import os import sys import contextlib import numpy as np from utils import std_utils from .device import device class nnlib(object): device = device #forwards nnlib.devicelib to device in order to use nnlib as standalone lib DeviceConfig = device.Config active_DeviceConfig = DeviceConfig() #default is one best GPU dlib = None keras = None keras_contrib = None tf = None tf_sess = None PML = None PMLK = None PMLTile= None code_import_keras = None code_import_keras_contrib = None code_import_all = None code_import_dlib = None ResNet = None UNet = None UNetTemporalPredictor = None NLayerDiscriminator = None code_import_keras_string = \ """ keras = nnlib.keras K = keras.backend Input = keras.layers.Input Dense = keras.layers.Dense Conv2D = keras.layers.Conv2D Conv2DTranspose = keras.layers.Conv2DTranspose SeparableConv2D = keras.layers.SeparableConv2D MaxPooling2D = keras.layers.MaxPooling2D UpSampling2D = keras.layers.UpSampling2D BatchNormalization = keras.layers.BatchNormalization LeakyReLU = keras.layers.LeakyReLU ReLU = keras.layers.ReLU PReLU = keras.layers.PReLU tanh = keras.layers.Activation('tanh') sigmoid = keras.layers.Activation('sigmoid') Dropout = keras.layers.Dropout Softmax = keras.layers.Softmax Lambda = keras.layers.Lambda Add = keras.layers.Add Concatenate = keras.layers.Concatenate Flatten = keras.layers.Flatten Reshape = keras.layers.Reshape ZeroPadding2D = keras.layers.ZeroPadding2D RandomNormal = keras.initializers.RandomNormal Model = keras.models.Model Adam = keras.optimizers.Adam modelify = nnlib.modelify gaussian_blur = nnlib.gaussian_blur style_loss = nnlib.style_loss dssim = nnlib.dssim PixelShuffler = nnlib.PixelShuffler SubpixelUpscaler = nnlib.SubpixelUpscaler Scale = nnlib.Scale #ReflectionPadding2D = nnlib.ReflectionPadding2D #AddUniformNoise = nnlib.AddUniformNoise """ code_import_keras_contrib_string = \ """ keras_contrib = nnlib.keras_contrib GroupNormalization = keras_contrib.layers.GroupNormalization InstanceNormalization = keras_contrib.layers.InstanceNormalization Padam = keras_contrib.optimizers.Padam """ code_import_dlib_string = \ """ dlib = nnlib.dlib """ code_import_all_string = \ """ DSSIMMSEMaskLoss = nnlib.DSSIMMSEMaskLoss ResNet = nnlib.ResNet UNet = nnlib.UNet UNetTemporalPredictor = nnlib.UNetTemporalPredictor NLayerDiscriminator = nnlib.NLayerDiscriminator """ @staticmethod def _import_tf(device_config): if nnlib.tf is not None: return nnlib.code_import_tf if 'TF_SUPPRESS_STD' in os.environ.keys() and os.environ['TF_SUPPRESS_STD'] == '1': suppressor = std_utils.suppress_stdout_stderr().__enter__() else: suppressor = None if 'CUDA_VISIBLE_DEVICES' in os.environ.keys(): os.environ.pop('CUDA_VISIBLE_DEVICES') os.environ['TF_MIN_GPU_MULTIPROCESSOR_COUNT'] = '2' os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' #tf log errors only import tensorflow as tf nnlib.tf = tf if device_config.cpu_only: config = tf.ConfigProto(device_count={'GPU': 0}) else: config = tf.ConfigProto() if device_config.backend != "tensorflow-generic": #tensorflow-generic is system with NVIDIA card, but w/o NVSMI #so dont hide devices and let tensorflow to choose best card visible_device_list = '' for idx in device_config.gpu_idxs: visible_device_list += str(idx) + ',' config.gpu_options.visible_device_list=visible_device_list[:-1] config.gpu_options.force_gpu_compatible = True config.gpu_options.allow_growth = device_config.allow_growth nnlib.tf_sess = tf.Session(config=config) if suppressor is not None: suppressor.