custom_model.py

import timm
import torch
from transformers import PreTrainedModel
from timm.models.resnet import BasicBlock, Bottleneck, ResNet

import custom_configuration


BLOCK_MAPPING = {"basic": BasicBlock, "bottleneck": Bottleneck}


class ResnetModel(PreTrainedModel):
    config_class = custom_configuration.ResnetConfig  # not necessary unless you want to register model with auto classes

    def __init__(self, config):
        super().__init__(config)
        block_layer = BLOCK_MAPPING[config.block_type]
        self.model = ResNet(
            block_layer,
            config.layers,
            num_classes=config.num_classes,
            in_chans=config.input_channels,
            cardinality=config.cardinality,
            base_width=config.base_width,
            stem_width=config.stem_width,
            stem_type=config.stem_type,
            avg_down=config.avg_down,
        )

    def forward(self, tensor):
        return self.model.forward_features(tensor)


class ResnetModelForImageClassification(PreTrainedModel):
    config_class = custom_configuration.ResnetConfig  # not necessary unless you want to register model with auto classes

    def __init__(self, config):
        super().__init__(config)
        block_layer = BLOCK_MAPPING[config.block_type]
        self.model = ResNet(
            block_layer,
            config.layers,
            num_classes=config.num_classes,
            in_chans=config.input_channels,
            cardinality=config.cardinality,
            base_width=config.base_width,
            stem_width=config.stem_width,
            stem_type=config.stem_type,
            avg_down=config.avg_down,
        )

    def forward(self, tensor, labels=None):
        logits = self.model(tensor)
        if labels is not None:
            loss = torch.nn.cross_entropy(logits, labels)
            return {"loss": loss, "logits": logits}  # this form, with a loss key, is usable by the Trainer class
        return {"logits": logits}


if __name__ == "__main__":
    resnet50d_config = custom_configuration.ResnetConfig(block_type="bottleneck", stem_width=32, stem_type="deep", avg_down=True)
    resnet50d = ResnetModelForImageClassification(resnet50d_config)
    # resnet50d.save_pretrained("resnet50d")
    # resnet50d.push_to_hub("resnet50d")

    # transfer weights from pretrained model
    pretrained_model = timm.create_model("resnet50d", pretrained=True)
    resnet50d.model.load_state_dict(pretrained_model.state_dict())