代码拉取完成，页面将自动刷新
# -*- coding: UTF-8 -*-
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import torch.nn as nn
from torch.nn import functional
import math
from collections import OrderedDict
class WingLoss(nn.Module):
def __init__(self, wing_w=10.0, wing_epsilon=2.0):
super(WingLoss, self).__init__()
self.wing_w = wing_w
self.wing_epsilon = wing_epsilon
self.wing_c = self.wing_w * (1.0 - math.log(1.0 + self.wing_w / self.wing_epsilon))
def forward(self, targets, predictions, euler_angle_weights=None):
abs_error = torch.abs(targets - predictions)
loss = torch.where(torch.le(abs_error, self.wing_w),
self.wing_w * torch.log(1.0 + abs_error / self.wing_epsilon), abs_error - self.wing_c)
loss_sum = torch.sum(loss, 1)
if euler_angle_weights is not None:
loss_sum *= euler_angle_weights
return torch.mean(loss_sum)
class LinearBottleneck(nn.Module):
def __init__(self, input_channels, out_channels, expansion, stride=1, activation=nn.ReLU6):
super(LinearBottleneck, self).__init__()
self.expansion_channels = input_channels * expansion
self.conv1 = nn.Conv2d(input_channels, self.expansion_channels, stride=1, kernel_size=1)
self.bn1 = nn.BatchNorm2d(self.expansion_channels)
self.depth_conv2 = nn.Conv2d(self.expansion_channels, self.expansion_channels, stride=stride, kernel_size=3,
groups=self.expansion_channels, padding=1)
self.bn2 = nn.BatchNorm2d(self.expansion_channels)
self.conv3 = nn.Conv2d(self.expansion_channels, out_channels, stride=1, kernel_size=1)
self.bn3 = nn.BatchNorm2d(out_channels)
self.activation = activation(inplace=True) # inplace=True
self.stride = stride
self.input_channels = input_channels
self.out_channels = out_channels
def forward(self, input):
residual = input
out = self.conv1(input)
out = self.bn1(out)
# out = self.activation(out)
out = self.depth_conv2(out)
out = self.bn2(out)
out = self.activation(out)
out = self.conv3(out)
out = self.bn3(out)
if self.stride == 1 and self.input_channels == self.out_channels:
out += residual
return out
class AuxiliaryNet(nn.Module):
def __init__(self, input_channels, nums_class=3, activation=nn.ReLU, first_conv_stride=2):
super(AuxiliaryNet, self).__init__()
self.input_channels = input_channels
# self.num_channels = [128, 128, 32, 128, 32]
self.num_channels = [512, 512, 512, 512, 1024]
self.conv1 = nn.Conv2d(self.input_channels, self.num_channels[0], kernel_size=3, stride=first_conv_stride,
padding=1)
self.bn1 = nn.BatchNorm2d(self.num_channels[0])
self.conv2 = nn.Conv2d(self.num_channels[0], self.num_channels[1], kernel_size=3, stride=1, padding=1)
self.bn2 = nn.BatchNorm2d(self.num_channels[1])
self.conv3 = nn.Conv2d(self.num_channels[1], self.num_channels[2], kernel_size=3, stride=2, padding=1)
self.bn3 = nn.BatchNorm2d(self.num_channels[2])
self.conv4 = nn.Conv2d(self.num_channels[2], self.num_channels[3], kernel_size=7, stride=1, padding=3)
self.bn4 = nn.BatchNorm2d(self.num_channels[3])
self.fc1 = nn.Linear(in_features=self.num_channels[3], out_features=self.num_channels[4])
self.fc2 = nn.Linear(in_features=self.num_channels[4], out_features=nums_class)
self.activation = activation(inplace=True)
self.init_params()
def init_params(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out')
if m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
nn.init.normal_(m.weight, std=0.01)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
def forward(self, input):
out = self.conv1(input)
out = self.bn1(out)
out = self.activation(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.activation(out)
out = self.conv3(out)
out = self.bn3(out)
out = self.activation(out)
out = self.conv4(out)
out = self.bn4(out)
out = self.activation(out)
out = functional.adaptive_avg_pool2d(out, 1).squeeze(-1).squeeze(-1)
#print(out.size())
# out = out.view(out.size(0), -1)
out = self.fc1(out)
euler_angles_pre = self.fc2(out)
return euler_angles_pre