__exit__() @staticmethod def import_keras(device_config = None): if nnlib.keras is not None: return nnlib.code_import_keras if device_config is None: device_config = nnlib.active_DeviceConfig nnlib.active_DeviceConfig = device_config if "tensorflow" in device_config.backend: nnlib._import_tf(device_config) device_config = nnlib.active_DeviceConfig elif device_config.backend == "plaidML": os.environ["KERAS_BACKEND"] = "plaidml.keras.backend" os.environ["PLAIDML_DEVICE_IDS"] = ",".join ( [ nnlib.device.getDeviceID(idx) for idx in device_config.gpu_idxs] ) if 'TF_SUPPRESS_STD' in os.environ.keys() and os.environ['TF_SUPPRESS_STD'] == '1': suppressor = std_utils.suppress_stdout_stderr().__enter__() #if "tensorflow" in device_config.backend: # nnlib.keras = nnlib.tf.keras #else: import keras as keras_ nnlib.keras = keras_ if device_config.backend == "plaidML": import plaidml import plaidml.tile nnlib.PML = plaidml nnlib.PMLK = plaidml.keras.backend nnlib.PMLTile = plaidml.tile if device_config.use_fp16: nnlib.keras.backend.set_floatx('float16') if "tensorflow" in device_config.backend: nnlib.keras.backend.set_session(nnlib.tf_sess) nnlib.keras.backend.set_image_data_format('channels_last') if 'TF_SUPPRESS_STD' in os.environ.keys() and os.environ['TF_SUPPRESS_STD'] == '1': suppressor.__exit__() nnlib.code_import_keras = compile (nnlib.code_import_keras_string,'','exec') nnlib.__initialize_keras_functions() return nnlib.code_import_keras @staticmethod def __initialize_keras_functions(): keras = nnlib.keras K = keras.backend def modelify(model_functor): def func(tensor): return keras.models.Model (tensor, model_functor(tensor)) return func nnlib.modelify = modelify def gaussian_blur(radius=2.0): def gaussian(x, mu, sigma): return np.exp(-(float(x) - float(mu)) ** 2 / (2 * sigma ** 2)) def make_kernel(sigma): kernel_size = max(3, int(2 * 2 * sigma + 1)) mean = np.floor(0.5 * kernel_size) kernel_1d = np.array([gaussian(x, mean, sigma) for x in range(kernel_size)]) np_kernel = np.outer(kernel_1d, kernel_1d).astype(dtype=K.floatx()) kernel = np_kernel / np.sum(np_kernel) return kernel gauss_kernel = make_kernel(radius) gauss_kernel = gauss_kernel[:, :,np.newaxis, np.newaxis] def func(input): inputs = [ input[:,:,:,i:i+1] for i in range( K.int_shape( input )[-1] ) ] outputs = [] for i in range(len(inputs)): outputs += [ K.conv2d( inputs[i] , K.constant(gauss_kernel) , strides=(1,1), padding="same") ] return K.concatenate (outputs, axis=-1) return func nnlib.gaussian_blur = gaussian_blur def style_loss(gaussian_blur_radius=0.0, loss_weight=1.0, wnd_size=0, step_size=1): if gaussian_blur_radius > 0.0: gblur = gaussian_blur(gaussian_blur_radius) def sd(content, style, loss_weight): content_nc = K.int_shape(content)[-1] style_nc = K.int_shape(style)[-1] if content_nc != style_nc: raise Exception("style_loss() content_nc != style_nc") axes = [1,2] c_mean, c_var = K.mean(content, axis=axes, keepdims=True), K.var(content, axis=axes, keepdims=True) s_mean, s_var = K.mean(style, axis=axes, keepdims=True), K.var(style, axis=axes, keepdims=True) c_std, s_std = K.sqrt(c_var + 1e-5), K.sqrt(s_var + 1e-5) mean_loss = K.sum(K.square(c_mean-s_mean)) std_loss = K.sum(K.square(c_std-s_std)) return (mean_loss + std_loss) * ( loss_weight / float(content_nc) ) def func(target, style): if wnd_size == 0: if gaussian_blur_radius > 0.0: return sd( gblur(target), gblur(style), loss_weight=loss_weight) else: return sd( target, style, loss_weight=loss_weight ) else: #currently unused if nnlib.tf is not None: sh = K.int_shape(target)[1] k = (sh-wnd_size) // step_size + 1 if gaussian_blur_radius > 0.0: target, style = gblur(target), gblur(style) target = nnlib.tf.image.extract_image_patches(target, [1,k,k,1], [1,1,1,1], [1,step_size,step_size,1], 'VALID') style = nnlib.tf.image.extract_image_patches(style, [1,k,k,1], [1,1,1,1], [1,step_size,step_size,1], 'VALID') return sd( target, style, loss_weight ) if nnlib.PML is not None: print ("Sorry, plaidML backend does not support style_loss") return 0 return func nnlib.style_loss = style_loss def dssim(k1=0.01, k2=0.03, max_value=1.0): # port of tf.image.ssim to pure keras in order to work on