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model.py
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model.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
def get_model_parameters(model):
total_parameters = 0
for layer in list(model.parameters()):
layer_parameter = 1
for l in list(layer.size()):
layer_parameter *= l
total_parameters += layer_parameter
return total_parameters
def _weights_init(m):
if isinstance(m, nn.Conv2d):
torch.nn.init.xavier_uniform_(m.weight)
if m.bias is not None:
torch.nn.init.zeros_(m.bias)
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_()
class h_sigmoid(nn.Module):
def __init__(self, inplace=True):
super(h_sigmoid, self).__init__()
self.inplace = inplace
def forward(self, x):
return F.relu6(x + 3., inplace=self.inplace) / 6.
class h_swish(nn.Module):
def __init__(self, inplace=True):
super(h_swish, self).__init__()
self.inplace = inplace
def forward(self, x):
out = F.relu6(x + 3., self.inplace) / 6.
return out * x
def _make_divisible(v, divisor=8, min_value=None):
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
class SqueezeBlock(nn.Module):
def __init__(self, exp_size, divide=4):
super(SqueezeBlock, self).__init__()
self.dense = nn.Sequential(
nn.Linear(exp_size, exp_size // divide),
nn.ReLU(inplace=True),
nn.Linear(exp_size // divide, exp_size),
h_sigmoid()
)
def forward(self, x):
batch, channels, height, width = x.size()
out = F.avg_pool2d(x, kernel_size=[height, width]).view(batch, -1)
out = self.dense(out)
out = out.view(batch, channels, 1, 1)
# out = hard_sigmoid(out)
return out * x
class MobileBlock(nn.Module):
def __init__(self, in_channels, out_channels, kernal_size, stride, nonLinear, SE, exp_size):
super(MobileBlock, self).__init__()
self.out_channels = out_channels
self.nonLinear = nonLinear
self.SE = SE
padding = (kernal_size - 1) // 2
self.use_connect = stride == 1 and in_channels == out_channels
if self.nonLinear == "RE":
activation = nn.ReLU
else:
activation = h_swish
self.conv = nn.Sequential(
nn.Conv2d(in_channels, exp_size, kernel_size=1, stride=1, padding=0, bias=False),
nn.BatchNorm2d(exp_size),
activation(inplace=True)
)
self.depth_conv = nn.Sequential(
nn.Conv2d(exp_size, exp_size, kernel_size=kernal_size, stride=stride, padding=padding, groups=exp_size),
nn.BatchNorm2d(exp_size),
)
if self.SE:
self.squeeze_block = SqueezeBlock(exp_size)
self.point_conv = nn.Sequential(
nn.Conv2d(exp_size, out_channels, kernel_size=1, stride=1, padding=0),
nn.BatchNorm2d(out_channels),
activation(inplace=True)
)
def forward(self, x):
# MobileNetV2
out = self.conv(x)
out = self.depth_conv(out)
# Squeeze and Excite
if self.SE:
out = self.squeeze_block(out)
# point-wise conv
out = self.point_conv(out)
# connection
if self.use_connect:
return x + out
else:
return out
class MobileNetV3(nn.Module):
def __init__(self, model_mode="LARGE", num_classes=1000, multiplier=1.0, dropout_rate=0.0):
super(MobileNetV3, self).__init__()
self.num_classes = num_classes
if model_mode == "LARGE":
layers = [
[16, 16, 3, 1, "RE", False, 16],
[16, 24, 3, 2, "RE", False, 64],
[24, 24, 3, 1, "RE", False, 72],
[24, 40, 5, 2, "RE", True, 72],
[40, 40, 5, 1, "RE", True, 120],
[40, 40, 5, 1, "RE", True, 120],
[40, 80, 3, 2, "HS", False, 240],
[80, 80, 3, 1, "HS", False, 200],
[80, 80, 3, 1, "HS", False, 184],
[80, 80, 3, 1, "HS", False, 184],
[80, 112, 3, 1, "HS", True, 480],
[112, 112, 3, 1, "HS", True, 672],
