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regnet.py
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regnet.py
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import torch
import torch.nn as nn
from torch.utils import model_zoo
class BlockX(nn.Module):
def __init__(self,
in_channels,
out_channels,
stride,
bottleneck_ratio=1.0,
group_width=1,
bn_eps=1e-5,
bn_momentum=0.1,
relu_inplace=False):
super(BlockX, self).__init__()
# projection convolution
if (in_channels != out_channels) or (stride != 1):
self.project = True
else:
self.project = False
if self.project:
self.project_conv = nn.Conv2d(in_channels=in_channels,
out_channels=out_channels,
kernel_size=1,
stride=stride,
padding=0,
bias=False)
self.project_bn = nn.BatchNorm2d(num_features=out_channels,
eps=bn_eps,
momentum=bn_momentum)
out_bottleneck = int(round(out_channels * bottleneck_ratio))
groups = out_bottleneck // group_width
self.conv_1 = nn.Conv2d(in_channels=in_channels,
out_channels=out_bottleneck,
kernel_size=1,
stride=1,
padding=0,
bias=False)
self.conv_1_bn = nn.BatchNorm2d(num_features=out_bottleneck,
eps=bn_eps,
momentum=bn_momentum)
self.conv_1_relu = nn.ReLU(inplace=relu_inplace)
self.conv_2 = nn.Conv2d(in_channels=out_bottleneck,
out_channels=out_bottleneck,
kernel_size=3,
stride=stride,
padding=1,
groups=groups,
bias=False)
self.conv_2_bn = nn.BatchNorm2d(num_features=out_bottleneck,
eps=bn_eps,
momentum=bn_momentum)
self.conv_2_relu = nn.ReLU(inplace=relu_inplace)
self.conv_3 = nn.Conv2d(in_channels=out_bottleneck,
out_channels=out_channels,
kernel_size=1,
stride=1,
padding=0,
bias=False)
self.conv_3_bn = nn.BatchNorm2d(num_features=out_channels,
eps=bn_eps,
momentum=bn_momentum)
self.conv_3_bn.final_bn = True
self.relu = nn.ReLU(inplace=relu_inplace)
return
def forward(self, x):
if self.project:
x_inp = self.project_bn(self.project_conv(x))
else:
x_inp = x
x = self.conv_1(x)
x = self.conv_1_bn(x)
x = self.conv_1_relu(x)
x = self.conv_2(x)
x = self.conv_2_bn(x)
x = self.conv_2_relu(x)
x = self.conv_3(x)
x = self.conv_3_bn(x)
x = x_inp + x
x = self.relu(x)
return x
class BlockY(nn.Module):
def __init__(self,
in_channels,
out_channels,
stride,
bottleneck_ratio=1.0,
group_width=1,
se_ratio=0.25,
bn_eps=1e-5,
bn_momentum=0.1,
relu_inplace=False):
super(BlockY, self).__init__()
# projection convolution
if (in_channels != out_channels) or (stride != 1):
self.project = True
else:
self.project = False
if self.project:
self.project_conv = nn.Conv2d(in_channels=in_channels,
out_channels=out_channels,
kernel_size=1,
stride=stride,
padding=0,
bias=False)
self.project_bn = nn.BatchNorm2d(num_features=out_channels,
eps=bn_eps,
momentum=bn_momentum)
out_bottleneck = int(round(out_channels * bottleneck_ratio))
groups = out_bottleneck // group_width
self.conv_1 = nn.Conv2d(in_channels=in_channels,
out_channels=out_bottleneck,
kernel_size=1,
stride=1,
