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seminar-breakout
Commit fe33c833 authored by Yuxin Wu's avatar Yuxin Wu
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Use tf.layers arguments in GAN examples.

parent 4744853b
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Showing with 100 additions and 103 deletions
examples/GAN/BEGAN.py
View file @ fe33c833
......@@ -31,10 +31,10 @@ class Model(GANModelDesc):
@auto_reuse_variable_scope
def decoder(self, z):
l = FullyConnected('fc', z, NF * 8 * 8, nl=tf.identity)
l = FullyConnected('fc', z, NF * 8 * 8)
l = tf.reshape(l, [-1, 8, 8, NF])
with argscope(Conv2D, nl=tf.nn.elu, kernel_shape=3, stride=1):
with argscope(Conv2D, activation=tf.nn.elu, kernel_size=3, strides=1):
l = (LinearWrap(l)
.Conv2D('conv1.1', NF)
.Conv2D('conv1.2', NF)
......@@ -47,12 +47,12 @@ class Model(GANModelDesc):
.tf.image.resize_nearest_neighbor([64, 64], align_corners=True)
.Conv2D('conv4.1', NF)
.Conv2D('conv4.2', NF)
.Conv2D('conv4.3', 3, nl=tf.identity)())
.Conv2D('conv4.3', 3, activation=tf.identity)())
return l
@auto_reuse_variable_scope
def encoder(self, imgs):
with argscope(Conv2D, nl=tf.nn.elu, kernel_shape=3, stride=1):
with argscope(Conv2D, activation=tf.nn.elu, kernel_size=3, strides=1):
l = (LinearWrap(imgs)
.Conv2D('conv1.1', NF)
.Conv2D('conv1.2', NF)
......@@ -70,7 +70,7 @@ class Model(GANModelDesc):
.Conv2D('conv4.1', NF * 4)
.Conv2D('conv4.2', NF * 4)
.FullyConnected('fc', NH, nl=tf.identity)())
.FullyConnected('fc', NH)())
return l
def _build_graph(self, inputs):
......@@ -86,7 +86,7 @@ class Model(GANModelDesc):
tf.summary.image(name, tf.cast(x, tf.uint8), max_outputs=30)
with argscope([Conv2D, FullyConnected],
W_init=tf.truncated_normal_initializer(stddev=0.02)):
kernel_initializer=tf.truncated_normal_initializer(stddev=0.02)):
with tf.variable_scope('gen'):
image_gen = self.decoder(z)
......
examples/GAN/ConditionalGAN-mnist.py
View file @ fe33c833
......@@ -45,16 +45,16 @@ class Model(GANModelDesc):
InputDesc(tf.int32, (None,), 'label')]
def generator(self, z, y):
l = FullyConnected('fc0', tf.concat([z, y], 1), 1024, nl=BNReLU)
l = FullyConnected('fc1', tf.concat([l, y], 1), 64 * 2 * 7 * 7, nl=BNReLU)
l = FullyConnected('fc0', tf.concat([z, y], 1), 1024, activation=BNReLU)
l = FullyConnected('fc1', tf.concat([l, y], 1), 64 * 2 * 7 * 7, activation=BNReLU)
l = tf.reshape(l, [-1, 7, 7, 64 * 2])
y = tf.reshape(y, [-1, 1, 1, 10])
l = tf.concat([l, tf.tile(y, [1, 7, 7, 1])], 3)
l = Deconv2D('deconv1', l, 64 * 2, 5, 2, nl=BNReLU)
l = Conv2DTranspose('deconv1', l, 64 * 2, 5, 2, activation=BNReLU)
l = tf.concat([l, tf.tile(y, [1, 14, 14, 1])], 3)
l = Deconv2D('deconv2', l, 1, 5, 2, nl=tf.identity)