# class AuxiliaryNet(nn.Module):
#
# def __init__(self, input_channels, nums_class=3, activation=nn.ReLU):
# super(AuxiliaryNet, self).__init__()
# self.input_channels = input_channels
# # self.num_channels = [128, 128, 32, 128, 32]
# self.num_channels = [512, 512, 512, 512, 1024]
# self.conv1 = nn.Conv2d(self.input_channels, self.num_channels[0], kernel_size=3, stride=2,
# padding=1)
# self.bn1 = nn.BatchNorm2d(self.num_channels[0])
#
# self.conv2 = nn.Conv2d(self.num_channels[0], self.num_channels[0], kernel_size=3, stride=2,
# padding=1)
# self.bn2 = nn.BatchNorm2d(self.num_channels[0])
#
# self.conv3 = nn.Conv2d(self.num_channels[0], self.num_channels[1], kernel_size=3, stride=1, padding=1)
# self.bn3 = nn.BatchNorm2d(self.num_channels[1])
#
# self.conv4 = nn.Conv2d(self.num_channels[1], self.num_channels[2], kernel_size=3, stride=2, padding=1)
# self.bn4 = nn.BatchNorm2d(self.num_channels[2])
#
# self.conv5 = nn.Conv2d(self.num_channels[2], self.num_channels[3], kernel_size=7, stride=1, padding=3)
# self.bn5 = nn.BatchNorm2d(self.num_channels[3])
#
# self.fc1 = nn.Linear(in_features=self.num_channels[3], out_features=self.num_channels[4])
# self.fc2 = nn.Linear(in_features=self.num_channels[4], out_features=nums_class)
#
# self.activation = activation(inplace=True)
#
# self.init_params()
#
# def init_params(self):
# for m in self.modules():
# if isinstance(m, nn.Conv2d):
# nn.init.kaiming_normal_(m.weight, mode='fan_out')
# if m.bias is not None:
# nn.init.constant_(m.bias, 0)
# elif isinstance(m, nn.BatchNorm2d):
# nn.init.constant_(m.weight, 1)
# nn.init.constant_(m.bias, 0)
# elif isinstance(m, nn.Linear):
# nn.init.normal_(m.weight, std=0.01)
# if m.bias is not None:
# nn.init.constant_(m.bias, 0)
#
# def forward(self, input):
#
# out = self.conv1(input)
# out = self.bn1(out)
# out = self.activation(out)
#
# out = self.conv2(out)
# out = self.bn2(out)
# out = self.activation(out)
#
# out = self.conv3(out)
# out = self.bn3(out)
# out = self.activation(out)
#
# out = self.conv4(out)
# out = self.bn4(out)
# out = self.activation(out)
#
# out = self.conv5(out)
# out = self.bn5(out)
# out = self.activation(out)
#
# out = functional.adaptive_avg_pool2d(out, 1).squeeze(-1).squeeze(-1)
#
# # out = out.view(out.size(0), -1)
# out = self.fc1(out)
# euler_angles_pre = self.fc2(out)
#
# return euler_angles_pre
class MobileNetV2(nn.Module):
def __init__(self, input_channels=3, num_of_channels=None, nums_class=136, activation=nn.ReLU6):
super(MobileNetV2, self).__init__()
assert num_of_channels is not None
self.num_of_channels = num_of_channels
self.conv1 = nn.Conv2d(input_channels, self.num_of_channels[0], kernel_size=3, stride=2, padding=1)
self.bn1 = nn.BatchNorm2d(self.num_of_channels[0])
self.depth_conv2 = nn.Conv2d(self.num_of_channels[0], self.num_of_channels[0], kernel_size=3, stride=1,
padding=1, groups=self.num_of_channels[0])
self.bn2 = nn.BatchNorm2d(self.num_of_channels[0])
self.stage0 = self.make_stage(self.num_of_channels[0], self.num_of_channels[0], stride=2, stage=0, times=5,
expansion=2, activation=activation)
self.stage1 = self.make_stage(self.num_of_channels[0], self.num_of_channels[1], stride=2, stage=1, times=7,
expansion=4, activation=activation)
self.linear_bottleneck_end = nn.Sequential(LinearBottleneck(self.num_of_channels[1], self.num_of_channels[2],
expansion=2, stride=1, activation=activation))
self.conv3 = nn.Conv2d(self.num_of_channels[2], self.num_of_channels[3], kernel_size=3, stride=2, padding=1)
self.bn3 = nn.BatchNorm2d(self.num_of_channels[3])
self.conv4 = nn.Conv2d(self.num_of_channels[3], self.num_of_channels[4], kernel_size=7, stride=1)
self.bn4 = nn.BatchNorm2d(self.num_of_channels[4])
self.activation = activation(inplace=True)
self.in_features = 14 * 14 * self.num_of_channels[2] + 7 * 7 * self.num_of_channels[3] + 1 * 1 * self.num_of_channels[4]
self.fc = nn.Linear(in_features=self.in_features, out_features=nums_class)
self.init_params()
def init_params(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out')
if m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
nn.init.normal_(m.weight, std=0.01)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
def make_stage(self, input_channels, out_channels, stride, stage, times, expansion, activation=nn.ReLU6):
modules = OrderedDict()
stage_name = 'LinearBottleneck{}'.format(stage)
module = LinearBottleneck(input_channels, out_channels, expansion=2,
stride=stride, activation=activation)
modules[stage_name+'_0'] = module
for i in range(times - 1):
module = LinearBottleneck(out_channels, out_channels, expansion=expansion, stride=1,
activation=activation)
module_name = stage_name+'_{}'.format(i+1)
modules[module_name] = module
return nn.Sequential(modules)
def forward(self, input):
out = self.conv1(input)
out = self.bn1(out)
out = self.activation(out)
# print(out.size())
out = self.depth_conv2(out)
out = self.bn2(out)
out = self.activation(out)
