A ConvNet for the 2020s

Official PyTorch implementation of ConvNeXt, from the following paper:

A ConvNet for the 2020s. arXiv 2022.
Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell and Saining Xie
Facebook AI Research, UC Berkeley

We propose ConvNeXt, a pure ConvNet model constructed entirely from standard ConvNet modules. ConvNeXt is accurate, efficient, scalable and very simple in design.

Catalog

ImageNet-1K Training Code
ImageNet-22K Pre-training Code
ImageNet-1K Fine-tuning Code
Downstream Transfer (Detection, Segmentation) Code

Results and Pre-trained Models

ImageNet-1K trained models

name	resolution	acc@1	#params	FLOPs	model
ConvNeXt-T	224x224	82.1	28M	4.5G	model
ConvNeXt-S	224x224	83.1	50M	8.7G	model
ConvNeXt-B	224x224	83.8	89M	15.4G	model
ConvNeXt-B	384x384	85.1	89M	45.0G	model
ConvNeXt-L	224x224	84.3	198M	34.4G	model
ConvNeXt-L	384x384	85.5	198M	101.0G	model

ImageNet-22K trained models

name	resolution	acc@1	#params	FLOPs	22k model	1k model
ConvNeXt-B	224x224	85.8	89M	15.4G	model	model
ConvNeXt-B	384x384	86.8	89M	47.0G	-	model
ConvNeXt-L	224x224	86.6	198M	34.4G	model	model
ConvNeXt-L	384x384	87.5	198M	101.0G	-	model
ConvNeXt-XL	224x224	87.0	350M	60.9G	model	model
ConvNeXt-XL	384x384	87.8	350M	179.0G	-	model

ImageNet-1K trained models (isotropic)

name	resolution	acc@1	#params	FLOPs	model
ConvNeXt-S	224x224	78.7	22M	4.3G	model
ConvNeXt-B	224x224	82.0	87M	16.9G	model
ConvNeXt-L	224x224	82.6	306M	59.7G	model

Installation

Please check INSTALL.md for installation instructions.

Evaluation

We give an example evaluation command for a ImageNet-22K pre-trained, then ImageNet-1K fine-tuned ConvNeXt-B:

Single-GPU

python main.py --model convnext_base --eval true \
--resume https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth \
--input_size 224 --drop_path 0.2 \
--data_path /path/to/imagenet-1k

Multi-GPU

python -m torch.distributed.launch --nproc_per_node=8 main.py \
--model convnext_base --eval true \
--resume https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth \
--input_size 224 --drop_path 0.2 \
--data_path /path/to/imagenet-1k

This should give

* Acc@1 85.820 Acc@5 97.868 loss 0.563

For evaluating other model variants, change --model, --resume, --input_size accordingly. You can get the url to pre-trained models from the tables above.
Setting model-specific --drop_path is not strictly required in evaluation, as the DropPath module in timm behaves the same during evaluation; but it is required in training. See TRAINING.md or our paper for the values used for different models.

Training

See TRAINING.md for training and fine-tuning instructions.

Acknowledgement

This repository is built using the timm library, DeiT and BEiT repositories.

License

This project is released under the MIT license. Please see the LICENSE file for more information.

Citation

If you find this repository helpful, please consider citing:

@Article{liu2021convnet,
  author  = {Zhuang Liu and Hanzi Mao and Chao-Yuan Wu and Christoph Feichtenhofer and Trevor Darrell and Saining Xie},
  title   = {A ConvNet for the 2020s},
  journal = {arXiv preprint arXiv:2201.03545},
  year    = {2022},
}

Hi,

I am trying to fine-tune convnext large model on the dataset in coco format, and after solving lot of error i am able to run training by using utils/train.py .

But i get mmdet - ERROR - The testing results of the whole dataset is empty. when i print out the output, it gives an empty array.

Since the dataset is strictly detection based , i commented out mask details from the config which now looks like this 👍



_base_ = [
    '../_base_/models/cascade_mask_rcnn_convnext_fpn.py',
    ../_base_/datasets/custom_dataset_detection.py',
    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]

model = dict(
    backbone=dict(
        in_chans=3,
        depths=[3, 3, 27, 3], 
        dims=[192, 384, 768, 1536], 
        drop_path_rate=0.7,
        layer_scale_init_value=1.0,
        out_indices=[0, 1, 2, 3],
    ),
    neck=dict(in_channels=[192, 384, 768, 1536]),
    roi_head=dict(
        bbox_head=[
            dict(
                type='ConvFCBBoxHead',
                num_shared_convs=4,
                num_shared_fcs=1,
                in_channels=256,
                conv_out_channels=256,
                fc_out_channels=1024,
                roi_feat_size=7,
                num_classes=97,
                bbox_coder=dict(
                    type='DeltaXYWHBBoxCoder',
                    target_means=[0., 0., 0., 0.],
                    target_stds=[0.1, 0.1, 0.2, 0.2]),
                reg_class_agnostic=False,
                reg_decoded_bbox=True,
                norm_cfg=dict(type='SyncBN', requires_grad=True),
                loss_cls=dict(
                    type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
                loss_bbox=dict(type='GIoULoss', loss_weight=10.0)),
            dict(
                type='ConvFCBBoxHead',
                num_shared_convs=4,
                num_shared_fcs=1,
                in_channels=256,
                conv_out_channels=256,
                fc_out_channels=1024,
                roi_feat_size=7,
                num_classes=97,
                bbox_coder=dict(
                    type='DeltaXYWHBBoxCoder',
                    target_means=[0., 0., 0., 0.],
                    target_stds=[0.05, 0.05, 0.1, 0.1]),
                reg_class_agnostic=False,
                reg_decoded_bbox=True,
                norm_cfg=dict(type='SyncBN', requires_grad=True),
                loss_cls=dict(
                    type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
                loss_bbox=dict(type='GIoULoss', loss_weight=10.0)),
            dict(
                type='ConvFCBBoxHead',
                num_shared_convs=4,
                num_shared_fcs=1,
                in_channels=256,
                conv_out_channels=256,
                fc_out_channels=1024,
                roi_feat_size=7,
                num_classes=97,
                bbox_coder=dict(
                    type='DeltaXYWHBBoxCoder',
                    target_means=[0., 0., 0., 0.],
                    target_stds=[0.033, 0.033, 0.067, 0.067]),
                reg_class_agnostic=False,
                reg_decoded_bbox=True,
                norm_cfg=dict(type='SyncBN', requires_grad=True),
                loss_cls=dict(
                    type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
                loss_bbox=dict(type='GIoULoss', loss_weight=10.0))
        ]))

img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)

