Merge branch 'yolo' of https://github.com/PurdueCAM2Project/tf-models into yolo

official/vision/beta/projects/yolo/modeling/layers/nn_blocks.py

@@ -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.
       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
@@ -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(
@@ -207,18 +206,18 @@ class DarkTiny(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
-                    of all batch norm layers to the models global statistics (across all input batches)
+      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
 
   '''
@@ -472,24 +471,24 @@ class CSPTiny(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
-      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.
       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__(
@@ -665,11 +664,11 @@ class CSPDownSample(ks.layers.Layer):
       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__(
@@ -767,11 +766,11 @@ class CSPConnect(ks.layers.Layer):
       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__(