`__. Đầu tiên tải
xuống, trích xuất và tải tập dữ liệu này.
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#@save
d2l.DATA_HUB['pokemon'] = (d2l.DATA_URL + 'pokemon.zip',
'c065c0e2593b8b161a2d7873e42418bf6a21106c')
data_dir = d2l.download_extract('pokemon')
pokemon = gluon.data.vision.datasets.ImageFolderDataset(data_dir)
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#@save
d2l.DATA_HUB['pokemon'] = (d2l.DATA_URL + 'pokemon.zip',
'c065c0e2593b8b161a2d7873e42418bf6a21106c')
data_dir = d2l.download_extract('pokemon')
pokemon = torchvision.datasets.ImageFolder(data_dir)
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#@save
d2l.DATA_HUB['pokemon'] = (d2l.DATA_URL + 'pokemon.zip',
'c065c0e2593b8b161a2d7873e42418bf6a21106c')
data_dir = d2l.download_extract('pokemon')
batch_size = 256
pokemon = tf.keras.preprocessing.image_dataset_from_directory(
data_dir, batch_size=batch_size, image_size=(64, 64))
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Downloading ../data/pokemon.zip from http://d2l-data.s3-accelerate.amazonaws.com/pokemon.zip...
Found 40597 files belonging to 721 classes.
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batch_size = 256
transformer = gluon.data.vision.transforms.Compose([
gluon.data.vision.transforms.Resize(64),
gluon.data.vision.transforms.ToTensor(),
gluon.data.vision.transforms.Normalize(0.5, 0.5)
])
data_iter = gluon.data.DataLoader(
pokemon.transform_first(transformer), batch_size=batch_size,
shuffle=True, num_workers=d2l.get_dataloader_workers())
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batch_size = 256
transformer = torchvision.transforms.Compose([
torchvision.transforms.Resize((64, 64)),
torchvision.transforms.ToTensor(),
torchvision.transforms.Normalize(0.5, 0.5)
])
pokemon.transform = transformer
data_iter = torch.utils.data.DataLoader(
pokemon, batch_size=batch_size,
shuffle=True, num_workers=d2l.get_dataloader_workers())
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def transform_func(X):
X = X / 255.
X = (X - 0.5) / (0.5)
return X
# For TF>=2.4 use `num_parallel_calls = tf.data.AUTOTUNE`
data_iter = pokemon.map(lambda x, y: (transform_func(x), y),
num_parallel_calls=tf.data.experimental.AUTOTUNE)
data_iter = data_iter.cache().shuffle(buffer_size=1000).prefetch(
buffer_size=tf.data.experimental.AUTOTUNE)
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d2l.set_figsize((4, 4))
for X, y in data_iter:
imgs = X[0:20,:,:,:].transpose(0, 2, 3, 1)/2+0.5
d2l.show_images(imgs, num_rows=4, num_cols=5)
break
.. figure:: output_dcgan_2541de_39_0.svg
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warnings.filterwarnings('ignore')
d2l.set_figsize((4, 4))
for X, y in data_iter:
imgs = X[0:20,:,:,:].permute(0, 2, 3, 1)/2+0.5
d2l.show_images(imgs, num_rows=4, num_cols=5)
break
.. figure:: output_dcgan_2541de_42_0.svg
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d2l.set_figsize(figsize=(4, 4))
for X, y in data_iter.take(1):
imgs = X[:20, :, :, :] / 2 + 0.5
d2l.show_images(imgs, num_rows=4, num_cols=5)
.. figure:: output_dcgan_2541de_45_0.svg
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class G_block(nn.Block):
def __init__(self, channels, kernel_size=4,
strides=2, padding=1, **kwargs):
super(G_block, self).__init__(**kwargs)
self.conv2d_trans = nn.Conv2DTranspose(
channels, kernel_size, strides, padding, use_bias=False)
self.batch_norm = nn.BatchNorm()
self.activation = nn.Activation('relu')
def forward(self, X):
return self.activation(self.batch_norm(self.conv2d_trans(X)))
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class G_block(nn.Module):
def __init__(self, out_channels, in_channels=3, kernel_size=4, strides=2,