# augmentation strategy originates from DETR / Sparse RCNN
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
    dict(type='RandomFlip', flip_ratio=0.5),
    dict(type='AutoAugment',
         policies=[
             [
                 dict(type='Resize',
                      img_scale=[(480, 1333), (512, 1333), (544, 1333), (576, 1333),
                                 (608, 1333), (640, 1333), (672, 1333), (704, 1333),
                                 (736, 1333), (768, 1333), (800, 1333)],
                      multiscale_mode='value',
                      keep_ratio=True)
             ],
             [
                 dict(type='Resize',
                      img_scale=[(400, 1333), (500, 1333), (600, 1333)],
                      multiscale_mode='value',
                      keep_ratio=True),
                 dict(type='RandomCrop',
                      crop_type='absolute_range',
                      crop_size=(384, 600),
                      allow_negative_crop=True),
                 dict(type='Resize',
                      img_scale=[(480, 1333), (512, 1333), (544, 1333),
                                 (576, 1333), (608, 1333), (640, 1333),
                                 (672, 1333), (704, 1333), (736, 1333),
                                 (768, 1333), (800, 1333)],
                      multiscale_mode='value',
                      override=True,
                      keep_ratio=True)
             ]
         ]),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='Pad', size_divisor=32),
    dict(type='DefaultFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]
data = dict(train=dict(pipeline=train_pipeline))

optimizer = dict(constructor='LearningRateDecayOptimizerConstructor', _delete_=True, type='AdamW', 
                 lr=0.0000002, betas=(0.9, 0.999), weight_decay=0.05,
                 paramwise_cfg={'decay_rate': 0.7,
                                'decay_type': 'layer_wise',
                                'num_layers': 12})
lr_config = dict(step=[3, 9, 27, 33])
runner = dict(type='EpochBasedRunnerAmp', max_epochs=36)

# do not use mmdet version fp16
fp16 = None
optimizer_config = dict(
    type="DistOptimizerHook",
    update_interval=1,
    grad_clip=None,
    coalesce=True,
    bucket_size_mb=-1,
    use_fp16=False,
)

And the corresponsing base/models/cascade_mask_rcnn_convnext_fpn.py config looks like :

# Copyright (c) Meta Platforms, Inc. and affiliates.

# All rights reserved.

# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.


# model settings
model = dict(
    type='CascadeRCNN',
    pretrained='checkpoint/cascade_mask_rcnn_convnext_large_22k_3x.pth',
    backbone=dict(
        type='ConvNeXt',
        in_chans=3,
        depths=[3, 3, 9, 3], 
        dims=[96, 192, 384, 768], 
        drop_path_rate=0.2,
        layer_scale_init_value=1e-6,
        out_indices=[0, 1, 2, 3],
    ),
    neck=dict(
        type='FPN',
        in_channels=[128, 256, 512, 1024],
        out_channels=256,
        num_outs=5),
    rpn_head=dict(
        type='RPNHead',
        in_channels=256,
        feat_channels=256,
        anchor_generator=dict(
            type='AnchorGenerator',
            scales=[8],
            ratios=[0.5, 1.0, 2.0],
            strides=[4, 8, 16, 32, 64]),
        bbox_coder=dict(
            type='DeltaXYWHBBoxCoder',
            target_means=[.0, .0, .0, .0],
            target_stds=[1.0, 1.0, 1.0, 1.0]),
        loss_cls=dict(
            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
        loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)),
    roi_head=dict(
        type='CascadeRoIHead',
        num_stages=3,
        stage_loss_weights=[1, 0.5, 0.25],
        bbox_roi_extractor=dict(
            type='SingleRoIExtractor',
            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
            out_channels=256,
            featmap_strides=[4, 8, 16, 32]),
        bbox_head=[
            dict(
                type='Shared2FCBBoxHead',
                in_channels=256,
                fc_out_channels=1024,
                roi_feat_size=7,
                num_classes=97,
                bbox_coder=dict(
                    type='DeltaXYWHBBoxCoder',
                    target_means=[0., 0., 0., 0.],
                    target_stds=[0.1, 0.1, 0.2, 0.2]),
                reg_class_agnostic=True,
                loss_cls=dict(
                    type='CrossEntropyLoss',
                    use_sigmoid=False,
                    loss_weight=1.0),
                loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
                               loss_weight=1.0)),
            dict(
                type='Shared2FCBBoxHead',
                in_channels=256,
                fc_out_channels=1024,
                roi_feat_size=7,
                num_classes=97,
                bbox_coder=dict(
                    type='DeltaXYWHBBoxCoder',
                    target_means=[0., 0., 0., 0.],
                    target_stds=[0.05, 0.05, 0.1, 0.1]),
                reg_class_agnostic=True,
                loss_cls=dict(
                    type='CrossEntropyLoss',
                    use_sigmoid=False,
                    loss_weight=1.0),
                loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
                               loss_weight=1.0)),
            dict(
                type='Shared2FCBBoxHead',
                in_channels=256,
                fc_out_channels=1024,
                roi_feat_size=7,
                num_classes=97,
                bbox_coder=dict(
                    type='DeltaXYWHBBoxCoder',
                    target_means=[0., 0., 0., 0.],
                    target_stds=[0.033, 0.033, 0.067, 0.067]),
                reg_class_agnostic=True,
                loss_cls=dict(
                    type='CrossEntropyLoss',
                    use_sigmoid=False,
                    loss_weight=1.0),
                loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))
        ]),
    # model training and testing settings
    train_cfg = dict(
        rpn=dict(
            assigner=dict(
                type='MaxIoUAssigner',
                pos_iou_thr=0.7,
                neg_iou_thr=0.3,
                min_pos_iou=0.3,
                match_low_quality=True,
                ignore_iof_thr=-1),
            sampler=dict(
                type='RandomSampler',
                num=256,
                pos_fraction=0.5,
                neg_pos_ub=-1,
                add_gt_as_proposals=False),
            allowed_border=0,
            pos_weight=-1,
            debug=False),
        rpn_proposal=dict(
            nms_across_levels=False,
            nms_pre=2000,
            nms_post=2000,
            max_per_img=2000,
            nms=dict(type='nms', iou_threshold=0.7),
            min_bbox_size=0),
        rcnn=[
            dict(
                assigner=dict(
                    type='MaxIoUAssigner',
                    pos_iou_thr=0.5,
                    neg_iou_thr=0.5,
                    min_pos_iou=0.5,
                    match_low_quality=False,
                    ignore_iof_thr=-1),
                sampler=dict(
                    type='RandomSampler',
                    num=512,
                    pos_fraction=0.25,
                    neg_pos_ub=-1,
                    add_gt_as_proposals=True),
                #mask_size=28,
                pos_weight=-1,
                debug=False),
            dict(
                assigner=dict(
                    type='MaxIoUAssigner',
                    pos_iou_thr=0.6,
                    neg_iou_thr=0.6,
                    min_pos_iou=0.6,
                    match_low_quality=False,
                    ignore_iof_thr=-1),
                sampler=dict(
                    type='RandomSampler',
                    num=512,
                    pos_fraction=0.25,
                    neg_pos_ub=-1,
                    add_gt_as_proposals=True),
                #mask_size=28,
                pos_weight=-1,
                debug=False),
            dict(
                assigner=dict(
                    type='MaxIoUAssigner',
                    pos_iou_thr=0.7,
                    neg_iou_thr=0.7,
                    min_pos_iou=0.7,
                    match_low_quality=False,
                    ignore_iof_thr=-1),
                sampler=dict(
                    type='RandomSampler',
                    num=512,
                    pos_fraction=0.25,
                    neg_pos_ub=-1,
                    add_gt_as_proposals=True),
                #mask_size=28,
                pos_weight=-1,
                debug=False)
        ]),
    test_cfg = dict(
        rpn=dict(
            nms_across_levels=False,
            nms_pre=1000,
            nms_post=1000,
            max_per_img=1000,
            nms=dict(type='nms', iou_threshold=0.7),
            min_bbox_size=0),
        rcnn=dict(
            score_thr=0.05,
            nms=dict(type='nms', iou_threshold=0.5),
            max_per_img=100)))

