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Commit 2186e9f2 authored by vishnubanna's avatar vishnubanna
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Merge branch 'yolo' of https://github.com/PurdueCAM2Project/tf-models into yolo

parents 669807e6 da0d0d27
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Showing with 73 additions and 74 deletions
official/vision/beta/projects/yolo/modeling/layers/nn_blocks.py
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......@@ -19,9 +19,9 @@ class Identity(ks.layers.Layer):
@ks.utils.register_keras_serializable(package='yolo')
class DarkConv(ks.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. The Layer also allows for feature grouping
suggested in the CSPNet paper
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. The Layer also allows for feature grouping
suggested in the CSPNet paper
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
......@@ -33,20 +33,20 @@ class DarkConv(ks.layers.Layer):
strides: integer of tuple how much to move the kernel after each kernel use
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
the how many pixels ina featur map to skip
use_bias: boolean to indicate wither to use bias in convolution layer
kernel_initializer: string to indicate which function to use to initialize weigths
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
bias_regularizer: string to indicate which function to use to regularizer bias
group_id: integer for which group of features to pass through the conv.
group_id: integer for which group of features to pass through the conv.
groups: integer for how many splits there should be in the convolution feature stack input
grouping_only: skip the convolution and only return the group of featuresindicated by grouping_only
use_bn: boolean for wether to use batchnormalization
use_sync_bn: boolean for wether sync batch normalization statistics
grouping_only: skip the convolution and only return the group of features indicated by grouping_only
use_bn: boolean for whether to use batch normalization
use_sync_bn: boolean for whether sync batch normalization statistics
of all batch norm layers to the models global statistics (across all input batches)
norm_moment: float for moment to use for batchnorm
norm_epsilon: float for batchnorm epsilon
norm_moment: float for moment to use for batch normalization
norm_epsilon: float for batch normalization epsilon
activation: string or None for activation function to use in layer,
if None activation is replaced by linear
leaky_alpha: float to use as alpha if activation function is leaky
......@@ -60,9 +60,9 @@ class DarkConv(ks.layers.Layer):
padding='same',
dilation_rate=(1, 1),
use_bias=True,
groups = 1,
group_id = 0,
grouping_only = False,
groups = 1,
group_id = 0,
grouping_only = False,
kernel_initializer='glorot_uniform',
bias_initializer='zeros',
bias_regularizer=None,
......@@ -74,7 +74,7 @@ class DarkConv(ks.layers.Layer):
activation='leaky',
leaky_alpha=0.1,
**kwargs):
# convolution params
self._filters = filters
......@@ -91,7 +91,7 @@ class DarkConv(ks.layers.Layer):
self._kernel_regularizer = kernel_regularizer
self._bias_regularizer = bias_regularizer
# batchnorm params
# batch normalization params
self._use_bn = use_bn
if self._use_bn:
self._use_bias = False
......@@ -137,7 +137,6 @@ class DarkConv(ks.layers.Layer):
kernel_regularizer=self._kernel_regularizer,
bias_regularizer=self._bias_regularizer)
#self.conv =tf.nn.convolution(filters=self._filters, strides=self._strides, padding=self._padding
if self._use_bn:
if self._use_sync_bn:
self.bn = tf.keras.layers.experimental.SyncBatchNormalization(
......@@ -162,7 +161,7 @@ class DarkConv(ks.layers.Layer):
def call(self, x):
if self._groups != 1:
x = tf.split(x, self._groups, axis=-1)
x = x[self._group_id] # grouping
x = x[self._group_id] # grouping
if not self._grouping_only:
x = self._zeropad(x)
x = self.conv(x)
......@@ -179,7 +178,7 @@ class DarkConv(ks.layers.Layer):
"padding": self._padding,
"dilation_rate": self._dilation_rate,
"use_bias": self._use_bias,
"groups": self._groups,
"groups": self._groups,
"group_id": self._group_id,
"grouping_only": self._grouping_only,
"kernel_initializer": self._kernel_initializer,
......@@ -203,22 +202,22 @@ class DarkConv(ks.layers.Layer):
@ks.utils.register_keras_serializable(package='yolo')
class DarkTiny(ks.layers.Layer):
"""
Automatic Maxpool Downsampling Convolution layer, created to make routing easier.
Automatic Maxpool Downsampling Convolution layer, created to make routing easier.