l = Conv2DTranspose('deconv2', l, 1, 5, 2, activation=tf.identity)
l = tf.nn.tanh(l, name='gen')
return l
......@@ -63,7 +63,7 @@ class Model(GANModelDesc):
""" return a (b, 1) logits"""
yv = y
y = tf.reshape(y, [-1, 1, 1, 10])
with argscope(Conv2D, nl=tf.identity, kernel_shape=5, stride=2):
with argscope(Conv2D, kernel_size=5, strides=1):
l = (LinearWrap(imgs)
.ConcatWith(tf.tile(y, [1, 28, 28, 1]), 3)
.Conv2D('conv0', 11)
......@@ -76,12 +76,12 @@ class Model(GANModelDesc):
.apply(batch_flatten)
.ConcatWith(yv, 1)
.FullyConnected('fc1', 1024, nl=tf.identity)
.FullyConnected('fc1', 1024, activation=tf.identity)
.BatchNorm('bn2')
.tf.nn.leaky_relu()
.ConcatWith(yv, 1)
.FullyConnected('fct', 1, nl=tf.identity)())
.FullyConnected('fct', 1, activation=tf.identity)())
return l
def _build_graph(self, inputs):
......@@ -92,8 +92,8 @@ class Model(GANModelDesc):
z = tf.random_uniform([BATCH, 100], -1, 1, name='z_train')
z = tf.placeholder_with_default(z, [None, 100], name='z') # clear the static shape
with argscope([Conv2D, Deconv2D, FullyConnected],
W_init=tf.truncated_normal_initializer(stddev=0.02)):
with argscope([Conv2D, Conv2DTranspose, FullyConnected],
kernel_initializer=tf.truncated_normal_initializer(stddev=0.02)):
with tf.variable_scope('gen'):
image_gen = self.generator(z, y)
tf.summary.image('gen', image_gen, 30)
......
examples/GAN/CycleGAN.py
View file @ fe33c833
......@@ -51,38 +51,38 @@ class Model(GANModelDesc):
input = x
return (LinearWrap(x)
.tf.pad([[0, 0], [0, 0], [1, 1], [1, 1]], mode='SYMMETRIC')
.Conv2D('conv0', chan, padding='VALID')
.Conv2D('conv0', chan, 3, padding='VALID')
.tf.pad([[0, 0], [0, 0], [1, 1], [1, 1]], mode='SYMMETRIC')
.Conv2D('conv1', chan, padding='VALID', nl=tf.identity)
.Conv2D('conv1', chan, 3, padding='VALID', activation=tf.identity)
.InstanceNorm('inorm')()) + input
@auto_reuse_variable_scope
def generator(self, img):
assert img is not None
with argscope([Conv2D, Deconv2D], nl=INReLU, kernel_shape=3):
with argscope([Conv2D, Conv2DTranspose], activation=INReLU):
l = (LinearWrap(img)
.tf.pad([[0, 0], [0, 0], [3, 3], [3, 3]], mode='SYMMETRIC')
.Conv2D('conv0', NF, kernel_shape=7, padding='VALID')
.Conv2D('conv1', NF * 2, stride=2)
.Conv2D('conv2', NF * 4, stride=2)())
.Conv2D('conv0', NF, 7, padding='VALID')
.Conv2D('conv1', NF * 2, 3, strides=2)
.Conv2D('conv2', NF * 4, 3, strides=2)())
for k in range(9):
l = Model.build_res_block(l, 'res{}'.format(k), NF * 4, first=(k == 0))
l = (LinearWrap(l)
.Deconv2D('deconv0', NF * 2, stride=2)
.Deconv2D('deconv1', NF * 1, stride=2)
.Conv2DTranspose('deconv0', NF * 2, 3, strides=2)
.Conv2DTranspose('deconv1', NF * 1, 3, strides=2)
.tf.pad([[0, 0], [0, 0], [3, 3], [3, 3]], mode='SYMMETRIC')
