fix_mmdetection

mmde/mmdet/.mim/configs/carafe/mask-rcnn_r50_fpn-carafe_1x_coco.py

+_base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    data_preprocessor=dict(pad_size_divisor=64),
+    neck=dict(
+        type='FPN_CARAFE',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        num_outs=5,
+        start_level=0,
+        end_level=-1,
+        norm_cfg=None,
+        act_cfg=None,
+        order=('conv', 'norm', 'act'),
+        upsample_cfg=dict(
+            type='carafe',
+            up_kernel=5,
+            up_group=1,
+            encoder_kernel=3,
+            encoder_dilation=1,
+            compressed_channels=64)),
+    roi_head=dict(
+        mask_head=dict(
+            upsample_cfg=dict(
+                type='carafe',
+                scale_factor=2,
+                up_kernel=5,
+                up_group=1,
+                encoder_kernel=3,
+                encoder_dilation=1,
+                compressed_channels=64))))

mmde/mmdet/.mim/configs/carafe/metafile.yml

+Collections:
+  - Name: CARAFE
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - RPN
+        - FPN_CARAFE
+        - ResNet
+        - RoIPool
+    Paper:
+      URL: https://arxiv.org/abs/1905.02188
+      Title: 'CARAFE: Content-Aware ReAssembly of FEatures'
+    README: configs/carafe/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.12.0/mmdet/models/necks/fpn_carafe.py#L11
+      Version: v2.12.0
+
+Models:
+  - Name: faster-rcnn_r50_fpn_carafe_1x_coco
+    In Collection: CARAFE
+    Config: configs/carafe/faster-rcnn_r50_fpn-carafe_1x_coco.py
+    Metadata:
+      Training Memory (GB): 4.26
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 38.6
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 38.6
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/carafe/faster_rcnn_r50_fpn_carafe_1x_coco/faster_rcnn_r50_fpn_carafe_1x_coco_bbox_mAP-0.386_20200504_175733-385a75b7.pth
+
+  - Name: mask-rcnn_r50_fpn_carafe_1x_coco
+    In Collection: CARAFE
+    Config: configs/carafe/mask-rcnn_r50_fpn-carafe_1x_coco.py
+    Metadata:
+      Training Memory (GB): 4.31
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 39.3
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 35.6
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/carafe/mask_rcnn_r50_fpn_carafe_1x_coco/mask_rcnn_r50_fpn_carafe_1x_coco_bbox_mAP-0.393__segm_mAP-0.358_20200503_135957-8687f195.pth

mmde/mmdet/.mim/configs/cascade_rcnn/cascade-mask-rcnn_r101-caffe_fpn_1x_coco.py

+_base_ = './cascade-mask-rcnn_r50-caffe_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron2/resnet101_caffe')))

mmde/mmdet/.mim/configs/cascade_rcnn/cascade-mask-rcnn_r101-caffe_fpn_ms-3x_coco.py

+_base_ = './cascade-mask-rcnn_r50-caffe_fpn_ms-3x_coco.py'
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron2/resnet101_caffe')))

mmde/mmdet/.mim/configs/cascade_rcnn/cascade-mask-rcnn_r101_fpn_1x_coco.py

+_base_ = './cascade-mask-rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))

mmde/mmdet/.mim/configs/cascade_rcnn/cascade-mask-rcnn_r101_fpn_20e_coco.py

+_base_ = './cascade-mask-rcnn_r50_fpn_20e_coco.py'
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))

mmde/mmdet/.mim/configs/cascade_rcnn/cascade-mask-rcnn_r101_fpn_ms-3x_coco.py

+_base_ = './cascade-mask-rcnn_r50_fpn_ms-3x_coco.py'
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))

mmde/mmdet/.mim/configs/cascade_rcnn/cascade-mask-rcnn_r50-caffe_fpn_1x_coco.py

+_base_ = ['./cascade-mask-rcnn_r50_fpn_1x_coco.py']
+
+model = dict(
+    data_preprocessor=dict(
+        mean=[103.530, 116.280, 123.675],
+        std=[1.0, 1.0, 1.0],
+        bgr_to_rgb=False),
+    backbone=dict(
+        norm_cfg=dict(requires_grad=False),
+        norm_eval=True,
+        style='caffe',
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron2/resnet50_caffe')))

mmde/mmdet/.mim/configs/cascade_rcnn/cascade-mask-rcnn_r50-caffe_fpn_ms-3x_coco.py

