def nn_init(self, layer, initializer_option):
if initializer_option is None:
return
if type(initializer_option) == pyparsing.ParseResults and type(
initializer_option[0]) == hypergan.parser.Pattern:
args = [initializer_option[0].layer_name
] + initializer_option[0].args
options = hc.Config(initializer_option[0].options)
else:
args = [initializer_option]
options = hc.Config({})
layer_data = layer.weight.data
if args[0] == "uniform":
a = float(args[1])
b = float(args[2])
nn.init.uniform_(layer_data, a, b)
elif args[0] == "normal":
mean = float(args[1])
std = float(args[2])
nn.init.normal_(layer_data, mean, std)
elif args[0] == "constant":
val = float(args[1])
nn.init.constant_(layer_data, val)
elif args[0] == "ones":
nn.init.ones_(layer_data)
elif args[0] == "zeros":
nn.init.zeros_(layer_data)
elif args[0] == "eye":
nn.init.eye_(layer_data)
elif args[0] == "dirac":
nn.init.dirac_(layer_data)
elif args[0] == "xavier_uniform":
gain = nn.init.calculate_gain(options.gain or "relu")
nn.init.xavier_uniform_(layer_data, gain=gain)
elif args[0] == "xavier_normal":
gain = nn.init.calculate_gain(options.gain or "relu")
nn.init.xavier_normal_(layer_data, gain=gain)
elif args[0] == "kaiming_uniform":
a = 0 #TODO wrong
nn.init.kaiming_uniform_(layer_data,
mode=(options.mode or "fan_in"),
nonlinearity=options.gain or "relu")
elif args[0] == "kaiming_normal":
a = 0 #TODO wrong
nn.init.kaiming_normal_(layer_data,
mode=(options.mode or "fan_in"),
nonlinearity=options.gain or "relu")
elif args[0] == "orthogonal":
if "gain" in options:
gain = nn.init.calculate_gain(options["gain"])
else:
gain = 1
nn.init.orthogonal_(layer_data, gain=gain)
else:
print("Warning: No initializer found for " + args[0])
if "gain" in options:
layer_data.mul_(nn.init.calculate_gain(options["gain"]))
return NoOp()
def __init__(self, gan, config, name=None, input=None, reuse=None, x=None, g=None, features=[], skip_connections=[]):
self.layers = []
self.skip_connections = skip_connections
self.layer_options = {}
self.layer_ops = {
"relational": self.layer_relational,
"minibatch": self.layer_minibatch,
"phase_shift": self.layer_phase_shift,
"conv": self.layer_conv,
"zeros": self.layer_zeros,
"zeros_like": self.layer_zeros_like,
"control": self.layer_controls,
"linear": self.layer_linear,
"identity": self.layer_identity,
"double_resolution": self.layer_double_resolution,
"attention": self.layer_attention,
"adaptive_instance_norm": self.layer_adaptive_instance_norm,
"subpixel": self.layer_subpixel,
"pixel_norm": self.layer_pixel_norm,
"gram_matrix": self.layer_gram_matrix,
"unpool": self.layer_unpool,
"crop": self.layer_crop,
"resize_images": self.layer_resize_images,
"concat_noise": self.layer_noise,
"concat": self.layer_concat,
"variational_noise": self.layer_variational_noise,
"variational": self.layer_variational,
"noise": self.layer_noise,
"pad": self.layer_pad,
"fractional_avg_pool": self.layer_fractional_avg_pool,
"two_sample_stack": self.layer_two_sample_stack,
"bicubic_conv": self.layer_bicubic_conv,
"conv_double": self.layer_conv_double,
"conv_reshape": self.layer_conv_reshape,
"reshape": self.layer_reshape,
"conv_dts": self.layer_conv_dts,
"deconv": self.layer_deconv,
"resize_conv": self.layer_resize_conv,
"squash": self.layer_squash,
"add": self.layer_add,
"avg_pool": self.layer_avg_pool,
"reference": self.layer_reference,
"image_statistics": self.layer_image_statistics,
"combine_features": self.layer_combine_features,
"resnet": self.layer_resnet,
"layer_filter": self.layer_filter,
"turing_test": self.layer_turing_test,