.Conv2D('convlast', 3, kernel_shape=7, padding='VALID', nl=tf.tanh, use_bias=True)())
.Conv2D('convlast', 3, 7, padding='VALID', activation=tf.tanh, use_bias=True)())
return l
@auto_reuse_variable_scope
def discriminator(self, img):
with argscope(Conv2D, nl=INLReLU, kernel_shape=4, stride=2):
with argscope(Conv2D, activation=INLReLU, kernel_size=4, strides=2):
l = (LinearWrap(img)
.Conv2D('conv0', NF, nl=tf.nn.leaky_relu)
.Conv2D('conv0', NF, activation=tf.nn.leaky_relu)
.Conv2D('conv1', NF * 2)
.Conv2D('conv2', NF * 4)
.Conv2D('conv3', NF * 8, stride=1)
.Conv2D('conv4', 1, stride=1, nl=tf.identity, use_bias=True)())
.Conv2D('conv3', NF * 8, strides=1)
.Conv2D('conv4', 1, strides=1, activation=tf.identity, use_bias=True)())
return l
def _build_graph(self, inputs):
......@@ -101,9 +101,9 @@ class Model(GANModelDesc):
tf.summary.image(name, im, max_outputs=50)
# use the initializers from torch
with argscope([Conv2D, Deconv2D], use_bias=False,
W_init=tf.random_normal_initializer(stddev=0.02)), \
argscope([Conv2D, Deconv2D, InstanceNorm], data_format='NCHW'):
with argscope([Conv2D, Conv2DTranspose], use_bias=False,
kernel_initializer=tf.random_normal_initializer(stddev=0.02)), \
argscope([Conv2D, Conv2DTranspose, InstanceNorm], data_format='channels_first'):
with tf.variable_scope('gen'):
with tf.variable_scope('B'):
AB = self.generator(A)
......@@ -211,10 +211,11 @@ if __name__ == '__main__':
logger.auto_set_dir()
data = get_data(args.data)
data = PrintData(data)
df = get_data(args.data)
df = PrintData(df)
data = StagingInput(QueueInput(df))
GANTrainer(QueueInput(data), Model()).train_with_defaults(
GANTrainer(data, Model()).train_with_defaults(
callbacks=[
ModelSaver(),
ScheduledHyperParamSetter(
......
examples/GAN/DCGAN.py
View file @ fe33c833
......@@ -46,14 +46,14 @@ class Model(GANModelDesc):
def generator(self, z):
""" return an image generated from z"""
nf = 64
l = FullyConnected('fc0', z, nf * 8 * 4 * 4, nl=tf.identity)
l = FullyConnected('fc0', z, nf * 8 * 4 * 4, activation=tf.identity)
l = tf.reshape(l, [-1, 4, 4, nf * 8])
l = BNReLU(l)
with argscope(Deconv2D, nl=BNReLU, kernel_shape=4, stride=2):
l = Deconv2D('deconv1', l, nf * 4)
l = Deconv2D('deconv2', l, nf * 2)
l = Deconv2D('deconv3', l, nf)
l = Deconv2D('deconv4', l, 3, nl=tf.identity)
with argscope(Conv2DTranspose, activation=BNReLU, kernel_size=4, strides=2):
l = Conv2DTranspose('deconv1', l, nf * 4)
l = Conv2DTranspose('deconv2', l, nf * 2)
l = Conv2DTranspose('deconv3', l, nf)
l = Conv2DTranspose('deconv4', l, 3, activation=tf.identity)
l = tf.tanh(l, name='gen')
return l
......@@ -61,9 +61,9 @@ class Model(GANModelDesc):
def discriminator(self, imgs):
""" return a (b, 1) logits"""
nf = 64
with argscope(Conv2D, nl=tf.identity, kernel_shape=4, stride=2):