padding=0,
bias=False)
self.conv_1_bn = nn.BatchNorm2d(num_features=out_bottleneck,
eps=bn_eps,
momentum=bn_momentum)
self.conv_1_relu = nn.ReLU(inplace=relu_inplace)
self.conv_2 = nn.Conv2d(in_channels=out_bottleneck,
out_channels=out_bottleneck,
kernel_size=3,
stride=stride,
padding=1,
groups=groups,
bias=False)
self.conv_2_bn = nn.BatchNorm2d(num_features=out_bottleneck,
eps=bn_eps,
momentum=bn_momentum)
self.conv_2_relu = nn.ReLU(inplace=relu_inplace)
# squeeze-and-excitation after 3x3 conv
w_se = int(round(in_channels * se_ratio))
self.se_avg_pool = nn.AdaptiveAvgPool2d((1, 1))
self.se_reduce = nn.Conv2d(out_bottleneck, w_se, 1, bias=True)
self.se_reduce_act = nn.ReLU(inplace=relu_inplace)
self.se_expand = nn.Conv2d(w_se, out_bottleneck, 1, bias=True)
self.se_expand_act = nn.Sigmoid()
self.conv_3 = nn.Conv2d(in_channels=out_bottleneck,
out_channels=out_channels,
kernel_size=1,
stride=1,
padding=0,
bias=False)
self.conv_3_bn = nn.BatchNorm2d(num_features=out_channels,
eps=bn_eps,
momentum=bn_momentum)
self.conv_3_bn.final_bn = True
self.relu = nn.ReLU(inplace=relu_inplace)
return
def forward(self, x):
if self.project:
x_inp = self.project_bn(self.project_conv(x))
else:
x_inp = x
x = self.conv_1(x)
x = self.conv_1_bn(x)
x = self.conv_1_relu(x)
x = self.conv_2(x)
x = self.conv_2_bn(x)
x = self.conv_2_relu(x)
x_se = x
x = self.se_avg_pool(x)
x = self.se_reduce(x)
x = self.se_reduce_act(x)
x = self.se_expand(x)
x = self.se_expand_act(x)
x = x_se * x
x = self.conv_3(x)
x = self.conv_3_bn(x)
x = x_inp + x
x = self.relu(x)
return x
def stage_x(num_blocks, in_channels, out_channels, stride, bottleneck_ratio,
group_width, bn_eps, bn_momentum, relu_inplace):
blocks = []
for i in range(num_blocks):
b_stride = stride if i == 0 else 1
b_in_channels = in_channels if i == 0 else out_channels
blocks.append(BlockX(in_channels=b_in_channels,
out_channels=out_channels,
stride=b_stride,
bottleneck_ratio=bottleneck_ratio,
group_width=group_width,
bn_eps=bn_eps,
bn_momentum=bn_momentum,
relu_inplace=relu_inplace))
return nn.Sequential(*blocks)
def stage_y(num_blocks, in_channels, out_channels, stride, bottleneck_ratio,
group_width, se_ratio, bn_eps, bn_momentum, relu_inplace):
blocks = []
for i in range(num_blocks):
b_stride = stride if i == 0 else 1
b_in_channels = in_channels if i == 0 else out_channels
blocks.append(BlockY(in_channels=b_in_channels,
out_channels=out_channels,
stride=b_stride,
bottleneck_ratio=bottleneck_ratio,
group_width=group_width,
se_ratio=se_ratio,
bn_eps=bn_eps,
bn_momentum=bn_momentum,
relu_inplace=relu_inplace))
return nn.Sequential(*blocks)
MODELS = {
'RegNetX-200MF': {'init_out_channels': 32,
'num_blocks': [1, 1, 4, 7],
'out_channels': [24, 56, 152, 368],
'strides': [2, 2, 2, 2],
'bottleneck_ratios': [1.0, 1.0, 1.0, 1.0],
'group_widths': [8, 8, 8, 8],
'weights_url': 'https://dl.fbaipublicfiles.com/pycls/dds_baselines/160905981/RegNetX-200MF_dds_8gpu.pyth',
