项目描述：对花进行分类
项目数据集：102种花的图片，数据集下载https://www.kaggle.com/datasets/eswarkamineni/flower-data
算法：使用迁移学习Resnet152，冻结所有卷积层，更改全连接层并进行训练
makejson.py文件代码如下，给每一种分类花一个名字，因为我们下载的数据集中每个分类文件命名为1，2，3，所以我们对应给个名称。
import json
f = open("cat_to_name.json","w")
f.write('{"21": "fire lily", "3": "canterbury bells", "45": "bolero deep blue", "1": "pink primrose", "34": "mexican aster", "27": "prince of wales feathers", "7": "moon orchid", "16": "globe-flower", "25": "grape hyacinth", "26": "corn poppy", "79": "toad lily", "39": "siam tulip", "24": "red ginger", "67": "spring crocus", "35": "alpine sea holly", "32": "garden phlox", "10": "globe thistle", "6": "tiger lily", "93": "ball moss", "33": "love in the mist", "9": "monkshood", "102": "blackberry lily", "14": "spear thistle", "19": "balloon flower", "100": "blanket flower", "13": "king protea", "49": "oxeye daisy", "15": "yellow iris", "61": "cautleya spicata", "31": "carnation", "64": "silverbush", "68": "bearded iris", "63": "black-eyed susan", "69": "windflower", "62": "japanese anemone", "20": "giant white arum lily", "38": "great masterwort", "4": "sweet pea", "86": "tree mallow", "101": "trumpet creeper", "42": "daffodil", "22": "pincushion flower", "2": "hard-leaved pocket orchid", "54": "sunflower", "66": "osteospermum", "70": "tree poppy", "85": "desert-rose", "99": "bromelia", "87": "magnolia", "5": "english marigold", "92": "bee balm", "28": "stemless gentian", "97": "mallow", "57": "gaura", "40": "lenten rose", "47": "marigold", "59": "orange dahlia", "48": "buttercup", "55": "pelargonium", "36": "ruby-lipped cattleya", "91": "hippeastrum", "29": "artichoke", "71": "gazania", "90": "canna lily", "18": "peruvian lily", "98": "mexican petunia", "8": "bird of paradise", "30": "sweet william", "17": "purple coneflower", "52": "wild pansy", "84": "columbine", "12": "colt\'s foot", "11": "snapdragon", "96": "camellia", "23": "fritillary", "50": "common dandelion", "44": "poinsettia", "53": "primula", "72": "azalea", "65": "californian poppy", "80": "anthurium", "76": "morning glory", "37": "cape flower", "56": "bishop of llandaff", "60": "pink-yellow dahlia", "82": "clematis", "58": "geranium", "75": "thorn apple", "41": "barbeton daisy", "95": "bougainvillea", "43": "sword lily", "83": "hibiscus", "78": "lotus lotus", "88": "cyclamen", "94": "foxglove", "81": "frangipani", "74": "rose", "89": "watercress", "73": "water lily", "46": "wallflower", "77": "passion flower", "51": "petunia"}')
f.close()
TorchVision.py文件代码如下
# torchvision模块实战
# torchvision.datasets模块包括数据集，数据加载方法
# torchvision.models模块包括一些经典的网络架构
# torchvision.transforms模块包括一些预处理图像增强方法
import os
import matplotlib.pyplot as plt
import numpy as np
import torch
from torch import nn
import torch.optim as optim
import torchvision
from torchvision import transforms, models, datasets
# import imageio
import time
import warnings
import random
import sys
import copy
import json
from PIL import Image
# 处理流程
# 数据预处理部分：
# 数据增强：torchvision.transforms
# 数据预处理：torchvisivon.transforms
# DataLoader读取batch数据
# 数据读取与预处理操作
data_dir = './flower_data'
train_dir = data_dir + 'train'
valid_dir = data_dir + 'valid'
# data_transforms中指定了所有图像的预处理操作
# ImageFolder假设所有文件按文件夹保存好，每个文件夹下面存储同一类别图片，文件夹的名字为分类的名字
data_transforms = {
