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from torch.nn import Linear, Conv2d, BatchNorm1d, BatchNorm2d, PReLU, ReLU, Sigmoid, Dropout2d, Dropout, AvgPool2d, \
MaxPool2d, AdaptiveAvgPool2d, Sequential, Module, Parameter
import torch.nn.functional as F
import torch
from collections import namedtuple
import math
import pdb
################################## Original Arcface Model #############################################################
class Flatten(Module):
def forward(self, input):
return input.view(input.size(0), -1)
def l2_norm(input, axis=1):
norm = torch.norm(input, 2, axis, True)
output = torch.div(input, norm)
return output
class SEModule(Module):
def __init__(self, channels, reduction):
super(SEModule, self).__init__()
self.avg_pool = AdaptiveAvgPool2d(1)
self.fc1 = Conv2d(
channels, channels // reduction, kernel_size=1, padding=0, bias=False)
self.relu = ReLU(inplace=True)
self.fc2 = Conv2d(
channels // reduction, channels, kernel_size=1, padding=0, bias=False)
self.sigmoid = Sigmoid()
def forward(self, x):
module_input = x
x = self.avg_pool(x)
x = self.fc1(x)
x = self.relu(x)
x = self.fc2(x)
x = self.sigmoid(x)
return module_input * x
# i = 0
class bottleneck_IR(Module):
def __init__(self, in_channel, depth, stride):
super(bottleneck_IR, self).__init__()
if in_channel == depth:
self.shortcut_layer = MaxPool2d(1, stride)
else:
self.shortcut_layer = Sequential(
Conv2d(in_channel, depth, (1, 1), stride, bias=False), BatchNorm2d(depth))
self.res_layer = Sequential(
BatchNorm2d(in_channel),
Conv2d(in_channel, depth, (3, 3), (1, 1), 1, bias=False), PReLU(depth),
Conv2d(depth, depth, (3, 3), stride, 1, bias=False), BatchNorm2d(depth))
i = 0
def forward(self, x):
shortcut = self.shortcut_layer(x)
# print(shortcut.shape)
# print('---s---')
res = self.res_layer(x)
# print(res.shape)
# print('---r---')
# i = i + 50
# print(i)
# print('50')
return res + shortcut
class bottleneck_IR_SE(Module):
def __init__(self, in_channel, depth, stride):
super(bottleneck_IR_SE, self).__init__()
if in_channel == depth:
self.shortcut_layer = MaxPool2d(1, stride)
else:
self.shortcut_layer = Sequential(
Conv2d(in_channel, depth, (1, 1), stride, bias=False),
BatchNorm2d(depth))
self.res_layer = Sequential(
BatchNorm2d(in_channel),
Conv2d(in_channel, depth, (3, 3), (1, 1), 1, bias=False),
PReLU(depth),
Conv2d(depth, depth, (3, 3), stride, 1, bias=False),
BatchNorm2d(depth),
SEModule(depth, 16)
)
def forward(self, x):
shortcut = self.shortcut_layer(x)
res = self.res_layer(x)
return res + shortcut
class Bottleneck(namedtuple('Block', ['in_channel', 'depth', 'stride'])):
'''A named tuple describing a ResNet block.'''
# print('50')
def get_block(in_channel, depth, num_units, stride=2):
return [Bottleneck(in_channel, depth, stride)] + [Bottleneck(depth, depth, 1) for i in range(num_units - 1)]
def get_blocks(num_layers):
if num_layers == 50:
blocks1 = [
get_block(in_channel=64, depth=64, num_units=3),
# get_block(in_channel=64, depth=128, num_units=4),
# get_block(in_channel=128, depth=256, num_units=14),
# get_block(in_channel=256, depth=512, num_units=3)
]
blocks2 = [
# get_block(in_channel=64, depth=64, num_units=3),
get_block(in_channel=64, depth=128, num_units=4),
# get_block(in_channel=128, depth=256, num_units=14),
# get_block(in_channel=256, depth=512, num_units=3)
]
blocks3 = [
# get_block(in_channel=64, depth=64, num_units=3),
# get_block(in_channel=64, depth=128, num_units=4),
get_block(in_channel=128, depth=256, num_units=14),
# get_block(in_channel=256, depth=512, num_units=3)
]
elif num_layers == 100:
blocks = [
get_block(in_channel=64, depth=64, num_units=3),
get_block(in_channel=64, depth=128, num_units=13),
get_block(in_channel=128, depth=256, num_units=30),
get_block(in_channel=256, depth=512, num_units=3)
]
elif num_layers == 152:
blocks = [
get_block(in_channel=64, depth=64, num_units=3),
