def load_model(model_name, weight_path, input_size, framework):
assert model_name in ['yolov3_tiny', 'yolov3', 'yolov4']
NUM_CLASS = len(utils.read_class_names(cfg.YOLO.CLASSES))
if framework == 'tf':
input_layer = tf.keras.layers.Input([input_size, input_size, 3])
if model_name == 'yolov3_tiny':
feature_maps = YOLOv3_tiny(input_layer, NUM_CLASS)
bbox_tensors = []
for i, fm in enumerate(feature_maps):
bbox_tensor = ops.decode(fm, NUM_CLASS)
bbox_tensors.append(bbox_tensor)
model = tf.keras.Model(input_layer, bbox_tensors)
elif model_name == 'yolov3':
feature_maps = YOLOv3(input_layer, NUM_CLASS)
bbox_tensors = []
for i, fm in enumerate(feature_maps):
bbox_tensor = ops.decode(fm, NUM_CLASS)
bbox_tensors.append(bbox_tensor)
model = tf.keras.Model(input_layer, bbox_tensors)
elif model_name == 'yolov4':
feature_maps = YOLOv4(input_layer, NUM_CLASS)
bbox_tensors = []
for i, fm in enumerate(feature_maps):
bbox_tensor = ops.decode(fm, NUM_CLASS)
bbox_tensors.append(bbox_tensor)
model = tf.keras.Model(input_layer, bbox_tensors)
else:
model = None
raise ValueError
if weight_path.split(".")[-1] == "weights":
if model_name == 'yolov3_tiny':
utils.load_weights_tiny(model, weight_path)
print('load yolo tiny 3')
elif model_name == 'yolov3':
utils.load_weights_v3(model, weight_path)
print('load yolo 3')
elif model_name == 'yolov4':
utils.load_weights(model, weight_path)
print('load yolo 4')
else:
raise ValueError
else:
model.load_weights(weight_path).expect_partial()
print('Restoring weights from: %s ' % weight_path)
return model
def transfer_tflite(model_name, weight_path, output, input_size):
assert model_name in ['yolov3_tiny', 'yolov3', 'yolov4']
NUM_CLASS = len(utils.read_class_names(cfg.YOLO.CLASSES))
input_layer = tf.keras.layers.Input([input_size, input_size, 3])
if model_name == 'yolov3_tiny':
feature_maps = YOLOv3_tiny(input_layer, NUM_CLASS)
bbox_tensors = []
for i, fm in enumerate(feature_maps):
bbox_tensor = ops.decode(fm, NUM_CLASS)
bbox_tensors.append(bbox_tensor)
model = tf.keras.Model(input_layer, bbox_tensors)
elif model_name == 'yolov3':
feature_maps = YOLOv3(input_layer, NUM_CLASS)
bbox_tensors = []
for i, fm in enumerate(feature_maps):
bbox_tensor = ops.decode(fm, NUM_CLASS)
bbox_tensors.append(bbox_tensor)
model = tf.keras.Model(input_layer, bbox_tensors)
elif model_name == 'yolov4':
feature_maps = YOLOv4(input_layer, NUM_CLASS)
bbox_tensors = []
for i, fm in enumerate(feature_maps):
bbox_tensor = ops.decode(fm, NUM_CLASS)
bbox_tensors.append(bbox_tensor)
model = tf.keras.Model(input_layer, bbox_tensors)
else:
model = None
raise ValueError
if weight_path.split(".")[-1] == "weights":
if model_name == 'yolov3_tiny':
utils.load_weights_tiny(model, weight_path)
elif model_name == ' yolov3':
utils.load_weights_v3(model, weight_path)
elif model_name == 'yolov4':
utils.load_weights(model, weight_path)
else:
raise ValueError
else:
model.load_weights(weight_path).expect_partial()
print('Restoring weights from: %s ... ' % weight_path)
converter = tf.lite.TFLiteConverter.from_keras_model(model)
# converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE]
tflite_model = converter.convert()
open(output, 'wb').write(tflite_model)
def transfer_tflite(model_name, weight_path, output, input_size):
assert model_name in ['yolov3_tiny']
NUM_CLASS = len(utils.read_class_names(cfg.YOLO.CLASSES))
input_layer = tf.keras.layers.Input([input_size, input_size, 3])
