diff --git a/README.md b/README.md index f407dc5..831ad5f 100644 --- a/README.md +++ b/README.md @@ -15,7 +15,7 @@ Ao Wang, Hui Chen, Zijia Lin, Hengjun Pu, and Guiguang Ding\ Abstract -Recently, lightweight Vision Transformers (ViTs) demonstrate superior performance and lower latency compared with lightweight Convolutional Neural Networks (CNNs) on resource-constrained mobile devices. This improvement is usually attributed to the multi-head self-attention module, which enables the model to learn global representations. However, the architectural disparities between lightweight ViTs and lightweight CNNs have not been adequately examined. In this study, we revisit the efficient design of lightweight CNNs and emphasize their potential for mobile devices. We incrementally enhance the mobile-friendliness of a standard lightweight CNN, specifically MobileNetV3, by integrating the efficient architectural choices of lightweight ViTs. This ends up with a new family of pure lightweight CNNs, namely RepViT. Extensive experiments show that RepViT outperforms existing state-of-the-art lightweight ViTs and exhibits favorable latency in various vision tasks. On ImageNet, RepViT achieves over 80\% top-1 accuracy with nearly 1ms latency on an iPhone 12, which is the first time for a lightweight model, to the best of our knowledge. Our largest model, RepViT-M3, obtains 81.4\% accuracy with only 1.3ms latency. +Recently, lightweight Vision Transformers (ViTs) demonstrate superior performance and lower latency compared with lightweight Convolutional Neural Networks (CNNs) on resource-constrained mobile devices. This improvement is usually attributed to the multi-head self-attention module, which enables the model to learn global representations. However, the architectural disparities between lightweight ViTs and lightweight CNNs have not been adequately examined. In this study, we revisit the efficient design of lightweight CNNs and emphasize their potential for mobile devices. We incrementally enhance the mobile-friendliness of a standard lightweight CNN, specifically MobileNetV3, by integrating the efficient architectural choices of lightweight ViTs. This ends up with a new family of pure lightweight CNNs, namely RepViT. Extensive experiments show that RepViT outperforms existing state-of-the-art lightweight ViTs and exhibits favorable latency in various vision tasks. On ImageNet, RepViT achieves over 80\% top-1 accuracy with 1ms latency on an iPhone 12, which is the first time for a lightweight model, to the best of our knowledge. Our largest model, RepViT-M2.3, obtains 83.7\% accuracy with only 2.3ms latency.
@@ -29,18 +29,21 @@ Recently, lightweight Vision Transformers (ViTs) demonstrate superior performanc ### Models -| Model | Top-1 (300)| #params | MACs | Latency | Ckpt | Core ML | Log | +| Model | Top-1 (300 / 450)| #params | MACs | Latency | Ckpt | Core ML | Log | |:---------------|:----:|:---:|:--:|:--:|:--:|:--:|:--:| -| RepViT-M1 | 78.5 | 5.1M | 0.8G | 0.9ms | [M1](https://github.com/jameslahm/RepViT/releases/download/v1.0/repvit_m1_distill_300.pth) | [M1](https://github.com/jameslahm/RepViT/releases/download/v1.0/repvit_m1_224.mlmodel) | [M1](./logs/repvit_m1_train.log) | -| RepViT-M2 | 80.6 | 8.2M | 1.3G | 1.1ms | [M2](https://github.com/jameslahm/RepViT/releases/download/v1.0/repvit_m2_distill_300.pth) | [M2](https://github.com/jameslahm/RepViT/releases/download/v1.0/repvit_m2_224.mlmodel) | [M2](./logs/repvit_m2_train.log) | -| RepViT-M3 | 81.4 | 10.1M | 1.9G | 1.3ms | [M3](https://github.com/jameslahm/RepViT/releases/download/v1.0/repvit_m3_distill_300.pth) | [M3](https://github.com/jameslahm/RepViT/releases/download/v1.0/repvit_m3_224.mlmodel) | [M3](./logs/repvit_m3_train.log) | +| RepViT-M0.9 | 78.7 / 79.1 | 5.1M | 0.8G | 0.9ms | [M0.9-300e]() / [M0.9-450e]() | [M0.9-300e]() / [M0.9-450e]() | [M0.9-300e](./logs/repvit_m0_9_distill_300e.txt) / [M0.9-450e](./logs/repvit_m0_9_distill_450e.txt) | +| RepViT-M1.0 | 80.0 / 80.3 | 6.8M | 1.1G | 1.0ms | [M1.0-300e]() / [M1.0-450e]() | [M1.0-300e]() / [M1.0-450e]() | [M1.0-300e](./logs/repvit_m1_0_distill_300e.txt) / [M1.0-450e](./logs/repvit_m1_0_distill_450e.txt) | +| RepViT-M1.1 | 80.7 / 81.1 | 8.2M | 1.3G | 1.1ms | [M1.1-300e]() / [M1.1-450e]() | [M1.1-300e]() / [M1.1-450e]() | [M1.1-300e](./logs/repvit_m1_1_distill_300e.txt) / [M1.1-450e](./logs/repvit_m1_1_distill_450e.txt) | +| RepViT-M1.5 | 82.3 / 82.5 | 14.0M | 2.3G | 1.5ms | [M1.5-300e]() / [M1.5-450e]() | [M1.5-300e]() / [M1.5-450e]() | [M1.5-300e](./logs/repvit_m1_5_distill_300e.txt) / [M1.5-450e](./logs/repvit_m1_5_distill_450e.txt) | +| RepViT-M2.3 | 83.3 / 83.7 | 22.9M | 4.5G | 2.3ms | [M2.3-300e]() / [M2.3-450e]() | [M2.3-300e]() / [M2.3-450e]() | [M2.3-300e](./logs/repvit_m2_3_distill_300e.txt) / [M2.3-450e](./logs/repvit_m2_3_distill_450e.txt) | + Tips: Convert a training-time RepViT into the inference-time structure ``` from timm.models import create_model import utils -model = create_model('repvit_m1') +model = create_model('repvit_m0_9') utils.replace_batchnorm(model) ``` @@ -49,15 +52,15 @@ utils.replace_batchnorm(model) The latency reported in RepViT for iPhone 12 (iOS 16) uses the benchmark tool from [XCode 14](https://developer.apple.com/videos/play/wwdc2022/10027/). For example, here is a latency measurement of RepViT-M1: -![](./figures/repvit_m1_latency.png) +![](./figures/repvit_m0_9_latency.png) Tips: export the model to Core ML model ``` -python export_coreml.py --model repvit_m1 --ckpt pretrain/repvit_m1_distill_300.pth +python export_coreml.py --model repvit_m0_9 --ckpt pretrain/repvit_m0_9_distill_300e.pth ``` Tips: measure the throughput on GPU ``` -python speed_gpu.py --model repvit_m1 +python speed_gpu.py --model repvit_m0_9 ``` @@ -83,14 +86,14 @@ Download and extract ImageNet train and val images from http://image-net.org/. T To train RepViT-M1 on an 8-GPU machine: ``` -python -m torch.distributed.launch --nproc_per_node=8 --master_port 12346 --use_env main.py --model repvit_m1 --data-path ~/imagenet --dist-eval +python -m torch.distributed.launch --nproc_per_node=8 --master_port 12346 --use_env main.py --model repvit_m0_9 --data-path ~/imagenet --dist-eval ``` Tips: specify your data path and model name! ### Testing For example, to test RepViT-M1: ``` -python main.py --eval --model repvit_m3 --resume pretrain/repvit_m3_distill_300.pth --data-path ~/imagenet +python main.py --eval --model repvit_m0_9 --resume pretrain/repvit_m0_9_distill_300e.pth --data-path ~/imagenet ``` ## Downstream Tasks diff --git a/detection/README.md b/detection/README.md index de0a43b..df829a5 100644 --- a/detection/README.md +++ b/detection/README.md @@ -5,8 +5,9 @@ Detection and instance segmentation on MS COCO 2017 is implemented based on [MMD ## Models | Model | $AP^b$ | $AP_{50}^b$ | $AP_{75}^b$ | $AP^m$ | $AP_{50}^m$ | $AP_{75}^m$ | Latency | Ckpt | Log | |:---------------|:----:|:---:|:--:|:--:|:--:|:--:|:--:|:--:|:--:| -| RepViT-M2 | 39.8 | 61.9 | 43.5 | 37.2 | 58.8 | 40.1 | 4.9ms | [M2](https://github.com/jameslahm/RepViT/releases/download/v1.0/repvit_m2_coco.pth) | [M2](./logs/repvit_m2_coco.json) | -| RepViT-M3 | 41.1 | 63.1 | 45.0 | 38.3 | 60.4 | 41.0 | 5.9ms | [M3](https://github.com/jameslahm/RepViT/releases/download/v1.0/repvit_m3_coco.pth) | [M3](./logs/repvit_m3_coco.json) | +| RepViT-M1_1 | 39.8 | 61.9 | 43.5 | 37.2 | 58.8 | 40.1 | 4.9ms | [M1_1]() | [M1_1](./logs/repvit_m1_1_coco.json) | +| RepViT-M1_5 | 41.6 | 63.2 | 45.3 | 38.6 | 60.5 | 41.5 | 43.6 | 6.4ms | [M1_5]() | [M1_5](./logs/repvit_m1_5_coco.json) | +| RepViT-M2_3 | 44.6 | 66.1 | 48.8 | 40.8 | 63.6 | 43.9 | 46.1 | 9.9ms | [M2_3]() | [M2_3](./logs/repvit_m2_3_coco.json) | ## Installation @@ -43,7 +44,7 @@ We provide a multi-GPU testing script, specify config file, checkpoint, and numb For example, to test RepViT-M1 on COCO 2017 on an 8-GPU machine, ``` -./dist_test.sh configs/mask_rcnn_repvit_m2_fpn_1x_coco.py path/to/repvit_m2_coco.pth 8 --eval bbox segm +./dist_test.sh configs/mask_rcnn_repvit_m1_1_fpn_1x_coco.py path/to/repvit_m1_1_coco.pth 8 --eval bbox segm ``` ## Training @@ -51,7 +52,7 @@ Download ImageNet-1K pretrained weights into `./pretrain` We provide PyTorch distributed data parallel (DDP) training script `dist_train.sh`, for example, to train RepViT-M1 on an 8-GPU machine: ``` -./dist_train.sh configs/mask_rcnn_repvit_m2_fpn_1x_coco.py 8 +./dist_train.sh configs/mask_rcnn_repvit_m1_1_fpn_1x_coco.py 8 ``` Tips: specify configs and #GPUs! diff --git a/detection/configs/mask_rcnn_repvit_m2_fpn_1x_coco.py b/detection/configs/mask_rcnn_repvit_m1_1_fpn_1x_coco.py similarity index 86% rename from detection/configs/mask_rcnn_repvit_m2_fpn_1x_coco.py rename to detection/configs/mask_rcnn_repvit_m1_1_fpn_1x_coco.py index 86f4772..24f0ac3 100644 --- a/detection/configs/mask_rcnn_repvit_m2_fpn_1x_coco.py +++ b/detection/configs/mask_rcnn_repvit_m1_1_fpn_1x_coco.py @@ -7,10 +7,10 @@ # optimizer model = dict( backbone=dict( - type='repvit_m2', + type='repvit_m1_1', init_cfg=dict( type='Pretrained', - checkpoint='pretrain/repvit_m2_distill_300.pth', + checkpoint='pretrain/repvit_m1_1_distill_300e.pth', ), out_indices = [2,6,20,24] ), diff --git a/detection/configs/mask_rcnn_repvit_m3_fpn_1x_coco.py b/detection/configs/mask_rcnn_repvit_m1_5_fpn_1x_coco.py similarity index 80% rename from detection/configs/mask_rcnn_repvit_m3_fpn_1x_coco.py rename to detection/configs/mask_rcnn_repvit_m1_5_fpn_1x_coco.py index 13b23a3..03b8b21 100644 --- a/detection/configs/mask_rcnn_repvit_m3_fpn_1x_coco.py +++ b/detection/configs/mask_rcnn_repvit_m1_5_fpn_1x_coco.py @@ -7,12 +7,12 @@ # optimizer model = dict( backbone=dict( - type='repvit_m3', + type='repvit_m1_5', init_cfg=dict( type='Pretrained', - checkpoint='pretrain/repvit_m3_distill_300.pth', + checkpoint='pretrain/repvit_m1_5_distill_300e.pth', ), - out_indices=[4,10,30,34] + out_indices=[4, 10, 36, 42] ), neck=dict( type='FPN', diff --git a/detection/configs/mask_rcnn_repvit_m2_3_fpn_1x_coco.py b/detection/configs/mask_rcnn_repvit_m2_3_fpn_1x_coco.py new file mode 100644 index 0000000..e82d2ed --- /dev/null +++ b/detection/configs/mask_rcnn_repvit_m2_3_fpn_1x_coco.py @@ -0,0 +1,24 @@ +_base_ = [ + '_base_/models/mask_rcnn_r50_fpn.py', + '_base_/datasets/coco_instance.py', + '_base_/schedules/schedule_1x.py', + '_base_/default_runtime.py' +] +# optimizer +model = dict( + backbone=dict( + type='repvit_m2_3', + init_cfg=dict( + type='Pretrained', + checkpoint='pretrain/repvit_m2_3_distill_450e.pth', + ), + out_indices=[6, 14, 50, 54] + ), + neck=dict( + type='FPN', + in_channels=[80, 160, 320, 640], + out_channels=256, + num_outs=5)) +# optimizer +optimizer = dict(_delete_=True, type='AdamW', lr=0.0002, weight_decay=0.05) # 0.0001 +optimizer_config = dict(grad_clip=None) diff --git a/detection/eval.sh b/detection/eval.sh index 918f242..7ba1b2b 100644 --- a/detection/eval.sh +++ b/detection/eval.sh @@ -1 +1 @@ -PORT=12345 ./dist_test.sh configs/mask_rcnn_repvit_m3_fpn_1x_coco.py det_pretrain/repvit_m3_coco.pth 8 --eval bbox segm \ No newline at end of file +PORT=12345 ./dist_test.sh configs/mask_rcnn_repvit_m1_1_fpn_1x_coco.py det_pretrain/repvit_m1_1_coco.pth 8 --eval bbox segm \ No newline at end of file diff --git a/detection/logs/repvit_m2_coco.json b/detection/logs/repvit_m1_1_coco.json similarity index 98% rename from detection/logs/repvit_m2_coco.json rename to detection/logs/repvit_m1_1_coco.json index 02a28f1..8c16fee 100644 --- a/detection/logs/repvit_m2_coco.json +++ b/detection/logs/repvit_m1_1_coco.json @@ -1,4 +1,4 @@ -{"env_info": "sys.platform: linux\nPython: 3.8.16 (default, Mar 2 2023, 03:21:46) [GCC 11.2.0]\nCUDA available: True\nGPU 0,1,2,3,4,5,6,7: NVIDIA GeForce RTX 4090\nCUDA_HOME: /home/inspur/cuda-11.7\nNVCC: Cuda compilation tools, release 11.7, V11.7.99\nGCC: gcc (Ubuntu 11.3.0-1ubuntu1~22.04.1) 11.3.0\nPyTorch: 2.0.1+cu117\nPyTorch compiling details: PyTorch built with:\n - GCC 9.3\n - C++ Version: 201703\n - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications\n - Intel(R) MKL-DNN v2.7.3 (Git Hash 6dbeffbae1f23cbbeae17adb7b5b13f1f37c080e)\n - OpenMP 201511 (a.k.a. OpenMP 4.5)\n - LAPACK is enabled (usually provided by MKL)\n - NNPACK is enabled\n - CPU capability usage: AVX2\n - CUDA Runtime 11.7\n - 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\n - CuDNN 8.9.2 (built against CUDA 12.1)\n - Built with CuDNN 8.5\n - Magma 2.6.1\n - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.7, CUDNN_VERSION=8.5.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wunused-local-typedefs -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -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 -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_DISABLE_GPU_ASSERTS=ON, TORCH_VERSION=2.0.1, 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=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, \n\nTorchVision: 0.15.2+cu117\nOpenCV: 4.7.0\nMMCV: 1.7.1\nMMCV Compiler: GCC 11.3\nMMCV CUDA Compiler: 11.7\nMMDetection: 2.28.2+25971f7", "config": "model = dict(\n type='MaskRCNN',\n pretrained='torchvision://resnet50',\n backbone=dict(\n type='repvit_m2',\n depth=50,\n num_stages=4,\n out_indices=[2, 6, 20, 24],\n frozen_stages=1,\n norm_cfg=dict(type='BN', requires_grad=True),\n norm_eval=True,\n style='pytorch',\n init_cfg=dict(\n type='Pretrained',\n checkpoint='pretrain/repvit_m2_new_distill_300.pth')),\n neck=dict(\n type='FPN',\n in_channels=[64, 128, 256, 512],\n out_channels=256,\n num_outs=5),\n rpn_head=dict(\n type='RPNHead',\n in_channels=256,\n feat_channels=256,\n anchor_generator=dict(\n type='AnchorGenerator',\n scales=[8],\n ratios=[0.5, 1.0, 2.0],\n strides=[4, 8, 16, 32, 64]),\n bbox_coder=dict(\n type='DeltaXYWHBBoxCoder',\n target_means=[0.0, 0.0, 0.0, 0.0],\n target_stds=[1.0, 1.0, 1.0, 1.0]),\n loss_cls=dict(\n type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),\n loss_bbox=dict(type='L1Loss', loss_weight=1.0)),\n roi_head=dict(\n type='StandardRoIHead',\n bbox_roi_extractor=dict(\n type='SingleRoIExtractor',\n roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),\n out_channels=256,\n featmap_strides=[4, 8, 16, 32]),\n bbox_head=dict(\n type='Shared2FCBBoxHead',\n in_channels=256,\n fc_out_channels=1024,\n roi_feat_size=7,\n num_classes=80,\n bbox_coder=dict(\n type='DeltaXYWHBBoxCoder',\n target_means=[0.0, 0.0, 0.0, 0.0],\n target_stds=[0.1, 0.1, 0.2, 0.2]),\n reg_class_agnostic=False,\n loss_cls=dict(\n type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),\n loss_bbox=dict(type='L1Loss', loss_weight=1.0)),\n mask_roi_extractor=dict(\n type='SingleRoIExtractor',\n roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),\n out_channels=256,\n featmap_strides=[4, 8, 16, 32]),\n mask_head=dict(\n type='FCNMaskHead',\n num_convs=4,\n in_channels=256,\n conv_out_channels=256,\n num_classes=80,\n loss_mask=dict(\n type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))),\n train_cfg=dict(\n rpn=dict(\n assigner=dict(\n type='MaxIoUAssigner',\n pos_iou_thr=0.7,\n neg_iou_thr=0.3,\n min_pos_iou=0.3,\n match_low_quality=True,\n ignore_iof_thr=-1),\n sampler=dict(\n type='RandomSampler',\n num=256,\n pos_fraction=0.5,\n neg_pos_ub=-1,\n add_gt_as_proposals=False),\n allowed_border=-1,\n pos_weight=-1,\n debug=False),\n rpn_proposal=dict(\n nms_pre=2000,\n max_per_img=1000,\n nms=dict(type='nms', iou_threshold=0.7),\n min_bbox_size=0),\n rcnn=dict(\n assigner=dict(\n type='MaxIoUAssigner',\n pos_iou_thr=0.5,\n neg_iou_thr=0.5,\n min_pos_iou=0.5,\n match_low_quality=True,\n ignore_iof_thr=-1),\n sampler=dict(\n type='RandomSampler',\n num=512,\n pos_fraction=0.25,\n neg_pos_ub=-1,\n add_gt_as_proposals=True),\n mask_size=28,\n pos_weight=-1,\n debug=False)),\n test_cfg=dict(\n rpn=dict(\n nms_pre=1000,\n max_per_img=1000,\n nms=dict(type='nms', iou_threshold=0.7),\n min_bbox_size=0),\n rcnn=dict(\n score_thr=0.05,\n nms=dict(type='nms', iou_threshold=0.5),\n max_per_img=100,\n mask_thr_binary=0.5)))\ndataset_type = 'CocoDataset'\ndata_root = 'data/coco/'\nimg_norm_cfg = dict(\n mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)\ntrain_pipeline = [\n dict(type='LoadImageFromFile'),\n dict(type='LoadAnnotations', with_bbox=True, with_mask=True),\n dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),\n dict(type='RandomFlip', flip_ratio=0.5),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='Pad', size_divisor=32),\n dict(type='DefaultFormatBundle'),\n dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks'])\n]\ntest_pipeline = [\n dict(type='LoadImageFromFile'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(1333, 800),\n flip=False,\n transforms=[\n dict(type='Resize', keep_ratio=True),\n dict(type='RandomFlip'),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='Pad', size_divisor=32),\n dict(type='ImageToTensor', keys=['img']),\n dict(type='Collect', keys=['img'])\n ])\n]\ndata = dict(\n samples_per_gpu=2,\n workers_per_gpu=2,\n train=dict(\n type='CocoDataset',\n ann_file='data/coco/annotations/instances_train2017.json',\n img_prefix='data/coco/train2017/',\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(type='LoadAnnotations', with_bbox=True, with_mask=True),\n dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),\n dict(type='RandomFlip', flip_ratio=0.5),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='Pad', size_divisor=32),\n