[112, 160, 5, 1, "HS", True, 672],
[160, 160, 5, 2, "HS", True, 672],
[160, 160, 5, 1, "HS", True, 960],
]
init_conv_out = _make_divisible(16 * multiplier)
self.init_conv = nn.Sequential(
nn.Conv2d(in_channels=3, out_channels=init_conv_out, kernel_size=3, stride=2, padding=1),
nn.BatchNorm2d(init_conv_out),
h_swish(inplace=True),
)
self.block = []
for in_channels, out_channels, kernal_size, stride, nonlinear, se, exp_size in layers:
in_channels = _make_divisible(in_channels * multiplier)
out_channels = _make_divisible(out_channels * multiplier)
exp_size = _make_divisible(exp_size * multiplier)
self.block.append(MobileBlock(in_channels, out_channels, kernal_size, stride, nonlinear, se, exp_size))
self.block = nn.Sequential(*self.block)
out_conv1_in = _make_divisible(160 * multiplier)
out_conv1_out = _make_divisible(960 * multiplier)
self.out_conv1 = nn.Sequential(
nn.Conv2d(out_conv1_in, out_conv1_out, kernel_size=1, stride=1),
nn.BatchNorm2d(out_conv1_out),
h_swish(inplace=True),
)
out_conv2_in = _make_divisible(960 * multiplier)
out_conv2_out = _make_divisible(1280 * multiplier)
self.out_conv2 = nn.Sequential(
nn.Conv2d(out_conv2_in, out_conv2_out, kernel_size=1, stride=1),
h_swish(inplace=True),
nn.Dropout(dropout_rate),
nn.Conv2d(out_conv2_out, self.num_classes, kernel_size=1, stride=1),
)
elif model_mode == "SMALL":
layers = [
[16, 16, 3, 2, "RE", True, 16],
[16, 24, 3, 2, "RE", False, 72],
[24, 24, 3, 1, "RE", False, 88],
[24, 40, 5, 2, "RE", True, 96],
[40, 40, 5, 1, "RE", True, 240],
[40, 40, 5, 1, "RE", True, 240],
[40, 48, 5, 1, "HS", True, 120],
[48, 48, 5, 1, "HS", True, 144],
[48, 96, 5, 2, "HS", True, 288],
[96, 96, 5, 1, "HS", True, 576],
[96, 96, 5, 1, "HS", True, 576],
]
init_conv_out = _make_divisible(16 * multiplier)
self.init_conv = nn.Sequential(
nn.Conv2d(in_channels=3, out_channels=init_conv_out, kernel_size=3, stride=2, padding=1),
nn.BatchNorm2d(init_conv_out),
h_swish(inplace=True),
)
self.block = []
for in_channels, out_channels, kernal_size, stride, nonlinear, se, exp_size in layers:
in_channels = _make_divisible(in_channels * multiplier)
out_channels = _make_divisible(out_channels * multiplier)
exp_size = _make_divisible(exp_size * multiplier)
self.block.append(MobileBlock(in_channels, out_channels, kernal_size, stride, nonlinear, se, exp_size))
self.block = nn.Sequential(*self.block)
out_conv1_in = _make_divisible(96 * multiplier)
out_conv1_out = _make_divisible(576 * multiplier)
self.out_conv1 = nn.Sequential(
nn.Conv2d(out_conv1_in, out_conv1_out, kernel_size=1, stride=1),
SqueezeBlock(out_conv1_out),
nn.BatchNorm2d(out_conv1_out),
h_swish(inplace=True),
)
out_conv2_in = _make_divisible(576 * multiplier)
out_conv2_out = _make_divisible(1280 * multiplier)
self.out_conv2 = nn.Sequential(
nn.Conv2d(out_conv2_in, out_conv2_out, kernel_size=1, stride=1),
h_swish(inplace=True),
nn.Dropout(dropout_rate),
nn.Conv2d(out_conv2_out, self.num_classes, kernel_size=1, stride=1),
)
self.apply(_weights_init)
def forward(self, x):
out = self.init_conv(x)
out = self.block(out)
out = self.out_conv1(out)
batch, channels, height, width = out.size()
out = F.avg_pool2d(out, kernel_size=[height, width])
out = self.out_conv2(out).view(batch, -1)
return out
# temp = torch.zeros((1, 3, 224, 224))
# model = MobileNetV3(model_mode="LARGE", num_classes=1000, multiplier=1.0)
# print(model(temp).shape)
# print(get_model_parameters(model))