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