resnet.py

# -*- coding: utf-8 -*-
"""
Created on Thu Jul 30 14:47:26 2020

@author: sshss
"""

from __future__ import print_function, division

import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
import numpy as np
import torchvision
from torchvision import datasets, models, transforms
import time
import os
import copy
from torch.autograd import  Variable
import cv2
from PIL import Image
import torch.nn.functional as F
from torchvision.utils import make_grid, save_image


class MyModule(nn.Module):
    def __init__(self):
        super(MyModule, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 38)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1, 16 * 5 * 5)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        x = F.softmax(x,dim=-1)
        return x




def train_model(model, criterion, optimizer, scheduler, num_epochs=500):
   

    best_model_wts = copy.deepcopy(model.state_dict())
    best_acc = 0.0

    for epoch in range(num_epochs):
        since = 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
            else:
                model.eval()   # Set model to evaluate mode

            running_loss = 0.0
            running_corrects = 0

            # Iterate over data.
            for inputs, labels in 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)
                    _, preds = torch.max(outputs, 1)
                    loss = criterion(outputs, labels)

                    # backward + optimize only if in training phase
                    if phase == 'train':
                        loss.backward()
                        optimizer.step()

                # statistics
                running_loss += loss.item() * inputs.size(0)
                running_corrects += torch.sum(preds == labels.data)
            if phase == 'train':
                scheduler.step()

            epoch_loss = running_loss / dataset_sizes[phase]
            epoch_acc = running_corrects.double() / dataset_sizes[phase]

            print('{} Loss: {:.4f} Acc: {:.4f}'.format(
                phase, epoch_loss, epoch_acc))

            # 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()

        time_elapsed = time.time() - since
        print('epoch 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


data_transforms = {
    'train': transforms.Compose([
#        transforms.Grayscale(),
        transforms.ToTensor(),
        # transforms.Normalize(mean=[0.4,0.4,0.2], std=[0.5,0.5,0.5]),
    ]),
    'val': transforms.Compose([
#        transforms.Grayscale(),
        transforms.ToTensor(),
        # transforms.Normalize(mean=[0.1,0.1,0.2], std=[0.1,0.12,0.13]),
    ]),
}

data_dir = 'datasets_ocr'
image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),
                                          data_transforms[x])
                  for x in ['train', 'val']}
dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=128,
                                              shuffle=True, pin_memory=True)
              for x in ['train', 'val']}
dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}
class_names = image_datasets['train'].classes

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")


# Visualize batch imgs
#for i,(data,label) in enumerate(dataloaders['val']):
#    img = make_grid(data,nrow=8)
#    save_image(img, 'batch-img-%s.jpg'%i)
#    if i >2:
#        break

model = torchvision.models.resnet18()
model.conv1.in_channels = 1
num_ftrs = model.fc.in_features
model.fc = nn.Linear(num_ftrs, 11)
model_conv = model.to(device)
print(model_conv.load_state_dict)

criterion = nn.CrossEntropyLoss()


optimizer_conv = optim.Adam(model_conv.parameters(), lr=0.001)


exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=10, gamma=0.1)

model_conv = train_model(model_conv, criterion, optimizer_conv,
                            exp_lr_scheduler, num_epochs=100)

torch.save(model_conv, 'resnet18_v1.pth')

torch.cuda.empty_cache()