Do i need to prune or update some layer's information for which i am getting below error :

unexpected key in source state_dict: backbone.downsample_layers.0.0.weight, backbone.downsample_layers.0.0.bias, backbone.downsample_layers.0.1.weight, backbone.downsample_layers.0.1.bias, backbone.downsample_layers.1.0.weight, backbone.downsample_layers.1.0.bias, backbone.downsample_layers.1.1.weight, backbone.downsample_layers.1.1.bias, backbone.downsample_layers.2.0.weight, backbone.downsample_layers.2.0.bias, backbone.downsample_layers.2.1.weight, backbone.downsample_layers.2.1.bias, backbone.downsample_layers.3.0.weight, backbone.downsample_layers.3.0.bias, backbone.downsample_layers.3.1.weight, backbone.downsample_layers.3.1.bias, backbone.stages.0.0.gamma, backbone.stages.0.0.dwconv.weight, backbone.stages.0.0.dwconv.bias, backbone.stages.0.0.norm.weight, backbone.stages.0.0.norm.bias, backbone.stages.0.0.pwconv1.weight, backbone.stages.0.0.pwconv1.bias, backbone.stages.0.0.pwconv2.weight, backbone.stages.0.0.pwconv2.bias, backbone.stages.0.1.gamma, backbone.stages.0.1.dwconv.weight, backbone.stages.0.1.dwconv.bias, backbone.stages.0.1.norm.weight, backbone.stages.0.1.norm.bias, backbone.stages.0.1.pwconv1.weight, backbone.stages.0.1.pwconv1.bias, backbone.stages.0.1.pwconv2.weight, backbone.stages.0.1.pwconv2.bias, backbone.stages.0.2.gamma, backbone.stages.0.2.dwconv.weight, backbone.stages.0.2.dwconv.bias, backbone.stages.0.2.norm.weight, backbone.stages.0.2.norm.bias, backbone.stages.0.2.pwconv1.weight, backbone.stages.0.2.pwconv1.bias, backbone.stages.0.2.pwconv2.weight, backbone.stages.0.2.pwconv2.bias, backbone.stages.1.0.gamma, backbone.stages.1.0.dwconv.weight, backbone.stages.1.0.dwconv.bias, backbone.stages.1.0.norm.weight, backbone.stages.1.0.norm.bias, backbone.stages.1.0.pwconv1.weight, backbone.stages.1.0.pwconv1.bias, backbone.stages.1.0.pwconv2.weight, backbone.stages.1.0.pwconv2.bias, backbone.stages.1.1.gamma, backbone.stages.1.1.dwconv.weight, backbone.stages.1.1.dwconv.bias, backbone.stages.1.1.norm.weight, backbone.stages.1.1.norm.bias, backbone.stages.1.1.pwconv1.weight, backbone.stages.1.1.pwconv1.bias, backbone.stages.1.1.pwconv2.weight, backbone.stages.1.1.pwconv2.bias, backbone.stages.1.2.gamma, backbone.stages.1.2.dwconv.weight, backbone.stages.1.2.dwconv.bias, backbone.stages.1.2.norm.weight, backbone.stages.1.2.norm.bias, backbone.stages.1.2.pwconv1.weight, backbone.stages.1.2.pwconv1.bias, backbone.stages.1.2.pwconv2.weight, backbone.stages.1.2.pwconv2.bias, backbone.stages.2.0.gamma, backbone.stages.2.0.dwconv.weight, backbone.stages.2.0.dwconv.bias, backbone.stages.2.0.norm.weight, backbone.stages.2.0.norm.bias, backbone.stages.2.0.pwconv1.weight, backbone.stages.2.0.pwconv1.bias, backbone.stages.2.0.pwconv2.weight, backbone.stages.2.0.pwconv2.bias, backbone.stages.2.1.gamma, backbone.stages.2.1.dwconv.weight, backbone.stages.2.1.dwconv.bias, backbone.stages.2.1.norm.weight, backbone.stages.2.1.norm.bias, backbone.stages.2.1.pwconv1.weight, backbone.stages.2.1.pwconv1.bias, backbone.stages.2.1.pwconv2.weight, backbone.stages.2.1.pwconv2.bias, backbone.stages.2.2.gamma, backbone.stages.2.2.dwconv.weight, backbone.stages.2.2.dwconv.bias, backbone.stages.2.2.norm.weight, backbone.stages.2.2.norm.bias, backbone.stages.2.2.pwconv1.weight, backbone.stages.2.2.pwconv1.bias, backbone.stages.2.2.pwconv2.weight, backbone.stages.2.2.pwconv2.bias, backbone.stages.2.3.gamma, backbone.stages.2.3.dwconv.weight, backbone.stages.2.3.dwconv.bias, backbone.stages.2.3.norm.weight, backbone.stages.2.3.norm.bias, backbone.stages.2.3.pwconv1.weight, backbone.stages.2.3.pwconv1.bias, backbone.stages.2.3.pwconv2.weight, backbone.stages.2.3.pwconv2.bias, backbone.stages.2.4.gamma, backbone.stages.2.4.dwconv.weight, backbone.stages.2.4.dwconv.bias, backbone.stages.2.4.norm.weight, backbone.stages.2.4.norm.bias, backbone.stages.2.4.pwconv1.weight, backbone.stages.2.4.pwconv1.bias, backbone.stages.2.4.pwconv2.weight, backbone.stages.2.4.pwconv2.bias, backbone.stages.2.5.gamma, 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Thank you !! Anshu

