note20参数详细参数（NOTE.20220601.YOLO）
导读：YOLOv5训练P R mAP等值为零...
YOLOv5训练P R mAP等值为零
两种方法：关掉重新训练；更换训练环境
多版本CUDA（原10.1，新10.2）
安装过程参考
版本切换：将系统环境中的10.2相关路径移动到10.1相关路径的前面
TensorFlow+CUDA+cuDNN版本传送门
torch
CUDA 10.2 版本安装命令，官方显示CUDA-10.2需要更换11.6.0：CUDA-10.2 PyTorch builds are no longer available for Windows, please use CUDA-11.6，实测远古10.2版本仍然可用Previous Versions
conda install pytorch==1.9.0 torchvision==0.10.0 torchaudio==0.9.0 cudatoolkit=10.2 -c pytorch -c conda-forge # conda install pytorch==1.9.0 torchvision==0.10.0 torchaudio==0.9.0 cudatoolkit=11.6 -c pytorch -c conda-forge # 加速 pip install torch==1.8.1+cu111 torchvision==0.9.1+cu111 torchaudio==0.8.1 -f https://download.pytorch.org/whl/torch_stable.html -i https://pypi.tuna.tsinghua.edu.cn/simple/ some-package --trusted-host mirrors.aliyun.com conda install pytorch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0 cudatoolkit=11.1 -c pytorch -c https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch/win-64/expected non-empty vector for x
YOLOv5 456c0e89 Python-3.8.0 torch-1.9.0 CUDA:0 (NVIDIA GeForce RTX 2060, 12288MiB) hyperparameters: lr0=0.01, lrf=0.1, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.3, cls_pw=1.0, obj=0.7, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.9, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.1, copy_paste=0.1 Weights & Biases: run pip install wandb to automatically track and visualize YOLOv5 runs (RECOMMENDED) TensorBoard: Start with tensorboard --logdir runs\train, view at http://localhost:6006/ from n params module arguments 0 -1 1 8800 models.common.Conv [3, 80, 6, 2, 2] 1 -1 1 115520 models.common.Conv [80, 160, 3, 2] 2 -1 4 309120 models.common.C3 [160, 160, 4] 3 -1 1 461440 models.common.Conv [160, 320, 3, 2] 4 -1 8 2259200 models.common.C3 [320, 320, 8] 5 -1 1 1844480 models.common.Conv [320, 640, 3, 2] 6 -1 12 13125120 models.common.C3 [640, 640, 12] 7 -1 1 7375360 models.common.Conv [640, 1280, 3, 2] 8 -1 4 19676160 models.common.C3 [1280, 1280, 4] 9 -1 1 4099840 models.common.SPPF [1280, 1280, 5] 10 -1 1 820480 models.common.Conv [1280, 640, 1, 1] 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, nearest] 12 [-1, 6] 1 0 models.common.Concat [1] 13 -1 4 5332480 models.common.C3 [1280, 640, 4, False] 14 -1 1 205440 models.common.Conv [640, 320, 1, 1] 15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, nearest] 16 [-1, 4] 1 0 models.common.Concat [1] 17 -1 4 1335040 models.common.C3 [640, 320, 4, False] 18 -1 1 922240 models.common.Conv [320, 320, 3, 2] 19 [-1, 14] 1 0 models.common.Concat [1] 20 -1 4 4922880 models.common.C3 [640, 640, 4, False] 21 -1 1 3687680 models.common.Conv [640, 640, 3, 2] 22 [-1, 10] 1 0 models.common.Concat [1] 23 -1 4 19676160 models.common.C3 [1280, 1280, 4, False] 24 [17, 20, 23] 1 107664 models.yolo.Detect [11, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [320, 640, 1280]] Model summary: 567 layers, 86285104 parameters, 86285104 gradients Transferred 745/745 items from runs\train\M5-13\weights\last.pt AMP: checks passed WARNING: --img-size 8 must be multiple of max stride 32, updating to 64 AutoBatch: Computing optimal batch size for --imgsz 64 AutoBatch: CUDA:0 (NVIDIA GeForce RTX 2060) 12.00G total, 0.69G reserved, 0.66G allocated, 10.65G free Params GFLOPs GPU_mem (GB) forward (ms) backward (ms) input output Unable to find a valid cuDNN algorithm