dict(type='DefaultFormatBundle'),\n dict(\n type='Collect',\n keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks'])\n ]),\n val=dict(\n type='CocoDataset',\n ann_file='data/coco/annotations/instances_val2017.json',\n img_prefix='data/coco/val2017/',\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(1333, 800),\n flip=False,\n transforms=[\n dict(type='Resize', keep_ratio=True),\n dict(type='RandomFlip'),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='Pad', size_divisor=32),\n dict(type='ImageToTensor', keys=['img']),\n dict(type='Collect', keys=['img'])\n ])\n ]),\n test=dict(\n type='CocoDataset',\n ann_file='data/coco/annotations/instances_val2017.json',\n img_prefix='data/coco/val2017/',\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(1333, 800),\n flip=False,\n transforms=[\n dict(type='Resize', keep_ratio=True),\n dict(type='RandomFlip'),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='Pad', size_divisor=32),\n dict(type='ImageToTensor', keys=['img']),\n dict(type='Collect', keys=['img'])\n ])\n ]))\nevaluation = dict(metric=['bbox', 'segm'])\noptimizer = dict(type='AdamW', lr=0.0002, weight_decay=0.05)\noptimizer_config = dict(grad_clip=None)\nlr_config = dict(\n policy='step',\n warmup='linear',\n warmup_iters=500,\n warmup_ratio=1e-06,\n step=[8, 11])\nrunner = dict(type='EpochBasedRunner', max_epochs=12)\ncheckpoint_config = dict(interval=1)\nlog_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])\ncustom_hooks = [dict(type='NumClassCheckHook')]\ndist_params = dict(backend='nccl')\nlog_level = 'INFO'\nload_from = None\nresume_from = None\nworkflow = [('train', 1)]\nwork_dir = './work_dirs/mask_rcnn_repvit_m2_fpn_1x_coco'\nauto_resume = False\ngpu_ids = range(0, 8)\n", "seed": 0, "exp_name": "mask_rcnn_repvit_m2_fpn_1x_coco.py"} +{"env_info": "sys.platform: linux\nPython: 3.8.16 (default, Mar 2 2023, 03:21:46) [GCC 11.2.0]\nCUDA available: True\nGPU 0,1,2,3,4,5,6,7: NVIDIA GeForce RTX 4090\nCUDA_HOME: /home/inspur/cuda-11.7\nNVCC: Cuda compilation tools, release 11.7, V11.7.99\nGCC: gcc (Ubuntu 11.3.0-1ubuntu1~22.04.1) 11.3.0\nPyTorch: 2.0.1+cu117\nPyTorch compiling details: PyTorch built with:\n - GCC 9.3\n - C++ Version: 201703\n - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications\n - Intel(R) MKL-DNN v2.7.3 (Git Hash 6dbeffbae1f23cbbeae17adb7b5b13f1f37c080e)\n - OpenMP 201511 (a.k.a. OpenMP 4.5)\n - LAPACK is enabled (usually provided by MKL)\n - NNPACK is enabled\n - CPU capability usage: AVX2\n - CUDA Runtime 11.7\n - 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\n - CuDNN 8.9.2 (built against CUDA 12.1)\n - Built with CuDNN 8.5\n - Magma 2.6.1\n - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.7, CUDNN_VERSION=8.5.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wunused-local-typedefs -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -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 -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_DISABLE_GPU_ASSERTS=ON, TORCH_VERSION=2.0.1, 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=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, \n\nTorchVision: 0.15.2+cu117\nOpenCV: 4.7.0\nMMCV: 1.7.1\nMMCV Compiler: GCC 11.3\nMMCV CUDA Compiler: 11.7\nMMDetection: 2.28.2+25971f7", "config": "model = dict(\n type='MaskRCNN',\n pretrained='torchvision://resnet50',\n backbone=dict(\n type='repvit_m1_1',\n depth=50,\n num_stages=4,\n out_indices=[2, 6, 20, 24],\n frozen_stages=1,\n norm_cfg=dict(type='BN', requires_grad=True),\n norm_eval=True,\n style='pytorch',\n init_cfg=dict(\n type='Pretrained',\n checkpoint='pretrain/repvit_m1_1_distill_300e.pth')),\n neck=dict(\n type='FPN',\n in_channels=[64, 128, 256, 512],\n out_channels=256,\n num_outs=5),\n rpn_head=dict(\n type='RPNHead',\n in_channels=256,\n feat_channels=256,\n anchor_generator=dict(\n type='AnchorGenerator',\n scales=[8],\n ratios=[0.5, 1.0, 2.0],\n strides=[4, 8, 16, 32, 64]),\n bbox_coder=dict(\n type='DeltaXYWHBBoxCoder',\n target_means=[0.0, 0.0, 0.0, 0.0],\n target_stds=[1.0, 1.0, 1.0, 1.0]),\n loss_cls=dict(\n type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),\n loss_bbox=dict(type='L1Loss', loss_weight=1.0)),\n roi_head=dict(\n type='StandardRoIHead',\n bbox_roi_extractor=dict(\n type='SingleRoIExtractor',\n roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),\n out_channels=256,\n featmap_strides=[4, 8, 16, 32]),\n bbox_head=dict(\n type='Shared2FCBBoxHead',\n in_channels=256,\n fc_out_channels=1024,\n roi_feat_size=7,\n num_classes=80,\n bbox_coder=dict(\n type='DeltaXYWHBBoxCoder',\n target_means=[0.0, 0.0, 0.0, 0.0],\n target_stds=[0.1, 0.1, 0.2, 0.2]),\n reg_class_agnostic=False,\n loss_cls=dict(\n type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),\n loss_bbox=dict(type='L1Loss', loss_weight=1.0)),\n mask_roi_extractor=dict(\n type='SingleRoIExtractor',\n roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),\n out_channels=256,\n featmap_strides=[4, 8, 16, 32]),\n mask_head=dict(\n type='FCNMaskHead',\n num_convs=4,\n in_channels=256,\n conv_out_channels=256,\n num_classes=80,\n loss_mask=dict(\n type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))),\n train_cfg=dict(\n rpn=dict(\n assigner=dict(\n type='MaxIoUAssigner',\n pos_iou_thr=0.7,\n neg_iou_thr=0.3,\n min_pos_iou=0.3,\n match_low_quality=True,\n ignore_iof_thr=-1),\n sampler=dict(\n type='RandomSampler',\n num=256,\n pos_fraction=0.5,\n neg_pos_ub=-1,\n add_gt_as_proposals=False),\n allowed_border=-1,\n pos_weight=-1,\n debug=False),\n rpn_proposal=dict(\n nms_pre=2000,\n max_per_img=1000,\n nms=dict(type='nms', iou_threshold=0.7),\n min_bbox_size=0),\n rcnn=dict(\n assigner=dict(\n type='MaxIoUAssigner',\n pos_iou_thr=0.5,\n neg_iou_thr=0.5,\n min_pos_iou=0.5,\n match_low_quality=True,\n ignore_iof_thr=-1),\n sampler=dict(\n type='RandomSampler',\n num=512,\n pos_fraction=0.25,\n neg_pos_ub=-1,\n add_gt_as_proposals=True),\n mask_size=28,\n pos_weight=-1,\n debug=False)),\n test_cfg=dict(\n rpn=dict(\n nms_pre=1000,\n max_per_img=1000,\n nms=dict(type='nms', iou_threshold=0.7),\n min_bbox_size=0),\n rcnn=dict(\n score_thr=0.05,\n nms=dict(type='nms', iou_threshold=0.5),\n max_per_img=100,\n mask_thr_binary=0.5)))\ndataset_type = 'CocoDataset'\ndata_root = 'data/coco/'\nimg_norm_cfg = dict(\n mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)\ntrain_pipeline = [\n dict(type='LoadImageFromFile'),\n dict(type='LoadAnnotations', with_bbox=True, with_mask=True),\n dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),\n dict(type='RandomFlip', flip_ratio=0.5),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='Pad', size_divisor=32),\n dict(type='DefaultFormatBundle'),\n dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks'])\n]\ntest_pipeline = [\n dict(type='LoadImageFromFile'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(1333, 800),\n flip=False,\n transforms=[\n dict(type='Resize', keep_ratio=True),\n dict(type='RandomFlip'),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='Pad', size_divisor=32),\n dict(type='ImageToTensor', keys=['img']),\n dict(type='Collect', keys=['img'])\n ])\n]\ndata = dict(\n samples_per_gpu=2,\n workers_per_gpu=2,\n train=dict(\n type='CocoDataset',\n ann_file='data/coco/annotations/instances_train2017.json',\n img_prefix='data/coco/train2017/',\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(type='LoadAnnotations', with_bbox=True, with_mask=True),\n dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),\n dict(type='RandomFlip', flip_ratio=0.5),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='Pad', size_divisor=32),\n dict(type='DefaultFormatBundle'),\n dict(\n type='Collect',\n keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks'])\n ]),\n val=dict(\n type='CocoDataset',\n ann_file='data/coco/annotations/instances_val2017.json',\n img_prefix='data/coco/val2017/',\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(1333, 800),\n flip=False,\n transforms=[\n dict(type='Resize', keep_ratio=True),\n dict(type='RandomFlip'),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='Pad', size_divisor=32),\n dict(type='ImageToTensor', keys=['img']),\n dict(type='Collect', keys=['img'])\n ])\n ]),\n test=dict(\n type='CocoDataset',\n ann_file='data/coco/annotations/instances_val2017.json',\n img_prefix='data/coco/val2017/',\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(1333, 800),\n flip=False,\n transforms=[\n dict(type='Resize', keep_ratio=True),\n dict(type='RandomFlip'),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='Pad', size_divisor=32),\n dict(type='ImageToTensor', keys=['img']),\n dict(type='Collect', keys=['img'])\n ])\n ]))\nevaluation = dict(metric=['bbox', 'segm'])\noptimizer = dict(type='AdamW', lr=0.0002, weight_decay=0.05)\noptimizer_config = dict(grad_clip=None)\nlr_config = dict(\n policy='step',\n warmup='linear',\n warmup_iters=500,\n warmup_ratio=1e-06,\n step=[8, 11])\nrunner = dict(type='EpochBasedRunner', max_epochs=12)\ncheckpoint_config = dict(interval=1)\nlog_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])\ncustom_hooks = [dict(type='NumClassCheckHook')]\ndist_params = dict(backend='nccl')\nlog_level = 'INFO'\nload_from = None\nresume_from = None\nworkflow = [('train', 1)]\nwork_dir = './work_dirs/mask_rcnn_repvit_m1_1_fpn_1x_coco'\nauto_resume = False\ngpu_ids = range(0, 8)\n", "seed": 0, "exp_name": "mask_rcnn_repvit_m1_1_fpn_1x_coco.py"} {"mode": "train", "epoch": 1, "iter": 50, "lr": 2e-05, "memory": 5484, "data_time": 0.11493, "loss_rpn_cls": 0.53125, "loss_rpn_bbox": 0.17528, "loss_cls": 2.15711, "acc": 67.11523, "loss_bbox": 0.07881, "loss_mask": 1.82539, "loss": 4.76784, "time": 0.45501} {"mode": "train", "epoch": 1, "iter": 100, "lr": 4e-05, "memory": 5685, "data_time": 0.03757, "loss_rpn_cls": 0.2529, "loss_rpn_bbox": 0.1119, "loss_cls": 0.42318, "acc": 94.50195, "loss_bbox": 0.18693, "loss_mask": 0.78322, "loss": 1.75813, "time": 0.25931} {"mode": "train", "epoch": 1, "iter": 150, "lr": 6e-05, "memory": 5685, "data_time": 0.04388, "loss_rpn_cls": 0.22443, "loss_rpn_bbox": 0.10909, "loss_cls": 0.3672, "acc": 94.34106, "loss_bbox": 0.19103, "loss_mask": 0.70588, "loss": 1.59763, "time": 0.2502} diff --git a/detection/logs/repvit_m1_5_coco.json b/detection/logs/repvit_m1_5_coco.json new file mode 100644 index 0000000..1779ea7 --- /dev/null +++ b/detection/logs/repvit_m1_5_coco.json @@ -0,0 +1,1765 @@ +{"env_info": "sys.platform: linux\nPython: 3.8.16 (default, Mar 2 2023, 03:21:46) [GCC 11.2.0]\nCUDA available: True\nGPU 0,1,2,3,4,5,6,7: GeForce RTX 3090\nCUDA_HOME: /home/wangao/cuda-11.2\nNVCC: Cuda compilation tools, release 11.2, V11.2.152\nGCC: gcc (GCC) 5.4.0\nPyTorch: 2.0.1+cu117\nPyTorch compiling details: PyTorch built with:\n - GCC 9.3\n - C++ Version: 201703\n - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications\n - Intel(R) MKL-DNN v2.7.3 (Git Hash 6dbeffbae1f23cbbeae17adb7b5b13f1f37c080e)\n - OpenMP 201511 (a.k.a. OpenMP 4.5)\n - LAPACK is enabled (usually provided by MKL)\n - NNPACK is enabled\n - CPU capability usage: AVX2\n - CUDA Runtime 11.7\n - 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\n - CuDNN 8.5\n - Magma 2.6.1\n - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.7, CUDNN_VERSION=8.5.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wunused-local-typedefs -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -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 -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_DISABLE_GPU_ASSERTS=ON, TORCH_VERSION=2.0.1, 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=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, \n\nTorchVision: 0.15.2+cu117\nOpenCV: 4.8.0\nMMCV: 1.7.1\nMMCV Compiler: GCC 5.4\nMMCV CUDA Compiler: 11.2\nMMDetection: 2.28.2+c54117a", "config": "model = dict(\n type='MaskRCNN',\n pretrained='torchvision://resnet50',\n backbone=dict(\n type='repvit_m4',\n depth=50,\n num_stages=4,\n out_indices=[4, 10, 36, 42],\n frozen_stages=1,\n norm_cfg=dict(type='BN', requires_grad=True),\n norm_eval=True,\n style='pytorch',\n init_cfg=dict(\n type='Pretrained',\n checkpoint='pretrain/repvit_m4_distill_300e.pth')),\n neck=dict(\n type='FPN',\n in_channels=[64, 128, 256, 512],\n out_channels=256,\n num_outs=5),\n rpn_head=dict(\n type='RPNHead',\n in_channels=256,\n feat_channels=256,\n anchor_generator=dict(\n type='AnchorGenerator',\n scales=[8],\n ratios=[0.5, 1.0, 2.0],\n strides=[4, 8, 16, 32, 64]),\n bbox_coder=dict(\n type='DeltaXYWHBBoxCoder',\n target_means=[0.0, 0.0, 0.0, 0.0],\n target_stds=[1.0, 1.0, 1.0, 1.0]),\n loss_cls=dict(\n type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),\n loss_bbox=dict(type='L1Loss', loss_weight=1.0)),\n roi_head=dict(\n type='StandardRoIHead',\n bbox_roi_extractor=dict(\n type='SingleRoIExtractor',\n roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),\n out_channels=256,\n featmap_strides=[4, 8, 16, 32]),\n bbox_head=dict(\n type='Shared2FCBBoxHead',\n in_channels=256,\n fc_out_channels=1024,\n roi_feat_size=7,\n num_classes=80,\n bbox_coder=dict(\n type='DeltaXYWHBBoxCoder',\n target_means=[0.0, 0.0, 0.0, 0.0],\n target_stds=[0.1, 0.1, 0.2, 0.2]),\n reg_class_agnostic=False,\n loss_cls=dict(\n type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),\n loss_bbox=dict(type='L1Loss', loss_weight=1.0)),\n mask_roi_extractor=dict(\n type='SingleRoIExtractor',\n roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),\n out_channels=256,\n featmap_strides=[4, 8, 16, 32]),\n mask_head=dict(\n type='FCNMaskHead',\n num_convs=4,\n in_channels=256,\n conv_out_channels=256,\n num_classes=80,\n loss_mask=dict(\n type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))),\n train_cfg=dict(\n rpn=dict(\n assigner=dict(\n type='MaxIoUAssigner',\n pos_iou_thr=0.7,\n neg_iou_thr=0.3,\n min_pos_iou=0.3,\n match_low_quality=True,\n ignore_iof_thr=-1),\n sampler=dict(\n type='RandomSampler',\n num=256,\n pos_fraction=0.5,\n neg_pos_ub=-1,\n add_gt_as_proposals=False),\n allowed_border=-1,\n pos_weight=-1,\n debug=False),\n rpn_proposal=dict(\n nms_pre=2000,\n max_per_img=1000,\n nms=dict(type='nms', iou_threshold=0.7),\n min_bbox_size=0),\n rcnn=dict(\n assigner=dict(\n type='MaxIoUAssigner',\n pos_iou_thr=0.5,\n neg_iou_thr=0.5,\n min_pos_iou=0.5,\n match_low_quality=True,\n ignore_iof_thr=-1),\n sampler=dict(\n type='RandomSampler',\n num=512,\n pos_fraction=0.25,\n neg_pos_ub=-1,\n add_gt_as_proposals=True),\n mask_size=28,\n pos_weight=-1,\n debug=False)),\n test_cfg=dict(\n rpn=dict(\n nms_pre=1000,\n max_per_img=1000,\n nms=dict(type='nms', iou_threshold=0.7),\n min_bbox_size=0),\n rcnn=dict(\n score_thr=0.05,\n nms=dict(type='nms', iou_threshold=0.5),\n max_per_img=100,\n mask_thr_binary=0.5)))\ndataset_type = 'CocoDataset'\ndata_root = 'data/coco/'\nimg_norm_cfg = dict(\n mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)\ntrain_pipeline = [\n dict(type='LoadImageFromFile'),\n dict(type='LoadAnnotations', with_bbox=True, with_mask=True),\n dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),\n dict(type='RandomFlip', flip_ratio=0.5),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='Pad', size_divisor=32),\n dict(type='DefaultFormatBundle'),\n dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks'])\n]\ntest_pipeline = [\n dict(type='LoadImageFromFile'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(1333, 800),\n flip=False,\n transforms=[\n dict(type='Resize', keep_ratio=True),\n dict(type='RandomFlip'),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='Pad', size_divisor=32),\n dict(type='ImageToTensor', keys=['img']),\n dict(type='Collect', keys=['img'])\n ])\n]\ndata = dict(\n samples_per_gpu=2,\n workers_per_gpu=2,\n train=dict(\n type='CocoDataset',\n ann_file='data/coco/annotations/instances_train2017.json',\n img_prefix='data/coco/train2017/',\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(type='LoadAnnotations', with_bbox=True, with_mask=True),\n dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),\n dict(type='RandomFlip', flip_ratio=0.5),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='Pad', size_divisor=32),\n dict(type='DefaultFormatBundle'),\n dict(\n type='Collect',\n keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks'])\n ]),\n val=dict(\n type='CocoDataset',\n ann_file='data/coco/annotations/instances_val2017.json',\n img_prefix='data/coco/val2017/',\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(1333, 800),\n flip=False,\n transforms=[\n dict(type='Resize', keep_ratio=True),\n dict(type='RandomFlip'),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='Pad', size_divisor=32),\n dict(type='ImageToTensor', keys=['img']),\n dict(type='Collect', keys=['img'])\n ])\n ]),\n test=dict(\n type='CocoDataset',\n ann_file='data/coco/annotations/instances_val2017.json',\n img_prefix='data/coco/val2017/',\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(1333, 800),\n flip=False,\n transforms=[\n dict(type='Resize', keep_ratio=True),\n dict(type='RandomFlip'),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='Pad', size_divisor=32),\n dict(type='ImageToTensor', keys=['img']),\n dict(type='Collect', keys=['img'])\n ])\n ]))\nevaluation = dict(metric=['bbox', 'segm'])\noptimizer = dict(type='AdamW', lr=0.0002, weight_decay=0.05)\noptimizer_config = dict(grad_clip=None)\nlr_config = dict(\n policy='step',\n warmup='linear',\n warmup_iters=500,\n warmup_ratio=1e-06,\n step=[8, 11])\nrunner = dict(type='EpochBasedRunner', max_epochs=12)\ncheckpoint_config = dict(interval=1)\nlog_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])\ncustom_hooks = [dict(type='NumClassCheckHook')]\ndist_params = dict(backend='nccl')\nlog_level = 'INFO'\nload_from = None\nresume_from = None\nworkflow = [('train', 1)]\nwork_dir = './work_dirs/mask_rcnn_repvit_m4_fpn_1x_coco'\nauto_resume = False\ngpu_ids = range(0, 8)\n", "seed": 0, "exp_name": "mask_rcnn_repvit_m4_fpn_1x_coco.py"} +{"mode": "train", "epoch": 1, "iter": 50, "lr": 2e-05, "memory": 7980, "data_time": 0.14548, "loss_rpn_cls": 0.53314, "loss_rpn_bbox": 0.14748, "loss_cls": 1.91489, "acc": 68.13208, "loss_bbox": 0.072, "loss_mask": 1.59976, "loss": 4.26727, "time": 0.68673} +{"mode": "train", "epoch": 1, "iter": 100, "lr": 4e-05, "memory": 8162, "data_time": 0.06063, "loss_rpn_cls": 0.25145, "loss_rpn_bbox": 0.11155, "loss_cls": 0.43818, "acc": 94.75, "loss_bbox": 0.17652, "loss_mask": 0.77439, "loss": 1.75208, "time": 0.47045} +{"mode": "train", "epoch": 1, "iter": 150, "lr": 6e-05, "memory": 8162, "data_time": 0.06035, "loss_rpn_cls": 0.20735, "loss_rpn_bbox": 0.10837, "loss_cls": 0.39381, "acc": 93.92725, "loss_bbox": 0.20736, "loss_mask": 0.70913, "loss": 1.62602, "time": 0.46687} +{"mode": "train", "epoch": 1, "iter": 200, "lr": 8e-05, "memory": 8162, "data_time": 0.05478, "loss_rpn_cls": 0.15562, "loss_rpn_bbox": 0.10826, "loss_cls": 0.43233, "acc": 92.84741, "loss_bbox": 0.25263, "loss_mask": 0.67926, "loss": 1.62809, "time": 0.45768} +{"mode": "train", "epoch": 1, "iter": 250, "lr": 0.0001, "memory": 8162, "data_time": 0.0551, "loss_rpn_cls": 0.12255, "loss_rpn_bbox": 0.10449, "loss_cls": 0.4498, "acc": 91.91943, "loss_bbox": 0.29686, "loss_mask": 0.63444, "loss": 1.60814, "time": 0.43649} +{"mode": "train", "epoch": 1, "iter": 300, "lr": 0.00012, "memory": 8212, "data_time": 0.07644, "loss_rpn_cls": 0.09739, "loss_rpn_bbox": 0.09526, "loss_cls": 0.49012, "acc": 90.57593, "loss_bbox": 0.35082, "loss_mask": 0.58461, "loss": 1.6182, "time": 0.45779} +{"mode": "train", "epoch": 1, "iter": 350, "lr": 0.00014, "memory": 8212, "data_time": 0.05278, "loss_rpn_cls": 0.0899, "loss_rpn_bbox": 0.09086, "loss_cls": 0.46009, "acc": 90.81299, "loss_bbox": 0.3403, "loss_mask": 0.52724, "loss": 1.50839, "time": 0.43938} +{"mode": "train", "epoch": 1, "iter": 400, "lr": 0.00016, "memory": 8351, "data_time": 0.05907, "loss_rpn_cls": 0.09468, "loss_rpn_bbox": 0.0926, "loss_cls": 0.4741, "acc": 90.06348, "loss_bbox": 0.35981, "loss_mask": 0.4983, "loss": 1.51949, "time": 0.4306} +{"mode": "train", "epoch": 1, "iter": 450, "lr": 0.00018, "memory": 8351, "data_time": 0.05434, "loss_rpn_cls": 0.08586, "loss_rpn_bbox": 0.08794, "loss_cls": 0.45477, "acc": 89.84888, "loss_bbox": 0.36565, "loss_mask": 0.45791, "loss": 1.45213, "time": 0.42242} +{"mode": "train", "epoch": 1, "iter": 500, "lr": 0.0002, "memory": 8351, "data_time": 0.04756, "loss_rpn_cls": 0.07821, "loss_rpn_bbox": 0.08602, "loss_cls": 0.45179, "acc": 89.7334, "loss_bbox": 0.37553, "loss_mask": 0.451, "loss": 1.44255, "time": 0.41952} +{"mode": "train", "epoch": 1, "iter": 550, "lr": 0.0002, "memory": 8398, "data_time": 0.05398, "loss_rpn_cls": 0.08005, "loss_rpn_bbox": 0.08015, "loss_cls": 0.44438, "acc": 89.64673, "loss_bbox": 0.37342, "loss_mask": 0.41911, "loss": 1.39711, "time": 0.43381} +{"mode": "train", "epoch": 1, "iter": 600, "lr": 0.0002, "memory": 8398, "data_time": 0.04911, "loss_rpn_cls": 0.07676, "loss_rpn_bbox": 0.08142, "loss_cls": 0.42188, "acc": 89.59058, "loss_bbox": 0.36926, "loss_mask": 0.40923, "loss": 1.35854, "time": 0.43607} +{"mode": "train", "epoch": 1, "iter": 650, "lr": 0.0002, "memory": 8398, "data_time": 0.07102, "loss_rpn_cls": 0.07519, "loss_rpn_bbox": 0.08478, "loss_cls": 0.41821, "acc": 89.52515, "loss_bbox": 0.37333, "loss_mask": 0.39809, "loss": 1.34961, "time": 0.43863} +{"mode": "train", "epoch": 1, "iter": 700, "lr": 0.0002, "memory": 8398, "data_time": 0.05426, "loss_rpn_cls": 0.07631, "loss_rpn_bbox": 0.08253, "loss_cls": 0.40514, "acc": 89.38013, "loss_bbox": 0.37422, "loss_mask": 0.39287, "loss": 1.33107, "time": 0.43034} +{"mode": "train", "epoch": 1, "iter": 750, "lr": 0.0002, "memory": 8398, "data_time": 0.04916, "loss_rpn_cls": 0.07243, "loss_rpn_bbox": 0.08025, "loss_cls": 0.3955, "acc": 89.45923, "loss_bbox": 0.36461, "loss_mask": 0.38045, "loss": 1.29324, "time": 0.43622} +{"mode": "train", "epoch": 1, "iter": 800, "lr": 0.0002, "memory": 8398, "data_time": 0.05913, "loss_rpn_cls": 0.06914, "loss_rpn_bbox": 0.08124, "loss_cls": 0.40151, "acc": 89.427, "loss_bbox": 0.36257, "loss_mask": 0.37968, "loss": 1.29413, "time": 0.54043} +{"mode": "train", "epoch": 1, "iter": 850, "lr": 0.0002, "memory": 8398, "data_time": 0.06153, "loss_rpn_cls": 0.06676, "loss_rpn_bbox": 0.07747, "loss_cls": 0.3726, "acc": 89.71411, "loss_bbox": 0.35481, "loss_mask": 0.36996, "loss": 1.2416, "time": 0.4233} +{"mode": "train", "epoch": 1, "iter": 900, "lr": 0.0002, "memory": 8404, "data_time": 0.05656, "loss_rpn_cls": 0.06777, "loss_rpn_bbox": 0.07761, "loss_cls": 0.38445, "acc": 89.5686, "loss_bbox": 0.35571, "loss_mask": 0.37542, "loss": 1.26096, "time": 0.41979} +{"mode": "train", "epoch": 1, "iter": 950, "lr": 0.0002, "memory": 8415, "data_time": 0.05396, "loss_rpn_cls": 0.06608, "loss_rpn_bbox": 0.0761, "loss_cls": 0.3778, "acc": 89.68164, "loss_bbox": 0.34412, "loss_mask": 0.36283, "loss": 1.22693, "time": 0.41275} +{"mode": "train", "epoch": 1, "iter": 1000, "lr": 0.0002, "memory": 8415, "data_time": 0.06083, "loss_rpn_cls": 0.06699, "loss_rpn_bbox": 0.0749, "loss_cls": 0.36141, "acc": 89.95605, "loss_bbox": 0.34437, "loss_mask": 0.35494, "loss": 1.20261, "time": 0.42294} +{"mode": "train", "epoch": 1, "iter": 1050, "lr": 0.0002, "memory": 8415, "data_time": 0.04988, "loss_rpn_cls": 0.06339, "loss_rpn_bbox": 0.07299, "loss_cls": 0.36277, "acc": 89.83081, "loss_bbox": 0.34577, "loss_mask": 0.35495, "loss": 1.19986, "time": 0.42698} +{"mode": "train", "epoch": 1, "iter": 1100, "lr": 0.0002, "memory": 8415, "data_time": 0.05954, "loss_rpn_cls": 0.05847, "loss_rpn_bbox": 0.07187, "loss_cls": 0.36026, "acc": 89.68848, "loss_bbox": 0.349, "loss_mask": 0.35461, "loss": 1.19421, "time": 0.41342} +{"mode": "train", "epoch": 1, "iter": 1150, "lr": 0.0002, "memory": 8415, "data_time": 0.05858, "loss_rpn_cls": 0.06511, "loss_rpn_bbox": 0.07882, "loss_cls": 0.36549, "acc": 89.56396, "loss_bbox": 0.35267, "loss_mask": 0.35172, "loss": 1.21381, "time": 0.41625} +{"mode": "train", "epoch": 1, "iter": 1200, "lr": 0.0002, "memory": 8415, "data_time": 0.06615, "loss_rpn_cls": 0.06678, "loss_rpn_bbox": 0.07934, "loss_cls": 0.36381, "acc": 89.5354, "loss_bbox": 0.35422, "loss_mask": 0.35489, "loss": 1.21904, "time": 0.42158} +{"mode": "train", "epoch": 1, "iter": 1250, "lr": 0.0002, "memory": 8415, "data_time": 0.05749, "loss_rpn_cls": 0.05694, "loss_rpn_bbox": 0.0691, "loss_cls": 0.35693, "acc": 89.9856, "loss_bbox": 0.33243, "loss_mask": 0.34468, "loss": 1.16008, "time": 0.40236} +{"mode": "train", "epoch": 1, "iter": 1300, "lr": 0.0002, "memory": 8415, "data_time": 0.05652, "loss_rpn_cls": 0.05882, "loss_rpn_bbox": 0.07072, "loss_cls": 0.3518, "acc": 89.92432, "loss_bbox": 0.3382, "loss_mask": 0.34099, "loss": 1.16052, "time": 0.47858} +{"mode": "train", "epoch": 1, "iter": 1350, "lr": 0.0002, "memory": 8415, "data_time": 0.0554, "loss_rpn_cls": 0.06632, "loss_rpn_bbox": 0.0772, "loss_cls": 0.35289, "acc": 89.81982, "loss_bbox": 0.33598, "loss_mask": 0.33655, "loss": 1.16894, "time": 0.41736} +{"mode": "train", "epoch": 1, "iter": 1400, "lr": 0.0002, "memory": 8415, "data_time": 0.05635, "loss_rpn_cls": 0.06142, "loss_rpn_bbox": 0.07092, "loss_cls": 0.32754, "acc": 90.54077, "loss_bbox": 0.32229, "loss_mask": 0.32667, "loss": 1.10883, "time": 0.41364} +{"mode": 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0.33634, "loss": 1.13416, "time": 0.40736} +{"mode": "train", "epoch": 1, "iter": 1650, "lr": 0.0002, "memory": 8415, "data_time": 0.06168, "loss_rpn_cls": 0.05807, "loss_rpn_bbox": 0.07231, "loss_cls": 0.32441, "acc": 90.44385, "loss_bbox": 0.32279, "loss_mask": 0.32701, "loss": 1.10459, "time": 0.40502} +{"mode": "train", "epoch": 1, "iter": 1700, "lr": 0.0002, "memory": 8415, "data_time": 0.05439, "loss_rpn_cls": 0.06002, "loss_rpn_bbox": 0.06796, "loss_cls": 0.33398, "acc": 90.16528, "loss_bbox": 0.33135, "loss_mask": 0.32746, "loss": 1.12077, "time": 0.42232} +{"mode": "train", "epoch": 1, "iter": 1750, "lr": 0.0002, "memory": 8415, "data_time": 0.04958, "loss_rpn_cls": 0.05742, "loss_rpn_bbox": 0.07281, "loss_cls": 0.33623, "acc": 89.95874, "loss_bbox": 0.33596, "loss_mask": 0.32664, "loss": 1.12907, "time": 0.42305} +{"mode": "train", "epoch": 1, "iter": 1800, "lr": 0.0002, "memory": 8415, "data_time": 0.06257, "loss_rpn_cls": 0.05794, "loss_rpn_bbox": 0.07209, "loss_cls": 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"loss_rpn_cls": 0.05445, "loss_rpn_bbox": 0.06809, "loss_cls": 0.32541, "acc": 90.42383, "loss_bbox": 0.32201, "loss_mask": 0.32235, "loss": 1.09231, "time": 0.40692} +{"mode": "train", "epoch": 1, "iter": 2050, "lr": 0.0002, "memory": 8423, "data_time": 0.05528, "loss_rpn_cls": 0.05437, "loss_rpn_bbox": 0.07047, "loss_cls": 0.32547, "acc": 90.26758, "loss_bbox": 0.3266, "loss_mask": 0.31934, "loss": 1.09626, "time": 0.42819} +{"mode": "train", "epoch": 1, "iter": 2100, "lr": 0.0002, "memory": 8423, "data_time": 0.05003, "loss_rpn_cls": 0.05406, "loss_rpn_bbox": 0.06614, "loss_cls": 0.31329, "acc": 90.59033, "loss_bbox": 0.31957, "loss_mask": 0.31193, "loss": 1.06499, "time": 0.4112} +{"mode": "train", "epoch": 1, "iter": 2150, "lr": 0.0002, "memory": 8423, "data_time": 0.05253, "loss_rpn_cls": 0.05467, "loss_rpn_bbox": 0.0672, "loss_cls": 0.30893, "acc": 90.62939, "loss_bbox": 0.3159, "loss_mask": 0.31678, "loss": 1.06348, "time": 0.40682} +{"mode": "train", "epoch": 1, "iter": 2200, 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0.21447, "loss_mask": 0.21825, "loss": 0.65955, "time": 0.39383} +{"mode": "train", "epoch": 12, "iter": 6700, "lr": 0.0, "memory": 8424, "data_time": 0.04409, "loss_rpn_cls": 0.02007, "loss_rpn_bbox": 0.04313, "loss_cls": 0.16996, "acc": 93.73511, "loss_bbox": 0.21635, "loss_mask": 0.22091, "loss": 0.67042, "time": 0.39887} +{"mode": "train", "epoch": 12, "iter": 6750, "lr": 0.0, "memory": 8424, "data_time": 0.05394, "loss_rpn_cls": 0.01922, "loss_rpn_bbox": 0.04279, "loss_cls": 0.15959, "acc": 94.03149, "loss_bbox": 0.20473, "loss_mask": 0.21819, "loss": 0.64453, "time": 0.4094} +{"mode": "train", "epoch": 12, "iter": 6800, "lr": 0.0, "memory": 8424, "data_time": 0.05122, "loss_rpn_cls": 0.0178, "loss_rpn_bbox": 0.03872, "loss_cls": 0.16093, "acc": 94.03442, "loss_bbox": 0.20509, "loss_mask": 0.21127, "loss": 0.63382, "time": 0.41902} +{"mode": "train", "epoch": 12, "iter": 6850, "lr": 0.0, "memory": 8424, "data_time": 0.05211, "loss_rpn_cls": 0.01809, "loss_rpn_bbox": 0.04162, "loss_cls": 0.16184, "acc": 93.95703, "loss_bbox": 0.20663, "loss_mask": 0.21209, "loss": 0.64026, "time": 0.40754} +{"mode": "train", "epoch": 12, "iter": 6900, "lr": 0.0, "memory": 8424, "data_time": 0.03964, "loss_rpn_cls": 0.01814, "loss_rpn_bbox": 0.04053, "loss_cls": 0.16623, "acc": 93.78467, "loss_bbox": 0.21073, "loss_mask": 0.21409, "loss": 0.64971, "time": 0.39998} +{"mode": "train", "epoch": 12, "iter": 6950, "lr": 0.0, "memory": 8424, "data_time": 0.04413, "loss_rpn_cls": 0.01988, "loss_rpn_bbox": 0.04286, "loss_cls": 0.16657, "acc": 93.73022, "loss_bbox": 0.2145, "loss_mask": 0.2151, "loss": 0.65891, "time": 0.41274} +{"mode": "train", "epoch": 12, "iter": 7000, "lr": 0.0, "memory": 8424, "data_time": 0.04871, "loss_rpn_cls": 0.01881, "loss_rpn_bbox": 0.04276, "loss_cls": 0.16037, "acc": 93.99292, "loss_bbox": 0.20678, "loss_mask": 0.22, "loss": 0.64872, "time": 0.40684} +{"mode": "train", "epoch": 12, "iter": 7050, "lr": 0.0, "memory": 8424, "data_time": 0.04371, "loss_rpn_cls": 0.0186, "loss_rpn_bbox": 0.04057, "loss_cls": 0.15792, "acc": 94.13794, "loss_bbox": 0.20039, "loss_mask": 0.2088, "loss": 0.62628, "time": 0.40826} +{"mode": "train", "epoch": 12, "iter": 7100, "lr": 0.0, "memory": 8424, "data_time": 0.05516, "loss_rpn_cls": 0.01804, "loss_rpn_bbox": 0.04232, "loss_cls": 0.16392, "acc": 93.93677, "loss_bbox": 0.20797, "loss_mask": 0.21711, "loss": 0.64935, "time": 0.40131} +{"mode": "train", "epoch": 12, "iter": 7150, "lr": 0.0, "memory": 8424, "data_time": 0.05134, "loss_rpn_cls": 0.01849, "loss_rpn_bbox": 0.04143, "loss_cls": 0.16247, "acc": 93.93066, "loss_bbox": 0.21037, "loss_mask": 0.2182, "loss": 0.65096, "time": 0.39192} +{"mode": "train", "epoch": 12, "iter": 7200, "lr": 0.0, "memory": 8424, "data_time": 0.03782, "loss_rpn_cls": 0.01809, "loss_rpn_bbox": 0.03723, "loss_cls": 0.15374, "acc": 94.323, "loss_bbox": 0.20083, "loss_mask": 0.20747, "loss": 0.61736, "time": 0.39124} +{"mode": "train", "epoch": 12, "iter": 7250, "lr": 0.0, "memory": 8424, "data_time": 0.04148, "loss_rpn_cls": 0.01936, "loss_rpn_bbox": 0.04313, "loss_cls": 0.16463, "acc": 93.84668, "loss_bbox": 0.21942, "loss_mask": 0.22223, "loss": 0.66876, "time": 0.41775} +{"mode": "train", "epoch": 12, "iter": 7300, "lr": 0.0, "memory": 8424, "data_time": 0.0426, "loss_rpn_cls": 0.0183, "loss_rpn_bbox": 0.04353, "loss_cls": 0.17257, "acc": 93.57227, "loss_bbox": 0.22043, "loss_mask": 0.21888, "loss": 0.67371, "time": 0.47587} +{"mode": "val", "epoch": 12, "iter": 625, "lr": 0.0, "bbox_mAP": 0.416, "bbox_mAP_50": 0.6325, "bbox_mAP_75": 0.4534, "bbox_mAP_s": 0.2413, "bbox_mAP_m": 0.4509, "bbox_mAP_l": 0.5483, "bbox_mAP_copypaste": "0.4160 0.6325 0.4534 0.2413 0.4509 0.5483", "segm_mAP": 0.3859, "segm_mAP_50": 0.6053, "segm_mAP_75": 0.4145, "segm_mAP_s": 0.1851, "segm_mAP_m": 0.4149, "segm_mAP_l": 0.5631, "segm_mAP_copypaste": "0.3859 0.6053 0.4145 0.1851 0.4149 0.5631"} diff --git a/detection/logs/repvit_m2_3_coco.json b/detection/logs/repvit_m2_3_coco.json new file mode 100644 index 0000000..8b1dea5 --- /dev/null +++ b/detection/logs/repvit_m2_3_coco.json @@ -0,0 +1,1765 @@ +{"env_info": "sys.platform: linux\nPython: 3.8.16 (default, Mar 2 2023, 03:21:46) [GCC 11.2.0]\nCUDA available: True\nGPU 0,1,2,3,4,5,6,7: GeForce RTX 3090\nCUDA_HOME: /home/wangao/cuda-11.2\nNVCC: Cuda compilation tools, release 11.2, V11.2.152\nGCC: gcc (GCC) 5.4.0\nPyTorch: 2.0.1+cu117\nPyTorch compiling details: PyTorch built with:\n - GCC 9.3\n - C++ Version: 201703\n - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications\n - Intel(R) MKL-DNN v2.7.3 (Git Hash 6dbeffbae1f23cbbeae17adb7b5b13f1f37c080e)\n - OpenMP 201511 (a.k.a. OpenMP 4.5)\n - LAPACK is enabled (usually provided by MKL)\n - NNPACK is enabled\n - CPU capability usage: AVX2\n - CUDA Runtime 11.7\n - 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\n - CuDNN 8.5\n - Magma 2.6.1\n - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.7, CUDNN_VERSION=8.5.