+_base_ = [
+    '../common/ms_3x_coco-instance.py',
+    '../_base_/models/cascade-mask-rcnn_r50_fpn.py'
+]
+
+model = dict(
+    # use caffe img_norm
+    data_preprocessor=dict(
+        mean=[103.530, 116.280, 123.675],
+        std=[1.0, 1.0, 1.0],
+        bgr_to_rgb=False),
+    backbone=dict(
+        norm_cfg=dict(requires_grad=False),
+        norm_eval=True,
+        style='caffe',
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron2/resnet50_caffe')))

mmde/mmdet/.mim/configs/cascade_rcnn/cascade-mask-rcnn_r50_fpn_1x_coco.py

+_base_ = [
+    '../_base_/models/cascade-mask-rcnn_r50_fpn.py',
+    '../_base_/datasets/coco_instance.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]

mmde/mmdet/.mim/configs/cascade_rcnn/cascade-mask-rcnn_r50_fpn_20e_coco.py

+_base_ = [
+    '../_base_/models/cascade-mask-rcnn_r50_fpn.py',
+    '../_base_/datasets/coco_instance.py',
+    '../_base_/schedules/schedule_20e.py', '../_base_/default_runtime.py'
+]

mmde/mmdet/.mim/configs/cascade_rcnn/cascade-mask-rcnn_r50_fpn_ms-3x_coco.py

+_base_ = [
+    '../common/ms_3x_coco-instance.py',
+    '../_base_/models/cascade-mask-rcnn_r50_fpn.py'
+]

mmde/mmdet/.mim/configs/cascade_rcnn/cascade-mask-rcnn_x101-32x4d_fpn_1x_coco.py

+_base_ = './cascade-mask-rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=32,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))

mmde/mmdet/.mim/configs/cascade_rcnn/cascade-mask-rcnn_x101-32x4d_fpn_20e_coco.py

+_base_ = './cascade-mask-rcnn_r50_fpn_20e_coco.py'
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=32,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))

mmde/mmdet/.mim/configs/cascade_rcnn/cascade-mask-rcnn_x101-32x4d_fpn_ms-3x_coco.py

+_base_ = './cascade-mask-rcnn_r50_fpn_ms-3x_coco.py'
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=32,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))

mmde/mmdet/.mim/configs/cascade_rcnn/cascade-mask-rcnn_x101-32x8d_fpn_ms-3x_coco.py

+_base_ = './cascade-mask-rcnn_r50_fpn_ms-3x_coco.py'
+
+model = dict(
+    # ResNeXt-101-32x8d model trained with Caffe2 at FB,
+    # so the mean and std need to be changed.
+    data_preprocessor=dict(
+        type='DetDataPreprocessor',
+        mean=[103.530, 116.280, 123.675],
+        std=[57.375, 57.120, 58.395],
+        bgr_to_rgb=False,
+        pad_size_divisor=32),
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=32,
+        base_width=8,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=False),
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron2/resnext101_32x8d')))

mmde/mmdet/.mim/configs/cascade_rcnn/cascade-mask-rcnn_x101-64x4d_fpn_1x_coco.py

+_base_ = './cascade-mask-rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=64,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))

mmde/mmdet/.mim/configs/cascade_rcnn/cascade-mask-rcnn_x101-64x4d_fpn_20e_coco.py

+_base_ = './cascade-mask-rcnn_r50_fpn_20e_coco.py'
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=64,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))

mmde/mmdet/.mim/configs/cascade_rcnn/cascade-mask-rcnn_x101-64x4d_fpn_ms-3x_coco.py

+_base_ = './cascade-mask-rcnn_r50_fpn_ms-3x_coco.py'
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=64,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))

mmde/mmdet/.mim/configs/cascade_rcnn/cascade-rcnn_r101-caffe_fpn_1x_coco.py

+_base_ = './cascade-rcnn_r50-caffe_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron2/resnet101_caffe')))