def test_build_feature_network(self):
config = hparams_config.get_efficientdet_config('efficientdet-d0')
with tf.Session(graph=tf.Graph()) as sess:
inputs = {
0: tf.ones([1, 512, 512, 3]),
1: tf.ones([1, 256, 256, 16]),
2: tf.ones([1, 128, 128, 24]),
3: tf.ones([1, 64, 64, 40]),
4: tf.ones([1, 32, 32, 112]),
5: tf.ones([1, 16, 16, 320])
}
tf.random.set_random_seed(SEED)
new_feats1 = efficientdet_arch_keras.build_feature_network(
inputs, config)
sess.run(tf.global_variables_initializer())
new_feats1 = sess.run(new_feats1)
with tf.Session(graph=tf.Graph()) as sess:
inputs = {
0: tf.ones([1, 512, 512, 3]),
1: tf.ones([1, 256, 256, 16]),
2: tf.ones([1, 128, 128, 24]),
3: tf.ones([1, 64, 64, 40]),
4: tf.ones([1, 32, 32, 112]),
5: tf.ones([1, 16, 16, 320])
}
tf.random.set_random_seed(SEED)
new_feats2 = legacy_arch.build_feature_network(inputs, config)
sess.run(tf.global_variables_initializer())
new_feats2 = sess.run(new_feats2)
for i in range(config.min_level, config.max_level + 1):
self.assertAllEqual(new_feats1[i], new_feats2[i])
def test_model_output(self):
inputs_shape = [1, 512, 512, 3]
config = hparams_config.get_efficientdet_config('efficientdet-d0')
with tf.Session(graph=tf.Graph()) as sess:
feats = tf.ones(inputs_shape)
tf.random.set_random_seed(SEED)
feats = efficientdet_arch_keras.build_backbone(feats, config)
feats = efficientdet_arch_keras.build_feature_network(
feats, config)
feats = efficientdet_arch_keras.build_class_and_box_outputs(
feats, config)
# TODO(tanmingxing): Fix the failure for keras Model.
# feats = efficientdet_arch_keras.EfficientDetModel(config=config)(feats)
sess.run(tf.global_variables_initializer())
keras_class_out, keras_box_out = sess.run(feats)
with tf.Session(graph=tf.Graph()) as sess:
feats = tf.ones(inputs_shape)
tf.random.set_random_seed(SEED)
feats = legacy_arch.efficientdet(feats, config=config)
sess.run(tf.global_variables_initializer())
legacy_class_out, legacy_box_out = sess.run(feats)
for i in range(3, 8):
self.assertAllEqual(keras_class_out[i - 3], legacy_class_out[i])
self.assertAllEqual(keras_box_out[i - 3], legacy_box_out[i])
feats = tf.ones(inputs_shape)
tf.random.set_random_seed(SEED)
model = efficientdet_arch_keras.EfficientDetModel(config=config)
eager_class_out, eager_box_out = model(feats)
for i in range(3, 8):
# TODO(tanmingxing): fix the failing case.
self.assertAllEqual(eager_class_out[i - 3], legacy_class_out[i])
self.assertAllEqual(eager_box_out[i - 3], legacy_box_out[i])
def test_model_output(self):
inputs_shape = [1, 512, 512, 3]
config = hparams_config.get_efficientdet_config('efficientdet-d0')
with tf.Session(graph=tf.Graph()) as sess:
feats = tf.ones(inputs_shape)
tf.random.set_random_seed(SEED)
feats, _ = efficientdet_arch_keras.build_backbone(feats, config)
feats = efficientdet_arch_keras.build_feature_network(
feats, config)
feats = efficientdet_arch_keras.build_class_and_box_outputs(
feats, config)
sess.run(tf.global_variables_initializer())
class_output1, box_output1 = sess.run(feats)
with tf.Session(graph=tf.Graph()) as sess:
feats = tf.ones(inputs_shape)
tf.random.set_random_seed(SEED)
feats = legacy_arch.build_backbone(feats, config)
feats = legacy_arch.build_feature_network(feats, config)
feats = legacy_arch.build_class_and_box_outputs(feats, config)
sess.run(tf.global_variables_initializer())
class_output2, box_output2 = sess.run(feats)
for i in range(3, 8):
self.assertAllEqual(class_output1[i], class_output2[i])
self.assertAllEqual(box_output1[i], box_output2[i])