def test_model_variables(self):
input_shape = (1, 512, 512, 3)
model = efficientdet_keras.EfficientDetNet('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_keras.EfficientDetNet('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(keras_train_vars, legacy_train_vars)
self.assertEqual(keras_model_vars, legacy_model_vars)
self.assertEqual(eager_train_vars, legacy_train_vars)
self.assertEqual(eager_model_vars, legacy_model_vars)
def main(_):
train_examples = info.splits['train'].num_examples
batch_size = 8
steps_per_epoch = train_examples // batch_size
train = dataset['train'].map(
load_image_train, num_parallel_calls=tf.data.experimental.AUTOTUNE)
test = dataset['test'].map(load_image_test)
train_dataset = train.cache().shuffle(1000).batch(batch_size).repeat()
train_dataset = train_dataset.prefetch(
buffer_size=tf.data.experimental.AUTOTUNE)
test_dataset = test.batch(batch_size)
model = efficientdet_keras.EfficientDetNet('efficientdet-d0')
model.build((1, 512, 512, 3))
model.compile(
optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
val_subsplits = 5
val_steps = info.splits['test'].num_examples // batch_size // val_subsplits
model.fit(train_dataset,
epochs=20,
steps_per_epoch=steps_per_epoch,
validation_steps=val_steps,
validation_data=test_dataset,
callbacks=[])
model.save_weights('./test/segmentation')
print(create_mask(model(tf.ones((1, 512, 512, 3)), False)))
def model_fn(inputs):
model = efficientdet_keras.EfficientDetNet(
config=hparams_config.Config(params))
cls_out_list, box_out_list = model(inputs,
params['is_training_bn'])
cls_outputs, box_outputs = {}, {}
for i in range(params['min_level'], params['max_level'] + 1):
cls_outputs[i] = cls_out_list[i - params['min_level']]
box_outputs[i] = box_out_list[i - params['min_level']]
return cls_outputs, box_outputs
def test_model_output(self):
inputs_shape = [1, 512, 512, 3]
config = hparams_config.get_efficientdet_config('efficientdet-d0')
config.heads = ['object_detection', 'segmentation']
tmp_ckpt = os.path.join(tempfile.mkdtemp(), 'ckpt')
with tf.Session(graph=tf.Graph()) as sess:
feats = tf.ones(inputs_shape)
tf.random.set_random_seed(SEED)
model = efficientdet_keras.EfficientDetNet(config=config)
outputs = model(feats, True)
sess.run(tf.global_variables_initializer())
keras_class_out, keras_box_out, keras_seg_out = sess.run(outputs)
model.save_weights(tmp_ckpt)
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)
model = efficientdet_keras.EfficientDetNet(config=config)
model.load_weights(tmp_ckpt)
eager_class_out, eager_box_out, eager_seg_out = model(feats, True)
for i in range(3, 8):
self.assertAllClose(eager_class_out[i - 3],
legacy_class_out[i],
rtol=1e-4,
atol=1e-4)
self.assertAllClose(eager_box_out[i - 3],
legacy_box_out[i],
rtol=1e-4,
atol=1e-4)
self.assertAllClose(eager_seg_out, keras_seg_out, rtol=1e-4, atol=1e-4)
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_keras.EfficientDetNet(config=config)
cls_out_list, box_out_list = model(feats, training=False)
# 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
def test_irregular_shape(self):
config = hparams_config.get_efficientdet_config('efficientdet-d0')
config.image_size = '896x1600'
model = efficientdet_keras.EfficientDetNet(config=config)
model(tf.ones([1, 896, 1600, 3]), False)
model(tf.ones([1, 499, 333, 3]), False)
def main(_):
config = hparams_config.get_efficientdet_config(FLAGS.model_name)
config.override(FLAGS.hparams)
config.batch_size = FLAGS.batch_size
config.val_json_file = FLAGS.val_json_file
config.nms_configs.max_nms_inputs = anchors.MAX_DETECTION_POINTS
base_height, base_width = utils.parse_image_size(config['image_size'])
# Network
model = efficientdet_keras.EfficientDetNet(config=config)
model.build((config.batch_size, base_height, base_width, 3))
model.load_weights(tf.train.latest_checkpoint(FLAGS.model_dir))
@tf.function
def f(imgs, labels, flip):
cls_outputs, box_outputs = model(imgs, training=False)
return postprocess.generate_detections(config, cls_outputs,
box_outputs,
labels['image_scales'],
labels['source_ids'], flip)
# in format (height, width, flip)
augmentations = []
if FLAGS.enable_tta:
for size_offset in (0, 128, 256):
for flip in (False, True):
augmentations.append((base_height + size_offset,
base_width + size_offset, flip))
else:
augmentations.append((base_height, base_width, False))
evaluator = None
detections_per_source = dict()
for height, width, flip in augmentations:
config.image_size = (height, width)
# dataset
ds = dataloader.InputReader(
FLAGS.val_file_pattern,
is_training=False,
use_fake_data=False,
max_instances_per_image=config.max_instances_per_image)(config)
# compute stats for all batches.
total_steps = FLAGS.eval_samples // FLAGS.batch_size
progress = tf.keras.utils.Progbar(total_steps)
for i, (images, labels) in enumerate(ds):
progress.update(i, values=None)
if i > total_steps:
break
if flip:
images = tf.image.flip_left_right(images)
detections = f(images, labels, flip)
for img_id, d in zip(labels['source_ids'], detections):
if img_id.numpy() in detections_per_source:
detections_per_source[img_id.numpy()] = tf.concat(
[d, detections_per_source[img_id.numpy()]], 0)
else:
detections_per_source[img_id.numpy()] = d
evaluator = coco_metric.EvaluationMetric(
filename=config.val_json_file)
for d in detections_per_source.values():
if FLAGS.enable_tta:
d = wbf.ensemble_detections(config, d, len(augmentations))
evaluator.update_state(
labels['groundtruth_data'].numpy(),
postprocess.transform_detections(tf.stack([d])).numpy())
# compute the final eval results.
if evaluator:
metrics = evaluator.result()
metric_dict = {}
for i, name in enumerate(evaluator.metric_names):
metric_dict[name] = metrics[i]
label_map = label_util.get_label_map(config.label_map)
if label_map:
for i, cid in enumerate(sorted(label_map.keys())):
name = 'AP_/%s' % label_map[cid]
metric_dict[name] = metrics[i - len(evaluator.metric_names)]
print(metric_dict)