[Feature] New config type (#539)
* update test.py * Update new config * Update new config * Update new confignew_config
Mashiro
GitHub
parent
d3cd028f4b
commit
4a309ce998
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# Copyright (c) OpenMMLab. All rights reserved.
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if '_base_':
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from mmyolo.configs.rtmdet.rtmdet_tiny_syncbn_fast_8xb32_300e_coco import *
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from mmrazor.models import (ChannelWiseDivergence, ConfigurableDistiller,
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ConvModuleConnector, FpnTeacherDistill,
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NormConnector)
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default_scope = None
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teacher_ckpt = 'https://download.openmmlab.com/mmyolo/v0/rtmdet/rtmdet_x_syncbn_fast_8xb32-300e_coco/rtmdet_x_syncbn_fast_8xb32-300e_coco_20221231_100345-b85cd476.pth' # noqa: E501
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norm_cfg = dict(type='BN', affine=False, track_running_stats=False)
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model = dict(
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type=FpnTeacherDistill,
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architecture=dict(
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cfg_path='mmyolo::rtmdet/rtmdet_l_syncbn_fast_8xb32-300e_coco.py'),
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teacher=dict(
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cfg_path='mmyolo::rtmdet/rtmdet_x_syncbn_fast_8xb32-300e_coco.py'),
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# teacher_ckpt=teacher_ckpt,
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distiller=dict(
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type=ConfigurableDistiller,
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# `recorders` are used to record various intermediate results during
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# the model forward.
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student_recorders=dict(
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fpn0=dict(type='ModuleOutputs', source='neck.out_layers.0.conv'),
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fpn1=dict(type='ModuleOutputs', source='neck.out_layers.1.conv'),
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fpn2=dict(type='ModuleOutputs', source='neck.out_layers.2.conv'),
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),
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teacher_recorders=dict(
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fpn0=dict(type='ModuleOutputs', source='neck.out_layers.0.conv'),
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fpn1=dict(type='ModuleOutputs', source='neck.out_layers.1.conv'),
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fpn2=dict(type='ModuleOutputs', source='neck.out_layers.2.conv')),
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# `connectors` are adaptive layers which usually map teacher's and
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# students features to the same dimension.
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connectors=dict(
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fpn0_s=dict(
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type=ConvModuleConnector,
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in_channel=256,
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out_channel=320,
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bias=False,
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norm_cfg=norm_cfg,
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act_cfg=None),
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fpn0_t=dict(
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type=NormConnector, in_channels=320, norm_cfg=norm_cfg),
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fpn1_s=dict(
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type=ConvModuleConnector,
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in_channel=256,
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out_channel=320,
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bias=False,
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norm_cfg=norm_cfg,
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act_cfg=None),
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fpn1_t=dict(
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type=NormConnector, in_channels=320, norm_cfg=norm_cfg),
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fpn2_s=dict(
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type=ConvModuleConnector,
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in_channel=256,
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out_channel=320,
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bias=False,
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norm_cfg=norm_cfg,
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act_cfg=None),
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fpn2_t=dict(
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type=NormConnector, in_channels=320, norm_cfg=norm_cfg)),
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distill_losses=dict(
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loss_fpn0=dict(type=ChannelWiseDivergence, loss_weight=1),
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loss_fpn1=dict(type=ChannelWiseDivergence, loss_weight=1),
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loss_fpn2=dict(type=ChannelWiseDivergence, loss_weight=1)),
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# `loss_forward_mappings` are mappings between distill loss forward
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# arguments and records.
