是否可以使用 RTX 4090 训练 Mask RCNN？

问题描述 投票：0回答：1

我想用 Nvidia RTX 4090 GPU 训练 Mask R-CNN 模型，但这似乎不可能。 似乎负重有问题。

我尝试了以下实现：

使用 Python 3.7.12 和 Tensorflow 1.14/1.15 以及 Keras 2.3.1 Mask-RCNN 效果很好。 但我无法将 RTX 4090 与这些版本一起使用。 我至少需要 Tensorflow 2.12.1 才能获得正确的 RTX 4090 CUDA 版本（CUDA 11.8）。

因此，当我使用 Tensorflow 2.12.1 或 2.13.1 时，存在一个特殊问题，即权重加载错误。权重总是随机加载的。 所以我训练模型 1 epoch，然后加载权重 3 次，权重不同。 看到这样的结果：

Results when loading the same weights 3 Times

请注意，结果当然会看起来很草图并且很奇怪，因为我没有训练该模式，但我预计在相同的权重下会得到相同的结果。

我尝试了 Tom Gross 的实现 和 mrk1992 的实现，并且都遇到了这个错误。

我查了很多问题，但没有发现任何东西。也许你能帮忙。

有人知道如何解决吗？

重现错误

为了娱乐，我给你我的 conda 环境。 创建环境.yml：

name: maskrcnn
channels:
  - conda-forge
  - defaults
dependencies:
  - _libgcc_mutex=0.1=conda_forge
  - _openmp_mutex=4.5=2_gnu
  - _tflow_select=2.3.0=mkl
  - absl-py=0.15.0=pyhd8ed1ab_0
  - aiohttp=3.7.4.post0=py37h5e8e339_1
  - alembic=1.13.1=pyhd8ed1ab_0
  - alsa-lib=1.2.8=h166bdaf_0
  - anyio=3.7.1=pyhd8ed1ab_0
  - aom=3.5.0=h27087fc_0
  - argon2-cffi=23.1.0=pyhd8ed1ab_0
  - argon2-cffi-bindings=21.2.0=py37h540881e_2
  - astor=0.8.1=pyh9f0ad1d_0
  - async-timeout=3.0.1=py_1000
  - attr=2.5.1=h166bdaf_1
  - attrs=23.2.0=pyh71513ae_0
  - backcall=0.2.0=pyh9f0ad1d_0
  - backports=1.0=pyhd8ed1ab_3
  - backports.functools_lru_cache=2.0.0=pyhd8ed1ab_0
  - bcrypt=3.2.2=py37h540881e_0
  - beautifulsoup4=4.12.3=pyha770c72_0
  - binutils_impl_linux-64=2.40=ha885e6a_0
  - binutils_linux-64=2.40=hdade7a5_3
  - bleach=6.1.0=pyhd8ed1ab_0
  - blinker=1.6.3=pyhd8ed1ab_0
  - bottleneck=1.3.5=py37hda87dfa_0
  - brotli=1.1.0=hd590300_1
  - brotli-bin=1.1.0=hd590300_1
  - brotli-python=1.0.9=py37hd23a5d3_7
  - bzip2=1.0.8=hd590300_5
  - c-ares=1.28.1=hd590300_0
  - ca-certificates=2024.2.2=hbcca054_0
  - cached-property=1.5.2=hd8ed1ab_1
  - cached_property=1.5.2=pyha770c72_1
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  - cloudpickle=2.2.1=pyhd8ed1ab_0
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  - colorlog=6.7.0=py37h89c1867_0
  - comm=0.2.2=pyhd8ed1ab_0
  - cryptography=38.0.2=py37h5994e8b_1
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  - ffmpeg=4.4.2=gpl_h8dda1f0_112
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  - flask=1.1.2=pyh9f0ad1d_0
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  - fontconfig=2.14.2=h14ed4e7_0
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  - geos=3.11.0=h27087fc_0
  - gettext=0.22.5=h59595ed_2
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  - glib=2.80.2=hf974151_0
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  - gmp=6.3.0=h59595ed_1
  - gnutls=3.7.9=hb077bed_0
  - google-auth=2.23.0=pyh1a96a4e_0
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  - graphite2=1.3.13=h59595ed_1003
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  - grpc-cpp=1.48.1=hc2bec63_1
