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OpenMMLab AI实战营 第三课笔记
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OpenMMLab AI实战营 第三课笔记
花朵五分类数据集：https://www.kaggle.com/datasets/alxmamaev/flowers-recognition 进入 mmclassification 目录In [1]: import os os.chdir('mmclassification') 导入工具包In [2]: import torch device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") print('device', device) device cuda:0 下载数据集In [3]: !wget https://zihao-openmmlab.obs.cn-east-3.myhuaweicloud.com/20220716-mmclassification/dataset/flower.zip -O data/flower.zip --2022-07-16 22:34:18-- https://zihao-openmmlab.obs.cn-east-3.myhuaweicloud.com/20220716-mmclassification/dataset/flower.zip Connecting to 172.16.0.13:5848... connected. Proxy request sent, awaiting response... 200 OK Length: 230662310 (220M) [application/zip] Saving to: ‘data/flower.zip’ data/flower.zip 100%[===================>] 219.98M 27.9MB/s in 7.8s 2022-07-16 22:34:28 (28.3 MB/s) - ‘data/flower.zip’ saved [230662310/230662310]In [4]: # 解压 !unzip data/flower.zip -d data >> /dev/nullIn [13]: from PIL import Image Image.open('data/flower/test/daisy/11023214096_b5b39fab08.jpg')
In [21]: ''' flower ├── classes.txt ├── train.txt ├── val.txt ├── test.txt ├── train │ ├── daisy │ ├── dandelion │ ├── rose │ ├── sunflower │ └── tulip ├── test │ ├── daisy │ ├── dandelion │ ├── rose │ ├── sunflower │ └── tulip └── val ├── daisy ├── dandelion ├── rose ├── sunflower └── tulip ''' 下载 config 配置文件In [30]: ''' Model config, which specify the basic structure of the model, e.g. number of the input channels. Dataset config, which contains details about the dataset, e.g. type of the dataset. Schedule config, which specify the training schedules, e.g. learning rate. Runtime config, which contains the rest of details, e.g. log config. '''In [11]: !wget https://zihao-openmmlab.obs.cn-east-3.myhuaweicloud.com/20220716-mmclassification/configs/mobilenet_v2_1x_flower.py -O configs/mobilenet_v2/mobilenet_v2_1x_flower.py --2022-07-16 22:51:45-- https://zihao-openmmlab.obs.cn-east-3.myhuaweicloud.com/20220716-mmclassification/configs/mobilenet_v2_1x_flower.py Connecting to 172.16.0.13:5848... connected. Proxy request sent, awaiting response... 200 OK Length: 1975 (1.9K) [binary/octet-stream] Saving to: ‘configs/mobilenet_v2/mobilenet_v2_1x_flower.py’ configs/mobilenet_v 100%[===================>] 1.93K --.-KB/s in 0s 2022-07-16 22:51:45 (8.72 MB/s) - ‘configs/mobilenet_v2/mobilenet_v2_1x_flower.py’ saved [1975/1975] 命令行-训练In [12]: !python tools/train.py \ configs/mobilenet_v2/mobilenet_v2_1x_flower.py \ --work-dir work_dirs/mobilenet_v2_1x_flower /home/featurize/work/MMClassification教程/mmclassification/mmcls/utils/setup_env.py:33: UserWarning: Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. f'Setting OMP_NUM_THREADS environment variable for each process ' /home/featurize/work/MMClassification教程/mmclassification/mmcls/utils/setup_env.py:43: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. f'Setting MKL_NUM_THREADS environment variable for each process ' 2022-07-16 22:51:55,465 - mmcls - INFO - Environment info: ------------------------------------------------------------ sys.platform: linux Python: 3.7.10 (default, Jun 4 2021, 14:48:32) [GCC 7.5.0] CUDA available: True GPU 0: NVIDIA RTX A4000 CUDA_HOME: /usr/local/cuda NVCC: Cuda compilation tools, release 11.2, V11.2.152 GCC: gcc (Ubuntu 9.3.0-17ubuntu1~20.04) 9.3.0 PyTorch: 1.10.0+cu113 PyTorch compiling details: PyTorch built with: - GCC 7.3 - C++ Version: 201402 - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications - Intel(R) MKL-DNN v2.2.3 (Git Hash 7336ca9f055cf1bfa13efb658fe15dc9b41f0740) - OpenMP 201511 (a.k.a. OpenMP 4.5) - LAPACK is enabled (usually provided by MKL) - NNPACK is enabled - CPU capability usage: AVX512 - CUDA Runtime 11.3 - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86 - CuDNN 8.2 - Magma 2.5.2 - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.10.