padding=1, **kwargs):
super(G_block, self).__init__(**kwargs)
self.conv2d_trans = nn.ConvTranspose2d(in_channels, out_channels,
kernel_size, strides, padding, bias=False)
self.batch_norm = nn.BatchNorm2d(out_channels)
self.activation = nn.ReLU()
def forward(self, X):
return self.activation(self.batch_norm(self.conv2d_trans(X)))
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class G_block(tf.keras.layers.Layer):
def __init__(self, out_channels, kernel_size=4, strides=2, padding="same",
**kwargs):
super().__init__(**kwargs)
self.conv2d_trans = tf.keras.layers.Conv2DTranspose(
out_channels, kernel_size, strides, padding, use_bias=False)
self.batch_norm = tf.keras.layers.BatchNormalization()
self.activation = tf.keras.layers.ReLU()
def call(self, X):
return self.activation(self.batch_norm(self.conv2d_trans(X)))
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x = np.zeros((2, 3, 16, 16))
g_blk = G_block(20)
g_blk.initialize()
g_blk(x).shape
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(2, 20, 32, 32)
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x = torch.zeros((2, 3, 16, 16))
g_blk = G_block(20)
g_blk(x).shape
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torch.Size([2, 20, 32, 32])
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x = tf.zeros((2, 16, 16, 3)) # Channel last convention
g_blk = G_block(20)
g_blk(x).shape
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TensorShape([2, 32, 32, 20])
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x = np.zeros((2, 3, 1, 1))
g_blk = G_block(20, strides=1, padding=0)
g_blk.initialize()
g_blk(x).shape
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(2, 20, 4, 4)
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x = torch.zeros((2, 3, 1, 1))
g_blk = G_block(20, strides=1, padding=0)
g_blk(x).shape
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torch.Size([2, 20, 4, 4])
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x = tf.zeros((2, 1, 1, 3))
# `padding="valid"` corresponds to no padding
g_blk = G_block(20, strides=1, padding="valid")
g_blk(x).shape
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TensorShape([2, 4, 4, 20])
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n_G = 64
net_G = nn.Sequential()
net_G.add(G_block(n_G*8, strides=1, padding=0), # Output: (64 * 8, 4, 4)
G_block(n_G*4), # Output: (64 * 4, 8, 8)
G_block(n_G*2), # Output: (64 * 2, 16, 16)
G_block(n_G), # Output: (64, 32, 32)
nn.Conv2DTranspose(
3, kernel_size=4, strides=2, padding=1, use_bias=False,
activation='tanh')) # Output: (3, 64, 64)
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n_G = 64
net_G = nn.Sequential(
G_block(in_channels=100, out_channels=n_G*8,
strides=1, padding=0), # Output: (64 * 8, 4, 4)
G_block(in_channels=n_G*8, out_channels=n_G*4), # Output: (64 * 4, 8, 8)
G_block(in_channels=n_G*4, out_channels=n_G*2), # Output: (64 * 2, 16, 16)
G_block(in_channels=n_G*2, out_channels=n_G), # Output: (64, 32, 32)
nn.ConvTranspose2d(in_channels=n_G, out_channels=3,
kernel_size=4, stride=2, padding=1, bias=False),
nn.Tanh()) # Output: (3, 64, 64)
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n_G = 64
net_G = tf.keras.Sequential([
# Output: (4, 4, 64 * 8)
G_block(out_channels=n_G*8, strides=1, padding="valid"),
G_block(out_channels=n_G*4), # Output: (8, 8, 64 * 4)
G_block(out_channels=n_G*2), # Output: (16, 16, 64 * 2)
G_block(out_channels=n_G), # Output: (32, 32, 64)
# Output: (64, 64, 3)
tf.keras.layers.Conv2DTranspose(
3, kernel_size=4, strides=2, padding="same", use_bias=False,
activation="tanh")
])
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x = np.zeros((1, 100, 1, 1))
net_G.initialize()
net_G(x).shape
