Your First GAN

import torch
from torch import nn
from tqdm.auto import tqdm
from torchvision import transforms
from torchvision.datasets import MNIST # Training dataset
from torchvision.utils import make_grid
from torch.utils.data import DataLoader
import matplotlib.pyplot as plt
torch.manual_seed(0) # Set for testing purposes, please do not change!

def show_tensor_images(image_tensor, num_images=25, size=(1, 28, 28)):
    '''
    Function for visualizing images: Given a tensor of images, number of images, and
    size per image, plots and prints the images in a uniform grid.
    '''
    image_unflat = image_tensor.detach().cpu().view(-1, *size)
    image_grid = make_grid(image_unflat[:num_images], nrow=5)
    plt.imshow(image_grid.permute(1, 2, 0).squeeze())
    plt.show()
    # UNQ_C1 (UNIQUE CELL IDENTIFIER, DO NOT EDIT)
# GRADED FUNCTION: get_generator_block
def get_generator_block(input_dim, output_dim):
    '''
    Function for returning a block of the generator's neural network
    given input and output dimensions.
    Parameters:
        input_dim: the dimension of the input vector, a scalar
        output_dim: the dimension of the output vector, a scalar
    Returns:
        a generator neural network layer, with a linear transformation 
          followed by a batch normalization and then a relu activation
    '''
    return nn.Sequential(
        # Hint: Replace all of the "None" with the appropriate dimensions.
        # The documentation may be useful if you're less familiar with PyTorch:
        # https://pytorch.org/docs/stable/nn.html.
        #### START CODE HERE ####
        nn.Linear(input_dim, output_dim),
        nn.BatchNorm1d(output_dim),
        nn.ReLU(inplace=True),
        #### END CODE HERE ####
    )
    # Verify the generator block function
def test_gen_block(in_features, out_features, num_test=1000):
    block = get_generator_block(in_features, out_features)

    # Check the three parts
    assert len(block) == 3
    assert type(block[0]) == nn.Linear
    assert type(block[1]) == nn.BatchNorm1d
    assert type(block[2]) == nn.ReLU

    # Check the output shape
    test_input = torch.randn(num_test, in_features)
    test_output = block(test_input)
    assert tuple(test_output.shape) == (num_test, out_features)
    assert test_output.std() > 0.55
    assert test_output.std() < 0.65

test_gen_block(25, 12)
test_gen_block(15, 28)
print("Success!")
# UNQ_C2 (UNIQUE CELL IDENTIFIER, DO NOT EDIT)
# GRADED FUNCTION: Generator
class Generator(nn.Module):
    '''
    Generator Class
    Values:
        z_dim: the dimension of the noise vector, a scalar
        im_dim: the dimension of the images, fitted for the dataset used, a scalar
          (MNIST images are 28 x 28 = 784 so that is your default)
        hidden_dim: the inner dimension, a scalar
    '''
    def __init__(self, z_dim=10, im_dim=784, hidden_dim=128):
        super(Generator, self).__init__()
        # Build the neural network
        self.gen = nn.Sequential(
            get_generator_block(z_dim, hidden_dim),
            get_generator_block(hidden_dim, hidden_dim * 2),
            get_generator_block(hidden_dim * 2, hidden_dim * 4),
            get_generator_block(hidden_dim * 4, hidden_dim * 8),
            # There is a dropdown with hints if you need them! 
            #### START CODE HERE ####
            nn.Linear(hidden_dim*8, im_dim),
            nn.Sigmoid(),
            #### END CODE HERE ####
        )
    def forward(self, noise):
        '''
        Function for completing a forward pass of the generator: Given a noise tensor, 
        returns generated images.
        Parameters:
            noise: a noise tensor with dimensions (n_samples, z_dim)
        '''
        return self.gen(noise)

    # Needed for grading
    def get_gen(self):
        '''
        Returns:
            the sequential model
        '''
        return self.gen
        # Verify the generator class
def test_generator(z_dim, im_dim, hidden_dim, num_test=10000):
    gen = Generator(z_dim, im_dim, hidden_dim).get_gen()

    # Check there are six modules in the sequential part
    assert len(gen) == 6
    test_input = torch.randn(num_test, z_dim)
    test_output = gen(test_input)

