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"""Save CTransPath model in TorchScript format.
Adapted from https://github.com/Xiyue-Wang/TransPath
Licensed GPL 3.0.
"""
import sys
# Use the TIMM library with modifications by the CTransPath authors.
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) # BCHW -> BNC
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(r"./ctranspath.pth")
model.load_state_dict(td["model"], strict=True)
jitted = torch.jit.script(model)
torch.jit.save(jitted, "torchscript_model.pt")
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