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networks.py
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networks.py
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# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# This work is licensed under the Creative Commons Attribution-NonCommercial
# 4.0 International License. To view a copy of this license, visit
# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
import numpy as np
import tensorflow as tf
# NOTE: Do not import any application-specific modules here!
#----------------------------------------------------------------------------
def lerp(a, b, t): return a + (b - a) * t
def lerp_clip(a, b, t): return a + (b - a) * tf.clip_by_value(t, 0.0, 1.0)
def cset(cur_lambda, new_cond, new_lambda): return lambda: tf.cond(new_cond, new_lambda, cur_lambda)
#----------------------------------------------------------------------------
# Get/create weight tensor for a convolutional or fully-connected layer.
def get_weight(shape, gain=np.sqrt(2), use_wscale=False, fan_in=None):
if fan_in is None: fan_in = np.prod(shape[:-1])
std = gain / np.sqrt(fan_in) # He init
if use_wscale:
wscale = tf.constant(np.float32(std), name='wscale')
return tf.get_variable('weight', shape=shape, initializer=tf.initializers.random_normal()) * wscale
else:
return tf.get_variable('weight', shape=shape, initializer=tf.initializers.random_normal(0, std))
#----------------------------------------------------------------------------
# Fully-connected layer.
def dense(x, fmaps, gain=np.sqrt(2), use_wscale=False):
if len(x.shape) > 2:
x = tf.reshape(x, [-1, np.prod([d.value for d in x.shape[1:]])])
w = get_weight([x.shape[1].value, fmaps], gain=gain, use_wscale=use_wscale)
w = tf.cast(w, x.dtype)
return tf.matmul(x, w)
#----------------------------------------------------------------------------
# Convolutional layer.
def conv2d(x, fmaps, kernel, gain=np.sqrt(2), use_wscale=False):
assert kernel >= 1 and kernel % 2 == 1
w = get_weight([kernel, kernel, x.shape[1].value, fmaps], gain=gain, use_wscale=use_wscale)
w = tf.cast(w, x.dtype)
return tf.nn.conv2d(x, w, strides=[1,1,1,1], padding='SAME', data_format='NCHW')
#----------------------------------------------------------------------------
# Apply bias to the given activation tensor.
def apply_bias(x):
b = tf.get_variable('bias', shape=[x.shape[1]], initializer=tf.initializers.zeros())
b = tf.cast(b, x.dtype)
if len(x.shape) == 2:
return x + b
else:
return x + tf.reshape(b, [1, -1, 1, 1])
#----------------------------------------------------------------------------
# Leaky ReLU activation. Same as tf.nn.leaky_relu, but supports FP16.
def leaky_relu(x, alpha=0.2):
with tf.name_scope('LeakyRelu'):
alpha = tf.constant(alpha, dtype=x.dtype, name='alpha')
return tf.maximum(x * alpha, x)
#----------------------------------------------------------------------------
# Nearest-neighbor upscaling layer.
def upscale2d(x, factor=2):
assert isinstance(factor, int) and factor >= 1
if factor == 1: return x
with tf.variable_scope('Upscale2D'):
s = x.shape
x = tf.reshape(x, [-1, s[1], s[2], 1, s[3], 1])
x = tf.tile(x, [1, 1, 1, factor, 1, factor])
x = tf.reshape(x, [-1, s[1], s[2] * factor, s[3] * factor])
return x
#----------------------------------------------------------------------------
# Fused upscale2d + conv2d.
# Faster and uses less memory than performing the operations separately.
def upscale2d_conv2d(x, fmaps, kernel, gain=np.sqrt(2), use_wscale=False):
assert kernel >= 1 and kernel % 2 == 1
w = get_weight([kernel, kernel, fmaps, x.shape[1].value], gain=gain, use_wscale=use_wscale, fan_in=(kernel**2)*x.shape[1].value)
w = tf.pad(w, [[1,1], [1,1], [0,0], [0,0]], mode='CONSTANT')
w = tf.add_n([w[1:, 1:], w[:-1, 1:], w[1:, :-1], w[:-1, :-1]])
w = tf.cast(w, x.dtype)
os = [tf.shape(x)[0], fmaps, x.shape[2] * 2, x.shape[3] * 2]
return tf.nn.conv2d_transpose(x, w, os, strides=[1,1,2,2], padding='SAME', data_format='NCHW')