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  - gst-plugins-base=1.21.3=h4243ec0_1
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  - jasper=2.0.33=h0ff4b12_1
  - jedi=0.19.1=pyhd8ed1ab_0
  - jinja2=3.1.4=pyhd8ed1ab_0
  - joblib=1.2.0=pyhd8ed1ab_0
  - jpeg=9e=h0b41bf4_3
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  - jupyter_client=7.4.9=pyhd8ed1ab_0
  - jupyter_console=6.5.1=pyhd8ed1ab_0
  - jupyter_core=4.11.1=py37h89c1867_0
  - jupyter_server=1.23.4=pyhd8ed1ab_0
  - jupyterlab_pygments=0.3.0=pyhd8ed1ab_1
  - jupyterlab_widgets=3.0.10=pyhd8ed1ab_0
  - keras=2.3.1=py37_0
  - keras-applications=1.0.8=py_1
  - keras-preprocessing=1.1.2=pyhd8ed1ab_0
  - kernel-headers_linux-64=2.6.32=he073ed8_17
  - keyutils=1.6.1=h166bdaf_0
  - kiwisolver=1.4.4=py37h7cecad7_0
  - krb5=1.20.1=h81ceb04_0
  - lame=3.100=h166bdaf_1003
  - lcms2=2.14=h6ed2654_0
  - ld_impl_linux-64=2.40=h55db66e_0
  - lerc=4.0.0=h27087fc_0
  - libabseil=20220623.0=cxx17_h05df665_6
  - libaec=1.1.3=h59595ed_0
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  - libasprintf-devel=0.22.5=h661eb56_2
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  - libgettextpo=0.22.5=h59595ed_2
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  - threadpoolctl=3.1.0=pyh8a188c0_0
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  - typing_extensions=4.7.1=pyha770c72_0
  - unicodedata2=14.0.0=py37h540881e_1
  - urllib3=1.26.18=pyhd8ed1ab_0
  - wcwidth=0.2.10=pyhd8ed1ab_0
  - webencodings=0.5.1=pyhd8ed1ab_2
  - websocket-client=1.6.1=pyhd8ed1ab_0
  - werkzeug=0.16.1=py_0
  - wheel=0.42.0=pyhd8ed1ab_0
  - widgetsnbextension=4.0.10=pyhd8ed1ab_0
  - wrapt=1.14.1=py37h540881e_0
  - x264=1!164.3095=h166bdaf_2
  - x265=3.5=h924138e_3
  - xcb-util=0.4.0=h516909a_0
  - xcb-util-image=0.4.0=h166bdaf_0
  - xcb-util-keysyms=0.4.0=h516909a_0
  - xcb-util-renderutil=0.3.9=h166bdaf_0
  - xcb-util-wm=0.4.1=h516909a_0
  - xkeyboard-config=2.38=h0b41bf4_0
  - xorg-fixesproto=5.0=h7f98852_1002
  - xorg-inputproto=2.3.2=h7f98852_1002
  - xorg-kbproto=1.0.7=h7f98852_1002
  - xorg-libice=1.1.1=hd590300_0
  - xorg-libsm=1.2.4=h7391055_0
  - xorg-libx11=1.8.4=h0b41bf4_0
  - xorg-libxau=1.0.11=hd590300_0
  - xorg-libxdmcp=1.1.3=h7f98852_0
  - xorg-libxext=1.3.4=h0b41bf4_2
  - xorg-libxfixes=5.0.3=h7f98852_1004
  - xorg-libxi=1.7.10=h7f98852_0
  - xorg-libxrender=0.9.10=h7f98852_1003
  - xorg-renderproto=0.11.1=h7f98852_1002
  - xorg-xextproto=7.3.0=h0b41bf4_1003
  - xorg-xproto=7.0.31=h7f98852_1007
  - xz=5.2.6=h166bdaf_0
  - yaml=0.2.5=h7f98852_2
  - yarl=1.7.2=py37h540881e_2
  - zeromq=4.3.5=h59595ed_1
  - zipp=3.15.0=pyhd8ed1ab_0
  - zlib=1.2.13=hd590300_5
  - zstd=1.5.6=ha6fb4c9_0
  - pip:
      - databricks-cli==0.18.0
      - tabulate==0.9.0
prefix: /home/local-admin/.conda/envs/maskrcnn

