add D2 conversion

examples/FasterRCNN/convert_d2/README.md

+## Detectron2 Conversion
+
+We provide a script to migrate a model trained in
+[detectron2](https://github.com/facebookresearch/detectron2)
+to tensorpack.
+
+The script reads a detectron2 config file and a detectron2 pkl file in the model zoo.
+It produces a corresponding tensorpack configs, as well as a tensorpack-compatible checkpoint.
+
+It currently supports ResNet{50,101}-{C4,FPN}-{Faster,Mask,Cascade} R-CNN models in
+[detectron2 model zoo](https://github.com/facebookresearch/detectron2/blob/master/MODEL_ZOO.md).
+
+### Usage:
+
+```
+# 1. Download the corresponding model from detectron2 model zoo
+# 2. Convert:
+
+$ python convert_d2.py --d2-config detectron2/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml --d2-pkl model_final_f10217.pkl --output R50FPN-d2-converted.npz
+# the script will print tensorpack configs
+'MODE_MASK=True' 'MODE_FPN=False' 'BACKBONE.STRIDE_1X1=True' 'PREPROC.PIXEL_MEAN=[123.675,116.28,103.53]' 'PREPROC.PIXEL_STD=[1.0,1.0,1.0]'
+
+# 3. Use the above configs to verify the conversion is correct:
+$ ./predict.py --evaluate out.json --load R50FPN-d2-converted.npz  --config DATA.BASEDIR=~/data/coco 'MODE_MASK=True' 'MODE_FPN=False' 'BACKBONE.STRIDE_1X1=True' 'PREPROC.PIXEL_MEAN=[123.675,116.28,103.53]' 'PREPROC.PIXEL_STD=[1.0,1.0,1.0]'
+
+# 4. Naively convert the model to a frozen pb file:
+$ ./predict.py --output-pb out.pb --load R50FPN-d2-converted.npz  --config DATA.BASEDIR=~/data/coco 'MODE_MASK=True' 'MODE_FPN=False' 'BACKBONE.STRIDE_1X1=True' 'PREPROC.PIXEL_MEAN=[123.675,116.28,103.53]' 'PREPROC.PIXEL_STD=[1.0,1.0,1.0]'
+```
+
+Note: this script does not support arbitrary detectron2 config.
+When run against an unsupported config, it may fail silently and produce
+erroneous models.
+
+For models in detectron2 model zoo, there is a small incompatibility:
+
+* `POOLER_SAMPLING_RATIO=0` in RoIAlign: there is no equivalence in TensorFlow.
+	Our RoIAlign only implements `POOLER_SAMPLING_RATIO=2`.
+	The results are quite similar, and the final AP may be different by <0.5.