plaidML backend. def func(y_true, y_pred): ch = K.shape(y_pred)[-1] def _fspecial_gauss(size, sigma): #Function to mimic the 'fspecial' gaussian MATLAB function. coords = np.arange(0, size, dtype=K.floatx()) coords -= (size - 1 ) / 2.0 g = coords**2 g *= ( -0.5 / (sigma**2) ) g = np.reshape (g, (1,-1)) + np.reshape(g, (-1,1) ) g = K.constant ( np.reshape (g, (1,-1)) ) g = K.softmax(g) g = K.reshape (g, (size, size, 1, 1)) g = K.tile (g, (1,1,ch,1)) return g kernel = _fspecial_gauss(11,1.5) def reducer(x): return K.depthwise_conv2d(x, kernel, strides=(1, 1), padding='valid') c1 = (k1 * max_value) ** 2 c2 = (k2 * max_value) ** 2 mean0 = reducer(y_true) mean1 = reducer(y_pred) num0 = mean0 * mean1 * 2.0 den0 = K.square(mean0) + K.square(mean1) luminance = (num0 + c1) / (den0 + c1) num1 = reducer(y_true * y_pred) * 2.0 den1 = reducer(K.square(y_true) + K.square(y_pred)) c2 *= 1.0 #compensation factor cs = (num1 - num0 + c2) / (den1 - den0 + c2) ssim_val = K.mean(luminance * cs, axis=(-3, -2) ) return K.mean( (1.0 - ssim_val ) / 2.0 ) return func nnlib.dssim = dssim class PixelShuffler(keras.layers.Layer): def __init__(self, size=(2, 2), data_format='channels_last', **kwargs): super(PixelShuffler, self).__init__(**kwargs) self.data_format = data_format self.size = size def call(self, inputs): input_shape = K.int_shape(inputs) if len(input_shape) != 4: raise ValueError('Inputs should have rank ' + str(4) + '; Received input shape:', str(input_shape)) if self.data_format == 'channels_first': batch_size, c, h, w = input_shape if batch_size is None: batch_size = -1 rh, rw = self.size oh, ow = h * rh, w * rw oc = c // (rh * rw) out = K.reshape(inputs, (batch_size, rh, rw, oc, h, w)) out = K.permute_dimensions(out, (0, 3, 4, 1, 5, 2)) out = K.reshape(out, (batch_size, oc, oh, ow)) return out elif self.data_format == 'channels_last': batch_size, h, w, c = input_shape if batch_size is None: batch_size = -1 rh, rw = self.size oh, ow = h * rh, w * rw oc = c // (rh * rw) out = K.reshape(inputs, (batch_size, h, w, rh, rw, oc)) out = K.permute_dimensions(out, (0, 1, 3, 2, 4, 5)) out = K.reshape(out, (batch_size, oh, ow, oc)) return out def compute_output_shape(self, input_shape): if len(input_shape) != 4: raise ValueError('Inputs should have rank ' + str(4) + '; Received input shape:', str(input_shape)) if self.data_format == 'channels_first': height = input_shape[2] * self.size[0] if input_shape[2] is not None else None width = input_shape[3] * self.size[1] if input_shape[3] is not None else None channels = input_shape[1] // self.size[0] // self.size[1] if channels * self.size[0] * self.size[1] != input_shape[1]: raise ValueError('channels of input and size are incompatible') return (input_shape[0], channels, height, width) elif self.data_format == 'channels_last': height = input_shape[1] * self.size[0] if input_shape[1] is not None else None width = input_shape[2] * self.size[1] if input_shape[2] is not None else None channels = input_shape[3] // self.size[0] // self.size[1] if channels * self.size[0] * self.size[1] != input_shape[3]: raise ValueError('channels of input and size are incompatible') return (input_shape[0], height, width, channels) def get_config(self): config = {'size': self.size, 'data_format': self.data_format} base_config = super(PixelShuffler, self).get_config() return dict(list(base_config.items()) + list(config.items())) nnlib.PixelShuffler = PixelShuffler nnlib.SubpixelUpscaler = PixelShuffler class Scale(keras.layers.Layer): """ GAN Custom Scal Layer Code borrows from https://github.com/flyyufelix/cnn_finetune """ def __init__(self, weights=None, axis=-1, gamma_init='zero', **kwargs): self.axis = axis self.gamma_init = keras.initializers.get(gamma_init) self.initial_weights = weights super(Scale, self).