Spaces:
Running
Running
File size: 1,914 Bytes
51ba5d6 8f8ef33 51ba5d6 8f8ef33 51ba5d6 0f2d9f6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 |
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)
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)
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())
|