    # Check that the output shape is correct
    assert tuple(test_output.shape) == (num_test, im_dim)
    assert test_output.max() < 1, "Make sure to use a sigmoid"
    assert test_output.min() > 0, "Make sure to use a sigmoid"
    assert test_output.std() > 0.05, "Don't use batchnorm here"
    assert test_output.std() < 0.15, "Don't use batchnorm here"

test_generator(5, 10, 20)
test_generator(20, 8, 24)
print("Success!")
# UNQ_C3 (UNIQUE CELL IDENTIFIER, DO NOT EDIT)
# GRADED FUNCTION: get_noise
def get_noise(n_samples, z_dim, device='cpu'):
    '''
    Function for creating noise vectors: Given the dimensions (n_samples, z_dim),
    creates a tensor of that shape filled with random numbers from the normal distribution.
    Parameters:
        n_samples: the number of samples to generate, a scalar
        z_dim: the dimension of the noise vector, a scalar
        device: the device type
    '''
    # NOTE: To use this on GPU with device='cuda', make sure to pass the device 
    # argument to the function you use to generate the noise.
    #### START CODE HERE ####
    return torch.randn(n_samples, z_dim).to(device)
    #### END CODE HERE ####
    # Verify the noise vector function
def test_get_noise(n_samples, z_dim, device='cpu'):
    noise = get_noise(n_samples, z_dim, device)

    # Make sure a normal distribution was used
    assert tuple(noise.shape) == (n_samples, z_dim)
    assert torch.abs(noise.std() - torch.tensor(1.0)) < 0.01
    assert str(noise.device).startswith(device)

test_get_noise(1000, 100, 'cpu')
if torch.cuda.is_available():
    test_get_noise(1000, 32, 'cuda')
print("Success!")
# UNQ_C4 (UNIQUE CELL IDENTIFIER, DO NOT EDIT)
# GRADED FUNCTION: get_discriminator_block
def get_discriminator_block(input_dim, output_dim):
    '''
    Discriminator Block
    Function for returning a neural network of the discriminator given input and output dimensions.
    Parameters:
        input_dim: the dimension of the input vector, a scalar
        output_dim: the dimension of the output vector, a scalar
    Returns:
        a discriminator neural network layer, with a linear transformation 
          followed by an nn.LeakyReLU activation with negative slope of 0.2 
          (https://pytorch.org/docs/master/generated/torch.nn.LeakyReLU.html)
    '''
    return nn.Sequential(
        #### START CODE HERE ####
        nn.Linear(input_dim,output_dim),
        nn.LeakyReLU(0.2),
        #### END CODE HERE ####
    )
    # Verify the discriminator block function
def test_disc_block(in_features, out_features, num_test=10000):
    block = get_discriminator_block(in_features, out_features)

    # Check there are two parts
    assert len(block) == 2
    test_input = torch.randn(num_test, in_features)
    test_output = block(test_input)

    # Check that the shape is right
    assert tuple(test_output.shape) == (num_test, out_features)

    # Check that the LeakyReLU slope is about 0.2
    assert -test_output.min() / test_output.max() > 0.1
    assert -test_output.min() / test_output.max() < 0.3
    assert test_output.std() > 0.3
    assert test_output.std() < 0.5

test_disc_block(25, 12)
test_disc_block(15, 28)
print("Success!")
# UNQ_C5 (UNIQUE CELL IDENTIFIER, DO NOT EDIT)
# GRADED FUNCTION: Discriminator
class Discriminator(nn.Module):
    '''
    Discriminator Class
    Values:
        im_dim: the dimension of the images, fitted for the dataset used, a scalar
            (MNIST images are 28x28 = 784 so that is your default)
        hidden_dim: the inner dimension, a scalar
    '''
    def __init__(self, im_dim=784, hidden_dim=128):
        super(Discriminator, self).__init__()
        self.disc = nn.Sequential(
            get_discriminator_block(im_dim, hidden_dim * 4),
            get_discriminator_block(hidden_dim * 4, hidden_dim * 2),
            get_discriminator_block(hidden_dim * 2, hidden_dim),
            # Hint: You want to transform the final output into a single value,
            #       so add one more linear map.
            #### START CODE HERE ####
            nn.Linear(hidden_dim,1)
            #### END CODE HERE ####
        )

    def forward(self, image):
        '''
        Function for completing a forward pass of the discriminator: Given an image tensor, 
        returns a 1-dimension tensor representing fake/real.
        Parameters:
            image: a flattened image tensor with dimension (im_dim)
        '''
        return self.disc(image)

