| import numpy as np |
| from face_feature.core.leras import nn |
| tf = nn.tf |
|
|
| class Conv2D(nn.LayerBase): |
| """ |
| default kernel_initializer - CA |
| use_wscale bool enables equalized learning rate, if kernel_initializer is None, it will be forced to random_normal |
| |
| |
| """ |
| def __init__(self, in_ch, out_ch, kernel_size, strides=1, padding='SAME', dilations=1, use_bias=True, use_wscale=False, kernel_initializer=None, bias_initializer=None, trainable=True, dtype=None, **kwargs ): |
| if not isinstance(strides, int): |
| raise ValueError ("strides must be an int type") |
| if not isinstance(dilations, int): |
| raise ValueError ("dilations must be an int type") |
| kernel_size = int(kernel_size) |
|
|
| if dtype is None: |
| dtype = nn.floatx |
|
|
| if isinstance(padding, str): |
| if padding == "SAME": |
| padding = ( (kernel_size - 1) * dilations + 1 ) // 2 |
| elif padding == "VALID": |
| padding = 0 |
| else: |
| raise ValueError ("Wrong padding type. Should be VALID SAME or INT or 4x INTs") |
|
|
| if isinstance(padding, int): |
| if padding != 0: |
| if nn.data_format == "NHWC": |
| padding = [ [0,0], [padding,padding], [padding,padding], [0,0] ] |
| else: |
| padding = [ [0,0], [0,0], [padding,padding], [padding,padding] ] |
| else: |
| padding = None |
|
|
| if nn.data_format == "NHWC": |
| strides = [1,strides,strides,1] |
| else: |
| strides = [1,1,strides,strides] |
|
|
| if nn.data_format == "NHWC": |
| dilations = [1,dilations,dilations,1] |
| else: |
| dilations = [1,1,dilations,dilations] |
|
|
| self.in_ch = in_ch |
| self.out_ch = out_ch |
| self.kernel_size = kernel_size |
| self.strides = strides |
| self.padding = padding |
| self.dilations = dilations |
| self.use_bias = use_bias |
| self.use_wscale = use_wscale |
| self.kernel_initializer = kernel_initializer |
| self.bias_initializer = bias_initializer |
| self.trainable = trainable |
| self.dtype = dtype |
| super().__init__(**kwargs) |
|
|
| def build_weights(self): |
| kernel_initializer = self.kernel_initializer |
| if self.use_wscale: |
| gain = 1.0 if self.kernel_size == 1 else np.sqrt(2) |
| fan_in = self.kernel_size*self.kernel_size*self.in_ch |
| he_std = gain / np.sqrt(fan_in) |
| self.wscale = tf.constant(he_std, dtype=self.dtype ) |
| if kernel_initializer is None: |
| kernel_initializer = tf.initializers.random_normal(0, 1.0, dtype=self.dtype) |
|
|
| if kernel_initializer is None: |
| kernel_initializer = nn.initializers.ca() |
|
|
| self.weight = tf.get_variable("weight", (self.kernel_size,self.kernel_size,self.in_ch,self.out_ch), dtype=self.dtype, initializer=kernel_initializer, trainable=self.trainable ) |
|
|
| if self.use_bias: |
| bias_initializer = self.bias_initializer |
| if bias_initializer is None: |
| bias_initializer = tf.initializers.zeros(dtype=self.dtype) |
|
|
| self.bias = tf.get_variable("bias", (self.out_ch,), dtype=self.dtype, initializer=bias_initializer, trainable=self.trainable ) |
|
|
| def get_weights(self): |
| weights = [self.weight] |
| if self.use_bias: |
| weights += [self.bias] |
| return weights |
|
|
| def forward(self, x): |
| weight = self.weight |
| if self.use_wscale: |
| weight = weight * self.wscale |
|
|
| if self.padding is not None: |
| x = tf.pad (x, self.padding, mode='CONSTANT') |
|
|
| x = tf.nn.conv2d(x, weight, self.strides, 'VALID', dilations=self.dilations, data_format=nn.data_format) |
| if self.use_bias: |
| if nn.data_format == "NHWC": |
| bias = tf.reshape (self.bias, (1,1,1,self.out_ch) ) |
| else: |
| bias = tf.reshape (self.bias, (1,self.out_ch,1,1) ) |
| x = tf.add(x, bias) |
| return x |
|
|
| def __str__(self): |
| r = f"{self.__class__.__name__} : in_ch:{self.in_ch} out_ch:{self.out_ch} " |
|
|
| return r |
| nn.Conv2D = Conv2D |