# print(out.size())
out = self.stage0(out)
# print(out.size())
out1 = self.stage1(out)
# print(out1.size())
out1 = self.linear_bottleneck_end(out1)
# print(out1.size())
out2 = self.conv3(out1)
out2 = self.bn3(out2)
out2 = self.activation(out2)
# print(out2.size())
out3 = self.conv4(out2)
out3 = self.bn4(out3)
out3 = self.activation(out3)
# print(out3.size())
out1 = out1.view(out1.size(0), -1)
# print(out1.size())
out2 = out2.view(out2.size(0), -1)
# print(out2.size())
out3 = out3.view(out3.size(0), -1)
# print(out3.size())
multi_scale = torch.cat([out1, out2, out3], 1)
# print(multi_scale.size())
assert multi_scale.size(1) == self.in_features
pre_landmarks = self.fc(multi_scale)
return pre_landmarks, out
# class MobileNetV2(nn.Module):
#
# def __init__(self, input_channels=3, num_of_channels=None, nums_class=136, activation=nn.ReLU6):
# super(MobileNetV2, self).__init__()
# assert num_of_channels is not None
# self.num_of_channels = num_of_channels
# self.conv1 = nn.Conv2d(input_channels, self.num_of_channels[0], kernel_size=3, stride=2, padding=1)
# self.bn1 = nn.BatchNorm2d(self.num_of_channels[0])
#
# self.depth_conv2 = nn.Conv2d(self.num_of_channels[0], self.num_of_channels[0], kernel_size=3, stride=1,
# padding=1, groups=self.num_of_channels[0])
# self.bn2 = nn.BatchNorm2d(self.num_of_channels[0])
#
# self.stage0 = self.make_stage(self.num_of_channels[0], self.num_of_channels[0], stride=2, stage=0, times=3,
# expansion=2, activation=activation)
#
# self.stage1 = self.make_stage(self.num_of_channels[0], self.num_of_channels[1], stride=2, stage=1, times=5,
# expansion=4, activation=activation)
#
# self.linear_bottleneck_end = nn.Sequential(LinearBottleneck(self.num_of_channels[1], self.num_of_channels[2],
# expansion=2, stride=1, activation=activation))
#
# self.conv3 = nn.Conv2d(self.num_of_channels[2], self.num_of_channels[3], kernel_size=3, stride=2, padding=1)
# self.bn3 = nn.BatchNorm2d(self.num_of_channels[3])
#
# self.conv4 = nn.Conv2d(self.num_of_channels[3], self.num_of_channels[4], kernel_size=7, stride=1)
# self.bn4 = nn.BatchNorm2d(self.num_of_channels[4])
#
# self.activation = activation(inplace=True)
#
# self.avg_pool1 = nn.AvgPool2d(kernel_size=14, stride=1)
#
# self.avg_pool2 = nn.AvgPool2d(kernel_size=7, stride=1)
#
# self.in_features = self.num_of_channels[2] + self.num_of_channels[3] + self.num_of_channels[4]
# self.fc = nn.Linear(in_features=self.in_features, out_features=nums_class)
#
# self.init_params()
#
# def init_params(self):
# for m in self.modules():
# if isinstance(m, nn.Conv2d):
# nn.init.kaiming_normal_(m.weight, mode='fan_out')
# if m.bias is not None:
# nn.init.constant_(m.bias, 0)
# elif isinstance(m, nn.BatchNorm2d):
# nn.init.constant_(m.weight, 1)
# nn.init.constant_(m.bias, 0)
# elif isinstance(m, nn.Linear):
# nn.init.normal_(m.weight, std=0.01)
# if m.bias is not None:
# nn.init.constant_(m.bias, 0)
#
# def make_stage(self, input_channels, out_channels, stride, stage, times, expansion, activation=nn.ReLU6):
# modules = OrderedDict()
# stage_name = 'LinearBottleneck{}'.format(stage)
#
# module = LinearBottleneck(input_channels, out_channels, expansion=2,
# stride=stride, activation=activation)
# modules[stage_name+'_0'] = module
#
# for i in range(times - 1):
# module = LinearBottleneck(out_channels, out_channels, expansion=expansion, stride=1,
# activation=activation)
# module_name = stage_name+'_{}'.format(i+1)
# modules[module_name] = module
#
# return nn.Sequential(modules)
#
# def forward(self, input):
# out = self.conv1(input)
# out = self.bn1(out)
# out = self.activation(out)
# # print(out.size())
#
# out = self.depth_conv2(out)
# out = self.bn2(out)
# out = self.activation(out)
# # print(out.size())
#
# out = self.stage0(out)
# # print(out.size())
#
# out1 = self.stage1(out)
# # print(out1.size())
#
# out1 = self.linear_bottleneck_end(out1)
# # print(out1.size())
#
# out2 = self.conv3(out1)
# out2 = self.bn3(out2)
# out2 = self.activation(out2)
# # print(out2.size())
#
# out3 = self.conv4(out2)
# out3 = self.bn4(out3)
# out3 = self.activation(out3)
# # print(out3.size())
#
# out1 = self.avg_pool1(out1)
#
# out2 = self.avg_pool2(out2)
#
# out1 = out1.view(out1.size(0), -1)
# # print(out1.size())
# out2 = out2.view(out2.size(0), -1)
# # print(out2.size())
# out3 = out3.view(out3.size(0), -1)
# # print(out3.size())
#
# multi_scale = torch.cat([out1, out2, out3], 1)
# # print(multi_scale.size())
#
# assert multi_scale.size(1) == self.in_features
#
# pre_landmarks = self.fc(multi_scale)
# return pre_landmarks, out
class BlazeBlock(nn.Module):