to run convolution Unable to find a valid cuDNN algorithm to run convolution Unable to find a valid cuDNN algorithm to run convolution CUDA out of memory. Tried to allocate 2.00 MiB (GPU 0; 12.00 GiB total capacity; 838.13 MiB already allocated; 9.33 GiB free; 866.00 MiB reserved in total by PyTorch) Unable to find a valid cuDNN algorithm to run convolution Traceback (most recent call last): File "train.py", line 675, in <module> main(opt) File "train.py", line 570, in main train(opt.hyp, opt, device, callbacks) File "train.py", line 147, in train batch_size = check_train_batch_size(model, imgsz, amp) File "D:\conda\YOLO\utils\autobatch.py", line 18, in check_train_batch_size return autobatch(deepcopy(model).train(), imgsz) # compute optimal batch size File "D:\conda\YOLO\utils\autobatch.py", line 57, in autobatch p = np.polyfit(batch_sizes[:len(y)], y, deg=1) # first degree polynomial fit File "<__array_function__ internals>", line 180, in polyfit File "D:\conda\envs\GTC1.7\lib\site-packages\numpy\lib\polynomial.py", line 638, in polyfit raise TypeError("expected non-empty vector for x") TypeError: expected non-empty vector for xlocal variable ‘results’ referenced before assignment
results 进行初始化之后再进行操作
Traceback (most recent call last): File "train.py", line 675, in <module> main(opt) File "train.py", line 570, in main train(opt.hyp, opt, device, callbacks) File "train.py", line 147, in train batch_size = check_train_batch_size(model, imgsz, amp) File "D:\conda\YOLO\utils\autobatch.py", line 18, in check_train_batch_size return autobatch(deepcopy(model).train(), imgsz) # compute optimal batch size File "D:\conda\YOLO\utils\autobatch.py", line 54, in autobatch if results : UnboundLocalError: local variable results referenced before assignment RuntimeError: CUDA out of memory. Tried to allocate 16.00 MiB (GPU 0; 12.00 GiB total capacity; 624.84 MiB already allocated; 9.56 GiB free; 654.00 MiB reserved in total by PyTorch)查看GPU使用率
Windows 10 ： nvidia-smi -l n （实时刷新，n为时间间隔，单位秒s）
watch -n t -d nvidia-smi （实时刷新，t为时间间隔，单位秒s）
（看到能够用GPU训练的那一刻，开心的像个二傻子，吼吼吼）
别惹训练过程中GPU也会跳变嘛，求指点，安利看训练过程的工具GPU-ZGPU-Z官方 国内源
Fan ：风扇转速
Temp ：显卡温度
Perf ：性能状态（PO->P12）
Pwr ：能耗，使用量/总量
Busld ：GPU总线
Disp.A ：GPU显示是否已经初始化
Memory-Usage ：显卡使用率
GPU-Util ：GPU利用率
Compute M ：计算模式GPU利用率低
增大batch-size，设置多线程加载数据
torchvision.datasets.ImageFolder( File "D:\conda\envs\TE\lib\site-packages\torchvision\datasets\folder.py", line 226, in __init__ super(ImageFolder, self).__init__(root, loader, IMG_EXTENSIONS if is_valid_file is None else None, File "D:\conda\envs\TE\lib\site-packages\torchvision\datasets\folder.py", line 114, in __init__ raise RuntimeError(msg) RuntimeError: Found 0 files in subfolders of: D:/Date/TEST/ Supported extensions are: .jpg,.jpeg,.png,.ppm,.bmp,.pgm,.tif,.tiff,.webp DataLoader(） 返回torch.Size([8, 3, 224, 224]) torch.Size([8]) torch.Size([8, 3, 224, 224]) # Batch,Channel,Width,Height torch.Size([8]) # Batch ValueError: optimizer got an empty parameter list NotImplementedError 可能是某个函数没有对齐的原因（T:forward写成_forward） TF 2.