#----------------------------------------------------------------------------
# Box filter downscaling layer.
def downscale2d(x, factor=2):
assert isinstance(factor, int) and factor >= 1
if factor == 1: return x
with tf.variable_scope('Downscale2D'):
ksize = [1, 1, factor, factor]
return tf.nn.avg_pool(x, ksize=ksize, strides=ksize, padding='VALID', data_format='NCHW') # NOTE: requires tf_config['graph_options.place_pruned_graph'] = True
#----------------------------------------------------------------------------
# Fused conv2d + downscale2d.
# Faster and uses less memory than performing the operations separately.
def conv2d_downscale2d(x, fmaps, kernel, gain=np.sqrt(2), use_wscale=False):
assert kernel >= 1 and kernel % 2 == 1
w = get_weight([kernel, kernel, x.shape[1].value, fmaps], gain=gain, use_wscale=use_wscale)
w = tf.pad(w, [[1,1], [1,1], [0,0], [0,0]], mode='CONSTANT')
w = tf.add_n([w[1:, 1:], w[:-1, 1:], w[1:, :-1], w[:-1, :-1]]) * 0.25
w = tf.cast(w, x.dtype)
return tf.nn.conv2d(x, w, strides=[1,1,2,2], padding='SAME', data_format='NCHW')
#----------------------------------------------------------------------------
# Pixelwise feature vector normalization.
def pixel_norm(x, epsilon=1e-8):
with tf.variable_scope('PixelNorm'):
return x * tf.rsqrt(tf.reduce_mean(tf.square(x), axis=1, keep_dims=True) + epsilon)
#----------------------------------------------------------------------------
# Minibatch standard deviation.
def minibatch_stddev_layer(x, group_size=4):
with tf.variable_scope('MinibatchStddev'):
group_size = tf.minimum(group_size, tf.shape(x)[0]) # Minibatch must be divisible by (or smaller than) group_size.
s = x.shape # [NCHW] Input shape.
y = tf.reshape(x, [group_size, -1, s[1], s[2], s[3]]) # [GMCHW] Split minibatch into M groups of size G.
y = tf.cast(y, tf.float32) # [GMCHW] Cast to FP32.
y -= tf.reduce_mean(y, axis=0, keep_dims=True) # [GMCHW] Subtract mean over group.
y = tf.reduce_mean(tf.square(y), axis=0) # [MCHW] Calc variance over group.
y = tf.sqrt(y + 1e-8) # [MCHW] Calc stddev over group.
y = tf.reduce_mean(y, axis=[1,2,3], keep_dims=True) # [M111] Take average over fmaps and pixels.
y = tf.cast(y, x.dtype) # [M111] Cast back to original data type.
y = tf.tile(y, [group_size, 1, s[2], s[3]]) # [N1HW] Replicate over group and pixels.
return tf.concat([x, y], axis=1) # [NCHW] Append as new fmap.
#----------------------------------------------------------------------------
# Generator network used in the paper.
def G_paper(
latents_in, # First input: Latent vectors [minibatch, latent_size].
labels_in, # Second input: Labels [minibatch, label_size].
num_channels = 1, # Number of output color channels. Overridden based on dataset.
resolution = 32, # Output resolution. Overridden based on dataset.
label_size = 0, # Dimensionality of the labels, 0 if no labels. Overridden based on dataset.
fmap_base = 8192, # Overall multiplier for the number of feature maps.
fmap_decay = 1.0, # log2 feature map reduction when doubling the resolution.
fmap_max = 512, # Maximum number of feature maps in any layer.
latent_size = None, # Dimensionality of the latent vectors. None = min(fmap_base, fmap_max).
normalize_latents = True, # Normalize latent vectors before feeding them to the network?
use_wscale = True, # Enable equalized learning rate?
use_pixelnorm = True, # Enable pixelwise feature vector normalization?
pixelnorm_epsilon = 1e-8, # Constant epsilon for pixelwise feature vector normalization.
use_leakyrelu = True, # True = leaky ReLU, False = ReLU.
dtype = 'float32', # Data type to use for activations and outputs.
fused_scale = True, # True = use fused upscale2d + conv2d, False = separate upscale2d layers.
structure = None, # 'linear' = human-readable, 'recursive' = efficient, None = select automatically.
is_template_graph = False, # True = template graph constructed by the Network class, False = actual evaluation.
