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import torch
import torch.nn as nn
import torchvision.models as tvm
class VGG19(nn.Module):
def __init__(self, pretrained=False, amp=False, amp_dtype=torch.float16) -> None:
super().__init__()
self.layers = nn.ModuleList(tvm.vgg19_bn(pretrained=pretrained).features[:40])
# Maxpool layers: 6, 13, 26, 39
self.amp = amp
self.amp_dtype = amp_dtype
def forward(self, x, **kwargs):
with torch.autocast("cuda", enabled=self.amp, dtype=self.amp_dtype):
feats = []
sizes = []
for layer in self.layers:
if isinstance(layer, nn.MaxPool2d):
feats.append(x)
sizes.append(x.shape[-2:])
x = layer(x)
return feats, sizes
class VGG(nn.Module):
def __init__(
self, size="19", pretrained=False, amp=False, amp_dtype=torch.float16
) -> None:
super().__init__()
if size == "11":
self.layers = nn.ModuleList(
tvm.vgg11_bn(pretrained=pretrained).features[:22]
)
elif size == "13":
self.layers = nn.ModuleList(
tvm.vgg13_bn(pretrained=pretrained).features[:28]
)
elif size == "19":
self.layers = nn.ModuleList(
tvm.vgg19_bn(pretrained=pretrained).features[:40]
)
# Maxpool layers: 6, 13, 26, 39
self.amp = amp
self.amp_dtype = amp_dtype
def forward(self, x, **kwargs):
with torch.autocast("cuda", enabled=self.amp, dtype=self.amp_dtype):
feats = []
sizes = []
for layer in self.layers:
if isinstance(layer, nn.MaxPool2d):
feats.append(x)
sizes.append(x.shape[-2:])
x = layer(x)
return feats, sizes
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