* 版本解决办法：升级 protoc >= 3.19.0 Traceback (most recent call last): File "D:/conda/TF/im_main.py", line 1, in <module> import tensorflow File "D:\conda\envs\TF\lib\site-packages\tensorflow\__init__.py", line 41, in <module> from tensorflow.python.tools import module_util as _module_util File "D:\conda\envs\TF\lib\site-packages\tensorflow\python\__init__.py", line 40, in <module> from tensorflow.python.eager import context File "D:\conda\envs\TF\lib\site-packages\tensorflow\python\eager\context.py", line 32, in <module> from tensorflow.core.framework import function_pb2 File "D:\conda\envs\TF\lib\site-packages\tensorflow\core\framework\function_pb2.py", line 16, in <module> from tensorflow.core.framework import attr_value_pb2 as tensorflow_dot_core_dot_framework_dot_attr__value__pb2 File "D:\conda\envs\TF\lib\site-packages\tensorflow\core\framework\attr_value_pb2.py", line 16, in <module> from tensorflow.core.framework import tensor_pb2 as tensorflow_dot_core_dot_framework_dot_tensor__pb2 File "D:\conda\envs\TF\lib\site-packages\tensorflow\core\framework\tensor_pb2.py", line 16, in <module> from tensorflow.core.framework import resource_handle_pb2 as tensorflow_dot_core_dot_framework_dot_resource__handle__pb2 File "D:\conda\envs\TF\lib\site-packages\tensorflow\core\framework\resource_handle_pb2.py", line 16, in <module> from tensorflow.core.framework import tensor_shape_pb2 as tensorflow_dot_core_dot_framework_dot_tensor__shape__pb2 File "D:\conda\envs\TF\lib\site-packages\tensorflow\core\framework\tensor_shape_pb2.py", line 36, in <module> _descriptor.FieldDescriptor( File "D:\conda\envs\TF\lib\site-packages\google\protobuf\descriptor.py", line 560, in __new__ _message.Message._CheckCalledFromGeneratedFile() TypeError: Descriptors cannot not be created directly. If this call came from a _pb2.py file, your generated code is out of date and must be regenerated with protoc >= 3.19.0. If you cannot immediately regenerate your protos, some other possible workarounds are: 1. Downgrade the protobuf package to 3.20.x or lower. 2. Set PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python (but this will use pure-Python parsing and will be much slower). More information: https://developers.google.com/protocol-buffers/docs/news/2022-05-06#python-updates TF 2.* 版本解决办法： import tensorflow as tf import tensorflow.keras import numpy as np import os import sys from tensorflow.keras.layers import Flatten , Dense , Dropout , Input from tensorflow.keras.applications import VGG16 from tensorflow.keras.models import Model from tensorflow.keras.models import load_model from tensorflow.keras.models import model_from_yaml 2022-06-13 10:05:14.663841: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cudart64_101.dll Traceback (most recent call last): File "D:/conda/TF/im_main.py", line 2, in <module> import keras File "D:\conda\envs\TF\lib\site-packages\keras\__init__.py", line 24, in <module> from keras import models File "D:\conda\envs\TF\lib\site-packages\keras\models\__init__.py", line 18, in <module> from keras.engine.functional import Functional File "D:\conda\envs\TF\lib\site-packages\keras\engine\functional.py", line 23, in <module> from keras import backend File "D:\conda\envs\TF\lib\site-packages\keras\backend.py", line 39, in <module> from tensorflow.python.eager.context import get_config ImportError: cannot import name get_config from tensorflow.python.eager.context (D:\conda\envs\TF\lib\site-packages\tensorflow\python\eager\context.py) 2022-06-13 14:01:57.323214: W tensorflow/core/framework/op_kernel.cc:1744] OP_REQUIRES failed at cast_op.cc:124 : Unimplemented: Cast string to float is not supported Traceback (most