__init__(**kwargs) def build(self, input_shape): self.input_spec = [keras.engine.InputSpec(shape=input_shape)] # Compatibility with TensorFlow >= 1.0.0 self.gamma = K.variable(self.gamma_init((1,)), name='{}_gamma'.format(self.name)) self.trainable_weights = [self.gamma] if self.initial_weights is not None: self.set_weights(self.initial_weights) del self.initial_weights def call(self, x, mask=None): return self.gamma * x def get_config(self): config = {"axis": self.axis} base_config = super(Scale, self).get_config() return dict(list(base_config.items()) + list(config.items())) nnlib.Scale = Scale ''' not implemented in plaidML class ReflectionPadding2D(keras.layers.Layer): def __init__(self, padding=(1, 1), **kwargs): self.padding = tuple(padding) self.input_spec = [keras.layers.InputSpec(ndim=4)] super(ReflectionPadding2D, self).__init__(**kwargs) def compute_output_shape(self, s): """ If you are using "channels_last" configuration""" return (s[0], s[1] + 2 * self.padding[0], s[2] + 2 * self.padding[1], s[3]) def call(self, x, mask=None): w_pad,h_pad = self.padding return tf.pad(x, [[0,0], [h_pad,h_pad], [w_pad,w_pad], [0,0] ], 'REFLECT') nnlib.ReflectionPadding2D = ReflectionPadding2D ''' @staticmethod def import_keras_contrib(device_config = None): if nnlib.keras_contrib is not None: return nnlib.code_import_keras_contrib import keras_contrib as keras_contrib_ nnlib.keras_contrib = keras_contrib_ nnlib.__initialize_keras_contrib_functions() nnlib.code_import_keras_contrib = compile (nnlib.code_import_keras_contrib_string,'','exec') @staticmethod def __initialize_keras_contrib_functions(): pass @staticmethod def import_dlib( device_config = None): if nnlib.dlib is not None: return nnlib.code_import_dlib import dlib as dlib_ nnlib.dlib = dlib_ if not device_config.cpu_only and "tensorflow" in device_config.backend and len(device_config.gpu_idxs) > 0: nnlib.dlib.cuda.set_device(device_config.gpu_idxs[0]) nnlib.code_import_dlib = compile (nnlib.code_import_dlib_string,'','exec') @staticmethod def import_all(device_config = None): if nnlib.code_import_all is None: nnlib.import_keras(device_config) nnlib.import_keras_contrib(device_config) nnlib.code_import_all = compile (nnlib.code_import_keras_string + '\n' + nnlib.code_import_keras_contrib_string + nnlib.code_import_all_string,'','exec') nnlib.__initialize_all_functions() return nnlib.code_import_all @staticmethod def __initialize_all_functions(): exec (nnlib.import_keras(), locals(), globals()) exec (nnlib.import_keras_contrib(), locals(), globals()) class DSSIMMSEMaskLoss(object): def __init__(self, mask, is_mse=False): self.mask = mask self.is_mse = is_mse def __call__(self,y_true, y_pred): total_loss = None mask = self.mask if self.is_mse: blur_mask = gaussian_blur(max(1, K.int_shape(mask)[1] // 64))(mask) return K.mean ( 50*K.square( y_true*blur_mask - y_pred*blur_mask ) ) else: return 10*dssim() (y_true*mask, y_pred*mask) nnlib.DSSIMMSEMaskLoss = DSSIMMSEMaskLoss ''' def ResNet(output_nc, use_batch_norm, ngf=64, n_blocks=6, use_dropout=False): exec (nnlib.import_all(), locals(), globals()) if not use_batch_norm: use_bias = True def XNormalization(x): return InstanceNormalization (axis=3, gamma_initializer=RandomNormal(1., 0.02))(x)#GroupNormalization (axis=3, groups=K.int_shape (x)[3] // 4, gamma_initializer=RandomNormal(1., 0.02))(x) else: use_bias = False def XNormalization(x): return BatchNormalization (axis=3, gamma_initializer=RandomNormal(1., 0.02))(x) def Conv2D (filters, kernel_size, strides=(1, 1), padding='valid', data_format=None, dilation_rate=(1, 1), activation=None, use_bias=use_bias, kernel_initializer=RandomNormal(0, 0.02), bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None): return