Conv2d(1, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False)\n    self.bn1 = norm_layer(self.inplanes)\n    self.relu = nn.ReLU(inplace=True)\n    # self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n    self.layer1 = self._make_layer(block, int(128 * s), layers[0], kernel_size= (img_size//2))\n    self.layer2 = self._make_layer(block, int(256 * s), layers[1], stride=2, kernel_size=(img_size//2),\n                                    dilate=replace_stride_with_dilation[0])\n    # self.layer3 = self._make_layer(block, int(512 * s), layers[2], stride=2, kernel_size=(img_size//4),\n    #                                dilate=replace_stride_with_dilation[1])\n    # self.layer4 = self._make_layer(block, int(1024 * s), layers[3], stride=2, kernel_size=(img_size//8),\n    #                                dilate=replace_stride_with_dilation[2])\n    \n    # Decoder\n    # self.decoder1 = nn.Conv2d(int(1024 *2*s)      ,        int(1024*2*s), kernel_size=3, stride=2, padding=1)\n    # self.decoder2 = nn.Conv2d(int(1024  *2*s)     , int(1024*s), kernel_size=3, stride=1, padding=1)\n    # self.decoder3 = nn.Conv2d(int(1024*s),  int(512*s), kernel_size=3, stride=1, padding=1)\n    self.decoder4 = nn.Conv2d(int(512*s) ,  int(256*s), kernel_size=3, stride=1, padding=1)\n    self.decoder5 = nn.Conv2d(int(256*s) , int(128*s) , kernel_size=3, stride=1, padding=1)\n    self.adjust   = nn.Conv2d(int(128*s) , num_classes, kernel_size=1, stride=1, padding=0)\n    self.soft     = nn.Softmax(dim=1)\n\n\n    self.conv1_p = nn.Conv2d(imgchan, self.inplanes, kernel_size=7, stride=2, padding=3,\n                            bias=False)\n    self.conv2_p = nn.Conv2d(self.inplanes,128, kernel_size=3, stride=1, padding=1,\n                            bias=False)\n    self.conv3_p = nn.Conv2d(128, self.inplanes, kernel_size=3, stride=1, padding=1,\n                            bias=False)\n    # self.conv1 = nn.Conv2d(1, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False)\n    self.bn1_p = norm_layer(self.inplanes)\n    self.bn2_p = norm_layer(128)\n    self.bn3_p = norm_layer(self.inplanes)\n\n    self.relu_p = nn.ReLU(inplace=True)\n\n    img_size_p = img_size // 4\n\n    self.layer1_p = self._make_layer(block_2, int(128 * s), layers[0], kernel_size= (img_size_p//2))\n    self.layer2_p = self._make_layer(block_2, int(256 * s), layers[1], stride=2, kernel_size=(img_size_p//2),\n                                    dilate=replace_stride_with_dilation[0])\n    self.layer3_p = self._make_layer(block_2, int(512 * s), layers[2], stride=2, kernel_size=(img_size_p//4),\n                                    dilate=replace_stride_with_dilation[1])\n    self.layer4_p = self._make_layer(block_2, int(1024 * s), layers[3], stride=2, kernel_size=(img_size_p//8),\n                                    dilate=replace_stride_with_dilation[2])\n    \n    # Decoder\n    self.decoder1_p = nn.Conv2d(int(1024 *2*s)      ,        int(1024*2*s), kernel_size=3, stride=2, padding=1)\n    self.decoder2_p = nn.Conv2d(int(1024  *2*s)     , int(1024*s), kernel_size=3, stride=1, padding=1)\n    self.decoder3_p = nn.Conv2d(int(1024*s),  int(512*s), kernel_size=3, stride=1, padding=1)\n    self.decoder4_p = nn.Conv2d(int(512*s) ,  int(256*s), kernel_size=3, stride=1, padding=1)\n    self.decoder5_p = nn.Conv2d(int(256*s) , int(128*s) , kernel_size=3, stride=1, padding=1)\n\n    self.decoderf = nn.Conv2d(int(128*s) , int(128*s) , kernel_size=3, stride=1, padding=1)\n    self.adjust_p   = nn.Conv2d(int(128*s) , num_classes, kernel_size=1, stride=1, padding=0)\n    self.soft_p     = nn.Softmax(dim=1)\n\n\n  def _make_layer(self, block, planes, blocks, kernel_size=56, stride=1, dilate=False):\n    norm_layer = self._norm_layer\n    downsample = None\n    previous_dilation = self.dilation\n    if dilate:\n        self.dilation *= stride\n        stride = 1\n    if stride != 1 or self.inplanes != planes * block.expansion:\n        downsample = nn.Sequential(\n            conv1x1(self.inplanes, planes * block.expansion, stride),\n            norm_layer(planes * block.expansion),\n        )\n\n    layers = []\n    layers.append(block(self.inplanes, planes, stride, downsample, groups=self.groups,\n                        base_width=self.base_width, dilation=previous_dilation, \n                        norm_layer=norm_layer, kernel_size=kernel_size))\n    self.inplanes = planes * block.expansion\n    if stride != 1:\n        kernel_size = kernel_size // 2\n\n    for _ in range(1, blocks):\n        layers.append(block(self.inplanes, planes, groups=self.groups,\n                            base_width=self.base_width, dilation=self.dilation,\n                            norm_layer=norm_layer, kernel_size=kernel_size))\n\n    return nn.Sequential(*layers)\n\n  def _forward_impl(self, x):\n\n    xin = x.clone()\n    x = self.conv1(x)\n    x = self.bn1(x)\n    x = self.relu(x)\n    x = self.conv2(x)\n    x = self.bn2(x)\n    x = self.relu(x)\n    x = self.conv3(x)\n    x = self.bn3(x)\n    # x = F.max_pool2d(x,2,2)\n    x = self.relu(x)\n    \n    # x = self.maxpool(x)\n    # pdb.set_trace()\n    x1 = self.layer1(x)\n    # print(x1.shape)\n    x2 = self.layer2(x1)\n    # print(x2.shape)\n    # x3 = self.layer3(x2)\n    # # print(x3.shape)\n    # x4 = self.layer4(x3)\n    # # print(x4.shape)\n    # x = F.relu(F.interpolate(self.decoder1(x4), scale_factor=(2,2), mode ='bilinear'))\n    # x = torch.add(x, x4)\n    # x = F.relu(F.interpolate(self.decoder2(x4) , scale_factor=(2,2), mode ='bilinear'))\n    # x = torch.add(x, x3)\n    # x = F.relu(F.interpolate(self.decoder3(x3) , scale_factor=(2,2), mode ='bilinear'))\n    # x = torch.add(x, x2)\n    x = F.relu(F.interpolate(self.decoder4(x2) , scale_factor=(2,2), mode ='bilinear'))\n    x = torch.add(x, x1)\n    x = F.relu(F.interpolate(self.decoder5(x) , scale_factor=(2,2), mode ='bilinear'))\n    # print(x.shape)\n    \n    # end of full image training \n\n    # y_out = torch.ones((1,2,128,128))\n    x_loc = x.clone()\n    # x = F.relu(F.interpolate(self.decoder5(x) , scale_factor=(2,2), mode ='bilinear'))\n    #start \n    for i in range(0,4):\n        for j in range(0,4):\n\n            x_p = xin[:,:,32*i:32*(i+1),32*j:32*(j+1)]\n            # begin patch wise\n            x_p = self.conv1_p(x_p)\n            x_p = self.bn1_p(x_p)\n            # x = F.max_pool2d(x,2,2)\n            x_p = self.relu(x_p)\n\n            x_p = self.conv2_p(x_p)\n            x_p = self.bn2_p(x_p)\n            # x = F.max_pool2d(x,2,2)\n            x_p = self.relu(x_p)\n            x_p = self.conv3_p(x_p)\n            x_p = self.bn3_p(x_p)\n            # x = F.max_pool2d(x,2,2)\n            x_p = self.relu(x_p)\n            \n            # x = self.maxpool(x)\n            # pdb.set_trace()\n            x1_p = self.layer1_p(x_p)\n            # print(x1.shape)\n            x2_p = self.layer2_p(x1_p)\n            # print(x2.shape)\n            x3_p = self.layer3_p(x2_p)\n            # # print(x3.shape)\n            x4_p = self.layer4_p(x3_p)\n            \n            x_p = F.relu(F.interpolate(self.decoder1_p(x4_p), scale_factor=(2,2), mode ='bilinear'))\n            x_p = torch.add(x_p, x4_p)\n            x_p = F.relu(F.interpolate(self.decoder2_p(x_p) , scale_factor=(2,2), mode ='bilinear'))\n            x_p = torch.add(x_p, x3_p)\n            x_p = F.relu(F.interpolate(self.decoder3_p(x_p) , scale_factor=(2,2), mode ='bilinear'))\n            x_p = torch.add(x_p, x2_p)\n            x_p = F.relu(F.interpolate(self.decoder4_p(x_p) , scale_factor=(2,2), mode ='bilinear'))\n            x_p = torch.add(x_p, x1_p)\n            x_p = F.relu(F.interpolate(self.decoder5_p(x_p) , scale_factor=(2,2), mode ='bilinear'))\n            \n            x_loc[:,:,32*i:32*(i+1),32*j:32*(j+1)] = x_p\n\n    x = torch.add(x,x_loc)\n    x = F.relu(self.decoderf(x))\n    \n    x = self.adjust(F.relu(x))\n\n    # pdb.set_trace()\n    return x\n\n  def forward(self, x):\n    return self._forward_impl(x)","metadata":{"id":"xcGaiR1amg6f","execution":{"iopub.status.busy":"2023-12-01T15:49:23.236006Z","iopub.execute_input":"2023-12-01T15:49:23.236280Z","iopub.status.idle":"2023-12-01T15:49:23.330128Z","shell.execute_reply.started":"2023-12-01T15:49:23.236246Z","shell.execute_reply":"2023-12-01T15:49:23.329374Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def MedT(pretrained=False, **kwargs):\n    model = medt_net(AxialBlock_dynamic,AxialBlock_wopos, [1, 2, 4, 1], s= 0.125,  **kwargs)\n    return model\n\ndef logo(pretrained=False, **kwargs):\n    model = medt_net(AxialBlock,AxialBlock, [1, 2, 4, 1], s= 0.125, **kwargs)\n    return model","metadata":{"id":"OfeVZvgHnE0A","execution":{"iopub.status.busy":"2023-12-01T15:49:32.850846Z","iopub.execute_input":"2023-12-01T15:49:32.851597Z","iopub.status.idle":"2023-12-01T15:49:32.900911Z","shell.execute_reply.started":"2023-12-01T15:49:32.851559Z","shell.execute_reply":"2023-12-01T15:49:32.900163Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Loss and Metric","metadata":{"id":"RgTY707XdxZB"}},{"cell_type":"code","source":"from torch.nn.functional import cross_entropy\nfrom torch.nn.modules.loss import _WeightedLoss","metadata":{"id":"-eSA9jNod2HY","execution":{"iopub.status.busy":"2023-12-01T15:49:39.900598Z","iopub.execute_input":"2023-12-01T15:49:39.900862Z","iopub.status.idle":"2023-12-01T15:49:39.948366Z","shell.execute_reply.started":"2023-12-01T15:49:39.900833Z","shell.execute_reply":"2023-12-01T15:49:39.947685Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import torch\nfrom torch.nn.functional import cross_entropy\nfrom torch.nn.modules.loss import _WeightedLoss\n\n\nEPSILON = 1e-32\n\n\nclass LogNLLLoss(_WeightedLoss):\n    __constants__ = ['weight', 'reduction', 'ignore_index']\n\n    def __init__(self, weight=None, size_average=None, reduce=None, reduction=None,\n                 ignore_index=-100):\n        super(LogNLLLoss, self).