def __init__(self, in_channels, out_channels, mid_channels=None, stride=1):
super(BlazeBlock, self).__init__()
mid_channels = mid_channels or in_channels
assert stride in [1, 2]
if stride > 1:
self.use_pool = True
else:
self.use_pool = False
self.branch1 = nn.Sequential(
nn.Conv2d(in_channels=in_channels, out_channels=mid_channels, kernel_size=5, stride=stride, padding=2,
groups=in_channels),
nn.BatchNorm2d(mid_channels),
nn.Conv2d(in_channels=mid_channels, out_channels=out_channels, kernel_size=1, stride=1),
nn.BatchNorm2d(out_channels),
)
if self.use_pool:
self.shortcut = nn.Sequential(
nn.MaxPool2d(kernel_size=3, stride=stride, padding=1),
nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=1),
nn.BatchNorm2d(out_channels),
)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
branch1 = self.branch1(x)
out = (branch1+self.shortcut(x)) if self.use_pool else (branch1+x)
return self.relu(out)
class DoubleBlazeBlock(nn.Module):
def __init__(self, in_channels, out_channels, mid_channels=None, stride=1):
super(DoubleBlazeBlock, self).__init__()
mid_channels = mid_channels or in_channels
assert stride in [1, 2]
if stride > 1:
self.use_pool = True
else:
self.use_pool = False
self.branch1 = nn.Sequential(
nn.Conv2d(in_channels=in_channels, out_channels=in_channels, kernel_size=5, stride=stride,
padding=2, groups=in_channels),
nn.BatchNorm2d(in_channels),
nn.Conv2d(in_channels=in_channels, out_channels=mid_channels, kernel_size=1, stride=1),
nn.BatchNorm2d(mid_channels),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels=mid_channels, out_channels=mid_channels, kernel_size=5, stride=1, padding=2,
groups=mid_channels),
nn.BatchNorm2d(mid_channels),
nn.Conv2d(in_channels=mid_channels, out_channels=out_channels, kernel_size=1, stride=1),
nn.BatchNorm2d(out_channels),
)
if self.use_pool:
self.shortcut = nn.Sequential(
nn.MaxPool2d(kernel_size=3, stride=stride, padding=1),
nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=1),
nn.BatchNorm2d(out_channels),
)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
branch1 = self.branch1(x)
out = (branch1 + self.shortcut(x)) if self.use_pool else (branch1 + x)
return self.relu(out)
class ASPP(nn.Module):
def __init__(self, in_channels=96, out_channels=96):
super(ASPP, self).__init__()
self.conv_1x1_1 = nn.Conv2d(in_channels, out_channels, kernel_size=1)
self.bn_conv_1x1_1 = nn.BatchNorm2d(out_channels)
self.conv_3x3_1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=2, dilation=2)
self.bn_conv_3x3_1 = nn.BatchNorm2d(out_channels)
self.conv_3x3_2 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=3, dilation=3)
self.bn_conv_3x3_2 = nn.BatchNorm2d(out_channels)
self.conv_3x3_3 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=5, dilation=5)
self.bn_conv_3x3_3 = nn.BatchNorm2d(out_channels)
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.conv_1x1_2 = nn.Conv2d(in_channels, out_channels, kernel_size=1)
self.bn_conv_1x1_2 = nn.BatchNorm2d(out_channels)
self.conv_1x1_3 = nn.Conv2d(in_channels*5, out_channels, kernel_size=1) # (480 = 5*96)
self.bn_conv_1x1_3 = nn.BatchNorm2d(out_channels)
def forward(self, feature_map):
feature_map_h = feature_map.size()[2]
feature_map_w = feature_map.size()[3]
out_1x1 = functional.relu(self.bn_conv_1x1_1(self.conv_1x1_1(feature_map)))
out_3x3_1 = functional.relu(self.bn_conv_3x3_1(self.conv_3x3_1(feature_map)))
out_3x3_2 = functional.relu(self.bn_conv_3x3_2(self.conv_3x3_2(feature_map)))
out_3x3_3 = functional.relu(self.bn_conv_3x3_3(self.conv_3x3_3(feature_map)))
out_img = self.avg_pool(feature_map)
out_img = functional.relu(self.bn_conv_1x1_2(self.conv_1x1_2(out_img)))
out_img = functional.interpolate(out_img, size=(feature_map_h, feature_map_w), mode="bilinear", align_corners=True)
out = torch.cat([out_1x1, out_3x3_1, out_3x3_2, out_3x3_3, out_img], 1)
out = functional.relu(self.bn_conv_1x1_3(self.conv_1x1_3(out)))
return out
class BlazeLandMark(nn.Module):
def __init__(self, nums_class=136):
super(BlazeLandMark, self).__init__()
self.firstconv = nn.Sequential(
nn.Conv2d(in_channels=3, out_channels=24, kernel_size=3, stride=2, padding=1),
nn.BatchNorm2d(24),
nn.ReLU(inplace=True),
# nn.Conv2d(in_channels=24, out_channels=24, kernel_size=3, stride=2, padding=1),
# nn.BatchNorm2d(24),
# nn.ReLU(inplace=True),
)