'train': transforms.Compose([transforms.RandomRotation(45), # 随机旋转，-45到45度之间随机旋转
transforms.CenterCrop(224), # 从中心开始裁剪
transforms.RandomHorizontalFlip(p=0.5), # 随机水平翻转 选择一个翻转概率
transforms.RandomVerticalFlip(p=0.5), # 随机垂直翻转
transforms.ColorJitter(brightness=0.2, contrast=0.1, saturation=0.1, hue=0.1),# 亮度，对比度，饱和度，色相
transforms.RandomGrayscale(p=0.025), # 概率转换为灰度率
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) # 标准化((x-均值)/标准差)，均值，标准差，拿别人的预训练数据的均值和标准差为了使训练效果更好
]),
'valid': transforms.Compose([transforms.Resize(256), # 我们的训练集比较小所以没有resize
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) # 大小和标准化必须和训练集一样
]),
}
batch_size = 8 # 显存不够把batch_size调小
# 构建数据集
image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x]) for x in ['train', 'valid']} # 传路径和预处理流程
dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=batch_size, shuffle=True) for x in ['train', 'valid']}
datasets_sizes = {x: len(image_datasets[x]) for x in ['train', 'valid']}
class_names = image_datasets['train'].classes
print(dataloaders)
print(datasets_sizes)
# 读取标签和对应的实际名字
with open('cat_to_name.json', 'r') as f:
cat_to_name = json.load(f)
print(cat_to_name)
# 展示数据
# 因为我们已经对数据做了处理，所以先将tensor数据转化为numpy格式，然后还原回标准化的结果
def im_convert(tensor):
image = tensor.to("cpu").clone().detach()
image = image.numpy().squeeze()
image = image.transpose(1,2,0) # torch颜色通道被放到了第一位，我们利用transpose将h,w,c还原回去
image = image * np.array((0.229, 0.224, 0.225)) + np.array((0.485, 0.456, 0.406)) # 去除标准化，成标准差加均值
image = image.clip(0, 1)
return image
fig = plt.figure(figsize=(20, 12))
columns = 4
rows = 2
dataiter = iter(dataloaders['valid']) # 迭代一次取一个batch数据
inputs, classes = dataiter.next() # 数据，标签
for idx in range(columns*rows):
ax = fig.add_subplot(rows, columns, idx+1, xticks=[], yticks=[])
ax.set_title(cat_to_name[str(int(class_names[classes[idx]]))])
plt.imshow(im_convert(inputs[idx]))
plt.show()
# 迁移学习
# 拿别人的卷积层，方案1在此卷积层的基础上继续训练，方案2冻结此卷积层作为我们的特征提取工具
# 全连接层都是要重写重训练的
# 网络模块设置：
# 加载预训练模型：torchvision.models，可以调用训练好的权重参数来继续训练，也就是所谓的迁移学习
# 注意：需要把最后一层改一下，改成我们自己的任务
# 训练时可以全部重头训练，也可以只训练我们的任务层，因为前几层都是做特征提取的，本质任务目标是一致的
model_name = 'resnet' # 可选择的模型比较多{'alexnet', 'vgg', 'resnet', 'squeezenet', 'densenet', 'inception'}
# 是否用人家训练好的特征来做
feature_extract = True
# 是否用GPU训练
train_on_gpu = torch.cuda.is_available()
if not train_on_gpu:
print('CUDA is not available. Training on CPU ...')
else:
print('CUDA is available! Training on GPU ...')
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
def set_parameter_requires_grad(model, feature_extracting):
if feature_extracting:
for param in model.parameters():
param.requires_grad = False
# 加载模型
model_ft = models.resnet152()
print(model_ft)
def initialize_model(model_name, num_classes, feature_extract, use_pretrained=True):
# 选择合适的模型，不同的模型的初始化方法稍微有点区别
model_ft = None
input_size = 0
if model_name =="resnet":
"""Resnet152
"""
model_ft = models.resnet152(pretrained=use_pretrained) # 下载预训练的模型参数
set_parameter_requires_grad(model_ft, feature_extract) # 冻住卷积层
num_ftrs = model_ft.fc.in_features # 拿到最后全连接层输入
model_ft.fc = nn.Sequential(nn.Linear(num_ftrs, 102), nn.LogSoftmax(dim=1)) # 重写全连接层
input_size = 224
else:
print("Invalid model name, exiting...")
