177 lines
5.8 KiB
Python
177 lines
5.8 KiB
Python
#####################################################
|
|
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 #
|
|
#####################################################
|
|
# MobileNetV2: Inverted Residuals and Linear Bottlenecks, CVPR 2018
|
|
#####################################################
|
|
from torch import nn
|
|
|
|
from ..initialization import initialize_resnet
|
|
from ..SharedUtils import parse_channel_info
|
|
|
|
|
|
class ConvBNReLU(nn.Module):
|
|
def __init__(
|
|
self,
|
|
in_planes,
|
|
out_planes,
|
|
kernel_size,
|
|
stride,
|
|
groups,
|
|
has_bn=True,
|
|
has_relu=True,
|
|
):
|
|
super(ConvBNReLU, self).__init__()
|
|
padding = (kernel_size - 1) // 2
|
|
self.conv = nn.Conv2d(
|
|
in_planes,
|
|
out_planes,
|
|
kernel_size,
|
|
stride,
|
|
padding,
|
|
groups=groups,
|
|
bias=False,
|
|
)
|
|
if has_bn:
|
|
self.bn = nn.BatchNorm2d(out_planes)
|
|
else:
|
|
self.bn = None
|
|
if has_relu:
|
|
self.relu = nn.ReLU6(inplace=True)
|
|
else:
|
|
self.relu = None
|
|
|
|
def forward(self, x):
|
|
out = self.conv(x)
|
|
if self.bn:
|
|
out = self.bn(out)
|
|
if self.relu:
|
|
out = self.relu(out)
|
|
return out
|
|
|
|
|
|
class InvertedResidual(nn.Module):
|
|
def __init__(self, channels, stride, expand_ratio, additive):
|
|
super(InvertedResidual, self).__init__()
|
|
self.stride = stride
|
|
assert stride in [1, 2], "invalid stride : {:}".format(stride)
|
|
assert len(channels) in [2, 3], "invalid channels : {:}".format(channels)
|
|
|
|
if len(channels) == 2:
|
|
layers = []
|
|
else:
|
|
layers = [ConvBNReLU(channels[0], channels[1], 1, 1, 1)]
|
|
layers.extend(
|
|
[
|
|
# dw
|
|
ConvBNReLU(channels[-2], channels[-2], 3, stride, channels[-2]),
|
|
# pw-linear
|
|
ConvBNReLU(channels[-2], channels[-1], 1, 1, 1, True, False),
|
|
]
|
|
)
|
|
self.conv = nn.Sequential(*layers)
|
|
self.additive = additive
|
|
if self.additive and channels[0] != channels[-1]:
|
|
self.shortcut = ConvBNReLU(channels[0], channels[-1], 1, 1, 1, True, False)
|
|
else:
|
|
self.shortcut = None
|
|
self.out_dim = channels[-1]
|
|
|
|
def forward(self, x):
|
|
out = self.conv(x)
|
|
# if self.additive: return additive_func(out, x)
|
|
if self.shortcut:
|
|
return out + self.shortcut(x)
|
|
else:
|
|
return out
|
|
|
|
|
|
class InferMobileNetV2(nn.Module):
|
|
def __init__(self, num_classes, xchannels, xblocks, dropout):
|
|
super(InferMobileNetV2, self).__init__()
|
|
block = InvertedResidual
|
|
inverted_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],
|
|
]
|
|
assert len(inverted_residual_setting) == len(
|
|
xblocks
|
|
), "invalid number of layers : {:} vs {:}".format(
|
|
len(inverted_residual_setting), len(xblocks)
|
|
)
|
|
for block_num, ir_setting in zip(xblocks, inverted_residual_setting):
|
|
assert block_num <= ir_setting[2], "{:} vs {:}".format(
|
|
block_num, ir_setting
|
|
)
|
|
xchannels = parse_channel_info(xchannels)
|
|
# for i, chs in enumerate(xchannels):
|
|
# if i > 0: assert chs[0] == xchannels[i-1][-1], 'Layer[{:}] is invalid {:} vs {:}'.format(i, xchannels[i-1], chs)
|
|
self.xchannels = xchannels
|
|
self.message = "InferMobileNetV2 : xblocks={:}".format(xblocks)
|
|
# building first layer
|
|
features = [ConvBNReLU(xchannels[0][0], xchannels[0][1], 3, 2, 1)]
|
|
last_channel_idx = 1
|
|
|
|
# building inverted residual blocks
|
|
for stage, (t, c, n, s) in enumerate(inverted_residual_setting):
|
|
for i in range(n):
|
|
stride = s if i == 0 else 1
|
|
additv = True if i > 0 else False
|
|
module = block(self.xchannels[last_channel_idx], stride, t, additv)
|
|
features.append(module)
|
|
self.message += "\nstage={:}, ilayer={:02d}/{:02d}, block={:03d}, Cs={:}, stride={:}, expand={:}, original-C={:}".format(
|
|
stage,
|
|
i,
|
|
n,
|
|
len(features),
|
|
self.xchannels[last_channel_idx],
|
|
stride,
|
|
t,
|
|
c,
|
|
)
|
|
last_channel_idx += 1
|
|
if i + 1 == xblocks[stage]:
|
|
out_channel = module.out_dim
|
|
for iiL in range(i + 1, n):
|
|
last_channel_idx += 1
|
|
self.xchannels[last_channel_idx][0] = module.out_dim
|
|
break
|
|
# building last several layers
|
|
features.append(
|
|
ConvBNReLU(
|
|
self.xchannels[last_channel_idx][0],
|
|
self.xchannels[last_channel_idx][1],
|
|
1,
|
|
1,
|
|
1,
|
|
)
|
|
)
|
|
assert last_channel_idx + 2 == len(self.xchannels), "{:} vs {:}".format(
|
|
last_channel_idx, len(self.xchannels)
|
|
)
|
|
# make it nn.Sequential
|
|
self.features = nn.Sequential(*features)
|
|
|
|
# building classifier
|
|
self.classifier = nn.Sequential(
|
|
nn.Dropout(dropout),
|
|
nn.Linear(self.xchannels[last_channel_idx][1], num_classes),
|
|
)
|
|
|
|
# weight initialization
|
|
self.apply(initialize_resnet)
|
|
|
|
def get_message(self):
|
|
return self.message
|
|
|
|
def forward(self, inputs):
|
|
features = self.features(inputs)
|
|
vectors = features.mean([2, 3])
|
|
predicts = self.classifier(vectors)
|
|
return features, predicts
|