def _residual(self, x):
if self.normalize:
x = self.norm1(x)
x = self.actv(x)
x = self.conv1(x)
if self.downsample:
x = F.avg_pool2d(x, 2)
if self.normalize:
x = self.norm2(x)
x = self.actv(x)
x = self.conv2(x)
return x
def _forward(self, x):
branch1x1 = self.branch1x1(x)
branch5x5 = self.branch5x5_1(x)
branch5x5 = self.branch5x5_2(branch5x5)
branch3x3dbl = self.branch3x3dbl_1(x)
branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl)
branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)
branch_pool = self.branch_pool(branch_pool)
outputs = [branch1x1, branch5x5, branch3x3dbl, branch_pool]
return outputs
def forward(self, x):
# N x 768 x 17 x 17
x = F.avg_pool2d(x, kernel_size=5, stride=3)
# N x 768 x 5 x 5
x = self.conv0(x)
# N x 128 x 5 x 5
x = self.conv1(x)
# N x 768 x 1 x 1
# Adaptive average pooling
x = F.adaptive_avg_pool2d(x, (1, 1))
# N x 768 x 1 x 1
x = torch.flatten(x, 1)
# N x 768
x = self.fc(x)
# N x 1000
return x
def _forward(self, level, inp):
up1 = inp
up1 = self._sub_layers['b1_' + str(level)](up1)
low1 = F.avg_pool2d(inp, 2, stride=2)
low1 = self._sub_layers['b2_' + str(level)](low1)
if level > 1:
low2 = self._forward(level - 1, low1)
else:
low2 = low1
low2 = self._sub_layers['b2_plus_' + str(level)](low2)
low3 = low2
low3 = self._sub_layers['b3_' + str(level)](low3)
up2 = F.interpolate(low3, scale_factor=2, mode='nearest')
return up1 + up2
def _forward(self, x):
branch1x1 = self.branch1x1(x)
branch7x7 = self.branch7x7_1(x)
branch7x7 = self.branch7x7_2(branch7x7)
branch7x7 = self.branch7x7_3(branch7x7)
branch7x7dbl = self.branch7x7dbl_1(x)
branch7x7dbl = self.branch7x7dbl_2(branch7x7dbl)
branch7x7dbl = self.branch7x7dbl_3(branch7x7dbl)
branch7x7dbl = self.branch7x7dbl_4(branch7x7dbl)
branch7x7dbl = self.branch7x7dbl_5(branch7x7dbl)
branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)
branch_pool = self.branch_pool(branch_pool)
outputs = [branch1x1, branch7x7, branch7x7dbl, branch_pool]
return outputs
def _forward(self, x):
branch1x1 = self.branch1x1(x)
branch3x3 = self.branch3x3_1(x)
branch3x3 = [
self.branch3x3_2a(branch3x3),
self.branch3x3_2b(branch3x3),
]
branch3x3 = torch.cat(branch3x3, 1)
branch3x3dbl = self.branch3x3dbl_1(x)
branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
branch3x3dbl = [
self.branch3x3dbl_3a(branch3x3dbl),
self.branch3x3dbl_3b(branch3x3dbl),
]
branch3x3dbl = torch.cat(branch3x3dbl, 1)
branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)
branch_pool = self.branch_pool(branch_pool)
outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool]
return outputs
def forward(self, x):
x, _ = self.conv1(x)
x = F.relu(self.bn1(x), True)
x = F.avg_pool2d(self.conv2(x), 2, stride=2)
x = self.conv3(x)
x = self.conv4(x)
outputs = []
boundary_channels = []
tmp_out = None
ll, boundary_channel = self._sub_layers['m0'](x, tmp_out)
ll = self._sub_layers['top_m_0'](ll)
ll = F.relu(
self._sub_layers['bn_end0'](self._sub_layers['conv_last0'](ll)),
True)
# Predict heatmaps
tmp_out = self._sub_layers['l0'](ll)
if self.end_relu:
tmp_out = F.relu(tmp_out) # HACK: Added relu
outputs.append(tmp_out)
boundary_channels.append(boundary_channel)
return outputs, boundary_channels
def _shortcut(self, x):
if self.learned_sc:
x = self.conv1x1(x)
if self.downsample:
x = F.avg_pool2d(x, 2)
return x
