def set_model(self, model: tf.keras.Model):
self.train_model = model
with tf.device('/cpu:0'):
self.model = efficientdet_keras.EfficientDetModel(
config=model.config)
height, width = utils.parse_image_size(model.config.image_size)
self.model.build((1, height, width, 3))
self.file_writer = tf.summary.create_file_writer(
model.config.model_dir)
def set_model(self, model: tf.keras.Model):
self.train_model = model
with tf.device('/cpu:0'):
self.model = efficientdet_keras.EfficientDetModel(config=model.config)
height, width = utils.parse_image_size(model.config.image_size)
self.model.build((1, height, width, 3))
self.file_writer = tf.summary.create_file_writer(self.output_dir)
self.min_score_thresh = self.model.config.nms_configs['score_thresh'] or 0.4
self.max_boxes_to_draw = self.model.config.nms_configs['max_output_size'] or 100
def build(self, params_override=None):
"""Build model and restore checkpoints."""
params = copy.deepcopy(self.params)
if params_override:
params.update(params_override)
config = hparams_config.get_efficientdet_config(self.model_name)
config.override(params)
self.model = efficientdet_keras.EfficientDetModel(config=config)
image_size = utils.parse_image_size(params['image_size'])
self.model.build((self.batch_size, *image_size, 3))
util_keras.restore_ckpt(self.model, self.ckpt_path,
params['moving_average_decay'])
def main(_):
# pylint: disable=line-too-long
# Prepare images and checkpoints: please run these commands in shell.
# !mkdir tmp
# !wget https://user-images.githubusercontent.com/11736571/77320690-099af300-6d37-11ea-9d86-24f14dc2d540.png -O tmp/img.png
# !wget https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco/efficientdet-d0.tar.gz -O tmp/efficientdet-d0.tar.gz
# !tar zxf tmp/efficientdet-d0.tar.gz -C tmp
imgs = [np.array(Image.open(FLAGS.image_path))]
nms_score_thresh, nms_max_output_size = 0.4, 100
# Create model config.
config = hparams_config.get_efficientdet_config('efficientdet-d0')
config.is_training_bn = False
config.image_size = '1920x1280'
config.nms_configs.score_thresh = nms_score_thresh
config.nms_configs.max_output_size = nms_max_output_size
# Use 'mixed_float16' if running on GPUs.
policy = tf.keras.mixed_precision.experimental.Policy('float32')
tf.keras.mixed_precision.experimental.set_policy(policy)
tf.config.experimental_run_functions_eagerly(FLAGS.debug)
# Create and run the model.
model = efficientdet_keras.EfficientDetModel(config=config)
height, width = utils.parse_image_size(config['image_size'])
model.build((1, height, width, 3))
model.load_weights(FLAGS.checkpoint)
model.summary()
@tf.function
def f(imgs):
return model(imgs, training=False, post_mode='global')
boxes, scores, classes, valid_len = f(imgs)
# Visualize results.
for i, img in enumerate(imgs):
length = valid_len[i]
img = inference.visualize_image(img,
boxes[i].numpy()[:length],
classes[i].numpy().astype(
np.int)[:length],
scores[i].numpy()[:length],
min_score_thresh=nms_score_thresh,
max_boxes_to_draw=nms_max_output_size)
output_image_path = os.path.join(FLAGS.output_dir, str(i) + '.jpg')
Image.fromarray(img).save(output_image_path)
print('writing annotated image to ', output_image_path)
def build(self, params_override=None):
"""Build model and restore checkpoints."""
params = copy.deepcopy(self.params)
if params_override:
params.update(params_override)
config = hparams_config.get_efficientdet_config(self.model_name)
config.override(params)
if self.only_network:
self.model = efficientdet_keras.EfficientDetNet(config=config)
else:
self.model = efficientdet_keras.EfficientDetModel(config=config)
image_size = utils.parse_image_size(params['image_size'])
self.model.build((self.batch_size, *image_size, 3))
util_keras.restore_ckpt(self.model,
self.ckpt_path,
skip_mismatch=False)
def setUp(self):
super().setUp()
model = efficientdet_keras.EfficientDetModel('efficientdet-d0')
self.tmp_path = tempfile.mkdtemp()
model.save_weights(os.path.join(self.tmp_path, 'model'))
def main(_):
# pylint: disable=line-too-long
# Prepare images and checkpoints: please run these commands in shell.
# !mkdir tmp
# !wget https://user-images.githubusercontent.com/11736571/77320690-099af300-6d37-11ea-9d86-24f14dc2d540.png -O tmp/img.png
# !wget https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco/efficientdet-d0.tar.gz -O tmp/efficientdet-d0.tar.gz
# !tar zxf tmp/efficientdet-d0.tar.gz -C tmp
imgs = [np.array(Image.open(FLAGS.image_path))] * 2
# Create model config.
config = hparams_config.get_efficientdet_config('efficientdet-d0')
config.is_training_bn = False
config.image_size = '1920x1280'
config.nms_configs.score_thresh = 0.4
config.nms_configs.max_output_size = 100
config.override(FLAGS.hparams)
# Use 'mixed_float16' if running on GPUs.
policy = tf.keras.mixed_precision.Policy('float32')
tf.keras.mixed_precision.set_global_policy(policy)
tf.config.run_functions_eagerly(FLAGS.debug)
