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Commit dc2af579 authored by anivegesana's avatar anivegesana
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Remove _use_bias parameter in DarkConv

parent e0ada7c6
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Showing with 8 additions and 23 deletions
official/vision/beta/projects/yolo/modeling/layers/nn_blocks.py
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......@@ -16,7 +16,7 @@ class Identity(tf.keras.layers.Layer):
class ConvBN(tf.keras.layers.Layer):
'''
Modified Convolution layer to match that of the DarkNet Library. The Layer is a standards combination of Conv BatchNorm Activation,
however, the use of bias in the conv is determined by the use of batch normalization.
however, the use of bias in the conv is determined by the use of batch normalization.
Cross Stage Partial networks (CSPNets) were proposed in:
[1] Chien-Yao Wang, Hong-Yuan Mark Liao, I-Hau Yeh, Yueh-Hua Wu, Ping-Yang Chen, Jun-Wei Hsieh
......@@ -29,7 +29,6 @@ class ConvBN(tf.keras.layers.Layer):
padding: string 'valid' or 'same', if same, then pad the image, else do not
dialtion_rate: tuple to indicate how much to modulate kernel weights and
how many pixels in a feature map to skip
use_bias: boolean to indicate whether to use bias in convolution layer
kernel_initializer: string to indicate which function to use to initialize weights
bias_initializer: string to indicate which function to use to initialize bias
kernel_regularizer: string to indicate which function to use to regularizer weights
......@@ -51,7 +50,6 @@ class ConvBN(tf.keras.layers.Layer):
strides=(1, 1),
padding='same',
dilation_rate=(1, 1),
use_bias=True,
kernel_initializer='glorot_uniform',
bias_initializer='zeros',
bias_regularizer=None,
......@@ -70,7 +68,6 @@ class ConvBN(tf.keras.layers.Layer):
self._strides = strides
self._padding = padding
self._dilation_rate = dilation_rate
self._use_bias = use_bias
self._kernel_initializer = kernel_initializer
self._bias_initializer = bias_initializer
self._kernel_regularizer = kernel_regularizer
......@@ -78,8 +75,6 @@ class ConvBN(tf.keras.layers.Layer):
# batch normalization params
self._use_bn = use_bn
if self._use_bn:
self._use_bias = False
self._use_sync_bn = use_sync_bn
self._norm_moment = norm_momentum
self._norm_epsilon = norm_epsilon
......@@ -107,13 +102,15 @@ class ConvBN(tf.keras.layers.Layer):
else:
self._zeropad = Identity()
use_bias = not self._use_bn
self.conv = tf.keras.layers.Conv2D(
filters=self._filters,
kernel_size=self._kernel_size,
strides=self._strides,
padding="valid",
dilation_rate=self._dilation_rate,
use_bias=self._use_bias,
use_bias=use_bias,
kernel_initializer=self._kernel_initializer,
bias_initializer=self._bias_initializer,
kernel_regularizer=self._kernel_regularizer,
......@@ -157,7 +154,6 @@ class ConvBN(tf.keras.layers.Layer):
"strides": self._strides,
"padding": self._padding,
"dilation_rate": self._dilation_rate,
"use_bias": self._use_bias,
"kernel_initializer": self._kernel_initializer,
"bias_initializer": self._bias_initializer,
"bias_regularizer": self._bias_regularizer,
......@@ -183,7 +179,6 @@ class DarkResidual(tf.keras.layers.Layer):
Args:
filters: integer for output depth, or the number of features to learn
use_bias: boolean to indicate whether to use bias in convolution layer
kernel_initializer: string to indicate which function to use to initialize weights
bias_initializer: string to indicate which function to use to initialize bias
kernel_regularizer: string to indicate which function to use to regularizer weights
......@@ -205,7 +200,6 @@ class DarkResidual(tf.keras.layers.Layer):
def __init__(self,
filters=1,
filter_scale=2,
use_bias=True,
kernel_initializer='glorot_uniform',
bias_initializer='zeros',
kernel_regularizer=None,
......@@ -226,7 +220,6 @@ class DarkResidual(tf.keras.layers.Layer):
# ConvBN params
self._filters = filters
self._filter_scale = filter_scale
self._use_bias = use_bias
self._kernel_initializer = kernel_initializer
self._bias_initializer = bias_initializer
