1.ShuffleNet V1网络架构
ShuffleNet v1网络
2.ShuffleNet V2网络架构
ShuffleNet V2网络
3.代码实现
3.1.model.py
from typing import List,Callable
import torch
from torch import Tensor
import torch.nn as nn
def channel_shuffle(x:Tensor,groups:int)->Tensor:
batch_size,num_channels,height,width = x.size()
channel_per_group = num_channels//groups
# reshape
# [batch_size, num_channels, height, width] -> [batch_size, groups, channels_per_group, height, width]
x = x.view(batch_size,groups,channel_per_group,height,width)
x = torch.transpose(x,1,2).contiguous()
# flatten
x = x.view(batch_size,-1,height,width)
return x
class InvertedResidual(nn.Module):
def __init__(self, input_c: int, output_c: int, stride: int):
super(InvertedResidual, self).__init__()
if stride not in [1, 2]:
raise ValueError("illegal stride value.")
self.stride = stride
assert output_c % 2 == 0
branch_features = output_c // 2
# 当stride为1时，input_channel应该是branch_features的两倍
# python中 '<<' 是位运算，可理解为计算×2的快速方法
assert (self.stride != 1) or (input_c == branch_features << 1)
if self.stride == 2:
self.branch1 = nn.Sequential(
self.depthwise_conv(input_c, input_c, kernel_s=3, stride=self.stride, padding=1),
nn.BatchNorm2d(input_c),
nn.Conv2d(input_c, branch_features, kernel_size=1, stride=1, padding=0, bias=False),
nn.BatchNorm2d(branch_features),
nn.ReLU(inplace=True)
)
else:
self.branch1 = nn.Sequential()
self.branch2 = nn.Sequential(
nn.Conv2d(input_c if self.stride > 1 else branch_features, branch_features, kernel_size=1,
stride=1, padding=0, bias=False),
nn.BatchNorm2d(branch_features),
nn.ReLU(inplace=True),
self.depthwise_conv(branch_features, branch_features, kernel_s=3, stride=self.stride, padding=1),
nn.BatchNorm2d(branch_features),
nn.Conv2d(branch_features, branch_features, kernel_size=1, stride=1, padding=0, bias=False),
nn.BatchNorm2d(branch_features),
nn.ReLU(inplace=True)
)
@staticmethod
def depthwise_conv(input_c:int,output_c:int,kernel_s:int,stride:int=1,padding:int=0,bias:bool=False)->nn.Conv2d:
return nn.Conv2d(in_channels=input_c,out_channels=output_c,kernel_size=kernel_s,stride=stride,padding=padding,bias=bias,groups=input_c)
def forward(self,x:Tensor)->Tensor:
if self.stride == 1:
x1,x2 = x.chunk(2,dim=1)
out = torch.cat((x1,self.branch2(x2)),dim=1)
else:
out =torch.cat((self.branch1(x),self.branch2(x)),dim=1)
out = channel_shuffle(out,2)
return out
class ShuffleNetV2(nn.Module):
def __init__(self,stages_repeats:List[int],stages_out_channels:List[int],num_classes:int=1000,inverted_residual:Callable[..., nn.Module]=InvertedResidual) -> None:
super(ShuffleNetV2,self).__init__()
if len(stages_repeats)!=3:
raise ValueError("expected stages_repeats as list of 3 positive ints")
if len(stages_out_channels)!=5:
raise ValueError("expected stages_out_channels as list of 5 positive ints")
self._stage_out_channels = stages_out_channels
# input RGB image
input_channels = 3
output_channels =self._stage_out_channels[0]
self.conv1 = nn.Sequential(
nn.Conv2d(input_channels,output_channels,kernel_size=3,stride=2,padding=1,bias=False),
nn.BatchNorm2d(output_channels),
nn.ReLU(inplace=True)
)
input_channels=output_channels
self.maxpool = nn.MaxPool2d(kernel_size=3,stride=2,padding=1)
# Static annotations for mypy
self.stage2:nn.Sequential
self.stage3:nn.Sequential
self.stage4:nn.Sequential
stage_names = ["stage{}".format(i) for i in [2,3,4]]
for name,repeats,output_channels in zip(stage_names,stages_repeats,self._stage_out_channels[1:]):
seq = [inverted_residual(input_channels,output_channels,2)]
for i in range(repeats-1):
seq.append(inverted_residual(output_channels,output_channels,1))
setattr(self,name,nn.Sequential(*seq))
input_channels = output_channels
output_channels = self._stage_out_channels[-1]
self.conv5 = nn.Sequential(
nn.Conv2d(input_channels,output_channels,kernel_size=1,padding=0,bias=False),
nn.BatchNorm2d(output_channels),
nn.ReLU(inplace=True)
)
self.fc = nn.Linear(output_channels,num_classes)
def _forward_impl(self,x:Tensor)->Tensor:
# See note [TorchScript super()]
x = self.conv1(x)
x = self.maxpool(x)
x = self.stage2(x)
x = self.stage3(x)
x = self.stage4(x)
x = self.conv5(x)
x = x.mean([2,3]) # global pool
x = self.fc(x)
return x
def forward(self,x:Tensor)->Tensor:
return self._forward_impl(x)
def shufflenet_v2_x1_0(num_classes=1000):
"""
Constructs a ShuffleNetV2 with 1.0x output channels, as described in
`"ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design"
<https://arxiv.org/abs/1807.11164>`.
