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add Faster rcnn c5 (open-mmlab#4043)
* add faster rcnn C5 * add trained faster rcnn C5 model url * fix typos * update based on 1-st comments * update unittest for ChannelMapper * small changes of ChannelMapper's docstring * small changes of ChannelMapper's docstring
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,107 @@ | ||
| # model settings | ||
| norm_cfg = dict(type='BN', requires_grad=False) | ||
| model = dict( | ||
| type='FasterRCNN', | ||
| pretrained='open-mmlab://detectron2/resnet50_caffe', | ||
| backbone=dict( | ||
| type='ResNet', | ||
| depth=50, | ||
| num_stages=4, | ||
| strides=(1, 2, 2, 1), | ||
| dilations=(1, 1, 1, 2), | ||
| out_indices=(3, ), | ||
| frozen_stages=1, | ||
| norm_cfg=norm_cfg, | ||
| norm_eval=True, | ||
| style='caffe'), | ||
| rpn_head=dict( | ||
| type='RPNHead', | ||
| in_channels=2048, | ||
| feat_channels=2048, | ||
| anchor_generator=dict( | ||
| type='AnchorGenerator', | ||
| scales=[2, 4, 8, 16, 32], | ||
| ratios=[0.5, 1.0, 2.0], | ||
| strides=[16]), | ||
| bbox_coder=dict( | ||
| type='DeltaXYWHBBoxCoder', | ||
| target_means=[.0, .0, .0, .0], | ||
| target_stds=[1.0, 1.0, 1.0, 1.0]), | ||
| loss_cls=dict( | ||
| type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), | ||
| loss_bbox=dict(type='L1Loss', loss_weight=1.0)), | ||
| roi_head=dict( | ||
| type='StandardRoIHead', | ||
| bbox_roi_extractor=dict( | ||
| type='SingleRoIExtractor', | ||
| roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0), | ||
| out_channels=2048, | ||
| featmap_strides=[16]), | ||
| bbox_head=dict( | ||
| type='Shared2FCBBoxHead', | ||
| in_channels=2048, | ||
| fc_out_channels=1024, | ||
| roi_feat_size=7, | ||
| num_classes=80, | ||
| bbox_coder=dict( | ||
| type='DeltaXYWHBBoxCoder', | ||
| target_means=[0., 0., 0., 0.], | ||
| target_stds=[0.1, 0.1, 0.2, 0.2]), | ||
| reg_class_agnostic=False, | ||
| loss_cls=dict( | ||
| type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), | ||
| loss_bbox=dict(type='L1Loss', loss_weight=1.0)))) | ||
| # model training and testing settings | ||
| train_cfg = dict( | ||
| rpn=dict( | ||
| assigner=dict( | ||
| type='MaxIoUAssigner', | ||
| pos_iou_thr=0.7, | ||
| neg_iou_thr=0.3, | ||
| min_pos_iou=0.3, | ||
| match_low_quality=True, | ||
| ignore_iof_thr=-1), | ||
| sampler=dict( | ||
| type='RandomSampler', | ||
| num=256, | ||
| pos_fraction=0.5, | ||
| neg_pos_ub=-1, | ||
| add_gt_as_proposals=False), | ||
| allowed_border=0, | ||
| pos_weight=-1, | ||
| debug=False), | ||
| rpn_proposal=dict( | ||
| nms_across_levels=False, | ||
| nms_pre=12000, | ||
| nms_post=2000, | ||
| max_num=2000, | ||
| nms_thr=0.7, | ||
| min_bbox_size=0), | ||
| rcnn=dict( | ||
| assigner=dict( | ||
| type='MaxIoUAssigner', | ||
| pos_iou_thr=0.5, | ||
| neg_iou_thr=0.5, | ||
| min_pos_iou=0.5, | ||
| match_low_quality=False, | ||
| ignore_iof_thr=-1), | ||
| sampler=dict( | ||
| type='RandomSampler', | ||
| num=512, | ||
| pos_fraction=0.25, | ||
| neg_pos_ub=-1, | ||
| add_gt_as_proposals=True), | ||
| pos_weight=-1, | ||
| debug=False)) | ||
| test_cfg = dict( | ||
| rpn=dict( | ||
