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Copy pathshufflenet_v2_liteconv.py
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shufflenet_v2_liteconv.py
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import torch
import torch.nn as nn
def channel_shuffle(x, groups):
# type: (torch.Tensor, int) -> torch.Tensor
batchsize, num_channels, height, width = x.data.size()
channels_per_group = num_channels // groups
# reshape
x = x.view(batchsize, groups,
channels_per_group, height, width)
x = torch.transpose(x, 1, 2).contiguous()
# flatten
x = x.view(batchsize, -1, height, width)
return x
class LiteConv_V1(nn.Module):
def __init__(self, channel, gate=1):
super(LiteConv_V1, self).__init__()
self.channel = channel
self.gate = gate
self.spatial_conv = nn.Sequential(
self.depthwise_conv(channel, channel, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(channel))
self.channel_conv = nn.Sequential(
nn.Conv2d(channel, gate, kernel_size=1, stride=1, padding=0, bias=False),
nn.BatchNorm2d(gate))
self.gc = nn.Sequential(
nn.Conv2d(channel * (self.gate+1), channel, kernel_size=3, stride=1, padding=1, groups=channel,bias=False),
nn.BatchNorm2d(channel),
nn.ReLU(inplace=True))
@staticmethod
def depthwise_conv(i, o, kernel_size, stride=1, padding=0, bias=False):
return nn.Conv2d(i, o, kernel_size, stride, padding, bias=bias, groups=i)
def forward(self, x):
x_dw = self.spatial_conv(x)
x_pw = self.channel_conv(x)
nB = x_dw.shape[0]
nDWC = x_dw.shape[1]
nH = x_dw.shape[2]
nW = x_dw.shape[3]
nPWC = x_pw.shape[1]
x_dw = x_dw.reshape(nB, nDWC, nH * nW)
x_pw = x_pw.reshape(nB, 1, -1)
x_pw = x_pw.repeat(1, nDWC, 1)
x_merge = torch.cat((x_dw, x_pw), dim=2)
x_merge = x_merge.reshape(nB, nDWC * (self.gate + 1), nH, nW)
x_out = self.gc(x_merge)
return x_out
class LiteConv_V2(nn.Module):
def __init__(self, channel):
super(LiteConv_V2, self).__init__()
self.channel = channel
self.dw1 = nn.Sequential(
self.depthwise_conv(channel+2, channel+2, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(channel+2))
self.dw2 = nn.Sequential(
self.depthwise_conv(channel + 2, channel + 2, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(channel+2))
self.dw3 = nn.Sequential(
self.depthwise_conv(channel + 2, channel + 2, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(channel+2))
@staticmethod
def depthwise_conv(i, o, kernel_size, stride=1, padding=0, bias=False):
return nn.Conv2d(i, o, kernel_size, stride, padding, bias=bias, groups=i)
def forward(self, x):
nB = x.shape[0]
nH = x.shape[2]
nW = x.shape[3]
dev = x.device
padding = torch.zeros((nB, 1, nH, nW)).to(dev)
x = torch.cat([padding, x, padding], dim=1)
x_dw1 = self.dw1(x)
x_dw2 = self.dw2(x)
x_dw3 = self.dw2(x)
x = x_dw1[:,0:self.channel] + x_dw2[:,1:self.channel+1] + x_dw3[:,2:self.channel+2]
x = nn.functional.relu(x)
return x
class InvertedResidual(nn.Module):
def __init__(self, inp, oup, stride):
super(InvertedResidual, self).__init__()
if not (1 <= stride <= 3):
raise ValueError('illegal stride value')
self.stride = stride
branch_features = oup // 2
assert (self.stride != 1) or (inp == branch_features << 1)
if self.stride > 1:
self.branch1 = nn.Sequential(
self.depthwise_conv(inp, inp, kernel_size=3, stride=self.stride, padding=1),
nn.BatchNorm2d(inp),
nn.Conv2d(inp, branch_features, kernel_size=1, stride=1, padding=0, bias=False),
nn.BatchNorm2d(branch_features),
nn.ReLU(inplace=True),