recent call last): File "D:/conda/TF/im_main.py", line 49, in <module> history = model.fit( File "D:\conda\envs\TF\lib\site-packages\tensorflow\python\keras\engine\training.py", line 108, in _method_wrapper return method(self, *args, **kwargs) File "D:\conda\envs\TF\lib\site-packages\tensorflow\python\keras\engine\training.py", line 1098, in fit tmp_logs = train_function(iterator) File "D:\conda\envs\TF\lib\site-packages\tensorflow\python\eager\def_function.py", line 780, in __call__ result = self._call(*args, **kwds) File "D:\conda\envs\TF\lib\site-packages\tensorflow\python\eager\def_function.py", line 840, in _call return self._stateless_fn(*args, **kwds) File "D:\conda\envs\TF\lib\site-packages\tensorflow\python\eager\function.py", line 2829, in __call__ return graph_function._filtered_call(args, kwargs) # pylint: disable=protected-access File "D:\conda\envs\TF\lib\site-packages\tensorflow\python\eager\function.py", line 1843, in _filtered_call return self._call_flat( File "D:\conda\envs\TF\lib\site-packages\tensorflow\python\eager\function.py", line 1923, in _call_flat return self._build_call_outputs(self._inference_function.call( File "D:\conda\envs\TF\lib\site-packages\tensorflow\python\eager\function.py", line 545, in call outputs = execute.execute( File "D:\conda\envs\TF\lib\site-packages\tensorflow\python\eager\execute.py", line 59, in quick_execute tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name, tensorflow.python.framework.errors_impl.UnimplementedError: Cast string to float is not supported [[node binary_crossentropy/Cast (defined at /conda/TF/im_main.py:49) ]] [Op:__inference_train_function_1598] Function call stack: train_function labels = np.ones_like(lists) ==> labels = np.ones_like(lists , dtype = int ) 2022-06-13 14:40:17.548683: W tensorflow/core/framework/op_kernel.cc:1744] OP_REQUIRES failed at cast_op.cc:124 : Unimplemented: Cast string to float is not supported Traceback (most recent call last): File "D:/conda/TF/im_main.py", line 50, in <module> history = model.fit( File "D:\conda\envs\TF\lib\site-packages\tensorflow\python\keras\engine\training_v1.py", line 790, in fit return func.fit( File "D:\conda\envs\TF\lib\site-packages\tensorflow\python\keras\engine\training_arrays.py", line 649, in fit return fit_loop( File "D:\conda\envs\TF\lib\site-packages\tensorflow\python\keras\engine\training_arrays.py", line 297, in model_iteration batch_outs = f(actual_inputs) File "D:\conda\envs\TF\lib\site-packages\tensorflow\python\keras\backend.py", line 3822, in __call__ self._make_callable(feed_arrays, feed_symbols, symbol_vals, session) File "D:\conda\envs\TF\lib\site-packages\tensorflow\python\keras\backend.py", line 3759, in _make_callable callable_fn = session._make_callable_from_options(callable_opts) File "D:\conda\envs\TF\lib\site-packages\tensorflow\python\client\session.py", line 1505, in _make_callable_from_options return BaseSession._Callable(self, callable_options) File "D:\conda\envs\TF\lib\site-packages\tensorflow\python\client\session.py", line 1459, in __init__ self._handle = tf_session.TF_SessionMakeCallable( tensorflow.python.framework.errors_impl.UnimplementedError: Cast string to float is not supported [[{{node Cast}}]] RuntimeError: [enforce fail at C:\actions-runner\_work\pytorch\pytorch\builder\windows\pytorch\c10\core\impl\alloc_cpu.cpp:81] data. DefaultCPUAllocator: not enough memory: you tried to allocate 3145728 bytes解决办法
