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198
nnlib/__init__.py
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198
nnlib/__init__.py
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def tf_image_histogram (tf, input):
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x = input
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x += 1 / 255.0
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output = []
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for i in range(256, 0, -1):
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v = i / 255.0
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y = (x - v) * 1000
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y = tf.clip_by_value (y, -1.0, 0.0) + 1
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output.append ( tf.reduce_sum (y) )
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x -= y*v
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return tf.stack ( output[::-1] )
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def tf_dssim(tf, t1, t2):
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return (1.0 - tf.image.ssim (t1, t2, 1.0)) / 2.0
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def tf_ssim(tf, t1, t2):
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return tf.image.ssim (t1, t2, 1.0)
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def DSSIMMaskLossClass(tf):
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class DSSIMMaskLoss(object):
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def __init__(self, mask_list, is_tanh=False):
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self.mask_list = mask_list
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self.is_tanh = is_tanh
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def __call__(self,y_true, y_pred):
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total_loss = None
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for mask in self.mask_list:
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if not self.is_tanh:
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loss = (1.0 - tf.image.ssim (y_true*mask, y_pred*mask, 1.0)) / 2.0
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else:
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loss = (1.0 - tf.image.ssim ( (y_true/2+0.5)*(mask/2+0.5), (y_pred/2+0.5)*(mask/2+0.5), 1.0)) / 2.0
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if total_loss is None:
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total_loss = loss
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else:
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total_loss += loss
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return total_loss
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return DSSIMMaskLoss
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def MSEMaskLossClass(keras):
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class MSEMaskLoss(object):
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def __init__(self, mask_list, is_tanh=False):
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self.mask_list = mask_list
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self.is_tanh = is_tanh
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def __call__(self,y_true, y_pred):
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K = keras.backend
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total_loss = None
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for mask in self.mask_list:
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if not self.is_tanh:
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loss = K.mean(K.square(y_true*mask - y_pred*mask))
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else:
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loss = K.mean(K.square( (y_true/2+0.5)*(mask/2+0.5) - (y_pred/2+0.5)*(mask/2+0.5) ))
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if total_loss is None:
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total_loss = loss
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else:
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total_loss += loss
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return total_loss
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return MSEMaskLoss
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def PixelShufflerClass(keras):
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class PixelShuffler(keras.engine.topology.Layer):
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def __init__(self, size=(2, 2), data_format=None, **kwargs):
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super(PixelShuffler, self).__init__(**kwargs)
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self.data_format = keras.utils.conv_utils.normalize_data_format(data_format)
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self.size = keras.utils.conv_utils.normalize_tuple(size, 2, 'size')
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def call(self, inputs):
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input_shape = keras.backend.int_shape(inputs)
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if len(input_shape) != 4:
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raise ValueError('Inputs should have rank ' +
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str(4) +
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'; Received input shape:', str(input_shape))
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if self.data_format == 'channels_first':
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batch_size, c, h, w = input_shape
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if batch_size is None:
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batch_size = -1
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rh, rw = self.size
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oh, ow = h * rh, w * rw
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oc = c // (rh * rw)
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out = keras.backend.reshape(inputs, (batch_size, rh, rw, oc, h, w))
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out = keras.backend.permute_dimensions(out, (0, 3, 4, 1, 5, 2))
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out = keras.backend.reshape(out, (batch_size, oc, oh, ow))
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return out
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elif self.data_format == 'channels_last':
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batch_size, h, w, c = input_shape
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if batch_size is None:
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batch_size = -1
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rh, rw = self.size
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oh, ow = h * rh, w * rw
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oc = c // (rh * rw)
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out = keras.backend.reshape(inputs, (batch_size, h, w, rh, rw, oc))
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out = keras.backend.permute_dimensions(out, (0, 1, 3, 2, 4, 5))
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out = keras.backend.reshape(out, (batch_size, oh, ow, oc))
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return out
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def compute_output_shape(self, input_shape):
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if len(input_shape) != 4:
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raise ValueError('Inputs should have rank ' +
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str(4) +
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'; Received input shape:', str(input_shape))
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if self.data_format == 'channels_first':
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height = input_shape[2] * self.size[0] if input_shape[2] is not None else None
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width = input_shape[3] * self.size[1] if input_shape[3] is not None else None
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channels = input_shape[1] // self.size[0] // self.size[1]
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if channels * self.size[0] * self.size[1] != input_shape[1]:
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raise ValueError('channels of input and size are incompatible')
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return (input_shape[0],
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channels,
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height,
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width)
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elif self.data_format == 'channels_last':
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height = input_shape[1] * self.size[0] if input_shape[1] is not None else None
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width = input_shape[2] * self.size[1] if input_shape[2] is not None else None
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channels = input_shape[3] // self.size[0] // self.size[1]
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if channels * self.size[0] * self.size[1] != input_shape[3]:
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raise ValueError('channels of input and size are incompatible')
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return (input_shape[0],
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height,
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width,
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channels)
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def get_config(self):
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config = {'size': self.size,
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'data_format': self.data_format}
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base_config = super(PixelShuffler, self).get_config()
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return dict(list(base_config.items()) + list(config.items()))
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return PixelShuffler
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def conv(keras, input_tensor, filters):
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x = input_tensor
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x = keras.layers.convolutional.Conv2D(filters, kernel_size=5, strides=2, padding='same')(x)
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x = keras.layers.advanced_activations.LeakyReLU(0.1)(x)
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return x
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def upscale(keras, input_tensor, filters, k_size=3):
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x = input_tensor
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x = keras.layers.convolutional.Conv2D(filters * 4, kernel_size=k_size, padding='same')(x)
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x = keras.layers.advanced_activations.LeakyReLU(0.1)(x)
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x = PixelShufflerClass(keras)()(x)
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return x
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def upscale4(keras, input_tensor, filters):
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x = input_tensor
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x = keras.layers.convolutional.Conv2D(filters * 16, kernel_size=3, padding='same')(x)
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x = keras.layers.advanced_activations.LeakyReLU(0.1)(x)
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x = PixelShufflerClass(keras)(size=(4, 4))(x)
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return x
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def res(keras, input_tensor, filters):
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x = input_tensor
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x = keras.layers.convolutional.Conv2D(filters, kernel_size=3, kernel_initializer=keras.initializers.RandomNormal(0, 0.02), use_bias=False, padding="same")(x)
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x = keras.layers.advanced_activations.LeakyReLU(alpha=0.2)(x)
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x = keras.layers.convolutional.Conv2D(filters, kernel_size=3, kernel_initializer=keras.initializers.RandomNormal(0, 0.02), use_bias=False, padding="same")(x)
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x = keras.layers.Add()([x, input_tensor])
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x = keras.layers.advanced_activations.LeakyReLU(alpha=0.2)(x)
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return x
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def resize_like(tf, keras, ref_tensor, input_tensor):
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def func(input_tensor, ref_tensor):
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H, W = ref_tensor.get_shape()[1], ref_tensor.get_shape()[2]
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return tf.image.resize_bilinear(input_tensor, [H.value, W.value])
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return keras.layers.Lambda(func, arguments={'ref_tensor':ref_tensor})(input_tensor)
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def total_variation_loss(keras, x):
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K = keras.backend
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assert K.ndim(x) == 4
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B,H,W,C = K.int_shape(x)
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a = K.square(x[:, :H - 1, :W - 1, :] - x[:, 1:, :W - 1, :])
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b = K.square(x[:, :H - 1, :W - 1, :] - x[:, :H - 1, 1:, :])
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return K.mean (a+b)
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