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(1, 3, 64, 64)
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x = torch.zeros((1, 100, 1, 1))
net_G(x).shape
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torch.Size([1, 3, 64, 64])
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x = tf.zeros((1, 1, 1, 100))
net_G(x).shape
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TensorShape([1, 64, 64, 3])
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alphas = [0, .2, .4, .6, .8, 1]
x = np.arange(-2, 1, 0.1)
Y = [nn.LeakyReLU(alpha)(x).asnumpy() for alpha in alphas]
d2l.plot(x.asnumpy(), Y, 'x', 'y', alphas)
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alphas = [0, .2, .4, .6, .8, 1]
x = torch.arange(-2, 1, 0.1)
Y = [nn.LeakyReLU(alpha)(x).detach().numpy() for alpha in alphas]
d2l.plot(x.detach().numpy(), Y, 'x', 'y', alphas)
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alphas = [0, .2, .4, .6, .8, 1]
x = tf.range(-2, 1, 0.1)
Y = [tf.keras.layers.LeakyReLU(alpha)(x).numpy() for alpha in alphas]
d2l.plot(x.numpy(), Y, 'x', 'y', alphas)
.. figure:: output_dcgan_2541de_117_0.svg
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class D_block(nn.Block):
def __init__(self, channels, kernel_size=4, strides=2,
padding=1, alpha=0.2, **kwargs):
super(D_block, self).__init__(**kwargs)
self.conv2d = nn.Conv2D(
channels, kernel_size, strides, padding, use_bias=False)
self.batch_norm = nn.BatchNorm()
self.activation = nn.LeakyReLU(alpha)
def forward(self, X):
return self.activation(self.batch_norm(self.conv2d(X)))
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class D_block(nn.Module):
def __init__(self, out_channels, in_channels=3, kernel_size=4, strides=2,
padding=1, alpha=0.2, **kwargs):
super(D_block, self).__init__(**kwargs)
self.conv2d = nn.Conv2d(in_channels, out_channels, kernel_size,
strides, padding, bias=False)
self.batch_norm = nn.BatchNorm2d(out_channels)
self.activation = nn.LeakyReLU(alpha, inplace=True)
def forward(self, X):
return self.activation(self.batch_norm(self.conv2d(X)))
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class D_block(tf.keras.layers.Layer):
def __init__(self, out_channels, kernel_size=4, strides=2, padding="same",
alpha=0.2, **kwargs):
super().__init__(**kwargs)
self.conv2d = tf.keras.layers.Conv2D(out_channels, kernel_size,
strides, padding, use_bias=False)
self.batch_norm = tf.keras.layers.BatchNormalization()
self.activation = tf.keras.layers.LeakyReLU(alpha)
def call(self, X):
return self.activation(self.batch_norm(self.conv2d(X)))
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x = np.zeros((2, 3, 16, 16))
d_blk = D_block(20)
d_blk.initialize()
d_blk(x).shape
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(2, 20, 8, 8)
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x = torch.zeros((2, 3, 16, 16))
d_blk = D_block(20)
d_blk(x).shape
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torch.Size([2, 20, 8, 8])
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x = tf.zeros((2, 16, 16, 3))
d_blk = D_block(20)
d_blk(x).shape
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TensorShape([2, 8, 8, 20])
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n_D = 64
net_D = nn.Sequential()
net_D.add(D_block(n_D), # Output: (64, 32, 32)
D_block(n_D*2), # Output: (64 * 2, 16, 16)
D_block(n_D*4), # Output: (64 * 4, 8, 8)
D_block(n_D*8), # Output: (64 * 8, 4, 4)
nn.Conv2D(1, kernel_size=4, use_bias=False)) # Output: (1, 1, 1)
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n_D = 64
net_D = nn.Sequential(
D_block(n_D), # Output: (64, 32, 32)
D_block(in_channels=n_D, out_channels=n_D*2), # Output: (64 * 2, 16, 16)