examples/FasterRCNN/convert_d2/convert_d2.py

+#!/usr/bin/env python
+
+import argparse
+import numpy as np
+import pickle
+from detectron2.config import get_cfg
+
+
+def convert_config(cfg):
+    ret = []
+    ret.append(("MODE_MASK", cfg.MODEL.MASK_ON))
+    has_fpn = "fpn" in cfg.MODEL.BACKBONE.NAME
+    ret.append(("MODE_FPN", has_fpn))
+    if not has_fpn:
+        # we only support C4 and FPN
+        assert cfg.MODEL.ROI_HEADS.NAME == "Res5ROIHeads"
+    else:
+        ret.append(("FPN.CASCADE", cfg.MODEL.ROI_HEADS.NAME == "CascadeROIHeads"))
+        assert len(cfg.MODEL.ROI_BOX_CASCADE_HEAD.IOUS) == 3
+    depth = cfg.MODEL.RESNETS.DEPTH
+    assert depth in [50, 101], depth
+    if depth == 101:
+        ret.append(("BACKBONE.RESNET_NUM_BLOCKS", [3, 4, 23, 3]))
+    ret.append(("BACKBONE.STRIDE_1X1", cfg.MODEL.RESNETS.STRIDE_IN_1X1))
+    ret.append(("PREPROC.PIXEL_MEAN", cfg.MODEL.PIXEL_MEAN[::-1]))
+    ret.append(("PREPROC.PIXEL_STD", cfg.MODEL.PIXEL_STD[::-1]))
+
+    assert cfg.MODEL.ROI_MASK_HEAD.POOLER_TYPE == "ROIAlignV2"
+    assert cfg.MODEL.ROI_BOX_HEAD.POOLER_TYPE == "ROIAlignV2"
+    return ret
+
+
+def convert_weights(d, cfg):
+    has_fpn = "fpn" in cfg.MODEL.BACKBONE.NAME
+
+    ret = {}
+
+    def _convert_conv(src, dst):
+        src_w = d.pop(src + ".weight").transpose(2, 3, 1, 0)
+        ret[dst + "/W"] = src_w
+        if src + ".norm.weight" in d:     # has norm
+            ret[dst + "/bn/gamma"] = d.pop(src + ".norm.weight")
+            ret[dst + "/bn/beta"] = d.pop(src + ".norm.bias")
+            ret[dst + "/bn/variance/EMA"] = d.pop(src + ".norm.running_var")
+            ret[dst + "/bn/mean/EMA"] = d.pop(src + ".norm.running_mean")
+        if src + ".bias" in d:
+            ret[dst + "/b"] = d.pop(src + ".bias")
+
+    def _convert_fc(src, dst):
+        ret[dst + "/W"] = d.pop(src + ".weight").transpose()
+        ret[dst + "/b"] = d.pop(src + ".bias")
+
+    if has_fpn:
+        backbone_prefix = "backbone.bottom_up."
+    else:
+        backbone_prefix = "backbone."
+    _convert_conv(backbone_prefix + "stem.conv1", "conv0")
+    for grpid in range(4):
+        if not has_fpn and grpid == 3:
+            backbone_prefix = "roi_heads."
+        for blkid in range([3, 4, 6 if cfg.MODEL.RESNETS.DEPTH == 50 else 23, 3][grpid]):
+            _convert_conv(backbone_prefix + f"res{grpid + 2}.{blkid}.conv1",
+                          f"group{grpid}/block{blkid}/conv1")
+            _convert_conv(backbone_prefix + f"res{grpid + 2}.{blkid}.conv2",
+                          f"group{grpid}/block{blkid}/conv2")
+            _convert_conv(backbone_prefix + f"res{grpid + 2}.{blkid}.conv3",
+                          f"group{grpid}/block{blkid}/conv3")
+            if blkid == 0:
+                _convert_conv(backbone_prefix + f"res{grpid + 2}.{blkid}.shortcut",
+                              f"group{grpid}/block{blkid}/convshortcut")
+
+    if has_fpn:
+        for lvl in range(2, 6):
+            _convert_conv(f"backbone.fpn_lateral{lvl}", f"fpn/lateral_1x1_c{lvl}")
+            _convert_conv(f"backbone.fpn_output{lvl}", f"fpn/posthoc_3x3_p{lvl}")
+
+    # RPN:
+    _convert_conv("proposal_generator.rpn_head.conv", "rpn/conv0")
+    _convert_conv("proposal_generator.rpn_head.objectness_logits", "rpn/class")
+    _convert_conv("proposal_generator.rpn_head.anchor_deltas", "rpn/box")
+
+
+    # bbox predictor
+    def _convert_box_predictor(src, dst):
+        if cfg.MODEL.ROI_BOX_HEAD.CLS_AGNOSTIC_BBOX_REG:
+            _convert_fc(src + ".bbox_pred", dst + "/box")
+        else:
+            v = d.pop(src + ".bbox_pred.bias")
+            ret[dst + "/box/b"] = np.concatenate((v[:4], v))
+            v = d.pop(src + ".bbox_pred.weight")
+            ret[dst + "/box/W"] = np.concatenate((v[:4, :], v), axis=0).transpose()
+
+        _convert_fc(src + ".cls_score", dst + "/class")
+
+        num_class = ret[dst + "/class/W"].shape[1] - 1
+        idxs = np.concatenate(((num_class, ), np.arange(num_class)))
+        ret[dst + "/class/W"] = ret[dst + "/class/W"][:, idxs]
+        ret[dst + "/class/b"] = ret[dst + "/class/b"][idxs]
+
+    # Fast R-CNN: box head
+    has_cascade = cfg.MODEL.ROI_HEADS.NAME == "CascadeROIHeads"
+    if has_cascade:
+        assert cfg.MODEL.ROI_BOX_HEAD.CLS_AGNOSTIC_BBOX_REG
+        for k in range(3):
+            for i in range(cfg.MODEL.ROI_BOX_HEAD.NUM_FC):
+                _convert_fc(f"roi_heads.box_head.{k}.fc{i+1}", f"cascade_rcnn_stage{k+1}/head/fc{i+6}")
+            _convert_box_predictor(f"roi_heads.box_predictor.{k}", f"cascade_rcnn_stage{k+1}/outputs")
+    else:
+        for i in range(cfg.MODEL.ROI_BOX_HEAD.NUM_FC):
+            _convert_fc(f"roi_heads.box_head.fc{i+1}", f"fastrcnn/fc{i+6}")
+        _convert_box_predictor("roi_heads.box_predictor", "fastrcnn/outputs" if has_fpn else "fastrcnn")
+
+    # mask head
+    for fcn in range(cfg.MODEL.ROI_MASK_HEAD.NUM_CONV):
+        _convert_conv(f"roi_heads.mask_head.mask_fcn{fcn+1}", f"maskrcnn/fcn{fcn}")
+    _convert_conv("roi_heads.mask_head.deconv", "maskrcnn/deconv")
+    _convert_conv("roi_heads.mask_head.predictor", "maskrcnn/conv")
+
+    for k in list(d.keys()):
+        if "cell_anchors" in k:
+            d.pop(k)
+    assert len(d) == 0, d.keys()
+    return ret
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--d2-config")
+    parser.add_argument("--d2-pkl")
+    parser.add_argument("--output")
+    args = parser.parse_args()
+
+    cfg = get_cfg()
+    cfg.merge_from_file(args.d2_config)
+
+    tp_cfg = convert_config(cfg)
+    for k, v in tp_cfg:
+        print("'{}={}'".format(k, v).replace(' ', ''), end=' ')
+
+    with open(args.d2_pkl, "rb") as f:
+        d2_dict = pickle.load(f)["model"]
+    tp_dict = convert_weights(d2_dict, cfg)
+
+    np.savez_compressed(args.output, **tp_dict)

