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* update test.py * Update new config * Update new config * Update new config
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,88 @@ | ||
| # Copyright (c) OpenMMLab. All rights reserved. | ||
| if '_base_': | ||
| from mmyolo.configs.rtmdet.rtmdet_tiny_syncbn_fast_8xb32_300e_coco import * | ||
|
|
||
| from mmrazor.models import (ChannelWiseDivergence, ConfigurableDistiller, | ||
| ConvModuleConnector, FpnTeacherDistill, | ||
| NormConnector) | ||
|
|
||
| default_scope = None | ||
| 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 | ||
|
|
||
| norm_cfg = dict(type='BN', affine=False, track_running_stats=False) | ||
|
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| model = dict( | ||
| type=FpnTeacherDistill, | ||
| architecture=dict( | ||
| cfg_path='mmyolo::rtmdet/rtmdet_l_syncbn_fast_8xb32-300e_coco.py'), | ||
| teacher=dict( | ||
| cfg_path='mmyolo::rtmdet/rtmdet_x_syncbn_fast_8xb32-300e_coco.py'), | ||
| # teacher_ckpt=teacher_ckpt, | ||
| distiller=dict( | ||
| type=ConfigurableDistiller, | ||
| # `recorders` are used to record various intermediate results during | ||
| # the model forward. | ||
| student_recorders=dict( | ||
| fpn0=dict(type='ModuleOutputs', source='neck.out_layers.0.conv'), | ||
| fpn1=dict(type='ModuleOutputs', source='neck.out_layers.1.conv'), | ||
| fpn2=dict(type='ModuleOutputs', source='neck.out_layers.2.conv'), | ||
| ), | ||
| teacher_recorders=dict( | ||
| fpn0=dict(type='ModuleOutputs', source='neck.out_layers.0.conv'), | ||
| fpn1=dict(type='ModuleOutputs', source='neck.out_layers.1.conv'), | ||
| fpn2=dict(type='ModuleOutputs', source='neck.out_layers.2.conv')), | ||
| # `connectors` are adaptive layers which usually map teacher's and | ||
| # students features to the same dimension. | ||
| connectors=dict( | ||
| fpn0_s=dict( | ||
| type=ConvModuleConnector, | ||
| in_channel=256, | ||
| out_channel=320, | ||
| bias=False, | ||
| norm_cfg=norm_cfg, | ||
| act_cfg=None), | ||
| fpn0_t=dict( | ||
| type=NormConnector, in_channels=320, norm_cfg=norm_cfg), | ||
| fpn1_s=dict( | ||
| type=ConvModuleConnector, | ||
| in_channel=256, | ||
| out_channel=320, | ||
| bias=False, | ||
| norm_cfg=norm_cfg, | ||
| act_cfg=None), | ||
| fpn1_t=dict( | ||
| type=NormConnector, in_channels=320, norm_cfg=norm_cfg), | ||
| fpn2_s=dict( | ||
| type=ConvModuleConnector, | ||
| in_channel=256, | ||
| out_channel=320, | ||
| bias=False, | ||
| norm_cfg=norm_cfg, | ||
| act_cfg=None), | ||
| fpn2_t=dict( | ||
| type=NormConnector, in_channels=320, norm_cfg=norm_cfg)), | ||
| distill_losses=dict( | ||
| loss_fpn0=dict(type=ChannelWiseDivergence, loss_weight=1), | ||
| loss_fpn1=dict(type=ChannelWiseDivergence, loss_weight=1), | ||
| loss_fpn2=dict(type=ChannelWiseDivergence, loss_weight=1)), | ||
| # `loss_forward_mappings` are mappings between distill loss forward | ||
| # arguments and records. | ||
| loss_forward_mappings=dict( | ||
| loss_fpn0=dict( | ||
| preds_S=dict( | ||
| from_student=True, recorder='fpn0', connector='fpn0_s'), | ||
| preds_T=dict( | ||
| from_student=False, recorder='fpn0', connector='fpn0_t')), | ||
| loss_fpn1=dict( | ||
| preds_S=dict( | ||
| from_student=True, recorder='fpn1', connector='fpn1_s'), | ||
| preds_T=dict( | ||
| from_student=False, recorder='fpn1', connector='fpn1_t')), | ||
| loss_fpn2=dict( | ||
| preds_S=dict( | ||
| from_student=True, recorder='fpn2', connector='fpn2_s'), | ||