with argscope(Conv2D, kernel_size=4, strides=2):
l = (LinearWrap(imgs)
.Conv2D('conv0', nf, nl=tf.nn.leaky_relu)
.Conv2D('conv0', nf, activation=tf.nn.leaky_relu)
.Conv2D('conv1', nf * 2)
.BatchNorm('bn1')
.tf.nn.leaky_relu()
......@@ -73,7 +73,7 @@ class Model(GANModelDesc):
.Conv2D('conv3', nf * 8)
.BatchNorm('bn3')
.tf.nn.leaky_relu()
.FullyConnected('fct', 1, nl=tf.identity)())
.FullyConnected('fct', 1)())
return l
def _build_graph(self, inputs):
......@@ -83,8 +83,8 @@ class Model(GANModelDesc):
z = tf.random_uniform([self.batch, self.zdim], -1, 1, name='z_train')
z = tf.placeholder_with_default(z, [None, self.zdim], name='z')
with argscope([Conv2D, Deconv2D, FullyConnected],
W_init=tf.truncated_normal_initializer(stddev=0.02)):
with argscope([Conv2D, Conv2DTranspose, FullyConnected],
kernel_initializer=tf.truncated_normal_initializer(stddev=0.02)):
with tf.variable_scope('gen'):
image_gen = self.generator(z)
tf.summary.image('generated-samples', image_gen, max_outputs=30)
......
examples/GAN/DiscoGAN-CelebA.py
View file @ fe33c833
......@@ -21,13 +21,6 @@ from GAN import SeparateGANTrainer, GANModelDesc
3. Start training gender transfer:
./DiscoGAN-CelebA.py --data /path/to/img_align_celeba --style-A Male
4. Visualize the gender conversion images in tensorboard.
With TF1.0.1, cuda 8.0, cudnn 5.1.10,
the training on 64x64 images of batch 64 runs 5.4 it/s on Tesla M40.
This is 2.4x as fast as the original PyTorch implementation.
The cause is probably that in the torch implementation,
a backward() computes gradients for ALL parameters, which is not necessary in GAN.
"""
SHAPE = 64
......@@ -48,29 +41,29 @@ class Model(GANModelDesc):
@auto_reuse_variable_scope
def generator(self, img):
assert img is not None
with argscope([Conv2D, Deconv2D],
nl=BNLReLU, kernel_shape=4, stride=2), \
argscope(Deconv2D, nl=BNReLU):
with argscope([Conv2D, Conv2DTranspose],
activation=BNLReLU, kernel_size=4, strides=2), \
argscope(Conv2DTranspose, activation=BNReLU):
l = (LinearWrap(img)
.Conv2D('conv0', NF, nl=tf.nn.leaky_relu)
.Conv2D('conv0', NF, activation=tf.nn.leaky_relu)
.Conv2D('conv1', NF * 2)
.Conv2D('conv2', NF * 4)
.Conv2D('conv3', NF * 8)
.Deconv2D('deconv0', NF * 4)
.Deconv2D('deconv1', NF * 2)
.Deconv2D('deconv2', NF * 1)
.Deconv2D('deconv3', 3, nl=tf.identity)
.Conv2DTranspose('deconv0', NF * 4)
.Conv2DTranspose('deconv1', NF * 2)
.Conv2DTranspose('deconv2', NF * 1)
.Conv2DTranspose('deconv3', 3, activation=tf.identity)
.tf.sigmoid()())
return l
@auto_reuse_variable_scope
def discriminator(self, img):
with argscope(Conv2D, nl=BNLReLU, kernel_shape=4, stride=2):
l = Conv2D('conv0', img, NF, nl=tf.nn.leaky_relu)