__init__(weight, size_average, reduce, reduction)\n        self.ignore_index = ignore_index\n\n    def forward(self, y_input, y_target):\n        # y_input = torch.log(y_input + EPSILON)\n        return cross_entropy(y_input, y_target, weight=self.weight,\n                             ignore_index=self.ignore_index)\n\n\ndef classwise_iou(output, gt):\n    \"\"\"\n    Args:\n        output: torch.Tensor of shape (n_batch, n_classes, image.shape)\n        gt: torch.LongTensor of shape (n_batch, image.shape)\n    \"\"\"\n    #dims = (0, *range(2, len(output.shape)))\n    #gt = torch.zeros_like(output).scatter_(1, gt[:, None, :], 1)\n    output = torch.argmax(output, dim=1)\n    intersection = output*gt\n    union = output + gt - intersection\n    #classwise_iou = (intersection.sum(dim=dims).float() + EPSILON) / (union.sum(dim=dims) + EPSILON)\n    classwise_iou = (intersection.sum().float() + EPSILON) / (union.sum() + EPSILON)\n    return classwise_iou\n\n\ndef classwise_f1(output, gt):\n    \"\"\"\n    Args:\n        output: torch.Tensor of shape (n_batch, n_classes, image.shape)\n        gt: torch.LongTensor of shape (n_batch, image.shape)\n    \"\"\"\n\n    epsilon = 1e-20\n    n_classes = output.shape[1]\n\n    output = torch.argmax(output, dim=1)\n    #print(output)\n    true_positives = torch.tensor([((output == i) * (gt == i)).sum() for i in range(n_classes)]).float()\n    true_positives = true_positives[1].item()\n    selected = ((output == 1)).sum().float()\n    relevant = ((gt == 1)).sum().float()\n    #selected = torch.tensor([(output == i).sum() for i in range(n_classes)]).float()\n    #relevant = torch.tensor([(gt == i).sum() for i in range(n_classes)]).float()\n    #print(\"relevant:\",relevant)\n    #print(\"selected:\",selected)\n    \n    precision = (true_positives + epsilon) / (selected + epsilon)\n    recall = (true_positives + epsilon) / (relevant + epsilon)\n    #print(precision)\n    #print(recall)\n    classwise_f1 = 2 * (precision * recall) / (precision + recall)\n\n    return classwise_f1\n\n\ndef make_weighted_metric(classwise_metric):\n    \"\"\"\n    Args:\n        classwise_metric: classwise metric like classwise_IOU or classwise_F1\n    \"\"\"\n\n    def weighted_metric(output, gt, weights=None):\n\n        # dimensions to sum over\n        dims = (0, *range(2, len(output.shape)))\n\n        # default weights\n        if weights == None:\n            weights = torch.ones(output.shape[1]) / output.shape[1]\n        else:\n            # creating tensor if needed\n            if len(weights) != output.shape[1]:\n                raise ValueError(\"The number of weights must match with the number of classes\")\n            if not isinstance(weights, torch.Tensor):\n                weights = torch.tensor(weights)\n            # normalizing weights\n            weights /= torch.sum(weights)\n\n        classwise_scores = classwise_metric(output, gt).cpu()\n\n        return classwise_scores \n\n    return weighted_metric","metadata":{"id":"VLP0LTw6gmLJ","execution":{"iopub.status.busy":"2023-12-01T15:49:43.432073Z","iopub.execute_input":"2023-12-01T15:49:43.432734Z","iopub.status.idle":"2023-12-01T15:49:43.494046Z","shell.execute_reply.started":"2023-12-01T15:49:43.432694Z","shell.execute_reply":"2023-12-01T15:49:43.493334Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#output, gt = torch.ones(3, 2, 5, 5), torch.ones(3, 5, 5).long()\ngt = torch.ones(3, 5, 5).long()\noutput = torch.stack([torch.zeros(3,5,5), torch.ones(3,5,5)], dim=1)\nprint(classwise_f1(output, gt))","metadata":{"id":"iEXIf5fhgdtL","outputId":"26da9747-df40-46e4-f9e3-94c503984cd1","execution":{"iopub.status.busy":"2023-12-01T15:49:50.898697Z","iopub.execute_input":"2023-12-01T15:49:50.898962Z","iopub.status.idle":"2023-12-01T15:49:50.955014Z","shell.execute_reply.started":"2023-12-01T15:49:50.898929Z","shell.execute_reply":"2023-12-01T15:49:50.954228Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"classwise_iou(output, gt)\n#print(output.shape)","metadata":{"id":"4TG4PMpqTYnp","outputId":"31b75b78-1f14-4d5b-902d-9de937bdb6da","execution":{"iopub.status.busy":"2023-12-01T15:49:53.994572Z","iopub.execute_input":"2023-12-01T15:49:53.995125Z","iopub.status.idle":"2023-12-01T15:49:54.045039Z","shell.execute_reply.started":"2023-12-01T15:49:53.995087Z","shell.execute_reply":"2023-12-01T15:49:54.044247Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Train and Test","metadata":{"id":"dtsRItgkhaEI"}},{"cell_type":"code","source":"from tqdm import tqdm","metadata":{"id":"m8QYEeuehdrI","execution":{"iopub.status.busy":"2023-12-01T15:49:57.876004Z","iopub.execute_input":"2023-12-01T15:49:57.876721Z","iopub.status.idle":"2023-12-01T15:49:57.924185Z","shell.execute_reply.started":"2023-12-01T15:49:57.876681Z","shell.execute_reply":"2023-12-01T15:49:57.923418Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"lr = 1e-3\nDEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\nNUM_EPOCHS = 10\nIMG_SIZE = 128\nIMG_CHANEL = 3\nsave_freq = 10","metadata":{"id":"hddwVtAriFRh","execution":{"iopub.status.busy":"2023-12-01T15:50:53.816560Z","iopub.execute_input":"2023-12-01T15:50:53.817169Z","iopub.status.idle":"2023-12-01T15:50:53.867546Z","shell.execute_reply.started":"2023-12-01T15:50:53.817131Z","shell.execute_reply":"2023-12-01T15:50:53.866867Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def save_checkpoint(state, filename=\"my_checkpoint.pth.tar\"):\n    print(\"=>Saving checkpoint\")\n    torch.save(state, filename)\ndef load_checkpoint(checkpoint, model):\n    print(\"=> Loading checkpoint\")\n    model.load_state_dict(checkpoint[\"state_dict\"])","metadata":{"id":"ziziAtm3s21g","execution":{"iopub.status.busy":"2023-12-01T15:50:55.957219Z","iopub.execute_input":"2023-12-01T15:50:55.957607Z","iopub.status.idle":"2023-12-01T15:50:56.007351Z","shell.execute_reply.started":"2023-12-01T15:50:55.957571Z","shell.execute_reply":"2023-12-01T15:50:56.006567Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model = MedT(img_size = IMG_SIZE, imgchan = IMG_CHANEL)\nmodel.cuda()\ncriterion = LogNLLLoss()\noptimizer = torch.optim.Adam(model.parameters(), lr=lr, weight_decay=1e-5)\nscaler = torch.cuda.amp.GradScaler()\nreset_seed()","metadata":{"id":"CFPWpcPchjP8","execution":{"iopub.status.busy":"2023-12-01T15:50:57.946372Z","iopub.execute_input":"2023-12-01T15:50:57.947129Z","iopub.status.idle":"2023-12-01T15:50:58.055108Z","shell.execute_reply.started":"2023-12-01T15:50:57.947085Z","shell.execute_reply":"2023-12-01T15:50:58.054423Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def train_fn(loader, model, optimizer, loss_fn, scaler):\n  train_running_loss = 0\n  counter = 0\n  loop = tqdm(loader)\n  for batch_idx, (data, targets) in enumerate(loop):\n    data = data.to(device=DEVICE)\n    targets = targets.long().to(DEVICE)\n\n    # forward\n\n    with torch.cuda.amp.autocast():\n      prediction = model(data)\n      loss = loss_fn(prediction, targets)\n    \"\"\"\n    # backward\n    optimizer.zero_grad()\n    scaler.scale(loss).backward()\n    #loss.backward()\n    scaler.step(optimizer)\n    scaler.update()\n    #optimizer.step()\n    \"\"\"\n    # backward\n    optimizer.zero_grad()\n    scaler.scale(loss).backward()\n    scaler.step(optimizer)\n    scaler.update()\n\n    # update tqdm loop\n    loop.set_postfix(loss = loss.item())\n    train_running_loss += loss.item()\n    counter += 1\n  return train_running_loss / counter\n\ndef check_accuracy(loader, model, loss_fn, device=\"cuda\"):\n  my_f1 = 0\n  my_iou = 0\n  val_running_loss = 0\n  with torch.no_grad():\n    for X, y in loader:\n      batch_size = X.shape[0]\n      X = X.to(device)\n      y = y.long().to(device)\n      preds = model(X)\n      loss = loss_fn(preds, y)\n      val_running_loss += loss.item()\n      tmp = preds.detach().cpu()\n      tmp[tmp >= 0.5] = 1\n      tmp[tmp < 0.5] = 0\n      tmp2 = y.detach().cpu()\n      \n      \n      tmp = tmp.to(torch.int32)\n      my_f1 += classwise_f1(tmp, tmp2).item()\n      my_iou += classwise_iou(tmp, tmp2).item()\n  print(f\"IoU score: {my_iou/len(loader)}\")\n  print(f\"F1 score: {my_f1/len(loader)} \")\n  return val_running_loss\n","metadata":{"id":"9GL2oqf5jTn4","execution":{"iopub.status.busy":"2023-12-01T15:51:08.562242Z","iopub.execute_input":"2023-12-01T15:51:08.562972Z","iopub.status.idle":"2023-12-01T15:51:08.619350Z","shell.execute_reply.started":"2023-12-01T15:51:08.562933Z","shell.execute_reply":"2023-12-01T15:51:08.618498Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#/kaggle/input/qu-data-my/Test Image\nmy_checkpoint = torch.load(\"/kaggle/input/check-point/8497dice_7398iou.pth.tar\")\nmodel.load_state_dict(my_checkpoint['state_dict'])","metadata":{"execution":{"iopub.status.busy":"2023-12-01T15:55:36.997878Z","iopub.execute_input":"2023-12-01T15:55:36.998149Z","iopub.status.idle":"2023-12-01T15:55:37.580148Z","shell.execute_reply.started":"2023-12-01T15:55:36.998122Z","shell.execute_reply":"2023-12-01T15:55:37.578585Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X,y = val_ds[0]\nprint(y.shape)","metadata":{"execution":{"iopub.status.busy":"2023-12-01T15:55:49.216168Z","iopub.execute_input":"2023-12-01T15:55:49.216592Z","iopub.status.idle":"2023-12-01T15:55:49.280047Z","shell.execute_reply.started":"2023-12-01T15:55:49.216553Z","shell.execute_reply":"2023-12-01T15:55:49.279193Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.imshow(X.transpose(0,2).transpose(0,1))","metadata":{"execution":{"iopub.status.busy":"2023-12-01T15:55:51.669098Z","iopub.execute_input":"2023-12-01T15:55:51.669754Z","iopub.status.idle":"2023-12-01T15:55:51.730519Z","shell.execute_reply.started":"2023-12-01T15:55:51.669708Z","shell.execute_reply":"2023-12-01T15:55:51.729418Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.imshow(y)","metadata":{"execution":{"iopub.status.busy":"2023-12-01T15:55:53.866095Z","iopub.execute_input":"2023-12-01T15:55:53.866595Z","iopub.status.idle":"2023-12-01T15:55:54.193401Z","shell.execute_reply.started":"2023-12-01T15:55:53.866547Z","shell.execute_reply":"2023-12-01T15:55:54.192693Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"for param in model.parameters():\n    param.requires_grad = True","metadata":{"execution":{"iopub.status.busy":"2023-12-01T15:56:04.689009Z","iopub.execute_input":"2023-12-01T15:56:04.689271Z","iopub.status.idle":"2023-12-01T15:56:04.738523Z","shell.execute_reply.started":"2023-12-01T15:56:04.689245Z","shell.execute_reply":"2023-12-01T15:56:04.737728Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"code","source":"loss_history = {\"train\":[], \"val\":[]}\nfor epoch in range(151, NUM_EPOCHS):\n  #model.cuda()\n  print(f\"============Epoch {epoch} ============ \")\n  train_loss = train_fn(train_loader, model, optimizer, criterion, scaler)\n  \"\"\"\n  if epoch == 10:\n    for param in model.parameters():\n      param.requires_grad = True\n  \"\"\"\n  if (epoch % save_freq) == 0:\n    checkpoint = {\n        \"state_dict\": model.state_dict(),\n        \"optimizer\" : optimizer.state_dict(),\n    }\n    save_checkpoint(checkpoint)\n    val_loss = check_accuracy(val_loader, model, criterion, DEVICE)\n    loss_history[\"val\"].append(val_loss)\n    loss_history[\"train\"].append(train_loss)\n","metadata":{"id":"7TZvxtptrU_h","outputId":"68aff297-ce3d-4e05-a506-b2752796b0d8","execution":{"iopub.status.busy":"2023-12-01T15:56:44.670968Z","iopub.execute_input":"2023-12-01T15:56:44.671726Z","iopub.status.idle":"2023-12-01T15:56:44.722456Z","shell.execute_reply.started":"2023-12-01T15:56:44.671688Z","shell.execute_reply":"2023-12-01T15:56:44.721620Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"save_checkpoint(checkpoint)","metadata":{"execution":{"iopub.status.busy":"2023-12-01T15:52:14.641586Z","iopub.execute_input":"2023-12-01T15:52:14.641862Z","iopub.status.idle":"2023-12-01T15:52:14.706686Z","shell.execute_reply.started":"2023-12-01T15:52:14.641833Z","shell.execute_reply":"2023-12-01T15:52:14.705716Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_loss = check_accuracy(test_loader, model, criterion, DEVICE)","metadata":{"execution":{"iopub.status.busy":"2023-12-01T15:52:17.183081Z","iopub.execute_input":"2023-12-01T15:52:17.183697Z","iopub.status.idle":"2023-12-01T15:52:29.057424Z","shell.execute_reply.started":"2023-12-01T15:52:17.183658Z","shell.execute_reply":"2023-12-01T15:52:29.056636Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"my_index = torch.randint(0, len(test_ds),size=(1,)).item()\nwith torch.no_grad():\n    X_test, y_test = test_ds[my_index]\n    pred_test = model(X_test.unsqueeze(0).cuda())\n    pred_test = torch.argmax(pred_test, dim=1)\n    my_figrue, axarr = plt.subplots(1,3)\n    axarr[0].imshow(X_test.cpu().transpose(0,2).transpose(0,1))\n    axarr[1].imshow(y_test.cpu(),cmap='gray')\n    axarr[2].imshow(pred_test.squeeze(0).cpu(),cmap='gray')","metadata":{"execution":{"iopub.status.busy":"2023-12-01T15:53:05.425590Z","iopub.execute_input":"2023-12-01T15:53:05.425872Z","iopub.status.idle":"2023-12-01T15:53:06.083050Z","shell.execute_reply.started":"2023-12-01T15:53:05.425842Z","shell.execute_reply":"2023-12-01T15:53:06.082296Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"my_index = torch.randint(0, len(test_ds),size=(1,)).item()\nwith torch.no_grad():\n    X_test, y_test = test_ds[my_index]\n    pred_test = model(X_test.unsqueeze(0).cuda())\n    pred_test = torch.argmax(pred_test, dim=1)\n    my_figrue, axarr = plt.subplots(1,3)\n    axarr[0].imshow(X_test.cpu().transpose(0,2).transpose(0,1))\n    axarr[1].imshow(y_test.cpu())\n    axarr[2].imshow(pred_test.squeeze(0).cpu())","metadata":{"execution":{"iopub.status.busy":"2023-12-01T15:52:38.877071Z","iopub.execute_input":"2023-12-01T15:52:38.877324Z","iopub.status.idle":"2023-12-01T15:52:39.534991Z","shell.execute_reply.started":"2023-12-01T15:52:38.877297Z","shell.execute_reply":"2023-12-01T15:52:39.534187Z"},"trusted":true},"execution_count":null,"outputs":[]}]}