"activation": self.layer_activation,
"knowledge_base": self.layer_knowledge_base,
"const": self.layer_const,
"progressive_replace": self.layer_progressive_replace
}
self.features = features
self.controls = {}
self.named_layers = {}
if not hasattr(gan, "named_layers"):
gan.named_layers = {}
self.subnets = hc.Config(hc.Config(config).subnets or {})
def layer_resnet(self, net, args, options):
options = hc.Config(options)
config = self.config
ops = self.ops
depth = int(args[0])
activation_s = options.activation or self.ops.config_option("activation")
activation = self.ops.lookup(activation_s)
stride = options.stride or 1
stride = int(stride)
shortcut = net
initializer = None # default to global
if self.ops.config_option("avg_pool"):
net = ops.conv2d(net, 3, 3, 1, 1, depth, initializer=initializer)
if stride != 1:
ksize = [1,stride,stride,1]
net = tf.nn.avg_pool(net, ksize=ksize, strides=ksize, padding='SAME')
else:
net = ops.conv2d(net, 3, 3, stride, stride, depth, initializer=initializer)
net = activation(net)
net = ops.conv2d(net, 3, 3, 1, 1, depth, initializer=initializer)
if ops.shape(net)[-1] != ops.shape(shortcut)[-1] or stride != 1:
if self.ops.config_option("avg_pool"):
shortcut = ops.conv2d(shortcut, 3, 3, 1, 1, depth, initializer=initializer)
if stride != 1:
ksize = [1,stride,stride,1]
shortcut = tf.nn.avg_pool(shortcut, ksize=ksize, strides=ksize, padding='SAME')
else:
shortcut = ops.conv2d(shortcut, 3, 3, stride, stride, depth, initializer=initializer)
net = shortcut + net
net = activation(net)
return net
def __init__(self, component, args, options):
super(ResizableStack, self).__init__(component, args, options)
self.size = LayerShape(component.gan.channels(), component.gan.height(), component.gan.width())
self.max_channels = options.max_channels or 256
self.segment_channels = options.segment_channels or 5
self.style = options.style or "w"
layers = []
sizes = self.sizes(component.current_size.height, component.current_size.width, component.gan.height(), component.gan.width(), self.segment_channels * 2 * component.gan.channels())
print("SIZES", sizes)
for i, size in enumerate(sizes[1:]):
c = min(size.channels, self.max_channels)
upsample = hg.layers.Upsample(component, [], hc.Config({"w": size.width, "h": size.height}))
component.current_size = upsample.output_size() #TODO abstract input_size
if options.normalize != False:
_, add = component.parse_layer("add self (ez_norm initializer=(xavier_normal) style=" + self.style + ")")
_, conv = component.parse_layer("conv2d " + str(size.channels) + " padding=0 initializer=(xavier_normal)")
if options.normalize == False:
layers += [upsample, conv]
else:
layers += [upsample, add, conv]
if i < len(sizes) - 2:
layers += [nn.ReLU()]
layers += [hg.layers.SegmentSoftmax(component, [component.gan.channels()], {})]
self.layers = nn.ModuleList(layers)
def test_run(self):
with self.test_session():
gan = mock_gan()
args = hc.Config({"size": "1"})
cli = hg.CLI(gan, args)
cli.run()
self.assertEqual(cli.gan, gan)
def layer_reference(self, net, args, options):
options = hc.Config(options)
obj = self
if "src" in options:
obj = getattr(self.gan, options.src)
return obj.layer(options.name)
def __init__(self, gan=None, config=None, trainer=None, name="ProgressCompressTrainHook"):
super().__init__(config=config, gan=gan, trainer=trainer, name=name)
d_loss = []
self.x = tf.Variable(tf.zeros_like(gan.inputs.x))
self.g = tf.Variable(tf.zeros_like(gan.generator.sample))
stacked = tf.concat([self.gan.inputs.x, self.gan.generator.sample], axis=0)
