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# © Recursion Pharmaceuticals 2024
from typing import Dict
import timm.models.vision_transformer as vit
import torch
def build_imagenet_baselines() -> Dict[str, torch.jit.ScriptModule]:
"""This returns the prepped imagenet encoders from timm, not bad for microscopy data."""
vit_backbones = [
_make_vit(vit.vit_small_patch16_384),
_make_vit(vit.vit_base_patch16_384),
_make_vit(vit.vit_base_patch8_224),
_make_vit(vit.vit_large_patch16_384),
]
model_names = [
"vit_small_patch16_384",
"vit_base_patch16_384",
"vit_base_patch8_224",
"vit_large_patch16_384",
]
imagenet_encoders = list(map(_make_torchscripted_encoder, vit_backbones))
return {name: model for name, model in zip(model_names, imagenet_encoders)}
def _make_torchscripted_encoder(vit_backbone) -> torch.jit.ScriptModule:
dummy_input = torch.testing.make_tensor(
(2, 6, 256, 256),
low=0,
high=255,
dtype=torch.uint8,
device=torch.device("cpu"),
)
encoder = torch.nn.Sequential(
Normalizer(),
torch.nn.LazyInstanceNorm2d(
affine=False, track_running_stats=False
), # this module performs self-standardization, very important
vit_backbone,
).to(device="cpu")
_ = encoder(dummy_input) # get those lazy modules built
return torch.jit.freeze(torch.jit.script(encoder.eval()))
def _make_vit(constructor):
return constructor(
pretrained=True, # download imagenet weights
img_size=256, # 256x256 crops
in_chans=6, # we expect 6-channel microscopy images
num_classes=0,
fc_norm=None,
class_token=True,
global_pool="avg", # minimal perf diff btwn "cls" and "avg"
)
class Normalizer(torch.nn.Module):
def forward(self, pixels: torch.Tensor) -> torch.Tensor:
pixels = pixels.float()
pixels /= 255.0
return pixels
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