    # Needed for grading
    def get_disc(self):
        '''
        Returns:
            the sequential model
        '''
        return self.disc
        # Verify the discriminator class
def test_discriminator(z_dim, hidden_dim, num_test=100):

    disc = Discriminator(z_dim, hidden_dim).get_disc()

    # Check there are three parts
    assert len(disc) == 4

    # Check the linear layer is correct
    test_input = torch.randn(num_test, z_dim)
    test_output = disc(test_input)
    assert tuple(test_output.shape) == (num_test, 1)

    # Make sure there's no sigmoid
    assert test_input.max() > 1
    assert test_input.min() < -1

test_discriminator(5, 10)
test_discriminator(20, 8)
print("Success!")
# Set your parameters
criterion = nn.BCEWithLogitsLoss()
n_epochs = 200
z_dim = 64
display_step = 500
batch_size = 128
lr = 0.00001

# Load MNIST dataset as tensors
dataloader = DataLoader(
    MNIST('.', download=False, transform=transforms.ToTensor()),
    batch_size=batch_size,
    shuffle=True)

### DO NOT EDIT ###
device = 'cuda'
gen = Generator(z_dim).to(device)
gen_opt = torch.optim.Adam(gen.parameters(), lr=lr)
disc = Discriminator().to(device) 
disc_opt = torch.optim.Adam(disc.parameters(), lr=lr)
# UNQ_C6 (UNIQUE CELL IDENTIFIER, DO NOT EDIT)
# GRADED FUNCTION: get_disc_loss
def get_disc_loss(gen, disc, criterion, real, num_images, z_dim, device):
    '''
    Return the loss of the discriminator given inputs.
    Parameters:
        gen: the generator model, which returns an image given z-dimensional noise
        disc: the discriminator model, which returns a single-dimensional prediction of real/fake
        criterion: the loss function, which should be used to compare 
               the discriminator's predictions to the ground truth reality of the images 
               (e.g. fake = 0, real = 1)
        real: a batch of real images
        num_images: the number of images the generator should produce, 
                which is also the length of the real images
        z_dim: the dimension of the noise vector, a scalar
        device: the device type
    Returns:
        disc_loss: a torch scalar loss value for the current batch
    '''
    #     These are the steps you will need to complete:
    #       1) Create noise vectors and generate a batch (num_images) of fake images. 
    #            Make sure to pass the device argument to the noise.
    #       2) Get the discriminator's prediction of the fake image 
    #            and calculate the loss. Don't forget to detach the generator!
    #            (Remember the loss function you set earlier -- criterion. You need a 
    #            'ground truth' tensor in order to calculate the loss. 
    #            For example, a ground truth tensor for a fake image is all zeros.)
    #       3) Get the discriminator's prediction of the real image and calculate the loss.
    #       4) Calculate the discriminator's loss by averaging the real and fake loss
    #            and set it to disc_loss.
    #     Note: Please do not use concatenation in your solution. The tests are being updated to 
    #           support this, but for now, average the two losses as described in step (4).
    #     *Important*: You should NOT write your own loss function here - use criterion(pred, true)!
    #### START CODE HERE ####
    real_label = torch.ones(num_images,1, device = device)
    fake_label = torch.zeros(num_images,1, device = device)
    noise = get_noise(num_images, z_dim, device=device)
    #gen_detached = gen.detach()
    gen_output = gen(noise)
    gen_detached = gen_output.detach()
    fake_output = disc(gen_detached)
    d_loss_fake = criterion(fake_output, fake_label)
    real_output = disc(real)
    d_loss_real = criterion(real_output, real_label)
    disc_loss = torch.div(torch.add(d_loss_fake, d_loss_real), 2)

    #### END CODE HERE ####
    return disc_loss
    def test_disc_reasonable(num_images=10):
    # Don't use explicit casts to cuda - use the device argument
    import inspect, re
    lines = inspect.getsource(get_disc_loss)
    assert (re.search(r"to\(.cuda.\)", lines)) is None
    assert (re.search(r"\.cuda\(\)", lines)) is None

    z_dim = 64
    gen = torch.zeros_like
    disc = lambda x: x.mean(1)[:, None]
    criterion = torch.mul # Multiply
    real = torch.ones(num_images, z_dim)
    disc_loss = get_disc_loss(gen, disc, criterion, real, num_images, z_dim, 'cpu')
    assert torch.all(torch.abs(disc_loss.mean() - 0.5) < 1e-5)