self.blazeBlock = nn.Sequential(
BlazeBlock(in_channels=24, out_channels=24),
BlazeBlock(in_channels=24, out_channels=24),
BlazeBlock(in_channels=24, out_channels=48, stride=2),
BlazeBlock(in_channels=48, out_channels=48),
BlazeBlock(in_channels=48, out_channels=48),
)
self.doubleBlazeBlock1 = nn.Sequential(
DoubleBlazeBlock(in_channels=48, out_channels=96, mid_channels=24, stride=2),
DoubleBlazeBlock(in_channels=96, out_channels=96, mid_channels=24),
DoubleBlazeBlock(in_channels=96, out_channels=96, mid_channels=24))
self.doubleBlazeBlock2 = nn.Sequential(
DoubleBlazeBlock(in_channels=96, out_channels=96, mid_channels=24, stride=2),
DoubleBlazeBlock(in_channels=96, out_channels=96, mid_channels=24),
DoubleBlazeBlock(in_channels=96, out_channels=96, mid_channels=24),
)
# self.firstconv = nn.Sequential(
# nn.Conv2d(in_channels=3, out_channels=12, kernel_size=3, stride=2, padding=1),
# nn.BatchNorm2d(12),
# nn.ReLU(inplace=True),
# )
# self.blazeBlock = nn.Sequential(
# BlazeBlock(in_channels=12, out_channels=12),
# BlazeBlock(in_channels=12, out_channels=12),
# BlazeBlock(in_channels=12, out_channels=24, stride=2),
# BlazeBlock(in_channels=24, out_channels=24),
# BlazeBlock(in_channels=24, out_channels=24),
# )
# self.doubleBlazeBlock1 = nn.Sequential(
# DoubleBlazeBlock(in_channels=24, out_channels=48, mid_channels=12, stride=2),
# DoubleBlazeBlock(in_channels=48, out_channels=48, mid_channels=12),
# DoubleBlazeBlock(in_channels=48, out_channels=48, mid_channels=12))
# self.doubleBlazeBlock2 = nn.Sequential(
# DoubleBlazeBlock(in_channels=48, out_channels=48, mid_channels=12, stride=2),
# DoubleBlazeBlock(in_channels=48, out_channels=48, mid_channels=12),
# DoubleBlazeBlock(in_channels=48, out_channels=48, mid_channels=12),
# )
self.secondconv = nn.Sequential(
nn.Conv2d(in_channels=96, out_channels=192, kernel_size=7, stride=1),
nn.BatchNorm2d(192),
nn.ReLU(inplace=True),
)
self.aspp = ASPP(in_channels=96, out_channels=96)
#self.in_features = 48 + 96 + 192
self.in_features = 96*7*7
self.fc = nn.Linear(in_features=self.in_features, out_features=nums_class)
self.init_params()
# def initialize(self):
# for m in self.modules():
# if isinstance(m, nn.Conv2d):
# nn.init.kaiming_normal_(m.weight)
# nn.init.constant_(m.bias, 0)
# elif isinstance(m, nn.BatchNorm2d):
# nn.init.constant_(m.weight, 1)
# nn.init.constant_(m.bias, 0)
def init_params(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out')
if m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
nn.init.normal_(m.weight, std=0.01)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
def forward(self, input):
fisrt_out = self.firstconv(input)
#out = self.blazeBlock(input)
#print(out.size())
#
#out1 = self.doubleBlazeBlock1(out)
# print(out1.size())
#
#out2 = self.doubleBlazeBlock2(out1)
# print(out2.size())
#
#out3 = self.secondconv(out2)
#
# assert out1.size(2) == 14 and out1.size(3) == 14
# out1 = self.avg_pool1(out1)
#
# assert out2.size(2) == 7 and out2.size(3) == 7
# out2 = self.avg_pool2(out2)
block_out1 = self.blazeBlock(fisrt_out)
# print(out1.size())
block_out2 = self.doubleBlazeBlock1(block_out1)
# print(out2.size())
block_out3 = self.aspp(self.doubleBlazeBlock2(block_out2))
# print(out3.size())
# assert out1.size(2) == 14 and out1.size(3) == 14
# out1_ = self.avg_pool1(out1)
# assert out2.size(2) == 7 and out2.size(3) == 7
# out2_ = self.avg_pool2(out2)
# assert out3.size(2) == 4 and out3.size(3) == 4
# out3_ = self.avg_pool3(out3)
# out1_ = out1_.view(out1_.size(0), -1)
# out2_ = out2_.view(out2_.size(0), -1)
# out3_ = out3_.view(out3_.size(0), -1)
# multi_scale = torch.cat([out1_, out2_, out3_], 1)
# assert multi_scale.size(1) == self.in_features
# pre_landmarks = self.fc(multi_scale)
# functional.adaptive_avg_pool2d: global avg_pool
# 1 means 1*1 feature map no matter what input size
# squeeze(-1): delete dims that number is 1
#block_out1_ = functional.adaptive_avg_pool2d(block_out1, 1).squeeze(-1).squeeze(-1)
#block_out2_ = functional.adaptive_avg_pool2d(block_out2, 1).squeeze(-1).squeeze(-1)
#block_out3_ = functional.adaptive_avg_pool2d(block_out3, 1).squeeze(-1).squeeze(-1)
#print(block_out3_.size())
block_out3 = block_out3.view(block_out3.size(0), -1)
assert block_out3.size(1) == self.in_features
pre_landmarks = self.fc(block_out3)
return pre_landmarks, block_out1
#-----------------Efficient-------------------------