weight: https://download.pytorch.org/models/shufflenetv2_x1-5666bf0f80.pth
:param num_classes:
:return:
"""
model = ShuffleNetV2(stages_repeats=[4,8,4],stages_out_channels=[24,116,232,464,1024],num_classes=num_classes)
return model
def shufflenet_v2_x0_5(num_classes=1000):
"""
Constructs a ShuffleNetV2 with 0.5x output channels, as described in
`"ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design"
<https://arxiv.org/abs/1807.11164>`.
weight: https://download.pytorch.org/models/shufflenetv2_x0.5-f707e7126e.pth
:param num_classes:
:return:
"""
model = ShuffleNetV2(stages_repeats=[4,8,4],stages_out_channels=[24,48,96,192,1024],num_classes=num_classes)
return model
model_x1_0 = shufflenet_v2_x1_0(num_classes=5)
print(model_x1_0)
3.2.train.py
import os
import math
import argparse
import torch
import torch.optim as optim
from torch.utils.tensorboard import SummaryWriter
from torchvision import transforms
import torch.optim.lr_scheduler as lr_scheduler
from model import shufflenet_v2_x1_0
from my_dataset import MyDataSet
from utils import read_split_data, train_one_epoch, evaluate
def main(args):
device = torch.device(args.device if torch.cuda.is_available() else "cpu")
print(args)
print('Start Tensorboard with "tensorboard --logdir=runs", view at http://localhost:6006/')
tb_writer = SummaryWriter()
if os.path.exists("./weights") is False:
os.makedirs("./weights")
train_images_path, train_images_label, val_images_path, val_images_label = read_split_data(args.data_path)
data_transform = {
"train": transforms.Compose([transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]),
"val": transforms.Compose([transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])}
# 实例化训练数据集
train_dataset = MyDataSet(images_path=train_images_path,
images_class=train_images_label,
transform=data_transform["train"])
# 实例化验证数据集
val_dataset = MyDataSet(images_path=val_images_path,
images_class=val_images_label,
transform=data_transform["val"])
batch_size = args.batch_size
nw = min([os.cpu_count(), batch_size if batch_size > 1 else 0, 8]) # number of workers
print('Using {} dataloader workers every process'.format(nw))
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=batch_size,
shuffle=True,
pin_memory=True,
num_workers=nw,
collate_fn=train_dataset.collate_fn)
val_loader = torch.utils.data.DataLoader(val_dataset,
batch_size=batch_size,
shuffle=False,
pin_memory=True,
num_workers=nw,
collate_fn=val_dataset.collate_fn)
# 如果存在预训练权重则载入
model = shufflenet_v2_x1_0(num_classes=args.num_classes).to(device)
if args.weights != "":
if os.path.exists(args.weights):
weights_dict = torch.load(args.weights, map_location=device)
load_weights_dict = {k: v for k, v in weights_dict.items()
if model.state_dict()[k].numel() == v.numel()}
print(model.load_state_dict(load_weights_dict, strict=False))
else:
raise FileNotFoundError("not found weights file: {}".format(args.weights))
# 是否冻结权重
if args.freeze_layers:
for name, para in model.named_parameters():
# 除最后的全连接层外，其他权重全部冻结
if "fc" not in name:
para.requires_grad_(False)
pg = [p for p in model.parameters() if p.requires_grad]
optimizer = optim.SGD(pg, lr=args.lr, momentum=0.9, weight_decay=4E-5)
# Scheduler https://arxiv.org/pdf/1812.01187.pdf
lf = lambda x: ((1 + math.cos(x * math.pi / args.epochs)) / 2) * (1 - args.lrf) + args.lrf # cosine
scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
for epoch in range(args.epochs):
# train
mean_loss = train_one_epoch(model=model,
optimizer=optimizer,
data_loader=train_loader,
device=device,
epoch=epoch)
scheduler.step()
# validate
acc = evaluate(model=model,
data_loader=val_loader,
device=device)
print("[epoch {}] accuracy: {}".format(epoch, round(acc, 3)))
tags = ["loss", "accuracy", "learning_rate"]
tb_writer.add_scalar(tags[0], mean_loss, epoch)
tb_writer.add_scalar(tags[1], acc, epoch)
tb_writer.add_scalar(tags[2], optimizer.param_groups[0]["lr"], epoch)