D_block(in_channels=n_D*2, out_channels=n_D*4), # Output: (64 * 4, 8, 8)
D_block(in_channels=n_D*4, out_channels=n_D*8), # Output: (64 * 8, 4, 4)
nn.Conv2d(in_channels=n_D*8, out_channels=1,
kernel_size=4, bias=False)) # Output: (1, 1, 1)
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n_D = 64
net_D = tf.keras.Sequential([
D_block(n_D), # Output: (32, 32, 64)
D_block(out_channels=n_D*2), # Output: (16, 16, 64 * 2)
D_block(out_channels=n_D*4), # Output: (8, 8, 64 * 4)
D_block(out_channels=n_D*8), # Outupt: (4, 4, 64 * 64)
# Output: (1, 1, 1)
tf.keras.layers.Conv2D(1, kernel_size=4, use_bias=False)
])
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x = np.zeros((1, 3, 64, 64))
net_D.initialize()
net_D(x).shape
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(1, 1, 1, 1)
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x = torch.zeros((1, 3, 64, 64))
net_D(x).shape
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torch.Size([1, 1, 1, 1])
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x = tf.zeros((1, 64, 64, 3))
net_D(x).shape
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TensorShape([1, 1, 1, 1])
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def train(net_D, net_G, data_iter, num_epochs, lr, latent_dim,
device=d2l.try_gpu()):
loss = gluon.loss.SigmoidBCELoss()
net_D.initialize(init=init.Normal(0.02), force_reinit=True, ctx=device)
net_G.initialize(init=init.Normal(0.02), force_reinit=True, ctx=device)
trainer_hp = {'learning_rate': lr, 'beta1': 0.5}
trainer_D = gluon.Trainer(net_D.collect_params(), 'adam', trainer_hp)
trainer_G = gluon.Trainer(net_G.collect_params(), 'adam', trainer_hp)
animator = d2l.Animator(xlabel='epoch', ylabel='loss',
xlim=[1, num_epochs], nrows=2, figsize=(5, 5),
legend=['discriminator', 'generator'])
animator.fig.subplots_adjust(hspace=0.3)
for epoch in range(1, num_epochs + 1):
# Train one epoch
timer = d2l.Timer()
metric = d2l.Accumulator(3) # loss_D, loss_G, num_examples
for X, _ in data_iter:
batch_size = X.shape[0]
Z = np.random.normal(0, 1, size=(batch_size, latent_dim, 1, 1))
X, Z = X.as_in_ctx(device), Z.as_in_ctx(device),
metric.add(d2l.update_D(X, Z, net_D, net_G, loss, trainer_D),
d2l.update_G(Z, net_D, net_G, loss, trainer_G),
batch_size)
# Show generated examples
Z = np.random.normal(0, 1, size=(21, latent_dim, 1, 1), ctx=device)
# Normalize the synthetic data to N(0, 1)
fake_x = net_G(Z).transpose(0, 2, 3, 1) / 2 + 0.5
imgs = np.concatenate(
[np.concatenate([fake_x[i * 7 + j] for j in range(7)], axis=1)
for i in range(len(fake_x)//7)], axis=0)
animator.axes[1].cla()
animator.axes[1].imshow(imgs.asnumpy())
# Show the losses
loss_D, loss_G = metric[0] / metric[2], metric[1] / metric[2]
animator.add(epoch, (loss_D, loss_G))
print(f'loss_D {loss_D:.3f}, loss_G {loss_G:.3f}, '
f'{metric[2] / timer.stop():.1f} examples/sec on {str(device)}')
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def train(net_D, net_G, data_iter, num_epochs, lr, latent_dim,
device=d2l.try_gpu()):
loss = nn.BCEWithLogitsLoss(reduction='sum')
for w in net_D.parameters():
nn.init.normal_(w, 0, 0.02)
for w in net_G.parameters():
nn.init.normal_(w, 0, 0.02)
net_D, net_G = net_D.to(device), net_G.to(device)
trainer_hp = {'lr': lr, 'betas': [0.5,0.999]}
trainer_D = torch.optim.Adam(net_D.parameters(), **trainer_hp)
trainer_G = torch.optim.Adam(net_G.parameters(), **trainer_hp)
animator = d2l.Animator(xlabel='epoch', ylabel='loss',
xlim=[1, num_epochs], nrows=2, figsize=(5, 5),
legend=['discriminator', 'generator'])
animator.fig.subplots_adjust(hspace=0.3)
for epoch in range(1, num_epochs + 1):
# Train one epoch
timer = d2l.Timer()
metric = d2l.Accumulator(3) # loss_D, loss_G, num_examples
