Mobilenetv2_第三版

发表于 | 阅读数

Mobilenet v2 第三版

在这一版中，纠正了原来 Model 中的错误……

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
import torch
import torch.utils.data as data
import torch.nn as nn
import torch.nn.functional as F
import torchvision as tv
import numpy as np
import matplotlib.pyplot as plt
from torchvision import transforms, datasets
from torchvision.transforms import ToPILImage
from torch.autograd import Variable
from torch import optim
from tensorboardX import SummaryWriter
import os
import datetime

mnist_mean = 0.1307
mnist_std = 0.3081
epoch_n = 5


class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()

        channels = [32, 64, 144, 192, 384]
        strides = [1, 2, 1, 1]
        ex_ch = [ch * 3 for ch in channels]

        self.conv1 = nn.Sequential(
            nn.Conv2d(1, channels[0], kernel_size=3, padding=1, stride=2, bias=False),
            nn.BatchNorm2d(channels[0]),
            nn.ReLU6()
        )

        # Bottleneck第一层和第三层都是pointwise卷积，也即kernel_size=1，groups=1的卷积，
        # 第二层是depthwise卷积，也即kernel_size=3，groups=channels的卷积
        self.bottleneck1 = nn.Sequential(
            nn.Conv2d(channels[0], ex_ch[0], kernel_size=1, padding=0, stride=1, bias=False),
            nn.BatchNorm2d(ex_ch[0]),
            nn.ReLU6(),
            nn.Conv2d(ex_ch[0], ex_ch[0], kernel_size=3, padding=1, stride=strides[0], groups=ex_ch[0], bias=False),
            nn.BatchNorm2d(ex_ch[0]),
            nn.ReLU6(),
            nn.Conv2d(ex_ch[0], channels[1], kernel_size=1, padding=0, stride=1, bias=False),
            nn.BatchNorm2d(channels[1])
        )
        self.bottleneck2 = nn.Sequential(
            nn.Conv2d(channels[1], ex_ch[1], kernel_size=1, padding=0, stride=1, bias=False),
            nn.BatchNorm2d(ex_ch[1]),
            nn.ReLU6(),
            nn.Conv2d(ex_ch[1], ex_ch[1], kernel_size=3, padding=1, stride=strides[1], groups=ex_ch[1], bias=False),
            nn.BatchNorm2d(ex_ch[1]),
            nn.ReLU6(),
            nn.Conv2d(ex_ch[1], channels[2], kernel_size=1, padding=0, stride=1, bias=False),
            nn.BatchNorm2d(channels[2])
        )
        self.bottleneck3 = nn.Sequential(
            nn.Conv2d(channels[2], ex_ch[2], kernel_size=1, padding=0, stride=1, bias=False),
            nn.BatchNorm2d(ex_ch[2]),
            nn.ReLU6(),
            nn.Conv2d(ex_ch[2], ex_ch[2], kernel_size=3, padding=1, stride=strides[2], groups=ex_ch[2], bias=False),
            nn.BatchNorm2d(ex_ch[2]),
            nn.ReLU6(),
            nn.Conv2d(ex_ch[2], channels[3], kernel_size=1, padding=0, stride=1, bias=False),
            nn.BatchNorm2d(channels[3])
        )
        self.bottleneck4 = nn.Sequential(
            nn.Conv2d(channels[3], ex_ch[3], kernel_size=1, padding=0, stride=1, bias=False),
            nn.BatchNorm2d(ex_ch[3]),
            nn.ReLU6(),
            nn.Conv2d(ex_ch[3], ex_ch[3], kernel_size=3, padding=1, stride=strides[3], groups=ex_ch[3], bias=False),
            nn.BatchNorm2d(ex_ch[3]),
            nn.ReLU6(),
            nn.Conv2d(ex_ch[3], channels[4], kernel_size=1, padding=0, stride=1, bias=False),
            nn.BatchNorm2d(channels[4])
        )

        self.last_ch = 512
        self.conv2 = nn.Sequential(
            nn.Conv2d(channels[4], self.last_ch, kernel_size=1, padding=0, stride=1, bias=False),
            nn.BatchNorm2d(self.last_ch),
            nn.ReLU6()
        )
        self.pool1 = nn.AvgPool2d(kernel_size=7)
        self.Dense = nn.Linear(self.last_ch, 10)