    gen = torch.ones_like
    criterion = torch.mul # Multiply
    real = torch.zeros(num_images, z_dim)
    assert torch.all(torch.abs(get_disc_loss(gen, disc, criterion, real, num_images, z_dim, 'cpu')) < 1e-5)

    gen = lambda x: torch.ones(num_images, 10)
    disc = lambda x: x.mean(1)[:, None] + 10
    criterion = torch.mul # Multiply
    real = torch.zeros(num_images, 10)
    assert torch.all(torch.abs(get_disc_loss(gen, disc, criterion, real, num_images, z_dim, 'cpu').mean() - 5) < 1e-5)

    gen = torch.ones_like
    disc = nn.Linear(64, 1, bias=False)
    real = torch.ones(num_images, 64) * 0.5
    disc.weight.data = torch.ones_like(disc.weight.data) * 0.5
    disc_opt = torch.optim.Adam(disc.parameters(), lr=lr)
    criterion = lambda x, y: torch.sum(x) + torch.sum(y)
    disc_loss = get_disc_loss(gen, disc, criterion, real, num_images, z_dim, 'cpu').mean()
    disc_loss.backward()
    assert torch.isclose(torch.abs(disc.weight.grad.mean() - 11.25), torch.tensor(3.75))

def test_disc_loss(max_tests = 10):
    z_dim = 64
    gen = Generator(z_dim).to(device)
    gen_opt = torch.optim.Adam(gen.parameters(), lr=lr)
    disc = Discriminator().to(device) 
    disc_opt = torch.optim.Adam(disc.parameters(), lr=lr)
    num_steps = 0
    for real, _ in dataloader:
        cur_batch_size = len(real)
        real = real.view(cur_batch_size, -1).to(device)

        ### Update discriminator ###
        # Zero out the gradient before backpropagation
        disc_opt.zero_grad()

        # Calculate discriminator loss
        disc_loss = get_disc_loss(gen, disc, criterion, real, cur_batch_size, z_dim, device)
        assert (disc_loss - 0.68).abs() < 0.05

        # Update gradients
        disc_loss.backward(retain_graph=True)

        # Check that they detached correctly
        assert gen.gen[0][0].weight.grad is None

        # Update optimizer
        old_weight = disc.disc[0][0].weight.data.clone()
        disc_opt.step()
        new_weight = disc.disc[0][0].weight.data

        # Check that some discriminator weights changed
        assert not torch.all(torch.eq(old_weight, new_weight))
        num_steps += 1
        if num_steps >= max_tests:
            break

test_disc_reasonable()
test_disc_loss()
print("Success!")
# UNQ_C7 (UNIQUE CELL IDENTIFIER, DO NOT EDIT)
# GRADED FUNCTION: get_gen_loss
def get_gen_loss(gen, disc, criterion, num_images, z_dim, device):
    '''
    Return the loss of the generator given inputs.
    Parameters:
        gen: the generator model, which returns an image given z-dimensional noise
        disc: the discriminator model, which returns a single-dimensional prediction of real/fake
        criterion: the loss function, which should be used to compare 
               the discriminator's predictions to the ground truth reality of the images 
               (e.g. fake = 0, real = 1)
        num_images: the number of images the generator should produce, 
                which is also the length of the real images
        z_dim: the dimension of the noise vector, a scalar
        device: the device type
    Returns:
        gen_loss: a torch scalar loss value for the current batch
    '''
    #     These are the steps you will need to complete:
    #       1) Create noise vectors and generate a batch of fake images. 
    #           Remember to pass the device argument to the get_noise function.
    #       2) Get the discriminator's prediction of the fake image.
    #       3) Calculate the generator's loss. Remember the generator wants
    #          the discriminator to think that its fake images are real
    #     *Important*: You should NOT write your own loss function here - use criterion(pred, true)!

    #### START CODE HERE ####
    real_label = torch.ones(num_images, 1, device = device)
    noise = get_noise(num_images, z_dim, device=device)
    fake_imgs = gen(noise)
    disc_output = disc(fake_imgs)
    gen_loss = criterion(disc_output,real_label)
    #### END CODE HERE ####
    return gen_loss
    def test_gen_reasonable(num_images=10):
    # Don't use explicit casts to cuda - use the device argument
    import inspect, re
    lines = inspect.getsource(get_gen_loss)
    assert (re.search(r"to\(.cuda.\)", lines)) is None
    assert (re.search(r"\.cuda\(\)", lines)) is None