# Create and run the model.
model = efficientdet_keras.EfficientDetModel(config=config)
model.build((None, None, None, 3))
model.load_weights(tf.train.latest_checkpoint(FLAGS.model_dir))
model.summary()
class ExportModel(tf.Module):
def __init__(self, model):
super().__init__()
self.model = model
@tf.function
def f(self, imgs):
return self.model(imgs, training=False, post_mode='global')
imgs = tf.convert_to_tensor(imgs, dtype=tf.uint8)
export_model = ExportModel(model)
if FLAGS.saved_model_dir:
tf.saved_model.save(
export_model,
FLAGS.saved_model_dir,
signatures=export_model.f.get_concrete_function(
tf.TensorSpec(shape=(None, None, None, 3), dtype=tf.uint8)))
export_model = tf.saved_model.load(FLAGS.saved_model_dir)
boxes, scores, classes, valid_len = export_model.f(imgs)
# Visualize results.
for i, img in enumerate(imgs):
length = valid_len[i]
img = inference.visualize_image(
img,
boxes[i].numpy()[:length],
classes[i].numpy().astype(np.int)[:length],
scores[i].numpy()[:length],
label_map=config.label_map,
min_score_thresh=config.nms_configs.score_thresh,
max_boxes_to_draw=config.nms_configs.max_output_size)
output_image_path = os.path.join(FLAGS.output_dir, str(i) + '.jpg')
Image.fromarray(img).save(output_image_path)
print('writing annotated image to %s' % output_image_path)
def main(_):
img = Image.open(FLAGS.image_path)
imgs = [np.array(img)]
# Create model config.
config = hparams_config.get_efficientdet_config(FLAGS.model_name)
config.is_training_bn = False
# config.image_size = '640x640'
# config.nms_configs.score_thresh = 0.01
config.nms_configs.score_thresh = 0.4
config.nms_configs.max_output_size = 100
config.override(FLAGS.hparams)
# Use 'mixed_float16' if running on GPUs.
policy = tf.keras.mixed_precision.experimental.Policy('float32')
tf.keras.mixed_precision.experimental.set_policy(policy)
tf.config.experimental_run_functions_eagerly(FLAGS.debug)
# Create model
model = efficientdet_keras.EfficientDetNet(config=config)
target_size = utils.parse_image_size(config.image_size)
target_size = target_size + (3, )
model_inputs = tf.keras.Input(shape=target_size)
model(model_inputs, False)
model.summary()
# output layers detailed
# for i in model.layers:
# print(i.name, i.input, i.output)
model.load_weights(tf.train.latest_checkpoint(FLAGS.model_dir))
# create new model to access intermediate layers
effdet_model = tf.keras.Model(
inputs=model.input,
outputs=[
model.get_layer(name='class_net').output,
model.get_layer(name='box_net').output,
model.backbone.layers[-3].output # last layer
])
# is only used for pre- and post-processing methods
effdet_methods = efficientdet_keras.EfficientDetModel(config=config)
# input image preprocessing
imgs = tf.convert_to_tensor(imgs)
inputs, scales = effdet_methods._preprocessing(imgs, config.image_size,
'infer')
with tf.GradientTape() as tape:
# Compute activations of the last conv layer and make the tape watch it
cls_outputs, box_outputs, efficientnet_last_layer = effdet_model(
inputs, False)
# save gradients
grads = None
if FLAGS.gradient_type == 'cls':
grads = tape.gradient(cls_outputs, efficientnet_last_layer)
elif FLAGS.gradient_type == 'box':
grads = tape.gradient(box_outputs, efficientnet_last_layer)
assert grads != None
grad_cam(grads, efficientnet_last_layer[0], img, imgs[0],
FLAGS.gradient_type)
### bounding box visualization ###
boxes, scores, classes, valid_len = effdet_methods._postprocess(
cls_outputs, box_outputs, scales)
# Visualize results.
for i, img in enumerate(imgs):
length = valid_len[i]
img = inference.visualize_image(
img,
boxes[i].numpy()[:length],
classes[i].numpy().astype(np.int)[:length],
scores[i].numpy()[:length],
min_score_thresh=config.nms_configs.score_thresh,
max_boxes_to_draw=config.nms_configs.max_output_size)
output_image_path = os.path.join(FLAGS.output_dir, str(i) + '.jpg')
Image.fromarray(img).save(output_image_path)
print('writing annotated image to ', output_image_path)
# Prepare images and checkpoints: please run these commands in shell.
# !mkdir tmp
# !wget https://user-images.githubusercontent.com/11736571/77320690-099af300-6d37-11ea-9d86-24f14dc2d540.png -O tmp/img.png
# !wget https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco/efficientdet-d0.tar.gz -O tmp/efficientdet-d0.tar.gz
# !tar zxf tmp/efficientdet-d0.tar.gz -C tmp
imgs = [np.array(Image.open('tmp/img.png'))]
nms_score_thresh, nms_max_output_size = 0.4, 100
# Create model.
config = hparams_config.get_efficientdet_config('efficientdet-d0')
config.is_training_bn = False
config.image_size = '1920x1280'
config.nms_configs.score_thresh = nms_score_thresh
config.nms_configs.max_output_size = nms_max_output_size
model = efficientdet_keras.EfficientDetModel(config=config)
model.build((1, 1280, 1920, 3))
model.load_weights('tmp/efficientdet-d0/model')
boxes, scores, classes, valid_len = model(imgs)
model.summary()
# Visualize results.
for i, img in enumerate(imgs):
img = inference.visualize_image(img,
boxes[i].numpy(),
classes[i].numpy().astype(np.int),
scores[i].numpy(),
min_score_thresh=nms_score_thresh,
max_boxes_to_draw=nms_max_output_size)
output_image_path = os.path.join('tmp/', str(i) + '.jpg')
Image.fromarray(img).save(output_image_path)