with argscope(Conv2D, activation=BNLReLU, kernel_size=4, strides=2):
l = Conv2D('conv0', img, NF, activation=tf.nn.leaky_relu)
relu1 = Conv2D('conv1', l, NF * 2)
relu2 = Conv2D('conv2', relu1, NF * 4)
relu3 = Conv2D('conv3', relu2, NF * 8)
logits = FullyConnected('fc', relu3, 1, nl=tf.identity)
logits = FullyConnected('fc', relu3, 1, activation=tf.identity)
return logits, [relu1, relu2, relu3]
def get_feature_match_loss(self, feats_real, feats_fake):
......@@ -91,11 +84,11 @@ class Model(GANModelDesc):
B = tf.transpose(B / 255.0, [0, 3, 1, 2])
# use the torch initializers
with argscope([Conv2D, Deconv2D, FullyConnected],
W_init=tf.variance_scaling_initializer(scale=0.333, distribution='uniform'),
with argscope([Conv2D, Conv2DTranspose, FullyConnected],
kernel_initializer=tf.variance_scaling_initializer(scale=0.333, distribution='uniform'),
use_bias=False), \
argscope(BatchNorm, gamma_init=tf.random_uniform_initializer()), \
argscope([Conv2D, Deconv2D, BatchNorm], data_format='NCHW'):
argscope([Conv2D, Conv2DTranspose, BatchNorm], data_format='NCHW'):
with tf.variable_scope('gen'):
with tf.variable_scope('B'):
AB = self.generator(A)
......@@ -194,7 +187,7 @@ def get_celebA_data(datadir, styleA, styleB=None):
imgaug.Resize(64)]
df = AugmentImageComponents(df, augs, (0, 1))
df = BatchData(df, BATCH)
df = PrefetchDataZMQ(df, 1)
df = PrefetchDataZMQ(df, 3)
return df
......
examples/GAN/Image2Image.py
View file @ fe33c833
......@@ -74,54 +74,54 @@ class Model(GANModelDesc):
with argscope(BatchNorm, use_local_stat=True), \
argscope(Dropout, is_training=True):
# always use local stat for BN, and apply dropout even in testing
with argscope(Conv2D, kernel_shape=4, stride=2, nl=BNLReLU):
e1 = Conv2D('conv1', imgs, NF, nl=tf.nn.leaky_relu)
with argscope(Conv2D, kernel_size=4, strides=2, activation=BNLReLU):
e1 = Conv2D('conv1', imgs, NF, activation=tf.nn.leaky_relu)
e2 = Conv2D('conv2', e1, NF * 2)
e3 = Conv2D('conv3', e2, NF * 4)
e4 = Conv2D('conv4', e3, NF * 8)
e5 = Conv2D('conv5', e4, NF * 8)
e6 = Conv2D('conv6', e5, NF * 8)
e7 = Conv2D('conv7', e6, NF * 8)
e8 = Conv2D('conv8', e7, NF * 8, nl=BNReLU) # 1x1
with argscope(Deconv2D, nl=BNReLU, kernel_shape=4, stride=2):
e8 = Conv2D('conv8', e7, NF * 8, activation=BNReLU) # 1x1
with argscope(Conv2DTranspose, activation=BNReLU, kernel_size=4, strides=2):
return (LinearWrap(e8)
.Deconv2D('deconv1', NF * 8)
.Conv2DTranspose('deconv1', NF * 8)
.Dropout()
.ConcatWith(e7, 3)
.Deconv2D('deconv2', NF * 8)
.Conv2DTranspose('deconv2', NF * 8)
.Dropout()
.ConcatWith(e6, 3)
.Deconv2D('deconv3', NF * 8)
.Conv2DTranspose('deconv3', NF * 8)
.Dropout()
.ConcatWith(e5, 3)
.Deconv2D('deconv4', NF * 8)
.Conv2DTranspose('deconv4', NF * 8)
.ConcatWith(e4, 3)
.Deconv2D('deconv5', NF * 4)