examples/FasterRCNN/predict.py

@@ -120,8 +120,8 @@ if __name__ == '__main__':
     parser.add_argument('--benchmark', action='store_true', help="Benchmark the speed of the model + postprocessing")
     parser.add_argument('--config', help="A list of KEY=VALUE to overwrite those defined in config.py",
                         nargs='+')
-    parser.add_argument('--compact', help='Save a model to .pb')
-    parser.add_argument('--serving', help='Save a model to serving file')
+    parser.add_argument('--output-pb', help='Save a model to .pb')
+    parser.add_argument('--output-serving', help='Save a model to serving file')
 
     args = parser.parse_args()
     if args.config:
@@ -150,10 +150,10 @@ if __name__ == '__main__':
             input_names=MODEL.get_inference_tensor_names()[0],
             output_names=MODEL.get_inference_tensor_names()[1])
 
-        if args.compact:
-            ModelExporter(predcfg).export_compact(args.compact, optimize=False)
-        elif args.serving:
-            ModelExporter(predcfg).export_serving(args.serving, optimize=False)
+        if args.output_pb:
+            ModelExporter(predcfg).export_compact(args.output_pb, optimize=False)
+        elif args.output_serving:
+            ModelExporter(predcfg).export_serving(args.output_serving, optimize=False)
 
         if args.predict:
             predictor = OfflinePredictor(predcfg)