modeling.py

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

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


class ResnetModel(PreTrainedModel):
    config_class = ResnetConfig

    def __init__(self, config: ResnetConfig):
        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, inputs):
        return self.model.forward_features(inputs)


class ResnetModelForImageClassification(PreTrainedModel):
    config_class = ResnetConfig

    def __init__(self, config: ResnetConfig):
        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.functional.cross_entropy(logits, labels)
            return {"loss": loss, "logits": logits}
        return {"logits": logits}


from transformers import AutoConfig, AutoModel, AutoModelForImageClassification

AutoConfig.register("custom_resnet50d", ResnetConfig)
AutoModel.register(ResnetConfig, ResnetModel)
AutoModelForImageClassification.register(
    ResnetConfig, ResnetModelForImageClassification
)

ResnetConfig.register_for_auto_class()
ResnetModel.register_for_auto_class("AutoModel")
ResnetModelForImageClassification.register_for_auto_class(
    "AutoModelForImageClassification"
)

__all__ = [
    "ResnetModel",
    "ResnetModelForImageClassification",
]