torch.save(model.state_dict(), "./weights/model-{}.pth".format(epoch))
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--num_classes', type=int, default=5)
parser.add_argument('--epochs', type=int, default=30)
parser.add_argument('--batch-size', type=int, default=16)
parser.add_argument('--lr', type=float, default=0.01)
parser.add_argument('--lrf', type=float, default=0.1)
# 数据集所在根目录
# https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz
parser.add_argument('--data-path', type=str,
default="./data/flower_photos")
# shufflenetv2_x1.0 官方权重下载地址
# https://download.pytorch.org/models/shufflenetv2_x1-5666bf0f80.pth
parser.add_argument('--weights', type=str, default='./shufflenetv2_x1.pth',
help='initial weights path')
parser.add_argument('--freeze-layers', type=bool, default=False)
parser.add_argument('--device', default='cuda:0', help='device id (i.e. 0 or 0,1 or cpu)')
opt = parser.parse_args(args=[])
main(opt)
3.3.predict.py
import os
import json
import torch
from PIL import Image
from torchvision import transforms
import matplotlib.pyplot as plt
from model import shufflenet_v2_x1_0
def main():
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
data_transform = transforms.Compose(
[transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
# load image
img_path = "./tulip.jpg"
assert os.path.exists(img_path), "file: '{}' dose not exist.".format(img_path)
img = Image.open(img_path)
plt.imshow(img)
# [N, C, H, W]
img = data_transform(img)
# expand batch dimension
img = torch.unsqueeze(img, dim=0)
# read class_indict
json_path = './class_indices.json'
assert os.path.exists(json_path), "file: '{}' dose not exist.".format(json_path)
with open(json_path, "r") as f:
class_indict = json.load(f)
# create model
model = shufflenet_v2_x1_0(num_classes=5).to(device)
# load model weights
model_weight_path = "./weights/model-2.pth"
model.load_state_dict(torch.load(model_weight_path, map_location=device))
model.eval()
with torch.no_grad():
# predict class
output = torch.squeeze(model(img.to(device))).cpu()
predict = torch.softmax(output, dim=0)
predict_cla = torch.argmax(predict).numpy()
print_res = "class: {} prob: {:.3}".format(class_indict[str(predict_cla)],
predict[predict_cla].numpy())
plt.title(print_res)
for i in range(len(predict)):
print("class: {:10} prob: {:.3}".format(class_indict[str(i)],
predict[i].numpy()))
plt.show()
if __name__ == '__main__':
main()
3.4.my_dataset.py
from PIL import Image
import torch
from torch.utils.data import Dataset
class MyDataSet(Dataset):
"""自定义数据集"""
def __init__(self, images_path: list, images_class: list, transform=None):
self.images_path = images_path
self.images_class = images_class
self.transform = transform
def __len__(self):
return len(self.images_path)
def __getitem__(self, item):
img = Image.open(self.images_path[item])
# RGB为彩色图片，L为灰度图片
if img.mode != 'RGB':
raise ValueError("image: {} isn't RGB mode.".format(self.images_path[item]))
label = self.images_class[item]
if self.transform is not None:
img = self.transform(img)
return img, label
@staticmethod
def collate_fn(batch):
# 官方实现的default_collate可以参考
# https://github.com/pytorch/pytorch/blob/67b7e751e6b5931a9f45274653f4f653a4e6cdf6/torch/utils/data/_utils/collate.py
images, labels = tuple(zip(*batch))
images = torch.stack(images, dim=0)
labels = torch.as_tensor(labels)
return images, labels
3.5.utils.py
import os
import sys
import json
import pickle
import random
import torch
from tqdm import tqdm
import matplotlib.pyplot as plt
def read_split_data(root: str, val_rate: float = 0.2):
random.seed(0) # 保证随机结果可复现
assert os.path.exists(root), "dataset root: {} does not exist.".format(root)
# 遍历文件夹，一个文件夹对应一个类别
flower_class = [cla for cla in os.listdir(root) if os.path.isdir(os.path.join(root, cla))]
# 排序，保证顺序一致
flower_class.sort()
# 生成类别名称以及对应的数字索引
class_indices = dict((k, v) for v, k in enumerate(flower_class))