if model_name == 'yolov3_tiny':
feature_maps = YOLOv3_tiny(input_layer, NUM_CLASS)
bbox_tensors = []
for i, fm in enumerate(feature_maps):
bbox_tensor = ops.decode(fm, NUM_CLASS)
bbox_tensors.append(bbox_tensor)
model = tf.keras.Model(input_layer, bbox_tensors)
else:
model = None
raise ValueError
if model_name == 'yolov3_tiny':
weight = np.load(weight_path, allow_pickle=True)
model.set_weights(weight)
else:
raise ValueError
print('Restoring weights from: %s ' % weight_path)
converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()
open(output, 'wb').write(tflite_model)
def load_model(model_name, weight_path, input_size):
assert model_name in ['yolov3_tiny']
NUM_CLASS = len(utils.read_class_names(cfg.YOLO.CLASSES))
input_layer = tf.keras.layers.Input([input_size, input_size, 3])
if model_name == 'yolov3_tiny':
feature_maps = YOLOv3_tiny(input_layer, NUM_CLASS)
bbox_tensors = []
for i, fm in enumerate(feature_maps):
bbox_tensor = ops.decode(fm, NUM_CLASS)
bbox_tensors.append(bbox_tensor)
model = tf.keras.Model(input_layer, bbox_tensors)
else:
model = None
raise ValueError
if weight_path:
weight = np.load(weight_path, allow_pickle=True)
model.set_weights(weight)
else:
raise ValueError
print('Restoring weights from: %s ' % weight_path)
return model
def save_tiny_npz(model_name, weight_path, output, input_size):
assert model_name in ['yolov3_tiny']
NUM_CLASS = len(utils.read_class_names(cfg.YOLO.CLASSES))
input_layer = tf.keras.layers.Input([input_size, input_size, 3])
if model_name == 'yolov3_tiny':
feature_maps = YOLOv3_tiny(input_layer, NUM_CLASS)
bbox_tensors = []
for i, fm in enumerate(feature_maps):
bbox_tensor = ops.decode(fm, NUM_CLASS)
bbox_tensors.append(bbox_tensor)
model = tf.keras.Model(input_layer, bbox_tensors)
else:
raise ValueError
if weight_path.split(".")[-1] == "weights":
if model_name == 'yolov3_tiny':
extract_weight = utils.extract_weights_tiny(model, weight_path)
else:
raise ValueError
else:
raise ValueError
print('Restoring weights from: %s ... ' % weight_path)
np.save(output, extract_weight)
def save_tflite(model_name, weight_path, quantize_mode, output, input_size):
assert model_name in ['yolov3_tiny']
assert quantize_mode in ['int8', 'float16', 'full_int8']
NUM_CLASS = len(utils.read_class_names(cfg.YOLO.CLASSES))
input_layer = tf.keras.layers.Input([input_size, input_size, 3])
if model_name == 'yolov3_tiny':
feature_maps = YOLOv3_tiny(input_layer, NUM_CLASS)
bbox_tensors = []
for i, fm in enumerate(feature_maps):
bbox_tensor = ops.decode(fm, NUM_CLASS)
bbox_tensors.append(bbox_tensor)
model = tf.keras.Model(input_layer, bbox_tensors)
else:
model = None
raise ValueError
if weight_path:
if model_name == 'yolov3_tiny':
weight = np.load(weight_path, allow_pickle=True)
model.set_weights(weight)
else:
raise ValueError
print('Restoring weights from: %s ' % weight_path)
converter = tf.lite.TFLiteConverter.from_keras_model(model)
# if tf.__version__ >= '2.2.0':
# converter.experimental_new_converter = False
if quantize_mode == 'int8':
converter.optimizations = [tf.lite.Optimize.DEFAULT]
elif quantize_mode == 'float16':
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.target_spec.supported_types = [
tf.compat.v1.lite.constants.FLOAT16
]
elif quantize_mode == 'full_int8':
converter.target_spec.supported_ops = [
tf.lite.OpsSet.TFLITE_BUILTINS_INT8
]
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.target_spec.supported_ops = [
