test=dygraph update eval

configs/architechture/cascade_mask_rcnn.yml

-# Model Achitecture
-CascadeRCNN:
-  # model anchor info flow
-  anchor: AnchorRPN
-  proposal: Proposal
-  mask: Mask
-  # model feat info flow
-  backbone: ResNet
-  rpn_head: RPNHead
-  bbox_head: BBoxHead
-  mask_head: MaskHead
-
-ResNet:
-  norm_type: 'affine'
-  depth: 50
-  freeze_at: 'res2'
-
-RPNHead:
-  rpn_feat:
-    name: RPNFeat
-    feat_in: 1024
-    feat_out: 1024
-  anchor_per_position: 15
-
-BBoxHead:
-  bbox_feat:
-    name: BBoxFeat
-    feat_in: 1024
-    feat_out: 512
-    roi_extractor:
-      resolution: 14
-      sampling_ratio: 0
-      spatial_scale: 0.0625
-      extractor_type: 'RoIAlign'
-
-MaskHead:
-  mask_feat:
-    name: MaskFeat
-    feat_in: 2048
-    feat_out: 256
-  feat_in: 256
-  resolution: 14
-
-AnchorRPN:
-  anchor_generator:
-    name: AnchorGeneratorRPN
-    anchor_sizes: [32, 64, 128, 256, 512]
-    aspect_ratios: [0.5, 1.0, 2.0]
-    stride: [16.0, 16.0]
-    variance: [1.0, 1.0, 1.0, 1.0]
-  anchor_target_generator:
-    name: AnchorTargetGeneratorRPN
-    batch_size_per_im: 256
-    fg_fraction: 0.5
-    negative_overlap: 0.3
-    positive_overlap: 0.7
-    straddle_thresh: 0.0
-
-Proposal:
-  proposal_generator:
-    name: ProposalGenerator
-    min_size: 0.0
-    nms_thresh: 0.7
-    train_pre_nms_top_n: 2000
-    train_post_nms_top_n: 2000
-    infer_pre_nms_top_n: 2000
-    infer_post_nms_top_n: 2000
-    return_rois_num: True
-  proposal_target_generator:
-    name: ProposalTargetGenerator
-    batch_size_per_im: 512
-    bbox_reg_weights: [[0.1, 0.1, 0.2, 0.2],[0.05, 0.05, 0.1, 0.1],[0.333333, 0.333333, 0.666666, 0.666666]]
-    bg_thresh_hi: [0.5, 0.6, 0.7]
-    bg_thresh_lo: [0.0, 0.0, 0.0]
-    fg_thresh: [0.5, 0.6, 0.7]
-    fg_fraction: 0.25
-  bbox_post_process: # used in infer
-    name: BBoxPostProcess
-    # decode -> clip -> nms
-    decode_clip_nms:
-      name: DecodeClipNms
-      keep_top_k: 100
-      score_threshold: 0.05
-      nms_threshold: 0.5
-
-Mask:
-  mask_target_generator:
-    name: MaskTargetGenerator
-    resolution: 14
-  mask_post_process:
-    name: MaskPostProcess

configs/base/mask_rcnn_r50_1x.yml

 architecture: MaskRCNN
-pretrain_weights: https://paddlemodels.bj.bcebos.com/object_detection/dygraph/resnet50.pdparams
+pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_cos_pretrained.tar
 weights: output/mask_rcnn_r50_1x/model_final
 use_gpu: true
 epoch: 24
@@ -9,7 +9,7 @@ log_smooth_window: 20
 save_dir: output
 metric: COCO
 num_classes: 81
-load_static_weights: true
+load_static_weights: false
 
 _READER_: '../reader/mask_rcnn.yml'
 _ARCHITECHTURE_: '../architechture/mask_rcnn.yml'

configs/base/mask_rcnn_r50_fpn_1x.yml

 architecture: MaskRCNN
-pretrain_weights: https://paddlemodels.bj.bcebos.com/object_detection/dygraph/resnet50.pdparams
+pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_cos_pretrained.tar
 weights: output/mask_rcnn_r50_1x/model_final
 use_gpu: true
 epoch: 24
@@ -9,7 +9,7 @@ log_smooth_window: 20
 save_dir: output
 metric: COCO
 num_classes: 81
-load_static_weights: true
+load_static_weights: false
 
 _READER_: '../reader/mask_rcnn.yml'
 _ARCHITECHTURE_: '../architechture/mask_rcnn_fpn.yml'

configs/example/mask_rcnn_r50_1x.yml

@@ -7,6 +7,8 @@ log_smooth_window: 20
 save_dir: output
 metric: COCO
 num_classes: 81
+load_static_weights: true
+log_iter: 20
 