},
'RegNetX-400MF': {'init_out_channels': 32,
'num_blocks': [1, 2, 7, 12],
'out_channels': [32, 64, 160, 384],
'strides': [2, 2, 2, 2],
'bottleneck_ratios': [1.0, 1.0, 1.0, 1.0],
'group_widths': [16, 16, 16, 16],
'weights_url': 'https://dl.fbaipublicfiles.com/pycls/dds_baselines/160905967/RegNetX-400MF_dds_8gpu.pyth',
},
'RegNetX-600MF': {'init_out_channels': 32,
'num_blocks': [1, 3, 5, 7],
'out_channels': [48, 96, 240, 528],
'strides': [2, 2, 2, 2],
'bottleneck_ratios': [1.0, 1.0, 1.0, 1.0],
'group_widths': [24, 24, 24, 24],
'weights_url': 'https://dl.fbaipublicfiles.com/pycls/dds_baselines/160906442/RegNetX-600MF_dds_8gpu.pyth',
},
'RegNetX-800MF': {'init_out_channels': 32,
'num_blocks': [1, 3, 7, 5],
'out_channels': [64, 128, 288, 672],
'strides': [2, 2, 2, 2],
'bottleneck_ratios': [1.0, 1.0, 1.0, 1.0],
'group_widths': [16, 16, 16, 16],
'weights_url': 'https://dl.fbaipublicfiles.com/pycls/dds_baselines/160906036/RegNetX-800MF_dds_8gpu.pyth',
},
'RegNetX-1.6GF': {'init_out_channels': 32,
'num_blocks': [2, 4, 10, 2],
'out_channels': [72, 168, 408, 912],
'strides': [2, 2, 2, 2],
'bottleneck_ratios': [1.0, 1.0, 1.0, 1.0],
'group_widths': [24, 24, 24, 24],
'weights_url': 'https://dl.fbaipublicfiles.com/pycls/dds_baselines/160990626/RegNetX-1.6GF_dds_8gpu.pyth',
},
'RegNetX-3.2GF': {'init_out_channels': 32,
'num_blocks': [2, 6, 15, 2],
'out_channels': [96, 192, 432, 1008],
'strides': [2, 2, 2, 2],
'bottleneck_ratios': [1.0, 1.0, 1.0, 1.0],
'group_widths': [48, 48, 48, 48],
'weights_url': 'https://dl.fbaipublicfiles.com/pycls/dds_baselines/160906139/RegNetX-3.2GF_dds_8gpu.pyth',
},
'RegNetX-4.0GF': {'init_out_channels': 32,
'num_blocks': [2, 5, 14, 2],
'out_channels': [80, 240, 560, 1360],
'strides': [2, 2, 2, 2],
'bottleneck_ratios': [1.0, 1.0, 1.0, 1.0],
'group_widths': [40, 40, 40, 40],
'weights_url': 'https://dl.fbaipublicfiles.com/pycls/dds_baselines/160906383/RegNetX-4.0GF_dds_8gpu.pyth',
},
'RegNetX-6.4GF': {'init_out_channels': 32,
'num_blocks': [2, 4, 10, 1],
'out_channels': [168, 392, 784, 1624],
'strides': [2, 2, 2, 2],
'bottleneck_ratios': [1.0, 1.0, 1.0, 1.0],
'group_widths': [56, 56, 56, 56],
'weights_url': 'https://dl.fbaipublicfiles.com/pycls/dds_baselines/161116590/RegNetX-6.4GF_dds_8gpu.pyth',
},
'RegNetX-8.0GF': {'init_out_channels': 32,
'num_blocks': [2, 5, 15, 1],
'out_channels': [80, 240, 720, 1920],
'strides': [2, 2, 2, 2],
'bottleneck_ratios': [1.0, 1.0, 1.0, 1.0],
'group_widths': [80, 120, 120, 120],
'weights_url': 'https://dl.fbaipublicfiles.com/pycls/dds_baselines/161107726/RegNetX-8.0GF_dds_8gpu.pyth',
},
'RegNetX-12GF': {'init_out_channels': 32,
'num_blocks': [2, 5, 11, 1],
'out_channels': [224, 448, 896, 2240],
'strides': [2, 2, 2, 2],
'bottleneck_ratios': [1.0, 1.0, 1.0, 1.0],
'group_widths': [112, 112, 112, 112],
