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finetune_inception.py
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# License: BSD
# Author: Sasank Chilamkurthy # https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html
from __future__ import print_function, division
import torch
import torch.nn as nn
import torch.optim as optim
import torch.utils.data
from torch.optim import lr_scheduler
import numpy as np
import torchvision
from torchvision import datasets, transforms
# from torchvision import datasets, models, transforms
from metric.inception_3 import inception_v3
import matplotlib.pyplot as plt
import time
import os
import copy
from PIL import Image
from torchsummary import summary
# Data augmentation and normalization for training
# Just normalization for validation
train_data_transforms = transforms.Compose([
transforms.Resize((299, 299)),
# transforms.RandomResizedCrop(299),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]) # [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]
]) # [0.5, 0.5, 0.5], [0.5, 0.5, 0.5]
train_dir = '' # 训练
train_image_datasets = datasets.ImageFolder(train_dir, train_data_transforms)
train_dataloaders = torch.utils.data.DataLoader(train_image_datasets, batch_size=32, shuffle=True, num_workers=4)
class_names = train_image_datasets.classes
train_sizes = len(train_image_datasets)
print('train images number:', train_sizes)
val_data_transforms = transforms.Compose([
transforms.Resize((299, 299)),
# transforms.RandomResizedCrop(299),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]) # [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]
])
val_dir = ''
val_image_datasets = datasets.ImageFolder(val_dir, val_data_transforms)
val_dataloaders = torch.utils.data.DataLoader(val_image_datasets, batch_size=32, shuffle=True, num_workers=4)
val_sizes = len(val_image_datasets)
print('val images number:', val_sizes)
# device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
device = torch.device("cuda:0")
def imshow(inp, title=None):
"""Imshow for Tensor."""
inp = inp.numpy().transpose((1, 2, 0))
mean = np.array([0.5, 0.5, 0.5])
std = np.array([0.5, 0.5, 0.5])
inp = std * inp + mean
inp = np.clip(inp, 0, 1)
plt.imshow(inp)
if title is not None:
plt.title(title)
plt.pause(10) # pause a bit so that plots are updated
# Get a batch of training data
# inputs, classes = next(iter(train_dataloaders))
# # Make a grid from batch
# out = torchvision.utils.make_grid(inputs)
# imshow(out, title=[class_names[x] for x in classes])
def train_model(model, criterion, optimizer, scheduler, num_epochs=25):
since = time.time()
best_model_wts = copy.deepcopy(model.state_dict())
best_acc = 0.0
for epoch in range(num_epochs):
start_t = time.time()
print('Epoch {}/{}'.format(epoch, num_epochs - 1))
print('-' * 10)
# Each epoch has a training and validation phase
for phase in ['train', 'val']:
if phase == 'train':
model.train() # Set model to training mode
dataloaders = train_dataloaders
dataset_sizes = train_sizes
else:
model.eval() # Set model to evaluate mode
dataloaders = val_dataloaders
dataset_sizes = val_sizes
running_loss = 0.0
running_corrects = 0
# Iterate over data.
for inputs, labels in dataloaders: # dataloaders[phase]:
inputs = inputs.to(device)
labels = labels.to(device)
# zero the parameter gradients
optimizer.zero_grad()
# forward
# track history if only in train
with torch.set_grad_enabled(phase == 'train'):
outputs = model(inputs) # outputs[0]是logits, outputs[1]是aux_logits
# print(outputs)
if phase == 'train':
logits = outputs[0]
else:
logits = outputs[0] # outputs
_, preds = torch.max(logits, 1) # outputs[0]
loss = criterion(logits, labels)
# backward + optimize only if in training phase
if phase == 'train':
loss.backward()
optimizer.step()
# statistics
running_loss += loss.item() * inputs.size(0)
batch_correct = torch.sum(preds == labels.data)
running_corrects += torch.sum(preds == labels.data) # 计算预测对的类别
# print('{} Loss: {:.4f} Acc: {:.4f}'. format(phase, loss.item(), batch_correct.double() / 32.))
if phase == 'train':
scheduler.step()
epoch_loss = running_loss / dataset_sizes
epoch_acc = running_corrects.double() / dataset_sizes
print('{} pred correct number: {:.1f}'.format(phase, running_corrects.double().item()))
# deep copy the model
if phase == 'val' and epoch_acc > best_acc:
best_acc = epoch_acc
best_model_wts = copy.deepcopy(model.state_dict())
print('{} Loss: {:.4f} Acc: {:.4f}, Time: {:.4f}'.
format(phase, epoch_loss, epoch_acc, time.time() - start_t))
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))
# load best model weights
model.load_state_dict(best_model_wts)
return model
def visualize_model(model, num_images=6):
was_training = model.training # True
model.eval()
images_so_far = 0
fig = plt.figure()
with torch.no_grad():
for i, (inputs, labels) in enumerate(val_dataloaders):
inputs = inputs.to(device)
labels = labels.to(device)
outputs = model(inputs)
_, preds = torch.max(outputs, 1)
for j in range(inputs.size()[0]):
images_so_far += 1
ax = plt.subplot(num_images//2, 2, images_so_far)
ax.axis('off')
ax.set_title('predicted: {}'.format(class_names[preds[j]]))
imshow(inputs.cpu().data[j])
if images_so_far == num_images:
model.train(mode=was_training)
return
model.train(mode=was_training)
model_ft = inception_v3(pretrained=True, transform_input=False)
# 冻结某些层训练
set_grad = False
for dix, parameter in enumerate(model_ft.named_parameters()): # 与model_ft.parameters()的参数相同 # model_ft.named_modules()
name = parameter[0]
param = parameter[1]
print(dix, name)
if name == 'fc.weight': # 该层之后的所有层都可训练 263 7a 7b # Mixed_7c.branch1x1.conv.weight; fc.weight
set_grad = True
if set_grad:
param.requires_grad = True
else:
param.requires_grad = False
# print('-' * 10)
num_ftrs = model_ft.fc.in_features
model_ft.fc = nn.Linear(num_ftrs, len(class_names)) # model_ft.fc是加载的inception v3模型中的层，fc相当与名，这里对该层进行了重新替换
model_ft = model_ft.to(device)
# print(model_ft)
print('-' * 30)
summary(model_ft, (3, 299, 299))
# 训练模型
criterion = nn.CrossEntropyLoss() # 使用nn.CrossEntropyLoss会自动加上Softmax层。因此在定义网络时无需在最后加softmax激活
# label是非one-hot的编码，输入是真实的类别，然后函数内部再转换成one-hot
# Observe that all parameters are being optimized
optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.01, momentum=0.9) # model_ft.fc.parameters()
# optimizer_ft = optim.RMSprop(model_ft.parameters(), lr=0.01, alpha=0.9)
# optimizer_ft = optim.Adam(model_ft.parameters(), lr=0.001)
# Decay LR by a factor of 0.1 every 15 epochs
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=15, gamma=0.1)
model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler, num_epochs=50)
torch.save(model_ft.state_dict(), 'birds_inception3_fc_sgd.pth') # 只保存模型参数 birds_inception3_all.pth