Args:
filters: integer for output depth, or the number of features to learn
use_bias: boolean to indicate wither to use bias in convolution layer
kernel_initializer: string to indicate which function to use to initialize weigths
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
bias_regularizer: string to indicate which function to use to regularizer bias
use_bn: boolean for wether to use batchnormalization
use_sync_bn: boolean for wether sync batch normalization statistics
of all batch norm layers to the models global statistics (across all input batches)
group_id: integer for which group of features to pass through the csp tiny stack.
kernel_regularizer: string to indicate which function to use to regularize weights
bias_regularizer: string to indicate which function to use to regularize bias
use_bn: boolean for whether to use batch normalization
use_sync_bn: boolean for whether to sync batch normalization statistics
of all batch norm layers to the models' global statistics (across all input batches)
group_id: integer for which group of features to pass through the csp tiny stack.
groups: integer for how many splits there should be in the convolution feature stack output
norm_moment: float for moment to use for batchnorm
norm_epsilon: float for batchnorm epsilon
norm_moment: float for moment to use for batch normalization
norm_epsilon: float for batch normalization epsilon
activation: string or None for activation function to use in layer,
if None activation is replaced by linear
**kwargs: Keyword Arguments
......@@ -314,22 +313,22 @@ class DarkResidual(ks.layers.Layer):
Args:
filters: integer for output depth, or the number of features to learn
use_bias: boolean to indicate wither to use bias in convolution layer
kernel_initializer: string to indicate which function to use to initialize weigths
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
bias_regularizer: string to indicate which function to use to regularizer bias
use_bn: boolean for wether to use batchnormalization
use_sync_bn: boolean for wether sync batch normalization statistics
use_bn: boolean for whether to use batch normalization
use_sync_bn: boolean for whether sync batch normalization statistics
of all batch norm layers to the models global statistics (across all input batches)
norm_moment: float for moment to use for batchnorm
norm_epsilon: float for batchnorm epsilon
norm_moment: float for moment to use for batch normalization
norm_epsilon: float for batch normalization epsilon
conv_activation: string or None for activation function to use in layer,
if None activation is replaced by linear
leaky_alpha: float to use as alpha if activation function is leaky
sc_activation: string for activation function to use in layer
downsample: boolean for if image input is larger than layer output, set downsample to True
so the dimentions are forced to match
so the dimensions are forced to match
**kwargs: Keyword Arguments
'''
......@@ -463,33 +462,33 @@ class DarkResidual(ks.layers.Layer):
@ks.utils.register_keras_serializable(package='yolo')
class CSPTiny(ks.layers.Layer):
"""
A Small size convolution block proposed in the CSPNet. The layer uses shortcuts, routing(concatnation), and feature grouping
in order to improve gradient variablity and allow for high efficency, low power residual learning for small networks.
A Small size convolution block proposed in the CSPNet. The layer uses shortcuts, routing(concatnation), and feature grouping
in order to improve gradient variablity and allow for high efficency, low power residual learning for small networks.
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:
filters: integer for output depth, or the number of features to learn
use_bias: boolean to indicate wither to use bias in convolution layer
kernel_initializer: string to indicate which function to use to initialize weigths
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 wether to use batchnormalization
use_bn: boolean for whether to use batch normalization
kernel_regularizer: string to indicate which function to use to regularizer weights
bias_regularizer: string to indicate which function to use to regularizer bias
use_sync_bn: boolean for wether sync batch normalization statistics
use_sync_bn: boolean for whether sync batch normalization statistics
of all batch norm layers to the models global statistics (across all input batches)
group_id: integer for which group of features to pass through the csp tiny stack.
group_id: integer for which group of features to pass through the csp tiny stack.