| preds_T=dict( | ||
| from_student=False, recorder='fpn2', | ||
| connector='fpn2_t'))))) | ||
|
|
||
| find_unused_parameters = True |
75 changes: 75 additions & 0 deletions
75
...pruning/mmdet/group_fisher/retinanet/group_fisher_act_deploy_retinanet_r50_fpn_1x_coco.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,75 @@ | ||
| # Copyright (c) OpenMMLab. All rights reserved. | ||
| ############################################################################# | ||
| """You have to fill these args. | ||
| _base_(str): The path to your pretrain config file. | ||
| fix_subnet (Union[dict,str]): The dict store the pruning structure or the | ||
| json file including it. | ||
| divisor (int): The divisor the make the channel number divisible. | ||
| """ | ||
| if '_base_': | ||
| from mmdet.configs.retinanet.retinanet_r50_fpn_1x_coco import * | ||
|
|
||
| from mmrazor.implementations.pruning.group_fisher import \ | ||
| GroupFisherDeploySubModel | ||
|
|
||
| fix_subnet = { | ||
| 'backbone.conv1_(0, 64)_64': 60, | ||
| 'backbone.layer1.0.conv1_(0, 64)_64': 48, | ||
| 'backbone.layer1.0.conv2_(0, 64)_64': 44, | ||
| 'backbone.layer1.0.conv3_(0, 256)_256': 250, | ||
| 'backbone.layer1.1.conv1_(0, 64)_64': 40, | ||
| 'backbone.layer1.1.conv2_(0, 64)_64': 41, | ||
| 'backbone.layer1.2.conv1_(0, 64)_64': 48, | ||
| 'backbone.layer1.2.conv2_(0, 64)_64': 62, | ||
| 'backbone.layer2.0.conv1_(0, 128)_128': 115, | ||
| 'backbone.layer2.0.conv2_(0, 128)_128': 127, | ||
| 'backbone.layer2.0.conv3_(0, 512)_512': 511, | ||
| 'backbone.layer2.1.conv1_(0, 128)_128': 69, | ||
| 'backbone.layer2.1.conv2_(0, 128)_128': 83, | ||
| 'backbone.layer2.2.conv1_(0, 128)_128': 111, | ||
| 'backbone.layer2.2.conv2_(0, 128)_128': 121, | ||
| 'backbone.layer2.3.conv1_(0, 128)_128': 122, | ||
| 'backbone.layer2.3.conv2_(0, 128)_128': 128, | ||
| 'backbone.layer3.0.conv1_(0, 256)_256': 255, | ||
| 'backbone.layer3.0.conv2_(0, 256)_256': 256, | ||
| 'backbone.layer3.0.conv3_(0, 1024)_1024': 1024, | ||
| 'backbone.layer3.1.conv1_(0, 256)_256': 216, | ||
| 'backbone.layer3.1.conv2_(0, 256)_256': 223, | ||
| 'backbone.layer3.2.conv1_(0, 256)_256': 229, | ||
| 'backbone.layer3.2.conv2_(0, 256)_256': 247, | ||
| 'backbone.layer3.3.conv1_(0, 256)_256': 239, | ||
| 'backbone.layer3.3.conv2_(0, 256)_256': 246, | ||
| 'backbone.layer3.4.conv1_(0, 256)_256': 237, | ||
| 'backbone.layer3.4.conv2_(0, 256)_256': 239, | ||
| 'backbone.layer3.5.conv1_(0, 256)_256': 233, | ||
| 'backbone.layer3.5.conv2_(0, 256)_256': 221, | ||
| 'backbone.layer4.0.conv1_(0, 512)_512': 499, | ||
| 'backbone.layer4.0.conv2_(0, 512)_512': 494, | ||
| 'backbone.layer4.0.conv3_(0, 2048)_2048': 2031, | ||
| 'backbone.layer4.1.conv1_(0, 512)_512': 451, | ||
| 'backbone.layer4.1.conv2_(0, 512)_512': 401, | ||
| 'backbone.layer4.2.conv1_(0, 512)_512': 396, | ||
| 'backbone.layer4.2.conv2_(0, 512)_512': 237, | ||
| 'neck.lateral_convs.0.conv_(0, 256)_256': 237, | ||
| 'neck.fpn_convs.0.conv_(0, 256)_256': 241, | ||
| 'bbox_head.cls_convs.0.conv_(0, 256)_256': 133, | ||
| 'bbox_head.cls_convs.1.conv_(0, 256)_256': 134, | ||
| 'bbox_head.cls_convs.2.conv_(0, 256)_256': 139, | ||
| 'bbox_head.cls_convs.3.conv_(0, 256)_256': 79, | ||
| 'bbox_head.reg_convs.0.conv_(0, 256)_256': 89, | ||
| 'bbox_head.reg_convs.1.conv_(0, 256)_256': 92, | ||
| 'bbox_head.reg_convs.2.conv_(0, 256)_256': 82, | ||
| 'bbox_head.reg_convs.3.conv_(0, 256)_256': 117 | ||