keras.layers.Conv2D( filters=filters, kernel_size=kernel_size, strides=strides, padding=padding, data_format=data_format, dilation_rate=dilation_rate, activation=activation, use_bias=use_bias, kernel_initializer=kernel_initializer, bias_initializer=bias_initializer, kernel_regularizer=kernel_regularizer, bias_regularizer=bias_regularizer, activity_regularizer=activity_regularizer, kernel_constraint=kernel_constraint, bias_constraint=bias_constraint ) def Conv2DTranspose(filters, kernel_size, strides=(1, 1), padding='valid', output_padding=None, data_format=None, dilation_rate=(1, 1), activation=None, use_bias=use_bias, kernel_initializer='glorot_uniform', bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None): return keras.layers.Conv2DTranspose(filters=filters, kernel_size=kernel_size, strides=strides, padding=padding, output_padding=output_padding, data_format=data_format, dilation_rate=dilation_rate, activation=activation, use_bias=use_bias, kernel_initializer=kernel_initializer, bias_initializer=bias_initializer, kernel_regularizer=kernel_regularizer, bias_regularizer=bias_regularizer, activity_regularizer=activity_regularizer, kernel_constraint=kernel_constraint, bias_constraint=bias_constraint) def func(input): def ResnetBlock(dim): def func(input): x = input x = ReflectionPadding2D((1,1))(x) x = Conv2D(dim, 3, 1, padding='valid')(x) x = XNormalization(x) x = ReLU()(x) if use_dropout: x = Dropout(0.5)(x) x = ReflectionPadding2D((1,1))(x) x = Conv2D(dim, 3, 1, padding='valid')(x) x = XNormalization(x) x = ReLU()(x) return Add()([x,input]) return func x = input x = ReflectionPadding2D((3,3))(x) x = Conv2D(ngf, 7, 1, 'valid')(x) x = ReLU()(XNormalization(Conv2D(ngf*2, 4, 2, 'same')(x))) x = ReLU()(XNormalization(Conv2D(ngf*4, 4, 2, 'same')(x))) for i in range(n_blocks): x = ResnetBlock(ngf*4)(x) x = ReLU()(XNormalization(PixelShuffler()(Conv2D(ngf*2 *4, 3, 1, 'same')(x)))) x = ReLU()(XNormalization(PixelShuffler()(Conv2D(ngf *4, 3, 1, 'same')(x)))) x = ReflectionPadding2D((3,3))(x) x = Conv2D(output_nc, 7, 1, 'valid')(x) x = tanh(x) return x return func nnlib.ResNet = ResNet # Defines the Unet generator. # |num_downs|: number of downsamplings in UNet. For example, # if |num_downs| == 7, image of size 128x128 will become of size 1x1 # at the bottleneck def UNet(output_nc, use_batch_norm, num_downs, ngf=64, use_dropout=False): exec (nnlib.import_all(), locals(), globals()) if not use_batch_norm: use_bias = True def XNormalization(x): return InstanceNormalization (axis=3, gamma_initializer=RandomNormal(1., 0.02))(x)#GroupNormalization (axis=3, groups=K.int_shape (x)[3] // 4, gamma_initializer=RandomNormal(1., 0.02))(x) else: use_bias = False def XNormalization(x): return BatchNormalization (axis=3, gamma_initializer=RandomNormal(1., 0.02))(x) def Conv2D (filters, kernel_size, strides=(1, 1), padding='valid', data_format=None, dilation_rate=(1, 1), activation=None, use_bias=use_bias, kernel_initializer=RandomNormal(0, 0.02), bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None): return keras.layers.Conv2D( filters=filters, kernel_size=kernel_size, strides=strides, padding=padding, data_format=data_format, dilation_rate=dilation_rate, activation=activation, use_bias=use_bias, kernel_initializer=kernel_initializer, bias_initializer=bias_initializer, kernel_regularizer=kernel_regularizer, bias_regularizer=bias_regularizer, activity_regularizer=activity_regularizer, kernel_constraint=kernel_constraint, bias_constraint=bias_constraint ) def Conv2DTranspose(filters, kernel_size, strides=(1, 1), padding='valid', output_padding=None, data_format=None, dilation_rate=(1, 1), activation=None, use_bias=use_bias, kernel_initializer='glorot_uniform', bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None): return