    z_dim = 64
    gen = torch.zeros_like
    disc = nn.Identity()
    criterion = torch.mul # Multiply
    gen_loss_tensor = get_gen_loss(gen, disc, criterion, num_images, z_dim, 'cpu')
    assert torch.all(torch.abs(gen_loss_tensor) < 1e-5)
    #Verify shape. Related to gen_noise parametrization
    assert tuple(gen_loss_tensor.shape) == (num_images, z_dim)

    gen = torch.ones_like
    disc = nn.Identity()
    criterion = torch.mul # Multiply
    real = torch.zeros(num_images, 1)
    gen_loss_tensor = get_gen_loss(gen, disc, criterion, num_images, z_dim, 'cpu')
    assert torch.all(torch.abs(gen_loss_tensor - 1) < 1e-5)
    #Verify shape. Related to gen_noise parametrization
    assert tuple(gen_loss_tensor.shape) == (num_images, z_dim)


def test_gen_loss(num_images):
    z_dim = 64
    gen = Generator(z_dim).to(device)
    gen_opt = torch.optim.Adam(gen.parameters(), lr=lr)
    disc = Discriminator().to(device) 
    disc_opt = torch.optim.Adam(disc.parameters(), lr=lr)

    gen_loss = get_gen_loss(gen, disc, criterion, num_images, z_dim, device)

    # Check that the loss is reasonable
    assert (gen_loss - 0.7).abs() < 0.1
    gen_loss.backward()
    old_weight = gen.gen[0][0].weight.clone()
    gen_opt.step()
    new_weight = gen.gen[0][0].weight
    assert not torch.all(torch.eq(old_weight, new_weight))


test_gen_reasonable(10)
test_gen_loss(18)
print("Success!")
# UNQ_C8 (UNIQUE CELL IDENTIFIER, DO NOT EDIT)
# GRADED FUNCTION: 

cur_step = 0
mean_generator_loss = 0
mean_discriminator_loss = 0
test_generator = True # Whether the generator should be tested
gen_loss = False
error = False
for epoch in range(n_epochs):

    # Dataloader returns the batches
    for real, _ in tqdm(dataloader):
        cur_batch_size = len(real)

        # Flatten the batch of real images from the dataset
        real = real.view(cur_batch_size, -1).to(device)

        ### Update discriminator ###
        # Zero out the gradients before backpropagation
        disc_opt.zero_grad()

        # Calculate discriminator loss
        disc_loss = get_disc_loss(gen, disc, criterion, real, cur_batch_size, z_dim, device)

        # Update gradients
        disc_loss.backward(retain_graph=True)

        # Update optimizer
        disc_opt.step()

        # For testing purposes, to keep track of the generator weights
        if test_generator:
            old_generator_weights = gen.gen[0][0].weight.detach().clone()

        ### Update generator ###
        #     Hint: This code will look a lot like the discriminator updates!
        #     These are the steps you will need to complete:
        #       1) Zero out the gradients.
        #       2) Calculate the generator loss, assigning it to gen_loss.
        #       3) Backprop through the generator: update the gradients and optimizer.
        #### START CODE HERE ####
        gen_opt.zero_grad()
        gen_loss = get_gen_loss(gen, disc, criterion, 10, z_dim, device)
        gen_loss.backward(retain_graph=True)
        gen_opt.step()
        #### END CODE HERE ####

        # For testing purposes, to check that your code changes the generator weights
        if test_generator:
            try:
                assert lr > 0.0000002 or (gen.gen[0][0].weight.grad.abs().max() < 0.0005 and epoch == 0)
                assert torch.any(gen.gen[0][0].weight.detach().clone() != old_generator_weights)
            except:
                error = True
                print("Runtime tests have failed")

        # Keep track of the average discriminator loss
        mean_discriminator_loss += disc_loss.item() / display_step

        # Keep track of the average generator loss
        mean_generator_loss += gen_loss.item() / display_step

        ### Visualization code ###
        if cur_step % display_step == 0 and cur_step > 0:
            print(f"Epoch {epoch}, step {cur_step}: Generator loss: {mean_generator_loss}, discriminator loss: {mean_discriminator_loss}")
            fake_noise = get_noise(cur_batch_size, z_dim, device=device)
            fake = gen(fake_noise)
            show_tensor_images(fake)
            show_tensor_images(real)
            mean_generator_loss = 0
            mean_discriminator_loss = 0
        cur_step += 1

最后拿了九分，是detach生成器那里写的有问题，不影响训练，之后再改吧