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f3507ef | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 | # Copyright 2023 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for assemblenet++ network."""
from absl.testing import parameterized
import numpy as np
import tensorflow as tf, tf_keras
from official.projects.assemblenet.configs import assemblenet as asn_config
from official.projects.assemblenet.modeling import assemblenet_plus as asnp
class AssembleNetPlusTest(parameterized.TestCase, tf.test.TestCase):
@parameterized.parameters((50, True, ''), (50, False, ''),
(50, False, 'peer'), (50, True, 'peer'),
(50, True, 'self'), (50, False, 'self'))
def test_network_creation(self, depth, use_object_input, attention_mode):
batch_size = 2
num_frames = 32
img_size = 64
num_classes = 101 # ufc-101
num_object_classes = 151 # 151 is for ADE-20k
if use_object_input:
vid_input = (batch_size * num_frames, img_size, img_size, 3)
obj_input = (batch_size * num_frames, img_size, img_size,
num_object_classes)
input_specs = (tf_keras.layers.InputSpec(shape=(vid_input)),
tf_keras.layers.InputSpec(shape=(obj_input)))
vid_inputs = np.random.rand(batch_size * num_frames, img_size, img_size,
3)
obj_inputs = np.random.rand(batch_size * num_frames, img_size, img_size,
num_object_classes)
inputs = [vid_inputs, obj_inputs]
# We are using the full_asnp50_structure, since we feed both video and
# object.
model_structure = asn_config.full_asnp50_structure # Uses object input.
edge_weights = asn_config.full_asnp_structure_weights
else:
# video input: (batch_size, FLAGS.num_frames, image_size, image_size, 3)
input_specs = tf_keras.layers.InputSpec(
shape=(batch_size, num_frames, img_size, img_size, 3))
inputs = np.random.rand(batch_size, num_frames, img_size, img_size, 3)
# Here, we are using model_structures.asn50_structure for AssembleNet++
# instead of full_asnp50_structure. By using asn50_structure, it
# essentially becomes AssembleNet++ without objects, only requiring RGB
# inputs (and optical flow to be computed inside the model).
model_structure = asn_config.asn50_structure
edge_weights = asn_config.asn_structure_weights
model = asnp.assemblenet_plus(
assemblenet_depth=depth,
num_classes=num_classes,
num_frames=num_frames,
model_structure=model_structure,
model_edge_weights=edge_weights,
input_specs=input_specs,
use_object_input=use_object_input,
attention_mode=attention_mode,
)
outputs = model(inputs)
self.assertAllEqual(outputs.shape.as_list(), [batch_size, num_classes])
if __name__ == '__main__':
tf.test.main()
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