keras.layers.Conv2DTranspose(filters=filters, kernel_size=kernel_size, strides=strides, padding=padding, output_padding=output_padding, data_format=data_format, dilation_rate=dilation_rate, activation=activation, use_bias=use_bias, kernel_initializer=kernel_initializer, bias_initializer=bias_initializer, kernel_regularizer=kernel_regularizer, bias_regularizer=bias_regularizer, activity_regularizer=activity_regularizer, kernel_constraint=kernel_constraint, bias_constraint=bias_constraint) def UNetSkipConnection(outer_nc, inner_nc, sub_model=None, outermost=False, innermost=False, use_dropout=False): def func(inp): x = inp x = Conv2D(inner_nc, 4, 2, 'valid')(ReflectionPadding2D( (1,1) )(x)) x = XNormalization(x) x = ReLU()(x) if not innermost: x = sub_model(x) if not outermost: x = Conv2DTranspose(outer_nc, 3, 2, 'same')(x) x = XNormalization(x) x = ReLU()(x) if not innermost: if use_dropout: x = Dropout(0.5)(x) x = Concatenate(axis=3)([inp, x]) else: x = Conv2DTranspose(outer_nc, 3, 2, 'same')(x) x = tanh(x) return x return func def func(input): unet_block = UNetSkipConnection(ngf * 8, ngf * 8, sub_model=None, innermost=True) for i in range(num_downs - 5): unet_block = UNetSkipConnection(ngf * 8, ngf * 8, sub_model=unet_block, use_dropout=use_dropout) unet_block = UNetSkipConnection(ngf * 4 , ngf * 8, sub_model=unet_block) unet_block = UNetSkipConnection(ngf * 2 , ngf * 4, sub_model=unet_block) unet_block = UNetSkipConnection(ngf , ngf * 2, sub_model=unet_block) unet_block = UNetSkipConnection(output_nc, ngf , sub_model=unet_block, outermost=True) return unet_block(input) return func nnlib.UNet = UNet #predicts based on two past_image_tensors def UNetTemporalPredictor(output_nc, use_batch_norm, num_downs, ngf=64, use_dropout=False): exec (nnlib.import_all(), locals(), globals()) def func(inputs): past_2_image_tensor, past_1_image_tensor = inputs x = Concatenate(axis=3)([ past_2_image_tensor, past_1_image_tensor ]) x = UNet(3, use_batch_norm, num_downs=num_downs, ngf=ngf, use_dropout=use_dropout) (x) return x return func nnlib.UNetTemporalPredictor = UNetTemporalPredictor def NLayerDiscriminator(use_batch_norm, ndf=64, n_layers=3): exec (nnlib.import_all(), locals(), globals()) if not use_batch_norm: use_bias = True def XNormalization(x): return InstanceNormalization (axis=3, gamma_initializer=RandomNormal(1., 0.02))(x)#GroupNormalization (axis=3, groups=K.int_shape (x)[3] // 4, gamma_initializer=RandomNormal(1., 0.02))(x) else: use_bias = False def XNormalization(x): return BatchNormalization (axis=3, gamma_initializer=RandomNormal(1., 0.02))(x) def Conv2D (filters, kernel_size, strides=(1, 1), padding='valid', data_format=None, dilation_rate=(1, 1), activation=None, use_bias=use_bias, kernel_initializer=RandomNormal(0, 0.02), bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None): return keras.layers.Conv2D( filters=filters, kernel_size=kernel_size, strides=strides, padding=padding, data_format=data_format, dilation_rate=dilation_rate, activation=activation, use_bias=use_bias, kernel_initializer=kernel_initializer, bias_initializer=bias_initializer, kernel_regularizer=kernel_regularizer, bias_regularizer=bias_regularizer, activity_regularizer=activity_regularizer, kernel_constraint=kernel_constraint, bias_constraint=bias_constraint ) def func(input): x = input x = ZeroPadding2D((1,1))(x) x = Conv2D( ndf, 4, 2, 'valid')(x) x = LeakyReLU(0.2)(x) for i in range(1, n_layers): x = ZeroPadding2D((1,1))(x) x = Conv2D( ndf * min(2 ** i, 8), 4, 2, 'valid')(x) x = XNormalization(x) x = LeakyReLU(0.2)(x) x = ZeroPadding2D((1,1))(x) x = Conv2D( ndf * min(2 ** n_layers, 8), 4, 1, 'valid')(x) x = XNormalization(x) x = LeakyReLU(0.2)(x) x = ZeroPadding2D((1,1))(x) return Conv2D( 1, 4, 1, 'valid')(x) return func nnlib.NLayerDiscriminator = NLayerDiscriminator ''' @staticmethod def finalize_all(): if nnlib.keras_contrib is not None: nnlib.keras_contrib = None if nnlib.keras is not None: nnlib.keras.backend.clear_session() nnlib.keras = None if nnlib.tf is not None: nnlib.tf_sess = None nnlib.tf = None