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feat: swift yolo mbnv4
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commit 87250f5
Author: mjq2020 <[email protected]>
Date:   Fri Apr 26 10:33:07 2024 +0000

    add: mobilenetv4 backbone

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    Fix: cls loss weight too high

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* feat: mobilenetv4 swift yolo backbone

* chore: modify mbnv4 medium/large outputs
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iChizer0 authored May 22, 2024
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164 changes: 164 additions & 0 deletions configs/swift_yolo/swift_yolo_1xb16_300e_coco_mbnv4s.py
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@@ -0,0 +1,164 @@
# Copyright (c) Seeed Technology Co.,Ltd. All rights reserved.
_base_ = ['./base_arch.py']

# ========================Suggested optional parameters========================
# MODEL
num_classes = 71
deepen_factor = 0.33
widen_factor = 1

# DATA
dataset_type = 'sscma.CustomYOLOv5CocoDataset'
train_ann = 'train/_annotations.coco.json'
train_data = 'train/' # Prefix of train image path
val_ann = 'valid/_annotations.coco.json'
val_data = 'valid/' # Prefix of val image path

# dataset link: https://universe.roboflow.com/team-roboflow/coco-128
data_root = 'https://universe.roboflow.com/ds/z5UOcgxZzD?key=bwx9LQUT0t'
height = 192
width = 192
batch = 16
workers = 2
val_batch = batch
val_workers = workers
imgsz = (width, height)

# TRAIN
persistent_workers = True

# ================================END=================================

# DATA
affine_scale = 0.5
# MODEL
strides = [8, 16, 32]

anchors = [
[(10, 13), (16, 30), (33, 23)], # P3/8
[(30, 61), (62, 45), (59, 119)], # P4/16
[(116, 90), (156, 198), (373, 326)], # P5/32
]

# default_scope = 'sscma'

model = dict(
type='mmyolo.YOLODetector',
backbone=dict(
_delete_=True,
type='sscma.MobileNetv4',
arch='small'
),
neck=dict(
type='mmyolo.YOLOv5PAFPN',
deepen_factor=deepen_factor,
widen_factor=widen_factor,
in_channels=[64, 96, 128],
out_channels=[64, 96, 128]
),
bbox_head=dict(
head_module=dict(
num_classes=num_classes,
in_channels=[64, 96, 128],
widen_factor=widen_factor,
),
),
)

# ======================datasets==================


batch_shapes_cfg = dict(
type='BatchShapePolicy',
batch_size=1,
img_size=imgsz[0],
# The image scale of padding should be divided by pad_size_divisor
size_divisor=32,
# Additional paddings for pixel scale
extra_pad_ratio=0.5,
)

albu_train_transforms = [
dict(type='Blur', p=0.01),
dict(type='MedianBlur', p=0.01),
dict(type='ToGray', p=0.01),
dict(type='CLAHE', p=0.01),
]

pre_transform = [
dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),
dict(type='LoadAnnotations', with_bbox=True, _scope_='sscma'),
]

train_pipeline = [
*pre_transform,
dict(type='Mosaic', img_scale=imgsz, pad_val=114.0, pre_transform=pre_transform, _scope_='sscma'),
dict(
type='YOLOv5RandomAffine',
max_rotate_degree=0.0,
max_shear_degree=0.0,
scaling_ratio_range=(1 - affine_scale, 1 + affine_scale),
# imgsz is (width, height)
border=(-imgsz[0] // 2, -imgsz[1] // 2),
border_val=(114, 114, 114),
_scope_='sscma'
),
dict(
type='mmdet.Albu',
transforms=albu_train_transforms,
bbox_params=dict(type='BboxParams', format='pascal_voc', label_fields=['gt_bboxes_labels', 'gt_ignore_flags']),
keymap={'img': 'image', 'gt_bboxes': 'bboxes'},
),
dict(type='YOLOv5HSVRandomAug', _scope_='sscma'),
dict(type='mmdet.RandomFlip', prob=0.5),
dict(
type='mmdet.PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip', 'flip_direction')
),
]

train_dataloader = dict(
batch_size=batch,
num_workers=workers,
persistent_workers=persistent_workers,
pin_memory=True,
sampler=dict(type='DefaultSampler', shuffle=True),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file=train_ann,
data_prefix=dict(img=train_data),
filter_cfg=dict(filter_empty_gt=False, min_size=32),
pipeline=train_pipeline,
),
)

