add mmdet example config

Reviewed By: theschnitz

Differential Revision: D28016190

fbshipit-source-id: ba305adb1624b09194c7d63b960c07917f044e05

configs/Misc/mmdet_mask_rcnn_R_50_FPN_1x.py

@@ -0,0 +1,151 @@
+# An example config to train a mmdetection model using detectron2.
+
+from ..common.data.coco import dataloader
+from ..common.coco_schedule import lr_multiplier_1x as lr_multiplier
+from ..common.optim import SGD as optimizer
+from ..common.train import train
+
+from detectron2.modeling.mmdet_wrapper import MMDetDetector
+from detectron2.config import LazyCall as L
+
+model = L(MMDetDetector)(
+    detector=dict(
+        type="MaskRCNN",
+        pretrained="torchvision://resnet50",
+        backbone=dict(
+            type="ResNet",
+            depth=50,
+            num_stages=4,
+            out_indices=(0, 1, 2, 3),
+            frozen_stages=1,
+            norm_cfg=dict(type="BN", requires_grad=True),
+            norm_eval=True,
+            style="pytorch",
+        ),
+        neck=dict(type="FPN", in_channels=[256, 512, 1024, 2048], out_channels=256, num_outs=5),
+        rpn_head=dict(
+            type="RPNHead",
+            in_channels=256,
+            feat_channels=256,
+            anchor_generator=dict(
+                type="AnchorGenerator",
+                scales=[8],
+                ratios=[0.5, 1.0, 2.0],
+                strides=[4, 8, 16, 32, 64],
+            ),
+            bbox_coder=dict(
+                type="DeltaXYWHBBoxCoder",
+                target_means=[0.0, 0.0, 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=256,
+                featmap_strides=[4, 8, 16, 32],
+            ),
+            bbox_head=dict(
+                type="Shared2FCBBoxHead",
+                in_channels=256,
+                fc_out_channels=1024,
+                roi_feat_size=7,
+                num_classes=80,
+                bbox_coder=dict(
+                    type="DeltaXYWHBBoxCoder",
+                    target_means=[0.0, 0.0, 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),
+            ),
+            mask_roi_extractor=dict(
+                type="SingleRoIExtractor",
+                roi_layer=dict(type="RoIAlign", output_size=14, sampling_ratio=0),
+                out_channels=256,
+                featmap_strides=[4, 8, 16, 32],
+            ),
+            mask_head=dict(
+                type="FCNMaskHead",
+                num_convs=4,
+                in_channels=256,
+                conv_out_channels=256,
+                num_classes=80,
+                loss_mask=dict(type="CrossEntropyLoss", use_mask=True, 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=-1,
+                pos_weight=-1,
+                debug=False,
+            ),
+            rpn_proposal=dict(
+                nms_pre=2000,
+                max_per_img=1000,
+                nms=dict(type="nms", iou_threshold=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=True,
+                    ignore_iof_thr=-1,
+                ),
+                sampler=dict(
+                    type="RandomSampler",
+                    num=512,
+                    pos_fraction=0.25,
+                    neg_pos_ub=-1,
+                    add_gt_as_proposals=True,
+                ),
+                mask_size=28,
+                pos_weight=-1,
+                debug=False,
+            ),
+        ),
+        test_cfg=dict(
+            rpn=dict(
+                nms_pre=1000,
+                max_per_img=1000,
+                nms=dict(type="nms", iou_threshold=0.7),
+                min_bbox_size=0,
+            ),
+            rcnn=dict(
+                score_thr=0.05,
+                nms=dict(type="nms", iou_threshold=0.5),
+                max_per_img=100,
+                mask_thr_binary=0.5,
+            ),
+        ),
+    ),
+    pixel_mean=[123.675, 116.280, 103.530],
+    pixel_std=[58.395, 57.120, 57.375],
+)
+
+dataloader.train.mapper.image_format = "RGB"  # torchvision pretrained model
+train.init_checkpoint = None  # pretrained model is loaded inside backbone

detectron2/modeling/mmdet_wrapper.py

@@ -173,7 +173,9 @@ def forward(self, batched_inputs: Tuple[Dict[str, torch.Tensor]]):
             c, h, w = input["image"].shape
             meta["img_shape"] = meta["ori_shape"] = (h, w, c)
             if rescale:
-                scale_factor = np.array([w / input["width"], h / input["height"]] * 2)
+                scale_factor = np.array(
+                    [w / input["width"], h / input["height"]] * 2, dtype="float32"
+                )
                 ori_shape = (input["height"], input["width"])
                 output_shapes.append(ori_shape)
                 meta["ori_shape"] = ori_shape + (c,)