self.assign_x = tf.assign(self.x, gan.inputs.x)
self.assign_g = tf.assign(self.g, gan.generator.sample)
self.re_init_d = [d.initializer for d in gan.discriminator.variables()]
gan.hack = self.g
self.assign_knowledge_base = []
bs = gan.batch_size()
real = gan.discriminator.named_layers['knowledge_base_target']#tf.reshape(gan.loss.sample[:2], [2,-1])
_inputs = hc.Config({'x':real})
inner_gan = KBGAN(config=self.config.knowledge_base, inputs=_inputs, x=real, latent=stacked)
self.kb_loss = inner_gan.loss
self.kb = inner_gan.generator
self.trainer = inner_gan.trainer
variables = inner_gan.variables()
#variables += self.kb.variables()
for c in gan.components:
if hasattr(c, 'knowledge_base'):
for name, net in c.knowledge_base:
assign = self.kb.named_layers[name]
if self.ops.shape(assign)[0] > self.ops.shape(net)[0]:
assign = tf.slice(assign,[0 for i in self.ops.shape(net)] , [self.ops.shape(net)[0]]+self.ops.shape(assign)[1:])
self.assign_knowledge_base.append(tf.assign(net, assign))
self.gan.add_metric('d_kb', self.kb_loss.sample[0])
self.gan.add_metric('g_kb', self.kb_loss.sample[1])
def __init__(self, config={}, device="/gpu:0"):
config = hc.Config(config)
dtype = config.dtype or "float32"
initializer = config.initializer or 'orthogonal'
orthogonal_gain = config.orthogonal_gain or 1.0
random_stddev = config.random_stddev or 0.02
self.dtype = self.parse_dtype(dtype)
self.scope_count = 0
self.description = ''
self.weights = []
self.biases = []
self.device = config.device
self.initialized = False
self._reuse = False
self.reuse_scope_count = 0
self.reuse_context = 0
self.config = config
if initializer == 'orthogonal':
self.initializer = self.orthogonal_initializer(orthogonal_gain)
elif initializer == 'he_normal':
self.initializer = self.he_normal_initializer()
elif initializer == 'xavier':
self.initializer = self.xavier_initializer()
else:
self.initializer = self.random_initializer(random_stddev)
def layer_linear(self, net, args, options):
options = hc.Config(options)
shape = [int(x) for x in str(args[0]).split("*")]
bias = True
if options.bias == False:
bias = False
output_size = 1
for dim in shape:
output_size *= dim
layers = []
if len(self.current_size.dims) != 1:
layers += [nn.Flatten()]
layers += [
nn.Linear(options.input_size or self.current_size.size(),
output_size,
bias=bias)
]
self.nn_init(layers[-1], options.initializer)
self.current_size = LayerShape(*list(reversed(shape)))
if len(shape) != 1:
layers.append(Reshape(*self.current_size.dims))
if self.is_latent:
self._latent_parameters += [layers[0].weight]
self.is_latent = False
return nn.Sequential(*layers)
def layer_conv3d(self, net, args, options):
if len(args) > 0:
channels = args[0]
else:
channels = self.current_size.channels
options = hc.Config(options)
stride = options.stride or 1
fltr = options.filter or 3
dilation = 1
padding = options.padding or 1 #self.get_same_padding(self.current_width, self.current_width, stride, dilation)
if options.padding0:
padding = [options.padding0, padding, padding]
if options.stride0:
stride = [options.stride0, stride, stride]
else:
stride = [stride, stride, stride]
layers = [
nn.Conv3d(options.input_channels or self.current_size.channels,
channels,
fltr,
stride,
padding=padding)
]
self.nn_init(layer, options.initializer)
self.current_size = LayerShape(
frames, channels, self.current_size.height // stride[1],
self.current_size.width // stride[2]
) #TODO this doesn't work, what is frames? Also chw calculation like conv2d
return nn.Sequential(*layers)
def layer_conv1d(self, net, args, options):
if len(args) > 0:
channels = args[0]
else:
channels = self.current_size.channels
print("Options:", options)
options = hc.Config(options)
stride = options.stride or 1
fltr = options.filter or 3
dilation = 1
padding = 1
if options.padding is not None:
padding = options.padding
layers = [
nn.Conv1d(options.input_channels or self.current_size.channels,
channels,
fltr,
stride,
padding=padding)
]
self.nn_init(layers[-1], options.initializer)
h, _ = self.conv_output_shape(
(self.current_size.height, self.current_size.height),
options.filter or 3, stride, padding, 1)
self.current_size = LayerShape(channels, h)
return nn.Sequential(*layers)
def layer_conv2d(self, net, args, options):
if len(args) > 0:
channels = args[0]
else:
channels = self.current_size.channels
options = hc.Config(options)
stride = 1
if options.stride is not None:
stride = options.stride
filter = 3
if options.filter is not None:
filter = options.filter
padding = 1
if options.padding is not None:
padding = options.padding
dilation = 1
layer = nn.Conv2d(options.input_channels or self.current_size.channels,
channels,
filter,
stride,
padding=(padding, padding))
self.nn_init(layer, options.initializer)
h, w = self.conv_output_shape(
(self.current_size.height, self.current_size.width), filter,
stride, padding, dilation)
self.current_size = LayerShape(channels, h, w)
return layer
def layer_two_sample_stack(self, net, args, options):
options = hc.Config(options)
def _slice(_net):
s = self.ops.shape(_net)
s[0] = s[0] // 2
_net1 = tf.slice(_net, [0, 0, 0, 0], s)
_net2 = tf.slice(_net, [s[0], 0, 0, 0], s)
return _net1, _net2
net1, net2 = _slice(net)
net1a, net1b = _slice(net1)
net2a, net2b = _slice(net2)
if options.mixup:
alpha = tf.random_uniform([1], 0, 1)
t1 = alpha * net1a + (1 - alpha) * net1b
t2 = alpha * net2a + (1 - alpha) * net2b
t1 = tf.reshape(t1, self.ops.shape(net1b))
t2 = tf.reshape(t2, self.ops.shape(net2b))
else:
t1 = tf.concat([net1a, net1b], axis=3)
t2 = tf.concat([net2a, net2b], axis=3)
# hack fixes shape expectations
#t1 = tf.concat([t1,t1], axis=0)
#t2 = tf.concat([t2,t2], axis=0)
target = tf.concat([t1, t2], axis=0)
s = self.ops.shape(net)
return target
def layer_subpixel(self, net, args, options):
options = hc.Config(options)
depth = int(args[0])
config = self.config
activation = options.activation or config.defaults.activation
r = options.r or 2
r = int(r)
def _PS(X, r, n_out_channel):
if n_out_channel >= 1:
bsize, a, b, c = X.get_shape().as_list()
bsize = tf.shape(X)[
0] # Handling Dimension(None) type for undefined batch dim
Xs = tf.split(X, r, 3) #b*h*w*r*r
Xr = tf.concat(Xs, 2) #b*h*(r*w)*r
X = tf.reshape(
Xr,
(bsize, r * a, r * b, n_out_channel)) # b*(r*h)*(r*w)*c
return X
args[0] = depth * (r**2)
if (activation == 'crelu' or activation == 'double_sided'):
args[0] //= 2
y1 = self.layer_conv(net, args, options)
ps = _PS(y1, r, depth)
return ps
def __init__(self, gan, args={}):
self.samples = 0
self.steps = 0
self.gan = gan
args = hc.Config(args)
self.args = args
crop = self.args.crop
self.config_name = self.args.config or 'default'
self.method = args.method or 'test'
self.total_steps = args.steps or -1
self.sample_every = self.args.sample_every or 100
self.sampler = CLI.sampler_for(args.sampler)(self.gan)
self.validate()
if self.args.save_file:
self.save_file = self.args.save_file
else:
default_save_path = os.path.abspath("saves/"+self.config_name)
self.save_file = default_save_path + "/model.ckpt"
self.create_path(self.save_file)
title = "[hypergan] " + self.config_name
GlobalViewer.title = title
GlobalViewer.enabled = self.args.viewer
