def build_model(self, keras=False):
with tf.Graph().as_default():
config = hparams_config.get_efficientdet_config()
inputs_shape = [1, 512, 512, 3]
inputs = dict()
for i in range(config.min_level, config.max_level + 1):
inputs[i] = tf.ones(shape=inputs_shape, name='input', dtype=tf.float32)
if not keras:
legacy_arch.build_class_and_box_outputs(inputs, config)
else:
efficientdet_arch_keras.build_class_and_box_outputs(inputs, config)
return [n.name for n in tf.global_variables()]
def build_model(self, keras=False):
with tf.Graph().as_default():
config = hparams_config.get_efficientdet_config('efficientdet-d0')
inputs_shape = [1, 512, 512, 3]
legacy_inputs, keras_inputs = dict(), []
for i in range(config.min_level, config.max_level + 1):
keras_inputs.append(
tf.ones(shape=inputs_shape, name='input', dtype=tf.float32))
legacy_inputs[i] = keras_inputs[-1]
if keras:
efficientdet_keras.build_class_and_box_outputs(keras_inputs, config)
else:
legacy_arch.build_class_and_box_outputs(legacy_inputs, config)
return [n.name for n in tf.global_variables()]
def test_output(self):
config = hparams_config.get_efficientdet_config('efficientdet-d0')
inputs_shape = [1, 512, 512, 3]
config.max_level = config.min_level + 1
with tf.Session(graph=tf.Graph()) as sess:
inputs = dict()
for i in range(config.min_level, config.max_level + 1):
inputs[i] = tf.ones(shape=inputs_shape,
name='input',
dtype=tf.float32)
tf.random.set_random_seed(SEED)
output1 = efficientdet_arch_keras.build_class_and_box_outputs(
inputs, config)
sess.run(tf.global_variables_initializer())
class_output1, box_output1 = sess.run(output1)
with tf.Session(graph=tf.Graph()) as sess:
inputs = dict()
for i in range(config.min_level, config.max_level + 1):
inputs[i] = tf.ones(shape=inputs_shape,
name='input',
dtype=tf.float32)
tf.random.set_random_seed(SEED)
output2 = legacy_arch.build_class_and_box_outputs(inputs, config)
sess.run(tf.global_variables_initializer())
class_output2, box_output2 = sess.run(output2)
for i in range(config.min_level, config.max_level + 1):
self.assertAllEqual(class_output1[i], class_output2[i])
self.assertAllEqual(box_output1[i], box_output2[i])
def test_output(self):
config = hparams_config.get_efficientdet_config('efficientdet-d0')
inputs_shape = [1, 512, 512, 3]
config.max_level = config.min_level + 1
with tf.Session(graph=tf.Graph()) as sess:
tf.random.set_random_seed(SEED)
keras_inputs = []
for i in range(config.min_level, config.max_level + 1):
keras_inputs.append(
tf.ones(shape=inputs_shape, name='input',
dtype=tf.float32))
output1 = efficientdet_keras.build_class_and_box_outputs(
keras_inputs, config)
sess.run(tf.global_variables_initializer())
keras_class, keras_box = sess.run(output1)
with tf.Session(graph=tf.Graph()) as sess:
tf.random.set_random_seed(SEED)
legacy_inputs = dict()
for i in range(config.min_level, config.max_level + 1):
legacy_inputs[i] = tf.ones(shape=inputs_shape,
name='input',
dtype=tf.float32)
output2 = legacy_arch.build_class_and_box_outputs(
legacy_inputs, config)
sess.run(tf.global_variables_initializer())
legacy_class, legacy_box = sess.run(output2)
for i in range(config.min_level, config.max_level + 1):
self.assertAllClose(keras_class[i - config.min_level],
legacy_class[i])
self.assertAllClose(keras_box[i - config.min_level], legacy_box[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])
