feat: swift yolo mbnv4

* Squashed commit of the following: commit 87250f5 Author: mjq2020 <[email protected]> Date: Fri Apr 26 10:33:07 2024 +0000 add: mobilenetv4 backbone commit 0771f18 Merge: 8e0b2f7 7f9c4e0 Author: mjq2020 <[email protected]> Date: Fri Apr 26 10:31:57 2024 +0000 Merge branch 'main' of https://github.com/mjq2020/EdgeLab into main commit 8e0b2f7 Merge: ac0f39d 9b00e64 Author: mjq2020 <[email protected]> Date: Fri Apr 19 06:22:49 2024 +0000 Merge branch 'main' of https://github.com/mjq2020/EdgeLab into main commit ac0f39d Merge: c4ea712 1f67493 Author: mjq2020 <[email protected]> Date: Mon Apr 1 10:02:11 2024 +0000 Merge branch 'main' of https://github.com/mjq2020/EdgeLab into main commit c4ea712 Author: mjq2020 <[email protected]> Date: Mon Apr 1 10:00:39 2024 +0000 Fix: cls loss weight too high commit b87fc04 Merge: f146c73 ee72f81 Author: mjq2020 <[email protected]> Date: Mon Apr 1 09:54:45 2024 +0000 Merge branch 'main' of https://github.com/mjq2020/EdgeLab into main commit f146c73 Merge: c068454 289360c Author: mjq2020 <[email protected]> Date: Tue Mar 19 02:16:47 2024 +0000 Merge branch 'main' of https://github.com/mjq2020/EdgeLab into main commit c068454 Author: mjq2020 <[email protected]> Date: Mon Mar 18 07:05:34 2024 +0000 Optim: model inference display commit fc8874f Author: mjq2020 <[email protected]> Date: Mon Mar 18 07:03:33 2024 +0000 Fix: data type bug commit f1d76fc Merge: 1378e1b 3c61e3e Author: mjq2020 <[email protected]> Date: Thu Mar 14 18:42:18 2024 +0800 Merge branch 'Seeed-Studio:main' into main commit 1378e1b Merge: 8c8ffd7 31c5291 Author: mjq2020 <[email protected]> Date: Tue Jan 30 11:41:16 2024 +0800 Merge branch 'Seeed-Studio:main' into main commit 8c8ffd7 Merge: c67ed2d ebb1ec2 Author: mjq2020 <[email protected]> Date: Fri Oct 13 11:09:55 2023 +0800 Merge branch 'Seeed-Studio:main' into main commit c67ed2d Merge: d70e424 9be0612 Author: mjq2020 <[email protected]> Date: Sat Sep 23 16:18:57 2023 +0800 Merge branch 'Seeed-Studio:main' into main * feat: mobilenetv4 swift yolo backbone * chore: modify mbnv4 medium/large outputs
Seeed-Studio · May 22, 2024 · 8e6ad23 · 8e6ad23
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diff --git a/configs/swift_yolo/swift_yolo_1xb16_300e_coco_mbnv4s.py b/configs/swift_yolo/swift_yolo_1xb16_300e_coco_mbnv4s.py
@@ -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
diff --git a/sscma/models/backbones/MobileNetv4.py b/sscma/models/backbones/MobileNetv4.py
@@ -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,
@@ -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
@@ -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
@@ -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),
@@ -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
@@ -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),
@@ -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
@@ -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),
@@ -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
@@ -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,
@@ -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)
diff --git a/sscma/models/backbones/__init__.py b/sscma/models/backbones/__init__.py
@@ -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
@@ -17,6 +18,7 @@
  'CustomShuffleNetV2',
  'AxesNet',
  'MobileNetV3',
+ 'MobileNetv4',
  'ShuffleNetV2',
  'SqueezeNet',
  'EfficientNet',