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Don't reuse nn.ReLU modules in CLIP ResNet #33

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Don't reuse nn.ReLU modules in CLIP ResNet #33

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23 changes: 12 additions & 11 deletions torchmultimodal/modules/encoders/clip_resnet_encoder.py
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Original file line number Diff line number Diff line change
Expand Up @@ -24,16 +24,18 @@ def __init__(self, inplanes: int, planes: int, stride: int = 1):
# an avgpool is performed after the second convolution when stride > 1
self.conv1 = nn.Conv2d(inplanes, planes, 1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.relu1 = nn.ReLU(inplace=True)

self.conv2 = nn.Conv2d(planes, planes, 3, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.relu2 = nn.ReLU(inplace=True)

self.avgpool = nn.AvgPool2d(stride) if stride > 1 else nn.Identity()

self.conv3 = nn.Conv2d(planes, planes * EXPANSION, 1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * EXPANSION)
self.relu3 = nn.ReLU(inplace=True)

self.relu = nn.ReLU(inplace=True)
self.downsample = None
self.stride = stride

Expand Down Expand Up @@ -62,16 +64,16 @@ def __init__(self, inplanes: int, planes: int, stride: int = 1):
def forward(self, x: torch.Tensor) -> torch.Tensor:
identity = x

out = self.relu(self.bn1(self.conv1(x)))
out = self.relu(self.bn2(self.conv2(out)))
out = self.relu1(self.bn1(self.conv1(x)))
out = self.relu2(self.bn2(self.conv2(out)))
out = self.avgpool(out)
out = self.bn3(self.conv3(out))

if self.downsample is not None:
identity = self.downsample(x)

out += identity
out = self.relu(out)
out = self.relu3(out)
return out


Expand Down Expand Up @@ -161,14 +163,16 @@ def __init__(
3, width // 2, kernel_size=3, stride=2, padding=1, bias=False
)
self.bn1 = nn.BatchNorm2d(width // 2)
self.relu1 = nn.ReLU(inplace=True)
self.conv2 = nn.Conv2d(
width // 2, width // 2, kernel_size=3, padding=1, bias=False
)
self.bn2 = nn.BatchNorm2d(width // 2)
self.relu2 = nn.ReLU(inplace=True)
self.conv3 = nn.Conv2d(width // 2, width, kernel_size=3, padding=1, bias=False)
self.bn3 = nn.BatchNorm2d(width)
self.relu3 = nn.ReLU(inplace=True)
self.avgpool = nn.AvgPool2d(2)
self.relu = nn.ReLU(inplace=True)

# residual layers
self._inplanes = width # this is a *mutable* variable used during construction
Expand Down Expand Up @@ -215,12 +219,9 @@ def initialize_parameters(self):

def forward(self, x: torch.Tensor) -> torch.Tensor:
def stem(x):
for conv, bn in [
(self.conv1, self.bn1),
(self.conv2, self.bn2),
(self.conv3, self.bn3),
]:
x = self.relu(bn(conv(x)))
x = self.relu1(self.bn1(self.conv1(x)))
x = self.relu2(self.bn2(self.conv2(x)))
x = self.relu3(self.bn3(self.conv3(x)))
x = self.avgpool(x)
return x

Expand Down