'weights_url': 'https://dl.fbaipublicfiles.com/pycls/dds_baselines/160906020/RegNetX-12GF_dds_8gpu.pyth',
},
'RegNetX-16GF': {'init_out_channels': 32,
'num_blocks': [2, 6, 13, 1],
'out_channels': [256, 512, 896, 2048],
'strides': [2, 2, 2, 2],
'bottleneck_ratios': [1.0, 1.0, 1.0, 1.0],
'group_widths': [128, 128, 128, 128],
'weights_url': 'https://dl.fbaipublicfiles.com/pycls/dds_baselines/158460855/RegNetX-16GF_dds_8gpu.pyth',
},
'RegNetX-32GF': {'init_out_channels': 32,
'num_blocks': [2, 7, 13, 1],
'out_channels': [336, 672, 1344, 2520],
'strides': [2, 2, 2, 2],
'bottleneck_ratios': [1.0, 1.0, 1.0, 1.0],
'group_widths': [168, 168, 168, 168],
'weights_url': 'https://dl.fbaipublicfiles.com/pycls/dds_baselines/158188473/RegNetX-32GF_dds_8gpu.pyth',
},
'RegNetY-200MF': {'init_out_channels': 32,
'num_blocks': [1, 1, 4, 7],
'out_channels': [24, 56, 152, 368],
'strides': [2, 2, 2, 2],
'bottleneck_ratios': [1.0, 1.0, 1.0, 1.0],
'group_widths': [8, 8, 8, 8],
'se_ratio': 0.25,
'weights_url': 'https://dl.fbaipublicfiles.com/pycls/dds_baselines/176245422/RegNetY-200MF_dds_8gpu.pyth',
},
'RegNetY-400MF': {'init_out_channels': 32,
'num_blocks': [1, 3, 6, 6],
'out_channels': [48, 104, 208, 440],
'strides': [2, 2, 2, 2],
'bottleneck_ratios': [1.0, 1.0, 1.0, 1.0],
'group_widths': [8, 8, 8, 8],
'se_ratio': 0.25,
'weights_url': 'https://dl.fbaipublicfiles.com/pycls/dds_baselines/160906449/RegNetY-400MF_dds_8gpu.pyth',
},
'RegNetY-600MF': {'init_out_channels': 32,
'num_blocks': [1, 3, 7, 4],
'out_channels': [48, 112, 256, 608],
'strides': [2, 2, 2, 2],
'bottleneck_ratios': [1.0, 1.0, 1.0, 1.0],
'group_widths': [16, 16, 16, 16],
'se_ratio': 0.25,
'weights_url': 'https://dl.fbaipublicfiles.com/pycls/dds_baselines/160981443/RegNetY-600MF_dds_8gpu.pyth',
},
'RegNetY-800MF': {'init_out_channels': 32,
'num_blocks': [1, 3, 8, 2],
'out_channels': [64, 128, 320, 768],
'strides': [2, 2, 2, 2],
'bottleneck_ratios': [1.0, 1.0, 1.0, 1.0],
'group_widths': [16, 16, 16, 16],
'se_ratio': 0.25,
'weights_url': 'https://dl.fbaipublicfiles.com/pycls/dds_baselines/160906567/RegNetY-800MF_dds_8gpu.pyth',
},
'RegNetY-1.6GF': {'init_out_channels': 32,
'num_blocks': [2, 6, 17, 2],
'out_channels': [48, 120, 336, 888],
'strides': [2, 2, 2, 2],
'bottleneck_ratios': [1.0, 1.0, 1.0, 1.0],
'group_widths': [24, 24, 24, 24],
'se_ratio': 0.25,
'weights_url': 'https://dl.fbaipublicfiles.com/pycls/dds_baselines/160906681/RegNetY-1.6GF_dds_8gpu.pyth',
},
'RegNetY-3.2GF': {'init_out_channels': 32,
'num_blocks': [2, 5, 13, 1],
'out_channels': [72, 216, 576, 1512],
'strides': [2, 2, 2, 2],
'bottleneck_ratios': [1.0, 1.0, 1.0, 1.0],
'group_widths': [24, 24, 24, 24],
'se_ratio': 0.25,
'weights_url': 'https://dl.fbaipublicfiles.com/pycls/dds_baselines/160906834/RegNetY-3.2GF_dds_8gpu.pyth',
},
'RegNetY-4.0GF': {'init_out_channels': 32,
'num_blocks': [2, 6, 12, 2],
'out_channels': [128, 192, 512, 1088],
'strides': [2, 2, 2, 2],
