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from transformers import PreTrainedModel
import torch
import torch.nn as nn
from torchvision import transforms
from transformers.models.mvp.modeling_mvp import CrossEntropyLoss
from .configuration_resnet import ResnetFeatureExtractorConfig
class ResnetFeatureExtractor(PreTrainedModel):
config_class = ResnetFeatureExtractorConfig
def __init__(self, config):
super().__init__(config)
if config.name == 'resnet152':
self.model = torch.hub.load('pytorch/vision:v0.10.0', 'resnet152', pretrained=False)
self.model.fc = nn.Identity()
self.model.to(self.device)
self.preprocess = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
def forward(self, images):
tensor = torch.stack([self.preprocess(image) for image in images]).to(self.device).float()
return self.model(tensor)
class ResnetModelForImageClassification(PreTrainedModel):
config_class = ResnetFeatureExtractorConfig
def __init__(self, config):
super().__init__(config)
if config.name == 'resnet152':
self.model = nn.Sequential(
nn.Linear(2048, 32),
nn.ReLU(),
nn.Linear(32, 2)
)
def forward(self, tensor, labels=None):
logits = self.model(tensor)
if labels is not None:
loss = CrossEntropyLoss()(logits, torch.tensor(labels))
return {"loss": loss, "logits": logits}
return {"logits": logits} |