From 845b462190b5b4d5b785ff16251c28e6713d8550 Mon Sep 17 00:00:00 2001 From: DE009 <57087096+DE009@users.noreply.github.com> Date: Fri, 1 Sep 2023 17:54:18 +0800 Subject: [PATCH] [CodeCamp2023-340] New Version of config Adapting MobileNet Algorithm (#1774) * add new config adapting MobileNetV2,V3 * add base model config for mobile net v3, modified all training configs of mobile net v3 inherit from the base model config * removed directory _base_/models/mobilenet_v3 --- .../configs/_base_/datasets/cifar10_bs16.py | 52 ++++++++++++ .../_base_/datasets/imagenet_bs128_mbv3.py | 75 ++++++++++++++++ .../datasets/imagenet_bs32_pil_resize.py | 60 +++++++++++++ .../configs/_base_/models/mobilenet_v2_1x.py | 17 ++++ .../_base_/models/mobilenet_v3_small.py | 25 ++++++ .../configs/_base_/schedules/cifar10_bs128.py | 20 +++++ .../schedules/imagenet_bs256_epochstep.py | 20 +++++ .../mobilenet_v2/mobilenet_v2_8xb32_in1k.py | 9 ++ .../mobilenet_v3_large_8xb128_in1k.py | 40 +++++++++ .../mobilenet_v3_small_050_8xb128_in1k.py | 85 +++++++++++++++++++ .../mobilenet_v3_small_075_8xb128_in1k.py | 83 ++++++++++++++++++ .../mobilenet_v3_small_8xb128_in1k.py | 34 ++++++++ .../mobilenet_v3_small_8xb16_cifar10.py | 34 ++++++++ 13 files changed, 554 insertions(+) create mode 100644 mmpretrain/configs/_base_/datasets/cifar10_bs16.py create mode 100644 mmpretrain/configs/_base_/datasets/imagenet_bs128_mbv3.py create mode 100644 mmpretrain/configs/_base_/datasets/imagenet_bs32_pil_resize.py create mode 100644 mmpretrain/configs/_base_/models/mobilenet_v2_1x.py create mode 100644 mmpretrain/configs/_base_/models/mobilenet_v3_small.py create mode 100644 mmpretrain/configs/_base_/schedules/cifar10_bs128.py create mode 100644 mmpretrain/configs/_base_/schedules/imagenet_bs256_epochstep.py create mode 100644 mmpretrain/configs/mobilenet_v2/mobilenet_v2_8xb32_in1k.py create mode 100644 mmpretrain/configs/mobilenet_v3/mobilenet_v3_large_8xb128_in1k.py create mode 100644 mmpretrain/configs/mobilenet_v3/mobilenet_v3_small_050_8xb128_in1k.py create mode 100644 mmpretrain/configs/mobilenet_v3/mobilenet_v3_small_075_8xb128_in1k.py create mode 100644 mmpretrain/configs/mobilenet_v3/mobilenet_v3_small_8xb128_in1k.py create mode 100644 mmpretrain/configs/mobilenet_v3/mobilenet_v3_small_8xb16_cifar10.py diff --git a/mmpretrain/configs/_base_/datasets/cifar10_bs16.py b/mmpretrain/configs/_base_/datasets/cifar10_bs16.py new file mode 100644 index 00000000..3737dbee --- /dev/null +++ b/mmpretrain/configs/_base_/datasets/cifar10_bs16.py @@ -0,0 +1,52 @@ +# Copyright (c) OpenMMLab. All rights reserved. +# This is a BETA new format config file, and the usage may change recently. +from mmengine.dataset import DefaultSampler + +from mmpretrain.datasets import CIFAR10, PackInputs, RandomCrop, RandomFlip +from mmpretrain.evaluation import Accuracy + +# dataset settings +dataset_type = CIFAR10 +data_preprocessor = dict( + num_classes=10, + # RGB format normalization parameters + mean=[125.307, 122.961, 113.8575], + std=[51.5865, 50.847, 51.255], + # loaded images are already RGB format + to_rgb=False) + +train_pipeline = [ + dict(type=RandomCrop, crop_size=32, padding=4), + dict(type=RandomFlip, prob=0.5, direction='horizontal'), + dict(type=PackInputs), +] + +test_pipeline = [ + dict(type=PackInputs), +] + +train_dataloader = dict( + batch_size=16, + num_workers=2, + dataset=dict( + type=dataset_type, + data_root='data/cifar10', + split='train', + pipeline=train_pipeline), + sampler=dict(type=DefaultSampler, shuffle=True), +) + +val_dataloader = dict( + batch_size=16, + num_workers=2, + dataset=dict( + type=dataset_type, + data_root='data/cifar10/', + split='test', + pipeline=test_pipeline), + sampler=dict(type=DefaultSampler, shuffle=False), +) +val_evaluator = dict(type=Accuracy, topk=(1, )) + +test_dataloader = val_dataloader +test_evaluator = val_evaluator diff --git a/mmpretrain/configs/_base_/datasets/imagenet_bs128_mbv3.py b/mmpretrain/configs/_base_/datasets/imagenet_bs128_mbv3.py new file mode 100644 index 00000000..cf0aa629 --- /dev/null +++ b/mmpretrain/configs/_base_/datasets/imagenet_bs128_mbv3.py @@ -0,0 +1,75 @@ +# Copyright (c) OpenMMLab. All rights reserved. +# This is a BETA new format config file, and the usage may change recently. +from mmengine.dataset import DefaultSampler + +from mmpretrain.datasets import (AutoAugment, CenterCrop, ImageNet, + LoadImageFromFile, PackInputs, RandomErasing, + RandomFlip, RandomResizedCrop, ResizeEdge) +from mmpretrain.evaluation import Accuracy + +# dataset settings +dataset_type = ImageNet +data_preprocessor = dict( + num_classes=1000, + # RGB format normalization parameters + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + # convert image from BGR to RGB + to_rgb=True, +) + +bgr_mean = data_preprocessor['mean'][::-1] +bgr_std = data_preprocessor['std'][::-1] + +train_pipeline = [ + dict(type=LoadImageFromFile), + dict(type=RandomResizedCrop, scale=224, backend='pillow'), + dict(type=RandomFlip, prob=0.5, direction='horizontal'), + dict( + type=AutoAugment, + policies='imagenet', + hparams=dict(pad_val=[round(x) for x in bgr_mean])), + dict( + type=RandomErasing, + erase_prob=0.2, + mode='rand', + min_area_ratio=0.02, + max_area_ratio=1 / 3, + fill_color=bgr_mean, + fill_std=bgr_std), + dict(type=PackInputs), +] + +test_pipeline = [ + dict(type=LoadImageFromFile), + dict(type=ResizeEdge, scale=256, edge='short', backend='pillow'), + dict(type=CenterCrop, crop_size=224), + dict(type=PackInputs), +] + +train_dataloader = dict( + batch_size=128, + num_workers=5, + dataset=dict( + type=dataset_type, + data_root='data/imagenet', + split='train', + pipeline=train_pipeline), + sampler=dict(type=DefaultSampler, shuffle=True), +) + +val_dataloader = dict( + batch_size=128, + num_workers=5, + dataset=dict( + type=dataset_type, + data_root='data/imagenet', + split='val', + pipeline=test_pipeline), + sampler=dict(type=DefaultSampler, shuffle=False), +) +val_evaluator = dict(type=Accuracy, topk=(1, 5)) + +# If you want standard test, please manually configure the test dataset +test_dataloader = val_dataloader +test_evaluator = val_evaluator diff --git a/mmpretrain/configs/_base_/datasets/imagenet_bs32_pil_resize.py b/mmpretrain/configs/_base_/datasets/imagenet_bs32_pil_resize.py new file mode 100644 index 00000000..f911bc20 --- /dev/null +++ b/mmpretrain/configs/_base_/datasets/imagenet_bs32_pil_resize.py @@ -0,0 +1,60 @@ +# Copyright (c) OpenMMLab. All rights reserved. +# This is a BETA new format config file, and the usage may change recently. +from mmengine.dataset import DefaultSampler + +from mmpretrain.datasets import (CenterCrop, ImageNet, LoadImageFromFile, + PackInputs, RandomFlip, RandomResizedCrop, + ResizeEdge) +from mmpretrain.evaluation import Accuracy + +# dataset settings +dataset_type = ImageNet +data_preprocessor = dict( + num_classes=1000, + # RGB format normalization parameters + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + # convert image from BGR to RGB + to_rgb=True, +) + +train_pipeline = [ + dict(type=LoadImageFromFile), + dict(type=RandomResizedCrop, scale=224, backend='pillow'), + dict(type=RandomFlip, prob=0.5, direction='horizontal'), + dict(type=PackInputs), +] + +test_pipeline = [ + dict(type=LoadImageFromFile), + dict(type=ResizeEdge, scale=256, edge='short', backend='pillow'), + dict(type=CenterCrop, crop_size=224), + dict(type=PackInputs), +] + +train_dataloader = dict( + batch_size=32, + num_workers=5, + dataset=dict( + type=dataset_type, + data_root='data/imagenet', + split='train', + pipeline=train_pipeline), + sampler=dict(type=DefaultSampler, shuffle=True), +) + +val_dataloader = dict( + batch_size=32, + num_workers=5, + dataset=dict( + type=dataset_type, + data_root='data/imagenet', + split='val', + pipeline=test_pipeline), + sampler=dict(type=DefaultSampler, shuffle=False), +) +val_evaluator = dict(type=Accuracy, topk=(1, 5)) + +# If you want standard test, please manually configure the test dataset +test_dataloader = val_dataloader +test_evaluator = val_evaluator diff --git a/mmpretrain/configs/_base_/models/mobilenet_v2_1x.py b/mmpretrain/configs/_base_/models/mobilenet_v2_1x.py new file mode 100644 index 00000000..17dbb9fd --- /dev/null +++ b/mmpretrain/configs/_base_/models/mobilenet_v2_1x.py @@ -0,0 +1,17 @@ +# Copyright (c) OpenMMLab. All rights reserved. +# This is a BETA new format config file, and the usage may change recently. +from mmpretrain.models import (CrossEntropyLoss, GlobalAveragePooling, + ImageClassifier, LinearClsHead, MobileNetV2) + +# model settings +model = dict( + type=ImageClassifier, + backbone=dict(type=MobileNetV2, widen_factor=1.0), + neck=dict(type=GlobalAveragePooling), + head=dict( + type=LinearClsHead, + num_classes=1000, + in_channels=1280, + loss=dict(type=CrossEntropyLoss, loss_weight=1.0), + topk=(1, 5), + )) diff --git a/mmpretrain/configs/_base_/models/mobilenet_v3_small.py b/mmpretrain/configs/_base_/models/mobilenet_v3_small.py new file mode 100644 index 00000000..83edab59 --- /dev/null +++ b/mmpretrain/configs/_base_/models/mobilenet_v3_small.py @@ -0,0 +1,25 @@ +# Copyright (c) OpenMMLab. All rights reserved. +# This is a BETA new format config file, and the usage may change recently. +from mmengine.model.weight_init import NormalInit +from torch.nn.modules.activation import Hardswish + +from mmpretrain.models import (CrossEntropyLoss, GlobalAveragePooling, + ImageClassifier, MobileNetV3, + StackedLinearClsHead) + +# model settings +model = dict( + type=ImageClassifier, + backbone=dict(type=MobileNetV3, arch='small'), + neck=dict(type=GlobalAveragePooling), + head=dict( + type=StackedLinearClsHead, + num_classes=1000, + in_channels=576, + mid_channels=[1024], + dropout_rate=0.2, + act_cfg=dict(type=Hardswish), + loss=dict(type=CrossEntropyLoss, loss_weight=1.0), + init_cfg=dict( + type=NormalInit, layer='Linear', mean=0., std=0.01, bias=0.), + topk=(1, 5))) diff --git a/mmpretrain/configs/_base_/schedules/cifar10_bs128.py b/mmpretrain/configs/_base_/schedules/cifar10_bs128.py new file mode 100644 index 00000000..8ab749e8 --- /dev/null +++ b/mmpretrain/configs/_base_/schedules/cifar10_bs128.py @@ -0,0 +1,20 @@ +# Copyright (c) OpenMMLab. All rights reserved. +# This is a BETA new format config file, and the usage may change recently. +from mmengine.optim import MultiStepLR +from torch.optim import SGD + +# optimizer +optim_wrapper = dict( + optimizer=dict(type=SGD, lr=0.1, momentum=0.9, weight_decay=0.0001)) +# learning policy +param_scheduler = dict( + type=MultiStepLR, by_epoch=True, milestones=[100, 150], gamma=0.1) + +# train, val, test setting +train_cfg = dict(by_epoch=True, max_epochs=200, val_interval=1) +val_cfg = dict() +test_cfg = dict() + +# NOTE: `auto_scale_lr` is for