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mobilenet_v2_tsm.py
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mobilenet_v2_tsm.py
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import torch.nn as nn
import torch
import math
def conv_bn(inp, oup, stride):
return nn.Sequential(
nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
nn.BatchNorm2d(oup),
nn.ReLU6(inplace=True)
)
def conv_1x1_bn(inp, oup):
return nn.Sequential(
nn.Conv2d(inp, oup, 1, 1, 0, bias=False),
nn.BatchNorm2d(oup),
nn.ReLU6(inplace=True)
)
def make_divisible(x, divisible_by=8):
import numpy as np
return int(np.ceil(x * 1. / divisible_by) * divisible_by)
class InvertedResidual(nn.Module):
def __init__(self, inp, oup, stride, expand_ratio):
super(InvertedResidual, self).__init__()
self.stride = stride
assert stride in [1, 2]
hidden_dim = int(inp * expand_ratio)
self.use_res_connect = self.stride == 1 and inp == oup
if expand_ratio == 1:
self.conv = nn.Sequential(
# dw
nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, bias=False),
nn.BatchNorm2d(hidden_dim),
nn.ReLU6(inplace=True),
# pw-linear
nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
nn.BatchNorm2d(oup),
)
else:
self.conv = nn.Sequential(
# pw
nn.Conv2d(inp, hidden_dim, 1, 1, 0, bias=False),
nn.BatchNorm2d(hidden_dim),
nn.ReLU6(inplace=True),
# dw
nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, bias=False),
nn.BatchNorm2d(hidden_dim),
nn.ReLU6(inplace=True),
# pw-linear
nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
nn.BatchNorm2d(oup),
)
def forward(self, x):
if self.use_res_connect:
return x + self.conv(x)
else:
return self.conv(x)
class InvertedResidualWithShift(nn.Module):
def __init__(self, inp, oup, stride, expand_ratio):
super(InvertedResidualWithShift, self).__init__()
self.stride = stride
assert stride in [1, 2]
assert expand_ratio > 1
hidden_dim = int(inp * expand_ratio)
self.use_res_connect = self.stride == 1 and inp == oup
assert self.use_res_connect
self.conv = nn.Sequential(
# pw
nn.Conv2d(inp, hidden_dim, 1, 1, 0, bias=False),
nn.BatchNorm2d(hidden_dim),
nn.ReLU6(inplace=True),
# dw
nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, bias=False),
nn.BatchNorm2d(hidden_dim),
nn.ReLU6(inplace=True),
# pw-linear
nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
nn.BatchNorm2d(oup),
)
def forward(self, x, shift_buffer):
c = x.size(1)
x1, x2 = x[:, : c // 8], x[:, c // 8:]
return x + self.conv(torch.cat((shift_buffer, x2), dim=1)), x1
class MobileNetV2(nn.Module):
def __init__(self, n_class=1000, input_size=224, width_mult=1.):
super(MobileNetV2, self).__init__()
input_channel = 32
last_channel = 1280
interverted_residual_setting = [
# t, c, n, s
[1, 16, 1, 1],
[6, 24, 2, 2],
[6, 32, 3, 2],
[6, 64, 4, 2],
[6, 96, 3, 1],
[6, 160, 3, 2],
[6, 320, 1, 1],
]
# building first layer
assert input_size % 32 == 0
# input_channel = make_divisible(input_channel * width_mult) # first channel is always 32!
self.last_channel = make_divisible(last_channel * width_mult) if width_mult > 1.0 else last_channel
self.features = [conv_bn(3, input_channel, 2)]
# building inverted residual blocks
global_idx = 0
shift_block_idx = [2, 4, 5, 7, 8, 9, 11, 12, 14, 15]
for t, c, n, s in interverted_residual_setting:
output_channel = make_divisible(c * width_mult) if t > 1 else c
for i in range(n):
if i == 0:
block = InvertedResidualWithShift if global_idx in shift_block_idx else InvertedResidual
self.features.append(block(input_channel, output_channel, s, expand_ratio=t))
global_idx += 1
else:
block = InvertedResidualWithShift if global_idx in shift_block_idx else InvertedResidual
self.features.append(block(input_channel, output_channel, 1, expand_ratio=t))
global_idx += 1
input_channel = output_channel
# building last several layers
self.features.append(conv_1x1_bn(input_channel, self.last_channel))
# make it nn.Sequential
self.features = nn.ModuleList(self.features)
# building classifier
self.classifier = nn.Linear(self.last_channel, n_class)
self._initialize_weights()
def forward(self, x, *shift_buffer):
shift_buffer_idx = 0
out_buffer = []
for f in self.features:
if isinstance(f, InvertedResidualWithShift):
x, s = f(x, shift_buffer[shift_buffer_idx])
shift_buffer_idx += 1
out_buffer.append(s)
else:
x = f(x)
x = x.mean(3).mean(2)
x = self.classifier(x)
return (x, *out_buffer)
def _initialize_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
if m.bias is not None:
m.bias.data.zero_()
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
elif isinstance(m, nn.Linear):
n = m.weight.size(1)
m.weight.data.normal_(0, 0.01)
m.bias.data.zero_()
def mobilenet_v2_140():
return MobileNetV2(width_mult=1.4)
if __name__ == '__main__':
net = MobileNetV2()
x = torch.rand(1, 3, 224, 224)
shift_buffer = [torch.zeros([1, 3, 56, 56]),
torch.zeros([1, 4, 28, 28]),
torch.zeros([1, 4, 28, 28]),
torch.zeros([1, 8, 14, 14]),
torch.zeros([1, 8, 14, 14]),
torch.zeros([1, 8, 14, 14]),
torch.zeros([1, 12, 14, 14]),
torch.zeros([1, 12, 14, 14]),
torch.zeros([1, 20, 7, 7]),
torch.zeros([1, 20, 7, 7])]
with torch.no_grad():
for _ in range(10):
y, shift_buffer = net(x, *shift_buffer)
print([s.shape for s in shift_buffer])