| nms_across_levels=False, | ||
| nms_pre=6000, | ||
| nms_post=1000, | ||
| max_num=1000, | ||
| nms_thr=0.7, | ||
| min_bbox_size=0), | ||
| rcnn=dict( | ||
| score_thr=0.05, | ||
| nms=dict(type='nms', iou_threshold=0.5), | ||
| max_per_img=100)) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,37 @@ | ||
| _base_ = [ | ||
| '../_base_/models/faster_rcnn_r50_caffe_dc5.py', | ||
| '../_base_/datasets/coco_detection.py', | ||
| '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' | ||
| ] | ||
| # use caffe img_norm | ||
| img_norm_cfg = dict( | ||
| mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) | ||
| train_pipeline = [ | ||
| dict(type='LoadImageFromFile'), | ||
| dict(type='LoadAnnotations', with_bbox=True), | ||
| dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), | ||
| dict(type='RandomFlip', flip_ratio=0.5), | ||
| dict(type='Normalize', **img_norm_cfg), | ||
| dict(type='Pad', size_divisor=32), | ||
| dict(type='DefaultFormatBundle'), | ||
| dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), | ||
| ] | ||
| test_pipeline = [ | ||
| dict(type='LoadImageFromFile'), | ||
| dict( | ||
| type='MultiScaleFlipAug', | ||
| img_scale=(1333, 800), | ||
| flip=False, | ||
| transforms=[ | ||
| dict(type='Resize', keep_ratio=True), | ||
| dict(type='RandomFlip'), | ||
| dict(type='Normalize', **img_norm_cfg), | ||
| dict(type='Pad', size_divisor=32), | ||
| dict(type='ImageToTensor', keys=['img']), | ||
| dict(type='Collect', keys=['img']), | ||
| ]) | ||
| ] | ||
| data = dict( | ||
| train=dict(pipeline=train_pipeline), | ||
| val=dict(pipeline=test_pipeline), | ||
| test=dict(pipeline=test_pipeline)) |
42 changes: 42 additions & 0 deletions
42
configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,42 @@ | ||
| _base_ = [ | ||
| '../_base_/models/faster_rcnn_r50_caffe_dc5.py', | ||
| '../_base_/datasets/coco_detection.py', | ||
| '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' | ||
| ] | ||
| # use caffe img_norm | ||
| img_norm_cfg = dict( | ||
| mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) | ||
| train_pipeline = [ | ||
| dict(type='LoadImageFromFile'), | ||
| dict(type='LoadAnnotations', with_bbox=True), | ||
| dict( | ||
| type='Resize', | ||
| img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), | ||
| (1333, 768), (1333, 800)], | ||
| multiscale_mode='value', | ||
| keep_ratio=True), | ||
| dict(type='RandomFlip', flip_ratio=0.5), | ||
| dict(type='Normalize', **img_norm_cfg), | ||
| dict(type='Pad', size_divisor=32), | ||
| dict(type='DefaultFormatBundle'), | ||
| dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), | ||
| ] | ||
| test_pipeline = [ | ||
| dict(type='LoadImageFromFile'), | ||
| dict( | ||
| type='MultiScaleFlipAug', | ||
| img_scale=(1333, 800), | ||
| flip=False, | ||
| transforms=[ | ||
| dict(type='Resize', keep_ratio=True), | ||
| dict(type='RandomFlip'), | ||
| dict(type='Normalize', **img_norm_cfg), | ||
| dict(type='Pad', size_divisor=32), | ||
| dict(type='ImageToTensor', keys=['img']), | ||
| dict(type='Collect', keys=['img']), | ||
| ]) | ||
| ] | ||
| data = dict( | ||
| train=dict(pipeline=train_pipeline), | ||
