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import numpy as np |
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from core.leras import nn |
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tf = nn.tf |
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class Conv2DTranspose(nn.LayerBase): |
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""" |
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use_wscale enables weight scale (equalized learning rate) |
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if kernel_initializer is None, it will be forced to random_normal |
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""" |
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def __init__(self, in_ch, out_ch, kernel_size, strides=2, padding='SAME', use_bias=True, use_wscale=False, kernel_initializer=None, bias_initializer=None, trainable=True, dtype=None, **kwargs ): |
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if not isinstance(strides, int): |
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raise ValueError ("strides must be an int type") |
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kernel_size = int(kernel_size) |
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if dtype is None: |
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dtype = nn.floatx |
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self.in_ch = in_ch |
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self.out_ch = out_ch |
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self.kernel_size = kernel_size |
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self.strides = strides |
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self.padding = padding |
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self.use_bias = use_bias |
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self.use_wscale = use_wscale |
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self.kernel_initializer = kernel_initializer |
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self.bias_initializer = bias_initializer |
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self.trainable = trainable |
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self.dtype = dtype |
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super().__init__(**kwargs) |
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def build_weights(self): |
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kernel_initializer = self.kernel_initializer |
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if self.use_wscale: |
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gain = 1.0 if self.kernel_size == 1 else np.sqrt(2) |
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fan_in = self.kernel_size*self.kernel_size*self.in_ch |
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he_std = gain / np.sqrt(fan_in) |
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self.wscale = tf.constant(he_std, dtype=self.dtype ) |
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if kernel_initializer is None: |
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kernel_initializer = tf.initializers.random_normal(0, 1.0, dtype=self.dtype) |
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self.weight = tf.get_variable("weight", (self.kernel_size,self.kernel_size,self.out_ch,self.in_ch), dtype=self.dtype, initializer=kernel_initializer, trainable=self.trainable ) |
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if self.use_bias: |
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bias_initializer = self.bias_initializer |
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if bias_initializer is None: |
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bias_initializer = tf.initializers.zeros(dtype=self.dtype) |
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self.bias = tf.get_variable("bias", (self.out_ch,), dtype=self.dtype, initializer=bias_initializer, trainable=self.trainable ) |
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def get_weights(self): |
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weights = [self.weight] |
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if self.use_bias: |
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weights += [self.bias] |
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return weights |
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def forward(self, x): |
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shape = x.shape |
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if nn.data_format == "NHWC": |
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h,w,c = shape[1], shape[2], shape[3] |
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output_shape = tf.stack ( (tf.shape(x)[0], |
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self.deconv_length(w, self.strides, self.kernel_size, self.padding), |
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self.deconv_length(h, self.strides, self.kernel_size, self.padding), |
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self.out_ch) ) |
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strides = [1,self.strides,self.strides,1] |
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else: |
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c,h,w = shape[1], shape[2], shape[3] |
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output_shape = tf.stack ( (tf.shape(x)[0], |
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self.out_ch, |
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self.deconv_length(w, self.strides, self.kernel_size, self.padding), |
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self.deconv_length(h, self.strides, self.kernel_size, self.padding), |
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) ) |
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strides = [1,1,self.strides,self.strides] |
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weight = self.weight |
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if self.use_wscale: |
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weight = weight * self.wscale |
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x = tf.nn.conv2d_transpose(x, weight, output_shape, strides, padding=self.padding, data_format=nn.data_format) |
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if self.use_bias: |
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if nn.data_format == "NHWC": |
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bias = tf.reshape (self.bias, (1,1,1,self.out_ch) ) |
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else: |
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bias = tf.reshape (self.bias, (1,self.out_ch,1,1) ) |
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x = tf.add(x, bias) |
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return x |
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def __str__(self): |
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r = f"{self.__class__.__name__} : in_ch:{self.in_ch} out_ch:{self.out_ch} " |
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return r |
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def deconv_length(self, dim_size, stride_size, kernel_size, padding): |
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assert padding in {'SAME', 'VALID', 'FULL'} |
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if dim_size is None: |
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return None |
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if padding == 'VALID': |
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dim_size = dim_size * stride_size + max(kernel_size - stride_size, 0) |
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elif padding == 'FULL': |
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dim_size = dim_size * stride_size - (stride_size + kernel_size - 2) |
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elif padding == 'SAME': |
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dim_size = dim_size * stride_size |
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return dim_size |
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nn.Conv2DTranspose = Conv2DTranspose |
