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107 lines
4.4 KiB
Python
107 lines
4.4 KiB
Python
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) # He init
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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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#if kernel_initializer is None:
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# kernel_initializer = nn.initializers.ca()
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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 |