in easycv/models/ocr/backbones/det_resnet_vd.py [0:0]
def __init__(self, in_channels=3, layers=50, **kwargs):
super(OCRDetResNet, self).__init__()
self.layers = layers
supported_layers = [18, 34, 50, 101, 152, 200]
assert layers in supported_layers, \
'supported layers are {} but input layer is {}'.format(
supported_layers, layers)
if layers == 18:
depth = [2, 2, 2, 2]
elif layers == 34 or layers == 50:
depth = [3, 4, 6, 3]
elif layers == 101:
depth = [3, 4, 23, 3]
elif layers == 152:
depth = [3, 8, 36, 3]
elif layers == 200:
depth = [3, 12, 48, 3]
num_channels = [64, 256, 512, 1024
] if layers >= 50 else [64, 64, 128, 256]
num_filters = [64, 128, 256, 512]
self.conv1_1 = ConvBNLayer(
in_channels=in_channels,
out_channels=32,
kernel_size=3,
stride=2,
act='relu',
name='conv1_1')
self.conv1_2 = ConvBNLayer(
in_channels=32,
out_channels=32,
kernel_size=3,
stride=1,
act='relu',
name='conv1_2')
self.conv1_3 = ConvBNLayer(
in_channels=32,
out_channels=64,
kernel_size=3,
stride=1,
act='relu',
name='conv1_3')
self.pool2d_max = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.stages = nn.ModuleList()
self.out_channels = []
if layers >= 50:
for block in range(len(depth)):
# block_list = []
block_list = nn.Sequential()
shortcut = False
for i in range(depth[block]):
if layers in [101, 152] and block == 2:
if i == 0:
conv_name = 'res' + str(block + 2) + 'a'
else:
conv_name = 'res' + str(block + 2) + 'b' + str(i)
else:
conv_name = 'res' + str(block + 2) + chr(97 + i)
bottleneck_block = BottleneckBlock(
in_channels=num_channels[block]
if i == 0 else num_filters[block] * 4,
out_channels=num_filters[block],
stride=2 if i == 0 and block != 0 else 1,
shortcut=shortcut,
if_first=block == i == 0,
name=conv_name)
shortcut = True
block_list.add_module('bb_%d_%d' % (block, i),
bottleneck_block)
self.out_channels.append(num_filters[block] * 4)
# self.stages.append(nn.Sequential(*block_list))
self.stages.append(block_list)
else:
for block in range(len(depth)):
# block_list = []
block_list = nn.Sequential()
shortcut = False
for i in range(depth[block]):
conv_name = 'res' + str(block + 2) + chr(97 + i)
basic_block = BasicBlock(
in_channels=num_channels[block]
if i == 0 else num_filters[block],
out_channels=num_filters[block],
stride=2 if i == 0 and block != 0 else 1,
shortcut=shortcut,
if_first=block == i == 0,
name=conv_name)
shortcut = True
block_list.add_module('bb_%d_%d' % (block, i), basic_block)
# block_list.append(basic_block)
self.out_channels.append(num_filters[block])
self.stages.append(block_list)