Error #15: Initializing libiomp5md.dll, but found libiomp5md.dll already initialized. Traceback (most recent call last): File "<string>", line 1, in <module> File "D:\conda\envs\Torch1.8.1\lib\multiprocessing\spawn.py", line 116, in spawn_main exitcode = _main(fd, parent_sentinel) File "D:\conda\envs\Torch1.8.1\lib\multiprocessing\spawn.py", line 125, in _main prepare(preparation_data) File "D:\conda\envs\Torch1.8.1\lib\multiprocessing\spawn.py", line 236, in prepare _fixup_main_from_path(data[init_main_from_path]) File "D:\conda\envs\Torch1.8.1\lib\multiprocessing\spawn.py", line 287, in _fixup_main_from_path main_content = runpy.run_path(main_path, File "D:\conda\envs\Torch1.8.1\lib\runpy.py", line 262, in run_path return _run_module_code(code, init_globals, run_name, File "D:\conda\envs\Torch1.8.1\lib\runpy.py", line 95, in _run_module_code _run_code(code, mod_globals, init_globals, File "D:\conda\envs\Torch1.8.1\lib\runpy.py", line 85, in _run_code exec(code, run_globals) File "D:\conda\YOLO\train.py", line 26, in <module> import torch File "D:\conda\envs\Torch1.8.1\lib\site-packages\torch\__init__.py", line 123, in <module> raise err OSError: [WinError 1455] 页面文件太小，无法完成操作。 Error loading "D:\conda\envs\Torch1.8.1\lib\site-packages\torch\lib\caffe2_detectron_ops_gpu.dll" or one of its dependencies. Traceback (most recent call last): Traceback (most recent call last): Traceback (most recent call last): File "<string>", line 1, in <module> File "<string>", line 1, in <module> File "<string>", line 1, in <module> File "D:\conda\envs\Torch1.8.1\lib\multiprocessing\spawn.py", line 116, in spawn_main File "D:\conda\envs\Torch1.8.1\lib\multiprocessing\spawn.py", line 116, in spawn_main File "D:\conda\envs\Torch1.8.1\lib\multiprocessing\spawn.py", line 116, in spawn_main exitcode = _main(fd, parent_sentinel) exitcode = _main(fd, parent_sentinel) exitcode = _main(fd, parent_sentinel) File "D:\conda\envs\Torch1.8.1\lib\multiprocessing\spawn.py", line 125, in _main File "D:\conda\envs\Torch1.8.1\lib\multiprocessing\spawn.py", line 125, in _main File "D:\conda\envs\Torch1.8.1\lib\multiprocessing\spawn.py", line 125, in _main prepare(preparation_data) prepare(preparation_data) File "D:\conda\envs\Torch1.8.1\lib\multiprocessing\spawn.py", line 236, in prepare prepare(preparation_data) File "D:\conda\envs\Torch1.8.1\lib\multiprocessing\spawn.py", line 236, in prepare _fixup_main_from_path(data[init_main_from_path]) File "D:\conda\envs\Torch1.8.1\lib\multiprocessing\spawn.py", line 236, in prepare _fixup_main_from_path(data[init_main_from_path]) File "D:\conda\envs\Torch1.8.1\lib\multiprocessing\spawn.py", line 287, in _fixup_main_from_path _fixup_main_from_path(data[init_main_from_path]) File "D:\conda\envs\Torch1.8.1\lib\multiprocessing\spawn.py", line 287, in _fixup_main_from_path main_content = runpy.run_path(main_path, File "D:\conda\envs\Torch1.8.1\lib\multiprocessing\spawn.py", line 287, in _fixup_main_from_path main_content = runpy.run_path(main_path, File "D:\conda\envs\Torch1.8.1\lib\runpy.py", line 262, in run_path main_content = runpy.run_path(main_path, File "D:\conda\envs\Torch1.8.1\lib\runpy.py", line 262, in run_path return _run_module_code(code, init_globals, run_name, File "D:\conda\envs\Torch1.8.1\lib\runpy.py", line 