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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",
]
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