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loss_forward_mappings=dict(
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loss_fpn0=dict(
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preds_S=dict(
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from_student=True, recorder='fpn0', connector='fpn0_s'),
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preds_T=dict(
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from_student=False, recorder='fpn0', connector='fpn0_t')),
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loss_fpn1=dict(
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preds_S=dict(
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from_student=True, recorder='fpn1', connector='fpn1_s'),
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preds_T=dict(
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from_student=False, recorder='fpn1', connector='fpn1_t')),
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loss_fpn2=dict(
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preds_S=dict(
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from_student=True, recorder='fpn2', connector='fpn2_s'),
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preds_T=dict(
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from_student=False, recorder='fpn2',
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connector='fpn2_t')))))
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find_unused_parameters = True
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# Copyright (c) OpenMMLab. All rights reserved.
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#############################################################################
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"""You have to fill these args.
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_base_(str): The path to your pretrain config file.
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fix_subnet (Union[dict,str]): The dict store the pruning structure or the
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json file including it.
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divisor (int): The divisor the make the channel number divisible.
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"""
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if '_base_':
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from mmdet.configs.retinanet.retinanet_r50_fpn_1x_coco import *
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from mmrazor.implementations.pruning.group_fisher import \
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GroupFisherDeploySubModel
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fix_subnet = {
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'backbone.conv1_(0, 64)_64': 60,
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'backbone.layer1.0.conv1_(0, 64)_64': 48,
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'backbone.layer1.0.conv2_(0, 64)_64': 44,
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'backbone.layer1.0.conv3_(0, 256)_256': 250,
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'backbone.layer1.1.conv1_(0, 64)_64': 40,
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'backbone.layer1.1.conv2_(0, 64)_64': 41,
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'backbone.layer1.2.conv1_(0, 64)_64': 48,
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'backbone.layer1.2.conv2_(0, 64)_64': 62,
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'backbone.layer2.0.conv1_(0, 128)_128': 115,
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'backbone.layer2.0.conv2_(0, 128)_128': 127,
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'backbone.layer2.0.conv3_(0, 512)_512': 511,
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'backbone.layer2.1.conv1_(0, 128)_128': 69,
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'backbone.layer2.1.conv2_(0, 128)_128': 83,
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'backbone.layer2.2.conv1_(0, 128)_128': 111,
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'backbone.layer2.2.conv2_(0, 128)_128': 121,
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'backbone.layer2.3.conv1_(0, 128)_128': 122,
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'backbone.layer2.3.conv2_(0, 128)_128': 128,
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'backbone.layer3.0.conv1_(0, 256)_256': 255,
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'backbone.layer3.0.conv2_(0, 256)_256': 256,
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'backbone.layer3.0.conv3_(0, 1024)_1024': 1024,
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'backbone.layer3.1.conv1_(0, 256)_256': 216,
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'backbone.layer3.1.conv2_(0, 256)_256': 223,
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'backbone.layer3.2.conv1_(0, 256)_256': 229,
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'backbone.layer3.2.conv2_(0, 256)_256': 247,
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'backbone.layer3.3.conv1_(0, 256)_256': 239,
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'backbone.layer3.3.conv2_(0, 256)_256': 246,
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'backbone.layer3.4.conv1_(0, 256)_256': 237,
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'backbone.layer3.4.conv2_(0, 256)_256': 239,
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'backbone.layer3.5.conv1_(0, 256)_256': 233,
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'backbone.layer3.5.conv2_(0, 256)_256': 221,
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'backbone.layer4.0.conv1_(0, 512)_512': 499,
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'backbone.layer4.0.conv2_(0, 512)_512': 494,
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'backbone.layer4.0.conv3_(0, 2048)_2048': 2031,
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'backbone.layer4.1.conv1_(0, 512)_512': 451,
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'backbone.layer4.1.conv2_(0, 512)_512': 401,
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'backbone.layer4.2.conv1_(0, 512)_512': 396,
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'backbone.layer4.2.conv2_(0, 512)_512': 237,
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'neck.lateral_convs.0.conv_(0, 256)_256': 237,
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'neck.fpn_convs.0.conv_(0, 256)_256': 241,
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'bbox_head.cls_convs.0.conv_(0, 256)_256': 133,
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'bbox_head.cls_convs.1.conv_(0, 256)_256': 134,
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'bbox_head.cls_convs.2.conv_(0, 256)_256': 139,
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'bbox_head.cls_convs.3.conv_(0, 256)_256': 79,
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'bbox_head.reg_convs.0.conv_(0, 256)_256': 89,
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'bbox_head.reg_convs.1.conv_(0, 256)_256': 92,
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'bbox_head.reg_convs.2.conv_(0, 256)_256': 82,
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'bbox_head.reg_convs.3.conv_(0, 256)_256': 117
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}
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divisor = 16
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##############################################################################
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architecture = model
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model = dict(
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type=GroupFisherDeploySubModel,
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architecture=architecture,
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fix_subnet=fix_subnet,
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divisor=divisor,
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)
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# Copyright (c) OpenMMLab. All rights reserved.
