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)