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import sys
import torch.nn as nn
import os.path as osp
from torchvision import models
import torch.nn.functional as F
from registry import MODEL_REGISTRY
root_path = osp.abspath(osp.join(__file__, osp.pardir, osp.pardir))
sys.path.append(root_path)
# ============================= ResNets =============================
# @MODEL_REGISTRY.register()
# class ResNet18(nn.Module):
# def __init__(self, model_args):
# super(ResNet18, self).__init__()
# self.num_classes = model_args.get("num_classes", 1)
# self.resnet = models.resnet18(weights=None, num_classes=self.num_classes)
# def forward(self, x, masks=None):
# return self.resnet(x)
# @MODEL_REGISTRY.register()
# class ResNet18(nn.Module):
# def __init__(self, model_args):
# super(ResNet18, self).__init__()
# self.num_classes = model_args.get("num_classes", 1)
# self.resnet = models.resnet18(weights=None, num_classes=self.num_classes)
# def forward(self, x, masks=None):
# # Calculate the padding dynamically based on the input size
# height, width = x.shape[2], x.shape[3]
# pad_height = max(0, (224 - height) // 2)
# pad_width = max(0, (224 - width) // 2)
# # Apply padding
# x = F.pad(
# x, (pad_width, pad_width, pad_height, pad_height), mode="constant", value=0
# )
# x = self.resnet(x)
# return x
@MODEL_REGISTRY.register()
class ResNet18(nn.Module):
def __init__(self, model_args):
super(ResNet18, self).__init__()
self.num_classes = model_args.get("num_classes", 1)
self.resnet = models.resnet18(weights=None)
self.regression_head = nn.Linear(1000, self.num_classes)
def forward(self, x, masks=None):
# Calculate the padding dynamically based on the input size
height, width = x.shape[2], x.shape[3]
pad_height = max(0, (224 - height) // 2)
pad_width = max(0, (224 - width) // 2)
# Apply padding
x = F.pad(
x, (pad_width, pad_width, pad_height, pad_height), mode="constant", value=0
)
x = self.resnet(x)
x = self.regression_head(x)
return x
# @MODEL_REGISTRY.register()
# class ResNet50(nn.Module):
# def __init__(self, model_args):
# super(ResNet50, self).__init__()
# self.num_classes = model_args.get("num_classes", 1)
# self.resnet = models.resnet50(weights=None, num_classes=self.num_classes)
# def forward(self, x, masks=None):
# return self.resnet(x)
# @MODEL_REGISTRY.register()
# class ResNet50(nn.Module):
# def __init__(self, model_args):
# super(ResNet50, self).__init__()
# self.num_classes = model_args.get("num_classes", 1)
# self.resnet = models.resnet50(weights=None, num_classes=self.num_classes)
# def forward(self, x, masks=None):
# # Calculate the padding dynamically based on the input size
# height, width = x.shape[2], x.shape[3]
# pad_height = max(0, (224 - height) // 2)
# pad_width = max(0, (224 - width) // 2)
# # Apply padding
# x = F.pad(
# x, (pad_width, pad_width, pad_height, pad_height), mode="constant", value=0
# )
# x = self.resnet(x)
# return x
@MODEL_REGISTRY.register()
class ResNet50(nn.Module):
def __init__(self, model_args):
super(ResNet50, self).__init__()
self.num_classes = model_args.get("num_classes", 1)
self.resnet = models.resnet50(weights=None)
self.regression_head = nn.Linear(1000, self.num_classes)
def forward(self, x, masks=None):
# Calculate the padding dynamically based on the input size
height, width = x.shape[2], x.shape[3]
pad_height = max(0, (224 - height) // 2)
pad_width = max(0, (224 - width) // 2)
# Apply padding
x = F.pad(
x, (pad_width, pad_width, pad_height, pad_height), mode="constant", value=0
)
x = self.resnet(x)
x = self.regression_head(x)
return x
# print("Registered models in MODEL_REGISTRY:", MODEL_REGISTRY.keys())
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