**kwargs): # Ignore unrecognized keyword args.
resolution_log2 = int(np.log2(resolution))
assert resolution == 2**resolution_log2 and resolution >= 4
def nf(stage): return min(int(fmap_base / (2.0 ** (stage * fmap_decay))), fmap_max)
def PN(x): return pixel_norm(x, epsilon=pixelnorm_epsilon) if use_pixelnorm else x
if latent_size is None: latent_size = nf(0)
if structure is None: structure = 'linear' if is_template_graph else 'recursive'
act = leaky_relu if use_leakyrelu else tf.nn.relu
latents_in.set_shape([None, latent_size])
labels_in.set_shape([None, label_size])
combo_in = tf.cast(tf.concat([latents_in, labels_in], axis=1), dtype)
lod_in = tf.cast(tf.get_variable('lod', initializer=np.float32(0.0), trainable=False), dtype)
# Building blocks.
def block(x, res): # res = 2..resolution_log2
with tf.variable_scope('%dx%d' % (2**res, 2**res)):
if res == 2: # 4x4
if normalize_latents: x = pixel_norm(x, epsilon=pixelnorm_epsilon)
with tf.variable_scope('Dense'):
x = dense(x, fmaps=nf(res-1)*16, gain=np.sqrt(2)/4, use_wscale=use_wscale) # override gain to match the original Theano implementation
x = tf.reshape(x, [-1, nf(res-1), 4, 4])
x = PN(act(apply_bias(x)))
with tf.variable_scope('Conv'):
x = PN(act(apply_bias(conv2d(x, fmaps=nf(res-1), kernel=3, use_wscale=use_wscale))))
else: # 8x8 and up
if fused_scale:
with tf.variable_scope('Conv0_up'):
x = PN(act(apply_bias(upscale2d_conv2d(x, fmaps=nf(res-1), kernel=3, use_wscale=use_wscale))))
else:
x = upscale2d(x)
with tf.variable_scope('Conv0'):
x = PN(act(apply_bias(conv2d(x, fmaps=nf(res-1), kernel=3, use_wscale=use_wscale))))
with tf.variable_scope('Conv1'):
x = PN(act(apply_bias(conv2d(x, fmaps=nf(res-1), kernel=3, use_wscale=use_wscale))))
return x
def torgb(x, res): # res = 2..resolution_log2
lod = resolution_log2 - res
with tf.variable_scope('ToRGB_lod%d' % lod):
return apply_bias(conv2d(x, fmaps=num_channels, kernel=1, gain=1, use_wscale=use_wscale))
# Linear structure: simple but inefficient.
if structure == 'linear':
x = block(combo_in, 2)
images_out = torgb(x, 2)
for res in range(3, resolution_log2 + 1):
lod = resolution_log2 - res
x = block(x, res)
img = torgb(x, res)
images_out = upscale2d(images_out)
with tf.variable_scope('Grow_lod%d' % lod):
images_out = lerp_clip(img, images_out, lod_in - lod)
# Recursive structure: complex but efficient.
if structure == 'recursive':
def grow(x, res, lod):
y = block(x, res)
img = lambda: upscale2d(torgb(y, res), 2**lod)
if res > 2: img = cset(img, (lod_in > lod), lambda: upscale2d(lerp(torgb(y, res), upscale2d(torgb(x, res - 1)), lod_in - lod), 2**lod))
if lod > 0: img = cset(img, (lod_in < lod), lambda: grow(y, res + 1, lod - 1))
return img()
images_out = grow(combo_in, 2, resolution_log2 - 2)
assert images_out.dtype == tf.as_dtype(dtype)
images_out = tf.identity(images_out, name='images_out')
return images_out
#----------------------------------------------------------------------------
# Discriminator network used in the paper.
def D_paper(
images_in, # Input: Images [minibatch, channel, height, width].
num_channels = 1, # Number of input color channels. Overridden based on dataset.
resolution = 32, # Input resolution. Overridden based on dataset.
label_size = 0, # Dimensionality of the labels, 0 if no labels. Overridden based on dataset.
