forked from hunglc007/tensorflow-yolov4-tflite
/
benchmarks.py
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/
benchmarks.py
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import numpy as np
import tensorflow as tf
import time
import cv2
from core.yolov4 import YOLOv4, YOLOv3_tiny, YOLOv3, decode
from absl import app, flags, logging
from absl.flags import FLAGS
from tensorflow.python.saved_model import tag_constants
from core import utils
from core.config import cfg
from tensorflow.compat.v1 import ConfigProto
from tensorflow.compat.v1 import InteractiveSession
flags.DEFINE_boolean('tiny', False, 'yolo or yolo-tiny')
flags.DEFINE_string('framework', 'tf', '(tf, tflite, trt')
flags.DEFINE_string('model', 'yolov4', 'yolov3 or yolov4')
flags.DEFINE_string('weights', './data/yolov4.weights', 'path to weights file')
flags.DEFINE_string('image', './data/kite.jpg', 'path to input image')
flags.DEFINE_integer('size', 416, 'resize images to')
def main(_argv):
if FLAGS.tiny:
STRIDES = np.array(cfg.YOLO.STRIDES_TINY)
ANCHORS = utils.get_anchors(cfg.YOLO.ANCHORS_TINY, FLAGS.tiny)
else:
STRIDES = np.array(cfg.YOLO.STRIDES)
if FLAGS.model == 'yolov4':
ANCHORS = utils.get_anchors(cfg.YOLO.ANCHORS, FLAGS.tiny)
else:
ANCHORS = utils.get_anchors(cfg.YOLO.ANCHORS_V3, FLAGS.tiny)
NUM_CLASS = len(utils.read_class_names(cfg.YOLO.CLASSES))
XYSCALE = cfg.YOLO.XYSCALE
config = ConfigProto()
config.gpu_options.allow_growth = True
session = InteractiveSession(config=config)
input_size = FLAGS.size
physical_devices = tf.config.experimental.list_physical_devices('GPU')
if len(physical_devices) > 0:
tf.config.experimental.set_memory_growth(physical_devices[0], True)
if FLAGS.framework == 'tf':
input_layer = tf.keras.layers.Input([input_size, input_size, 3])
if FLAGS.tiny:
feature_maps = YOLOv3_tiny(input_layer, NUM_CLASS)
bbox_tensors = []
for i, fm in enumerate(feature_maps):
bbox_tensor = decode(fm, NUM_CLASS, i)
bbox_tensors.append(bbox_tensor)
model = tf.keras.Model(input_layer, bbox_tensors)
utils.load_weights_tiny(model, FLAGS.weights)
else:
if FLAGS.model == 'yolov3':
feature_maps = YOLOv3(input_layer, NUM_CLASS)
bbox_tensors = []
for i, fm in enumerate(feature_maps):
bbox_tensor = decode(fm, NUM_CLASS, i)
bbox_tensors.append(bbox_tensor)
model = tf.keras.Model(input_layer, bbox_tensors)
utils.load_weights_v3(model, FLAGS.weights)
elif FLAGS.model == 'yolov4':
feature_maps = YOLOv4(input_layer, NUM_CLASS)
bbox_tensors = []
for i, fm in enumerate(feature_maps):
bbox_tensor = decode(fm, NUM_CLASS, i)
bbox_tensors.append(bbox_tensor)
model = tf.keras.Model(input_layer, bbox_tensors)
utils.load_weights(model, FLAGS.weights)
elif FLAGS.framework == 'trt':
saved_model_loaded = tf.saved_model.load(FLAGS.weights, tags=[tag_constants.SERVING])
signature_keys = list(saved_model_loaded.signatures.keys())
print(signature_keys)
infer = saved_model_loaded.signatures['serving_default']
logging.info('weights loaded')
@tf.function
def run_model(x):
return model(x)
# Test the TensorFlow Lite model on random input data.
sum = 0
original_image = cv2.imread(FLAGS.image)
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), [FLAGS.size, FLAGS.size])
image_data = image_data[np.newaxis, ...].astype(np.float32)
img_raw = tf.image.decode_image(
open(FLAGS.image, 'rb').read(), channels=3)
img_raw = tf.expand_dims(img_raw, 0)
img_raw = tf.image.resize(img_raw, (FLAGS.size, FLAGS.size))
batched_input = tf.constant(image_data)
for i in range(1000):
prev_time = time.time()
# pred_bbox = model.predict(image_data)
if FLAGS.framework == 'tf':
pred_bbox = []
result = run_model(image_data)
for value in result:
value = value.numpy()
pred_bbox.append(value)
if FLAGS.model == '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, 0.25)
bboxes = utils.nms(bboxes, 0.213, method='nms')
elif FLAGS.framework == 'trt':
pred_bbox = []
result = infer(batched_input)
for key, value in result.items():
value = value.numpy()
pred_bbox.append(value)
if FLAGS.model == '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, 0.25)
bboxes = utils.nms(bboxes, 0.213, method='nms')
# pred_bbox = pred_bbox.numpy()
curr_time = time.time()
exec_time = curr_time - prev_time
if i == 0: continue
sum += (1 / exec_time)
info = str(i) + " time:" + str(round(exec_time, 3)) + " average FPS:" + str(round(sum / i, 2)) + ", FPS: " + str(
round((1 / exec_time), 1))
print(info)
if __name__ == '__main__':
try:
app.run(main)
except SystemExit:
pass