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wunused-local-typedefs -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -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 -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_DISABLE_GPU_ASSERTS=ON, TORCH_VERSION=2.0.1, 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=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, \n\nTorchVision: 0.15.2+cu117\nOpenCV: 4.8.0\nMMCV: 1.7.1\nMMCV Compiler: GCC 5.4\nMMCV CUDA Compiler: 11.2\nMMDetection: 2.28.2+ab02657", "config": "model = dict(\n type='MaskRCNN',\n pretrained='torchvision://resnet50',\n backbone=dict(\n type='repvit_m6',\n depth=50,\n num_stages=4,\n out_indices=[6, 14, 50, 54],\n frozen_stages=1,\n norm_cfg=dict(type='BN', requires_grad=True),\n norm_eval=True,\n style='pytorch',\n init_cfg=dict(\n type='Pretrained',\n checkpoint='pretrain/repvit_m6_distill_450e.pth')),\n neck=dict(\n type='FPN',\n in_channels=[80, 160, 320, 640],\n out_channels=256,\n num_outs=5),\n rpn_head=dict(\n type='RPNHead',\n in_channels=256,\n feat_channels=256,\n anchor_generator=dict(\n type='AnchorGenerator',\n scales=[8],\n ratios=[0.5, 1.0, 2.0],\n strides=[4, 8, 16, 32, 64]),\n bbox_coder=dict(\n type='DeltaXYWHBBoxCoder',\n target_means=[0.0, 0.0, 0.0, 0.0],\n target_stds=[1.0, 1.0, 1.0, 1.0]),\n loss_cls=dict(\n type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),\n loss_bbox=dict(type='L1Loss', loss_weight=1.0)),\n roi_head=dict(\n type='StandardRoIHead',\n bbox_roi_extractor=dict(\n type='SingleRoIExtractor',\n roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),\n out_channels=256,\n featmap_strides=[4, 8, 16, 32]),\n bbox_head=dict(\n type='Shared2FCBBoxHead',\n in_channels=256,\n fc_out_channels=1024,\n roi_feat_size=7,\n num_classes=80,\n bbox_coder=dict(\n type='DeltaXYWHBBoxCoder',\n target_means=[0.0, 0.0, 0.0, 0.0],\n target_stds=[0.1, 0.1, 0.2, 0.2]),\n reg_class_agnostic=False,\n loss_cls=dict(\n type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),\n loss_bbox=dict(type='L1Loss', loss_weight=1.0)),\n mask_roi_extractor=dict(\n type='SingleRoIExtractor',\n roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),\n out_channels=256,\n featmap_strides=[4, 8, 16, 32]),\n mask_head=dict(\n type='FCNMaskHead',\n num_convs=4,\n in_channels=256,\n conv_out_channels=256,\n num_classes=80,\n loss_mask=dict(\n type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))),\n train_cfg=dict(\n rpn=dict(\n assigner=dict(\n type='MaxIoUAssigner',\n pos_iou_thr=0.7,\n neg_iou_thr=0.3,\n min_pos_iou=0.3,\n match_low_quality=True,\n ignore_iof_thr=-1),\n sampler=dict(\n type='RandomSampler',\n num=256,\n pos_fraction=0.5,\n neg_pos_ub=-1,\n add_gt_as_proposals=False),\n allowed_border=-1,\n pos_weight=-1,\n debug=False),\n rpn_proposal=dict(\n nms_pre=2000,\n max_per_img=1000,\n nms=dict(type='nms', iou_threshold=0.7),\n min_bbox_size=0),\n rcnn=dict(\n assigner=dict(\n type='MaxIoUAssigner',\n pos_iou_thr=0.5,\n neg_iou_thr=0.5,\n min_pos_iou=0.5,\n match_low_quality=True,\n ignore_iof_thr=-1),\n sampler=dict(\n type='RandomSampler',\n num=512,\n pos_fraction=0.25,\n neg_pos_ub=-1,\n add_gt_as_proposals=True),\n mask_size=28,\n pos_weight=-1,\n debug=False)),\n test_cfg=dict(\n rpn=dict(\n nms_pre=1000,\n max_per_img=1000,\n nms=dict(type='nms', iou_threshold=0.7),\n min_bbox_size=0),\n rcnn=dict(\n score_thr=0.05,\n nms=dict(type='nms', iou_threshold=0.5),\n max_per_img=100,\n mask_thr_binary=0.5)))\ndataset_type = 'CocoDataset'\ndata_root = 'data/coco/'\nimg_norm_cfg = dict(\n mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)\ntrain_pipeline = [\n dict(type='LoadImageFromFile'),\n dict(type='LoadAnnotations', with_bbox=True, with_mask=True),\n dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),\n dict(type='RandomFlip', flip_ratio=0.5),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='Pad', size_divisor=32),\n dict(type='DefaultFormatBundle'),\n dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks'])\n]\ntest_pipeline = [\n dict(type='LoadImageFromFile'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(1333, 800),\n flip=False,\n transforms=[\n dict(type='Resize', keep_ratio=True),\n dict(type='RandomFlip'),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='Pad', size_divisor=32),\n dict(type='ImageToTensor', keys=['img']),\n dict(type='Collect', keys=['img'])\n ])\n]\ndata = dict(\n samples_per_gpu=2,\n workers_per_gpu=2,\n train=dict(\n type='CocoDataset',\n ann_file='data/coco/annotations/instances_train2017.json',\n img_prefix='data/coco/train2017/',\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(type='LoadAnnotations', with_bbox=True, with_mask=True),\n dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),\n dict(type='RandomFlip', flip_ratio=0.5),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='Pad', size_divisor=32),\n dict(type='DefaultFormatBundle'),\n dict(\n type='Collect',\n keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks'])\n ]),\n val=dict(\n type='CocoDataset',\n ann_file='data/coco/annotations/instances_val2017.json',\n img_prefix='data/coco/val2017/',\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(1333, 800),\n flip=False,\n transforms=[\n dict(type='Resize', keep_ratio=True),\n dict(type='RandomFlip'),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='Pad', size_divisor=32),\n dict(type='ImageToTensor', keys=['img']),\n dict(type='Collect', keys=['img'])\n ])\n ]),\n test=dict(\n type='CocoDataset',\n ann_file='data/coco/annotations/instances_val2017.json',\n img_prefix='data/coco/val2017/',\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(1333, 800),\n flip=False,\n transforms=[\n dict(type='Resize', keep_ratio=True),\n dict(type='RandomFlip'),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='Pad', size_divisor=32),\n dict(type='ImageToTensor', keys=['img']),\n dict(type='Collect', keys=['img'])\n ])\n ]))\nevaluation = dict(metric=['bbox', 'segm'])\noptimizer = dict(type='AdamW', lr=0.0002, weight_decay=0.05)\noptimizer_config = dict(grad_clip=None)\nlr_config = dict(\n policy='step',\n warmup='linear',\n warmup_iters=500,\n warmup_ratio=1e-06,\n step=[8, 11])\nrunner = dict(type='EpochBasedRunner', max_epochs=12)\ncheckpoint_config = dict(interval=1)\nlog_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])\ncustom_hooks = [dict(type='NumClassCheckHook')]\ndist_params = dict(backend='nccl')\nlog_level = 'INFO'\nload_from = None\nresume_from = None\nworkflow = [('train', 1)]\nwork_dir = './work_dirs/mask_rcnn_repvit_m6_fpn_1x_coco'\nauto_resume = False\ngpu_ids = range(0, 8)\n", "seed": 0, "exp_name": "mask_rcnn_repvit_m6_fpn_1x_coco.py"} +{"mode": "train", "epoch": 1, "iter": 50, "lr": 2e-05, "memory": 12069, "data_time": 0.15192, "loss_rpn_cls": 0.53878, "loss_rpn_bbox": 0.1359, "loss_cls": 1.98109, "acc": 67.09521, "loss_bbox": 0.05794, "loss_mask": 1.75779, "loss": 4.4715, "time": 0.80169} +{"mode": "train", "epoch": 1, "iter": 100, "lr": 4e-05, "memory": 12284, "data_time": 0.05602, 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"epoch": 12, "iter": 7100, "lr": 0.0, "memory": 12589, "data_time": 0.05068, "loss_rpn_cls": 0.01383, "loss_rpn_bbox": 0.03602, "loss_cls": 0.14291, "acc": 94.64233, "loss_bbox": 0.18698, "loss_mask": 0.20351, "loss": 0.58325, "time": 0.49483} +{"mode": "train", "epoch": 12, "iter": 7150, "lr": 0.0, "memory": 12589, "data_time": 0.05352, "loss_rpn_cls": 0.01293, "loss_rpn_bbox": 0.03505, "loss_cls": 0.13901, "acc": 94.73145, "loss_bbox": 0.18858, "loss_mask": 0.20467, "loss": 0.58023, "time": 0.50044} +{"mode": "train", "epoch": 12, "iter": 7200, "lr": 0.0, "memory": 12589, "data_time": 0.04563, "loss_rpn_cls": 0.01325, "loss_rpn_bbox": 0.03197, "loss_cls": 0.134, "acc": 95.06396, "loss_bbox": 0.18083, "loss_mask": 0.19477, "loss": 0.55482, "time": 0.49091} +{"mode": "train", "epoch": 12, "iter": 7250, "lr": 0.0, "memory": 12589, "data_time": 0.04553, "loss_rpn_cls": 0.01436, "loss_rpn_bbox": 0.03742, "loss_cls": 0.14561, "acc": 94.4375, "loss_bbox": 0.19722, "loss_mask": 0.20765, "loss": 0.60227, "time": 0.50736} +{"mode": "train", "epoch": 12, "iter": 7300, "lr": 0.0, "memory": 12589, "data_time": 0.04574, "loss_rpn_cls": 0.01298, "loss_rpn_bbox": 0.03739, "loss_cls": 0.14838, "acc": 94.40747, "loss_bbox": 0.19729, "loss_mask": 0.20685, "loss": 0.60289, "time": 0.50516} +{"mode": "val", "epoch": 12, "iter": 625, "lr": 0.0, "bbox_mAP": 0.4456, "bbox_mAP_50": 0.6614, "bbox_mAP_75": 0.4881, "bbox_mAP_s": 0.2779, "bbox_mAP_m": 0.4811, "bbox_mAP_l": 0.5832, "bbox_mAP_copypaste": "0.4456 0.6614 0.4881 0.2779 0.4811 0.5832", "segm_mAP": 0.4081, "segm_mAP_50": 0.6365, "segm_mAP_75": 0.4392, "segm_mAP_s": 0.2143, "segm_mAP_m": 0.4337, "segm_mAP_l": 0.5899, "segm_mAP_copypaste": "0.4081 0.6365 0.4392 0.2143 0.4337 0.5899"} diff --git a/detection/logs/repvit_m3_coco.json b/detection/logs/repvit_m3_coco.json deleted file mode 100644 index b882d07..0000000 --- a/detection/logs/repvit_m3_coco.json +++ /dev/null @@ -1,1765 +0,0 @@ -{"env_info": "sys.platform: linux\nPython: 3.8.16 (default, Mar 2 2023, 03:21:46) [GCC 11.2.0]\nCUDA available: True\nGPU 0,1,2,3,4,5,6,7: NVIDIA GeForce RTX 3090\nCUDA_HOME: /usr/local/cuda\nNVCC: Cuda compilation tools, release 11.3, V11.3.109\nGCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0\nPyTorch: 2.0.1+cu117\nPyTorch compiling details: PyTorch built with:\n - GCC 9.3\n - C++ Version: 201703\n - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications\n - Intel(R) MKL-DNN v2.7.3 (Git Hash 6dbeffbae1f23cbbeae17adb7b5b13f1f37c080e)\n - OpenMP 201511 (a.k.a. OpenMP 4.5)\n - LAPACK is enabled (usually provided by MKL)\n - NNPACK is enabled\n - CPU capability usage: AVX2\n - CUDA Runtime 11.7\n - 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\n - CuDNN 8.9.2 (built against CUDA 12.1)\n - Built with CuDNN 8.5\n - Magma 2.6.1\n - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.7, CUDNN_VERSION=8.5.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wunused-local-typedefs -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -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 -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_DISABLE_GPU_ASSERTS=ON, TORCH_VERSION=2.0.1, 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=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, \n\nTorchVision: 0.15.2+cu117\nOpenCV: 4.8.0\nMMCV: 1.7.1\nMMCV Compiler: GCC 9.4\nMMCV CUDA Compiler: 11.3\nMMDetection: 2.28.2+ceca238", "config": "model = dict(\n type='MaskRCNN',\n pretrained='torchvision://resnet50',\n backbone=dict(\n type='repvit_m3',\n depth=50,\n num_stages=4,\n out_indices=[4, 10, 30, 34],\n frozen_stages=1,\n norm_cfg=dict(type='BN', requires_grad=True),\n norm_eval=True,\n style='pytorch',\n init_cfg=dict(\n type='Pretrained',\n checkpoint='pretrain/repvit_m3_distill_300.pth')),\n neck=dict(\n type='FPN',\n in_channels=[64, 128, 256, 512],\n out_channels=256,\n num_outs=5),\n rpn_head=dict(\n type='RPNHead',\n in_channels=256,\n feat_channels=256,\n anchor_generator=dict(\n type='AnchorGenerator',\n scales=[8],\n ratios=[0.5, 1.0, 2.0],\n strides=[4, 8, 16, 32, 64]),\n bbox_coder=dict(\n type='DeltaXYWHBBoxCoder',\n target_means=[0.0, 0.0, 0.0, 0.0],\n target_stds=[1.0, 1.0, 1.0, 1.0]),\n loss_cls=dict(\n type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),\n loss_bbox=dict(type='L1Loss', loss_weight=1.0)),\n roi_head=dict(\n type='StandardRoIHead',\n bbox_roi_extractor=dict(\n type='SingleRoIExtractor',\n roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),\n out_channels=256,\n featmap_strides=[4, 8, 16, 32]),\n bbox_head=dict(\n type='Shared2FCBBoxHead',\n in_channels=256,\n fc_out_channels=1024,\n roi_feat_size=7,\n num_classes=80,\n bbox_coder=dict(\n type='DeltaXYWHBBoxCoder',\n target_means=[0.0, 0.0, 0.0, 0.0],\n target_stds=[0.1, 0.1, 0.2, 0.2]),\n reg_class_agnostic=False,\n loss_cls=dict(\n type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),\n loss_bbox=dict(type='L1Loss', loss_weight=1.0)),\n mask_roi_extractor=dict(\n type='SingleRoIExtractor',\n roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),\n out_channels=256,\n featmap_strides=[4, 8, 16, 32]),\n mask_head=dict(\n type='FCNMaskHead',\n num_convs=4,\n in_channels=256,\n conv_out_channels=256,\n num_classes=80,\n loss_mask=dict(\n type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))),\n train_cfg=dict(\n rpn=dict(\n assigner=dict(\n type='MaxIoUAssigner',\n pos_iou_thr=0.7,\n neg_iou_thr=0.3,\n min_pos_iou=0.3,\n match_low_quality=True,\n ignore_iof_thr=-1),\n sampler=dict(\n type='RandomSampler',\n num=256,\n pos_fraction=0.5,\n neg_pos_ub=-1,\n add_gt_as_proposals=False),\n allowed_border=-1,\n pos_weight=-1,\n debug=False),\n rpn_proposal=dict(\n nms_pre=2000,\n max_per_img=1000,\n nms=dict(type='nms', iou_threshold=0.7),\n min_bbox_size=0),\n rcnn=dict(\n assigner=dict(\n type='MaxIoUAssigner',\n pos_iou_thr=0.5,\n neg_iou_thr=0.5,\n min_pos_iou=0.5,\n match_low_quality=True,\n ignore_iof_thr=-1),\n sampler=dict(\n type='RandomSampler',\n num=512,\n pos_fraction=0.25,\n neg_pos_ub=-1,\n add_gt_as_proposals=True),\n mask_size=28,\n pos_weight=-1,\n debug=False)),\n test_cfg=dict(\n rpn=dict(\n nms_pre=1000,\n max_per_img=1000,\n nms=dict(type='nms', iou_threshold=0.7),\n min_bbox_size=0),\n rcnn=dict(\n score_thr=0.05,\n nms=dict(type='nms', iou_threshold=0.5),\n max_per_img=100,\n mask_thr_binary=0.5)))\ndataset_type = 'CocoDataset'\ndata_root = 'data/coco/'\nimg_norm_cfg = dict(\n mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)\ntrain_pipeline = [\n dict(type='LoadImageFromFile'),\n dict(type='LoadAnnotations', with_bbox=True, with_mask=True),\n dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),\n dict(type='RandomFlip', flip_ratio=0.5),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='Pad', size_divisor=32),\n dict(type='DefaultFormatBundle'),\n dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks'])\n]\ntest_pipeline = [\n dict(type='LoadImageFromFile'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(1333, 800),\n flip=False,\n transforms=[\n dict(type='Resize', keep_ratio=True),\n dict(type='RandomFlip'),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='Pad', size_divisor=32),\n dict(type='ImageToTensor', keys=['img']),\n dict(type='Collect', keys=['img'])\n ])\n]\ndata = dict(\n samples_per_gpu=2,\n workers_per_gpu=2,\n train=dict(\n type='CocoDataset',\n ann_file='data/coco/annotations/instances_train2017.json',\n img_prefix='data/coco/train2017/',\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(type='LoadAnnotations', with_bbox=True, with_mask=True),\n dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),\n dict(type='RandomFlip', flip_ratio=0.5),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='Pad', size_divisor=32),\n dict(type='DefaultFormatBundle'),\n dict(\n type='Collect',\n keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks'])\n ]),\n val=dict(\n type='CocoDataset',\n ann_file='data/coco/annotations/instances_val2017.json',\n img_prefix='data/coco/val2017/',\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(1333, 800),\n flip=False,\n transforms=[\n dict(type='Resize', keep_ratio=True),\n dict(type='RandomFlip'),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='Pad', size_divisor=32),\n dict(type='ImageToTensor', keys=['img']),\n dict(type='Collect', keys=['img'])\n ])\n ]),\n test=dict(\n type='CocoDataset',\n ann_file='data/coco/annotations/instances_val2017.json',\n img_prefix='data/coco/val2017/',\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(1333, 800),\n flip=False,\n transforms=[\n dict(type='Resize', keep_ratio=True),\n dict(type='RandomFlip'),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='Pad', size_divisor=32),\n dict(type='ImageToTensor', keys=['img']),\n dict(type='Collect', keys=['img'])\n ])\n ]))\nevaluation = dict(metric=['bbox', 'segm'])\noptimizer = dict(type='AdamW', lr=0.0002, weight_decay=0.05)\noptimizer_config = dict(grad_clip=None)\nlr_config = dict(\n policy='step',\n warmup='linear',\n warmup_iters=500,\n warmup_ratio=1e-06,\n step=[8, 11])\nrunner = dict(type='EpochBasedRunner', max_epochs=12)\ncheckpoint_config = dict(interval=1)\nlog_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])\ncustom_hooks = [dict(type='NumClassCheckHook')]\ndist_params = dict(backend='nccl')\nlog_level = 'INFO'\nload_from = None\nresume_from = None\nworkflow = [('train', 1)]\nwork_dir = './work_dirs/mask_rcnn_repvit_m3_fpn_1x_coco'\nauto_resume = False\ngpu_ids = range(0, 8)\n", "seed": 0, "exp_name": "mask_rcnn_repvit_m3_fpn_1x_coco.py"} -{"mode": "train", "epoch": 1, "iter": 50, "lr": 2e-05, "memory": 7210, "data_time": 0.11259, "loss_rpn_cls": 0.53979, "loss_rpn_bbox": 0.16942, "loss_cls": 1.98668, "acc": 68.76807, "loss_bbox": 0.06602, "loss_mask": 2.50328, "loss": 5.26519, "time": 0.52656} -{"mode": "train", "epoch": 1, "iter": 100, "lr": 4e-05, "memory": 7422, "data_time": 0.0442, "loss_rpn_cls": 0.25092, "loss_rpn_bbox": 0.11199, "loss_cls": 0.42925, "acc": 94.38892, "loss_bbox": 0.19288, "loss_mask": 0.83511, "loss": 1.82015, "time": 0.38495} -{"mode": "train", "epoch": 1, "iter": 150, "lr": 6e-05, "memory": 7422, "data_time": 0.07578, "loss_rpn_cls": 0.21635, "loss_rpn_bbox": 0.10693, "loss_cls": 0.38825, "acc": 93.98047, "loss_bbox": 0.2077, "loss_mask": 0.71694, "loss": 1.63618, "time": 0.39222} -{"mode": "train", "epoch": 1, "iter": 200, "lr": 8e-05, "memory": 7422, "data_time": 0.07599, "loss_rpn_cls": 0.16993, "loss_rpn_bbox": 0.10746, "loss_cls": 0.43496, "acc": 92.86743, "loss_bbox": 0.25253, "loss_mask": 0.6827, "loss": 1.64757, "time": 0.4079} -{"mode": "train", "epoch": 1, "iter": 250, "lr": 0.0001, "memory": 7422, "data_time": 0.08036, "loss_rpn_cls": 0.13591, "loss_rpn_bbox": 0.10474, "loss_cls": 0.45247, "acc": 92.15796, "loss_bbox": 0.28385, "loss_mask": 0.6428, "loss": 1.61977, "time": 0.39205} -{"mode": "train", "epoch": 1, "iter": 300, "lr": 0.00012, "memory": 7525, "data_time": 0.09891, "loss_rpn_cls": 0.10741, "loss_rpn_bbox": 0.09556, "loss_cls": 0.49368, "acc": 90.92529, "loss_bbox": 0.34045, "loss_mask": 0.6126, "loss": 1.6497, "time": 0.40027} -{"mode": "train", "epoch": 1, "iter": 350, "lr": 0.00014, "memory": 7525, "data_time": 0.08414, "loss_rpn_cls": 0.09357, "loss_rpn_bbox": 0.09249, "loss_cls": 0.47525, "acc": 90.96509, "loss_bbox": 0.33904, "loss_mask": 0.57477, "loss": 1.57512, "time": 0.40004} -{"mode": "train", "epoch": 1, "iter": 400, "lr": 0.00016, "memory": 7638, "data_time": 0.09406, "loss_rpn_cls": 0.09576, "loss_rpn_bbox": 0.09405, "loss_cls": 0.48517, "acc": 90.25317, "loss_bbox": 0.35541, "loss_mask": 0.54805, "loss": 1.57844, "time": 0.40831} -{"mode": "train", "epoch": 1, "iter": 450, "lr": 0.00018, "memory": 7638, "data_time": 0.07678, "loss_rpn_cls": 0.08855, "loss_rpn_bbox": 0.08875, "loss_cls": 0.45592, "acc": 90.35547, 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-__all__ = ['repvit_m1'] - - def _make_divisible(v, divisor, min_value=None): """ This function is taken from the original tf repo. @@ -95,16 +91,17 @@ class RepVGGDW(torch.nn.Module): def __init__(self, ed) -> None: super().