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mobilenet.py
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mobilenet.py
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"""
Creates a MobileNetV2 Model as defined in:
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen. (2018).
MobileNetV2: Inverted Residuals and Linear Bottlenecks
arXiv preprint arXiv:1801.04381.
import from https://github.com/tonylins/pytorch-mobilenet-v2
"""
import logging
import math
import torch
import torch.nn as nn
from fastreid.layers import get_norm
from fastreid.utils.checkpoint import get_missing_parameters_message, get_unexpected_parameters_message
from .build import BACKBONE_REGISTRY
logger = logging.getLogger(__name__)
def _make_divisible(v, divisor, min_value=None):
"""
This function is taken from the original tf repo.
It ensures that all layers have a channel number that is divisible by 8
It can be seen here:
https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
:param v:
:param divisor:
:param min_value:
:return:
"""
if min_value is None:
min_value = divisor
new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
# Make sure that round down does not go down by more than 10%.
if new_v < 0.9 * v:
new_v += divisor
return new_v
def conv_3x3_bn(inp, oup, stride, bn_norm):
return nn.Sequential(
nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
get_norm(bn_norm, oup),
nn.ReLU6(inplace=True)
)
def conv_1x1_bn(inp, oup, bn_norm):
return nn.Sequential(
nn.Conv2d(inp, oup, 1, 1, 0, bias=False),
get_norm(bn_norm, oup),
nn.ReLU6(inplace=True)
)
class InvertedResidual(nn.Module):
def __init__(self, inp, oup, bn_norm, stride, expand_ratio):
super(InvertedResidual, self).__init__()
assert stride in [1, 2]
hidden_dim = round(inp * expand_ratio)
self.identity = 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),
get_norm(bn_norm, hidden_dim),
nn.ReLU6(inplace=True),
# pw-linear
nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
get_norm(bn_norm, oup),
)
else:
self.conv = nn.Sequential(
# pw
nn.Conv2d(inp, hidden_dim, 1, 1, 0, bias=False),
get_norm(bn_norm, hidden_dim),
nn.ReLU6(inplace=True),
# dw
nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, bias=False),
get_norm(bn_norm, 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.identity:
return x + self.conv(x)
else:
return self.conv(x)
class MobileNetV2(nn.Module):
def __init__(self, bn_norm, width_mult=1.):
super(MobileNetV2, self).__init__()
# setting of inverted residual blocks
self.cfgs = [
# 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
input_channel = _make_divisible(32 * width_mult, 4 if width_mult == 0.1 else 8)
layers = [conv_3x3_bn(3, input_channel, 2, bn_norm)]
# building inverted residual blocks
block = InvertedResidual
for t, c, n, s in self.cfgs:
output_channel = _make_divisible(c * width_mult, 4 if width_mult == 0.1 else 8)
for i in range(n):
layers.append(block(input_channel, output_channel, bn_norm, s if i == 0 else 1, t))
input_channel = output_channel
self.features = nn.Sequential(*layers)
# building last several layers
output_channel = _make_divisible(1280 * width_mult, 4 if width_mult == 0.1 else 8) if width_mult > 1.0 else 1280
self.conv = conv_1x1_bn(input_channel, output_channel, bn_norm)
self._initialize_weights()
def forward(self, x):
x = self.features(x)
x = self.conv(x)
return x
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):
m.weight.data.normal_(0, 0.01)
m.bias.data.zero_()
@BACKBONE_REGISTRY.register()
def build_mobilenetv2_backbone(cfg):
"""
Create a MobileNetV2 instance from config.
Returns:
MobileNetV2: a :class: `MobileNetV2` instance.
"""
# fmt: off
pretrain = cfg.MODEL.BACKBONE.PRETRAIN
pretrain_path = cfg.MODEL.BACKBONE.PRETRAIN_PATH
bn_norm = cfg.MODEL.BACKBONE.NORM
depth = cfg.MODEL.BACKBONE.DEPTH
# fmt: on
width_mult = {
"1.0x": 1.0,
"0.75x": 0.75,
"0.5x": 0.5,
"0.35x": 0.35,
'0.25x': 0.25,
'0.1x': 0.1,
}[depth]
model = MobileNetV2(bn_norm, width_mult)
if pretrain:
try:
state_dict = torch.load(pretrain_path, map_location=torch.device('cpu'))
logger.info(f"Loading pretrained model from {pretrain_path}")
except FileNotFoundError as e:
logger.info(f'{pretrain_path} is not found! Please check this path.')
raise e
except KeyError as e:
logger.info("State dict keys error! Please check the state dict.")
raise e
incompatible = model.load_state_dict(state_dict, strict=False)
if incompatible.missing_keys:
logger.info(
get_missing_parameters_message(incompatible.missing_keys)
)
if incompatible.unexpected_keys:
logger.info(
get_unexpected_parameters_message(incompatible.unexpected_keys)
)
return model