[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

mmpretrain/configs/_base_/datasets/cifar10_bs16.py

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+# 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

mmpretrain/configs/_base_/datasets/imagenet_bs128_mbv3.py

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+# 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

mmpretrain/configs/_base_/datasets/imagenet_bs32_pil_resize.py

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+# 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

mmpretrain/configs/_base_/models/mobilenet_v2_1x.py

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+# 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),
+    ))

mmpretrain/configs/_base_/models/mobilenet_v3_small.py

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+# 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)))

mmpretrain/configs/_base_/schedules/cifar10_bs128.py

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+# 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)

mmpretrain/configs/_base_/schedules/imagenet_bs256_epochstep.py

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+# 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)

mmpretrain/configs/mobilenet_v2/mobilenet_v2_8xb32_in1k.py

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+# 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 *

mmpretrain/configs/mobilenet_v3/mobilenet_v3_large_8xb128_in1k.py

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+# 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)