然后只需输入

conda env create -n maskrcnn python=3.8.19 -f environment.yml

还下载 Maks RCNN 模型：

git clone -b tensorflow-2.0 https://github.com/tomgross/Mask_RCNN.git Mask_RCNN

这是测试模型的代码：

import sys
sys.path.append("./Mask_RCNN")

import os

import numpy as np
import cv2
import imutils
import matplotlib.pyplot as plt

import tensorflow as tf

from IPython.display import clear_output


from mrcnn.config import Config
from mrcnn import model as modellib
from mrcnn import visualize

img_path = f"./test.jpg"

image = cv2.imread(img_path)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = imutils.resize(image, width=512)

plt.figure(figsize=(10, 10))
plt.axis("off")
plt.imshow(image);

class TestConfig(Config):
    NAME = "mask-rcnn test"
    GPU_COUNT = 1
    IMAGES_PER_GPU = 1
    NUM_CLASSES = 2
    # BACKBONE = "resnet50"
    # IMAGE_MIN_DIM = 800
    # IMAGE_MAX_DIM = 1024

CLASS_NAMES = ['BG', 'FG']
TEST_MODEL_PATH = "./test_model.h5"
config = TestConfig()
# config.display()

# create new model with random weights
model = modellib.MaskRCNN(mode="training", config=config, model_dir=os.getcwd())
model.keras_model.save_weights(TEST_MODEL_PATH)

clear_output()

# prepare visualization
fig, ax = plt.subplots(ncols=3, nrows=1, figsize=(20, 5))
fig.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0.01, hspace=None)

# load the created random weights 3 times
weights = []
differences = []
for i in range(3):
    model = modellib.MaskRCNN(mode="inference", config=config, model_dir=os.getcwd())
    model.load_weights(TEST_MODEL_PATH, by_name=True)
    result = model.detect([image], verbose=0)[0]
    ax[i].set_title(f"{i}. Results")
    ax[i].axis("off")
    visualize.display_instances(image, result['rois'], result['masks'], result['class_ids'], CLASS_NAMES, result['scores'],
                                ax=ax[i], show_mask=True, show_bbox=True)
    weights += [model.keras_model.get_weights()]

clear_output()

# detect differences between the models
for cur_weights_1 in weights:
    cur_differences = []
    for cur_weights_2 in weights:
        all_weigt_differences = [np.abs(w1-w2) for w1, w2 in zip(cur_weights_1, cur_weights_2)]
        all_layer_differences = [np.sum(diff) for diff in all_weigt_differences]
        cur_differences += [np.sum(all_layer_differences)]
    differences += [cur_differences]
complete_sum_difference = np.sum(differences)
if complete_sum_difference == 0.0:
    print("Congratulations!\nYour implementation works and is ready to segment!")
else:
    print("Difference detected. Your Mask RCNN seem to have an problem.")
    print("Check if you correctly applied all steps of the installation.")
    print("\nMore detail of differences:")
    print("Absolute Weight Differences:", complete_sum_difference)
    print("Comparison: Weight 1, Weight 2, Weight 3")
    for idx in range(len(differences)):
        print(f"Weight {idx+1}: {differences[idx]}")

# try to remove the test model file
try:
    os.remove(TEST_MODEL_PATH)
except Exception:
    print(f"Wasn't able to delete the test model at: {TEST_MODEL_PATH}")

plt.show();

这是测试图像：

Test Image

我希望这能让您的娱乐变得轻松。

有人有解决办法吗？

我尝试过：

我还尝试了小于 2.12.1 的张量流版本，但这些不起作用。您还可以在此处查看 TensorFlow 版本的 CUDA 版本：https://www.tensorflow.org/install/source。

我期望加载相同重量 3 次时得到相同的重量。为了验证，在我在 GTX 1080 Ti 上训练和使用模型之前，使用 Python 3.7.12、tensorflow 1.15 和 Keras 2.3.1，上面的代码成功并且权重相等，这也是预期的。 有了新的 GPU，这看起来就不一样了。

python machine-learning deep-learning mask-rcnn instance-segmentation
1个回答
0
投票

抱歉，为什么您不使用

torchvision
中的模型？您可以加载它进行推理，并且 torch 还提供了从头开始训练它的方法：

from torchvision.models.detection import (
    MaskRCNN_ResNet50_FPN_Weights, 
    maskrcnn_resnet50_fpn
)
import torch
import PIL
import numpy as np

weights = MaskRCNN_ResNet50_FPN_Weights.COCO_V1

# image preprocessing
transform = weights.transforms()
model = maskrcnn_resnet50_fpn(weights=weights)


img = PIL.Image.open("airplane.jpeg")
input = transform(img)


model.eval()

# add batch dimension
input = input[None,...] 


preds = model(input)
print(preds[0]['labels']) # tensor([5])


# for training check this scripts
# https://github.com/pytorch/vision/tree/main/references/detection#mask-r-cnn
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