无法将大小为1665179的数组重塑为形状(512,512,3,3) [英] cannot reshape array of size 1665179 into shape (512,512,3,3)
问题描述
用于执行检测的脚本.
重量文件是yolov4 coco预先训练的模型，可以在这里找到.( https://drive.google.com/file/d/1cewMfusmPjYWbrnuJRuKhPMwRe_b9PaT/view )
Weight file was yolov4 coco pre-trained model and that can be found over here.(https://drive.google.com/file/d/1cewMfusmPjYWbrnuJRuKhPMwRe_b9PaT/view)
import time
from absl import app, flags, logging
from absl.flags import FLAGS
import core.utils as utils
from core.yolov4 import YOLOv4, YOLOv3, YOLOv3_tiny, decode
from PIL import Image
from core.config import cfg
import cv2
import numpy as np
import tensorflow as tf
flags.DEFINE_string('framework', 'tf', '(tf, tflite')
flags.DEFINE_string('weights', './data/yolov4.weights',
'path to weights file')
flags.DEFINE_integer('size', 608, 'resize images to')
flags.DEFINE_boolean('tiny', False, 'yolo or yolo-tiny')
flags.DEFINE_string('model', 'yolov4', 'yolov3 or yolov4')
flags.DEFINE_string('image', './data/kite.jpg', 'path to input image')
flags.DEFINE_string('output', 'result.png', 'path to output image')
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
input_size = FLAGS.size
image_path = FLAGS.image
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 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)
if FLAGS.weights.split(".")[len(FLAGS.weights.split(".")) - 1] == "weights":
utils.load_weights(model, FLAGS.weights)
else:
model.load_weights(FLAGS.weights).expect_partial()
model.summary()
pred_bbox = model.predict(image_data)
else:
# Load TFLite model and allocate tensors.
interpreter = tf.lite.Interpreter(model_path=FLAGS.weights)
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)
interpreter.invoke()
pred_bbox = [interpreter.get_tensor(output_details[i]['index']) for i in range(len(output_details))]
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')
image = utils.draw_bbox(original_image, bboxes)
image = Image.fromarray(image)
image.show()
# image = cv2.cvtColor(np.array(image), cv2.COLOR_BGR2RGB)
# cv2.imwrite(FLAGS.output, image)
if __name__ == '__main__':
try:
app.run(main)
except SystemExit:
pass
错误日志:
Traceback (most recent call last):
File "detect.py", line 100, in <module>
app.run(main)
File "/usr/local/lib/python3.6/dist-packages/absl/app.py", line 299, in run
_run_main(main, args)
File "/usr/local/lib/python3.6/dist-packages/absl/app.py", line 250, in _run_main
sys.exit(main(argv))
File "detect.py", line 68, in main
utils.load_weights(model, FLAGS.weights)
File "/content/tensorflow-yolov4/core/utils.py", line 114, in load_weights
conv_weights = conv_weights.reshape(conv_shape).transpose([2, 3, 1, 0])
ValueError: cannot reshape array of size 1665179 into shape (512,512,3,3)
您可以使用github重现此错误: https://github.com/hunglc007/tensorflow-yolov4-tflite/blob/master/detect.py 并尝试使用预先训练的体重进行演示检测
You can reproduce this error with github : https://github.com/hunglc007/tensorflow-yolov4-tflite/blob/master/detect.py and trying to do demo detection with the pre-trained weight
推荐答案
我也遇到了类似的重塑错误，但是我在使用此存储库的自定义tflite转换模型的Android代码示例中获得了此错误.此评论解决了我的问题，
I had similar reshaping error, but I got it on Android code sample for custom tflite converted model with this repository. This comment fixed my problem,
https://github.com/hunglc007/tensorflow-yolov4-tflite/issues/147#issuecomment-666736983 .
在配置文件中， coco.names 是硬编码的.
In the config file coco.names is hardcoded.
应使用包含定制培训课程的所需yolov4 obj.names 进行更改.这里，在先前的代码中，有 traffic.names .
It should be changed with desired yolov4 obj.names containing custom training classes. Here, in the code in previous there was traffic.names.
https://github.com/hunglc007/tensorflow-yolov4-tflite/commit/31b6d91503ecf57aa409c67b97f1596ee4ffdefc#diff-97ed6d1d0787c0eb92725a8c161e43c9b5281d1f671cc27d91809406ff084368R14
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