self._bias_regularizer = bias_regularizer
......@@ -247,7 +240,6 @@ class DarkResidual(tf.keras.layers.Layer):
def build(self, input_shape):
_dark_conv_args = {
"use_bias": self._use_bias,
"kernel_initializer": self._kernel_initializer,
"bias_initializer": self._bias_initializer,
"bias_regularizer": self._bias_regularizer,
......@@ -302,7 +294,6 @@ class DarkResidual(tf.keras.layers.Layer):
# used to store/share parameters to reconstruct the model
layer_config = {
"filters": self._filters,
"use_bias": self._use_bias,
"kernel_initializer": self._kernel_initializer,
"bias_initializer": self._bias_initializer,
"kernel_regularizer": self._kernel_regularizer,
......@@ -331,7 +322,6 @@ class CSPTiny(tf.keras.layers.Layer):
Args:
filters: integer for output depth, or the number of features to learn
use_bias: boolean to indicate whether to use bias in convolution layer
kernel_initializer: string to indicate which function to use to initialize weights
bias_initializer: string to indicate which function to use to initialize bias
use_bn: boolean for whether to use batch normalization
......@@ -353,7 +343,6 @@ class CSPTiny(tf.keras.layers.Layer):
"""
def __init__(self,
filters=1,
use_bias=True,
kernel_initializer='glorot_uniform',
bias_initializer='zeros',
bias_regularizer=None,
......@@ -371,7 +360,6 @@ class CSPTiny(tf.keras.layers.Layer):
# ConvBN params
self._filters = filters
self._use_bias = use_bias
self._kernel_initializer = kernel_initializer
self._bias_initializer = bias_initializer
self._bias_regularizer = bias_regularizer
......@@ -394,7 +382,6 @@ class CSPTiny(tf.keras.layers.Layer):
def build(self, input_shape):
_dark_conv_args = {
"use_bias": self._use_bias,
"kernel_initializer": self._kernel_initializer,
"bias_initializer": self._bias_initializer,
"bias_regularizer": self._bias_regularizer,
......@@ -416,7 +403,6 @@ class CSPTiny(tf.keras.layers.Layer):
kernel_size=(3, 3),
strides=(1, 1),
padding='same',
use_bias=self._use_bias,
kernel_initializer=self._kernel_initializer,
bias_initializer=self._bias_initializer,
bias_regularizer=self._bias_regularizer,
......@@ -463,7 +449,6 @@ class CSPTiny(tf.keras.layers.Layer):
# used to store/share parameters to reconsturct the model
layer_config = {
"filters": self._filters,
"use_bias": self._use_bias,
"strides": self._strides,
"kernel_initializer": self._kernel_initializer,
"bias_initializer": self._bias_initializer,
......@@ -671,16 +656,16 @@ class CSPConnect(tf.keras.layers.Layer):
class CSPStack(tf.keras.layers.Layer):
"""
CSP full stack, combines the route and the connect in case you dont want to jsut quickly wrap an existing callable or list of layers to
make it a cross stage partial. Added for ease of use. you should be able to wrap any layer stack with a CSP independent of wether it belongs
to the Darknet family. if filter_scale = 2, then the blocks in the stack passed into the the CSP stack should also have filters = filters/filter_scale
CSP full stack, combines the route and the connect in case you dont want to jsut quickly wrap an existing callable or list of layers to
make it a cross stage partial. Added for ease of use. you should be able to wrap any layer stack with a CSP independent of wether it belongs
to the Darknet family. if filter_scale = 2, then the blocks in the stack passed into the the CSP stack should also have filters = filters/filter_scale
Cross Stage Partial networks (CSPNets) were proposed in:
[1] Chien-Yao Wang, Hong-Yuan Mark Liao, I-Hau Yeh, Yueh-Hua Wu, Ping-Yang Chen, Jun-Wei Hsieh
CSPNet: A New Backbone that can Enhance Learning Capability of CNN. arXiv:1911.11929
Args:
model_to_wrap: callable Model or a list of callable objects that will process the output of CSPRoute, and be input into CSPConnect.
model_to_wrap: callable Model or a list of callable objects that will process the output of CSPRoute, and be input into CSPConnect.
list will be called sequentially.
downsample: down_sample the input
filters: integer for output depth, or the number of features to learn
......
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