automatically scaling LR +# based on the actual training batch size. +auto_scale_lr = dict(base_batch_size=128) diff --git a/mmpretrain/configs/_base_/schedules/imagenet_bs256_epochstep.py b/mmpretrain/configs/_base_/schedules/imagenet_bs256_epochstep.py new file mode 100644 index 00000000..9d245ebb --- /dev/null +++ b/mmpretrain/configs/_base_/schedules/imagenet_bs256_epochstep.py @@ -0,0 +1,20 @@ +# Copyright (c) OpenMMLab. All rights reserved. +# This is a BETA new format config file, and the usage may change recently. +from mmengine.optim import StepLR +from torch.optim import SGD + +# optimizer +optim_wrapper = dict( + optimizer=dict(type=SGD, lr=0.045, momentum=0.9, weight_decay=0.00004)) + +# learning policy +param_scheduler = dict(type=StepLR, by_epoch=True, step_size=1, gamma=0.98) + +# train, val, test setting +train_cfg = dict(by_epoch=True, max_epochs=300, val_interval=1) +val_cfg = dict() +test_cfg = dict() + +# NOTE: `auto_scale_lr` is for automatically scaling LR, +# based on the actual training batch size. +auto_scale_lr = dict(base_batch_size=256) diff --git a/mmpretrain/configs/mobilenet_v2/mobilenet_v2_8xb32_in1k.py b/mmpretrain/configs/mobilenet_v2/mobilenet_v2_8xb32_in1k.py new file mode 100644 index 00000000..79eec635 --- /dev/null +++ b/mmpretrain/configs/mobilenet_v2/mobilenet_v2_8xb32_in1k.py @@ -0,0 +1,9 @@ +# Copyright (c) OpenMMLab. All rights reserved. +# This is a BETA new format config file, and the usage may change recently. +from mmengine.config import read_base + +with read_base(): + from .._base_.datasets.imagenet_bs32_pil_resize import * + from .._base_.default_runtime import * + from .._base_.models.mobilenet_v2_1x import * + from .._base_.schedules.imagenet_bs256_epochstep import * diff --git a/mmpretrain/configs/mobilenet_v3/mobilenet_v3_large_8xb128_in1k.py b/mmpretrain/configs/mobilenet_v3/mobilenet_v3_large_8xb128_in1k.py new file mode 100644 index 00000000..3f1bee1c --- /dev/null +++ b/mmpretrain/configs/mobilenet_v3/mobilenet_v3_large_8xb128_in1k.py @@ -0,0 +1,40 @@ +# Copyright (c) OpenMMLab. All rights reserved. +# This is a BETA new format config file, and the usage may change recently. + +# Refers to https://pytorch.org/blog/ml-models-torchvision-v0.9/#classification +from mmengine.config import read_base + +with read_base(): + from .._base_.models.mobilenet_v3_small import * + from .._base_.datasets.imagenet_bs128_mbv3 import * + from .._base_.default_runtime import * + +from mmengine.optim import StepLR +from torch.optim import RMSprop + +# model settings +model.merge( + dict( + backbone=dict(arch='large'), + head=dict(in_channels=960, mid_channels=[1280]), + )) +# schedule settings +optim_wrapper = dict( + optimizer=dict( + type=RMSprop, + lr=0.064, + alpha=0.9, + momentum=0.9, + eps=0.0316, + weight_decay=1e-5)) + +param_scheduler = dict(type=StepLR, by_epoch=True, step_size=2, gamma=0.973) + +train_cfg = dict(by_epoch=True, max_epochs=600, val_interval=1) +val_cfg = dict() +test_cfg = dict() + +# NOTE: `auto_scale_lr` is for automatically scaling LR +# based on the actual training batch size. +# base_batch_size = (8 GPUs) x (128 samples per GPU) +auto_scale_lr = dict(base_batch_size=1024) diff --git a/mmpretrain/configs/mobilenet_v3/mobilenet_v3_small_050_8xb128_in1k.py b/mmpretrain/configs/mobilenet_v3/mobilenet_v3_small_050_8xb128_in1k.py new file mode 100644 index 00000000..50e1ffc6 --- /dev/null +++ b/mmpretrain/configs/mobilenet_v3/mobilenet_v3_small_050_8xb128_in1k.py @@ -0,0 +1,85 @@ +# Copyright (c) OpenMMLab. All rights reserved. +# This is a BETA new format config