groups: integer for how many splits there should be in the convolution feature stack output
norm_moment: float for moment to use for batchnorm
norm_epsilon: float for batchnorm epsilon
norm_moment: float for moment to use for batch normalization
norm_epsilon: float for batch normalization epsilon
conv_activation: string or None for activation function to use in layer,
if None activation is replaced by linear
leaky_alpha: float to use as alpha if activation function is leaky
sc_activation: string for activation function to use in layer
downsample: boolean for if image input is larger than layer output, set downsample to True
so the dimentions are forced to match
so the dimensions are forced to match
**kwargs: Keyword Arguments
"""
def __init__(
......@@ -556,7 +555,7 @@ class CSPTiny(ks.layers.Layer):
strides=(1, 1),
padding='same',
use_bias=self._use_bias,
groups = self._groups,
groups = self._groups,
group_id = self._group_id,
kernel_initializer=self._kernel_initializer,
bias_initializer=self._bias_initializer,
......@@ -610,11 +609,11 @@ class CSPTiny(ks.layers.Layer):
def call(self, inputs):
x1 = self._convlayer1(inputs)
x2 = self._convlayer2(x1) # grouping
x2 = self._convlayer2(x1) # grouping
x3 = self._convlayer3(x2)
x4 = tf.concat([x3, x2], axis=-1) # csp partial using grouping
x5 = self._convlayer4(x4)
x = tf.concat([x1, x5], axis=-1) # csp connect
x = tf.concat([x1, x5], axis=-1) # csp connect
if self._downsample:
x = self._maxpool(x)
return x, x5
......@@ -643,33 +642,33 @@ class CSPTiny(ks.layers.Layer):
@ks.utils.register_keras_serializable(package='yolo')
class CSPDownSample(ks.layers.Layer):
"""
Down sampling layer to take the place of down sampleing done in Residual networks. This is
Down sampling layer to take the place of down sampleing done in Residual networks. This is
the first of 2 layers needed to convert any Residual Network model to a CSPNet. At the start of a new
level change, this CSPDownsample layer creates a learned identity that will act as a cross stage connection,
that is used to inform the inputs to the next stage. It is called cross stage partial because the number of filters
level change, this CSPDownsample layer creates a learned identity that will act as a cross stage connection,
that is used to inform the inputs to the next stage. It is called cross stage partial because the number of filters
required in every intermitent Residual layer is reduced by half. The sister layer will take the partial generated by
this layer and concatnate it with the output of the final residual layer in the stack to create a fully feature level
output. This concatnation merges the partial blocks of 2 levels as input to the next allowing the gradients of each
level to be more unique, and reducing the number of parameters required by each level by 50% while keeping accuracy
consistent.
this layer and concatnate it with the output of the final residual layer in the stack to create a fully feature level
output. This concatnation merges the partial blocks of 2 levels as input to the next allowing the gradients of each
level to be more unique, and reducing the number of parameters required by each level by 50% while keeping accuracy
consistent.
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:
filters: integer for output depth, or the number of features to learn
filter_reduce: integer dicating (filters//2) or the number of filters in the partial feature stack
filter_reduce: integer dicating (filters//2) or the number of filters in the partial feature stack
activation: string for activation function to use in 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
bias_regularizer: string to indicate which function to use to regularizer bias
use_bn: boolean for wether to use batchnormalization
use_sync_bn: boolean for wether sync batch normalization statistics
use_bn: boolean for whether to use batch normalization
use_sync_bn: boolean for whether sync batch normalization statistics
of all batch norm layers to the models global statistics (across all input batches)
norm_moment: float for moment to use for batchnorm
norm_epsilon: float for batchnorm epsilon
norm_moment: float for moment to use for batch normalization
norm_epsilon: float for batch normalization epsilon
**kwargs: Keyword Arguments
"""
def __init__(
......@@ -752,26 +751,26 @@ class CSPDownSample(ks.layers.Layer):
@ks.utils.register_keras_serializable(package='yolo')
class CSPConnect(ks.layers.Layer):
"""
Sister Layer to the CSPDownsample layer. Merges the partial feature stacks generated by the CSPDownsampling layer,
and the finaly output of the residual stack. Suggested in the CSPNet paper.
Sister Layer to the CSPDownsample layer. Merges the partial feature stacks generated by the CSPDownsampling layer,
and the finaly output of the residual stack. Suggested in the CSPNet paper.
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:
filters: integer for output depth, or the number of features to learn
filter_reduce: integer dicating (filters//2) or the number of filters in the partial feature stack
filter_reduce: integer dicating (filters//2) or the number of filters in the partial feature stack
activation: string for activation function to use in 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
bias_regularizer: string to indicate which function to use to regularizer bias
use_bn: boolean for wether to use batchnormalization
use_sync_bn: boolean for wether sync batch normalization statistics
use_bn: boolean for whether to use batch normalization
use_sync_bn: boolean for whether sync batch normalization statistics
of all batch norm layers to the models global statistics (across all input batches)
norm_moment: float for moment to use for batchnorm
norm_epsilon: float for batchnorm epsilon
norm_moment: float for moment to use for batch normalization
norm_epsilon: float for batch normalization epsilon
**kwargs: Keyword Arguments
"""
def __init__(
......
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