test_pipeline = [
dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),
dict(type='YOLOv5KeepRatioResize', scale=imgsz, _scope_='sscma'),
dict(type='sscma.LetterResize', scale=imgsz, allow_scale_up=False, pad_val=dict(img=114), _scope_='sscma'),
dict(type='LoadAnnotations', with_bbox=True, _scope_='sscma'),
dict(
type='mmdet.PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'pad_param'),
),
]

val_dataloader = dict(
batch_size=val_batch,
num_workers=val_workers,
persistent_workers=persistent_workers,
pin_memory=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
test_mode=True,
data_prefix=dict(img=val_data),
ann_file=val_ann,
pipeline=test_pipeline,
batch_shapes_cfg=batch_shapes_cfg,
),
)

test_dataloader = val_dataloader
52 changes: 34 additions & 18 deletions sscma/models/backbones/MobileNetv4.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,7 +5,8 @@
from torch import Tensor

from sscma.models.base.general import ConvNormActivation
from sscma.registry import BACKBONES
from sscma.registry import MODELS

from sscma.models.layers.nn_blocks import (
UniversalInvertedBottleneckBlock,
InvertedBottleneckBlock,
Expand Down Expand Up @@ -129,7 +130,8 @@ def mhsa_large_12px():
)


@BACKBONES.register_module()

@MODELS.register_module()
class MobileNetv4(nn.Module):
'''
Architecture: https://arxiv.org/abs/2404.10518
Expand All @@ -144,7 +146,7 @@ class MobileNetv4(nn.Module):
'small': [
('convbn', 'ReLU', 3, None, None, False, 2, 32, None, False), # 1/2
('fused_ib', 'ReLU', 3, None, None, False, 2, 32, 1, False), # 1/4
('fused_ib', 'ReLU', 3, None, None, False, 2, 64, 3, False), # 1/8
('fused_ib', 'ReLU', 3, None, None, False, 2, 64, 3, True), # 1/8
('uib', 'ReLU', None, 5, 5, True, 2, 96, 3.0, False), # 1/16
('uib', 'ReLU', None, 0, 3, True, 1, 96, 2.0, False), # IB
('uib', 'ReLU', None, 0, 3, True, 1, 96, 2.0, False), # IB
Expand Down Expand Up @@ -193,7 +195,7 @@ class MobileNetv4(nn.Module):
],
'large': [
('convbn', 'ReLU', 3, None, None, False, 2, 24, None, False),
('fused_ib', 'ReLU', 3, None, None, False, 2, 48, 4.0, True),
('fused_ib', 'ReLU', 3, None, None, False, 2, 48, 4.0, False),
('uib', 'ReLU', None, 3, 5, True, 2, 96, 4.0, False),
('uib', 'ReLU', None, 3, 3, True, 1, 96, 4.0, True),
('uib', 'ReLU', None, 3, 5, True, 2, 192, 4.0, False),
Expand Down Expand Up @@ -227,9 +229,9 @@ class MobileNetv4(nn.Module):
],
'hybridmedium': [
('convbn', 'ReLU', 3, None, None, False, 2, 32, None, False), # 1/2
('fused_ib', 'ReLU', 3, None, None, False, 2, 48, 4, True), # 1/4
('fused_ib', 'ReLU', 3, None, None, False, 2, 48, 4, False), # 1/4
('uib', 'ReLU', None, 3, 5, True, 2, 80, 4.0, False), # IB
('uib', 'ReLU', None, 3, 3, True, 1, 80, 2.0, False), # IB
('uib', 'ReLU', None, 3, 3, True, 1, 80, 2.0, True), # IB
('uib', 'ReLU', None, 3, 5, True, 2, 160, 6.0, False), # IB
('uib', 'ReLU', None, 0, 0, True, 1, 160, 2.0, False), # IB
('uib', 'ReLU', None, 3, 3, True, 1, 160, 4.0, False), # IB
Expand All @@ -242,7 +244,7 @@ class MobileNetv4(nn.Module):
('uib', 'ReLU', None, 3, 3, True, 1, 160, 4.0, False),
mhsa_medium_24px(),
('uib', 'ReLU', None, 3, 0, True, 1, 160, 4.0, True),
('uib', 'ReLU', None, 5, 5, True, 2, 256, 6.0, True),
('uib', 'ReLU', None, 5, 5, True, 2, 256, 6.0, False),
('uib', 'ReLU', None, 5, 5, True, 1, 256, 4.0, False),
('uib', 'ReLU', None, 3, 5, True, 1, 256, 4.0, False),
('uib', 'ReLU', None, 3, 5, True, 1, 256, 4.0, False),
Expand All @@ -265,7 +267,7 @@ class MobileNetv4(nn.Module):
],
'hybridlarge': [
('convbn', 'GELU', 3, None, None, False, 2, 24, None, False), # 1/2
('fused_ib', 'GELU', 3, None, None, False, 2, 48, 4, True), # 1/4
('fused_ib', 'GELU', 3, None, None, False, 2, 48, 4, False), # 1/4
('uib', 'GELU', None, 3, 5, True, 2, 96, 4.0, False), # IB
('uib', 'GELU', None, 3, 3, True, 1, 96, 4.0, True), # IB
('uib', 'GELU', None, 3, 5, True, 2, 192, 4.0, False), # IB
Expand All @@ -283,7 +285,7 @@ class MobileNetv4(nn.Module):
('uib', 'GELU', None, 5, 3, True, 1, 192, 4.0, False),
mhsa_large_24px(),
('uib', 'GELU', None, 3, 0, True, 1, 192, 4.0, True), # output
('uib', 'GELU', None, 5, 5, True, 2, 512, 4.0, True),
('uib', 'GELU', None, 5, 5, True, 2, 512, 4.0, False),
('uib', 'GELU', None, 5, 5, True, 1, 512, 4.0, False),
('uib', 'GELU', None, 5, 5, True, 1, 512, 4.0, False),
('uib', 'GELU', None, 5, 5, True, 1, 512, 4.0, False),
Expand Down Expand Up @@ -326,23 +328,29 @@ def __init__(