file, and the usage may change recently. +# Refers to https://pytorch.org/blog/ml-models-torchvision-v0.9/#classification + +from mmengine.config import read_base + +with read_base(): + from .._base_.models.mobilenet_v3_small import * + from .._base_.datasets.imagenet_bs128_mbv3 import * + from .._base_.default_runtime import * + +from mmengine.optim import StepLR +from torch.nn.modules.batchnorm import BatchNorm2d +from torch.optim import RMSprop + +# model settings +model.merge( + dict( + backbone=dict( + arch='small_050', + norm_cfg=dict(type=BatchNorm2d, eps=1e-5, momentum=0.1)), + head=dict(in_channels=288), + )) + +train_pipeline = [ + dict(type=LoadImageFromFile), + dict( + type=RandomResizedCrop, + scale=224, + backend='pillow', + interpolation='bicubic'), + dict(type=RandomFlip, prob=0.5, direction='horizontal'), + dict( + type=AutoAugment, + policies='imagenet', + hparams=dict(pad_val=[round(x) for x in [103.53, 116.28, 123.675]])), + dict( + type=RandomErasing, + erase_prob=0.2, + mode='rand', + min_area_ratio=0.02, + max_area_ratio=1 / 3, + fill_color=[103.53, 116.28, 123.675], + fill_std=[57.375, 57.12, 58.395]), + dict(type=PackInputs), +] + +test_pipeline = [ + dict(type=LoadImageFromFile), + dict( + type=ResizeEdge, + scale=256, + edge='short', + backend='pillow', + interpolation='bicubic'), + dict(type=CenterCrop, crop_size=224), + dict(type=PackInputs), +] + +train_dataloader.merge(dict(dataset=dict(pipeline=train_pipeline))) + +val_dataloader.merge(dict(dataset=dict(pipeline=test_pipeline))) +# If you want standard test, please manually configure the test dataset +test_dataloader = val_dataloader + +# schedule settings +optim_wrapper = dict( + optimizer=dict( + type=RMSprop, + lr=0.064, + alpha=0.9, + momentum=0.9, + eps=0.0316, + weight_decay=1e-5)) + +param_scheduler = dict(type=StepLR, by_epoch=True, step_size=2, gamma=0.973) + +train_cfg = dict(by_epoch=True, max_epochs=600, val_interval=10) +val_cfg = dict() +test_cfg = dict() + +# NOTE: `auto_scale_lr` is for automatically scaling LR +# based on the actual training batch size. +# base_batch_size = (8 GPUs) x (128 samples per GPU) +auto_scale_lr = dict(base_batch_size=1024) diff --git a/mmpretrain/configs/mobilenet_v3/mobilenet_v3_small_075_8xb128_in1k.py b/mmpretrain/configs/mobilenet_v3/mobilenet_v3_small_075_8xb128_in1k.py new file mode 100644 index 00000000..c8c640cd --- /dev/null +++ b/mmpretrain/configs/mobilenet_v3/mobilenet_v3_small_075_8xb128_in1k.py @@ -0,0 +1,83 @@ +# Copyright (c) OpenMMLab. All rights reserved. +# This is a BETA new format config file, and the usage may change recently. +# Refers to https://pytorch.org/blog/ml-models-torchvision-v0.9/#classification + +from mmengine.config import read_base + +with read_base(): + from .._base_.models.mobilenet_v3_small import * + from .._base_.datasets.imagenet_bs128_mbv3 import * + from .._base_.default_runtime import * + +from mmengine.optim import StepLR +from torch.nn.modules.batchnorm import BatchNorm2d +from torch.optim import RMSprop + +# model settings +model.merge( + dict( + backbone=dict( + arch='small_075', + norm_cfg=dict(type=BatchNorm2d, eps=1e-5, momentum=0.1)), + head=dict(in_channels=432), + )) + +train_pipeline = [ + dict(type=LoadImageFromFile), + dict( + type=RandomResizedCrop, + scale=224, + backend='pillow', + interpolation='bicubic'), + dict(type=RandomFlip, prob=0.5, direction='horizontal'), + dict( + type=AutoAugment, + policies='imagenet', + hparams=dict(pad_val=[round(x) for x in [103.53, 116.28, 123.675]])), + dict( + type=RandomErasing, + erase_prob=0.2, + mode='rand', + min_area_ratio=0.02, + max_area_ratio=1 / 3, + fill_color=[103.53, 116.28, 123.675], + fill_std=[57.375, 57.12, 58.395]), + dict(type=PackInputs), +] + +test_pipeline = [ + dict(type=LoadImageFromFile), + dict( + type=ResizeEdge, + scale=256, + edge='short', + backend='pillow', + interpolation='bicubic'), + dict(type=CenterCrop, crop_size=224), + dict(type=PackInputs), +] + +train_dataloader.merge(dict(dataset=dict(pipeline=train_pipeline))) +val_dataloader.merge(dict(dataset=dict(pipeline=test_pipeline))) +test_dataloader = val_dataloader + +# schedule settings +optim_wrapper = dict( + optimizer=dict( + type=RMSprop, + lr=0.064, + alpha=0.9, + momentum=0.9, + eps=0.0316, + weight_decay=1e-5)) + +param_scheduler = dict(type=StepLR, by_epoch=True, step_size=2, gamma=0.973) + +train_cfg = dict(by_epoch=True, max_epochs=600, val_interval=10) +val_cfg = dict() +test_cfg = dict() + +# NOTE: `auto_scale_lr` is for automatically scaling LR +# based on the actual training batch size. +# base_batch_size = (8 GPUs) x (128 samples per GPU) +auto_scale_lr = dict(base_batch_size=1024) diff --git a/mmpretrain/configs/mobilenet_v3/mobilenet_v3_small_8xb128_in1k.py b/mmpretrain/configs/mobilenet_v3/mobilenet_v3_small_8xb128_in1k.py new file mode 100644 index 00000000..0c220a01 --- /dev/null +++ b/mmpretrain/configs/mobilenet_v3/mobilenet_v3_small_8xb128_in1k.py @@ -0,0 +1,34 @@ +# Copyright (c) OpenMMLab. All rights reserved. +# This is a BETA new format config file, and the usage may change recently. +# Refers to https://pytorch.org/blog/ml-models-torchvision-v0.9/#classification + +from mmengine.config import read_base + +with read_base(): + from .._base_.models.mobilenet_v3_small import * + from .._base_.datasets.imagenet_bs128_mbv3 import * + from .._base_.default_runtime import * + +from mmengine.optim import StepLR +from torch.optim import RMSprop + +# schedule settings +optim_wrapper = dict( + optimizer=dict( + type=RMSprop, + lr=0.064, + alpha=0.9, + momentum=0.9, + eps=0.0316, + weight_decay=1e-5)) + +param_scheduler = dict(type=StepLR, by_epoch=True, step_size=2, gamma=0.973) + +train_cfg = dict(by_epoch=True, max_epochs=600, val_interval=1) +val_cfg = dict() +test_cfg = dict() + +# NOTE: `auto_scale_lr` is for automatically scaling LR +# based on the actual training batch size. +# base_batch_size = (8 GPUs) x (128 samples per GPU) +auto_scale_lr = dict(base_batch_size=1024) diff --git a/mmpretrain/configs/mobilenet_v3/mobilenet_v3_small_8xb16_cifar10.py b/mmpretrain/configs/mobilenet_v3/mobilenet_v3_small_8xb16_cifar10.py new file mode 100644 index 00000000..0f91ee38 --- /dev/null +++ b/mmpretrain/configs/mobilenet_v3/mobilenet_v3_small_8xb16_cifar10.py @@ -0,0 +1,34 @@ +# Copyright (c) OpenMMLab. All rights reserved. +# This is a BETA new format config file, and the usage may change recently. +from mmengine.config import read_base + +with read_base(): + from .._base_.models.mobilenet_v3_small import * + from .._base_.datasets.cifar10_bs16 import * + from .._base_.schedules.cifar10_bs128 import * + from .._base_.default_runtime import * + +from mmengine.optim import MultiStepLR + +# model settings +model.merge( + dict( + head=dict( + _delete_=True, + type=StackedLinearClsHead, + num_classes=10, + in_channels=576, + mid_channels=[1280], + act_cfg=dict(type=Hardswish), + loss=dict(type=CrossEntropyLoss, loss_weight=1.0), + topk=(1, 5)))) +# schedule settings +param_scheduler.merge( + dict( + type=MultiStepLR, + by_epoch=True, + milestones=[120, 170], + gamma=0.1, + )) + +train_cfg.merge(dict(by_epoch=True, max_epochs=200))