__init__() self.conv = Conv2d_BN(ed, ed, 3, 1, 1, groups=ed) - self.conv1 = Conv2d_BN(ed, ed, 1, 1, 0, groups=ed) + self.conv1 = torch.nn.Conv2d(ed, ed, 1, 1, 0, groups=ed) self.dim = ed + self.bn = torch.nn.BatchNorm2d(ed) def forward(self, x): - return self.conv(x) + self.conv1(x) + x + return self.bn((self.conv(x) + self.conv1(x)) + x) @torch.no_grad() def fuse(self): conv = self.conv.fuse() - conv1 = self.conv1.fuse() + conv1 = self.conv1 conv_w = conv.weight conv_b = conv.bias @@ -120,6 +117,14 @@ def fuse(self): conv.weight.data.copy_(final_conv_w) conv.bias.data.copy_(final_conv_b) + + bn = self.bn + w = bn.weight / (bn.running_var + bn.eps)**0.5 + w = conv.weight * w[:, None, None, None] + b = bn.bias + (conv.bias - bn.running_mean) * bn.weight / \ + (bn.running_var + bn.eps)**0.5 + conv.weight.data.copy_(w) + conv.bias.data.copy_(b) return conv @@ -267,43 +272,7 @@ def forward(self, x): from timm.models import register_model @BACKBONES.register_module() -def repvit_m1(pretrained=False, num_classes = 1000, distillation=False, init_cfg=None, out_indices=[], **kwargs): - """ - Constructs a MobileNetV3-Large model - """ - cfgs = [ - # k, t, c, SE, HS, s - [3, 2, 48, 1, 0, 1], - [3, 2, 48, 0, 0, 1], - [3, 2, 48, 0, 0, 1], - [3, 2, 96, 0, 0, 2], - [3, 2, 96, 1, 0, 1], - [3, 2, 96, 0, 0, 1], - [3, 2, 96, 0, 0, 1], - [3, 2, 192, 0, 1, 2], - [3, 2, 192, 1, 1, 1], - [3, 2, 192, 0, 1, 1], - [3, 2, 192, 1, 1, 1], - [3, 2, 192, 0, 1, 1], - [3, 2, 192, 1, 1, 1], - [3, 2, 192, 0, 1, 1], - [3, 2, 192, 1, 1, 1], - [3, 2, 192, 0, 1, 1], - [3, 2, 192, 1, 1, 1], - [3, 2, 192, 0, 1, 1], - [3, 2, 192, 1, 1, 1], - [3, 2, 192, 0, 1, 1], - [3, 2, 192, 1, 1, 1], - [3, 2, 192, 0, 1, 1], - [3, 2, 192, 0, 1, 1], - [3, 2, 384, 0, 1, 2], - [3, 2, 384, 1, 1, 1], - [3, 2, 384, 0, 1, 1] - ] - return RepViT(cfgs, init_cfg=init_cfg, pretrained=pretrained, distillation=distillation, out_indices=out_indices) - -@BACKBONES.register_module() -def repvit_m2(pretrained=False, num_classes = 1000, distillation=False, init_cfg=None, out_indices=[], **kwargs): +def repvit_m1_1(pretrained=False, num_classes = 1000, distillation=False, init_cfg=None, out_indices=[], **kwargs): """ Constructs a MobileNetV3-Large model """ @@ -336,8 +305,9 @@ def repvit_m2(pretrained=False, num_classes = 1000, distillation=False, init_cfg ] return RepViT(cfgs, init_cfg=init_cfg, pretrained=pretrained, distillation=distillation, out_indices=out_indices) + @BACKBONES.register_module() -def repvit_m3(pretrained=False, num_classes = 1000, distillation=False, init_cfg=None, out_indices=[], **kwargs): +def repvit_m1_5(pretrained=False, num_classes = 1000, distillation=False, init_cfg=None, out_indices=[], **kwargs): """ Constructs a MobileNetV3-Large model """ @@ -373,9 +343,87 @@ def repvit_m3(pretrained=False, num_classes = 1000, distillation=False, init_cfg [3, 2, 256, 0, 1, 1], [3, 2, 256, 1, 1, 1], [3, 2, 256, 0, 1, 1], + [3, 2, 256, 1, 1, 1], + [3, 2, 256, 0, 1, 1], + [3, 2, 256, 1, 1, 1], + [3, 2, 256, 0, 1, 1], + [3, 2, 256, 1, 1, 1], + [3, 2, 256, 0, 1, 1], [3, 2, 256, 0, 1, 1], [3, 2, 512, 0, 1, 2], [3, 2, 512, 1, 1, 1], + [3, 2, 512, 0, 1, 1], + [3, 2, 512, 1, 1, 1], [3, 2, 512, 0, 1, 1] ] return RepViT(cfgs, init_cfg=init_cfg, pretrained=pretrained, distillation=distillation, out_indices=out_indices) + + + +@BACKBONES.register_module() +def repvit_m2_3(pretrained=False, num_classes = 1000, distillation=False, init_cfg=None, out_indices=[], **kwargs): + """ + Constructs a MobileNetV3-Large model + """ + cfgs = [ + # k, t, c, SE, HS, s + [3, 2, 80, 1, 0, 1], + [3, 2, 80, 0, 0, 1], + [3, 2, 80, 1, 0, 1], + [3, 2, 80, 0, 0, 1], + [3, 2, 80, 1, 0, 1], + [3, 2, 80, 0, 0, 1], + [3, 2, 80, 0, 0, 1], + [3, 2, 160, 0, 0, 2], + [3, 2, 160, 1, 0, 1], + [3, 2, 160, 0, 0, 1], + [3, 2, 160, 1, 0, 1], + [3, 2, 160, 0, 0, 1], + [3, 2, 160, 1, 0, 1], + [3, 2, 160, 0, 0, 1], + [3, 2, 160, 0, 0, 1], + [3, 2, 320, 0, 1, 2], + [3, 2, 320, 1, 1, 1], + [3, 2, 320, 0, 1, 1], + [3, 2, 320, 1, 1, 1], + [3, 2, 320, 0, 1, 1], + [3, 2, 320, 1, 1, 1], + [3, 2, 320, 0, 1, 1], + [3, 2, 320, 1, 1, 1], + [3, 2, 320, 0, 1, 1], + [3, 2, 320, 1, 1, 1], + [3, 2, 320, 0, 1, 1], + [3, 2, 320, 1, 1, 1], + [3, 2, 320, 0, 1, 1], + [3, 2, 320, 1, 1, 1], + [3, 2, 320, 0, 1, 1], + [3, 2, 320, 1, 1, 1], + [3, 2, 320, 0, 1, 1], + [3, 2, 320, 1, 1, 1], + [3, 2, 320, 0, 1, 1], + [3, 2, 320, 1, 1, 1], + [3, 2, 320, 0, 1, 1], + [3, 2, 320, 1, 1, 1], + [3, 2, 320, 0, 1, 1], + [3, 2, 320, 1, 1, 1], + [3, 2, 320, 0, 1, 1], + [3, 2, 320, 1, 1, 1], + [3, 2, 320, 0, 1, 1], + [3, 2, 320, 1, 1, 1], + [3, 2, 320, 0, 1, 1], + [3, 2, 320, 1, 1, 1], + [3, 2, 320, 0, 1, 1], + [3, 2, 320, 1, 1, 1], + [3, 2, 320, 0, 1, 1], + [3, 2, 320, 1, 1, 1], + [3, 2, 320, 0, 1, 1], + # [3, 2, 320, 1, 1, 1], + # [3, 2, 320, 0, 1, 1], + [3, 2, 320, 0, 1, 1], + [3, 2, 640, 0, 1, 2], + [3, 2, 640, 1, 1, 1], + [3, 2, 640, 0, 1, 1], + # [3, 2, 640, 1, 1, 1], + # [3, 2, 640, 0, 1, 1] + ] + return RepViT(cfgs, init_cfg=init_cfg, pretrained=pretrained, distillation=distillation, out_indices=out_indices) \ No newline at end of file diff --git a/detection/train.sh b/detection/train.sh index dbde92f..a029f1e 100644 --- a/detection/train.sh +++ b/detection/train.sh @@ -1 +1 @@ -./dist_train.sh configs/mask_rcnn_repvit_m2_fpn_1x_coco.py 8 \ No newline at end of file +./dist_train.sh configs/mask_rcnn_repvit_m1_1_fpn_1x_coco.py 8 \ No newline at end of file diff --git a/eval.sh b/eval.sh index c083983..9b9a797 100644 --- a/eval.sh +++ b/eval.sh @@ -1 +1 @@ -python main.py --eval --model repvit_m1 --resume pretrain/repvit_m1_distill_300.pth --data-path ~/imagenet \ No newline at end of file +python main.py --eval --model repvit_m1_1 --resume pretrain/repvit_m1_1_distill_300e.pth --data-path ~/imagenet \ No newline at end of file diff --git a/export_coreml.py b/export_coreml.py index ba0cf1a..af03e0b 100644 --- a/export_coreml.py +++ b/export_coreml.py @@ -11,7 +11,7 @@ parser = ArgumentParser() -parser.add_argument('--model', default='repvit_m1', type=str) +parser.add_argument('--model', default='repvit_m1_1', type=str) parser.add_argument('--resolution', default=224, type=int) parser.add_argument('--ckpt', default=None, type=str) diff --git a/flops.py b/flops.py index 342628e..71b80af 100644 --- a/flops.py +++ b/flops.py @@ -9,7 +9,7 @@ T1 = 10 for n, batch_size, resolution in [ - ('repvit_m2', 1024, 224), + ('repvit_m0_9', 1024, 224), ]: inputs = torch.randn(1, 3, resolution, resolution) diff --git a/logs/repvit_m0_9_distill_300e.txt b/logs/repvit_m0_9_distill_300e.txt new file mode 100644 index 0000000..d0f889a --- /dev/null +++ b/logs/repvit_m0_9_distill_300e.txt @@ -0,0 +1,300 @@ +{"train_lr": 1.0000000000000165e-06, "train_loss": 6.982649914080576, "test_loss": 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a/main.py b/main.py index 3c433ea..1bea89e 100644 --- a/main.py +++ b/main.py @@ -33,7 +33,7 @@ def get_args_parser(): parser.add_argument('--epochs', default=300, type=int) # Model parameters - parser.add_argument('--model', default='repvit_m1', type=str, metavar='MODEL', + parser.add_argument('--model', default='repvit_m1_1', type=str, metavar='MODEL', help='Name of model to train') parser.add_argument('--input-size', default=224, type=int, help='images input size') diff --git a/model/repvit.py b/model/repvit.py index af2af06..e719744 100644 --- a/model/repvit.py +++ b/model/repvit.py @@ -1,9 +1,5 @@ import torch.nn as nn - -__all__ = ['repvit_m1'] - - def _make_divisible(v, divisor, min_value=None): """ This function is taken from the original tf repo. @@ -88,16 +84,17 @@ class RepVGGDW(torch.nn.Module): def __init__(self, ed) -> None: super().__init__() self.conv = Conv2d_BN(ed, ed, 3, 1, 1, groups=ed) - self.conv1 = Conv2d_BN(ed, ed, 1, 1, 0, groups=ed) + self.conv1 = torch.nn.Conv2d(ed, ed, 1, 1, 0, groups=ed) self.dim = ed + self.bn = torch.nn.BatchNorm2d(ed) def forward(self, x): - return self.conv(x) + self.conv1(x) + x + return self.bn((self.conv(x) + self.conv1(x)) + x) @torch.no_grad() def fuse(self): conv = self.conv.fuse() - conv1 = self.conv1.fuse() + conv1 = self.conv1 conv_w = conv.weight conv_b = conv.bias @@ -113,6 +110,14 @@ def fuse(self): conv.weight.data.copy_(final_conv_w) conv.bias.data.copy_(final_conv_b) + + bn = self.bn + w = bn.weight / (bn.running_var + bn.eps)**0.5 + w = conv.weight * w[:, None, None, None] + b = bn.bias + (conv.bias - bn.running_mean) * bn.weight / \ + (bn.running_var + bn.eps)**0.5 + conv.weight.data.copy_(w) + conv.bias.data.copy_(b) return conv @@ -242,7 +247,7 @@ def forward(self, x): from timm.models import register_model @register_model -def repvit_m1(pretrained=False, num_classes = 1000, distillation=False): +def repvit_m0_9(pretrained=False, num_classes = 1000, distillation=False): """ Constructs a MobileNetV3-Large model """ @@ -278,7 +283,44 @@ def repvit_m1(pretrained=False, num_classes = 1000, distillation=False): return RepViT(cfgs, num_classes=num_classes, distillation=distillation) @register_model -def repvit_m2(pretrained=False, num_classes = 1000, distillation=False): +def repvit_m1_0(pretrained=False, num_classes = 1000, distillation=False): + """ + Constructs a MobileNetV3-Large model + """ + cfgs = [ + # k, t, c, SE, HS, s + [3, 2, 56, 1, 0, 1], + [3, 2, 56, 0, 0, 1], + [3, 2, 56, 0, 0, 1], + [3, 2, 112, 0, 0, 2], + [3, 2, 112, 1, 0, 1], + [3, 2, 112, 0, 0, 1], + [3, 2, 112, 0, 0, 1], + [3, 2, 224, 0, 1, 2], + [3, 2, 224, 1, 1, 1], + [3, 2, 224, 0, 1, 1], + [3, 2, 224, 1, 1, 1], + [3, 2, 224, 0, 1, 1], + [3, 2, 224, 1, 1, 1], + [3, 2, 224, 0, 1, 1], + [3, 2, 224, 1, 1, 1], + [3, 2, 224, 0, 1, 1], + [3, 2, 224, 1, 1, 1], + [3, 2, 224, 0, 1, 1], + [3, 2, 224, 1, 1, 1], + [3, 2, 224, 0, 1, 1], + [3, 2, 224, 1, 1, 1], + [3, 2, 224, 0, 1, 1], + [3, 2, 224, 0, 1, 1], + [3, 2, 448, 0, 1, 2], + [3, 2, 448, 1, 1, 1], + [3, 2, 448, 0, 1, 1] + ] + return RepViT(cfgs, num_classes=num_classes, distillation=distillation) + + +@register_model +def repvit_m1_1(pretrained=False, num_classes = 1000, distillation=False): """ Constructs a MobileNetV3-Large model """ @@ -311,8 +353,9 @@ def repvit_m2(pretrained=False, num_classes = 1000, distillation=False): ] return RepViT(cfgs, num_classes=num_classes, distillation=distillation) + @register_model -def repvit_m3(pretrained=False, num_classes = 1000, distillation=False): +def repvit_m1_5(pretrained=False, num_classes = 1000, distillation=False): """ Constructs a MobileNetV3-Large model """ @@ -348,9 +391,87 @@ def repvit_m3(pretrained=False, num_classes = 1000, distillation=False): [3, 2, 256, 0, 1, 1], [3, 2, 256, 1, 1, 1], [3, 2, 256, 0, 1, 1], + [3, 2, 256, 1, 1, 1], + [3, 2, 256, 0, 1, 1], + [3, 2, 256, 1, 1, 1], + [3, 2, 256, 0, 1, 1], + [3, 2, 256, 1, 1, 1], + [3, 2, 256, 0, 1, 1], [3, 2, 256, 0, 1, 1], [3, 2, 512, 0, 1, 2], [3, 2, 512, 1, 1, 1], + [3, 2, 512, 0, 1, 1], + [3, 2, 512, 1, 1, 1], [3, 2, 512, 0, 1, 1] ] return RepViT(cfgs, num_classes=num_classes, distillation=distillation) + + + +@register_model +def repvit_m2_3(pretrained=False, num_classes = 1000, distillation=False): + """ + Constructs a MobileNetV3-Large model + """ + cfgs = [ + # k, t, c, SE, HS, s + [3, 2, 80, 1, 0, 1], + [3, 2, 80, 0, 0, 1], + [3, 2, 80, 1, 0, 1], + [3, 2, 80, 0, 0, 1], + [3, 2, 80, 1, 0, 1], + [3, 2, 80, 0, 0, 1], + [3, 2, 80, 0, 0, 1], + [3, 2, 160, 0, 0, 2], + [3, 2, 160, 1, 0, 1], + [3, 2, 160, 0, 0, 1], + [3, 2, 160, 1, 0, 1], + [3, 2, 160, 0, 0, 1], + [3, 2, 160, 1, 0, 1], + [3, 2, 160, 0, 0, 1], + [3, 2, 160, 0, 0, 1], + [3, 2, 320, 0, 1, 2], + [3, 2, 320, 1, 1, 1], + [3, 2, 320, 0, 1, 1], + [3, 2, 320, 1, 1, 1], + [3, 2, 320, 0, 1, 1], + [3, 2, 320, 1, 1, 1], + [3, 2, 320, 0, 1, 1], + [3, 2, 320, 1, 1, 1], + [3, 2, 320, 0, 1, 1], + [3, 2, 320, 1, 1, 1], + [3, 2, 320, 0, 1, 1], + [3, 2, 320, 1, 1, 1], + [3, 2, 320, 0, 1, 1], + [3, 2, 320, 1, 1, 1], + [3, 2, 320, 0, 1, 1], + [3, 2, 320, 1, 1, 1], + [3, 2, 320, 0, 1, 1], + [3, 2, 320, 1, 1, 1], + [3, 2, 320, 0, 1, 1], + [3, 2, 320, 1, 1, 1], + [3, 2, 320, 0, 1, 1], + [3, 2, 320, 1, 1, 1], + [3, 2, 320, 0, 1, 1], + [3, 2, 320, 1, 1, 1], + [3, 2, 320, 0, 1, 1], + [3, 2, 320, 1, 1, 1], + [3, 2, 320, 0, 1, 1], + [3, 2, 320, 1, 1, 1], + [3, 2, 320, 0, 1, 1], + [3, 2, 320, 1, 1, 1], + [3, 2, 320, 0, 1, 1], + [3, 2, 320, 1, 1, 1], + [3, 2, 320, 0, 1, 1], + [3, 2, 320, 1, 1, 1], + [3, 2, 320, 0, 1, 1], + # [3, 2, 320, 1, 1, 1], + # [3, 2, 320, 0, 1, 1], + [3, 2, 320, 0, 1, 1], + [3, 2, 640, 0, 1, 2], + [3, 2, 640, 1, 1, 1], + [3, 2, 640, 0, 1, 1], + # [3, 2, 640, 1, 1, 1], + # [3, 2, 640, 0, 1, 1] + ] + return RepViT(cfgs, num_classes=num_classes, distillation=distillation) \ No newline at end of file diff --git a/segmentation/README.md b/segmentation/README.md index 7fe77d7..1e31344 100644 --- a/segmentation/README.md +++ b/segmentation/README.md @@ -5,8 +5,9 @@ Segmentation on ADE20K is implemented based on [MMSegmentation](https://github.c ## Models | Model | mIoU | Latency | Ckpt | Log | |:---------------|:----:|:---:|:--:|:--:| -| RepViT-M2 | 40.6 | 4.9ms | [M2](https://github.com/jameslahm/RepViT/releases/download/v1.0/repvit_m2_ade20k.pth) | [M2](./logs/repvit_m2_ade20k.json) | -| RepViT-M3 | 42.8 | 5.9ms | [M3](https://github.com/jameslahm/RepViT/releases/download/v1.0/repvit_m3_ade20k.pth) | [M3](./logs/repvit_m3_ade20k.json) | +| RepViT-M1_1 | 40.6 | 4.9ms | [M1_1]() | [M1_1](./logs/repvit_m1_1_ade20k.json) | +| RepViT-M1_5 | 43.6 | 6.4ms | [M1_5]() | [M1_5](./logs/repvit_m1_5_ade20k.json) | +| RepViT-M2_3 | 46.1 | 9.9ms | [M2_3]() | [M2_3](./logs/repvit_m2_3_ade20k.json) | The backbone latency is measured with image crops of 512x512 on iPhone 12 by Core ML Tools. @@ -50,7 +51,7 @@ We provide a multi-GPU testing script, specify config file, checkpoint, and numb For example, to test RepViT-M1 on ADE20K on an 8-GPU machine, ``` -./tools/dist_test.sh configs/sem_fpn/fpn_repvit_m2_ade20k_40k.py path/to/repvit_m2_ade20k.pth 8 --eval mIoU +./tools/dist_test.sh configs/sem_fpn/fpn_repvit_m1_1_ade20k_40k.py path/to/repvit_m1_1_ade20k.pth 8 --eval mIoU ``` ## Training @@ -58,6 +59,6 @@ Download ImageNet-1K pretrained weights into `./pretrain` We provide PyTorch distributed data parallel (DDP) training script `dist_train.sh`, for example, to train EfficientFormer-L1 on an 8-GPU machine: ``` -./tools/dist_train.sh configs/sem_fpn/fpn_repvit_m2_ade20k_40k.py 8 +./tools/dist_train.sh configs/sem_fpn/fpn_repvit_m1_1_ade20k_40k.py 8 ``` Tips: specify configs and #GPUs! diff --git a/segmentation/configs/sem_fpn/fpn_repvit_m2_ade20k_40k.py b/segmentation/configs/sem_fpn/fpn_repvit_m1_1_ade20k_40k.py similarity index 91% rename from segmentation/configs/sem_fpn/fpn_repvit_m2_ade20k_40k.py rename to segmentation/configs/sem_fpn/fpn_repvit_m1_1_ade20k_40k.py index dfaff5d..a974105 100644 --- a/segmentation/configs/sem_fpn/fpn_repvit_m2_ade20k_40k.py +++ b/segmentation/configs/sem_fpn/fpn_repvit_m1_1_ade20k_40k.py @@ -7,11 +7,11 @@ model = dict( type='EncoderDecoder', backbone=dict( - type='repvit_m2', + type='repvit_m1_1', style='pytorch', init_cfg=dict( type='Pretrained', - checkpoint='pretrain/repvit_m2_distill_300.pth', + checkpoint='pretrain/repvit_m1_1_distill_300e.pth', ), out_indices = [3,7,21,24] ), diff --git a/segmentation/configs/sem_fpn/fpn_repvit_m3_ade20k_40k.py b/segmentation/configs/sem_fpn/fpn_repvit_m1_5_ade20k_40k.py similarity index 87% rename from segmentation/configs/sem_fpn/fpn_repvit_m3_ade20k_40k.py rename to segmentation/configs/sem_fpn/fpn_repvit_m1_5_ade20k_40k.py index 6080999..bd924f0 100644 --- a/segmentation/configs/sem_fpn/fpn_repvit_m3_ade20k_40k.py +++ b/segmentation/configs/sem_fpn/fpn_repvit_m1_5_ade20k_40k.py @@ -7,13 +7,13 @@ model = dict( type='EncoderDecoder', backbone=dict( - type='repvit_m3', + type='repvit_m1_5', style='pytorch', init_cfg=dict( type='Pretrained', - checkpoint='pretrain/repvit_m3_distill_300.pth', + checkpoint='pretrain/repvit_m1_5_distill_300e.pth', ), - out_indices=[5,11,31,34] + out_indices=[5, 11, 37, 42] ), neck=dict(in_channels=[64, 128, 256, 512]), decode_head=dict(num_classes=150)) diff --git a/segmentation/configs/sem_fpn/fpn_repvit_m2_3_ade20k_40k.py b/segmentation/configs/sem_fpn/fpn_repvit_m2_3_ade20k_40k.py new file mode 100644 index 0000000..7633b01 --- /dev/null +++ b/segmentation/configs/sem_fpn/fpn_repvit_m2_3_ade20k_40k.py @@ -0,0 +1,30 @@ +_base_ = [ + '../_base_/models/fpn_r50.py', + '../_base_/datasets/ade20k.py', + '../_base_/default_runtime.py' +] +# model settings +model = dict( + type='EncoderDecoder', + backbone=dict( + type='repvit_m2_3', + style='pytorch', + init_cfg=dict( + type='Pretrained', + checkpoint='pretrain/repvit_m2_3_distill_450e.pth', + ), + out_indices=[7, 15, 51, 54] + ), + neck=dict(in_channels=[80, 160, 320, 640]), + decode_head=dict(num_classes=150)) + +gpu_multiples = 2 # we use 8 gpu instead of 4 in mmsegmentation, so lr*2 and max_iters/2 +# optimizer +optimizer = dict(type='AdamW', lr=0.0001 * gpu_multiples, weight_decay=0.0001) +optimizer_config = dict() +# learning policy +lr_config = dict(policy='poly', power=0.9, min_lr=1e-6, by_epoch=False) +# runtime settings +runner = dict(type='IterBasedRunner', max_iters=80000 // gpu_multiples) +checkpoint_config = dict(by_epoch=False, interval=8000 // gpu_multiples) +evaluation = dict(interval=8000 // gpu_multiples, metric='mIoU') diff --git a/segmentation/eval.sh b/segmentation/eval.sh index a71610f..d791888 100644 --- a/segmentation/eval.sh +++ b/segmentation/eval.sh @@ -1 +1 @@ -PORT=12345 ./tools/dist_test.sh configs/sem_fpn/fpn_repvit_m3_ade20k_40k.py seg_pretrain/repvit_m3_ade20k.pth 8 --eval mIoU \ No newline at end of file +PORT=12345 ./tools/dist_test.sh configs/sem_fpn/fpn_repvit_m1_1_ade20k_40k.py seg_pretrain/repvit_m1_1_ade20k.pth 8 --eval mIoU \ No newline at end of file diff --git a/segmentation/logs/repvit_m2_ade20k.json b/segmentation/logs/repvit_m1_1_ade20k.json similarity index 95% rename from segmentation/logs/repvit_m2_ade20k.json rename to segmentation/logs/repvit_m1_1_ade20k.json index bf8ea84..2f97653 100644 --- a/segmentation/logs/repvit_m2_ade20k.json +++ b/segmentation/logs/repvit_m1_1_ade20k.json @@ -1,4 +1,4 @@ -{"env_info": "sys.platform: linux\nPython: 3.8.16 (default, Mar 2 2023, 03:21:46) [GCC 11.2.0]\nCUDA available: True\nGPU 0,1,2,3,4,5,6,7: NVIDIA GeForce RTX 3090\nCUDA_HOME: /usr/local/cuda\nNVCC: Cuda compilation tools, release 11.3, V11.3.109\nGCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0\nPyTorch: 2.0.1+cu117\nPyTorch compiling details: PyTorch built with:\n - GCC 9.3\n - C++ Version: 201703\n - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications\n - Intel(R) MKL-DNN v2.7.3 (Git Hash 6dbeffbae1f23cbbeae17adb7b5b13f1f37c080e)\n - OpenMP 201511 (a.k.a. OpenMP 4.5)\n - LAPACK is enabled (usually provided by MKL)\n - NNPACK is enabled\n - CPU capability usage: AVX2\n - CUDA Runtime 11.7\n - 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\n - CuDNN 8.9.2 (built against CUDA 12.1)\n - Built with CuDNN 8.5\n - Magma 2.6.1\n - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.7, CUDNN_VERSION=8.5.