 TrainReader:
   inputs_def:

configs/example/mask_rcnn_r50_fpn_1x.yml

@@ -8,6 +8,7 @@ save_dir: output
 metric: COCO
 num_classes: 81
 weights: output/mask_r50_fpn_1x/model_final.pdparams
+
 EvalReader:
   dataset:
     dataset_dir: /home/ai/dataset/COCO17

configs/reader/faster_rcnn.yml

@@ -3,9 +3,10 @@ TrainReader:
     fields: ['image', 'im_info', 'im_id', 'gt_bbox', 'gt_class', 'is_crowd']
   dataset:
     name: COCODataset
-    dataset_dir: /home/ai/dataset/COCO17/
+    dataset_dir: dataset/coco
     image_dir: train2017
     anno_path: annotations/instances_train2017.json
+    with_background: true
   sample_transforms:
   - DecodeImage: {to_rgb: true}
   - RandomFlipImage: {prob: 0.5}
@@ -24,14 +25,15 @@ EvalReader:
     name: COCODataset
     image_dir: val2017
     anno_path: annotations/instances_val2017.json
-    dataset_dir: /home/ai/dataset/COCO17
+    dataset_dir: dataset/coco
+    with_background: true
   sample_transforms:
   - DecodeImage: {to_rgb: true}
   - NormalizeImage: {is_channel_first: false, is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
   - ResizeImage: {interp: 1, max_size: 1333, target_size: 800, use_cv2: true}
   - Permute: {channel_first: true, to_bgr: false}
   batch_transforms:
-  - PadBatch: {pad_to_stride: 0, use_padded_im_info: false, pad_gt: True}
+  - PadBatch: {pad_to_stride: 0, use_padded_im_info: false, pad_gt: False}
   batch_size: 2
   shuffle: false
   drop_empty: false

configs/reader/mask_rcnn.yml

 worker_num: 0
-use_prefetch: False
+use_prefetch: false
 
 TrainReader:
   inputs_def:
@@ -9,6 +9,7 @@ TrainReader:
     dataset_dir: dataset/coco
     image_dir: train2017
     anno_path: annotations/instances_train2017.json
+    with_background: true
   sample_transforms:
   - DecodeImage: {to_rgb: true}
   - RandomFlipImage: {prob: 0.5, is_mask_flip: true}
@@ -16,7 +17,7 @@ TrainReader:
   - ResizeImage: {target_size: 800, max_size: 1333, interp: 1, use_cv2: true}
   - Permute: {to_bgr: false, channel_first: true}
   batch_transforms:
-  - PadBatch: {pad_to_stride: 32, use_padded_im_info: false, pad_gt: True}
+  - PadBatch: {pad_to_stride: 32, use_padded_im_info: false, pad_gt: true}
   batch_size: 1
   shuffle: true
   drop_last: false
@@ -24,19 +25,20 @@ TrainReader:
 
 EvalReader:
   inputs_def:
-    fields: ['image', 'im_info', 'im_id', 'im_shape']
+    fields: ['image', 'im_info', 'im_id']
   dataset:
     name: COCODataset
     dataset_dir: dataset/coco
     image_dir: val2017
     anno_path: annotations/instances_val2017.json
+    with_background: true
   sample_transforms:
   - DecodeImage: {to_rgb: true}
   - NormalizeImage: {is_channel_first: false, is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
   - ResizeImage: {interp: 1, max_size: 1333, target_size: 800, use_cv2: true}
   - Permute: {channel_first: true, to_bgr: false}
   batch_transforms:
-  - PadBatch: {pad_to_stride: 32, use_padded_im_info: false, pad_gt: True}
+  - PadBatch: {pad_to_stride: 32, use_padded_im_info: false, pad_gt: false}
   batch_size: 1
   shuffle: false
   drop_last: false
@@ -45,7 +47,7 @@ EvalReader:
 
 TestReader:
   inputs_def:
-    fields: ['image', 'im_info', 'im_id', 'im_shape']
+    fields: ['image', 'im_info', 'im_id']
   dataset:
     name: ImageFolder
     anno_path: annotations/instances_val2017.json

ppdet/core/workspace.py

@@ -66,73 +66,62 @@ class AttrDict(dict):
 
 global_config = AttrDict()
 
-READER_KEY = '_READER_'
-
 
 def load_config(file_path):
-    """
-    Load config from file.
+    cfg = load_cfg(file_path)
+    if '_BASE_' in cfg.keys():
+        base_cfg = load_cfg(cfg['_BASE_'])
+        del cfg['_BASE_']
+        # merge cfg into base_cfg
+        cfg = merge_config(cfg, base_cfg)
+    # merge cfg int global_config
+    merge_config(cfg)
+    return global_config
 
-    Args:
-        file_path (str): Path of the config file to be loaded.
 