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, TorchVision: 0.11.1+cu113 OpenCV: 4.5.4 MMCV: 1.6.0 MMCV Compiler: GCC 9.3 MMCV CUDA Compiler: 11.3 MMClassification: 0.23.1+d2e5054 ------------------------------------------------------------ 2022-07-16 22:51:55,465 - mmcls - INFO - Distributed training: False 2022-07-16 22:51:55,601 - mmcls - INFO - Config: model = dict( type='ImageClassifier', backbone=dict(type='MobileNetV2', widen_factor=1.0), neck=dict(type='GlobalAveragePooling'), head=dict( type='LinearClsHead', num_classes=5, in_channels=1280, loss=dict(type='CrossEntropyLoss', loss_weight=1.0), topk=(1, 3))) dataset_type = 'ImageNet' img_norm_cfg = dict( mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) train_pipeline = [ dict(type='LoadImageFromFile'), dict(type='RandomResizedCrop', size=224, backend='pillow'), dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='ImageToTensor', keys=['img']), dict(type='ToTensor', keys=['gt_label']), dict(type='Collect', keys=['img', 'gt_label']) ] test_pipeline = [ dict(type='LoadImageFromFile'), dict(type='Resize', size=(256, -1), backend='pillow'), dict(type='CenterCrop', crop_size=224), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']) ] data = dict( samples_per_gpu=32, workers_per_gpu=2, train=dict( type='ImageNet', data_prefix='data/flower/train', pipeline=[ dict(type='LoadImageFromFile'), dict(type='RandomResizedCrop', size=224, backend='pillow'), dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='ImageToTensor', keys=['img']), dict(type='ToTensor', keys=['gt_label']), dict(type='Collect', keys=['img', 'gt_label']) ], classes='data/flower/classes.txt'), val=dict( type='ImageNet', data_prefix='data/flower/val', ann_file='data/flower/val.txt', pipeline=[ dict(type='LoadImageFromFile'), dict(type='Resize', size=(256, -1), backend='pillow'), dict(type='CenterCrop', crop_size=224), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']) ], classes='data/flower/classes.txt'), test=dict( type='ImageNet', data_prefix='data/flower/test', ann_file='data/flower/test.txt', pipeline=[ dict(type='LoadImageFromFile'), dict(type='Resize', size=(256, -1), backend='pillow'), dict(type='CenterCrop', crop_size=224), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']) ], classes='data/flower/classes.txt')) evaluation = dict( interval=1, metric=['accuracy', 'precision', 'f1_score'], metric_options=dict(topk=(1, ))) optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) optimizer_config = dict(grad_clip=None) lr_config = dict(policy='step', gamma=0.98, step=[1]) runner = dict(type='EpochBasedRunner', max_epochs=2) checkpoint_config = dict(interval=1) log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) dist_params = dict(backend='nccl') log_level = 'INFO' load_from = 'https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth' resume_from = None workflow = [('train', 1)] work_dir = 'work_dirs/mobilenet_v2_1x_flower' gpu_ids = [0] 2022-07-16 22:51:55,601 - mmcls - INFO - Set random seed to 943425345, deterministic: False 2022-07-16 22:51:55,802 - mmcls - INFO - initialize MobileNetV2 with init_cfg [{'type': 'Kaiming', 'layer': ['Conv2d']}, {'type': 'Constant', 'val': 1, 'layer': ['_BatchNorm', 'GroupNorm']}] 2022-07-16 22:51:55,832 - mmcls - INFO - initialize LinearClsHead with init_cfg {'type': 'Normal', 'layer': 'Linear', 'std': 0.01} 2022-07-16 22:52:02,074 - mmcls - INFO - load checkpoint from http path: https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth 2022-07-16 22:52:02,104 - mmcls - WARNING - The model and loaded state dict do not match exactly size mismatch for head.fc.weight: copying a param with shape torch.Size([1000, 1280]) from checkpoint, the shape in current model is torch.Size([5, 1280]). size mismatch for head.fc.bias: copying a param with shape torch.Size([1000]) from checkpoint, the shape in current model is torch.Size([5]). 