Hello I have an initial problem

Traceback (most recent call last): File "C:/Users/janice/Desktop/covnet/ConvNeXt-main/main.py", line 477, in main(args) File "C:/Users/janice/Desktop/covnet/ConvNeXt-main/main.py", line 205, in main utils.init_distributed_mode(args) File "C:\Users\janice\Desktop\covnet\ConvNeXt-main\utils.py", line 329, in init_distributed_mode torch.distributed.init_process_group(backend=args.dist_backend, init_method=args.dist_url, File "C:\Users\janice\anaconda3\envs\covnet\lib\site-packages\torch\distributed\distributed_c10d.py", line 503, in init_process_group _update_default_pg(_new_process_group_helper( File "C:\Users\janice\anaconda3\envs\covnet\lib\site-packages\torch\distributed\distributed_c10d.py", line 597, in _new_process_group_helper raise RuntimeError("Distributed package doesn't have NCCL " RuntimeError: Distributed package doesn't have NCCL built in Killing subprocess 14712 Traceback (most recent call last): File "C:\Users\janice\anaconda3\envs\covnet\lib\runpy.py", line 194, in _run_module_as_main return _run_code(code, main_globals, None, File "C:\Users\janice\anaconda3\envs\covnet\lib\runpy.py", line 87, in _run_code exec(code, run_globals) File "C:\Users\janice\anaconda3\envs\covnet\lib\site-packages\torch\distributed\launch.py", line 340, in main() File "C:\Users\janice\anaconda3\envs\covnet\lib\site-packages\torch\distributed\launch.py", line 326, in main sigkill_handler(signal.SIGTERM, None) # not coming back File "C:\Users\janice\anaconda3\envs\covnet\lib\site-packages\torch\distributed\launch.py", line 301, in sigkill_handler raise subprocess.CalledProcessError(returncode=last_return_code, cmd=cmd) subprocess.CalledProcessError: Command '['C:\Users\janice\anaconda3\envs\covnet\python.exe', '-u', 'C:/Users/janice/Desktop/covnet/ConvNeXt-main/main .py', '--local_rank=0', '--model', 'convnext_tiny', '--drop_path', '0.1', '--batch_size', '128', '--lr', '4e-3', '--update_freq', '4', '--model_ema', 'true ', '--model_ema_eval', 'true', '--data_path', 'C:/Users/janice/Desktop/covnet/train_val_test/training', '--output_dir', 'C:/Users/janice/Desktop/covnet/res ults']' returned non-zero exit status 1.

I have no idea about this error

opened by iamwic 12
hello，what is this problem，thanks~

Traceback (most recent call last): File "run_with_submitit.py", line 122, in main() File "run_with_submitit.py", line 113, in main args.dist_url = get_init_file().as_uri() File "run_with_submitit.py", line 41, in get_init_file os.makedirs(str(get_shared_folder()), exist_ok=True) File "run_with_submitit.py", line 37, in get_shared_folder raise RuntimeError("No shared folder available") RuntimeError: No shared folder available

opened by lxwkobe24 12
Add Weights and Biases Integration
This PR adds support for Weights and Biases metric and model checkpointing.

Usage

I have tested the implementation by training ConvNext Tiny on CIFAR 100 dataset for 10 epochs. To enable logging metrics using W&B use --enable_wandb true. To save the model checkpoints as versioned artifacts use --wandb_ckpt true along with --enable_wandb.

python main.py --epochs 10 --model convnext_tiny --data_set CIFAR --data_path datasets --num_workers 8 --warmup_epochs 0 --save_ckpt true --output_dir model_ckpt --finetune path/to/model.pth --cutmix 0 --mixup 0 --lr 4e-4 --enable_wandb true --wandb_ckpt true

You can also set the name of the W&B project where all the runs will be logged using the --project argument (default='convnext').

Result (Feature Addition)

Able to easily share the experiments. Here's an example W&B run that shows train and test metrics from fine-tuning a ConvNext tiny on CIFAR 100 for 10 epochs.

Ability to check out the exact configuration used to train the model. Configs can thus be easily compared.

CPU/GPU metrics - memory allocated, memory utilized, etc.

Versioned model checkpoints which are easy to share.

Note: I achieved a ~88% test top 1 accuracy which is really impressive. Thanks for working on this.

The screen recording below summarizes the features being added for a few lines of code.

https://user-images.githubusercontent.com/31141479/150026875-91e4f38d-f465-49b6-9f12-3c8559c2934a.mov

Note

I have tested the implementation on a single machine with one GPU and with 2 GPUs and seems to work as expected. You can find the W&B run associated with training on 2 K80s.

If --enable_wandb true is not passed the code will work as expected, thus the addition is not enforcing any dependency on W&B.

CLA Signed
opened by ayulockin 12

'ConvNeXt is not in the models registry' . How to register the ConvNeXt ,i add the all files to Swin-T ，but did not work

custom_hooks = [dict(type='NumClassCheckHook')]
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
classes = ('loose_l', 'loose_s', 'poor_l', 'porous')
work_dir = './work_dirs\radar_convnext'
gpu_ids = range(0, 1)
  File "D:\App\anaconda\envs\convnext\lib\site-packages\mmcv\utils\registry.py", line 212, in build
    return self.build_func(*args, **kwargs, registry=self)
  File "D:\App\anaconda\envs\convnext\lib\site-packages\mmcv\cnn\builder.py", line 27, in build_model_from_cfg
    return build_from_cfg(cfg, registry, default_args)
  File "D:\App\anaconda\envs\convnext\lib\site-packages\mmcv\utils\registry.py", line 45, in build_from_cfg
    f'{obj_type} is not in the {registry.name} registry')
KeyError: 'ConvNeXt is not in the models registry'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "tools/train.py", line 187, in <module>
    main()
  File "tools/train.py", line 161, in main
    test_cfg=cfg.get('test_cfg'))
  File "D:\App\anaconda\envs\convnext\lib\site-packages\mmdet\models\builder.py", line 59, in build_detector
    cfg, default_args=dict(train_cfg=train_cfg, test_cfg=test_cfg))
  File "D:\App\anaconda\envs\convnext\lib\site-packages\mmcv\utils\registry.py", line 212, in build
    return self.build_func(*args, **kwargs, registry=self)
  File "D:\App\anaconda\envs\convnext\lib\site-packages\mmcv\cnn\builder.py", line 27, in build_model_from_cfg
    return build_from_cfg(cfg, registry, default_args)
  File "D:\App\anaconda\envs\convnext\lib\site-packages\mmcv\utils\registry.py", line 55, in build_from_cfg
    raise type(e)(f'{obj_cls.__name__}: {e}')
KeyError: "CascadeRCNN: 'ConvNeXt is not in the models registry'"