from efficientnet.model import EfficientNet
class EFFNet(nn.Module):
def __init__(self, compound_coef=0, load_weights=False):
super(EFFNet, self).__init__()
print(f'efficientnet-b{compound_coef}')
model = EfficientNet.from_pretrained(f'efficientnet-b{compound_coef}', load_weights)
del model._conv_head
del model._bn1
del model._avg_pooling
del model._dropout
del model._fc
self.model = model
def forward(self, x):
x = self.model._conv_stem(x)
x = self.model._bn0(x)
x = self.model._swish(x)
feature_maps = []
last_x = None
for idx, block in enumerate(self.model._blocks):
drop_connect_rate = self.model._global_params.drop_connect_rate
if drop_connect_rate:
drop_connect_rate *= float(idx) / len(self.model._blocks)
x = block(x, drop_connect_rate)
#print('x', x.size())
if block._depthwise_conv.stride == [2, 2]:
feature_maps.append(last_x)
elif idx == len(self.model._blocks) - 1:
feature_maps.append(x)
last_x = x
#print('last_x', last_x.size())
del last_x
return feature_maps[1:]
class EfficientLM(nn.Module):
def __init__(self, nums_class=136, compound_coef=0, load_weights=False):
super(EfficientLM, self).__init__()
self.nums_class = nums_class
self.compound_coef = compound_coef
self.backbone_coef = [0, 1, 2, 3, 4, 5, 6, 7]
self.inchannels_list = [320, 320, 352, 384, 448, 512, 576, 640]
self.p8_outchannels_list = [40, 40, 48, 48, 56, 64, 72, 80]
self.p8_outchannels = self.p8_outchannels_list[self.compound_coef]
self.backbone = EFFNet(self.backbone_coef[self.compound_coef], load_weights=load_weights)
self.in_features = self.inchannels_list[self.compound_coef] * 4 * 4 # 112*112
self.fc = nn.Linear(in_features=self.in_features, out_features=self.nums_class)
def forward(self, x):
p4, p8, p16, p32 = self.backbone(x)
p32 = p32.view(p32.size(0), -1)
output = self.fc(p32)
return output, p8
# HRNetV2
import numpy as np
import torch.nn.functional as F
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=False)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.downsample = downsample
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
residual = self.downsample(x)
out = out + residual
out = self.relu(out)
return out
class BottleNeck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(BottleNeck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(planes, planes*self.expansion, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes*self.expansion)
self.relu = nn.ReLU(inplace=False)
self.downsample = downsample
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
residual = self.downsample(x)
out = out + residual
out = self.relu(out)
return out
blocks_dict = {
'BASIC':BasicBlock,
'BOTTLENECK':BottleNeck
}
class HighResolutionModule(nn.Module):
def __init__(self, num_branches, blocks, num_blocks, num_inchannels, num_channels, fuse_method, multi_scale_output=True):
super(HighResolutionModule, self).__init__()
self._check_branches(num_branches, blocks, num_blocks, num_inchannels, num_channels)
self.num_inchannels = num_inchannels
self.fuse_method = fuse_method
self.num_branches = num_branches
self.multi_scale_output = multi_scale_output
self.branches = self._make_branches(num_branches, blocks, num_blocks, num_channels)
self.fuse_layers = self._make_fuse_layers()
self.relu = nn.ReLU(inplace=False)
def _check_branches(self, num_branches, blocks, num_blocks, num_inchannels, num_channels):
if num_branches != len(num_blocks):
error_msg = 'NUM_BRANCHES({}) <> NUM_BLOCKS({})'.format(num_branches, len(num_blocks))
raise ValueError(error_msg)
if num_branches != len(num_channels):
error_msg = 'NUM_BRANCHES({}) <> NUM_CHANNELS({})'.format(num_branches, len(num_channels))
raise ValueError(error_msg)
if num_branches != len(num_inchannels):
error_msg = 'NUM_BRANCHES({}) <> NUM_INCHANNELS({})'.format(num_branches, len(num_inchannels))
raise ValueError(error_msg)
def _make_one_branch(self, branch_index, block, num_blocks, num_channels, stride=1):
downsample = None
if stride != 1 or \
self.num_inchannels[branch_index] != num_channels[branch_index] * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.num_inchannels[branch_index], num_channels[branch_index] * block.expansion,
kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(num_channels[branch_index] * block.expansion),
)
layers = []
layers.append(block(self.num_inchannels[branch_index], num_channels[branch_index], stride, downsample))