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#############################################################################
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"""You have to fill these args.
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_base_(str): The path to your pretrain config file.
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fix_subnet (Union[dict,str]): The dict store the pruning structure or the
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json file including it.
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divisor (int): The divisor the make the channel number divisible.
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"""
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if '_base_':
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from mmpose.configs.body_2d_keypoint.rtmpose.coco.rtmpose_s_8xb256_420e_aic_coco_256x192 import * # noqa: E501
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from mmrazor.implementations.pruning.group_fisher import \
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GroupFisherDeploySubModel
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fix_subnet = {
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'backbone.stem.0.conv_(0, 16)_16': 8,
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'backbone.stem.1.conv_(0, 16)_16': 9,
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'backbone.stem.2.conv_(0, 32)_32': 9,
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'backbone.stage1.0.conv_(0, 64)_64': 32,
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'backbone.stage1.1.short_conv.conv_(0, 32)_32': 30,
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'backbone.stage1.1.main_conv.conv_(0, 32)_32': 29,
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'backbone.stage1.1.blocks.0.conv1.conv_(0, 32)_32': 24,
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'backbone.stage1.1.final_conv.conv_(0, 64)_64': 27,
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'backbone.stage2.0.conv_(0, 128)_128': 62,
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'backbone.stage2.1.short_conv.conv_(0, 64)_64': 63,
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'backbone.stage2.1.main_conv.conv_(0, 64)_64': 64,
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'backbone.stage2.1.blocks.0.conv1.conv_(0, 64)_64': 56,
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'backbone.stage2.1.blocks.1.conv1.conv_(0, 64)_64': 62,
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'backbone.stage2.1.final_conv.conv_(0, 128)_128': 65,
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'backbone.stage3.0.conv_(0, 256)_256': 167,
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'backbone.stage3.1.short_conv.conv_(0, 128)_128': 127,
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'backbone.stage3.1.main_conv.conv_(0, 128)_128': 128,
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'backbone.stage3.1.blocks.0.conv1.conv_(0, 128)_128': 124,
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'backbone.stage3.1.blocks.1.conv1.conv_(0, 128)_128': 123,
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'backbone.stage3.1.final_conv.conv_(0, 256)_256': 172,
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'backbone.stage4.0.conv_(0, 512)_512': 337,
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'backbone.stage4.1.conv1.conv_(0, 256)_256': 256,
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'backbone.stage4.1.conv2.conv_(0, 512)_512': 379,
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'backbone.stage4.2.short_conv.conv_(0, 256)_256': 188,
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'backbone.stage4.2.main_conv.conv_(0, 256)_256': 227,
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'backbone.stage4.2.blocks.0.conv1.conv_(0, 256)_256': 238,
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'backbone.stage4.2.blocks.0.conv2.pointwise_conv.conv_(0, 256)_256': 195,
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'backbone.stage4.2.final_conv.conv_(0, 512)_512': 163
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}
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divisor = 8
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##############################################################################
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architecture = model
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model = dict(
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type=GroupFisherDeploySubModel,
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architecture=architecture,
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fix_subnet=fix_subnet,
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divisor=divisor,
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)
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