def test_model_variables(self):
input_shape = (1, 512, 512, 3)
model = efficientdet_arch_keras.EfficientDetModel('efficientdet-d0')
model.build(input_shape)
eager_train_vars = sorted(
[var.name for var in model.trainable_variables])
eager_model_vars = sorted([var.name for var in model.variables])
with tf.Graph().as_default():
feats = tf.ones([1, 512, 512, 3])
model = efficientdet_arch_keras.EfficientDetModel(
'efficientdet-d0')
model.build(input_shape)
keras_train_vars = sorted(
[var.name for var in model.trainable_variables])
keras_model_vars = sorted([var.name for var in model.variables])
with tf.Graph().as_default():
feats = tf.ones([1, 512, 512, 3])
legacy_arch.efficientdet(feats, 'efficientdet-d0')
legacy_train_vars = sorted(
[var.name for var in tf.trainable_variables()])
legacy_model_vars = sorted(
[var.name for var in tf.global_variables()])
self.assertEqual(eager_train_vars, legacy_train_vars)
self.assertEqual(eager_model_vars, legacy_model_vars)
self.assertEqual(keras_train_vars, legacy_train_vars)
self.assertEqual(keras_model_vars, legacy_model_vars)
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)
# v = efficientdet_arch_keras.build_class_and_box_outputs(feats, config)
v = efficientdet_arch_keras.EfficientDetModel(config=config)(feats)
sess.run(tf.global_variables_initializer())
keras_class_out, keras_box_out = sess.run(v)
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.assertAllClose(keras_class_out[i - 3],
legacy_class_out[i],
rtol=1e-4,
atol=1e-4)
self.assertAllClose(keras_box_out[i - 3],
legacy_box_out[i],
rtol=1e-4,
atol=1e-4)
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) # pylint: disable=unused-variable
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 model_arch(feats, model_name=None, **kwargs):
"""Construct a model arch for keras models."""
config = hparams_config.get_efficientdet_config(model_name)
config.override(kwargs)
model = efficientdet_arch_keras.EfficientDetModel(config=config)
cls_out_list, box_out_list = model(feats)
# convert the list of model outputs to a dictionary with key=level.
assert len(cls_out_list) == config.max_level - config.min_level + 1
assert len(box_out_list) == config.max_level - config.min_level + 1
cls_outputs, box_outputs = {}, {}
for i in range(config.min_level, config.max_level + 1):
cls_outputs[i] = cls_out_list[i - config.min_level]
box_outputs[i] = box_out_list[i - config.min_level]
return cls_outputs, box_outputs