opened by joeyslv 11

my model_ema not updat

I turned on ema during training,But this operation doesn't seem to work。The model is updated normally, but the ema model is not updated with it.I don't know if I'm missing something.I have tried many times and the ema model will not update

opened by TAOSHss 9
where are these files: ./_base_/models/cascade_mask_rcnn_convnext_fpn.py', '../_base_/datasets/coco_instance.py', '../_base_/schedules/schedule_1x.py',

where are these files: ./_base_/models/cascade_mask_rcnn_convnext_fpn.py', '../_base_/datasets/coco_instance.py', '../_base_/schedules/schedule_1x.py',

opened by Williamlizl 9
convnext is slower than swin ?

I use convnext_tiny as pretrained model to finetune on my own dataset, but i found convnext_tiny is slower than swin_tiny, I use 4 nvidia 1080ti. convnext tiny cost three more times than swin_tiny . But the flops of two model is similar, I don't know why convnext is slower?

opened by lichun-wang 8
The batch-sizes of single machine commands are not adjusted
On the training doc, I believe we need to adjust the batch-size (or the LR) on the single machine commands to maintain the total batch-size the same.

For example, currently the ConvNeXt-S reports:

Multi-node: --nodes 4 --ngpus 8 --batch_size 128 --lr 4e-3

Single-machine: --nproc_per_node=8 --batch_size 128 --lr 4e-3 <- I believe here it should be --batch_size 512

Same applies for the other variants.
opened by datumbox 7
ConvNeXt-S and ConvNeXt-T models pretrained on ImageNet-22k

Hi, this is certainly great work, and thank you for the contribution!

Models pretrained on ImageNet-22k outperform the ones without significantly. Wonder whether it's possible for you guys to release the ConvNeXt-S&T models that have been pretrained on ImageNet-22k? If possible, it would motivate lots of on-device downstream tasks to integrate ConvNeXt as the backbone. I've tried the current ImageNet-1k trained models with panoptic segmentation and it works like a charm. ConvNext-S&T pretrained with ImageNet-22k will be greatly appreciated. Thanks!

opened by CanyonWind 6
Speed of the convnext

Hello, I recently tried to use the convnext structure for a task that attaches great importance to the inference speed (the faster the better, not a certain speed), the inference speed of convnext is more than twice as slow as 33 convolution, I Just use pytorch for inference, is there any way to improve the speed (can sacrifice a little precision), or if it is written using cudnn, will it narrow the gap with 33 convolution.

opened by shengkelong 5
A question about processing ImageNet-22K dataset

Thanks for sharing your fantastic work~ I want to know how to process ImageNet-22K dataset that download from ImageNet.org, where can i get data labels?

opened by peiyingxin 5

LayerNorm, what is going on?

Hello!

I am unsure of what the LayerNorm on images is actually supposed to do. LayerNorm channels first works kinda like BatchNorm2d, however with quite suspicious vertical lines. LayerNorm channels last however completely breaks the image, as each pixel is normalized individually. Is this intended behaviour?

Please have a look at the experiment below, to understand what I mean.

Related #112 possibly #115

import torch
from torch import nn
import torchvision
from models.convnext import LayerNorm


img = torchvision.io.read_image("cat.jpg")
img = img*0.005
torchvision.utils.save_image(img, "cat0.jpg")

cat

Batch Norm 2d

bn = nn.BatchNorm2d(3)
img_bn = bn(img.unsqueeze(0))[0]
torchvision.utils.save_image(img_bn*.28+.7, "cat_bn.jpg")

cat_bn

Layer Norm

channels first

ln_cf = LayerNorm(3, data_format="channels_first")
img_cf = ln_cf(img)
torchvision.utils.save_image(img_cf*.28+.7, "cat_cf.jpg")

cat_cf

channels last

ln_cl = LayerNorm(3, data_format="channels_last")
img_cl = ln_cl(img.permute(1,2,0)).permute(2,0,1)
torchvision.utils.save_image(img_cl*.28+.7, "cat_cl.jpg")

cat_cl

opened by ju-w 1

Hyperparameter setting for training from scratch on CIFAR-10
Hi,

I am trying to train a convext on CIFAR-10 for a research project that doesn't allow using BN. I use the following configuration:

python -m torch.distributed.launch --nproc_per_node=4 main.py \ --data_set image_folder --data_path ./CIFAR-10-images/train --eval_data_path ./CIFAR-10-images/test \ --nb_classes 10 --num_workers 8 --warmup_epochs 0 \ --save_ckpt false \ --cutmix 0 --mixup 0 \ --model_ema_eval true \ --model convnext_tiny \ --epochs 100 --lr 4e-4 --weight_decay 5e-2 --opt 'sgd' --input_size 32\ --output_dir results/100epochs_lr_4e-4_wd_5e-2_sgd_inputsize_32 \

And the accuracy is only 75% percent (standard ResNet18 is about 93%). If I change the optimizer from AdamW to SGD, the best accuracy actually drops to below 50%. If I use the default input size 224, the accuracy is 84%, still significantly low.

Can ConvNeXt work on CIFAR10 without fine-tuning from a pretrained model? Could you provide a recommended set of hyper parameters for CIFAR10 (that should be robust to different types of optimizers and without mix-up and cutmix)?

Also I have another question on fine-tuning on CIFAR10: it seems that in the colab file the input_size is the default 224. However CIFAR10 image is 32*32. Does this mean that in the data preparation stage the image will be padded to 224 * 224?

Thank you!
opened by Yuancheng-Xu 1
Error in Convnext training with Swin transformer framework - The testing results of the whole dataset is empty.
Hi,

I am trying to fine-tune convnext large model on the dataset in coco format, and after solving lot of error i am able to run training by using utils/train.py .

But i get mmdet - ERROR - The testing results of the whole dataset is empty. when i print out the output, it gives an empty array.