tf.lite.OpsSet.TFLITE_BUILTINS, tf.lite.OpsSet.SELECT_TF_OPS
]
converter.allow_custom_ops = True
converter.representative_dataset = representative_data_gen
else:
raise ValueError
tflite_model = converter.convert()
open(output, 'wb').write(tflite_model)
logging.info("model saved to: {}".format(output))
def detect(model_name, weight_path, input_size, image_path):
assert model_name in ['yolov3_tiny']
NUM_CLASS = len(utils.read_class_names(cfg.YOLO.CLASSES))
STRIDES = np.array(cfg.YOLO.STRIDES_TINY)
ANCHORS = utils.get_anchors(cfg.YOLO.ANCHORS_TINY, tiny=True)
original_image = cv2.imread(image_path)
original_image = cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB)
original_image_size = original_image.shape[:2]
image_data = utils.image_preprocess(np.copy(original_image),
[input_size, input_size])
image_data = image_data[np.newaxis, ...].astype(np.float32)
input_layer = tf.keras.layers.Input([input_size, input_size, 3])
if model_name == 'yolov3_tiny':
feature_maps = YOLOv3_tiny(input_layer, NUM_CLASS)
bbox_tensors = []
for i, fm in enumerate(feature_maps):
bbox_tensor = ops.decode(fm, NUM_CLASS)
bbox_tensors.append(bbox_tensor)
model = tf.keras.Model(input_layer, bbox_tensors)
else:
raise ValueError
if weight_path:
if model_name == 'yolov3_tiny':
weight = np.load(weight_path, allow_pickle=True)
model.set_weights(weight)
else:
raise ValueError
print('Restoring weights from: %s ' % weight_path)
# model.summary()
start_time = time.time()
pred_bbox = model.predict(image_data)
print(time.time() - start_time)
pred_bbox = utils.postprocess_bbbox(pred_bbox, ANCHORS, STRIDES)
bboxes = utils.postprocess_boxes(pred_bbox, original_image_size,
input_size, 0.5)
bboxes = utils.nms(bboxes, 0.3, method='nms')
image = visualize.draw_bbox(original_image,
bboxes,
classes=utils.read_class_names(
cfg.YOLO.CLASSES))
image = Image.fromarray(image)
image.show()
def detect(image_path, weight_path, input_size):
STRIDES = np.array(cfg.YOLO.STRIDES)
ANCHORS = utils.get_anchors(cfg.YOLO.ANCHORS, tiny=False)
original_image = cv2.imread(image_path)
original_image = cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB)
original_image_size = original_image.shape[:2]
image_data = utils.image_preprocess(np.copy(original_image),
[input_size, input_size])
image_data = image_data[np.newaxis, ...].astype(np.float32)
NUM_CLASS = len(utils.read_class_names(cfg.YOLO.CLASSES))
input_layer = tf.keras.layers.Input([input_size, input_size, 3])
feature_maps = YOLOv3(input_layer, NUM_CLASS)
bbox_tensors = []
for i, fm in enumerate(feature_maps):
bbox_tensor = ops.decode(fm, NUM_CLASS)
bbox_tensors.append(bbox_tensor)
model = tf.keras.Model(input_layer, bbox_tensors)
if weight_path:
weight = np.load(weight_path, allow_pickle=True)
model.set_weights(weight)
print('Restoring weights from: %s ' % weight_path)
pred_bbox = model.predict(image_data)
pred_bbox = utils.postprocess_bbbox(pred_bbox, ANCHORS, STRIDES)
bboxes = utils.postprocess_boxes(pred_bbox, original_image_size,
input_size, 0.25)
bboxes = utils.nms(bboxes, 0.2, method='nms')
image = visualize.draw_bbox(original_image,
bboxes,
classes=utils.read_class_names(
cfg.YOLO.CLASSES))
image = Image.fromarray(image)
image.show()
def detect(model_name, weight_path, input_size, image_path, framework):
assert model_name in ['yolov3_tiny', 'yolov3', 'yolov4']
if model_name == 'yolov3_tiny':
STRIDES = np.array(cfg.YOLO.STRIDES_TINY)