)
else:
self.branch1 = nn.Sequential()
self.branch2 = nn.Sequential(
nn.Conv2d(inp if (self.stride > 1) else branch_features,
branch_features, kernel_size=1, stride=1, padding=0, bias=False),
nn.BatchNorm2d(branch_features),
nn.ReLU(inplace=True),
self.depthwise_conv(branch_features, branch_features, kernel_size=3, stride=self.stride, padding=1),
nn.BatchNorm2d(branch_features),
#nn.Conv2d(branch_features, branch_features, kernel_size=1, stride=1, padding=0, bias=False),
#nn.BatchNorm2d(branch_features),
#nn.ReLU(inplace=True),
)
@staticmethod
def depthwise_conv(i, o, kernel_size, stride=1, padding=0, bias=False):
return nn.Conv2d(i, o, kernel_size, stride, padding, bias=bias, groups=i)
def forward(self, x):
if self.stride == 1:
x1, x2 = x.chunk(2, dim=1)
out = torch.cat((x1, self.branch2(x2)), dim=1)
else:
out = torch.cat((self.branch1(x), self.branch2(x)), dim=1)
out = channel_shuffle(out, 2)
return out
class ShuffleNetV2LiteConv(nn.Module):
def __init__(self, arg_dict):
super(ShuffleNetV2LiteConv, self).__init__()
num_classes = arg_dict['class_num']
width_mult = arg_dict['channel_ratio']
inverted_residual=InvertedResidual
if width_mult == 0.5:
stages_repeats = [4, 8, 4]
stages_out_channels = [24, 48, 96, 192, 1024]
elif width_mult == 1.0:
stages_repeats = [4, 8, 4]
stages_out_channels = [24, 116, 232, 464, 1024]
elif width_mult == 1.5:
stages_repeats = [4, 8, 4]
stages_out_channels = [24, 176, 352, 704, 1024]
elif width_mult == 2.0:
stages_repeats = [4, 8, 4]
stages_out_channels = [24, 244, 488, 976, 2048]
elif width_mult == 0.25:
stages_repeats = [4, 8, 4]
stages_out_channels = [24, 28, 48, 96, 512]
else:
assert(False)
if len(stages_repeats) != 3:
raise ValueError('expected stages_repeats as list of 3 positive ints')
if len(stages_out_channels) != 5:
raise ValueError('expected stages_out_channels as list of 5 positive ints')
self._stage_out_channels = stages_out_channels
input_channels = 3
output_channels = self._stage_out_channels[0]
self.conv1 = nn.Sequential(
nn.Conv2d(input_channels, output_channels, 3, 2, 1, bias=False),
nn.BatchNorm2d(output_channels),
nn.ReLU(inplace=True),
)
input_channels = output_channels
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
stage_names = ['stage{}'.format(i) for i in [2, 3, 4]]
for name, repeats, output_channels in zip(
stage_names, stages_repeats, self._stage_out_channels[1:]):
seq = [inverted_residual(input_channels, output_channels, 2)]
for i in range(repeats - 1):
seq.append(inverted_residual(output_channels, output_channels, 1))
setattr(self, name, nn.Sequential(*seq))
input_channels = output_channels
output_channels = self._stage_out_channels[-1]
self.conv5 = nn.Sequential(
nn.Conv2d(input_channels, output_channels, 1, 1, 0, bias=False),
nn.BatchNorm2d(output_channels),
nn.ReLU(inplace=True),
)
self.fc = nn.Linear(output_channels, num_classes)
def _forward_impl(self, x):
# See note [TorchScript super()]
x = self.conv1(x)
x = self.maxpool(x)
x = self.stage2(x)
x = self.stage3(x)
x = self.stage4(x)
x = self.conv5(x)
x = x.mean([2, 3]) # globalpool
x = self.fc(x)
return x
def forward(self, x):
x = self._forward_impl(x)
if self.training:
return x
else:
return torch.softmax(x, dim=1)
def train_step(self, images, labels):
out = self.forward(images)
loss = nn.functional.cross_entropy(out, labels)
return loss
def eval_step(self, images):
out = self.forward(images)
return out