get_block(in_channel=64, depth=128, num_units=8),
get_block(in_channel=128, depth=256, num_units=36),
get_block(in_channel=256, depth=512, num_units=3)
]
return blocks1, blocks2, blocks3
class Backbone(Module):
def __init__(self, num_layers, drop_ratio, mode='ir'):
super(Backbone, self).__init__()
# assert num_layers in [50, 100, 152], 'num_layers should be 50,100, or 152'
assert mode in ['ir', 'ir_se'], 'mode should be ir or ir_se'
blocks1, blocks2, blocks3 = get_blocks(num_layers)
# blocks2 = get_blocks(num_layers)
if mode == 'ir':
unit_module = bottleneck_IR
elif mode == 'ir_se':
unit_module = bottleneck_IR_SE
self.input_layer = Sequential(Conv2d(3, 64, (3, 3), 1, 1, bias=False),
BatchNorm2d(64),
PReLU(64))
self.output_layer = Sequential(BatchNorm2d(512),
Dropout(drop_ratio),
Flatten(),
Linear(512 * 7 * 7, 512),
BatchNorm1d(512))
modules1 = []
for block in blocks1:
for bottleneck in block:
modules1.append(
unit_module(bottleneck.in_channel,
bottleneck.depth,
bottleneck.stride))
modules2 = []
for block in blocks2:
for bottleneck in block:
modules2.append(
unit_module(bottleneck.in_channel,
bottleneck.depth,
bottleneck.stride))
modules3 = []
for block in blocks3:
for bottleneck in block:
modules3.append(
unit_module(bottleneck.in_channel,
bottleneck.depth,
bottleneck.stride))
# modules4 = []
# for block in blocks4:
# for bottleneck in block:
# modules4.append(
# unit_module(bottleneck.in_channel,
# bottleneck.depth,
# bottleneck.stride))
self.body1 = Sequential(*modules1)
self.body2 = Sequential(*modules2)
self.body3 = Sequential(*modules3)
# self.body4 = Sequential(*modules4)
def forward(self, x):
x = F.interpolate(x, size=112)
x = self.input_layer(x)
x1 = self.body1(x)
x2 = self.body2(x1)
x3 = self.body3(x2)
# x = self.output_layer(x)
# return l2_norm(x)
return x1, x2, x3
def load_pretrained_weights(model, checkpoint):
import collections
if 'state_dict' in checkpoint:
state_dict = checkpoint['state_dict']
else:
state_dict = checkpoint
model_dict = model.state_dict()
new_state_dict = collections.OrderedDict()
matched_layers, discarded_layers = [], []
for i, (k, v) in enumerate(state_dict.items()):
# print(i)
# If the pretrained state_dict was saved as nn.DataParallel,
# keys would contain "module.", which should be ignored.
if k.startswith('module.'):
k = k[7:]
if k in model_dict and model_dict[k].size() == v.size():
new_state_dict[k] = v
matched_layers.append(k)
else:
# print(k)
discarded_layers.append(k)
# new_state_dict.requires_grad = False
model_dict.update(new_state_dict)
model.load_state_dict(model_dict)
print('load_weight', len(matched_layers))
return model
# model = Backbone(50, 0.0, 'ir')
# ir_checkpoint = torch.load(r'C:\Users\86187\Desktop\project\mixfacial\models\pretrain\new_ir50.pth')
# print('hello')
# i1, i2, i3 = 0, 0, 0
# ir_checkpoint = torch.load(r'C:\Users\86187\Desktop\project\mixfacial\models\pretrain\ir50.pth', map_location=lambda storage, loc: storage)
# for (k1, v1), (k2, v2) in zip(model.state_dict().items(), ir_checkpoint.items()):
# print(f'k1:{k1}, k2:{k2}')
# model.state_dict()[k1] = v2
# torch.save(model.state_dict(), r'C:\Users\86187\Desktop\project\mixfacial\models\pretrain\new_ir50.pth')
# print(k)
# if k.startswith('body1'):
# i1+=1
# if k.startswith('body2'):
# i2+=1
# if k.startswith('body3'):
# i3+=1
# print(f'i1:{i1}, i2:{i2}, i3:{i3}')
# print('-'*100)
# ir_checkpoint = torch.load(r'C:\Users\86187\Desktop\project\mixfacial\models\pretrain\ir50.pth', map_location=lambda storage, loc: storage)
# le = 0
# for k, v in ir_checkpoint.items():
# # print(k)
# if k.startswith('body'):
# if le < i1:
# le += 1
# key = k.split('.')[0] + str(1) + k.split('.')[1:]
# print(key)
# # ir_checkpoint = ir_checkpoint["model"]
# model = load_pretrained_weights(model, ir_checkpoint)
# img = torch.rand(size=(2,3,224,224))
# out1, out2, out3 = model(img)
# print(out1.shape, out2.shape, out3.shape) |