for X, _ in data_iter:
batch_size = X.shape[0]
Z = torch.normal(0, 1, size=(batch_size, latent_dim, 1, 1))
X, Z = X.to(device), Z.to(device)
metric.add(d2l.update_D(X, Z, net_D, net_G, loss, trainer_D),
d2l.update_G(Z, net_D, net_G, loss, trainer_G),
batch_size)
# Show generated examples
Z = torch.normal(0, 1, size=(21, latent_dim, 1, 1), device=device)
# Normalize the synthetic data to N(0, 1)
fake_x = net_G(Z).permute(0, 2, 3, 1) / 2 + 0.5
imgs = torch.cat(
[torch.cat([
fake_x[i * 7 + j].cpu().detach() for j in range(7)], dim=1)
for i in range(len(fake_x)//7)], dim=0)
animator.axes[1].cla()
animator.axes[1].imshow(imgs)
# Show the losses
loss_D, loss_G = metric[0] / metric[2], metric[1] / metric[2]
animator.add(epoch, (loss_D, loss_G))
print(f'loss_D {loss_D:.3f}, loss_G {loss_G:.3f}, '
f'{metric[2] / timer.stop():.1f} examples/sec on {str(device)}')
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def train(net_D, net_G, data_iter, num_epochs, lr, latent_dim,
device=d2l.try_gpu()):
loss = tf.keras.losses.BinaryCrossentropy(
from_logits=True, reduction=tf.keras.losses.Reduction.SUM)
for w in net_D.trainable_variables:
w.assign(tf.random.normal(mean=0, stddev=0.02, shape=w.shape))
for w in net_G.trainable_variables:
w.assign(tf.random.normal(mean=0, stddev=0.02, shape=w.shape))
optimizer_hp = {"lr": lr, "beta_1": 0.5, "beta_2": 0.999}
optimizer_D = tf.keras.optimizers.Adam(**optimizer_hp)
optimizer_G = tf.keras.optimizers.Adam(**optimizer_hp)
animator = d2l.Animator(xlabel='epoch', ylabel='loss',
xlim=[1, num_epochs], nrows=2, figsize=(5, 5),
legend=['discriminator', 'generator'])
animator.fig.subplots_adjust(hspace=0.3)
for epoch in range(1, num_epochs + 1):
# Train one epoch
timer = d2l.Timer()
metric = d2l.Accumulator(3) # loss_D, loss_G, num_examples
for X, _ in data_iter:
batch_size = X.shape[0]
Z = tf.random.normal(mean=0, stddev=1,
shape=(batch_size, 1, 1, latent_dim))
metric.add(d2l.update_D(X, Z, net_D, net_G, loss, optimizer_D),
d2l.update_G(Z, net_D, net_G, loss, optimizer_G),
batch_size)
# Show generated examples
Z = tf.random.normal(mean=0, stddev=1, shape=(21, 1, 1, latent_dim))
# Normalize the synthetic data to N(0, 1)
fake_x = net_G(Z) / 2 + 0.5
imgs = tf.concat([tf.concat([fake_x[i * 7 + j] for j in range(7)],
axis=1)
for i in range(len(fake_x) // 7)], axis=0)
animator.axes[1].cla()
animator.axes[1].imshow(imgs)
# Show the losses
loss_D, loss_G = metric[0] / metric[2], metric[1] / metric[2]
animator.add(epoch, (loss_D, loss_G))
print(f'loss_D {loss_D:.3f}, loss_G {loss_G:.3f}, '
f'{metric[2] / timer.stop():.1f} examples/sec on {str(device)}')
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latent_dim, lr, num_epochs = 100, 0.005, 20
train(net_D, net_G, data_iter, num_epochs, lr, latent_dim)
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:class: output
loss_D 0.383, loss_G 3.322, 2592.6 examples/sec on gpu(0)
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latent_dim, lr, num_epochs = 100, 0.005, 20
train(net_D, net_G, data_iter, num_epochs, lr, latent_dim)
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loss_D 0.154, loss_G 7.906, 1062.5 examples/sec on cuda:0
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latent_dim, lr, num_epochs = 100, 0.0005, 40
train(net_D, net_G, data_iter, num_epochs, lr, latent_dim)
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:class: output
loss_D 0.223, loss_G 3.809, 2240.4 examples/sec on
.. figure:: output_dcgan_2541de_189_1.svg
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`Discussions `__
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`Discussions `__
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