self.num_inchannels[branch_index] = num_channels[branch_index] * block.expansion
for i in range(1, num_blocks[branch_index]):
layers.append(block(self.num_inchannels[branch_index], num_channels[branch_index]))
return nn.Sequential(*layers)
def _make_branches(self, num_branches, block, num_blocks, num_channels):
branches = []
for i in range(num_branches):
branches.append(self._make_one_branch(i, block, num_blocks, num_channels))
return nn.ModuleList(branches)
def _make_fuse_layers(self):
if self.num_branches == 1:
return None
num_branches = self.num_branches
num_inchannels = self.num_inchannels
fuse_layers = []
for i in range(num_branches if self.multi_scale_output else 1):
fuse_layer = []
for j in range(num_branches):
if j > i:
fuse_layer.append(nn.Sequential(
nn.Conv2d(num_inchannels[j], num_inchannels[i], 1, 1, 0, bias=False),
nn.BatchNorm2d(num_inchannels[i])))
elif j == i:
fuse_layer.append(None)
else:
conv3x3s = []
for k in range(i-j):
if k == i - j - 1:
num_outchannels_conv3x3 = num_inchannels[i]
conv3x3s.append(nn.Sequential(
nn.Conv2d(num_inchannels[j], num_outchannels_conv3x3, 3, 2, 1, bias=False),
nn.BatchNorm2d(num_outchannels_conv3x3)))
else:
num_outchannels_conv3x3 = num_inchannels[j]
conv3x3s.append(nn.Sequential(
nn.Conv2d(num_inchannels[j], num_outchannels_conv3x3, 3, 2, 1, bias=False),
nn.BatchNorm2d(num_outchannels_conv3x3),
nn.ReLU(inplace=False)))
fuse_layer.append(nn.Sequential(*conv3x3s))
fuse_layers.append(nn.ModuleList(fuse_layer))
return nn.ModuleList(fuse_layers)
def get_num_inchannels(self):
return self.num_inchannels
def forward(self, x):
if self.num_branches == 1:
return [self.branches[0](x[0])]
for i in range(self.num_branches):
x[i] = self.branches[i](x[i])
x_fuse = []
for i in range(len(self.fuse_layers)):
y = x[0] if i == 0 else self.fuse_layers[i][0](x[0])
for j in range(1, self.num_branches):
if i == j:
y = y + x[j]
elif j > i:
width_output = x[i].shape[-1]
height_output = x[i].shape[-2]
y = y + F.interpolate(self.fuse_layers[i][j](x[j]), size=[height_output, width_output], mode='bilinear', align_corners=True)
else:
y = y + self.fuse_layers[i][j](x[j])
x_fuse.append(self.relu(y))
return x_fuse
class HighResolutionNet(nn.Module):
def __init__(self, nums_class=136):
super(HighResolutionNet, self).__init__()
self.num_branches1 = 2
self.num_branches2 = 3
self.num_branches3 = 4
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=False)
self.layer1 = self._make_layer(BottleNeck, 64, 64, 4)
layer1_out_channel = BottleNeck.expansion*64
num_channels1 = [48, 96]
num_channels_expansion1 = [num_channels1[i] * BasicBlock.expansion for i in range(len(num_channels1))]
self.transition1 = self._make_transition_layer([layer1_out_channel], num_channels_expansion1)
# layer_config['NUM_MODULES'] layer_config['NUM_BRANCHES'] layer_config['NUM_BLOCKS']
# layer_config['NUM_CHANNELS'] blocks_dict[layer_config['BLOCK']] layer_config['FUSE_METHOD']
self.stage2, pre_stage_channels = self._make_stage(1, self.num_branches1, [4, 4], num_channels1, BasicBlock, 'SUM', num_channels_expansion1)
num_channels2 = [48, 96, 192]
num_channels_expansion2 = [num_channels2[i] * BasicBlock.expansion for i in range(len(num_channels2))]
self.transition2 = self._make_transition_layer(pre_stage_channels, num_channels_expansion2)
self.stage3, pre_stage_channels = self._make_stage(4, self.num_branches2, [4, 4, 4], num_channels2, BasicBlock, 'SUM', num_channels_expansion2)
num_channels3 = [48, 96, 192, 384]
num_channels_expansion3 = [num_channels3[i] * BasicBlock.expansion for i in range(len(num_channels3))]
self.transition3 = self._make_transition_layer(pre_stage_channels, num_channels_expansion3)
self.stage4, pre_stage_channels = self._make_stage(3, self.num_branches3, [4, 4, 4, 4], num_channels3, BasicBlock, 'SUM', num_channels_expansion3, multi_scale_output=True)
last_inp_channels = np.int(np.sum(pre_stage_channels))
self.FINAL_CONV_KERNEL = 1
self.last_layer = nn.Sequential(
nn.Conv2d(in_channels=last_inp_channels, out_channels=last_inp_channels, kernel_size=1, stride=1, padding=0),
nn.BatchNorm2d(last_inp_channels),
nn.ReLU(inplace=False),
# nn.Conv2d(in_channels=last_inp_channels, out_channels=nums_class, kernel_size=self.FINAL_CONV_KERNEL,
# stride=1, padding=1 if self.FINAL_CONV_KERNEL == 3 else 0)
)
self.in_features = last_inp_channels * 28 * 28
self.fc = nn.Linear(in_features=self.in_features, out_features=nums_class)
self.init_weights()