2022-07-16 22:52:02,105 - mmcls - INFO - Start running, host: featurize@featurize, work_dir: /home/featurize/work/MMClassification教程/mmclassification/work_dirs/mobilenet_v2_1x_flower 2022-07-16 22:52:02,105 - mmcls - INFO - Hooks will be executed in the following order: before_run: (VERY_HIGH ) StepLrUpdaterHook (NORMAL ) CheckpointHook (LOW ) EvalHook (VERY_LOW ) TextLoggerHook -------------------- before_train_epoch: (VERY_HIGH ) StepLrUpdaterHook (LOW ) IterTimerHook (LOW ) EvalHook (VERY_LOW ) TextLoggerHook -------------------- before_train_iter: (VERY_HIGH ) StepLrUpdaterHook (LOW ) IterTimerHook (LOW ) EvalHook -------------------- after_train_iter: (ABOVE_NORMAL) OptimizerHook (NORMAL ) CheckpointHook (LOW ) IterTimerHook (LOW ) EvalHook (VERY_LOW ) TextLoggerHook -------------------- after_train_epoch: (NORMAL ) CheckpointHook (LOW ) EvalHook (VERY_LOW ) TextLoggerHook -------------------- before_val_epoch: (LOW ) IterTimerHook (VERY_LOW ) TextLoggerHook -------------------- before_val_iter: (LOW ) IterTimerHook -------------------- after_val_iter: (LOW ) IterTimerHook -------------------- after_val_epoch: (VERY_LOW ) TextLoggerHook -------------------- after_run: (VERY_LOW ) TextLoggerHook -------------------- 2022-07-16 22:52:02,105 - mmcls - INFO - workflow: [('train', 1)], max: 2 epochs 2022-07-16 22:52:02,105 - mmcls - INFO - Checkpoints will be saved to /home/featurize/work/MMClassification教程/mmclassification/work_dirs/mobilenet_v2_1x_flower by HardDiskBackend. 2022-07-16 22:52:11,810 - mmcls - INFO - Saving checkpoint at 1 epochs [>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 715/715, 354.3 task/s, elapsed: 2s, ETA: 0s2022-07-16 22:52:13,944 - mmcls - INFO - Epoch(val) [1][23] accuracy_top-1: 66.1538, precision: 73.5692, f1_score: 65.5141 2022-07-16 22:52:23,245 - mmcls - INFO - Saving checkpoint at 2 epochs [>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 715/715, 360.9 task/s, elapsed: 2s, ETA: 0s2022-07-16 22:52:25,354 - mmcls - INFO - Epoch(val) [2][23] accuracy_top-1: 88.6713, precision: 89.7683, f1_score: 88.7995 用训练得到的图像分类模型，对新图像预测In [16]: import matplotlib.pyplot as plt import mmcv from mmcls.apis import inference_model, init_model, show_result_pyplot img = mmcv.imread('data/flower/test/daisy/11023214096_b5b39fab08.jpg') # img = mmcv.imread('data/cat2.jpg') # 图像分类模型 config 配置文件 config_file = 'configs/mobilenet_v2/mobilenet_v2_1x_flower.py' # 图像分类模型 checkpoint 权重文件 checkpoint_file = 'work_dirs/mobilenet_v2_1x_flower/latest.pth' # 通过 config 配置文件 和 checkpoint 权重文件 构建模型 model = init_model(config_file, checkpoint_file, device=device) result = inference_model(model, img) print('类别', result['pred_class'], '置信度', result['pred_score']) show_result_pyplot(model, img, result) load checkpoint from local path: work_dirs/mobilenet_v2_1x_flower/latest.pth 类别 daisy 置信度 0.9996930360794067
In [17]: !python tools/test.py \ configs/mobilenet_v2/mobilenet_v2_1x_flower.py \ work_dirs/mobilenet_v2_1x_flower/latest.pth \ --out testset_result.json /home/featurize/work/MMClassification教程/mmclassification/mmcls/utils/setup_env.py:33: UserWarning: Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. f'Setting OMP_NUM_THREADS environment variable for each process ' /home/featurize/work/MMClassification教程/mmclassification/mmcls/utils/setup_env.py:43: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. f'Setting MKL_NUM_THREADS environment variable for each process ' load checkpoint from local path: work_dirs/mobilenet_v2_1x_flower/latest.pth [>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 715/715, 358.6 task/s, elapsed: 2s, ETA: 0s dumping results to results_flower.json 将训练得到的模型在测试集上预测，获得图像分类评估结果In [18]: !python tools/test.py \ configs/mobilenet_v2/mobilenet_v2_1x_flower.py \ work_dirs/mobilenet_v2_1x_flower/latest.pth \ --metrics accuracy precision recall f1_score support \ --metric-options topk=1 /home/featurize/work/MMClassification教程/mmclassification/mmcls/utils/setup_env.py:33: UserWarning: Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. f'Setting OMP_NUM_THREADS environment variable for each process ' /home/featurize/work/MMClassification教程/mmclassification/mmcls/utils/setup_env.py:43: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. f'Setting MKL_NUM_THREADS environment variable for each process ' load checkpoint from local path: work_dirs/mobilenet_v2_1x_flower/latest.pth [>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 715/715, 352.3 task/s, elapsed: 2s, ETA: 0s accuracy : 88.67 support : 715.0 precision : 89.77 recall : 88.83 f1_score : 88.8 |
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