262, in run_path return _run_module_code(code, init_globals, run_name, File "D:\conda\envs\Torch1.8.1\lib\runpy.py", line 95, in _run_module_code return _run_module_code(code, init_globals, run_name, File "D:\conda\envs\Torch1.8.1\lib\runpy.py", line 95, in _run_module_code _run_code(code, mod_globals, init_globals, File "D:\conda\envs\Torch1.8.1\lib\runpy.py", line 95, in _run_module_code _run_code(code, mod_globals, init_globals, File "D:\conda\envs\Torch1.8.1\lib\runpy.py", line 85, in _run_code _run_code(code, mod_globals, init_globals, File "D:\conda\envs\Torch1.8.1\lib\runpy.py", line 85, in _run_code exec(code, run_globals) File "D:\conda\envs\Torch1.8.1\lib\runpy.py", line 85, in _run_code exec(code, run_globals) File "D:\conda\YOLO\train.py", line 26, in <module> exec(code, run_globals) File "D:\conda\YOLO\train.py", line 26, in <module> import torch File "D:\conda\YOLO\train.py", line 26, in <module> File "D:\conda\envs\Torch1.8.1\lib\site-packages\torch\__init__.py", line 123, in <module> import torch raise err import torch File "D:\conda\envs\Torch1.8.1\lib\site-packages\torch\__init__.py", line 123, in <module> OSError: [WinError 1455] 页面文件太小，无法完成操作。 Error loading "D:\conda\envs\Torch1.8.1\lib\site-packages\torch\lib\caffe2_detectron_ops_gpu.dll" or one of its dependencies. File "D:\conda\envs\Torch1.8.1\lib\site-packages\torch\__init__.py", line 123, in <module> raise err raise err OSError: [WinError 1455] 页面文件太小，无法完成操作。 Error loading "D:\conda\envs\Torch1.8.1\lib\site-packages\torch\lib\caffe2_detectron_ops_gpu.dll" or one of its dependencies. OSError: [WinError 1455] 页面文件太小，无法完成操作。 Error loading "D:\conda\envs\Torch1.8.1\lib\site-packages\torch\lib\caffe2_detectron_ops_gpu.dll" or one of its dependencies. Traceback (most recent call last): File "<string>", line 1, in <module> File "D:\conda\envs\Torch1.8.1\lib\multiprocessing\spawn.py", line 116, in spawn_main exitcode = _main(fd, parent_sentinel) File "D:\conda\envs\Torch1.8.1\lib\multiprocessing\spawn.py", line 125, in _main prepare(preparation_data) File "D:\conda\envs\Torch1.8.1\lib\multiprocessing\spawn.py", line 236, in prepare _fixup_main_from_path(data[init_main_from_path]) File "D:\conda\envs\Torch1.8.1\lib\multiprocessing\spawn.py", line 287, in _fixup_main_from_path main_content = runpy.run_path(main_path, File "D:\conda\envs\Torch1.8.1\lib\runpy.py", line 262, in run_path return _run_module_code(code, init_globals, run_name, File "D:\conda\envs\Torch1.8.1\lib\runpy.py", line 95, in _run_module_code _run_code(code, mod_globals, init_globals, File "D:\conda\envs\Torch1.8.1\lib\runpy.py", line 85, in _run_code exec(code, run_globals) File "D:\conda\YOLO\train.py", line 26, in <module> import torch File "D:\conda\envs\Torch1.8.1\lib\site-packages\torch\__init__.py", line 123, in <module> raise err OSError: [WinError 1455] 页面文件太小，无法完成操作。 