Since the dataset is strictly detection based , i commented out mask details from the config which now looks like this 👍

_base_ = [ '../_base_/models/cascade_mask_rcnn_convnext_fpn.py', ../_base_/datasets/custom_dataset_detection.py', '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' ] model = dict( backbone=dict( in_chans=3, depths=[3, 3, 27, 3], dims=[192, 384, 768, 1536], drop_path_rate=0.7, layer_scale_init_value=1.0, out_indices=[0, 1, 2, 3], ), neck=dict(in_channels=[192, 384, 768, 1536]), roi_head=dict( bbox_head=[ dict( type='ConvFCBBoxHead', num_shared_convs=4, num_shared_fcs=1, in_channels=256, conv_out_channels=256, fc_out_channels=1024, roi_feat_size=7, num_classes=97, bbox_coder=dict( type='DeltaXYWHBBoxCoder', target_means=[0., 0., 0., 0.], target_stds=[0.1, 0.1, 0.2, 0.2]), reg_class_agnostic=False, reg_decoded_bbox=True, norm_cfg=dict(type='SyncBN', requires_grad=True), loss_cls=dict( type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), loss_bbox=dict(type='GIoULoss', loss_weight=10.0)), dict( type='ConvFCBBoxHead', num_shared_convs=4, num_shared_fcs=1, in_channels=256, conv_out_channels=256, fc_out_channels=1024, roi_feat_size=7, num_classes=97, bbox_coder=dict( type='DeltaXYWHBBoxCoder', target_means=[0., 0., 0., 0.], target_stds=[0.05, 0.05, 0.1, 0.1]), reg_class_agnostic=False, reg_decoded_bbox=True, norm_cfg=dict(type='SyncBN', requires_grad=True), loss_cls=dict( type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), loss_bbox=dict(type='GIoULoss', loss_weight=10.0)), dict( type='ConvFCBBoxHead', num_shared_convs=4, num_shared_fcs=1, in_channels=256, conv_out_channels=256, fc_out_channels=1024, roi_feat_size=7, num_classes=97, bbox_coder=dict( type='DeltaXYWHBBoxCoder', target_means=[0., 0., 0., 0.], target_stds=[0.033, 0.033, 0.067, 0.067]), reg_class_agnostic=False, reg_decoded_bbox=True, norm_cfg=dict(type='SyncBN', requires_grad=True), loss_cls=dict( type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), loss_bbox=dict(type='GIoULoss', loss_weight=10.0)) ])) img_norm_cfg = dict( mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) # augmentation strategy originates from DETR / Sparse RCNN train_pipeline = [ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations', with_bbox=True, with_mask=True), dict(type='RandomFlip', flip_ratio=0.5), dict(type='AutoAugment', policies=[ [ dict(type='Resize', img_scale=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), (608, 1333), (640, 1333), (672, 1333), (704, 1333), (736, 1333), (768, 1333), (800, 1333)], multiscale_mode='value', keep_ratio=True) ], [ dict(type='Resize', img_scale=[(400, 1333), (500, 1333), (600, 1333)], multiscale_mode='value', keep_ratio=True), dict(type='RandomCrop', crop_type='absolute_range', crop_size=(384, 600), allow_negative_crop=True), dict(type='Resize', img_scale=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), (608, 1333), (640, 1333), (672, 1333), (704, 1333), (736, 1333), (768, 1333), (800, 1333)], multiscale_mode='value', override=True, keep_ratio=True) ] ]), dict(type='Normalize', **img_norm_cfg), dict(type='Pad', size_divisor=32), dict(type='DefaultFormatBundle'), dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), ] data = dict(train=dict(pipeline=train_pipeline)) optimizer = dict(constructor='LearningRateDecayOptimizerConstructor', _delete_=True, type='AdamW', lr=0.0000002, betas=(0.9, 0.999), weight_decay=0.05, paramwise_cfg={'decay_rate': 0.7, 'decay_type': 'layer_wise', 'num_layers': 12}) lr_config = dict(step=[3, 9, 27, 33]) runner = dict(type='EpochBasedRunnerAmp', max_epochs=36) # do not use mmdet version fp16 fp16 = None optimizer_config = dict( type="DistOptimizerHook", update_interval=1, grad_clip=None, coalesce=True, bucket_size_mb=-1, use_fp16=False, )

And the corresponsing base/models/cascade_mask_rcnn_convnext_fpn.py config looks like :

# Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # This source code is licensed under the license found in the # LICENSE file in the root directory of this source tree. # model settings model = dict( type='CascadeRCNN', pretrained='checkpoint/cascade_mask_rcnn_convnext_large_22k_3x.pth', backbone=dict( type='ConvNeXt', in_chans=3, depths=[3, 3, 9, 3], dims=[96, 192, 384, 768], drop_path_rate=0.2, layer_scale_init_value=1e-6, out_indices=[0, 1, 2, 3], ), neck=dict( type='FPN', in_channels=[128, 256, 512, 1024], out_channels=256, num_outs=5), rpn_head=dict( type='RPNHead', in_channels=256, feat_channels=256, anchor_generator=dict( type='AnchorGenerator', scales=[8], ratios=[0.5, 1.0, 2.0], strides=[4, 8, 16, 32, 64]), bbox_coder=dict( type='DeltaXYWHBBoxCoder', target_means=[.0, .0, .0, .0], target_stds=[1.0, 1.0, 1.0, 1.0]), loss_cls=dict( type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)), roi_head=dict( type='CascadeRoIHead', num_stages=3, stage_loss_weights=[1, 0.5, 0.25], bbox_roi_extractor=dict( type='SingleRoIExtractor', roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0), out_channels=256, featmap_strides=[4, 8, 16, 32]), bbox_head=[ dict( type='Shared2FCBBoxHead', in_channels=256, fc_out_channels=1024, roi_feat_size=7, num_classes=97, bbox_coder=dict( type='DeltaXYWHBBoxCoder', target_means=[0., 0., 0., 0.], target_stds=[0.1, 0.1, 0.2, 0.2]), reg_class_agnostic=True, loss_cls=dict( type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)), dict( type='Shared2FCBBoxHead', in_channels=256, fc_out_channels=1024, roi_feat_size=7, num_classes=97, bbox_coder=dict( type='DeltaXYWHBBoxCoder', target_means=[0., 0., 0., 0.], target_stds=[0.05, 0.05, 0.1, 0.1]), reg_class_agnostic=True, loss_cls=dict( type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)), dict( type='Shared2FCBBoxHead', in_channels=256, fc_out_channels=1024, roi_feat_size=7, num_classes=97, bbox_coder=dict( type='DeltaXYWHBBoxCoder', target_means=[0., 0., 0., 0.], target_stds=[0.033, 0.033, 0.067, 0.067]), reg_class_agnostic=True, loss_cls=dict( type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)) ]), # model training and testing settings train_cfg = dict( rpn=dict( assigner=dict( type='MaxIoUAssigner', pos_iou_thr=0.7, neg_iou_thr=0.3, min_pos_iou=0.3, match_low_quality=True, ignore_iof_thr=-1), sampler=dict( type='RandomSampler', num=256, pos_fraction=0.5, neg_pos_ub=-1, add_gt_as_proposals=False), allowed_border=0, pos_weight=-1, debug=False), rpn_proposal=dict( nms_across_levels=False, nms_pre=2000, nms_post=2000, max_per_img=2000, nms=dict(type='nms', iou_threshold=0.7), min_bbox_size=0), rcnn=[ dict( assigner=dict( type='MaxIoUAssigner', pos_iou_thr=0.5, neg_iou_thr=0.5, min_pos_iou=0.5, match_low_quality=False, ignore_iof_thr=-1), sampler=dict( type='RandomSampler', num=512, pos_fraction=0.25, neg_pos_ub=-1, add_gt_as_proposals=True), #mask_size=28, pos_weight=-1, debug=False), dict( assigner=dict( type='MaxIoUAssigner', pos_iou_thr=0.6, neg_iou_thr=0.6, min_pos_iou=0.6, match_low_quality=False, ignore_iof_thr=-1), sampler=dict( type='RandomSampler', num=512, pos_fraction=0.25, neg_pos_ub=-1, add_gt_as_proposals=True), #mask_size=28, pos_weight=-1, debug=False), dict( assigner=dict( type='MaxIoUAssigner', pos_iou_thr=0.7, neg_iou_thr=0.7, min_pos_iou=0.7, match_low_quality=False, ignore_iof_thr=-1), sampler=dict( type='RandomSampler', num=512, pos_fraction=0.25, neg_pos_ub=-1, add_gt_as_proposals=True), #mask_size=28, pos_weight=-1, debug=False) ]), test_cfg = dict( rpn=dict( nms_across_levels=False, nms_pre=1000, nms_post=1000, max_per_img=1000, nms=dict(type='nms', iou_threshold=0.7), min_bbox_size=0), rcnn=dict( score_thr=0.05, nms=dict(type='nms', iou_threshold=0.5), max_per_img=100)))

Do i need to prune or update some layer's information for which i am getting below error :

