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https://github.com/iperov/DeepFaceLab.git
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changing SubpixelUpscaler to variable H,W dims,
tensorflow backend : using depth_to_space in SubpixelUpscaler, so training speed increased by 4%
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iperov
parent
689aefeb2f
commit
4683c362ac
1 changed files with 114 additions and 63 deletions
177
nnlib/nnlib.py
177
nnlib/nnlib.py
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@ -321,84 +321,135 @@ NLayerDiscriminator = nnlib.NLayerDiscriminator
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nnlib.dssim = dssim
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class PixelShuffler(KL.Layer):
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def __init__(self, size=(2, 2), data_format='channels_last', **kwargs):
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super(PixelShuffler, self).__init__(**kwargs)
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self.data_format = data_format
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self.size = size
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if 'tensorflow' in backend:
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class PixelShuffler(keras.layers.Layer):
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def __init__(self, size=(2, 2), data_format='channels_last', **kwargs):
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super(PixelShuffler, self).__init__(**kwargs)
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self.data_format = data_format
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self.size = size
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def call(self, inputs):
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input_shape = K.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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def call(self, inputs):
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input_shape = K.shape(inputs)
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if K.int_shape(input_shape)[0] != 4:
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raise ValueError('Inputs should have rank 4; Received input shape:', str(K.int_shape(inputs)))
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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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if self.data_format == 'channels_first':
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return K.tf.depth_to_space(inputs, self.size[0], 'NCHW')
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out = K.reshape(inputs, (batch_size, rh, rw, oc, h, w))
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out = K.permute_dimensions(out, (0, 3, 4, 1, 5, 2))
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out = K.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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return K.tf.depth_to_space(inputs, self.size[0], 'NHWC')
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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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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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out = K.reshape(inputs, (batch_size, h, w, rh, rw, oc))
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out = K.permute_dimensions(out, (0, 1, 3, 2, 4, 5))
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out = K.reshape(out, (batch_size, oh, ow, oc))
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return out
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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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def compute_output_shape(self, input_shape):
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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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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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return (input_shape[0],
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channels,
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height,
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width)
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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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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[1]:
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raise ValueError('channels of input and size are incompatible')
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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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channels,
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height,
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width)
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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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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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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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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 dict(list(base_config.items()) + list(config.items()))
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else:
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class PixelShuffler(KL.Layer):
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def __init__(self, size=(2, 2), data_format='channels_last', **kwargs):
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super(PixelShuffler, self).__init__(**kwargs)
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self.data_format = data_format
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self.size = size
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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 call(self, inputs):
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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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input_shape = K.shape(inputs)
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if K.int_shape(input_shape)[0] != 4:
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raise ValueError('Inputs should have rank 4; Received input shape:', str(K.int_shape(inputs)))
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return dict(list(base_config.items()) + list(config.items()))
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if self.data_format == 'channels_first':
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batch_size, c, h, w = input_shape[0], K.int_shape(inputs)[1], input_shape[2], input_shape[3]
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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 = K.reshape(inputs, (batch_size, rh, rw, oc, h, w))
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out = K.permute_dimensions(out, (0, 3, 4, 1, 5, 2))
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out = K.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[0], input_shape[1], input_shape[2], K.int_shape(inputs)[-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 = K.reshape(inputs, (batch_size, h, w, rh, rw, oc))
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out = K.permute_dimensions(out, (0, 1, 3, 2, 4, 5))
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out = K.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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nnlib.PixelShuffler = PixelShuffler
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nnlib.SubpixelUpscaler = PixelShuffler
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