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import torch
import torchvision
from torch import nn
def create_resnet50_model(num_classes:int=10, # 4
seed:int=42):
"""Creates an ResNet50 feature extractor model and transforms.
Args:
num_classes (int, optional): number of classes in the classifier head.
Defaults to 3.
seed (int, optional): random seed value. Defaults to 42.
Returns:
model (torch.nn.Module): ResNet50 feature extractor model.
transforms (torchvision.transforms): ResNet50 image transforms.
"""
# 1, 2, 3. Create ResNet50 pretrained weights, transforms and model
weights = torchvision.models.ResNet50_Weights.DEFAULT
transforms = weights.transforms()
model = torchvision.models.resnet50(weights=weights)
model.fc = nn.Linear(2048, 10)
# 4. Freeze all layers in base model
for param in model.parameters():
param.requires_grad = True # Set to False for model's other than ResNet
# 5. Change classifier head with random seed for reproducibility
torch.manual_seed(seed)
model.classifier = nn.Sequential(
nn.Dropout(p=0.3, inplace=True),
nn.Linear(in_features=2048
, out_features=num_classes), # If using EffnetB2 in_features = 1408, EffnetB0 in_features = 1280, if ResNet50 in_features = 2048
)
return model, transforms
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