Error loading "D:\conda\envs\Torch1.8.1\lib\site-packages\torch\lib\caffe2_detectron_ops_gpu.dll" or one of its dependencies. Traceback (most recent call last): File "<string>", line 1, in <module> File "D:\conda\envs\Torch1.8.1\lib\multiprocessing\spawn.py", line 116, in spawn_main Traceback (most recent call last): exitcode = _main(fd, parent_sentinel) File "<string>", line 1, in <module> File "D:\conda\envs\Torch1.8.1\lib\multiprocessing\spawn.py", line 125, in _main File "D:\conda\envs\Torch1.8.1\lib\multiprocessing\spawn.py", line 116, in spawn_main prepare(preparation_data) exitcode = _main(fd, parent_sentinel) File "D:\conda\envs\Torch1.8.1\lib\multiprocessing\spawn.py", line 236, in prepare Traceback (most recent call last): File "D:\conda\envs\Torch1.8.1\lib\multiprocessing\spawn.py", line 125, in _main _fixup_main_from_path(data[init_main_from_path]) File "<string>", line 1, in <module> prepare(preparation_data) File "D:\conda\envs\Torch1.8.1\lib\multiprocessing\spawn.py", line 287, in _fixup_main_from_path File "D:\conda\envs\Torch1.8.1\lib\multiprocessing\spawn.py", line 116, in spawn_main File "D:\conda\envs\Torch1.8.1\lib\multiprocessing\spawn.py", line 236, in prepare main_content = runpy.run_path(main_path, File "D:\conda\envs\Torch1.8.1\lib\runpy.py", line 262, in run_path return _run_module_code(code, init_globals, run_name, exitcode = _main(fd, parent_sentinel) _fixup_main_from_path(data[init_main_from_path]) File "D:\conda\envs\Torch1.8.1\lib\runpy.py", line 95, in _run_module_code File "D:\conda\envs\Torch1.8.1\lib\multiprocessing\spawn.py", line 125, in _main File "D:\conda\envs\Torch1.8.1\lib\multiprocessing\spawn.py", line 287, in _fixup_main_from_path _run_code(code, mod_globals, init_globals, prepare(preparation_data) File "D:\conda\envs\Torch1.8.1\lib\multiprocessing\spawn.py", line 236, in prepare main_content = runpy.run_path(main_path, File "D:\conda\envs\Torch1.8.1\lib\runpy.py", line 85, in _run_code _fixup_main_from_path(data[init_main_from_path]) File "D:\conda\envs\Torch1.8.1\lib\runpy.py", line 262, in run_path exec(code, run_globals) return _run_module_code(code, init_globals, run_name, File "D:\conda\envs\Torch1.8.1\lib\multiprocessing\spawn.py", line 287, in _fixup_main_from_path File "D:\conda\YOLO\train.py", line 26, in <module> File "D:\conda\envs\Torch1.8.1\lib\runpy.py", line 95, in _run_module_code main_content = runpy.run_path(main_path, import torch _run_code(code, mod_globals, init_globals, File "D:\conda\envs\Torch1.8.1\lib\runpy.py", line 262, in run_path File "D:\conda\envs\Torch1.8.1\lib\site-packages\torch\__init__.py", line 123, in <module> File "D:\conda\envs\Torch1.8.1\lib\runpy.py", line 85, in _run_code return _run_module_code(code, init_globals, run_name, raise err exec(code, run_globals) File "D:\conda\envs\Torch1.8.1\lib\runpy.py", line 95, in _run_module_code OSError: [WinError 1455] 页面文件太小，无法完成操作。 Error loading "D:\conda\envs\Torch1.8.1\lib\site-packages\torch\lib\caffe2_detectron_ops_gpu.dll" or one of its dependencies. File "D:\conda\YOLO\train.py", line 26, in <module> _run_code(code, mod_globals, init_globals, import torch File "D:\conda\envs\Torch1.8.1\lib\runpy.py", line 85, in _run_code File "D:\conda\envs\Torch1.8.1\lib\site-packages\torch\__init__.py", line 123, in <module> exec(code, run_globals) raise err File "D:\conda\YOLO\train.py", line 26, in <module> OSError: [WinError 1455] 页面文件太小，无法完成操作。 Error loading "D:\conda\envs\Torch1.8.1\lib\site-packages\torch\lib\caffe2_detectron_ops_gpu.dll" or one of its dependencies. import torch File "D:\conda\envs\Torch1.8.1\lib\site-packages\torch\__init__.py", line 123, in <module> raise err OSError: [WinError 1455] 页面文件太小，无法完成操作。 