def test_resample_feature_map(self):
feat = tf.random.uniform([1, 16, 16, 320])
for apply_bn in [True, False]:
for is_training in [True, False]:
for strategy in ['tpu', '']:
with self.subTest(apply_bn=apply_bn,
is_training=is_training,
strategy=strategy):
tf.random.set_random_seed(SEED)
expect_result = legacy_arch.resample_feature_map(
feat,
name='resample_p0',
target_height=8,
target_width=8,
target_num_channels=64,
apply_bn=apply_bn,
is_training=is_training,
strategy=strategy)
tf.random.set_random_seed(SEED)
resample_layer = efficientdet_arch_keras.ResampleFeatureMap(
name='resample_p0',
target_height=8,
target_width=8,
target_num_channels=64,
apply_bn=apply_bn,
is_training=is_training,
strategy=strategy)
actual_result = resample_layer(feat)
self.assertAllCloseAccordingToType(
expect_result, actual_result)
def test_resample_feature_map(self):
feat = tf.random.uniform([1, 16, 16, 320])
for apply_fn in [True, False]:
for is_training in [True, False]:
for use_tpu in [True, False]:
with self.subTest(apply_fn=apply_fn,
is_training=is_training,
use_tpu=use_tpu):
tf.random.set_random_seed(111111)
expect_result = efficientdet_arch.resample_feature_map(
feat,
name='resample_p0',
target_height=8,
target_width=8,
target_num_channels=64,
apply_bn=apply_fn,
is_training=is_training,
use_tpu=use_tpu)
tf.random.set_random_seed(111111)
actual_result = efficientdet_arch_keras.ResampleFeatureMap(
name='resample_p0',
target_height=8,
target_width=8,
target_num_channels=64,
apply_bn=apply_fn,
is_training=is_training,
use_tpu=use_tpu)(feat)
self.assertAllCloseAccordingToType(
expect_result, actual_result)
def test_name(self):
feat = tf.random.uniform([1, 16, 16, 320])
actual_result = efficientdet_arch_keras.ResampleFeatureMap(
name='p0', target_height=8, target_width=8,
target_num_channels=64)(feat)
self.assertEqual("resample_p0/max_pooling2d/MaxPool:0",
actual_result.name)
def test_op_name(self):
with tf.Graph().as_default():
feat = tf.random.uniform([1, 16, 16, 320])
resample_layer = efficientdet_arch_keras.ResampleFeatureMap(
name='p0', target_height=8, target_width=8, target_num_channels=64)
result = resample_layer(feat)
self.assertEqual('resample_p0/max_pooling2d/MaxPool:0', result.name)
def test_var_names(self):
with tf.Graph().as_default():
feat = tf.random.uniform([1, 16, 16, 320])
resample_layer = efficientdet_arch_keras.ResampleFeatureMap(
name='resample_p0',
target_height=8,
target_width=8,
target_num_channels=64)
resample_layer(feat)
vars1 = [var.name for var in tf.trainable_variables()]
with tf.Graph().as_default():
feat = tf.random.uniform([1, 16, 16, 320])
legacy_arch.resample_feature_map(feat,
name='p0',
target_height=8,
target_width=8,
target_num_channels=64)
vars2 = [var.name for var in tf.trainable_variables()]
self.assertEqual(vars1, vars2)