exit()
return model_ft, input_size
model_ft, input_size = initialize_model(model_name, 102, feature_extract, use_pretrained=True)
# GPU计算
model_ft = model_ft.to(device)
# 指定保存模型的名字
filename = 'checkpoint.pth'
# 是否训练所有层,一般策略是先学习自己的层，在观察学习全部层
params_to_update = model_ft.parameters()
print("Params to learn:")
if feature_extract:
params_to_update = []
for name, param in model_ft.named_parameters():
if param.requires_grad == True:
params_to_update.append(param)
print("\t", name)
else:
for name, param in model_ft.named_parameters():
if param.requires_grad == True:
print("\t", name)
# 看一下现在的网络层
print(model_ft)
# 优化器设置
optimizer_ft = optim.Adam(params_to_update, lr=1e-2)
scheduler = optim.lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1) # 学习率每7个epoch衰减为原来的0.1
# 最后一层LogSoftmax()了，所以不能nn.CrossEntropyLoss()来计算了，nn.CrossEntropyLoss()相当于logSoftmax()和nn.NLLLoss()整合
criterion = nn.NLLLoss()
def train_model(model, dataloaders, criterion, optimizer, num_epochs=25, is_inception=False, filename=filename):
since = time.time()
best_acc = 0
"""
"""
model.to(device)
val_acc_history = []
train_acc_history = []
train_losses = []
valid_losses = []
LRs = [optimizer.param_groups[0]['lr']]
best_model_wts = copy.deepcopy(model.state_dict())
for epoch in range(num_epochs):
print('Epoch {}/{}'.format(epoch, num_epochs - 1))
print('-' * 10)
# 训练和验证
for phase in ['train', 'valid']:
if phase == 'train':
model.train()
else:
model.eval()
running_loss = 0.0
running_corrects = 0
# 把数据都取个遍
for inputs, labels in dataloaders[phase]:
inputs = inputs.to(device)
labels = labels.to(device)
# 梯度清零
optimizer.zero_grad()
# 只有训练的时候计算和更新梯度
with torch.set_grad_enabled(phase == 'train'):
outputs = model(inputs)
loss = criterion(outputs, labels)
_, preds = torch.max(outputs, 1) # 获取概率最大的类别
# 训练更新权重
if phase == 'train':
loss.backward()
optimizer.step()
# 计算损失
running_loss += loss.item() * inputs.size(0)
running_corrects += torch.sum(preds == labels.data)
epoch_loss = running_loss / len(dataloaders[phase].dataset)
epoch_acc = running_corrects.double() / len(dataloaders[phase].dataset)
time_elapsed = time.time() - since
print('Time elapsed {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))
print('{} Loss: {:.4f} Acc: {:.4f}'.format(phase, epoch_loss, epoch_acc))
# 得到最好次好的模型
if phase == 'valid' and epoch_acc > best_acc:
best_acc = epoch_acc
best_model_wts = copy.deepcopy(model.state_dict())
state = {
'state_dict': model.state_dict(),
'best_acc': best_acc,
'optimizer': optimizer.state_dict(),
}
torch.save(state, filename)
if phase == 'valid':
val_acc_history.append(epoch_acc)
valid_losses.append(epoch_loss)
scheduler.step(epoch_loss)
if phase == 'train':
train_acc_history.append(epoch_acc)
train_losses.append(epoch_loss)
print('Optimizer learning rate : {:.7f}'.format(optimizer.param_groups[0]['lr']))
LRs.append(optimizer.param_group[0]['lr'])
print()
time_elapsed = time.time() - since
print('Training complete in {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))
print('Best val Acc: {:4f}'.format(best_acc))
# 训练完后用最好的一次当作模型最终结果
model.load_state_dict(best_model_wts)
return model, val_acc_history, train_acc_history, valid_losses, train_losses, LRs
model_ft, val_acc_history, train_acc_history, valid_losses, train_losses, LRs = train_model(model_ft, dataloaders, criterion, optimizer_ft, num_epochs=10, is_inception=False, filename=filename)
# 模型保存可以带有选择性，例如在验证集中如果效果好则保存
模型训练结果，在这里我用的,Linux系统，3090显卡。在上述代码的基础上，将batch_size改为了64，并在DataLoader最后加入了num_workers=8。如果你使用的是windows系统则不能设置num_workers，会报错，我本地电脑，windows+1050显卡，一个周期要7m11s。差距太大了，所以我建议大家还是租显卡，或者买张显卡。
Params to learn:
fc.0.weight
fc.0.bias
Epoch 0/49
----------
Time elapsed 0m 16s
train Loss: 6.2446 Acc: 0.4113
Time elapsed 0m 20s