unexpected key in source state_dict: backbone.downsample_layers.0.0.weight, backbone.downsample_layers.0.0.bias, backbone.downsample_layers.0.1.weight, backbone.downsample_layers.0.1.bias, backbone.downsample_layers.1.0.weight, backbone.downsample_layers.1.0.bias, backbone.downsample_layers.1.1.weight, backbone.downsample_layers.1.1.bias, backbone.downsample_layers.2.0.weight, backbone.downsample_layers.2.0.bias, backbone.downsample_layers.2.1.weight, backbone.downsample_layers.2.1.bias, backbone.downsample_layers.3.0.weight, backbone.downsample_layers.3.0.bias, backbone.downsample_layers.3.1.weight, backbone.downsample_layers.3.1.bias, backbone.stages.0.0.gamma, backbone.stages.0.0.dwconv.weight, backbone.stages.0.0.dwconv.bias, backbone.stages.0.0.norm.weight, backbone.stages.0.0.norm.bias, backbone.stages.0.0.pwconv1.weight, backbone.stages.0.0.pwconv1.bias, backbone.stages.0.0.pwconv2.weight, backbone.stages.0.0.pwconv2.bias, backbone.stages.0.1.gamma, backbone.stages.0.1.dwconv.weight, backbone.stages.0.1.dwconv.bias, backbone.stages.0.1.norm.weight, backbone.stages.0.1.norm.bias, backbone.stages.0.1.pwconv1.weight, backbone.stages.0.1.pwconv1.bias, backbone.stages.0.1.pwconv2.weight, backbone.stages.0.1.pwconv2.bias, backbone.stages.0.2.gamma, backbone.stages.0.2.dwconv.weight, backbone.stages.0.2.dwconv.bias, backbone.stages.0.2.norm.weight, backbone.stages.0.2.norm.bias, backbone.stages.0.2.pwconv1.weight, backbone.stages.0.2.pwconv1.bias, backbone.stages.0.2.pwconv2.weight, backbone.stages.0.2.pwconv2.bias, backbone.stages.1.0.gamma, backbone.stages.1.0.dwconv.weight, backbone.stages.1.0.dwconv.bias, backbone.stages.1.0.norm.weight, backbone.stages.1.0.norm.bias, backbone.stages.1.0.pwconv1.weight, backbone.stages.1.0.pwconv1.bias, backbone.stages.1.0.pwconv2.weight, backbone.stages.1.0.pwconv2.bias, backbone.stages.1.1.gamma, backbone.stages.1.1.dwconv.weight, backbone.stages.1.1.dwconv.bias, backbone.stages.1.1.norm.weight, backbone.stages.1.1.norm.bias, backbone.stages.1.1.pwconv1.weight, backbone.stages.1.1.pwconv1.bias, backbone.stages.1.1.pwconv2.weight, backbone.stages.1.1.pwconv2.bias, backbone.stages.1.2.gamma, backbone.stages.1.2.dwconv.weight, backbone.stages.1.2.dwconv.bias, backbone.stages.1.2.norm.weight, backbone.stages.1.2.norm.bias, backbone.stages.1.2.pwconv1.weight, backbone.stages.1.2.pwconv1.bias, backbone.stages.1.2.pwconv2.weight, backbone.stages.1.2.pwconv2.bias, backbone.stages.2.0.gamma, backbone.stages.2.0.dwconv.weight, backbone.stages.2.0.dwconv.bias, backbone.stages.2.0.norm.weight, backbone.stages.2.0.norm.bias, backbone.stages.2.0.pwconv1.weight, backbone.stages.2.0.pwconv1.bias, backbone.stages.2.0.pwconv2.weight, backbone.stages.2.0.pwconv2.bias, backbone.stages.2.1.gamma, backbone.stages.2.1.dwconv.weight, backbone.stages.2.1.dwconv.bias, backbone.stages.2.1.norm.weight, backbone.stages.2.1.norm.bias, backbone.stages.2.1.pwconv1.weight, backbone.stages.2.1.pwconv1.bias, backbone.stages.2.1.pwconv2.weight, backbone.stages.2.1.pwconv2.bias, backbone.stages.2.2.gamma, backbone.stages.2.2.dwconv.weight, backbone.stages.2.2.dwconv.bias, backbone.stages.2.2.norm.weight, backbone.stages.2.2.norm.bias, backbone.stages.2.2.pwconv1.weight, backbone.stages.2.2.pwconv1.bias, backbone.stages.2.2.pwconv2.weight, backbone.stages.2.2.pwconv2.bias, backbone.stages.2.3.gamma, backbone.stages.2.3.dwconv.weight, backbone.stages.2.3.dwconv.bias, backbone.stages.2.3.norm.weight, backbone.stages.2.3.norm.bias, backbone.stages.2.3.pwconv1.weight, backbone.stages.2.3.pwconv1.bias, backbone.stages.2.3.pwconv2.weight, backbone.stages.2.3.pwconv2.bias, backbone.stages.2.4.gamma, backbone.stages.2.4.dwconv.weight, backbone.stages.2.4.dwconv.bias, backbone.stages.2.4.norm.weight, backbone.stages.2.4.norm.bias, backbone.stages.2.4.pwconv1.weight, backbone.stages.2.4.pwconv1.bias, backbone.stages.2.4.pwconv2.weight, backbone.stages.2.4.pwconv2.bias, backbone.stages.2.5.gamma, 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backbone.stages.2.8.norm.bias, backbone.stages.2.8.pwconv1.weight, backbone.stages.2.8.pwconv1.bias, backbone.stages.2.8.pwconv2.weight, backbone.stages.2.8.pwconv2.bias, backbone.stages.2.9.gamma, backbone.stages.2.9.dwconv.weight, backbone.stages.2.9.dwconv.bias, backbone.stages.2.9.norm.weight, backbone.stages.2.9.norm.bias, backbone.stages.2.9.pwconv1.weight, backbone.stages.2.9.pwconv1.bias, backbone.stages.2.9.pwconv2.weight, backbone.stages.2.9.pwconv2.bias, backbone.stages.2.10.gamma, backbone.stages.2.10.dwconv.weight, backbone.stages.2.10.dwconv.bias, backbone.stages.2.10.norm.weight, backbone.stages.2.10.norm.bias, backbone.stages.2.10.pwconv1.weight, backbone.stages.2.10.pwconv1.bias, backbone.stages.2.10.pwconv2.weight, backbone.stages.2.10.pwconv2.bias, backbone.stages.2.11.gamma, backbone.stages.2.11.dwconv.weight, backbone.stages.2.11.dwconv.bias, backbone.stages.2.11.norm.weight, backbone.stages.2.11.norm.bias, backbone.stages.2.11.pwconv1.weight, 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backbone.stages.2.14.pwconv2.bias, backbone.stages.2.15.gamma, backbone.stages.2.15.dwconv.weight, backbone.stages.2.15.dwconv.bias, backbone.stages.2.15.norm.weight, backbone.stages.2.15.norm.bias, backbone.stages.2.15.pwconv1.weight, backbone.stages.2.15.pwconv1.bias, backbone.stages.2.15.pwconv2.weight, backbone.stages.2.15.pwconv2.bias, backbone.stages.2.16.gamma, backbone.stages.2.16.dwconv.weight, backbone.stages.2.16.dwconv.bias, backbone.stages.2.16.norm.weight, backbone.stages.2.16.norm.bias, backbone.stages.2.16.pwconv1.weight, backbone.stages.2.16.pwconv1.bias, backbone.stages.2.16.pwconv2.weight, backbone.stages.2.16.pwconv2.bias, backbone.stages.2.17.gamma, backbone.stages.2.17.dwconv.weight, backbone.stages.2.17.dwconv.bias, backbone.stages.2.17.norm.weight, backbone.stages.2.17.norm.bias, backbone.stages.2.17.pwconv1.weight, backbone.stages.2.17.pwconv1.bias, backbone.stages.2.17.pwconv2.weight, backbone.stages.2.17.pwconv2.bias, backbone.stages.2.18.gamma, 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missing keys in source state_dict: downsample_layers.0.0.weight, downsample_layers.0.0.bias, downsample_layers.0.1.weight, downsample_layers.0.1.bias, downsample_layers.1.0.weight, downsample_layers.1.0.bias, downsample_layers.1.1.weight, downsample_layers.1.1.bias, downsample_layers.2.0.weight, downsample_layers.2.0.bias, downsample_layers.2.1.weight, downsample_layers.2.1.bias, downsample_layers.3.0.weight, downsample_layers.3.0.bias, downsample_layers.3.1.weight, downsample_layers.3.1.bias, stages.0.0.gamma, stages.0.0.dwconv.weight, stages.0.0.dwconv.bias, stages.0.0.norm.weight, stages.0.0.norm.bias, stages.0.0.pwconv1.weight, stages.0.0.pwconv1.bias, stages.0.0.pwconv2.weight, stages.0.0.pwconv2.bias, stages.0.1.gamma, stages.0.1.dwconv.weight, stages.0.1.dwconv.bias, stages.0.1.norm.weight, stages.0.1.norm.bias, stages.0.1.pwconv1.weight, stages.0.1.pwconv1.bias, stages.0.1.pwconv2.weight, stages.0.1.pwconv2.bias, stages.0.2.gamma, stages.0.2.dwconv.weight, 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Thank you !! Anshu
opened by anshudaur 0
Why LayerNorm before conv in downsampling layers ?
Thanks for your awesome work!

While stem is coherent in regard to Blocks where we have the ordering conv->norm, in dowsampling layers you put LayerNorm before convolution.

The full path is:

conv2d 4x4, stride 4

layernorm

residual stage 1

layernorm

conv2d 2x2, stride 2

residual stage 2

layernorm

conv2d 2x2, stride 2

residual stage 3

layernorm

conv2d 2x2, stride 2

residual stage 4

Which means that if residual stage 1 converges to identity, we have a layernorm into a layernorm which seems weird to me:

conv2d 4x4, stride 4

layernorm

layernorm

conv2d 2x2, stride 2

Can you explain this design choice ?
opened by F-Barto 0

Infinite loss value when training under amp

Hi, I encounter the infinite loss value assertion failure when training using mixed precision. The trackback like this:

Traceback (most recent call last):
  File "main.py", line 498, in <module>
    main(args)
  File "main.py", line 409, in main
    train_stats = train_one_epoch(
  File "ConvNeXt/engine.py", line 63, in train_one_epoch
    assert math.isfinite(loss_value)
AssertionError

I wonder how I could fix this problem. Thanks very much!

opened by jameslahm 0