| | """Save CTransPath model in TorchScript format. |
| | |
| | Adapted from https://github.com/Xiyue-Wang/TransPath |
| | |
| | Licensed GPL 3.0. |
| | """ |
| |
|
| | import sys |
| |
|
| | |
| | sys.path.append("timm-0.5.4/") |
| |
|
| | import timm |
| | from timm.models.layers.helpers import to_2tuple |
| | import torch |
| | import torch.nn as nn |
| |
|
| | assert timm.__version__ == "0.5.4" |
| |
|
| |
|
| | class ConvStem(nn.Module): |
| | def __init__( |
| | self, |
| | img_size=224, |
| | patch_size=4, |
| | in_chans=3, |
| | embed_dim=768, |
| | norm_layer=None, |
| | flatten=True, |
| | ): |
| | super().__init__() |
| |
|
| | assert patch_size == 4 |
| | assert embed_dim % 8 == 0 |
| |
|
| | img_size = to_2tuple(img_size) |
| | patch_size = to_2tuple(patch_size) |
| | self.img_size = img_size |
| | self.patch_size = patch_size |
| | self.grid_size = (img_size[0] // patch_size[0], img_size[1] // patch_size[1]) |
| | self.num_patches = self.grid_size[0] * self.grid_size[1] |
| | self.flatten = flatten |
| |
|
| | stem = [] |
| | input_dim, output_dim = 3, embed_dim // 8 |
| | for l in range(2): |
| | stem.append( |
| | nn.Conv2d( |
| | input_dim, |
| | output_dim, |
| | kernel_size=3, |
| | stride=2, |
| | padding=1, |
| | bias=False, |
| | ) |
| | ) |
| | stem.append(nn.BatchNorm2d(output_dim)) |
| | stem.append(nn.ReLU(inplace=True)) |
| | input_dim = output_dim |
| | output_dim *= 2 |
| | stem.append(nn.Conv2d(input_dim, embed_dim, kernel_size=1)) |
| | self.proj = nn.Sequential(*stem) |
| |
|
| | self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity() |
| |
|
| | def forward(self, x): |
| | B, C, H, W = x.shape |
| | assert ( |
| | H == self.img_size[0] and W == self.img_size[1] |
| | ), f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})." |
| | x = self.proj(x) |
| | if self.flatten: |
| | x = x.flatten(2).transpose(1, 2) |
| | x = self.norm(x) |
| | return x |
| |
|
| |
|
| | def ctranspath(): |
| | model = timm.create_model( |
| | "swin_tiny_patch4_window7_224", embed_layer=ConvStem, pretrained=False |
| | ) |
| | return model |
| |
|
| |
|
| | model = ctranspath() |
| | model.head = torch.nn.Identity() |
| | td = torch.load("ctranspath.pth") |
| | model.load_state_dict(td["model"], strict=True) |
| |
|
| | jitted = torch.jit.script(model) |
| | torch.jit.save(jitted, "torchscript_model.pt") |
| |
|
| | torch.onnx.export( |
| | model, |
| | args=torch.ones(1, 3, 224, 224), |
| | f="model.onnx", |
| | input_names=["image"], |
| | output_names=["embedding"], |
| | dynamic_axes={ |
| | "image": {0: "batch_size"}, |
| | "embedding": {0: "batch_size"}, |
| | }, |
| | ) |
| |
|