self._output_stride: int = (1,)

self.blocks_setting = []
self.block_settings = []
for setting in arch_setting:
if isinstance(setting, tuple):
self.blocks_setting.append(BlockConfig(*setting, input_channels=input_channels))
self.block_settings.append(BlockConfig(*setting, input_channels=input_channels))
else:
self.blocks_setting.append(BlockConfig(**setting, input_channels=input_channels))
if self.blocks_setting[-1].output_channels is not None:
input_channels = self.blocks_setting[-1].output_channels
self.block_settings.append(BlockConfig(**setting, input_channels=input_channels))
if self.block_settings[-1].output_channels is not None:
input_channels = self.block_settings[-1].output_channels

self._forward_blocks = self.build_layers()
last_output_block = 0
for i, block in enumerate(self.block_settings):
if block.isoutputblock:
last_output_block = i

self._forward_blocks = self.build_layers()[: last_output_block + 1]

def build_layers(self):
layers = []
block: BlockConfig
current_stride = 1
rate = 1
for block in self.blocks_setting:

for block in self.block_settings:

if not block.stride:
block.stride = 1
Expand All @@ -355,6 +363,7 @@ def build_layers(self):
layer_stride = block.stride
layer_rate = 1
current_stride *= block.stride

if block.block_name == 'convbn':
layer = ConvNormActivation(
block.input_channels,
Expand Down Expand Up @@ -422,9 +431,16 @@ def build_layers(self):
)
else:
raise ValueError(f'block name "{block.block_name}" is not supported')

layers.append(layer)

return nn.Sequential(*layers)

def forward(self, x):
x = self._forward_blocks(x)
return x
outs = []
for cfg, blk in zip(self.block_settings, self._forward_blocks):
x = blk(x)
if cfg.isoutputblock:
outs.append(x)

return tuple(outs)
2 changes: 2 additions & 0 deletions sscma/models/backbones/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,6 +3,7 @@
from .EfficientNet import EfficientNet
from .MobileNetv2 import MobileNetv2
from .MobileNetv3 import MobileNetV3
from .MobileNetv4 import MobileNetv4
from .pfld_mobilenet_v2 import PfldMobileNetV2
from .ShuffleNetV2 import ShuffleNetV2, CustomShuffleNetV2, FastShuffleNetV2
from .SoundNet import SoundNetRaw
Expand All @@ -17,6 +18,7 @@
'CustomShuffleNetV2',
'AxesNet',
'MobileNetV3',
'MobileNetv4',
'ShuffleNetV2',
'SqueezeNet',
'EfficientNet',
Expand Down

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