Error loading "D:\conda\envs\Torch1.8.1\lib\site-packages\torch\lib\caffe2_detectron_ops_gpu.dll" or one of its dependencies. train: Scanning D:\VisDrone\VisDrone2019-DET-train\labels images and labels...: 0%| | 0/6471 [00:08<?, ?it/s] Traceback (most recent call last): File "D:\conda\YOLO\utils\dataloaders.py", line 450, in __init__ assert cache[hash] == get_hash(self.label_files + self.im_files) # same hash AssertionError During handling of the above exception, another exception occurred: Traceback (most recent call last): File "D:\conda\envs\Torch1.8.1\lib\multiprocessing\pool.py", line 848, in next item = self._items.popleft() IndexError: pop from an empty deque During handling of the above exception, another exception occurred: Traceback (most recent call last): File "train.py", line 677, in <module> main(opt) File "train.py", line 572, in main train(opt.hyp, opt, device, callbacks) File "train.py", line 224, in train train_loader, dataset = create_dataloader(train_path, File "D:\conda\YOLO\utils\dataloaders.py", line 114, in create_dataloader dataset = LoadImagesAndLabels( File "D:\conda\YOLO\utils\dataloaders.py", line 452, in __init__ cache, exists = self.cache_labels(cache_path, prefix), False # cache File "D:\conda\YOLO\utils\dataloaders.py", line 543, in cache_labels for im_file, lb, shape, segments, nm_f, nf_f, ne_f, nc_f, msg in pbar: File "D:\conda\envs\Torch1.8.1\lib\site-packages\tqdm\std.py", line 1195, in __iter__ for obj in iterable: File "D:\conda\envs\Torch1.8.1\lib\multiprocessing\pool.py", line 853, in next self._cond.wait(timeout) File "D:\conda\envs\Torch1.8.1\lib\threading.py", line 302, in wait waiter.acquire() KeyboardInterrupt Traceback (most recent call last): File "train.py", line 663, in <module> main(opt) File "train.py", line 558, in main train(opt.hyp, opt, device, callbacks) File "train.py", line 315, in train for i, (imgs, targets, paths, _) in pbar: # batch ------------------------------------------------------------- File "D:\conda\envs\Torch1.8.1\lib\site-packages\tqdm\std.py", line 1195, in __iter__ for obj in iterable: File "D:\conda\YOLO\utils\dataloaders.py", line 158, in __iter__ yield next(self.iterator) File "D:\conda\envs\Torch1.8.1\lib\site-packages\torch\utils\data\dataloader.py", line 517, in __next__ data = self._next_data() File "D:\conda\envs\Torch1.8.1\lib\site-packages\torch\utils\data\dataloader.py", line 557, in _next_data data = self._dataset_fetcher.fetch(index) # may raise StopIteration File "D:\conda\envs\Torch1.8.1\lib\site-packages\torch\utils\data\_utils\fetch.py", line 44, in fetch data = [self.dataset[idx] for idx in possibly_batched_index] File "D:\conda\envs\Torch1.8.1\lib\site-packages\torch\utils\data\_utils\fetch.py", line 44, in <listcomp> data = [self.dataset[idx] for idx in possibly_batched_index] File "D:\conda\YOLO\utils\dataloaders.py", line 587, in __getitem__ img, labels = self.load_mosaic(index) File "D:\conda\YOLO\utils\dataloaders.py", line 687, in load_mosaic img, _, (h, w) = self.load_image(index) File "D:\conda\YOLO\utils\dataloaders.py", line 661, in load_image im = cv2.imread(f) # BGR File "D:\conda\YOLO\utils\general.py", line 999, in imread return cv2.imdecode(np.fromfile(path, np.uint8), flags) cv2.error: OpenCV(4.1.2) C:\projects\opencv-python\opencv\modules\core\src\alloc.cpp:73: error: (-4:Insufficient memory) Failed to allocate 9000000 bytes in function cv::OutOfMemoryError创心域SEO版权声明：以上内容作者已申请原创保护，未经允许不得转载，侵权必究！授权事宜、对本内容有异议或投诉，敬请联系网站管理员，我们将尽快回复您，谢谢合作！