.Conv2DTranspose('deconv5', NF * 4)
.ConcatWith(e3, 3)
.Deconv2D('deconv6', NF * 2)
.Conv2DTranspose('deconv6', NF * 2)
.ConcatWith(e2, 3)
.Deconv2D('deconv7', NF * 1)
.Conv2DTranspose('deconv7', NF * 1)
.ConcatWith(e1, 3)
.Deconv2D('deconv8', OUT_CH, nl=tf.tanh)())
.Conv2DTranspose('deconv8', OUT_CH, activation=tf.tanh)())
@auto_reuse_variable_scope
def discriminator(self, inputs, outputs):
""" return a (b, 1) logits"""
l = tf.concat([inputs, outputs], 3)
with argscope(Conv2D, kernel_shape=4, stride=2, nl=BNLReLU):
with argscope(Conv2D, kernel_size=4, strides=2, activation=BNLReLU):
l = (LinearWrap(l)
.Conv2D('conv0', NF, nl=tf.nn.leaky_relu)
.Conv2D('conv0', NF, activation=tf.nn.leaky_relu)
.Conv2D('conv1', NF * 2)
.Conv2D('conv2', NF * 4)
.Conv2D('conv3', NF * 8, stride=1, padding='VALID')
.Conv2D('convlast', 1, stride=1, padding='VALID', nl=tf.identity)())
.Conv2D('conv3', NF * 8, strides=1, padding='VALID')
.Conv2D('convlast', 1, strides=1, padding='VALID', activation=tf.identity)())
return l
def _build_graph(self, inputs):
input, output = inputs
input, output = input / 128.0 - 1, output / 128.0 - 1
with argscope([Conv2D, Deconv2D], W_init=tf.truncated_normal_initializer(stddev=0.02)):
with argscope([Conv2D, Conv2DTranspose], kernel_initializer=tf.truncated_normal_initializer(stddev=0.02)):
with tf.variable_scope('gen'):
fake_output = self.generator(input)
with tf.variable_scope('discrim'):
......
examples/GAN/Improved-WGAN.py
View file @ fe33c833
......@@ -26,9 +26,9 @@ class Model(DCGAN.Model):
@auto_reuse_variable_scope
def discriminator(self, imgs):
nf = 64
with argscope(Conv2D, nl=tf.identity, kernel_shape=4, stride=2):
with argscope(Conv2D, activation=tf.identity, kernel_size=4, strides=2):
l = (LinearWrap(imgs)
.Conv2D('conv0', nf, nl=tf.nn.leaky_relu)
.Conv2D('conv0', nf, activation=tf.nn.leaky_relu)
.Conv2D('conv1', nf * 2)
.LayerNorm('ln1')
.tf.nn.leaky_relu()
......@@ -38,7 +38,7 @@ class Model(DCGAN.Model):
.Conv2D('conv3', nf * 8)
.LayerNorm('ln3')
.tf.nn.leaky_relu()
.FullyConnected('fct', 1, nl=tf.identity)())
.FullyConnected('fct', 1, activation=tf.identity)())
return tf.reshape(l, [-1])
def _build_graph(self, inputs):
......@@ -48,8 +48,8 @@ class Model(DCGAN.Model):
z = tf.random_normal([self.batch, self.zdim], name='z_train')
z = tf.placeholder_with_default(z, [None, self.zdim], name='z')
with argscope([Conv2D, Deconv2D, FullyConnected],
W_init=tf.truncated_normal_initializer(stddev=0.02)):
with argscope([Conv2D, Conv2DTranspose, FullyConnected],
kernel_initializer=tf.truncated_normal_initializer(stddev=0.02)):
with tf.variable_scope('gen'):
image_gen = self.generator(z)
tf.summary.image('generated-samples', image_gen, max_outputs=30)
......
