import torch as t
import torch.nn as nn
import torch.nn.functional as F

import torch
'''
初始化網(wǎng)絡(luò)
初始化Loss函數(shù) & 優(yōu)化器
進(jìn)入step循環(huán)：
　　梯度清零
　　向前傳播
　　計(jì)算本次Loss
　　向后傳播
　　更新參數(shù)
'''
class LeNet(nn.Module):
? ? def __init__(self):
? ? ? ? super(LeNet, self).__init__()
? ? ? ? self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
? ? ? ? self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
? ? ? ? self.conv2_drop = nn.Dropout2d()
? ? ? ? self.fc1 = nn.Linear(320, 50)
? ? ? ? self.fc2 = nn.Linear(50, 10)
? ? def forward(self, x):
? ? ? ? x = F.relu(F.max_pool2d(self.conv1(x), 2))
? ? ? ? x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
? ? ? ? x = x.view(-1, 320)
? ? ? ? x = F.relu(self.fc1(x))
? ? ? ? x = F.dropout(x, training=self.training)
? ? ? ? x = self.fc2(x)
? ? ? ? return x


if __name__ == "__main__":
? ? net = LeNet()

? ? # #########訓(xùn)練網(wǎng)絡(luò)#########
? ? from torch import optim
? ? # from torchvision.datasets import MNIST
? ? import ?torchvision
? ? import numpy
? ? from torchvision import transforms
? ? from torch.utils.data import DataLoader

? ? # 初始化Loss函數(shù) & 優(yōu)化器
? ? loss_fn = nn.CrossEntropyLoss()
? ? optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

? ? # transforms = transforms.Compose([])

? ? DOWNLOAD = False
? ? BATCH_SIZE = 32
? ? transform = transforms.Compose([
? ? ? ? transforms.ToTensor()
? ? ])
? ? #transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) ?# 歸一化

? ? train_dataset = torchvision.datasets.MNIST(root='./', train=True, transform=transform, download=DOWNLOAD)
? ? test_dataset = torchvision.datasets.MNIST(root='./data/mnist',
? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? train=False,
? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? transform=torchvision.transforms.ToTensor(),
? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? download=True)
? ?
? ? train_loader = DataLoader(dataset=train_dataset,
? ? ? ? ? ? ? ? ? ? ? ? ? ? ? batch_size=BATCH_SIZE,
? ? ? ? ? ? ? ? ? ? ? ? ? ? ? shuffle=True)
? ? test_loader = DataLoader(dataset=test_dataset, batch_size=BATCH_SIZE)

? ? for epoch in range(200):
? ? ? ? running_loss = 0.0
? ? ? ? for step, data in enumerate(train_loader): ?
? ? ? ? ? ? inputs, labels = data
? ? ? ? ? ? inputs, labels = t.autograd.Variable(inputs), t.autograd.Variable(labels)
? ? ? ? ? ? # inputs = torch.from_numpy(inputs).unsqueeze(1)
? ? ? ? ? ? # labels = torch.from_numpy(numpy.array(labels))
? ? ? ? ? ? # 梯度清零
? ? ? ? ? ? optimizer.zero_grad()

? ? ? ? ? ? # forward
? ? ? ? ? ? outputs = net(inputs)
? ? ? ? ? ? # backward
? ? ? ? ? ? loss = loss_fn(outputs, labels)
? ? ? ? ? ? loss.backward()
? ? ? ? ? ? # update
? ? ? ? ? ? optimizer.step()

? ? ? ? ? ? running_loss += loss.item()
? ? ? ? ? ? if step % 10 == 9:
? ? ? ? ? ? ? ? print("[{0:d}, {1:5d}] loss: {2:3f}".format(epoch + 1, step + 1, running_loss / 2000))
? ? ? ? ? ? ? ? running_loss = 0.
? ? print("Finished Training")

? ?# save the trained net
? ? torch.save(net, 'net.pkl')

? ? # load the trained net
? ? net1 = torch.load('net.pkl')

? ? # test the trained net
? ? correct = 0
? ? total = 1
? ? for images, labels in test_loader:
? ? ? ? preds = net(images)
? ? ? ? predicted = torch.argmax(preds, 1)
? ? ? ? total += labels.size(0)
? ? ? ? correct += (predicted == labels).sum().item()

? ? accuracy = correct / total
? ? print('accuracy of test data:{:.1%}'.format(accuracy))

class Rescale(object):
? ? """Rescale the image in a sample to a given size.

? ? Args:
? ? ? ? output_size (tuple or int): Desired output size. If tuple, output is
? ? ? ? ? ? matched to output_size. If int, smaller of image edges is matched
? ? ? ? ? ? to output_size keeping aspect ratio the same.
? ? """

? ? def __init__(self, output_size):
? ? ? ? assert isinstance(output_size, (int, tuple))
? ? ? ? self.output_size = output_size

? ? def __call__(self, sample):
? ? ? ? image, landmarks = sample['image'], sample['landmarks']

? ? ? ? h, w = image.shape[:2]
? ? ? ? if isinstance(self.output_size, int):
? ? ? ? ? ? if h > w:
? ? ? ? ? ? ? ? new_h, new_w = self.output_size * h / w, self.output_size
? ? ? ? ? ? else:
? ? ? ? ? ? ? ? new_h, new_w = self.output_size, self.output_size * w / h
? ? ? ? else:
? ? ? ? ? ? new_h, new_w = self.output_size

? ? ? ? new_h, new_w = int(new_h), int(new_w)

? ? ? ? img = transform.resize(image, (new_h, new_w))

? ? ? ? # h and w are swapped for landmarks because for images,
? ? ? ? # x and y axes are axis 1 and 0 respectively
? ? ? ? landmarks = landmarks * [new_w / w, new_h / h]

? ? ? ? return {'image': img, 'landmarks': landmarks}


class RandomCrop(object):
? ? """Crop randomly the image in a sample.

