Yolov3.weights未被完全读取错误

Question

从main.py文件运行代码时，utils.py文件中出现错误，其中没有读取yolov3.weights文件。此错误发生在load_yolo_weights函数的utils.py文件中。weights_file将程序引导到正确的文件位置。YOLO_TYPE是yolov3

以下是代码。

main.py:

import cv2
import numpy as np
import argparse
import sys
import os
import tensorflow as tf
from yolov3.utils import Load_Yolo_model
# starting deep sort
maxCosineDist = 0.7
nn_budget = None
modelFilename = 'mars-small128.pb'
encoder = gendetect.create_box_encoder(modelFilename, batch_size=1)
metric = nn_matching.NearestNeighborDistanceMetric("cosine", maxCosineDist, nn_budget)
tracker = Tracker(metric)
yolo = Load_Yolo_model()
# Read image
out = "Yolo_Output.avi"
if args.image:
if not os.path.isfile(args.image):
print(args.image, " doesn't exist")
sys.exit(1)
vid = cv2.VideoCapture(args.image)
out = args.image[:-4] + "Yolo_Output.jpg"
# Load class names
classesFile = "coco.names"
classes = read_class_names(classesFile)
keyList = list(classes.keys())
valList = list(classes.values())
# Create window for output
windowTitle = "Sports Tracking and Analytics"
cv2.namedWindow(windowTitle, cv2.WINDOW_NORMAL)

while True:
_, frame = vid.read()
try:
origFrame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
origFrame = cv2.cvtColor(origFrame, cv2.COLOR_BGR2RGB)
except:
break
imgData = image_preprocess(np.copy(origFrame), [416, 416])
imgData = imgData[np.newaxis, ...].astype(np.float32)
predBox = yolo.predict(imgData)

utils.py

import cv2
import time
import random
import colorsys
from yolov3.yolov4 import *
from yolov3.configs import *
import tensorflow as tf
import numpy as np
from tensorflow.python.saved_model import tag_constants
def load_yolo_weights(model, weights_file):
tf.keras.backend.clear_session() # used to reset layer names
# load Darknet original weights to TensorFlow model
if YOLO_TYPE == "yolov3":
range1 = 75 if not TRAIN_YOLO_TINY else 13
range2 = [58, 66, 74] if not TRAIN_YOLO_TINY else [9, 12]
if YOLO_TYPE == "yolov4":
range1 = 110 if not TRAIN_YOLO_TINY else 21
range2 = [93, 101, 109] if not TRAIN_YOLO_TINY else [17, 20]
with open(weights_file, 'rb') as wf:
major, minor, revision, seen, _ = np.fromfile(wf, dtype=np.int32, count=5)
j = 0
for i in range(range1):
if i > 0:
conv_layer_name = 'conv2d_%d' %i
else:
conv_layer_name = 'conv2d'
if j > 0:
bn_layer_name = 'batch_normalization_%d' %j
else:
bn_layer_name = 'batch_normalization'
conv_layer = model.get_layer(conv_layer_name)
filters = conv_layer.filters
k_size = conv_layer.kernel_size[0]
in_dim = conv_layer.input_shape[-1]
if i not in range2:
# darknet weights: [beta, gamma, mean, variance]
bn_weights = np.fromfile(wf, dtype=np.float32, count=4 * filters)
# tf weights: [gamma, beta, mean, variance]
bn_weights = bn_weights.reshape((4, filters))[[1, 0, 2, 3]]
bn_layer = model.get_layer(bn_layer_name)
j += 1
else:
conv_bias = np.fromfile(wf, dtype=np.float32, count=filters)
# darknet shape (out_dim, in_dim, height, width)
conv_shape = (filters, in_dim, k_size, k_size)
conv_weights = np.fromfile(wf, dtype=np.float32, count=np.product(conv_shape))
# tf shape (height, width, in_dim, out_dim)
conv_weights = conv_weights.reshape(conv_shape).transpose([2, 3, 1, 0])
if i not in range2:
conv_layer.set_weights([conv_weights])
bn_layer.set_weights(bn_weights)
else:
conv_layer.set_weights([conv_weights, conv_bias])
assert len(wf.read()) == 0, 'failed to read all data'
def Load_Yolo_model():
gpus = tf.config.experimental.list_physical_devices('GPU')
if len(gpus) > 0:
print(f'GPUs {gpus}')
try: tf.config.experimental.set_memory_growth(gpus[0], True)
except RuntimeError: pass
if YOLO_FRAMEWORK == "tf": # TensorFlow detection
if YOLO_TYPE == "yolov4":
Darknet_weights = YOLO_V4_TINY_WEIGHTS if TRAIN_YOLO_TINY else YOLO_V4_WEIGHTS
if YOLO_TYPE == "yolov3":
Darknet_weights = YOLO_V3_TINY_WEIGHTS if TRAIN_YOLO_TINY else YOLO_V3_WEIGHTS
if YOLO_CUSTOM_WEIGHTS == False:
print("Loading Darknet_weights from:", Darknet_weights)
yolo = Create_Yolo(input_size=416, CLASSES="coco.names")
load_yolo_weights(yolo, Darknet_weights) # use Darknet weights
else:
checkpoint = f"./checkpoints/{TRAIN_MODEL_NAME}"
if TRAIN_YOLO_TINY:
checkpoint += "_Tiny"
print("Loading custom weights from:", checkpoint)
yolo = Create_Yolo(input_size=416, CLASSES=TRAIN_CLASSES)
yolo.load_weights(checkpoint)  # use custom weights
elif YOLO_FRAMEWORK == "trt": # TensorRT detection
saved_model_loaded = tf.saved_model.load(YOLO_CUSTOM_WEIGHTS, tags=[tag_constants.SERVING])
signature_keys = list(saved_model_loaded.signatures.keys())
yolo = saved_model_loaded.signatures['serving_default']
return yolo

任何帮助都将不胜感激。

Answer 1

已解决！

这个错误是由coco.names文件引起的，因为我使用的文件之前已经编辑过了。