运行蒙版RCNN代码，并停留在“将稀疏IndexedSlices转换为形状未知的密集张量”

Question

我是Python和Tensorflow的新手从this tutorial运行Mask RCNN代码，并被卡在“ 将稀疏IndexedSlices转换为未知形状的密集Tensor。”

这是train.py的我的配置部分，如下所示

import os
import sys
import json
import datetime
import numpy as np
import skimage.draw
import cv2
from mrcnn.visualize import display_instances
import matplotlib.pyplot as plt

# Root directory of the project
ROOT_DIR = ""

# Import Mask RCNN
sys.path.append(ROOT_DIR)  # To find local version of the library
from mrcnn.config import Config
from mrcnn import model as modellib, utils

# Path to trained weights file
COCO_WEIGHTS_PATH = os.path.join(ROOT_DIR, "mask_rcnn_coco.h5")

# Directory to save logs and model checkpoints, if not provided
# through the command line argument --logs
DEFAULT_LOGS_DIR = os.path.join(ROOT_DIR, "logs")

class CustomConfig(Config):
    """Configuration for training on the toy  dataset.
    Derives from the base Config class and overrides some values.
    """
    # Give the configuration a recognizable name
    NAME = "BH"

    # We use a GPU with 12GB memory, which can fit two images.
    # Adjust down if you use a smaller GPU.
    IMAGES_PER_GPU = 1

    # Number of classes (including background)
    NUM_CLASSES = 1 + 1  # Background + toy

    # Number of training steps per epoch
    STEPS_PER_EPOCH = 100

    # Skip detections with < 90% confidence
    DETECTION_MIN_CONFIDENCE = 0.9

我已经等了半个小时，看来无法继续下一步。请提供一些建议。

Answer 1

不是“卡住”的，它在训练中。时代1/10表示其当前在第一个时代，并且每个时代有100步，每个时代的速度可以根据代码中的不同细节而变化。例如