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| class HighResolutionModule(nn.Module):
def __init__(self, num_branches, blocks, num_blocks, num_inchannels,
num_channels, fuse_method, multi_scale_output=True):
super(HighResolutionModule, self).__init__()
self._check_branches(
num_branches, blocks, num_blocks, num_inchannels, num_channels)
self.num_inchannels = num_inchannels
self.fuse_method = fuse_method
self.num_branches = num_branches
self.multi_scale_output = multi_scale_output
self.branches = self._make_branches(
num_branches, blocks, num_blocks, num_channels)
self.fuse_layers = self._make_fuse_layers()
self.relu = nn.ReLU(True)
def _check_branches(self, num_branches, blocks, num_blocks,
num_inchannels, num_channels):
if num_branches != len(num_blocks):
error_msg = 'NUM_BRANCHES({}) <> NUM_BLOCKS({})'.format(
num_branches, len(num_blocks))
logger.error(error_msg)
raise ValueError(error_msg)
if num_branches != len(num_channels):
error_msg = 'NUM_BRANCHES({}) <> NUM_CHANNELS({})'.format(
num_branches, len(num_channels))
logger.error(error_msg)
raise ValueError(error_msg)
if num_branches != len(num_inchannels):
error_msg = 'NUM_BRANCHES({}) <> NUM_INCHANNELS({})'.format(
num_branches, len(num_inchannels))
logger.error(error_msg)
raise ValueError(error_msg)
def _make_one_branch(self, branch_index, block, num_blocks, num_channels,
stride=1):
downsample = None
if stride != 1 or \
self.num_inchannels[branch_index] != num_channels[branch_index] * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(
self.num_inchannels[branch_index],
num_channels[branch_index] * block.expansion,
kernel_size=1, stride=stride, bias=False
),
nn.BatchNorm2d(
num_channels[branch_index] * block.expansion,
momentum=BN_MOMENTUM
),
)
layers = []
layers.append(
block(
self.num_inchannels[branch_index],
num_channels[branch_index],
stride,
downsample
)
)
self.num_inchannels[branch_index] = \
num_channels[branch_index] * block.expansion
for i in range(1, num_blocks[branch_index]):
layers.append(
block(
self.num_inchannels[branch_index],
num_channels[branch_index]
)
)
return nn.Sequential(*layers)
def _make_branches(self, num_branches, block, num_blocks, num_channels):
branches = []
for i in range(num_branches):
branches.append(
self._make_one_branch(i, block, num_blocks, num_channels)
)
return nn.ModuleList(branches)
def _make_fuse_layers(self):
if self.num_branches == 1:
return None
num_branches = self.num_branches
num_inchannels = self.num_inchannels
fuse_layers = []
for i in range(num_branches if self.multi_scale_output else 1):
fuse_layer = []
for j in range(num_branches):
if j > i:
fuse_layer.append(
nn.Sequential(
nn.Conv2d(
num_inchannels[j],
num_inchannels[i],
1, 1, 0, bias=False
),
nn.BatchNorm2d(num_inchannels[i]),
nn.Upsample(scale_factor=2**(j-i), mode='nearest')
)
)
elif j == i:
fuse_layer.append(None)
else:
conv3x3s = []
for k in range(i-j):
if k == i - j - 1:
num_outchannels_conv3x3 = num_inchannels[i]
conv3x3s.append(
nn.Sequential(
nn.Conv2d(
num_inchannels[j],
num_outchannels_conv3x3,
3, 2, 1, bias=False
),
nn.BatchNorm2d(num_outchannels_conv3x3)
)
)
else:
num_outchannels_conv3x3 = num_inchannels[j]
conv3x3s.append(
nn.Sequential(
nn.Conv2d(
num_inchannels[j],
num_outchannels_conv3x3,
3, 2, 1, bias=False
),
nn.BatchNorm2d(num_outchannels_conv3x3),
nn.ReLU(True)
)
)
fuse_layer.append(nn.Sequential(*conv3x3s))
fuse_layers.append(nn.ModuleList(fuse_layer))
return nn.ModuleList(fuse_layers)
def get_num_inchannels(self):
return self.num_inchannels
def forward(self, x):
if self.num_branches == 1:
return [self.branches[0](x[0])]
for i in range(self.num_branches):
x[i] = self.branches[i](x[i])
x_fuse = []
for i in range(len(self.fuse_layers)):
y = x[0] if i == 0 else self.fuse_layers[i][0](x[0])
for j in range(1, self.num_branches):
if i == j:
y = y + x[j]
else:
y = y + self.fuse_layers[i][j](x[j])
x_fuse.append(self.relu(y))
return x_fuse
|