json_str = json.dumps(dict((val, key) for key, val in class_indices.items()), indent=4)
with open('class_indices.json', 'w') as json_file:
json_file.write(json_str)
train_images_path = [] # 存储训练集的所有图片路径
train_images_label = [] # 存储训练集图片对应索引信息
val_images_path = [] # 存储验证集的所有图片路径
val_images_label = [] # 存储验证集图片对应索引信息
every_class_num = [] # 存储每个类别的样本总数
supported = [".jpg", ".JPG", ".png", ".PNG"] # 支持的文件后缀类型
# 遍历每个文件夹下的文件
for cla in flower_class:
cla_path = os.path.join(root, cla)
# 遍历获取supported支持的所有文件路径
images = [os.path.join(root, cla, i) for i in os.listdir(cla_path)
if os.path.splitext(i)[-1] in supported]
# 获取该类别对应的索引
image_class = class_indices[cla]
# 记录该类别的样本数量
every_class_num.append(len(images))
# 按比例随机采样验证样本
val_path = random.sample(images, k=int(len(images) * val_rate))
for img_path in images:
if img_path in val_path: # 如果该路径在采样的验证集样本中则存入验证集
val_images_path.append(img_path)
val_images_label.append(image_class)
else: # 否则存入训练集
train_images_path.append(img_path)
train_images_label.append(image_class)
print("{} images were found in the dataset.".format(sum(every_class_num)))
print("{} images for training.".format(len(train_images_path)))
print("{} images for validation.".format(len(val_images_path)))
plot_image = False
if plot_image:
# 绘制每种类别个数柱状图
plt.bar(range(len(flower_class)), every_class_num, align='center')
# 将横坐标0,1,2,3,4替换为相应的类别名称
plt.xticks(range(len(flower_class)), flower_class)
# 在柱状图上添加数值标签
for i, v in enumerate(every_class_num):
plt.text(x=i, y=v + 5, s=str(v), ha='center')
# 设置x坐标
plt.xlabel('image class')
# 设置y坐标
plt.ylabel('number of images')
# 设置柱状图的标题
plt.title('flower class distribution')
plt.show()
return train_images_path, train_images_label, val_images_path, val_images_label
def plot_data_loader_image(data_loader):
batch_size = data_loader.batch_size
plot_num = min(batch_size, 4)
json_path = './class_indices.json'
assert os.path.exists(json_path), json_path + " does not exist."
json_file = open(json_path, 'r')
class_indices = json.load(json_file)
for data in data_loader:
images, labels = data
for i in range(plot_num):
# [C, H, W] -> [H, W, C]
img = images[i].numpy().transpose(1, 2, 0)
# 反Normalize操作
img = (img * [0.229, 0.224, 0.225] + [0.485, 0.456, 0.406]) * 255
label = labels[i].item()
plt.subplot(1, plot_num, i+1)
plt.xlabel(class_indices[str(label)])
plt.xticks([]) # 去掉x轴的刻度
plt.yticks([]) # 去掉y轴的刻度
plt.imshow(img.astype('uint8'))
plt.show()
def write_pickle(list_info: list, file_name: str):
with open(file_name, 'wb') as f:
pickle.dump(list_info, f)
def read_pickle(file_name: str) -> list:
with open(file_name, 'rb') as f:
info_list = pickle.load(f)
return info_list
def train_one_epoch(model, optimizer, data_loader, device, epoch):
model.train()
loss_function = torch.nn.CrossEntropyLoss()
mean_loss = torch.zeros(1).to(device)
optimizer.zero_grad()
data_loader = tqdm(data_loader, file=sys.stdout)
for step, data in enumerate(data_loader):
images, labels = data
pred = model(images.to(device))
loss = loss_function(pred, labels.to(device))
loss.backward()
mean_loss = (mean_loss * step + loss.detach()) / (step + 1) # update mean losses
data_loader.desc = "[epoch {}] mean loss {}".format(epoch, round(mean_loss.item(), 3))
if not torch.isfinite(loss):
print('WARNING: non-finite loss, ending training ', loss)
sys.exit(1)
optimizer.step()
optimizer.zero_grad()
return mean_loss.item()
@torch.no_grad()
def evaluate(model, data_loader, device):
model.eval()
# 验证样本总个数
total_num = len(data_loader.dataset)
# 用于存储预测正确的样本个数
sum_num = torch.zeros(1).to(device)
data_loader = tqdm(data_loader, file=sys.stdout)
for step, data in enumerate(data_loader):
images, labels = data
pred = model(images.to(device))
pred = torch.max(pred, dim=1)[1]
sum_num += torch.eq(pred, labels.to(device)).sum()
return sum_num.item() / total_num
4.预测结果
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