| } | ||
| divisor = 16 | ||
|
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||
| ############################################################################## | ||
|
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| architecture = model | ||
| model = dict( | ||
| type=GroupFisherDeploySubModel, | ||
| architecture=architecture, | ||
| fix_subnet=fix_subnet, | ||
| divisor=divisor, | ||
| ) |
56 changes: 56 additions & 0 deletions
56
...pruning/mmpose/group_fisher/group_fisher_deploy_rtmpose-s_8xb256-420e_aic-coco-256x192.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,56 @@ | ||
| # Copyright (c) OpenMMLab. All rights reserved. | ||
| ############################################################################# | ||
| """You have to fill these args. | ||
| _base_(str): The path to your pretrain config file. | ||
| fix_subnet (Union[dict,str]): The dict store the pruning structure or the | ||
| json file including it. | ||
| divisor (int): The divisor the make the channel number divisible. | ||
| """ | ||
| if '_base_': | ||
| from mmpose.configs.body_2d_keypoint.rtmpose.coco.rtmpose_s_8xb256_420e_aic_coco_256x192 import * # noqa: E501 | ||
|
|
||
| from mmrazor.implementations.pruning.group_fisher import \ | ||
| GroupFisherDeploySubModel | ||
|
|
||
| fix_subnet = { | ||
| 'backbone.stem.0.conv_(0, 16)_16': 8, | ||
| 'backbone.stem.1.conv_(0, 16)_16': 9, | ||
| 'backbone.stem.2.conv_(0, 32)_32': 9, | ||
| 'backbone.stage1.0.conv_(0, 64)_64': 32, | ||
| 'backbone.stage1.1.short_conv.conv_(0, 32)_32': 30, | ||
| 'backbone.stage1.1.main_conv.conv_(0, 32)_32': 29, | ||
| 'backbone.stage1.1.blocks.0.conv1.conv_(0, 32)_32': 24, | ||
| 'backbone.stage1.1.final_conv.conv_(0, 64)_64': 27, | ||
| 'backbone.stage2.0.conv_(0, 128)_128': 62, | ||
| 'backbone.stage2.1.short_conv.conv_(0, 64)_64': 63, | ||
| 'backbone.stage2.1.main_conv.conv_(0, 64)_64': 64, | ||
| 'backbone.stage2.1.blocks.0.conv1.conv_(0, 64)_64': 56, | ||
| 'backbone.stage2.1.blocks.1.conv1.conv_(0, 64)_64': 62, | ||
| 'backbone.stage2.1.final_conv.conv_(0, 128)_128': 65, | ||
| 'backbone.stage3.0.conv_(0, 256)_256': 167, | ||
| 'backbone.stage3.1.short_conv.conv_(0, 128)_128': 127, | ||
| 'backbone.stage3.1.main_conv.conv_(0, 128)_128': 128, | ||
| 'backbone.stage3.1.blocks.0.conv1.conv_(0, 128)_128': 124, | ||
| 'backbone.stage3.1.blocks.1.conv1.conv_(0, 128)_128': 123, | ||
| 'backbone.stage3.1.final_conv.conv_(0, 256)_256': 172, | ||
| 'backbone.stage4.0.conv_(0, 512)_512': 337, | ||
| 'backbone.stage4.1.conv1.conv_(0, 256)_256': 256, | ||
| 'backbone.stage4.1.conv2.conv_(0, 512)_512': 379, | ||
| 'backbone.stage4.2.short_conv.conv_(0, 256)_256': 188, | ||
| 'backbone.stage4.2.main_conv.conv_(0, 256)_256': 227, | ||
| 'backbone.stage4.2.blocks.0.conv1.conv_(0, 256)_256': 238, | ||
| 'backbone.stage4.2.blocks.0.conv2.pointwise_conv.conv_(0, 256)_256': 195, | ||
| 'backbone.stage4.2.final_conv.conv_(0, 512)_512': 163 | ||
| } | ||
| divisor = 8 | ||
| ############################################################################## | ||
|
|
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| architecture = model | ||
|
|
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| model = dict( | ||
| type=GroupFisherDeploySubModel, | ||
| architecture=architecture, | ||
| fix_subnet=fix_subnet, | ||
| divisor=divisor, | ||
| ) |
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