猫狗识别：提交第一版的代码

src/python/playground/dogs-vs-cats/Pytorch-CNN.py

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+#!/usr/bin/python
+# coding: utf-8
+
+import os
+from PIL import Image
+import torch
+import torch.nn as nn
+import torch.nn.parallel
+import torch.optim
+from torch.autograd import Variable
+from torch.utils import data
+from torchvision import transforms as T
+
+from models import AlexNet
+
+"""
+参考链接：https://github.com/pytorch/examples/blob/27e2a46c1d1505324032b1d94fc6ce24d5b67e97/imagenet/main.py
+"""
+# 1. 加载数据
+
+
+class DogCat(data.Dataset):
+    def __init__(self, root, transforms=None, train=True, test=False):
+        '''
+        目标：获取所有图片地址，并根据训练、验证、测试划分数据
+        '''
+        self.test = test
+        imgs = [os.path.join(root, img) for img in os.listdir(root)]
+
+        # test1: data/test1/8973.jpg
+        # train: data/train/cat.10004.jpg 
+        if self.test:
+            imgs = sorted(
+                imgs, key=lambda x: int(x.split('.')[-2].split('/')[-1]))
+        else:
+            imgs = sorted(imgs, key=lambda x: int(x.split('.')[-2]))
+
+        imgs_num = len(imgs)
+
+        # 划分训练、验证集，验证:训练 = 3:7
+        if self.test:
+            self.imgs = imgs
+        elif train:
+            self.imgs = imgs[:int(0.7 * imgs_num)]
+        else:
+            self.imgs = imgs[int(0.7 * imgs_num):]
+
+        if transforms is None:
+
+            # 数据转换操作，测试验证和训练的数据转换有所区别
+
+            normalize = T.Normalize(
+                mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+
+            # 测试集和验证集
+            if self.test or not train:
+                self.transforms = T.Compose(
+                    [T.Resize(224), T.CenterCrop(224), T.ToTensor(), normalize])
+            # 训练集
+            else:
+                self.transforms = T.Compose([
+                    T.Resize(256), T.RandomResizedCrop(224),
+                    T.RandomHorizontalFlip(), T.ToTensor(), normalize
+                ])
+
+    def __getitem__(self, index):
+        '''
+        返回一张图片的数据
+        对于测试集，没有label，返回图片id，如1000.jpg返回1000
+        '''
+        img_path = self.imgs[index]
+        if self.test:
+            label = int(self.imgs[index].split('.')[-2].split('/')[-1])
+        else:
+            label = 1 if 'dog' in img_path.split('/')[-1] else 0
+        data = Image.open(img_path)
+        data = self.transforms(data)
+        return data, label
+
+    def __len__(self):
+        '''
+        返回数据集中所有图片的个数
+        '''
+        return len(self.imgs)
+
+
+traindir = '/opt/data/kaggle/playground/dogs-vs-cats/sample_train'
+train_dataset = DogCat(traindir, train=True)
+loader_train = data.DataLoader(train_dataset, batch_size=20, shuffle=True, num_workers=1)
+
+testdir = '/opt/data/kaggle/playground/dogs-vs-cats/sample_test'
+test_dataset = DogCat(testdir, train=True)
+loader_test = data.DataLoader(test_dataset, batch_size=3, shuffle=True, num_workers=1)
+
+
+# 2. 创建 CNN 模型
+cnn = AlexNet()
+print(cnn)
+# 3. 设置优化器和损失函数
+optimizer = torch.optim.Adam(cnn.parameters(), lr=0.005, betas=(0.9, 0.99))  # optimize all cnn parameters
+loss_func = nn.CrossEntropyLoss()  # the target label is not one-hotted
+
+# 4. 训练模型
+EPOCH = 10  # train the training data n times, to save time, we just train 1 epoch
+# training and testing
+for epoch in range(EPOCH):
+        num = 0
+        # gives batch data, normalize x when iterate train_loader
+        for step, (x, y) in enumerate(loader_train):
+            b_x = Variable(x)  # batch x
+            b_y = Variable(y)  # batch y
+
+            output = cnn(b_x)  # cnn output
+            loss = loss_func(output, b_y)  # cross entropy loss
+            optimizer.zero_grad()  # clear gradients for this training step
+            loss.backward()  # backpropagation, compute gradients
+            optimizer.step()  # apply gradients
+
+            # print('-'*30, step)
+            if step % 20 == 0:
+                num += 1
+                for _, (x_t, y_test) in enumerate(loader_test):
+                    x_test = Variable(x_t)  # batch x
+                    test_output = cnn(x_test)
+                    pred_y = torch.max(test_output, 1)[1].data.squeeze()
+                    accuracy = sum(pred_y == y_test) / float(y_test.size(0))
+                    print('Epoch: ', epoch, '| Num: ',  num, '| Step: ',  step, '| train loss: %.4f' % loss.data[0], '| test accuracy: %.4f' % accuracy)