def _make_layer(self, block, inplanes, planes, blocks, stride=1):
downsample = None
if stride != 1 or inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(planes * block.expansion),
)
layers = []
layers.append(block(inplanes, planes, stride, downsample))
inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(inplanes, planes))
return nn.Sequential(*layers)
def _make_transition_layer(self, num_channels_pre_layer, num_channels_cur_layer):
num_branches_cur = len(num_channels_cur_layer)
num_branches_pre = len(num_channels_pre_layer)
transition_layers = []
for i in range(num_branches_cur):
if i < num_branches_pre:
if num_channels_cur_layer[i] != num_channels_pre_layer[i]:
transition_layers.append(nn.Sequential(
nn.Conv2d(num_channels_pre_layer[i], num_channels_cur_layer[i], 3, 1, 1, bias=False),
nn.BatchNorm2d(num_channels_cur_layer[i]),
nn.ReLU(inplace=False)))
else:
transition_layers.append(None)
else:
conv3x3s = []
for j in range(i+1-num_branches_pre):
inchannels = num_channels_pre_layer[-1]
outchannels = num_channels_cur_layer[i] \
if j == i-num_branches_pre else inchannels
conv3x3s.append(nn.Sequential(
nn.Conv2d(inchannels, outchannels, 3, 2, 1, bias=False),
nn.BatchNorm2d(outchannels),
nn.ReLU(inplace=False)))
transition_layers.append(nn.Sequential(*conv3x3s))
return nn.ModuleList(transition_layers)
def _make_stage(self, num_modules, num_branches, num_blocks, num_channels, block, fuse_method, num_inchannels, multi_scale_output=True):
modules = []
for i in range(num_modules):
# multi_scale_output is only used last module
if not multi_scale_output and i == num_modules - 1:
reset_multi_scale_output = False
else:
reset_multi_scale_output = True
modules.append(
HighResolutionModule(num_branches, block, num_blocks, num_inchannels, num_channels, fuse_method, reset_multi_scale_output)
)
num_inchannels = modules[-1].get_num_inchannels()
return nn.Sequential(*modules), num_inchannels
def init_weights(self):
# print('=> init weights from normal distribution')
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out')
if m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
nn.init.normal_(m.weight, std=0.01)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
def forward(self, x):
x = self.relu(self.bn1(self.conv1(x)))
axn_input = self.relu(self.bn2(self.conv2(x)))
out = self.layer1(axn_input)
x_list = []
for i in range(self.num_branches1):
if self.transition1[i] is not None:
x_list.append(self.transition1[i](out))
else:
x_list.append(out)
y_list = self.stage2(x_list)
x_list = []
for i in range(self.num_branches2):
if self.transition2[i] is not None:
if i < self.num_branches1:
x_list.append(self.transition2[i](y_list[i]))
else:
x_list.append(self.transition2[i](y_list[-1]))
else:
x_list.append(y_list[i])
y_list = self.stage3(x_list)
x_list = []
for i in range(self.num_branches3):
if self.transition3[i] is not None:
if i < self.num_branches2:
x_list.append(self.transition3[i](y_list[i]))
else:
x_list.append(self.transition3[i](y_list[-1]))
else:
x_list.append(y_list[i])
out = self.stage4(x_list)
# print(out[0].size(), out[1].size(), out[2].size(), out[3].size())
# Upsampling
x0_h, x0_w = out[0].size(2), out[0].size(3)
x1 = F.interpolate(out[1], size=(x0_h, x0_w), mode='bilinear', align_corners=True)
x2 = F.interpolate(out[2], size=(x0_h, x0_w), mode='bilinear', align_corners=True)
x3 = F.interpolate(out[3], size=(x0_h, x0_w), mode='bilinear', align_corners=True)
out = torch.cat([out[0], x1, x2, x3], 1)
out = self.last_layer(out)
out = out.view(out.size(0), -1)
out = self.fc(out)
# print(out.size(), axn_input.size())
return out, axn_input
# resNest50
from resnest.torch import resnest50
class MyResNest50(nn.Module):
def __init__(self, nums_class=136):
super(MyResNest50, self).__init__()
self.resnest = resnest50(pretrained=True)
self.resnest_backbone1 = nn.Sequential(*list(self.resnest.children())[:-6])
self.resnest_backbone_end = nn.Sequential(*list(self.resnest.children())[-6:-2])
self.in_features = 2048 * 4 * 4
self.fc = nn.Linear(in_features=self.in_features, out_features=nums_class)
def forward(self, x):
# (x has shape (batch_size, 3, h, w))
# pass x through (parts of) the pretrained ResNet:
auxnet = self.resnest_backbone1(x)
#print(auxnet.size())
out = self.resnest_backbone_end(auxnet)
#print(out.size())
out = out.view(out.size(0), -1)
out = self.fc(out)
return out, auxnet
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