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from transformers import PreTrainedModel
from timm.models.resnet import BasicBlock, Bottleneck, ResNet
from resnet_model.configuration_resnet import ResnetConfig
import torch
import timm
BLOCK_MAPPING = {"basic": BasicBlock, "bottleneck": Bottleneck}
class ResnetModel(PreTrainedModel): # 继承基类
config_class = ResnetConfig
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): # 继承基类
config_class = ResnetConfig
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): # 前向方法
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
# resnet50d_config = ResnetConfig.from_pretrained("../custom-resnet")
# resnet50d = ResnetModelForImageClassification(resnet50d_config)
# pretrained_model = timm.create_model("resnet50d", pretrained=True)
# resnet50d.model.load_state_dict(pretrained_model.state_dict())
AutoConfig.register("resnet-t", ResnetConfig) # 注册配置
AutoModel.register(ResnetConfig, ResnetModel) # 注册普适模型
AutoModelForImageClassification.register(ResnetConfig, ResnetModelForImageClassification) # 注册图像分类模型 |