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wunused-local-typedefs -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -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 -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_DISABLE_GPU_ASSERTS=ON, TORCH_VERSION=2.0.1, 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=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, \n\nTorchVision: 0.15.2+cu117\nOpenCV: 4.8.0\nMMCV: 1.7.1\nMMCV Compiler: GCC 9.4\nMMCV CUDA Compiler: 11.3\nMMSegmentation: 0.30.0+25971f7", "seed": null, "exp_name": "fpn_repvit_m2_ade20k_40k.py", "mmseg_version": "0.30.0+25971f7", "config": "norm_cfg = dict(type='SyncBN', requires_grad=True)\nmodel = dict(\n type='EncoderDecoder',\n pretrained='open-mmlab://resnet50_v1c',\n backbone=dict(\n type='repvit_m2',\n depth=50,\n num_stages=4,\n out_indices=[3, 7, 21, 24],\n dilations=(1, 1, 1, 1),\n strides=(1, 2, 2, 2),\n norm_cfg=dict(type='SyncBN', requires_grad=True),\n norm_eval=False,\n style='pytorch',\n contract_dilation=True,\n init_cfg=dict(\n type='Pretrained',\n checkpoint='pretrain/repvit_m2_new_distill_300.pth'),\n pretrained='open-mmlab://resnet50_v1c'),\n neck=dict(\n type='FPN',\n in_channels=[64, 128, 256, 512],\n out_channels=256,\n num_outs=4),\n decode_head=dict(\n type='FPNHead',\n in_channels=[256, 256, 256, 256],\n in_index=[0, 1, 2, 3],\n feature_strides=[4, 8, 16, 32],\n channels=128,\n dropout_ratio=0.1,\n num_classes=150,\n norm_cfg=dict(type='SyncBN', requires_grad=True),\n align_corners=False,\n loss_decode=dict(\n type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),\n train_cfg=dict(),\n test_cfg=dict(mode='whole'))\ndataset_type = 'ADE20KDataset'\ndata_root = 'data/ade/ADEChallengeData2016'\nimg_norm_cfg = dict(\n mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)\ncrop_size = (512, 512)\ntrain_pipeline = [\n dict(type='LoadImageFromFile'),\n dict(type='LoadAnnotations', reduce_zero_label=True),\n dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)),\n dict(type='RandomCrop', crop_size=(512, 512), cat_max_ratio=0.75),\n dict(type='RandomFlip', prob=0.5),\n dict(type='PhotoMetricDistortion'),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='Pad', size=(512, 512), pad_val=0, seg_pad_val=255),\n dict(type='DefaultFormatBundle'),\n dict(type='Collect', keys=['img', 'gt_semantic_seg'])\n]\ntest_pipeline = [\n dict(type='LoadImageFromFile'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(2048, 512),\n flip=False,\n transforms=[\n dict(type='AlignResize', keep_ratio=True, size_divisor=32),\n dict(type='RandomFlip'),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='ImageToTensor', keys=['img']),\n dict(type='Collect', keys=['img'])\n ])\n]\ndata = dict(\n samples_per_gpu=4,\n workers_per_gpu=4,\n train=dict(\n type='RepeatDataset',\n times=50,\n dataset=dict(\n type='ADE20KDataset',\n data_root='data/ade/ADEChallengeData2016',\n img_dir='images/training',\n ann_dir='annotations/training',\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(type='LoadAnnotations', reduce_zero_label=True),\n dict(\n type='Resize',\n img_scale=(2048, 512),\n ratio_range=(0.5, 2.0)),\n dict(\n type='RandomCrop',\n crop_size=(512, 512),\n cat_max_ratio=0.75),\n dict(type='RandomFlip', prob=0.5),\n dict(type='PhotoMetricDistortion'),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='Pad', size=(512, 512), pad_val=0, seg_pad_val=255),\n dict(type='DefaultFormatBundle'),\n dict(type='Collect', keys=['img', 'gt_semantic_seg'])\n ])),\n val=dict(\n type='ADE20KDataset',\n data_root='data/ade/ADEChallengeData2016',\n img_dir='images/validation',\n ann_dir='annotations/validation',\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(2048, 512),\n flip=False,\n transforms=[\n dict(type='AlignResize', keep_ratio=True, size_divisor=32),\n dict(type='RandomFlip'),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='ImageToTensor', keys=['img']),\n dict(type='Collect', keys=['img'])\n ])\n ]),\n test=dict(\n type='ADE20KDataset',\n data_root='data/ade/ADEChallengeData2016',\n img_dir='images/validation',\n ann_dir='annotations/validation',\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(2048, 512),\n flip=False,\n transforms=[\n dict(type='AlignResize', keep_ratio=True, size_divisor=32),\n dict(type='RandomFlip'),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='ImageToTensor', keys=['img']),\n dict(type='Collect', keys=['img'])\n ])\n ]))\nlog_config = dict(\n interval=50, hooks=[dict(type='TextLoggerHook', by_epoch=False)])\ndist_params = dict(backend='nccl')\nlog_level = 'INFO'\nload_from = None\nresume_from = None\nworkflow = [('train', 1)]\ncudnn_benchmark = True\ngpu_multiples = 2\noptimizer = dict(type='AdamW', lr=0.0002, weight_decay=0.0001)\noptimizer_config = dict()\nlr_config = dict(policy='poly', power=0.9, min_lr=1e-06, by_epoch=False)\nrunner = dict(type='IterBasedRunner', max_iters=40000)\ncheckpoint_config = dict(by_epoch=False, interval=4000)\nevaluation = dict(interval=4000, metric='mIoU')\nwork_dir = './work_dirs/fpn_repvit_m2_ade20k_40k'\ngpu_ids = range(0, 8)\ndevice = 'cuda'\nseed = None\n", "CLASSES": ["wall", "building", "sky", "floor", "tree", "ceiling", "road", "bed ", "windowpane", "grass", "cabinet", "sidewalk", "person", "earth", "door", "table", "mountain", "plant", "curtain", "chair", "car", "water", "painting", "sofa", "shelf", "house", "sea", "mirror", "rug", "field", "armchair", "seat", "fence", "desk", "rock", "wardrobe", "lamp", "bathtub", "railing", "cushion", "base", "box", "column", "signboard", "chest of drawers", "counter", "sand", "sink", "skyscraper", "fireplace", "refrigerator", "grandstand", "path", "stairs", "runway", "case", "pool table", "pillow", "screen door", "stairway", "river", "bridge", "bookcase", "blind", "coffee table", "toilet", "flower", "book", "hill", "bench", "countertop", "stove", "palm", "kitchen island", "computer", "swivel chair", "boat", "bar", "arcade machine", "hovel", "bus", "towel", "light", "truck", "tower", "chandelier", "awning", "streetlight", "booth", "television receiver", "airplane", "dirt track", "apparel", "pole", "land", "bannister", "escalator", "ottoman", "bottle", "buffet", "poster", "stage", "van", "ship", "fountain", "conveyer belt", "canopy", "washer", "plaything", "swimming pool", "stool", "barrel", "basket", "waterfall", "tent", "bag", "minibike", "cradle", "oven", "ball", "food", "step", "tank", "trade name", "microwave", "pot", "animal", "bicycle", "lake", "dishwasher", "screen", "blanket", "sculpture", "hood", "sconce", "vase", "traffic light", "tray", "ashcan", "fan", "pier", "crt screen", "plate", "monitor", "bulletin board", "shower", "radiator", "glass", "clock", "flag"], "PALETTE": [[120, 120, 120], [180, 120, 120], [6, 230, 230], [80, 50, 50], [4, 200, 3], [120, 120, 80], [140, 140, 140], [204, 5, 255], [230, 230, 230], [4, 250, 7], [224, 5, 255], [235, 255, 7], [150, 5, 61], [120, 120, 70], [8, 255, 51], [255, 6, 82], [143, 255, 140], [204, 255, 4], [255, 51, 7], [204, 70, 3], [0, 102, 200], [61, 230, 250], [255, 6, 51], [11, 102, 255], [255, 7, 71], [255, 9, 224], [9, 7, 230], [220, 220, 220], [255, 9, 92], [112, 9, 255], [8, 255, 214], [7, 255, 224], [255, 184, 6], [10, 255, 71], [255, 41, 10], [7, 255, 255], [224, 255, 8], [102, 8, 255], [255, 61, 6], [255, 194, 7], [255, 122, 8], [0, 255, 20], [255, 8, 41], [255, 5, 153], [6, 51, 255], [235, 12, 255], [160, 150, 20], [0, 163, 255], [140, 140, 140], [250, 10, 15], [20, 255, 0], [31, 255, 0], [255, 31, 0], [255, 224, 0], [153, 255, 0], [0, 0, 255], [255, 71, 0], [0, 235, 255], [0, 173, 255], [31, 0, 255], [11, 200, 200], [255, 82, 0], [0, 255, 245], [0, 61, 255], [0, 255, 112], [0, 255, 133], [255, 0, 0], [255, 163, 0], [255, 102, 0], [194, 255, 0], [0, 143, 255], [51, 255, 0], [0, 82, 255], [0, 255, 41], [0, 255, 173], [10, 0, 255], [173, 255, 0], [0, 255, 153], [255, 92, 0], [255, 0, 255], [255, 0, 245], [255, 0, 102], [255, 173, 0], [255, 0, 20], [255, 184, 184], [0, 31, 255], [0, 255, 61], [0, 71, 255], [255, 0, 204], [0, 255, 194], [0, 255, 82], [0, 10, 255], [0, 112, 255], [51, 0, 255], [0, 194, 255], [0, 122, 255], [0, 255, 163], [255, 153, 0], [0, 255, 10], [255, 112, 0], [143, 255, 0], [82, 0, 255], [163, 255, 0], [255, 235, 0], [8, 184, 170], [133, 0, 255], [0, 255, 92], [184, 0, 255], [255, 0, 31], [0, 184, 255], [0, 214, 255], [255, 0, 112], [92, 255, 0], [0, 224, 255], [112, 224, 255], [70, 184, 160], [163, 0, 255], [153, 0, 255], [71, 255, 0], [255, 0, 163], [255, 204, 0], [255, 0, 143], [0, 255, 235], [133, 255, 0], [255, 0, 235], [245, 0, 255], [255, 0, 122], [255, 245, 0], [10, 190, 212], [214, 255, 0], [0, 204, 255], [20, 0, 255], [255, 255, 0], [0, 153, 255], [0, 41, 255], [0, 255, 204], [41, 0, 255], [41, 255, 0], [173, 0, 255], [0, 245, 255], [71, 0, 255], [122, 0, 255], [0, 255, 184], [0, 92, 255], [184, 255, 0], [0, 133, 255], [255, 214, 0], [25, 194, 194], [102, 255, 0], [92, 0, 255]]} +{"env_info": "sys.platform: linux\nPython: 3.8.16 (default, Mar 2 2023, 03:21:46) [GCC 11.2.0]\nCUDA available: True\nGPU 0,1,2,3,4,5,6,7: NVIDIA GeForce RTX 3090\nCUDA_HOME: /usr/local/cuda\nNVCC: Cuda compilation tools, release 11.3, V11.3.109\nGCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0\nPyTorch: 2.0.1+cu117\nPyTorch compiling details: PyTorch built with:\n - GCC 9.3\n - C++ Version: 201703\n - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications\n - Intel(R) MKL-DNN v2.7.3 (Git Hash 6dbeffbae1f23cbbeae17adb7b5b13f1f37c080e)\n - OpenMP 201511 (a.k.a. OpenMP 4.5)\n - LAPACK is enabled (usually provided by MKL)\n - NNPACK is enabled\n - CPU capability usage: AVX2\n - CUDA Runtime 11.7\n - 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\n - CuDNN 8.9.2 (built against CUDA 12.1)\n - Built with CuDNN 8.5\n - Magma 2.6.1\n - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.7, CUDNN_VERSION=8.5.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wunused-local-typedefs -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -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 -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_DISABLE_GPU_ASSERTS=ON, TORCH_VERSION=2.0.1, 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=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, \n\nTorchVision: 0.15.2+cu117\nOpenCV: 4.8.0\nMMCV: 1.7.1\nMMCV Compiler: GCC 9.4\nMMCV CUDA Compiler: 11.3\nMMSegmentation: 0.30.0+25971f7", "seed": null, "exp_name": "fpn_repvit_m1_1_ade20k_40k.py", "mmseg_version": "0.30.0+25971f7", "config": "norm_cfg = dict(type='SyncBN', requires_grad=True)\nmodel = dict(\n type='EncoderDecoder',\n pretrained='open-mmlab://resnet50_v1c',\n backbone=dict(\n type='repvit_m1_1',\n depth=50,\n num_stages=4,\n out_indices=[3, 7, 21, 24],\n dilations=(1, 1, 1, 1),\n strides=(1, 2, 2, 2),\n norm_cfg=dict(type='SyncBN', requires_grad=True),\n norm_eval=False,\n style='pytorch',\n contract_dilation=True,\n init_cfg=dict(\n type='Pretrained',\n checkpoint='pretrain/repvit_m1_1_distill_300e.pth'),\n pretrained='open-mmlab://resnet50_v1c'),\n neck=dict(\n type='FPN',\n in_channels=[64, 128, 256, 512],\n out_channels=256,\n num_outs=4),\n decode_head=dict(\n type='FPNHead',\n in_channels=[256, 256, 256, 256],\n in_index=[0, 1, 2, 3],\n feature_strides=[4, 8, 16, 32],\n channels=128,\n dropout_ratio=0.1,\n num_classes=150,\n norm_cfg=dict(type='SyncBN', requires_grad=True),\n align_corners=False,\n loss_decode=dict(\n type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),\n train_cfg=dict(),\n test_cfg=dict(mode='whole'))\ndataset_type = 'ADE20KDataset'\ndata_root = 'data/ade/ADEChallengeData2016'\nimg_norm_cfg = dict(\n mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)\ncrop_size = (512, 512)\ntrain_pipeline = [\n dict(type='LoadImageFromFile'),\n dict(type='LoadAnnotations', reduce_zero_label=True),\n dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)),\n dict(type='RandomCrop', crop_size=(512, 512), cat_max_ratio=0.75),\n dict(type='RandomFlip', prob=0.5),\n dict(type='PhotoMetricDistortion'),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='Pad', size=(512, 512), pad_val=0, seg_pad_val=255),\n dict(type='DefaultFormatBundle'),\n dict(type='Collect', keys=['img', 'gt_semantic_seg'])\n]\ntest_pipeline = [\n dict(type='LoadImageFromFile'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(2048, 512),\n flip=False,\n transforms=[\n dict(type='AlignResize', keep_ratio=True, size_divisor=32),\n dict(type='RandomFlip'),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='ImageToTensor', keys=['img']),\n dict(type='Collect', keys=['img'])\n ])\n]\ndata = dict(\n samples_per_gpu=4,\n workers_per_gpu=4,\n train=dict(\n type='RepeatDataset',\n times=50,\n dataset=dict(\n type='ADE20KDataset',\n data_root='data/ade/ADEChallengeData2016',\n img_dir='images/training',\n ann_dir='annotations/training',\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(type='LoadAnnotations', reduce_zero_label=True),\n dict(\n type='Resize',\n img_scale=(2048, 512),\n ratio_range=(0.5, 2.0)),\n dict(\n type='RandomCrop',\n crop_size=(512, 512),\n cat_max_ratio=0.75),\n dict(type='RandomFlip', prob=0.5),\n dict(type='PhotoMetricDistortion'),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='Pad', size=(512, 512), pad_val=0, seg_pad_val=255),\n dict(type='DefaultFormatBundle'),\n dict(type='Collect', keys=['img', 'gt_semantic_seg'])\n ])),\n val=dict(\n type='ADE20KDataset',\n data_root='data/ade/ADEChallengeData2016',\n img_dir='images/validation',\n ann_dir='annotations/validation',\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(2048, 512),\n flip=False,\n transforms=[\n dict(type='AlignResize', keep_ratio=True, size_divisor=32),\n dict(type='RandomFlip'),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='ImageToTensor', keys=['img']),\n dict(type='Collect', keys=['img'])\n ])\n ]),\n test=dict(\n type='ADE20KDataset',\n data_root='data/ade/ADEChallengeData2016',\n img_dir='images/validation',\n ann_dir='annotations/validation',\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(2048, 512),\n flip=False,\n transforms=[\n dict(type='AlignResize', keep_ratio=True, size_divisor=32),\n dict(type='RandomFlip'),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='ImageToTensor', keys=['img']),\n dict(type='Collect', keys=['img'])\n ])\n ]))\nlog_config = dict(\n interval=50, hooks=[dict(type='TextLoggerHook', by_epoch=False)])\ndist_params = dict(backend='nccl')\nlog_level = 'INFO'\nload_from = None\nresume_from = None\nworkflow = [('train', 1)]\ncudnn_benchmark = True\ngpu_multiples = 2\noptimizer = dict(type='AdamW', lr=0.0002, weight_decay=0.0001)\noptimizer_config = dict()\nlr_config = dict(policy='poly', power=0.9, min_lr=1e-06, by_epoch=False)\nrunner = dict(type='IterBasedRunner', max_iters=40000)\ncheckpoint_config = dict(by_epoch=False, interval=4000)\nevaluation = dict(interval=4000, metric='mIoU')\nwork_dir = './work_dirs/fpn_repvit_m1_1_ade20k_40k'\ngpu_ids = range(0, 8)\ndevice = 'cuda'\nseed = None\n", "CLASSES": ["wall", "building", "sky", "floor", "tree", "ceiling", "road", "bed ", "windowpane", "grass", "cabinet", "sidewalk", "person", "earth", "door", "table", "mountain", "plant", "curtain", "chair", "car", "water", "painting", "sofa", "shelf", "house", "sea", "mirror", "rug", "field", "armchair", "seat", "fence", "desk", "rock", "wardrobe", "lamp", "bathtub", "railing", "cushion", "base", "box", "column", "signboard", "chest of drawers", "counter", "sand", "sink", "skyscraper", "fireplace", "refrigerator", "grandstand", "path", "stairs", "runway", "case", "pool table", "pillow", "screen door", "stairway", "river", "bridge", "bookcase", "blind", "coffee table", "toilet", "flower", "book", "hill", "bench", "countertop", "stove", "palm", "kitchen island", "computer", "swivel chair", "boat", "bar", "arcade machine", "hovel", "bus", "towel", "light", "truck", "tower", "chandelier", "awning", "streetlight", "booth", "television