ANCHORS = utils.get_anchors(cfg.YOLO.ANCHORS_TINY, True)
elif model_name == 'yolov3':
STRIDES = np.array(cfg.YOLO.STRIDES)
ANCHORS = utils.get_anchors(cfg.YOLO.ANCHORS_V3, False)
elif model_name == 'yolov4':
STRIDES = np.array(cfg.YOLO.STRIDES)
ANCHORS = utils.get_anchors(cfg.YOLO.ANCHORS, False)
else:
raise ValueError
NUM_CLASS = len(utils.read_class_names(cfg.YOLO.CLASSES))
XYSCALE = cfg.YOLO.XYSCALE
original_image = cv2.imread(image_path)
original_image = cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB)
original_image_size = original_image.shape[:2]
image_data = utils.image_preprocess(np.copy(original_image),
[input_size, input_size])
image_data = image_data[np.newaxis, ...].astype(np.float32)
if framework == 'tf':
input_layer = tf.keras.layers.Input([input_size, input_size, 3])
if model_name == 'yolov3_tiny':
feature_maps = YOLOv3_tiny(input_layer, NUM_CLASS)
bbox_tensors = []
for i, fm in enumerate(feature_maps):
bbox_tensor = ops.decode(fm, NUM_CLASS)
bbox_tensors.append(bbox_tensor)
model = tf.keras.Model(input_layer, bbox_tensors)
elif model_name == 'yolov3':
feature_maps = YOLOv3(input_layer, NUM_CLASS)
bbox_tensors = []
for i, fm in enumerate(feature_maps):
bbox_tensor = ops.decode(fm, NUM_CLASS)
bbox_tensors.append(bbox_tensor)
model = tf.keras.Model(input_layer, bbox_tensors)
elif model_name == 'yolov4':
feature_maps = YOLOv4(input_layer, NUM_CLASS)
bbox_tensors = []
for i, fm in enumerate(feature_maps):
bbox_tensor = ops.decode(fm, NUM_CLASS)
bbox_tensors.append(bbox_tensor)
model = tf.keras.Model(input_layer, bbox_tensors)
else:
model = None
raise ValueError
if weight_path.split(".")[-1] == "weights":
if model_name == 'yolov3_tiny':
utils.load_weights_tiny(model, weight_path)
# utils.extract_weights_tiny(model, weight_path)
print('load yolo tiny 3')
elif model_name == 'yolov3':
utils.load_weights_v3(model, weight_path)
print('load yolo 3')
elif model_name == 'yolov4':
utils.load_weights(model, weight_path)
print('load yolo 4')
else:
raise ValueError
elif weight_path.split(".")[-1] == "npy":
if model_name == 'yolov3_tiny':
# utils.load_weights_tiny_npy(model, weight_path)
print('load yolo tiny 3 npy')
else:
model.load_weights(weight_path)
print('Restoring weights from: %s ' % weight_path)
# weight = np.load('D:\\coursera\\YoLoSerirs\\checkpoint\\yolo3_tiny.npy', allow_pickle=True)
# model.set_weights(weight)
# model.summary()
start_time = time.time()
pred_bbox = model.predict(image_data)
print(time.time() - start_time)
else:
# Load TFLite model and allocate tensors.
interpreter = tf.lite.Interpreter(model_path=weight_path)
interpreter.allocate_tensors()
# Get input and output tensors.
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
print(input_details)
print(output_details)
interpreter.set_tensor(input_details[0]['index'], image_data)
start_time = time.time()
interpreter.invoke()
pred_bbox = [
interpreter.get_tensor(output_details[i]['index'])
for i in range(len(output_details))
]
print(time.time() - start_time)
if model_name == 'yolov4':
pred_bbox = utils.postprocess_bbbox(pred_bbox, ANCHORS, STRIDES,
XYSCALE)
else:
pred_bbox = utils.postprocess_bbbox(pred_bbox, ANCHORS, STRIDES)
bboxes = utils.postprocess_boxes(pred_bbox, original_image_size,
input_size, 0.5)
bboxes = utils.nms(bboxes, 0.3, method='nms')
image = visualize.draw_bbox(original_image, bboxes)
image = Image.fromarray(image)
image.show()