    def forward(self, x):
        out = self.conv1(x)
        out = self.bottleneck1(out)
        out = self.bottleneck2(out)
        out = self.bottleneck3(out)
        out = self.bottleneck4(out)
        out = self.conv2(out)
        out = self.pool1(out)
        out = out.view(-1, self.last_ch)
        out = self.Dense(out)
        return out


def get_trainloader(batch_size):
    dataset = datasets.MNIST(root="./mmnist/", train=True, download=True,
                             transform=transforms.Compose([
                                 transforms.ToTensor(),
                                 transforms.Normalize(
                                     (mnist_mean,), (mnist_std,)
                                 )
                             ]))
    return data.DataLoader(
        dataset=dataset,
        batch_size=batch_size,
        shuffle=True,
        num_workers=2,
        pin_memory=True,
        drop_last=False,
    )


def get_testloader(batch_size):
    dataset = datasets.MNIST(root="./mmnist/", train=False, download=True,
                             transform=transforms.Compose([
                                 transforms.ToTensor(),
                                 transforms.Normalize(
                                     (mnist_mean,), (mnist_std,)
                                 )
                             ]))
    return data.DataLoader(
        dataset=dataset,
        batch_size=batch_size,
        shuffle=False,                   # 每个epoch是否混淆
        num_workers=2,                   # 多进程并发装载
        pin_memory=True,                 # 是否使用锁页内存
        drop_last=False,                 # 是否丢弃最后一个不完整的batch
    )


def train(train_data_loader, optimizer):
    epoch_acc = 0.0
    epoch_loss = 0.0
    train_dataset_length = 0.0
    tot_it = len(train_data_loader)
    for it, (x_train, y_train) in enumerate(train_data_loader):
        train_dataset_length += len(y_train)
        y_pred = model(x_train)
        optimizer.zero_grad()
        loss = nn.functional.cross_entropy(y_pred, y_train)
        loss.backward()
        optimizer.step()
        epoch_loss += loss.item()
        y_pred = torch.argmax(y_pred, dim=1)
        epoch_acc += y_pred.eq(y_train).sum().item()
        print(it)

        if it % 32 == 0:
            print(f'it:{it}/{tot_it}, '
                  f'Loss:{epoch_loss}/{it+1} = {epoch_loss/(it+1)}, '
                  f'Acc:{epoch_acc}/{train_dataset_length} = {epoch_acc/train_dataset_length}')

    print("train_Epoch:Loss is:{:4f},Acc is:{:4f}".format(epoch_loss/tot_it, epoch_acc/train_dataset_length))


def validation(test_data_loader):
    with torch.no_grad():
        model.eval()
        epoch_acc = 0.0
        epoch_loss = 0.0
        test_dataset_length = 0.0
        tot_it = len(test_data_loader)
        for it, (x_test, y_test) in enumerate(test_data_loader):
            test_dataset_length += len(y_test)
            y_pred = model(x_test)
            loss = nn.functional.cross_entropy(y_pred, y_test)
            epoch_loss += loss.item()
            y_pred = torch.argmax(y_pred, dim=1)
            epoch_acc += y_pred.eq(y_test).sum().item()

            if it % 32 == 0:
                print(f'it:{it}/{tot_it}, '
                      f'Loss:{epoch_loss}/{it+1} = {epoch_loss / (it+1)}, '
                      f'Acc:{epoch_acc}/{test_dataset_length} = {epoch_acc / test_dataset_length}')

        model.train()
        print("test_Epoch:Loss is:{:4f},Acc is:{:4f}".format(epoch_loss/tot_it, epoch_acc/test_dataset_length))


model = Model()


def main():
    PATH = './Mobilenetv2.pth'
    # pretrained_net = torch.load(PATH)
    # model.load_state_dict(pretrained_net)
    optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
    train_data_loader = get_trainloader(64)
    test_data_loader = get_testloader(64)
    for epoch in range(epoch_n):
        train(train_data_loader=train_data_loader, optimizer=optimizer)
        validation(test_data_loader=test_data_loader)
    torch.save(model.state_dict(), PATH)


if __name__ == '__main__':
    main()