'bottleneck_ratios': [1.0, 1.0, 1.0, 1.0],
'group_widths': [64, 64, 64, 64],
'se_ratio': 0.25,
'weights_url': 'https://dl.fbaipublicfiles.com/pycls/dds_baselines/160906838/RegNetY-4.0GF_dds_8gpu.pyth',
},
'RegNetY-6.4GF': {'init_out_channels': 32,
'num_blocks': [2, 7, 14, 2],
'out_channels': [144, 288, 576, 1296],
'strides': [2, 2, 2, 2],
'bottleneck_ratios': [1.0, 1.0, 1.0, 1.0],
'group_widths': [72, 72, 72, 72],
'se_ratio': 0.25,
'weights_url': 'https://dl.fbaipublicfiles.com/pycls/dds_baselines/160907112/RegNetY-6.4GF_dds_8gpu.pyth',
},
'RegNetY-8.0GF': {'init_out_channels': 32,
'num_blocks': [2, 4, 10, 1],
'out_channels': [168, 448, 896, 2016],
'strides': [2, 2, 2, 2],
'bottleneck_ratios': [1.0, 1.0, 1.0, 1.0],
'group_widths': [56, 56, 56, 56],
'se_ratio': 0.25,
'weights_url': 'https://dl.fbaipublicfiles.com/pycls/dds_baselines/161160905/RegNetY-8.0GF_dds_8gpu.pyth',
},
'RegNetY-12GF': {'init_out_channels': 32,
'num_blocks': [2, 5, 11, 1],
'out_channels': [224, 448, 896, 2240],
'strides': [2, 2, 2, 2],
'bottleneck_ratios': [1.0, 1.0, 1.0, 1.0],
'group_widths': [112, 112, 112, 112],
'se_ratio': 0.25,
'weights_url': 'https://dl.fbaipublicfiles.com/pycls/dds_baselines/160907100/RegNetY-12GF_dds_8gpu.pyth',
},
'RegNetY-16GF': {'init_out_channels': 32,
'num_blocks': [2, 4, 11, 1],
'out_channels': [224, 448, 1232, 3024],
'strides': [2, 2, 2, 2],
'bottleneck_ratios': [1.0, 1.0, 1.0, 1.0],
'group_widths': [112, 112, 112, 112],
'se_ratio': 0.25,
'weights_url': 'https://dl.fbaipublicfiles.com/pycls/dds_baselines/161303400/RegNetY-16GF_dds_8gpu.pyth',
},
'RegNetY-32GF': {'init_out_channels': 32,
'num_blocks': [2, 5, 12, 1],
'out_channels': [232, 696, 1392, 3712],
'strides': [2, 2, 2, 2],
'bottleneck_ratios': [1.0, 1.0, 1.0, 1.0],
'group_widths': [232, 232, 232, 232],
'se_ratio': 0.25,
'weights_url': 'https://dl.fbaipublicfiles.com/pycls/dds_baselines/161277763/RegNetY-32GF_dds_8gpu.pyth',
},
}
class RegNet(nn.Module):
def __init__(self,
name,
in_channels=3,
num_classes=1000,
bn_eps=1e-5,
bn_momentum=0.1,
relu_inplace=False,
pretrained=False,
progress=False,
):
super(RegNet, self).__init__()
config = MODELS.get(name)
if config is None:
raise ValueError('Invalid model name: %s' % name)
self.init_conv = nn.Conv2d(in_channels=in_channels,
out_channels=config['init_out_channels'],
kernel_size=3,
stride=2,
padding=1,
bias=False)
self.init_bn = nn.BatchNorm2d(num_features=config['init_out_channels'],
eps=bn_eps,
momentum=bn_momentum)
self.init_relu = nn.ReLU(inplace=relu_inplace)
prev_out_channels = config['init_out_channels']
self.stages = nn.ModuleList()
if 'RegNetX' in name:
for i in range(len(config['num_blocks'])):
stage = stage_x(num_blocks=config['num_blocks'][i],
in_channels=prev_out_channels,
out_channels=config['out_channels'][i],
stride=config['strides'][i],
bottleneck_ratio=config['bottleneck_ratios'][i],
group_width=config['group_widths'][i],
