在 Google Colab 上的自定义数据集上微调 EfficientDet 时出现 Gather_Nd 错误答案

【问题标题】：Gather_Nd error when fine-tuning EfficientDet on a custom dataset on Google Colab在 Google Colab 上的自定义数据集上微调 EfficientDet 时出现 Gather_Nd 错误
【发布时间】：2021-09-03 01:49:29
【问题描述】：

我正在尝试使用 Google Colab（免费）在自定义数据集上微调 EfficientDet，以进行多对象检测。我是 tf 的新手，所以我尝试复制/修改现有笔记本（这个：https://colab.research.google.com/drive/1iOydvFQVE-syG-ixEyam04X3E40Lx7NA?usp=sharing）

这就是问题所在。训练时出现以下错误：

(0) Invalid argument: indices[2] = [2] does not index into param shape [1,1], node name: parser/GatherNd_1
     [[{{node parser/GatherNd_1}}]]
     [[IteratorGetNext]]
     [[IteratorGetNext/_4303]]

尽管我知道它可能来自 TFrecord 文件，但无法知道它的来源。我的火车数据集由 png 图像（大小调整为 256x256）和边界框的相关元数据组成。以下是我生成 tfrecord 文件的方法：

def create_tf_example(filepath, df_label):

    encoded_image_data = open(filepath, "rb").read()
    key = hashlib.sha256(encoded_image_data).hexdigest()
    filename = os.path.basename(filepath)
    image_name = filename.replace(".png", "")
    height0 = df_label["height0"].loc[df_label["id"]==image_name].iloc[0]
    width0 = df_label["width0"].loc[df_label["id"]==image_name].iloc[0]
    image_format = b'png'
    width = 256
    height = 256

    xmins = [x / width0 for x in df_label["xmins0"].loc[df_label["id"]==image_name].iloc[0]]
    xmaxs = [x / width0 for x in df_label["xmaxs0"].loc[df_label["id"]==image_name].iloc[0]]
    ymins = [x / height0 for x in df_label["ymins0"].loc[df_label["id"]==image_name].iloc[0]]
    ymaxs = [x / height0 for x in df_label["ymaxs0"].loc[df_label["id"]==image_name].iloc[0]]
    classes_text = ["opacity".encode("utf-8")]
    classes = [1]

    tf_example = tf.train.Example(features=tf.train.Features(feature={
        'image/height': tf.train.Feature(int64_list=tf.train.Int64List(value=[height])),
        'image/width': tf.train.Feature(int64_list=tf.train.Int64List(value=[width])),
        "image/filename": tf.train.Feature(bytes_list=tf.train.BytesList(value=[filename.encode("utf-8")])),
        "image/source_id": tf.train.Feature(bytes_list=tf.train.BytesList(value=['0'.encode("utf-8")])), # Pb with image names solved with this hack
        "image/key/sha256": tf.train.Feature(bytes_list=tf.train.BytesList(value=[key.encode("utf-8")])),
        "image/encoded": tf.train.Feature(bytes_list=tf.train.BytesList(value=[encoded_image_data])),
        "image/format": tf.train.Feature(bytes_list=tf.train.BytesList(value=["png".encode("utf-8")])),
        "image/object/bbox/xmin": tf.train.Feature(float_list=tf.train.FloatList(value=xmins)),
        "image/object/bbox/xmax": tf.train.Feature(float_list=tf.train.FloatList(value=xmaxs)),
        "image/object/bbox/ymin": tf.train.Feature(float_list=tf.train.FloatList(value=ymins)),
        "image/object/bbox/ymax": tf.train.Feature(float_list=tf.train.FloatList(value=ymaxs)),
        "image/object/class/text": tf.train.Feature(bytes_list=tf.train.BytesList(value=classes_text)),
        "image/object/class/label": tf.train.Feature(int64_list=tf.train.Int64List(value=classes)),
        }))
    return tf_example

writer_train = tf.io.TFRecordWriter('/content/drive/MyDrive/siim-covid19-detection/TFRecords/train/train.tfrecord')

for filepath in train_filepaths:
    tf_example = create_tf_example(filepath, df_train)
    writer_train.write(tf_example.SerializeToString())

writer_train.close()

val.tfrecord 的代码相同。

我用这个下载了模型：

if not os.path.isdir("automl"):

    !git clone --depth 1 https://github.com/google/automl
    %cd automl
    !git checkout f2b4480703278250fb05abe38a2f4ecbb16ba463 # Recent commit

    %cd efficientdet

    %pip install -r requirements.txt
    %pip install -U "git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI"

MODEL = "efficientdet-d0"
if not os.path.exists(f"{MODEL}.tar.gz"):
    !curl -O https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco2/{MODEL}.tar.gz
    !tar xvzf {MODEL}.tar.gz

配置是这样的：

PROJ_DIR = "/content/MODEL"
CONFIG_DIR = os.path.join(PROJ_DIR, "configs")
CONFIG_FILE = os.path.join(CONFIG_DIR, "default.yaml")
if not os.path.exists(CONFIG_DIR):
    os.mkdir(CONFIG_DIR)

config_text = \
"""image_size: 256x256 # this is the size of my images
num_classes: 1
label_map: {1: opacity}
input_rand_hflip: true
jitter_min: 0.8
jitter_max: 1.2
"""

with open(CONFIG_FILE, "w") as fwrite:
    fwrite.write(config_text)