def layer_layer(self, net, args, options):
options = hc.Config(options)
if "src" in options:
obj = getattr(self.gan, options.src)
else:
obj = self
return obj.layer(args[0])
def layer_conv_reshape(self, net, args, options):
options = hc.Config(options)
config = self.config
ops = self.ops
activation_s = options.activation or config.defaults.activation
activation = self.ops.lookup(activation_s)
stride = options.stride or config.defaults.stride or [1, 1]
fltr = options.filter or config.defaults.filter or [3, 3]
if type(fltr) == type(""):
fltr = [int(fltr), int(fltr)]
if type(stride) == type(""):
stride = [int(stride), int(stride)]
depth = int(args[0])
initializer = None # default to global
trainable = True
if options.trainable == 'false':
trainable = False
net = ops.conv2d(net,
fltr[0],
fltr[1],
stride[0],
stride[1],
depth * 4,
initializer=initializer,
trainable=trainable)
s = ops.shape(net)
net = tf.reshape(net, [s[0], s[1] * 2, s[2] * 2, depth])
if activation:
#net = self.layer_regularizer(net)
net = activation(net)
return net
def create_trainer(self, cycloss, z_cycloss, encoder, generator,
encoder_loss, standard_loss, standard_discriminator,
encoder_discriminator):
metrics = []
metrics.append(standard_loss.metrics)
d_vars = standard_discriminator.variables()
g_vars = generator.variables() + encoder.variables()
print("D_VARS", d_vars)
print("G_VARS", g_vars)
#d_loss = standard_loss.d_loss
#g_loss = standard_loss.g_loss + cycloss
loss1 = ("g_loss", standard_loss.g_loss)
loss2 = ("d_loss", standard_loss.d_loss)
loss = hc.Config({
'sample': [standard_loss.d_loss, standard_loss.g_loss],
'metrics': {
'g_loss': loss1[1],
'd_loss': loss2[1]
}
})
trainer = ConsensusTrainer(self,
self.config.trainer,
loss=loss,
g_vars=g_vars,
d_vars=d_vars)
return trainer
def __init__(self, gan, args={}):
self.samples = 0
self.steps = 0
self.gan = gan
if gan is not None:
self.gan.cli = self
args = hc.Config(args)
self.args = args
crop = self.args.crop
self.config_name = self.args.config or 'default'
self.method = args.method or 'test'
self.total_steps = args.steps or -1
self.sample_every = self.args.sample_every or 100
self.sampler_name = args.sampler
self.sampler = None
self.validate()
if self.args.save_file:
self.save_file = self.args.save_file
else:
default_save_path = os.path.abspath("saves/" + self.config_name)
self.save_file = default_save_path + "/model.ckpt"
self.create_path(self.save_file)
if self.gan is not None:
self.gan.save_file = self.save_file
title = "[hypergan] " + self.config_name
GlobalViewer.enable_menu = self.args.menu
GlobalViewer.title = title
GlobalViewer.viewer_size = self.args.viewer_size
GlobalViewer.enabled = self.args.viewer
GlobalViewer.zoom = self.args.zoom
def layer_pad(self, net, args, options):
options = hc.Config(options)
s = self.ops.shape(net)
sizew = s[1]//2
sizeh = s[2]//2
net,_,_ = tf.pad(net, [[0,0],[ sizew,sizew],[ sizeh,sizeh],[ 0,0]])
return net
def lookup(self, symbol):
if symbol == None:
return None
if type(symbol) == type([]):
return [self.lookup(k) for k in symbol]
if type(symbol) == type({}) or type(symbol) == hc.Config:
return hc.Config(
{k: self.lookup(symbol[k])
for k in symbol.keys()})
if type(symbol) != type(""):
return symbol
if symbol.startswith('function:'):
return self.lookup_function(symbol)
if symbol.startswith('class:'):
return self.lookup_class(symbol)
if symbol == 'tanh':
return tf.nn.tanh
if symbol == 'sigmoid':
return tf.nn.sigmoid
if symbol == 'batch_norm':
return layer_regularizers.batch_norm_1
if symbol == 'layer_norm':