receiver", "airplane", "dirt track", "apparel", "pole", "land", "bannister", "escalator", "ottoman", "bottle", "buffet", "poster", "stage", "van", "ship", "fountain", "conveyer belt", "canopy", "washer", "plaything", "swimming pool", "stool", "barrel", "basket", "waterfall", "tent", "bag", "minibike", "cradle", "oven", "ball", "food", "step", "tank", "trade name", "microwave", "pot", "animal", "bicycle", "lake", "dishwasher", "screen", "blanket", "sculpture", "hood", "sconce", "vase", "traffic light", "tray", "ashcan", "fan", "pier", "crt screen", "plate", "monitor", "bulletin board", "shower", "radiator", "glass", "clock", "flag"], "PALETTE": [[120, 120, 120], [180, 120, 120], [6, 230, 230], [80, 50, 50], [4, 200, 3], [120, 120, 80], [140, 140, 140], [204, 5, 255], [230, 230, 230], [4, 250, 7], [224, 5, 255], [235, 255, 7], [150, 5, 61], [120, 120, 70], [8, 255, 51], [255, 6, 82], [143, 255, 140], [204, 255, 4], [255, 51, 7], [204, 70, 3], [0, 102, 200], [61, 230, 250], [255, 6, 51], [11, 102, 255], [255, 7, 71], [255, 9, 224], [9, 7, 230], [220, 220, 220], [255, 9, 92], [112, 9, 255], [8, 255, 214], [7, 255, 224], [255, 184, 6], [10, 255, 71], [255, 41, 10], [7, 255, 255], [224, 255, 8], [102, 8, 255], [255, 61, 6], [255, 194, 7], [255, 122, 8], [0, 255, 20], [255, 8, 41], [255, 5, 153], [6, 51, 255], [235, 12, 255], [160, 150, 20], [0, 163, 255], [140, 140, 140], [250, 10, 15], [20, 255, 0], [31, 255, 0], [255, 31, 0], [255, 224, 0], [153, 255, 0], [0, 0, 255], [255, 71, 0], [0, 235, 255], [0, 173, 255], [31, 0, 255], [11, 200, 200], [255, 82, 0], [0, 255, 245], [0, 61, 255], [0, 255, 112], [0, 255, 133], [255, 0, 0], [255, 163, 0], [255, 102, 0], [194, 255, 0], [0, 143, 255], [51, 255, 0], [0, 82, 255], [0, 255, 41], [0, 255, 173], [10, 0, 255], [173, 255, 0], [0, 255, 153], [255, 92, 0], [255, 0, 255], [255, 0, 245], [255, 0, 102], [255, 173, 0], [255, 0, 20], [255, 184, 184], [0, 31, 255], [0, 255, 61], [0, 71, 255], [255, 0, 204], [0, 255, 194], [0, 255, 82], [0, 10, 255], [0, 112, 255], [51, 0, 255], [0, 194, 255], [0, 122, 255], [0, 255, 163], [255, 153, 0], [0, 255, 10], [255, 112, 0], [143, 255, 0], [82, 0, 255], [163, 255, 0], [255, 235, 0], [8, 184, 170], [133, 0, 255], [0, 255, 92], [184, 0, 255], [255, 0, 31], [0, 184, 255], [0, 214, 255], [255, 0, 112], [92, 255, 0], [0, 224, 255], [112, 224, 255], [70, 184, 160], [163, 0, 255], [153, 0, 255], [71, 255, 0], [255, 0, 163], [255, 204, 0], [255, 0, 143], [0, 255, 235], [133, 255, 0], [255, 0, 235], [245, 0, 255], [255, 0, 122], [255, 245, 0], [10, 190, 212], [214, 255, 0], [0, 204, 255], [20, 0, 255], [255, 255, 0], [0, 153, 255], [0, 41, 255], [0, 255, 204], [41, 0, 255], [41, 255, 0], [173, 0, 255], [0, 245, 255], [71, 0, 255], [122, 0, 255], [0, 255, 184], [0, 92, 255], [184, 255, 0], [0, 133, 255], [255, 214, 0], [25, 194, 194], [102, 255, 0], [92, 0, 255]]} {"mode": "train", "epoch": 1, "iter": 50, "lr": 0.0002, "memory": 4602, "data_time": 0.02081, "decode.loss_ce": 2.6497, "decode.acc_seg": 41.65224, "loss": 2.6497, "time": 0.26173} {"mode": "train", "epoch": 1, "iter": 100, "lr": 0.0002, "memory": 4602, "data_time": 0.01155, "decode.loss_ce": 1.82189, "decode.acc_seg": 52.23696, "loss": 1.82189, "time": 0.14413} {"mode": "train", "epoch": 1, "iter": 150, "lr": 0.0002, "memory": 4602, "data_time": 0.01169, "decode.loss_ce": 1.65146, "decode.acc_seg": 54.48229, "loss": 1.65146, "time": 0.14572} diff --git a/segmentation/logs/repvit_m1_5_ade20k.json b/segmentation/logs/repvit_m1_5_ade20k.json new file mode 100644 index 0000000..b5ba88a --- /dev/null +++ b/segmentation/logs/repvit_m1_5_ade20k.json @@ -0,0 +1,811 @@ +{"env_info": "sys.platform: linux\nPython: 3.8.16 (default, Mar 2 2023, 03:21:46) [GCC 11.2.0]\nCUDA available: True\nGPU 0,1,2,3,4,5,6,7: NVIDIA GeForce RTX 3090\nCUDA_HOME: /home/shijiang/wangao/cuda\nNVCC: Cuda compilation tools, release 11.8, V11.8.89\nGCC: gcc (Ubuntu 9.3.0-17ubuntu1~20.04) 9.3.0\nPyTorch: 2.0.1+cu117\nPyTorch compiling details: PyTorch built with:\n - GCC 9.3\n - C++ Version: 201703\n - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications\n - Intel(R) MKL-DNN v2.7.3 (Git Hash 6dbeffbae1f23cbbeae17adb7b5b13f1f37c080e)\n - OpenMP 201511 (a.k.a. OpenMP 4.5)\n - LAPACK is enabled (usually provided by MKL)\n - NNPACK is enabled\n - CPU capability usage: AVX2\n - CUDA Runtime 11.7\n - 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\n - CuDNN 8.5\n - Magma 2.6.1\n - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.7, CUDNN_VERSION=8.5.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wunused-local-typedefs -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -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 -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_DISABLE_GPU_ASSERTS=ON, TORCH_VERSION=2.0.1, 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=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, \n\nTorchVision: 0.15.2+cu117\nOpenCV: 4.8.0\nMMCV: 1.7.1\nMMCV Compiler: GCC 9.3\nMMCV CUDA Compiler: 11.8\nMMSegmentation: 0.30.0+c54117a", "seed": null, "exp_name": "fpn_repvit_m4_ade20k_40k.py", "mmseg_version": "0.30.0+c54117a", "config": "norm_cfg = dict(type='SyncBN', requires_grad=True)\nmodel = dict(\n type='EncoderDecoder',\n pretrained='open-mmlab://resnet50_v1c',\n backbone=dict(\n type='repvit_m4',\n depth=50,\n num_stages=4,\n out_indices=[5, 11, 37, 42],\n dilations=(1, 1, 1, 1),\n strides=(1, 2, 2, 2),\n norm_cfg=dict(type='SyncBN', requires_grad=True),\n norm_eval=False,\n style='pytorch',\n contract_dilation=True,\n init_cfg=dict(\n type='Pretrained',\n checkpoint='pretrain/repvit_m4_distill_300e.pth'),\n pretrained='open-mmlab://resnet50_v1c'),\n neck=dict(\n type='FPN',\n in_channels=[64, 128, 256, 512],\n out_channels=256,\n num_outs=4),\n decode_head=dict(\n type='FPNHead',\n in_channels=[256, 256, 256, 256],\n in_index=[0, 1, 2, 3],\n feature_strides=[4, 8, 16, 32],\n channels=128,\n dropout_ratio=0.1,\n num_classes=150,\n norm_cfg=dict(type='SyncBN', requires_grad=True),\n align_corners=False,\n loss_decode=dict(\n type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),\n train_cfg=dict(),\n test_cfg=dict(mode='whole'))\ndataset_type = 'ADE20KDataset'\ndata_root = 'data/ade/ADEChallengeData2016'\nimg_norm_cfg = dict(\n mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)\ncrop_size = (512, 512)\ntrain_pipeline = [\n dict(type='LoadImageFromFile'),\n dict(type='LoadAnnotations', reduce_zero_label=True),\n dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)),\n dict(type='RandomCrop', crop_size=(512, 512), cat_max_ratio=0.75),\n dict(type='RandomFlip', prob=0.5),\n dict(type='PhotoMetricDistortion'),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='Pad', size=(512, 512), pad_val=0, seg_pad_val=255),\n dict(type='DefaultFormatBundle'),\n dict(type='Collect', keys=['img', 'gt_semantic_seg'])\n]\ntest_pipeline = [\n dict(type='LoadImageFromFile'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(2048, 512),\n flip=False,\n transforms=[\n dict(type='AlignResize', keep_ratio=True, size_divisor=32),\n dict(type='RandomFlip'),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='ImageToTensor', keys=['img']),\n dict(type='Collect', keys=['img'])\n ])\n]\ndata = dict(\n samples_per_gpu=4,\n workers_per_gpu=4,\n train=dict(\n type='RepeatDataset',\n times=50,\n dataset=dict(\n type='ADE20KDataset',\n data_root='data/ade/ADEChallengeData2016',\n img_dir='images/training',\n ann_dir='annotations/training',\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(type='LoadAnnotations', reduce_zero_label=True),\n dict(\n type='Resize',\n img_scale=(2048, 512),\n ratio_range=(0.5, 2.0)),\n dict(\n type='RandomCrop',\n crop_size=(512, 512),\n cat_max_ratio=0.75),\n dict(type='RandomFlip', prob=0.5),\n dict(type='PhotoMetricDistortion'),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='Pad', size=(512, 512), pad_val=0, seg_pad_val=255),\n dict(type='DefaultFormatBundle'),\n dict(type='Collect', keys=['img', 'gt_semantic_seg'])\n ])),\n val=dict(\n type='ADE20KDataset',\n data_root='data/ade/ADEChallengeData2016',\n img_dir='images/validation',\n ann_dir='annotations/validation',\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(2048, 512),\n flip=False,\n transforms=[\n dict(type='AlignResize', keep_ratio=True, size_divisor=32),\n dict(type='RandomFlip'),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='ImageToTensor', keys=['img']),\n dict(type='Collect', keys=['img'])\n ])\n ]),\n test=dict(\n type='ADE20KDataset',\n data_root='data/ade/ADEChallengeData2016',\n img_dir='images/validation',\n ann_dir='annotations/validation',\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(2048, 512),\n flip=False,\n transforms=[\n dict(type='AlignResize', keep_ratio=True, size_divisor=32),\n dict(type='RandomFlip'),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='ImageToTensor', keys=['img']),\n dict(type='Collect', keys=['img'])\n ])\n ]))\nlog_config = dict(\n interval=50, hooks=[dict(type='TextLoggerHook', by_epoch=False)])\ndist_params = dict(backend='nccl')\nlog_level = 'INFO'\nload_from = None\nresume_from = None\nworkflow = [('train', 1)]\ncudnn_benchmark = True\ngpu_multiples = 2\noptimizer = dict(type='AdamW', lr=0.0002, weight_decay=0.0001)\noptimizer_config = dict()\nlr_config = dict(policy='poly', power=0.9, min_lr=1e-06, by_epoch=False)\nrunner = dict(type='IterBasedRunner', max_iters=40000)\ncheckpoint_config = dict(by_epoch=False, interval=4000)\nevaluation = dict(interval=4000, metric='mIoU')\nwork_dir = './work_dirs/fpn_repvit_m4_ade20k_40k'\ngpu_ids = range(0, 8)\ndevice = 'cuda'\nseed = None\n", "CLASSES": ["wall", "building", "sky", "floor", "tree", "ceiling", "road", "bed ", "windowpane", "grass", "cabinet", "sidewalk", "person", "earth", "door", "table", "mountain", "plant", "curtain", "chair", "car", "water", "painting", "sofa", "shelf", "house", "sea", "mirror", "rug", "field", "armchair", "seat", "fence", "desk", "rock", "wardrobe", "lamp", "bathtub", "railing", "cushion", "base", "box", "column", "signboard", "chest of drawers", "counter", "sand", "sink", "skyscraper", "fireplace", "refrigerator", "grandstand", "path", "stairs", "runway", "case", "pool table", "pillow", "screen door", "stairway", "river", "bridge", "bookcase", "blind", "coffee table", "toilet", "flower", "book", "hill", "bench", "countertop", "stove", "palm", "kitchen island", "computer", "swivel chair", "boat", "bar", "arcade machine", "hovel", "bus", "towel", "light", "truck", "tower", "chandelier", "awning", "streetlight", "booth", "television receiver", "airplane", "dirt track", "apparel", "pole", "land", "bannister", "escalator", "ottoman", "bottle", "buffet", "poster", "stage", "van", "ship", "fountain", "conveyer belt", "canopy", "washer", "plaything", "swimming pool", "stool", "barrel", "basket", "waterfall", "tent", "bag", "minibike", "cradle", "oven", "ball", "food", "step", "tank", "trade name", "microwave", "pot", "animal", "bicycle", "lake", "dishwasher", "screen", "blanket", "sculpture", "hood", "sconce", "vase", "traffic light", "tray", "ashcan", "fan", "pier", "crt screen", "plate", "monitor", "bulletin board", "shower", "radiator", "glass", "clock", "flag"], "PALETTE": [[120, 120, 120], [180, 120, 120], [6, 230, 230], [80, 50, 50], [4, 200, 3], [120, 120, 80], [140, 140, 140], [204, 5, 255], [230, 230, 230], [4, 250, 7], [224, 5, 255], [235, 255, 7], [150, 5, 61], [120, 120, 70], [8, 255, 51], [255, 6, 82], [143, 255, 140], [204, 255, 4], [255, 51, 7], [204, 70, 3], 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V11.3.109\nGCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0\nPyTorch: 2.0.1+cu117\nPyTorch compiling details: PyTorch built with:\n - GCC 9.3\n - C++ Version: 201703\n - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications\n - Intel(R) MKL-DNN v2.7.3 (Git Hash 6dbeffbae1f23cbbeae17adb7b5b13f1f37c080e)\n - OpenMP 201511 (a.k.a. OpenMP 4.5)\n - LAPACK is enabled (usually provided by MKL)\n - NNPACK is enabled\n - CPU capability usage: AVX2\n - CUDA Runtime 11.7\n - 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\n - CuDNN 8.9.2 (built against CUDA 12.1)\n - Built with CuDNN 8.5\n - Magma 2.6.1\n - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.7, CUDNN_VERSION=8.5.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wunused-local-typedefs -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -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 -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_DISABLE_GPU_ASSERTS=ON, TORCH_VERSION=2.0.1, 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=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, \n\nTorchVision: 0.15.2+cu117\nOpenCV: 4.8.0\nMMCV: 1.7.1\nMMCV Compiler: GCC 9.4\nMMCV CUDA Compiler: 11.3\nMMSegmentation: 0.30.0+c54117a", "seed": 0, "exp_name": "fpn_repvit_m6_ade20k_40k.py", "mmseg_version": "0.30.0+c54117a", "config": "norm_cfg = dict(type='SyncBN', requires_grad=True)\nmodel = dict(\n type='EncoderDecoder',\n pretrained='open-mmlab://resnet50_v1c',\n backbone=dict(\n type='repvit_m6',\n depth=50,\n num_stages=4,\n out_indices=[7, 15, 51, 54],\n dilations=(1, 1, 1, 1),\n strides=(1, 2, 2, 2),\n norm_cfg=dict(type='SyncBN', requires_grad=True),\n norm_eval=False,\n style='pytorch',\n contract_dilation=True,\n init_cfg=dict(\n type='Pretrained',\n checkpoint='pretrain/repvit_m6_distill_450e.pth'),\n pretrained='open-mmlab://resnet50_v1c'),\n neck=dict(\n type='FPN',\n in_channels=[80, 160, 320, 640],\n out_channels=256,\n num_outs=4),\n decode_head=dict(\n type='FPNHead',\n in_channels=[256, 256, 256, 256],\n in_index=[0, 1, 2, 3],\n feature_strides=[4, 8, 16, 32],\n channels=128,\n dropout_ratio=0.1,\n num_classes=150,\n norm_cfg=dict(type='SyncBN', requires_grad=True),\n align_corners=False,\n loss_decode=dict(\n type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),\n train_cfg=dict(),\n test_cfg=dict(mode='whole'))\ndataset_type = 'ADE20KDataset'\ndata_root = 'data/ade/ADEChallengeData2016'\nimg_norm_cfg = dict(\n mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)\ncrop_size = (512, 512)\ntrain_pipeline = [\n dict(type='LoadImageFromFile'),\n dict(type='LoadAnnotations', reduce_zero_label=True),\n dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)),\n dict(type='RandomCrop', crop_size=(512, 512), cat_max_ratio=0.75),\n dict(type='RandomFlip', prob=0.5),\n dict(type='PhotoMetricDistortion'),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='Pad', size=(512, 512), pad_val=0, seg_pad_val=255),\n dict(type='DefaultFormatBundle'),\n dict(type='Collect', keys=['img', 'gt_semantic_seg'])\n]\ntest_pipeline = [\n dict(type='LoadImageFromFile'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(2048, 512),\n flip=False,\n transforms=[\n dict(type='AlignResize', keep_ratio=True, size_divisor=32),\n dict(type='RandomFlip'),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='ImageToTensor', keys=['img']),\n dict(type='Collect', keys=['img'])\n ])\n]\ndata = dict(\n samples_per_gpu=4,\n workers_per_gpu=4,\n train=dict(\n type='RepeatDataset',\n times=50,\n dataset=dict(\n type='ADE20KDataset',\n data_root='data/ade/ADEChallengeData2016',\n img_dir='images/training',\n ann_dir='annotations/training',\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(type='LoadAnnotations', reduce_zero_label=True),\n dict(\n type='Resize',\n img_scale=(2048, 512),\n ratio_range=(0.5, 2.0)),\n dict(\n type='RandomCrop',\n crop_size=(512, 512),\n cat_max_ratio=0.75),\n dict(type='RandomFlip', prob=0.5),\n dict(type='PhotoMetricDistortion'),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='Pad', size=(512, 512), pad_val=0, seg_pad_val=255),\n dict(type='DefaultFormatBundle'),\n dict(type='Collect', keys=['img', 'gt_semantic_seg'])\n ])),\n val=dict(\n type='ADE20KDataset',\n data_root='data/ade/ADEChallengeData2016',\n img_dir='images/validation',\n ann_dir='annotations/validation',\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(2048, 512),\n flip=False,\n transforms=[\n dict(type='AlignResize', keep_ratio=True, size_divisor=32),\n dict(type='RandomFlip'),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='ImageToTensor', keys=['img']),\n dict(type='Collect', keys=['img'])\n ])\n ]),\n test=dict(\n type='ADE20KDataset',\n data_root='data/ade/ADEChallengeData2016',\n img_dir='images/validation',\n ann_dir='annotations/validation',\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(2048, 512),\n flip=False,\n transforms=[\n dict(type='AlignResize', keep_ratio=True, size_divisor=32),\n dict(type='RandomFlip'),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='ImageToTensor', keys=['img']),\n dict(type='Collect', keys=['img'])\n ])\n ]))\nlog_config = dict(\n interval=50, hooks=[dict(type='TextLoggerHook', by_epoch=False)])\ndist_params = dict(backend='nccl')\nlog_level = 'INFO'\nload_from = None\nresume_from = None\nworkflow = [('train', 1)]\ncudnn_benchmark = True\ngpu_multiples = 2\noptimizer = dict(type='AdamW', lr=0.0002, weight_decay=0.0001)\noptimizer_config = dict()\nlr_config = dict(policy='poly', power=0.9, min_lr=1e-06, by_epoch=False)\nrunner = dict(type='IterBasedRunner', max_iters=40000)\ncheckpoint_config = dict(by_epoch=False, interval=4000)\nevaluation = dict(interval=4000, metric='mIoU')\nwork_dir = './work_dirs/fpn_repvit_m6_ade20k_40k'\ngpu_ids = range(0, 8)\ndevice = 'cuda'\nseed = 0\n", "CLASSES": ["wall", "building", "sky", "floor", "tree", "ceiling", "road", "bed ", "windowpane", "grass", "cabinet", "sidewalk", "person", "earth", "door", "table", "mountain", "plant", "curtain", "chair", "car", "water", "painting", "sofa", "shelf", "house", "sea", "mirror", "rug", "field", "armchair", "seat", "fence", "desk", "rock", "wardrobe", "lamp", "bathtub", "railing", "cushion", "base", "box", "column", "signboard", "chest of drawers", "counter", "sand", "sink", "skyscraper", "fireplace", "refrigerator", "grandstand", "path", "stairs", "runway", "case", "pool table", "pillow", "screen door", "stairway", "river", "bridge", "bookcase", "blind", "coffee table", "toilet", "flower", "book", "hill", "bench", "countertop", "stove", "palm", "kitchen island", "computer", "swivel chair", "boat", "bar", "arcade machine", "hovel", "bus", "towel", "light", "truck", "tower", "chandelier", "awning", "streetlight", "booth", "television receiver", "airplane", "dirt track", "apparel", "pole", "land", "bannister", "escalator", "ottoman", "bottle", "buffet", "poster", "stage", "van", "ship", "fountain", "conveyer belt", "canopy", "washer", "plaything", "swimming pool", "stool", "barrel", "basket", "waterfall", "tent", "bag", "minibike", "cradle", "oven", "ball", "food", "step", "tank", "trade name", "microwave", "pot", "animal", "bicycle", "lake", "dishwasher", "screen", "blanket", "sculpture", "hood", "sconce", "vase", "traffic light", "tray", "ashcan", "fan", "pier", "crt screen", "plate", "monitor", "bulletin board", "shower", "radiator", "glass", "clock", "flag"], "PALETTE": [[120, 120, 120], [180, 120, 120], [6, 230, 230], [80, 50, 50], [4, 200, 3], [120, 120, 80], [140, 140, 140], [204, 5, 255], [230, 230, 230], [4, 250, 7], [224, 5, 255], [235, 255, 7], [150, 5, 61], [120, 120, 70], [8, 255, 51], [255, 6, 82], [143, 255, 140], [204, 255, 4], 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-1,811 +0,0 @@ -{"env_info": "sys.platform: linux\nPython: 3.8.16 (default, Mar 2 2023, 03:21:46) [GCC 11.2.0]\nCUDA available: True\nGPU 0,1,2,3,4,5,6,7: NVIDIA GeForce RTX 3090\nCUDA_HOME: /usr/local/cuda\nNVCC: Cuda compilation tools, release 11.3, V11.3.109\nGCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0\nPyTorch: 2.0.1+cu117\nPyTorch compiling details: PyTorch built with:\n - GCC 9.3\n - C++ Version: 201703\n - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications\n - Intel(R) MKL-DNN v2.7.3 (Git Hash 6dbeffbae1f23cbbeae17adb7b5b13f1f37c080e)\n - OpenMP 201511 (a.k.a. OpenMP 4.5)\n - LAPACK is enabled (usually provided by MKL)\n - NNPACK is enabled\n - CPU capability usage: AVX2\n - CUDA Runtime 11.7\n - 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\n - CuDNN 8.5\n - Magma 2.6.1\n - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.7, CUDNN_VERSION=8.5.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wunused-local-typedefs -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -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 -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_DISABLE_GPU_ASSERTS=ON, TORCH_VERSION=2.0.1, 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=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, \n\nTorchVision: 0.15.2+cu117\nOpenCV: 4.8.0\nMMCV: 1.7.1\nMMCV Compiler: GCC 9.4\nMMCV CUDA Compiler: 11.3\nMMSegmentation: 0.30.0+6943757", "seed": null, "exp_name": "fpn_repvit_m3_ade20k_40k.py", "mmseg_version": "0.30.0+6943757", "config": "norm_cfg = dict(type='SyncBN', requires_grad=True)\nmodel = dict(\n type='EncoderDecoder',\n pretrained='open-mmlab://resnet50_v1c',\n backbone=dict(\n type='repvit_m3',\n depth=50,\n num_stages=4,\n out_indices=[5, 11, 31, 34],\n dilations=(1, 1, 1, 1),\n strides=(1, 2, 2, 2),\n norm_cfg=dict(type='SyncBN', requires_grad=True),\n norm_eval=False,\n style='pytorch',\n contract_dilation=True,\n init_cfg=dict(\n type='Pretrained',\n checkpoint='pretrain/repvit_m3_distill_300.pth'),\n pretrained='open-mmlab://resnet50_v1c'),\n neck=dict(\n type='FPN',\n in_channels=[64, 128, 256, 512],\n out_channels=256,\n num_outs=4),\n decode_head=dict(\n type='FPNHead',\n in_channels=[256, 256, 256, 256],\n in_index=[0, 1, 2, 3],\n feature_strides=[4, 8, 16, 32],\n channels=128,\n dropout_ratio=0.1,\n num_classes=150,\n norm_cfg=dict(type='SyncBN', requires_grad=True),\n align_corners=False,\n loss_decode=dict(\n type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),\n train_cfg=dict(),\n test_cfg=dict(mode='whole'))\ndataset_type = 'ADE20KDataset'\ndata_root = 'data/ade/ADEChallengeData2016'\nimg_norm_cfg = dict(\n mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)\ncrop_size = (512, 512)\ntrain_pipeline = [\n dict(type='LoadImageFromFile'),\n dict(type='LoadAnnotations', reduce_zero_label=True),\n dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)),\n dict(type='RandomCrop', crop_size=(512, 512), cat_max_ratio=0.75),\n dict(type='RandomFlip', prob=0.5),\n dict(type='PhotoMetricDistortion'),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='Pad', size=(512, 512), pad_val=0, seg_pad_val=255),\n dict(type='DefaultFormatBundle'),\n dict(type='Collect', keys=['img', 'gt_semantic_seg'])\n]\ntest_pipeline = [\n dict(type='LoadImageFromFile'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(2048, 512),\n flip=False,\n transforms=[\n dict(type='AlignResize', keep_ratio=True, size_divisor=32),\n dict(type='RandomFlip'),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='ImageToTensor', keys=['img']),\n dict(type='Collect', keys=['img'])\n ])\n]\ndata = dict(\n samples_per_gpu=4,\n workers_per_gpu=4,\n train=dict(\n type='RepeatDataset',\n times=50,\n dataset=dict(\n type='ADE20KDataset',\n data_root='data/ade/ADEChallengeData2016',\n img_dir='images/training',\n ann_dir='annotations/training',\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(type='LoadAnnotations', reduce_zero_label=True),\n dict(\n type='Resize',\n img_scale=(2048, 512),\n ratio_range=(0.5, 2.0)),\n dict(\n type='RandomCrop',\n crop_size=(512, 512),\n cat_max_ratio=0.75),\n dict(type='RandomFlip', prob=0.5),\n dict(type='PhotoMetricDistortion'),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='Pad', size=(512, 512), pad_val=0, seg_pad_val=255),\n dict(type='DefaultFormatBundle'),\n dict(type='Collect', keys=['img', 'gt_semantic_seg'])\n ])),\n val=dict(\n type='ADE20KDataset',\n data_root='data/ade/ADEChallengeData2016',\n img_dir='images/validation',\n ann_dir='annotations/validation',\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(2048, 512),\n flip=False,\n transforms=[\n dict(type='AlignResize', keep_ratio=True, size_divisor=32),\n dict(type='RandomFlip'),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='ImageToTensor', keys=['img']),\n dict(type='Collect', keys=['img'])\n ])\n ]),\n test=dict(\n type='ADE20KDataset',\n data_root='data/ade/ADEChallengeData2016',\n img_dir='images/validation',\n ann_dir='annotations/validation',\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(2048, 512),\n flip=False,\n transforms=[\n dict(type='AlignResize', keep_ratio=True, size_divisor=32),\n dict(type='RandomFlip'),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='ImageToTensor', keys=['img']),\n dict(type='Collect', keys=['img'])\n ])\n ]))\nlog_config = dict(\n interval=50, hooks=[dict(type='TextLoggerHook', by_epoch=False)])\ndist_params = dict(backend='nccl')\nlog_level = 'INFO'\nload_from = None\nresume_from = None\nworkflow = [('train', 1)]\ncudnn_benchmark = True\ngpu_multiples = 2\noptimizer = dict(type='AdamW', lr=0.0002, weight_decay=0.0001)\noptimizer_config = dict()\nlr_config = dict(policy='poly', power=0.9, min_lr=1e-06, by_epoch=False)\nrunner = dict(type='IterBasedRunner', max_iters=40000)\ncheckpoint_config = dict(by_epoch=False, interval=4000)\nevaluation = dict(interval=4000, metric='mIoU')\nwork_dir = './work_dirs/fpn_repvit_m3_ade20k_40k'\ngpu_ids = range(0, 8)\ndevice = 'cuda'\nseed = None\n", "CLASSES": ["wall", "building", "sky", "floor", "tree", "ceiling", "road", "bed ", "windowpane", "grass", "cabinet", "sidewalk", "person", "earth", "door", "table", "mountain", "plant", "curtain", "chair", "car", "water", "painting", "sofa", "shelf", "house", "sea", "mirror", "rug", "field", "armchair", "seat", "fence", "desk", "rock", "wardrobe", "lamp", "bathtub", "railing", "cushion", "base", "box", "column", "signboard", "chest of drawers", "counter", "sand", "sink", "skyscraper", "fireplace", "refrigerator", "grandstand", "path", "stairs", "runway", "case", "pool table", "pillow", "screen door", "stairway", "river", "bridge", "bookcase", "blind", "coffee table", "toilet", "flower", "book", "hill", "bench", "countertop", "stove", "palm", "kitchen island", "computer", "swivel chair", "boat", "bar", "arcade machine", "hovel", "bus", "towel", "light", "truck", "tower", "chandelier", 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"Acc.bus": 0.9255, "Acc.towel": 0.6515, "Acc.light": 0.4574, "Acc.truck": 0.2284, "Acc.tower": 0.3816, "Acc.chandelier": 0.7842, "Acc.awning": 0.2033, "Acc.streetlight": 0.2772, "Acc.booth": 0.4857, "Acc.television receiver": 0.7185, "Acc.airplane": 0.6255, "Acc.dirt track": 0.1607, "Acc.apparel": 0.5297, "Acc.pole": 0.2221, "Acc.land": 0.0411, "Acc.bannister": 0.0815, "Acc.escalator": 0.3968, "Acc.ottoman": 0.4714, "Acc.bottle": 0.2357, "Acc.buffet": 0.4313, "Acc.poster": 0.321, "Acc.stage": 0.084, "Acc.van": 0.459, "Acc.ship": 0.8469, "Acc.fountain": 0.2095, "Acc.conveyer belt": 0.8094, "Acc.canopy": 0.249, "Acc.washer": 0.6988, "Acc.plaything": 0.3086, "Acc.swimming pool": 0.7114, "Acc.stool": 0.4211, "Acc.barrel": 0.6301, "Acc.basket": 0.3019, "Acc.waterfall": 0.5291, "Acc.tent": 0.9741, "Acc.bag": 0.12, "Acc.minibike": 0.7287, "Acc.cradle": 0.8336, "Acc.oven": 0.5694, "Acc.ball": 0.5607, "Acc.food": 0.58, "Acc.step": 0.0369, "Acc.tank": 0.3842, "Acc.trade name": 0.242, "Acc.microwave": 0.4325, "Acc.pot": 0.3834, "Acc.animal": 0.6469, "Acc.bicycle": 0.6915, "Acc.lake": 0.5782, "Acc.dishwasher": 0.6538, "Acc.screen": 0.8382, "Acc.blanket": 0.0666, "Acc.sculpture": 0.3828, "Acc.hood": 0.5497, "Acc.sconce": 0.3468, "Acc.vase": 0.4135, "Acc.traffic light": 0.3429, "Acc.tray": 0.0314, "Acc.ashcan": 0.4502, "Acc.fan": 0.649, "Acc.pier": 0.4394, "Acc.crt screen": 0.2096, "Acc.plate": 0.4849, "Acc.monitor": 0.2936, "Acc.bulletin board": 0.4203, "Acc.shower": 0.0, "Acc.radiator": 0.5789, "Acc.glass": 0.1207, "Acc.clock": 0.2438, "Acc.flag": 0.3067} diff --git a/segmentation/repvit.py b/segmentation/repvit.py index a4e03c4..b912b02 100644 --- a/segmentation/repvit.py +++ b/segmentation/repvit.py @@ -8,9 +8,6 @@ from torch.nn.modules.batchnorm import _BatchNorm -__all__ = ['repvit_m1'] - - def _make_divisible(v, divisor, min_value=None): """ This function is taken from the original tf repo. @@ -95,16 +92,17 @@ class RepVGGDW(torch.nn.Module): def __init__(self, ed) -> None: super().__init__() self.conv = Conv2d_BN(ed, ed, 3, 1, 1, groups=ed) - self.conv1 = Conv2d_BN(ed, ed, 1, 1, 0, groups=ed) + self.conv1 = torch.nn.Conv2d(ed, ed, 1, 1, 0, groups=ed) self.dim = ed + self.bn = torch.nn.BatchNorm2d(ed) def forward(self, x): - return self.conv(x) + self.conv1(x) + x + return self.bn((self.conv(x) + self.conv1(x)) + x) @torch.no_grad() def fuse(self): conv = self.conv.fuse() - conv1 = self.conv1.fuse() + conv1 = self.conv1 conv_w = conv.weight conv_b = conv.bias @@ -120,6 +118,14 @@ def fuse(self): conv.weight.data.copy_(final_conv_w) conv.bias.data.copy_(final_conv_b) + + bn = self.bn + w = bn.weight / (bn.running_var + bn.eps)**0.5 + w = conv.weight * w[:, None, None, None] + b = bn.bias + (conv.bias - bn.running_mean) * bn.weight / \ + (bn.running_var + bn.eps)**0.5 + conv.weight.data.copy_(w) + conv.bias.data.copy_(b) return conv @@ -266,44 +272,9 @@ def forward(self, x): from timm.models import register_model -@BACKBONES.register_module() -def repvit_m1(pretrained=False, num_classes = 1000, distillation=False, init_cfg=None, out_indices=[], **kwargs): - """ - Constructs a MobileNetV3-Large model - """ - cfgs = [ - # k, t, c, SE, HS, s - [3, 2, 48, 1, 0, 1], - [3, 2, 48, 0, 0, 1], - [3, 2, 48, 0, 0, 1], - [3, 2, 96, 0, 0, 2], - [3, 2, 96, 1, 0, 1], - [3, 2, 96, 0, 0, 1], - [3, 2, 96, 0, 0, 1], - [3, 2, 192, 0, 1, 2], - [3, 2, 192, 1, 1, 1], - [3, 2, 192, 0, 1, 1], - [3, 2, 192, 1, 1, 1], - [3, 2, 192, 0, 1, 1], - [3, 2, 192, 1, 1, 1], - [3, 2, 192, 0, 1, 1], - [3, 2, 192, 1, 1, 1], - [3, 2, 192, 0, 1, 1], - [3, 2, 192, 1, 1, 1], - [3, 2, 192, 0, 1, 1], - [3, 2, 192, 1, 1, 1], - [3, 2, 192, 0, 1, 1], - [3, 2, 192, 1, 1, 1], - [3, 2, 192, 0, 1, 1], - [3, 2, 192, 0, 1, 1], - [3, 2, 384, 0, 1, 2], - [3, 2, 384, 1, 1, 1], - [3, 2, 384, 0, 1, 1] - ] - return RepViT(cfgs, init_cfg=init_cfg, pretrained=pretrained, distillation=distillation, out_indices=out_indices) @BACKBONES.register_module() -def repvit_m2(pretrained=False, num_classes = 1000, distillation=False, init_cfg=None, out_indices=[], **kwargs): +def repvit_m1_1(pretrained=False, num_classes = 1000, distillation=False, init_cfg=None, out_indices=[], **kwargs): """ Constructs a MobileNetV3-Large model """ @@ -337,7 +308,7 @@ def repvit_m2(pretrained=False, num_classes = 1000, distillation=False, init_cfg return RepViT(cfgs, init_cfg=init_cfg, pretrained=pretrained, distillation=distillation, out_indices=out_indices) @BACKBONES.register_module() -def repvit_m3(pretrained=False, num_classes = 1000, distillation=False, init_cfg=None, out_indices=[], **kwargs): +def repvit_m1_5(pretrained=False, num_classes = 1000, distillation=False, init_cfg=None, out_indices=[], **kwargs): """ Constructs a MobileNetV3-Large model """ @@ -373,9 +344,86 @@ def repvit_m3(pretrained=False, num_classes = 1000, distillation=False, init_cfg [3, 2, 256, 0, 1, 1], [3, 2, 256, 1, 1, 1], [3, 2, 256, 0, 1, 1], + [3, 2, 256, 1, 1, 1], + [3, 2, 256, 0, 1, 1], + [3, 2, 256, 1, 1, 1], + [3, 2, 256, 0, 1, 1], + [3, 2, 256, 1, 1, 1], + [3, 2, 256, 0, 1, 1], [3, 2, 256, 0, 1, 1], [3, 2, 512, 0, 1, 2], [3, 2, 512, 1, 1, 1], + [3, 2, 512, 0, 1, 1], + [3, 2, 512, 1, 1, 1], [3, 2, 512, 0, 1, 1] ] return RepViT(cfgs, init_cfg=init_cfg, pretrained=pretrained, distillation=distillation, out_indices=out_indices) + + +@BACKBONES.register_module() +def repvit_m2_3(pretrained=False, num_classes = 1000, distillation=False, init_cfg=None, out_indices=[], **kwargs): + """ + Constructs a MobileNetV3-Large model + """ + cfgs = [ + # k, t, c, SE, HS, s + [3, 2, 80, 1, 0, 1], + [3, 2, 80, 0, 0, 1], + [3, 2, 80, 1, 0, 1], + [3, 2, 80, 0, 0, 1], + [3, 2, 80, 1, 0, 1], + [3, 2, 80, 0, 0, 1], + [3, 2, 80, 0, 0, 1], + [3, 2, 160, 0, 0, 2], + [3, 2, 160, 1, 0, 1], + [3, 2, 160, 0, 0, 1], + [3, 2, 160, 1, 0, 1], + [3, 2, 160, 0, 0, 1], + [3, 2, 160, 1, 0, 1], + [3, 2, 160, 0, 0, 1], + [3, 2, 160, 0, 0, 1], + [3, 2, 320, 0, 1, 2], + [3, 2, 320, 1, 1, 1], + [3, 2, 320, 0, 1, 1], + [3, 2, 320, 1, 1, 1], + [3, 2, 320, 0, 1, 1], + [3, 2, 320, 1, 1, 1], + [3, 2, 320, 0, 1, 1], + [3, 2, 320, 1, 1, 1], + [3, 2, 320, 0, 1, 1], + [3, 2, 320, 1, 1, 1], + [3, 2, 320, 0, 1, 1], + [3, 2, 320, 1, 1, 1], + [3, 2, 320, 0, 1, 1], + [3, 2, 320, 1, 1, 1], + [3, 2, 320, 0, 1, 1], + [3, 2, 320, 1, 1, 1], + [3, 2, 320, 0, 1, 1], + [3, 2, 320, 1, 1, 1], + [3, 2, 320, 0, 1, 1], + [3, 2, 320, 1, 1, 1], + [3, 2, 320, 0, 1, 1], + [3, 2, 320, 1, 1, 1], + [3, 2, 320, 0, 1, 1], + [3, 2, 320, 1, 1, 1], + [3, 2, 320, 0, 1, 1], + [3, 2, 320, 1, 1, 1], + [3, 2, 320, 0, 1, 1], + [3, 2, 320, 1, 1, 1], + [3, 2, 320, 0, 1, 1], + [3, 2, 320, 1, 1, 1], + [3, 2, 320, 0, 1, 1], + [3, 2, 320, 1, 1, 1], + [3, 2, 320, 0, 1, 1], + [3, 2, 320, 1, 1, 1], + [3, 2, 320, 0, 1, 1], + # [3, 2, 320, 1, 1, 1], + # [3, 2, 320, 0, 1, 1], + [3, 2, 320, 0, 1, 1], + [3, 2, 640, 0, 1, 2], + [3, 2, 640, 1, 1, 1], + [3, 2, 640, 0, 1, 1], + # [3, 2, 640, 1, 1, 1], + # [3, 2, 640, 0, 1, 1] + ] + return RepViT(cfgs, init_cfg=init_cfg, pretrained=pretrained, distillation=distillation, out_indices=out_indices) \ No newline at end of file diff --git a/segmentation/train.sh b/segmentation/train.sh index 67990ce..11c706f 100644 --- a/segmentation/train.sh +++ b/segmentation/train.sh @@ -1 +1 @@ -./tools/dist_train.sh configs/sem_fpn/fpn_repvit_m2_ade20k_40k.py 8 +./tools/dist_train.sh configs/sem_fpn/fpn_repvit_m1_1_ade20k_40k.py 8 diff --git a/speed_gpu.py b/speed_gpu.py index af4dda2..740c42d 100644 --- a/speed_gpu.py +++ b/speed_gpu.py @@ -32,7 +32,7 @@ def throughput(name, model, device, batch_size, resolution=224): parser = ArgumentParser() -parser.add_argument('--model', default='repvit_m1', type=str) +parser.add_argument('--model', default='repvit_m0_9', type=str) parser.add_argument('--resolution', default=224, type=int) parser.add_argument('--batch-size', default=2048, type=int) diff --git a/train.sh b/train.sh index 02d35c4..aef1b10 100644 --- a/train.sh +++ b/train.sh @@ -1 +1 @@ -NCCL_P2P_DISABLE=1 python -m torch.distributed.launch --nproc_per_node=8 --master_port 12346 --use_env main.py --model repvit_m1 --data-path ~/imagenet --dist-eval +NCCL_P2P_DISABLE=1 python -m torch.distributed.launch --nproc_per_node=8 --master_port 12346 --use_env main.py --model repvit_m0_9 --data-path ~/imagenet --dist-eval