TFRECORD_DIR = "/content/drive/MyDrive/siim-covid19-detection/TFRecords"

CKPT = MODEL

TRAIN_SET = os.path.join(TFRECORD_DIR, "train/train.tfrecord")
VAL_SET = os.path.join(TFRECORD_DIR, "val/val.tfrecord")
MODEL_DIR_TMP = os.path.join(PROJ_DIR, "tmp", f"{MODEL}-finetune")
TRAIN_NUM_EXAMPLES = len(train_filepaths)
EVAL_NUM_EXAMPLES = len(val_filepaths)
EPOCHS = 2
BATCH_SIZE = 16

以下是我开始培训的方式：

!python -m main \
    --mode=train_and_eval \
    --train_file_pattern={TRAIN_SET} \
    --val_file_pattern={VAL_SET} \
    --model_name={MODEL} \
    --model_dir={MODEL_DIR_TMP} \
    --ckpt={CKPT} \
    --train_batch_size={BATCH_SIZE} \
    --eval_batch_size={BATCH_SIZE} \
    --num_epochs={EPOCHS} \
    --num_examples_per_epoch={TRAIN_NUM_EXAMPLES} \
    --eval_samples={EVAL_NUM_EXAMPLES} \
    --hparams={CONFIG_FILE}

提前感谢您的帮助！

【问题讨论】：

能否分享您使用的 Colab 和/或完整的堆栈跟踪信息？
嗨，艾伦，感谢您的回复。这是链接：colab.research.google.com/drive/…
感谢分享 Colab！我不确定错误到底来自哪里，但这里有一个很好的提示：``` INFO:tensorflow:Loss for final step: 0.97554517. I0617 20:25:41.305148 140287598352256 estimator.py:350] 最后一步的损失：0.97554517。 =====>开始评估，纪元：1。```所以基本上训练纪元1是成功的，但只有在我们评估时才会出现错误。我会检查你是否为 eval 做了不同的事情，也许它来自 here?。
感谢您的评论艾伦！这似乎确实是一个很好的提示......我会检查一下，如果这能解决我的问题，请告诉你。

标签： python google-colaboratory tensorflow2.0

【解决方案1】：

好吧，我发现了我的错误：类和类文本的数量与 tfrecord 文件中样本的 bbox 数量不匹配。我像这样更改了代码，它成功了：

def create_tf_example(filepath, df_label):

    encoded_image_data = open(filepath, "rb").read()
    key = hashlib.sha256(encoded_image_data).hexdigest()
    filename = os.path.basename(filepath)
    image_name = filename.replace(".png", "")
    height0 = df_label["height0"].loc[df_label["id"]==image_name].iloc[0]
    width0 = df_label["width0"].loc[df_label["id"]==image_name].iloc[0]
    image_format = b'png'
    width = 256
    height = 256

    xmins = [x / width0 for x in df_label["xmins0"].loc[df_label["id"]==image_name].iloc[0]]
    xmaxs = [x / width0 for x in df_label["xmaxs0"].loc[df_label["id"]==image_name].iloc[0]]
    ymins = [x / height0 for x in df_label["ymins0"].loc[df_label["id"]==image_name].iloc[0]]
    ymaxs = [x / height0 for x in df_label["ymaxs0"].loc[df_label["id"]==image_name].iloc[0]]

    classes_text = ["opacity".encode("utf-8")]*len(xmins) # now it creates a list of strings with length equal to the number of bbox
    classes = [0]*len(xmins) # now it creates a list of int with length equal to the number of bbox

    tf_example = tf.train.Example(features=tf.train.Features(feature={
        'image/height': tf.train.Feature(int64_list=tf.train.Int64List(value=[height])),
        'image/width': tf.train.Feature(int64_list=tf.train.Int64List(value=[width])),
        "image/filename": tf.train.Feature(bytes_list=tf.train.BytesList(value=[filename.encode("utf-8")])),
        "image/source_id": tf.train.Feature(bytes_list=tf.train.BytesList(value=['0'.encode("utf-8")])), # Pb with image names solved with this hack
        "image/key/sha256": tf.train.Feature(bytes_list=tf.train.BytesList(value=[key.encode("utf-8")])),
        "image/encoded": tf.train.Feature(bytes_list=tf.train.BytesList(value=[encoded_image_data])),
        "image/format": tf.train.Feature(bytes_list=tf.train.BytesList(value=["png".encode("utf-8")])),
        "image/object/bbox/xmin": tf.train.Feature(float_list=tf.train.FloatList(value=xmins)),
        "image/object/bbox/xmax": tf.train.Feature(float_list=tf.train.FloatList(value=xmaxs)),
        "image/object/bbox/ymin": tf.train.Feature(float_list=tf.train.FloatList(value=ymins)),
        "image/object/bbox/ymax": tf.train.Feature(float_list=tf.train.FloatList(value=ymaxs)),
        "image/object/class/text": tf.train.Feature(bytes_list=tf.train.BytesList(value=classes_text)),
        "image/object/class/label": tf.train.Feature(int64_list=tf.train.Int64List(value=classes)),
        }))
    return tf_example

【讨论】：