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c29babb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 | import timm
import torch
import torch.nn as nn
from PIL import Image
from timm.models.eva import Eva
class PerceptionEncoder(nn.Module):
def __init__(
self,
model_name="vit_pe_core_large_patch14_336",
img_size: None | int = None,
):
super().__init__()
if img_size is not None:
dynamic_img_size = True
self.backbone: Eva = timm.create_model(
model_name,
pretrained=True,
dynamic_img_size=dynamic_img_size,
)
# Get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(self.backbone)
if img_size is not None:
data_config["input_size"] = (3, img_size, img_size)
self._preprocess = timm.data.create_transform(**data_config, is_training=False)
# Remove head
self.backbone.head = nn.Identity()
self.features_dim = self.backbone.num_features
def preprocess(self, image: Image.Image) -> torch.Tensor:
return self._preprocess(image)
def forward(self, inputs: torch.Tensor) -> torch.Tensor:
return self.backbone(inputs)
def get_features_dim(self) -> int:
return self.features_dim
if __name__ == "__main__":
import autorootcwd # noqa: F401
from src.config import Backbone
from src.encoders._common import inference
model = PerceptionEncoder(Backbone.PerceptionEncoder_B_p16_224.value, img_size=224)
inference(model)
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