? ? Args:
? ? ? ? output_size (tuple or int): Desired output size. If int, square crop
? ? ? ? ? ? is made.
? ? """

? ? def __init__(self, output_size):
? ? ? ? assert isinstance(output_size, (int, tuple))
? ? ? ? if isinstance(output_size, int):
? ? ? ? ? ? self.output_size = (output_size, output_size)
? ? ? ? else:
? ? ? ? ? ? assert len(output_size) == 2
? ? ? ? ? ? self.output_size = output_size

? ? def __call__(self, sample):
? ? ? ? image, landmarks = sample['image'], sample['landmarks']

? ? ? ? h, w = image.shape[:2]
? ? ? ? new_h, new_w = self.output_size

? ? ? ? top = np.random.randint(0, h - new_h)
? ? ? ? left = np.random.randint(0, w - new_w)

? ? ? ? image = image[top: top + new_h,
? ? ? ? ? ? ? ? ? ? ? left: left + new_w]

? ? ? ? landmarks = landmarks - [left, top]

? ? ? ? return {'image': image, 'landmarks': landmarks}


class ToTensor(object):
? ? """Convert ndarrays in sample to Tensors."""

? ? def __call__(self, sample):
? ? ? ? image, landmarks = sample['image'], sample['landmarks']

? ? ? ? # swap color axis because
? ? ? ? # numpy image: H x W x C
? ? ? ? # torch image: C X H X W
? ? ? ? image = image.transpose((2, 0, 1))
? ? ? ? return {'image': torch.from_numpy(image),
? ? ? ? ? ? ? ? 'landmarks': torch.from_numpy(landmarks)}

torchvision.datasets
torchvision.models
torchvision.transforms
torchvision.utils

import torchvision
from torch.utils.data import DataLoader

DOWNLOAD = False
BATCH_SIZE = 32
transform = transforms.Compose([
? ? transforms.ToTensor()
])
#transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) ?# 歸一化

train_dataset = torchvision.datasets.MNIST(root='./', train=True, transform=transform, download=DOWNLOAD)

train_loader = DataLoader(dataset=train_dataset,
? ? ? ? ? ? ? ? ? ? ? ? ?batch_size=BATCH_SIZE,
? ? ? ? ? ? ? ? ? ? ? ? ?shuffle=True)

import torchvision.models as models
resnet18 = models.resnet18(pretrained=True)
alexnet = models.alexnet(pretrained=True)

# 圖像預(yù)處理步驟
transform = transforms.Compose([
? ? transforms.Resize(96), # 縮放到 96 * 96 大小
? ? transforms.ToTensor(),
? ? transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) # 歸一化
])

__all__ = ["Compose", "ToTensor", "PILToTensor", "ConvertImageDtype", "ToPILImage", "Normalize", "Resize", "Scale",
? ? ? ? ? ?"CenterCrop", "Pad", "Lambda", "RandomApply", "RandomChoice", "RandomOrder", "RandomCrop",
? ? ? ? ? ?"RandomHorizontalFlip", "RandomVerticalFlip", "RandomResizedCrop", "RandomSizedCrop", "FiveCrop", "TenCrop",
? ? ? ? ? ?"LinearTransformation", "ColorJitter", "RandomRotation", "RandomAffine", "Grayscale", "RandomGrayscale",
? ? ? ? ? ?"RandomPerspective", "RandomErasing", "GaussianBlur", "InterpolationMode", "RandomInvert", "RandomPosterize",
? ? ? ? ? ?"RandomSolarize", "RandomAdjustSharpness", "RandomAutocontrast", "RandomEqualize"]

from PIL import Image
# from torch.utils.tensorboard import SummaryWriter
from torchvision import transforms

from torch.autograd import Variable
from torchvision.transforms import functional as F

tensor數(shù)據(jù)類型
# 通過transforms.ToTensor去看兩個(gè)問題

img_path = "./k.jpg"
img = Image.open(img_path)

# writer = SummaryWriter("logs")

tensor_trans = transforms.ToTensor()
tensor_img = tensor_trans(img)

tensor_img1 = F.to_tensor(img)

print(tensor_img.type(),tensor_img1.type())
print(tensor_img.shape)

'''
transforms.Normalize使用如下公式進(jìn)行歸一化：
channel=（channel-mean）/std(因?yàn)閠ransforms.ToTensor()已經(jīng)把數(shù)據(jù)處理成[0,1],那么(x-0.5)/0.5就是[-1.0, 1.0])
'''

# writer.add_image("Tensor_img", tensor_img)
# writer.close()

### class torchvision.transforms.Scale(size, interpolation=2)

```python
from torchvision import transforms
from PIL import Image
crop = transforms.Scale(12)
img = Image.open('test.jpg')

print(type(img))
print(img.size)

croped_img=crop(img)
print(type(croped_img))
print(croped_img.size)

class torchvision.transforms.Compose(transforms)

class torchvision.transforms.Normalize(mean, std)

class torchvision.transforms.RandomSizedCrop(size, interpolation=2)

class torchvision.transforms.RandomCrop(size, padding=0)

class torchvision.transforms.CenterCrop(size)