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110 lines
4.2 KiB
Python
110 lines
4.2 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 DepthwiseConv2D(nn.LayerBase):
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"""
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default kernel_initializer - CA
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use_wscale bool enables equalized learning rate, 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, kernel_size, strides=1, padding='SAME', depth_multiplier=1, dilations=1, 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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if not isinstance(dilations, int):
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raise ValueError ("dilations 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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if isinstance(padding, str):
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if padding == "SAME":
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padding = ( (kernel_size - 1) * dilations + 1 ) // 2
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elif padding == "VALID":
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padding = 0
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else:
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raise ValueError ("Wrong padding type. Should be VALID SAME or INT or 4x INTs")
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if isinstance(padding, int):
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if padding != 0:
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if nn.data_format == "NHWC":
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padding = [ [0,0], [padding,padding], [padding,padding], [0,0] ]
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else:
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padding = [ [0,0], [0,0], [padding,padding], [padding,padding] ]
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else:
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padding = None
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if nn.data_format == "NHWC":
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strides = [1,strides,strides,1]
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else:
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strides = [1,1,strides,strides]
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if nn.data_format == "NHWC":
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dilations = [1,dilations,dilations,1]
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else:
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dilations = [1,1,dilations,dilations]
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self.in_ch = in_ch
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self.depth_multiplier = depth_multiplier
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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.dilations = dilations
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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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#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.in_ch,self.depth_multiplier), 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.in_ch*self.depth_multiplier,), 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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weight = self.weight
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if self.use_wscale:
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weight = weight * self.wscale
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if self.padding is not None:
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x = tf.pad (x, self.padding, mode='CONSTANT')
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x = tf.nn.depthwise_conv2d(x, weight, self.strides, 'VALID', 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.in_ch*self.depth_multiplier) )
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else:
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bias = tf.reshape (self.bias, (1,self.in_ch*self.depth_multiplier,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} depth_multiplier:{self.depth_multiplier} "
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return r
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nn.DepthwiseConv2D = DepthwiseConv2D |