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Commit 66e8a904 authored by Kaushik Shivakumar's avatar Kaushik Shivakumar
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make fixes

parent 3475ebda
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Showing with 6 additions and 5 deletions
research/object_detection/meta_architectures/context_rcnn_lib.py
View file @ 66e8a904
...@@ -61,10 +61,11 @@ class AttentionBlock(tf.keras.layers.Layer): ...@@ -61,10 +61,11 @@ class AttentionBlock(tf.keras.layers.Layer):
def build(self, input_shapes): def build(self, input_shapes):
self.feature_proj = ContextProjection(input_shapes[0][-1], self.freeze_batchnorm) self.feature_proj = ContextProjection(input_shapes[0][-1], self.freeze_batchnorm)
self.key_proj.build(input_shapes[0]) #self.key_proj.build(input_shapes[0])
self.val_proj.build(input_shapes[0]) #self.val_proj.build(input_shapes[0])
self.query_proj.build(input_shapes[0]) #self.query_proj.build(input_shapes[0])
self.feature_proj.build(input_shapes[0]) #self.feature_proj.build(input_shapes[0])
pass
def filter_weight_value(self, weights, values, valid_mask): def filter_weight_value(self, weights, values, valid_mask):
"""Filters weights and values based on valid_mask. """Filters weights and values based on valid_mask.
...@@ -140,7 +141,7 @@ class AttentionBlock(tf.keras.layers.Layer): ...@@ -140,7 +141,7 @@ class AttentionBlock(tf.keras.layers.Layer):
projected_features = layer(features, is_training) projected_features = layer(features, is_training)
projected_features = tf.reshape((batch_size, -1, bottleneck_dimension))(projected_features) projected_features = tf.reshape(projected_features, [batch_size, -1, bottleneck_dimension])
print(projected_features.shape) print(projected_features.shape)
if normalize: if normalize:
......
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