return layer_regularizers.layer_norm_1
if symbol == "crelu":
return tf.nn.crelu
if symbol == "prelu":
return self.prelu()
if symbol == "selu":
return selu
if symbol == "lrelu":
return lrelu
if symbol == "relu":
return tf.nn.relu
if symbol == 'square':
return tf.square
if symbol == 'reduce_mean':
return tf.reduce_mean
if symbol == 'reduce_min':
return tf.reduce_min
if symbol == 'reduce_sum':
return tf.reduce_sum
if symbol == 'reduce_logsumexp':
return tf.reduce_logsumexp
if symbol == 'reduce_linear':
return self.reduce_linear()
if symbol == 'l1_distance':
return l1_distance
if symbol == 'l2_distance':
return l2_distance
return symbol
def parse_layer(self, layer_defn):
print("Parsing layer:", layer_defn)
parsed = self.parser.parse_string(layer_defn)
parsed.parsed_options = hc.Config(parsed.options)
parsed.layer_defn = layer_defn
print("Parsed layer:", parsed.to_list())
layer = self.build_layer(parsed.layer_name, parsed.args,
parsed.parsed_options)
return parsed, layer
def get_array(self, name, shape=None):
if shape:
shape = [int(x) for x in shape]
connections = hc.Config(self.connections)
if name in connections:
conns = connections[name]
else:
conns = []
return [con[1] for con in conns if shape is None or con[0] == shape]
def parse_opts(self, opts):
options = {}
for opt in opts.split(","):
if opt == "":
continue
name, val = opt.split("=")
value = self.configurable_param(val)
options[name]=value
return hc.Config(options)
def layer_resize_images(self, net, args, options):
options = hc.Config(options)
if len(args) == 0:
w = self.gan.width()
h = self.gan.height()
else:
w = int(args[0])
h = int(args[1])
method = options.method or 1
return tf.image.resize_images(net, [w, h], method=method)
def layer_reference(self, net, args, options):
options = hc.Config(options)
obj = self
if "src" in options:
obj = getattr(self.gan, options.src)
if "resize_images" in options:
return self.layer_resize_images(getattr(obj, options.name), options["resize_images"].split("*"), options)
else:
return obj.layer(options.name)
def parse_args(self, strs):
options = hc.Config({})
args = []
for x in strs:
if '=' in x:
lhs, rhs = x.split('=')
options[lhs] = rhs
else:
args.append(x)
return args, options
def forward_loss(self):
losses = []
for d_real, d_fake in zip(d_reals, d_fakes):
loss = self.create_component(config.loss, discriminator=d, split=len(d_terms))
d_loss, g_loss = loss.forward(d_real, d_fake)
d_loss = [self.configurable_param(config.term_gammas[i]) * d_loss, self.configurable_param(config.term_gammas[i]) * g_loss]
losses += [[d_loss, g_loss]]
self.loss = hc.Config({
'sample': [sum([l.sample[0] for l in losses]), sum([l.sample[1] for l in losses])]
})
def layer_zeros(self, net, args, options):
options = hc.Config(options)
config = self.config
ops = self.ops
self.ops.activation_name = options.activation_name
reshape = [ops.shape(net)[0]] + [int(x) for x in args[0].split("*")]
size = reduce(operator.mul, reshape)
net = tf.zeros(reshape)
return net
def layer_const(self, net, args, options):
options = hc.Config(options)
s = [1] + [int(x) for x in args[0].split("*")]
trainable = True
if "trainable" in options and options["trainable"] == "false":
trainable = False
initializer = None
if "initializer" in options and options["initializer"] is not None:
initializer = self.ops.lookup_initializer(options["initializer"], options)
with tf.variable_scope(self.ops.generate_name(), reuse=self.ops._reuse):
return tf.tile(self.ops.get_weight(s, name='const', trainable=trainable, initializer=initializer), [self.gan.batch_size(), 1,1,1])