examples/GAN/InfoGAN-mnist.py
View file @ fe33c833
......@@ -109,33 +109,33 @@ class Model(GANModelDesc):
return [InputDesc(tf.float32, (None, 28, 28), 'input')]
def generator(self, z):
l = FullyConnected('fc0', z, 1024, nl=BNReLU)
l = FullyConnected('fc1', l, 128 * 7 * 7, nl=BNReLU)
l = FullyConnected('fc0', z, 1024, activation=BNReLU)
l = FullyConnected('fc1', l, 128 * 7 * 7, activation=BNReLU)
l = tf.reshape(l, [-1, 7, 7, 128])
l = Deconv2D('deconv1', l, 64, 4, 2, nl=BNReLU)
l = Deconv2D('deconv2', l, 1, 4, 2, nl=tf.identity)
l = Conv2DTranspose('deconv1', l, 64, 4, 2, activation=BNReLU)
l = Conv2DTranspose('deconv2', l, 1, 4, 2, activation=tf.identity)
l = tf.sigmoid(l, name='gen')
return l
@auto_reuse_variable_scope
def discriminator(self, imgs):
with argscope(Conv2D, nl=tf.identity, kernel_shape=4, stride=2):
with argscope(Conv2D, kernel_size=4, strides=2):
l = (LinearWrap(imgs)
.Conv2D('conv0', 64)
.tf.nn.leaky_relu()
.Conv2D('conv1', 128)
.BatchNorm('bn1')
.tf.nn.leaky_relu()
.FullyConnected('fc1', 1024, nl=tf.identity)
.FullyConnected('fc1', 1024)
.BatchNorm('bn2')
.tf.nn.leaky_relu()())
logits = FullyConnected('fct', l, 1, nl=tf.identity)
logits = FullyConnected('fct', l, 1)
encoder = (LinearWrap(l)
.FullyConnected('fce1', 128, nl=tf.identity)
.FullyConnected('fce1', 128)
.BatchNorm('bne')
.tf.nn.leaky_relu()
.FullyConnected('fce-out', DIST_PARAM_DIM, nl=tf.identity)())
.FullyConnected('fce-out', DIST_PARAM_DIM)())
return logits, encoder
def _build_graph(self, inputs):
......@@ -148,8 +148,8 @@ class Model(GANModelDesc):
tf.random_uniform([BATCH, NOISE_DIM], -1, 1), 0, name='z_noise')
z = tf.concat([zc, z_noise], 1, name='z')
with argscope([Conv2D, Deconv2D, FullyConnected],
W_init=tf.truncated_normal_initializer(stddev=0.02)):
with argscope([Conv2D, Conv2DTranspose, FullyConnected],
kernel_initializer=tf.truncated_normal_initializer(stddev=0.02)):
with tf.variable_scope('gen'):
fake_sample = self.generator(z)
fake_sample_viz = tf.cast((fake_sample) * 255.0, tf.uint8, name='viz')
......
tensorpack/models/conv2d.py
View file @ fe33c833
......@@ -9,7 +9,7 @@ from ..tfutils.common import get_tf_version_number
from ..utils.argtools import shape2d, shape4d, get_data_format
from .tflayer import rename_get_variable, convert_to_tflayer_args
__all__ = ['Conv2D', 'Deconv2D']
__all__ = ['Conv2D', 'Deconv2D', 'Conv2DTranspose']
@layer_register(log_shape=True)
......@@ -125,7 +125,7 @@ def Conv2D(
'kernel_shape': 'kernel_size',
'stride': 'strides',
})
def Deconv2D(
def Conv2DTranspose(
inputs,
filters,
kernel_size,
......@@ -172,3 +172,6 @@ def Deconv2D(
if use_bias:
ret.variables.b = layer.bias
return tf.identity(ret, name='output')
Deconv2D = Conv2DTranspose
tensorpack/models/fc.py
View file @ fe33c833
......@@ -28,7 +28,7 @@ def FullyConnected(
activity_regularizer=None):
"""
A wrapper around `tf.layers.Dense`.
One differences to maintain backward-compatibility:
One difference to maintain backward-compatibility:
Default weight initializer is variance_scaling_initializer(2.0).
Variable Names:
......
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