def test_output_shapes(self, resnet_type, output_layer_names):
if resnet_type == 'resnet_v1_34':
model = resnet_v1.resnet_v1_34(weights=None)
else:
model = resnet_v1.resnet_v1_18(weights=None)
outputs = [
model.get_layer(output_layer_name).output
for output_layer_name in output_layer_names
]
resnet_model = tf.keras.models.Model(inputs=model.input,
outputs=outputs)
outputs = resnet_model(np.zeros((2, 64, 64, 3), dtype=np.float32))
# Check the shape of 'conv2_block3_out':
self.assertEqual(outputs[0].shape, [2, 16, 16, 64])
# Check the shape of 'conv3_block4_out':
self.assertEqual(outputs[1].shape, [2, 8, 8, 128])
# Check the shape of 'conv4_block6_out':
self.assertEqual(outputs[2].shape, [2, 4, 4, 256])
# Check the shape of 'conv5_block3_out':
self.assertEqual(outputs[3].shape, [2, 2, 2, 512])
def __init__(self, resnet_type, channel_means=(0., 0., 0.),
channel_stds=(1., 1., 1.), bgr_ordering=False):
"""Initializes the feature extractor with a specific ResNet architecture.
Args:
resnet_type: A string specifying which kind of ResNet to use. Currently
only `resnet_v1_50` and `resnet_v1_101` are supported.
channel_means: A tuple of floats, denoting the mean of each channel
which will be subtracted from it.
channel_stds: A tuple of floats, denoting the standard deviation of each
channel. Each channel will be divided by its standard deviation value.
bgr_ordering: bool, if set will change the channel ordering to be in the
[blue, red, green] order.
"""
super(CenterNetResnetV1FpnFeatureExtractor, self).__init__(
channel_means=channel_means, channel_stds=channel_stds,
bgr_ordering=bgr_ordering)
if resnet_type == 'resnet_v1_50':
self._base_model = tf.keras.applications.ResNet50(weights=None)
elif resnet_type == 'resnet_v1_101':
self._base_model = tf.keras.applications.ResNet101(weights=None)
elif resnet_type == 'resnet_v1_18':
self._base_model = resnet_v1.resnet_v1_18(weights=None)
elif resnet_type == 'resnet_v1_34':
self._base_model = resnet_v1.resnet_v1_34(weights=None)
else:
raise ValueError('Unknown Resnet Model {}'.format(resnet_type))
output_layers = _RESNET_MODEL_OUTPUT_LAYERS[resnet_type]
outputs = [self._base_model.get_layer(output_layer_name).output
for output_layer_name in output_layers]
self._resnet_model = tf.keras.models.Model(inputs=self._base_model.input,
outputs=outputs)
resnet_outputs = self._resnet_model(self._base_model.input)
# Construct the top-down feature maps.
top_layer = resnet_outputs[-1]
residual_op = tf.keras.layers.Conv2D(filters=256, kernel_size=1,
strides=1, padding='same')
top_down = residual_op(top_layer)
num_filters_list = [256, 128, 64]
for i, num_filters in enumerate(num_filters_list):
level_ind = 2 - i
# Upsample.
upsample_op = tf.keras.layers.UpSampling2D(2, interpolation='nearest')
top_down = upsample_op(top_down)
# Residual (skip-connection) from bottom-up pathway.
residual_op = tf.keras.layers.Conv2D(filters=num_filters, kernel_size=1,
strides=1, padding='same')
residual = residual_op(resnet_outputs[level_ind])
# Merge.
top_down = top_down + residual
next_num_filters = num_filters_list[i+1] if i + 1 <= 2 else 64
conv = tf.keras.layers.Conv2D(filters=next_num_filters,
kernel_size=3, strides=1, padding='same')
top_down = conv(top_down)
top_down = tf.keras.layers.BatchNormalization()(top_down)
top_down = tf.keras.layers.ReLU()(top_down)
self._feature_extractor_model = tf.keras.models.Model(
inputs=self._base_model.input, outputs=top_down)
'output_layer_names': [
'conv2_block3_out', 'conv3_block4_out', 'conv4_block6_out',
'conv5_block3_out'
]
}, {
'resnet_type':
'resnet_v1_18',
'output_layer_names': [
'conv2_block2_out', 'conv3_block2_out', 'conv4_block2_out',
'conv5_block2_out'
]
})
def test_output_shapes(self, resnet_type, output_layer_names):
if resnet_type == 'resnet_v1_34':
<<<<<<< HEAD
model = resnet_v1.resnet_v1_34(weights=None)
else:
model = resnet_v1.resnet_v1_18(weights=None)
=======
model = resnet_v1.resnet_v1_34(input_shape=(64, 64, 3), weights=None)
else:
model = resnet_v1.resnet_v1_18(input_shape=(64, 64, 3), weights=None)
>>>>>>> a811a3b7e640722318ad868c99feddf3f3063e36
outputs = [
model.get_layer(output_layer_name).output
for output_layer_name in output_layer_names
]
resnet_model = tf.keras.models.Model(inputs=model.input, outputs=outputs)
outputs = resnet_model(np.zeros((2, 64, 64, 3), dtype=np.float32))
# Check the shape of 'conv2_block3_out':