| val=dict(pipeline=test_pipeline), | ||
| test=dict(pipeline=test_pipeline)) |
4 changes: 4 additions & 0 deletions
4
configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco.py
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|---|---|---|
| @@ -0,0 +1,4 @@ | ||
| _base_ = './faster_rcnn_r50_caffe_dc5_mstrain_1x_coco.py' | ||
| # learning policy | ||
| lr_config = dict(step=[28, 34]) | ||
| total_epochs = 36 |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,74 @@ | ||
| import torch.nn as nn | ||
| from mmcv.cnn import ConvModule, xavier_init | ||
|
|
||
| from ..builder import NECKS | ||
|
|
||
|
|
||
| @NECKS.register_module() | ||
| class ChannelMapper(nn.Module): | ||
| r"""Channel Mapper to reduce/increase channels of backbone features. | ||
| This is used to reduce/increase channels of backbone features. | ||
| Args: | ||
| in_channels (List[int]): Number of input channels per scale. | ||
| out_channels (int): Number of output channels (used at each scale). | ||
| kernel_size (int, optional): kernel_size for reducing channels (used | ||
| at each scale). Default: 3. | ||
| conv_cfg (dict, optional): Config dict for convolution layer. | ||
| Default: None. | ||
| norm_cfg (dict, optional): Config dict for normalization layer. | ||
| Default: None. | ||
| act_cfg (dict, optional): Config dict for activation layer in | ||
| ConvModule. Default: dict(type='ReLU'). | ||
| Example: | ||
| >>> import torch | ||
| >>> in_channels = [2, 3, 5, 7] | ||
| >>> scales = [340, 170, 84, 43] | ||
| >>> inputs = [torch.rand(1, c, s, s) | ||
| ... for c, s in zip(in_channels, scales)] | ||
| >>> self = ChannelMapper(in_channels, 11, 3).eval() | ||
| >>> outputs = self.forward(inputs) | ||
| >>> for i in range(len(outputs)): | ||
| ... print(f'outputs[{i}].shape = {outputs[i].shape}') | ||
| outputs[0].shape = torch.Size([1, 11, 340, 340]) | ||
| outputs[1].shape = torch.Size([1, 11, 170, 170]) | ||
| outputs[2].shape = torch.Size([1, 11, 84, 84]) | ||
| outputs[3].shape = torch.Size([1, 11, 43, 43]) | ||
| """ | ||
|
|
||
| def __init__(self, | ||
| in_channels, | ||
| out_channels, | ||
| kernel_size=3, | ||
| conv_cfg=None, | ||
| norm_cfg=None, | ||
| act_cfg=dict(type='ReLU')): | ||
| super(ChannelMapper, self).__init__() | ||
| assert isinstance(in_channels, list) | ||
|
|
||
| self.convs = nn.ModuleList() | ||
| for in_channel in in_channels: | ||
| self.convs.append( | ||
| ConvModule( | ||
| in_channel, | ||
| out_channels, | ||
| kernel_size, | ||
| padding=(kernel_size - 1) // 2, | ||
| conv_cfg=conv_cfg, | ||
| norm_cfg=norm_cfg, | ||
| act_cfg=act_cfg)) | ||
|
|
||
| # default init_weights for conv(msra) and norm in ConvModule | ||
| def init_weights(self): | ||
| """Initialize the weights of ChannelMapper module.""" | ||
| for m in self.modules(): | ||
| if isinstance(m, nn.Conv2d): | ||
| xavier_init(m, distribution='uniform') | ||
|
|
||
| def forward(self, inputs): | ||
| """Forward function.""" | ||
| assert len(inputs) == len(self.convs) | ||
| outs = [self.convs[i](inputs[i]) for i in range(len(inputs))] | ||
| return tuple(outs) |
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