def test_crnn_net_three_branches(self):
feature_extractor_text_proto = """
crnn_net {
net_type: THREE_BRANCHES
conv_hyperparams {
op: CONV
regularizer { l2_regularizer { weight: 1e-4 } }
initializer { variance_scaling_initializer { } }
batch_norm { }
}
summarize_activations: false
}
"""
convnet_proto = convnet_pb2.Convnet()
text_format.Merge(feature_extractor_text_proto, convnet_proto)
convnet_object = convnet_builder.build(convnet_proto, True)
self.assertTrue(isinstance(convnet_object, crnn_net.CrnnNet))
test_image_shape = [2, 32, 128, 3]
test_input_image = tf.random_uniform(test_image_shape,
minval=0,
maxval=255.0,
dtype=tf.float32,
seed=1)
feature_maps = convnet_object.extract_features(test_input_image)
self.assertTrue(len(feature_maps) == 3)
print(
'Outputs of test_crnn_net_three_branches: {}'.format(feature_maps))
def test_resnet_50layer(self):
feature_extractor_text_proto = """
resnet {
net_type: SINGLE_BRANCH
net_depth: RESNET_50
conv_hyperparams {
op: CONV
regularizer { l2_regularizer { weight: 1e-4 } }
initializer { variance_scaling_initializer { } }
batch_norm { }
}
summarize_activations: false
}
"""
convnet_proto = convnet_pb2.Convnet()
text_format.Merge(feature_extractor_text_proto, convnet_proto)
convnet_object = convnet_builder.build(convnet_proto, True)
self.assertTrue(isinstance(convnet_object, resnet.Resnet50Layer))
test_image_shape = [2, 32, 128, 3]
test_input_image = tf.random_uniform(test_image_shape,
minval=0,
maxval=255.0,
dtype=tf.float32,
seed=1)
feature_maps = convnet_object.extract_features(test_input_image)
self.assertTrue(len(feature_maps) == 1)
print('Outputs of test_resnet_single_branch: {}'.format(feature_maps))
def test_build_stn_convnet_tiny(self):
text_proto = """
stn_convnet {
conv_hyperparams {
op: CONV
regularizer { l2_regularizer { weight: 1e-4 } }
initializer { variance_scaling_initializer { } }
batch_norm { decay: 0.99 }
}
tiny: true
}
"""
convnet_proto = convnet_pb2.Convnet()
text_format.Merge(text_proto, convnet_proto)
convnet_object = convnet_builder.build(convnet_proto, True)
self.assertTrue(isinstance(convnet_object, stn_convnet.StnConvnetTiny))
test_image_shape = [2, 64, 128, 3]
test_input_image = tf.random_uniform(test_image_shape,
minval=0,
maxval=255.0,
dtype=tf.float32,
seed=1)
feature_maps = convnet_object.extract_features(test_input_image)
self.assertTrue(len(feature_maps) == 1)
print(
'Outputs of test_build_stn_convnet_tiny: {}'.format(feature_maps))