fmap_base = 8192, # Overall multiplier for the number of feature maps.
fmap_decay = 1.0, # log2 feature map reduction when doubling the resolution.
fmap_max = 512, # Maximum number of feature maps in any layer.
use_wscale = True, # Enable equalized learning rate?
mbstd_group_size = 4, # Group size for the minibatch standard deviation layer, 0 = disable.
dtype = 'float32', # Data type to use for activations and outputs.
fused_scale = True, # True = use fused conv2d + downscale2d, False = separate downscale2d layers.
structure = None, # 'linear' = human-readable, 'recursive' = efficient, None = select automatically
is_template_graph = False, # True = template graph constructed by the Network class, False = actual evaluation.
**kwargs): # Ignore unrecognized keyword args.
resolution_log2 = int(np.log2(resolution))
assert resolution == 2**resolution_log2 and resolution >= 4
def nf(stage): return min(int(fmap_base / (2.0 ** (stage * fmap_decay))), fmap_max)
if structure is None: structure = 'linear' if is_template_graph else 'recursive'
act = leaky_relu
images_in.set_shape([None, num_channels, resolution, resolution])
images_in = tf.cast(images_in, dtype)
lod_in = tf.cast(tf.get_variable('lod', initializer=np.float32(0.0), trainable=False), dtype)
# Building blocks.
def fromrgb(x, res): # res = 2..resolution_log2
with tf.variable_scope('FromRGB_lod%d' % (resolution_log2 - res)):
return act(apply_bias(conv2d(x, fmaps=nf(res-1), kernel=1, use_wscale=use_wscale)))
def block(x, res): # res = 2..resolution_log2
with tf.variable_scope('%dx%d' % (2**res, 2**res)):
if res >= 3: # 8x8 and up
with tf.variable_scope('Conv0'):
x = act(apply_bias(conv2d(x, fmaps=nf(res-1), kernel=3, use_wscale=use_wscale)))
if fused_scale:
with tf.variable_scope('Conv1_down'):
x = act(apply_bias(conv2d_downscale2d(x, fmaps=nf(res-2), kernel=3, use_wscale=use_wscale)))
else:
with tf.variable_scope('Conv1'):
x = act(apply_bias(conv2d(x, fmaps=nf(res-2), kernel=3, use_wscale=use_wscale)))
x = downscale2d(x)
else: # 4x4
if mbstd_group_size > 1:
x = minibatch_stddev_layer(x, mbstd_group_size)
with tf.variable_scope('Conv'):
x = act(apply_bias(conv2d(x, fmaps=nf(res-1), kernel=3, use_wscale=use_wscale)))
with tf.variable_scope('Dense0'):
x = act(apply_bias(dense(x, fmaps=nf(res-2), use_wscale=use_wscale)))
with tf.variable_scope('Dense1'):
x = apply_bias(dense(x, fmaps=1+label_size, gain=1, use_wscale=use_wscale))
return x
# Linear structure: simple but inefficient.
if structure == 'linear':
img = images_in
x = fromrgb(img, resolution_log2)
for res in range(resolution_log2, 2, -1):
lod = resolution_log2 - res
x = block(x, res)
img = downscale2d(img)
y = fromrgb(img, res - 1)
with tf.variable_scope('Grow_lod%d' % lod):
x = lerp_clip(x, y, lod_in - lod)
combo_out = block(x, 2)
# Recursive structure: complex but efficient.
if structure == 'recursive':
def grow(res, lod):
x = lambda: fromrgb(downscale2d(images_in, 2**lod), res)
if lod > 0: x = cset(x, (lod_in < lod), lambda: grow(res + 1, lod - 1))
x = block(x(), res); y = lambda: x
if res > 2: y = cset(y, (lod_in > lod), lambda: lerp(x, fromrgb(downscale2d(images_in, 2**(lod+1)), res - 1), lod_in - lod))
return y()
combo_out = grow(2, resolution_log2 - 2)
assert combo_out.dtype == tf.as_dtype(dtype)
scores_out = tf.identity(combo_out[:, :1], name='scores_out')
labels_out = tf.identity(combo_out[:, 1:], name='labels_out')
return scores_out, labels_out
#----------------------------------------------------------------------------