bn_eps=bn_eps,
bn_momentum=bn_momentum,
relu_inplace=relu_inplace)
self.stages.append(stage)
prev_out_channels = config['out_channels'][i]
elif 'RegNetY' in name:
for i in range(len(config['num_blocks'])):
stage = stage_y(num_blocks=config['num_blocks'][i],
in_channels=prev_out_channels,
out_channels=config['out_channels'][i],
stride=config['strides'][i],
bottleneck_ratio=config['bottleneck_ratios'][i],
group_width=config['group_widths'][i],
se_ratio=config['se_ratio'],
bn_eps=bn_eps,
bn_momentum=bn_momentum,
relu_inplace=relu_inplace)
self.stages.append(stage)
prev_out_channels = config['out_channels'][i]
self.avpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(in_features=prev_out_channels,
out_features=num_classes,
bias=True)
if pretrained:
self._load_state(name, in_channels, num_classes, progress)
return
def _load_state(self, name, in_channels, num_classes, progress):
state_dict = model_zoo.load_url(
MODELS[name]['weights_url'], progress=progress, check_hash=False)
state_dict = state_dict['model_state']
self_state_dict = self.state_dict()
assert len(state_dict) == len(
self_state_dict), f"{len(state_dict)} {len(self_state_dict)}"
strict = True
with torch.no_grad():
for key, self_key in zip(list(state_dict.keys()), list(self_state_dict.keys())):
if key in ['head.fc.weight', 'head.fc.bias']:
if num_classes != 1000:
strict = False
continue
elif key == 'stem.conv.weight':
if in_channels != 3:
strict = False
continue
else:
# original models were trained with images in BGR format,
# swap B and R filters in initial convolution weights in order to
# apadt model to RGB images
t = state_dict[key].detach().clone()
state_dict[key][:, 0, :, :] = state_dict[key][:, 2, :, :]
state_dict[key][:, 2, :, :] = t[:, 0, :, :]
assert state_dict[key].shape == self_state_dict[
self_key].shape, f"{key}, {self_key}, {state_dict[key].shape}, {self_state_dict[self_key].shape}"
self_state_dict[self_key] = state_dict[key]
self.load_state_dict(self_state_dict, strict=strict)
return
def get_features(self, x):
"""
Computer and return only intermediate features, no final fc layer
"""
x = self.init_conv(x)
x = self.init_bn(x)
x = self.init_relu(x)
out = []
for stage in self.stages:
x = stage(x)
out.append(x)
return out
def forward(self, x):
"""
forward pass
"""
x = self.init_conv(x)
x = self.init_bn(x)
x = self.init_relu(x)
for stage in self.stages:
x = stage(x)
x = self.avpool(x)
x = torch.flatten(x, 1)
x = self.fc(x)
return x
if __name__ == "__main__":
model = RegNet('RegNetY-200MF',
in_channels=3,
num_classes=50,
pretrained=True
)
# inp - tensor of shape [batch_size, in_channels, image_height, image_width]
inp = torch.randn([10, 3, 224, 224])
# to get predictions:
pred = model(inp)
print('out shape:', pred.shape)
# to extract features:
features = model.get_features(inp)
for i, feature in enumerate(features):
print('feature %d shape:' % i, feature.shape)