valid Loss: 2.4209 Acc: 0.5513
Optimizer learning rate : 0.0100000
Epoch 1/49
----------
Time elapsed 0m 36s
train Loss: 1.5336 Acc: 0.6784
Time elapsed 0m 40s
valid Loss: 2.2870 Acc: 0.5929
Optimizer learning rate : 0.0100000
Epoch 2/49
----------
Time elapsed 0m 57s
train Loss: 1.4210 Acc: 0.7121
Time elapsed 1m 1s
valid Loss: 2.8051 Acc: 0.5452
Optimizer learning rate : 0.0100000
Epoch 3/49
----------
Time elapsed 1m 17s
train Loss: 1.5040 Acc: 0.7346
Time elapsed 1m 21s
valid Loss: 2.8843 Acc: 0.6174
Optimizer learning rate : 0.0100000
Epoch 4/49
----------
Time elapsed 1m 38s
train Loss: 1.3956 Acc: 0.7511
Time elapsed 1m 41s
valid Loss: 2.5887 Acc: 0.6308
Optimizer learning rate : 0.0100000
Epoch 5/49
----------
Time elapsed 1m 57s
train Loss: 1.4859 Acc: 0.7486
Time elapsed 2m 1s
valid Loss: 3.4337 Acc: 0.6015
Optimizer learning rate : 0.0100000
Epoch 6/49
----------
Time elapsed 2m 17s
train Loss: 1.3451 Acc: 0.7764
Time elapsed 2m 21s
valid Loss: 3.3750 Acc: 0.6455
Optimizer learning rate : 0.0100000
Epoch 7/49
----------
Time elapsed 2m 37s
train Loss: 1.5002 Acc: 0.7746
Time elapsed 2m 41s
valid Loss: 3.5827 Acc: 0.5856
Optimizer learning rate : 0.0100000
Epoch 8/49
----------
Time elapsed 2m 57s
train Loss: 1.3252 Acc: 0.7863
Time elapsed 3m 0s
valid Loss: 3.1641 Acc: 0.6516
Optimizer learning rate : 0.0100000
Epoch 9/49
----------
Time elapsed 3m 17s
train Loss: 1.4956 Acc: 0.7856
Time elapsed 3m 20s
valid Loss: 3.5925 Acc: 0.6015
Optimizer learning rate : 0.0100000
# 加载训练好的模型
model_ft, input_size = initialize_model(model_name, 102, feature_extract, use_pretrained=True)
# GPU模式
model_ft = model_ft.to(device)
# 保存文件的名字
filename = 'checkpoint.pth'
# 加载模型
checkpoint = torch.load(filename)
best_acc = checkpoint['best_acc']
model_ft.load_state_dict(checkpoint['state_dict'])
# 测试数据预处理
def process_image(image_path):
# 读取测试数据
img = Image.open(image_path)
# Resize，thumbnail方法只能进行缩小，所以进行判断
if img.size[0] > img.size[1]:
img.thumbnail((10000,256))
else:
img.thumbnail((256,10000))
# 裁剪操作
left_margin = (img.width - 224)/2
bottom_margin = (img.height - 224)/2
right_margin = left_margin + 224
top_margin = bottom_margin + 224
img = img.crop((left_margin, bottom_margin, right_margin, top_margin))
# 相同的归一化，正则化
img = np.array(img)/255
mean = np.array([0.485, 0.456, 0.406])
std = np.array([0.229, 0.224, 0.225])
img = (img - mean)/std
# 注意颜色通道放到第一个位置
img = img.transpose((2, 0 ,1))
return img
def imshow(image, ax=None, title=None):
"""展示数据"""
if ax is None:
fig, ax = plt.subplots()
# 颜色通道还原
image = np.array(image).transpose((1, 2, 0))
# 预处理还原
mean = np.array([0.485, 0.456, 0.406])
std = np.array([0.229, 0.224, 0.225])
image = std * image + mean
image = np.clip(image, 0, 1)
ax.imshow(image)
ax.set_title(title)
return ax
image_path = './flower_data/test/1/image_05087.jpg'
img = process_image(image_path)
imshow(img)
print(img.shape)
取一个图片看看
取一个batch_size数据进行测试
# 得到一个batch的测试数据
dataiter = iter(dataloaders['valid'])
images, labels = dataiter.next()
model_ft.eval()
if train_on_gpu:
output = model_ft(images.cuda())
else:
output = model_ft(images)
print(output.shape)
# torch.Size([8, 102])
# 得到概率最大的那个
_, preds_tensor = torch.max(output, 1)
preds = np.squeeze(preds_tensor.numpy()) if not train_on_gpu else np.squeeze(preds_tensor.cpu().numpy())
print(preds)
# 预测结果展示
fig = plt.figure(figsize=(20, 20))
columns = 4
rows = 2
for idx in range (columns*rows):
ax = fig.add_subplot(rows, columns, idx+1, xticks=[], yticks=[])
plt.imshow(im_convert(images[idx]))
ax.set_title("{} ({})".format(cat_to_name[str(preds[idx])], cat_to_name[str(labels[idx].item())]),
color = ("green" if cat_to_name[str(preds[idx])] == cat_to_name[str(labels[idx].item())] else "red"))
plt.show()
我们可以看到只有一个花预测错误，其他都预测正确。