-    Returns: global config
-    """
+def load_cfg(file_path):
     _, ext = os.path.splitext(file_path)
     assert ext in ['.yml', '.yaml'], "only support yaml files for now"
-
     cfg = AttrDict()
     with open(file_path) as f:
         cfg = merge_config(yaml.load(f, Loader=yaml.Loader), cfg)
+    cfg = load_part_cfg(cfg, file_path)
+    return cfg
 
-    if READER_KEY in cfg:
-        reader_cfg = cfg[READER_KEY]
-        if reader_cfg.startswith("~"):
-            reader_cfg = os.path.expanduser(reader_cfg)
-        if not reader_cfg.startswith('/'):
-            reader_cfg = os.path.join(os.path.dirname(file_path), reader_cfg)
 
-        with open(reader_cfg) as f:
-            merge_config(yaml.load(f, Loader=yaml.Loader))
-        del cfg[READER_KEY]
+PART_KEY = ['_READER_', '_ARCHITECHTURE_', '_OPTIMIZE_']
 
-    merge_config(cfg)
-    return global_config
 
+def load_part_cfg(cfg, file_path):
+    for part_k in PART_KEY:
+        if part_k in cfg:
+            part_cfg = cfg[part_k]
+            if part_cfg.startswith("~"):
+                part_cfg = os.path.expanduser(part_cfg)
+            if not part_cfg.startswith('/'):
+                part_cfg = os.path.join(os.path.dirname(file_path), part_cfg)
 
-def dict_merge(dct, merge_dct):
-    """ Recursive dict merge. Inspired by :meth:``dict.update()``, instead of
-    updating only top-level keys, dict_merge recurses down into dicts nested
-    to an arbitrary depth, updating keys. The ``merge_dct`` is merged into
-    ``dct``.
+            with open(part_cfg) as f:
+                merge_config(yaml.load(f, Loader=yaml.Loader), cfg)
+            del cfg[part_k]
 
-    Args:
-        dct: dict onto which the merge is executed
-        merge_dct: dct merged into dct
-
-    Returns: dct
-    """
-    for k, v in merge_dct.items():
-        if (k in dct and isinstance(dct[k], dict) and
-                isinstance(merge_dct[k], collections.Mapping)):
-            dict_merge(dct[k], merge_dct[k])
-        else:
-            dct[k] = merge_dct[k]
-    return dct
+    return cfg
 
 
-def merge_config(config, another_cfg=None):
-    """
-    Merge config into global config or another_cfg.
+def merge_config(config, other_cfg=None):
+    global global_config
+    dct = other_cfg if other_cfg is not None else global_config
+    return merge_dict(dct, config)
 
-    Args:
-        config (dict): Config to be merged.
 
-    Returns: global config
-    """
-    global global_config
-    dct = another_cfg if another_cfg is not None else global_config
-    return dict_merge(dct, config)
+def merge_dict(dct, other_dct):
+    for k, v in other_dct.items():
+        if (k in dct and isinstance(dct[k], dict) and
+                isinstance(other_dct[k], collections.Mapping)):
+            merge_dict(dct[k], other_dct[k])
+        else:
+            dct[k] = other_dct[k]
+    return dct
 
 
 def get_registered_modules():
@@ -251,7 +240,4 @@ def create(cls_or_name, **kwargs):
                     kwargs[k] = new_dict
             else:
                 raise ValueError("Unsupported injection type:", target_key)
-    # prevent modification of global config values of reference types
-    # (e.g., list, dict) from within the created module instances
-    #kwargs = copy.deepcopy(kwargs)
     return cls(**kwargs)

ppdet/data/loader.py

@@ -24,7 +24,6 @@ class Compose(object):
         self.transforms_cls = []
         for t in self.transforms:
             for k, v in t.items():
-                print(k, v)
                 op_cls = getattr(from_, k)
                 self.transforms_cls.append(op_cls(**v))
                 if hasattr(op_cls, 'num_classes'):
@@ -131,7 +130,7 @@ class BaseDataLoader(object):
                  drop_empty=True,
                  num_classes=81):
         # dataset 
-        self._dataset = dataset  #create(dataset['name'])
+        self._dataset = dataset
         self._dataset.parse_dataset()
         # out fields 
         self._fields = copy.deepcopy(inputs_def[

ppdet/data/source/coco.py

@@ -20,7 +20,6 @@ class COCODataset(DetDataset):
                                           with_background, sample_num)
         self.load_image_only = False
         self.load_semantic = False
-        #self.parse_dataset()
 
     def parse_dataset(self):
         anno_path = os.path.join(self.dataset_dir, self.anno_path)

ppdet/modeling/head/bbox_head.py

@@ -5,15 +5,34 @@ from paddle.fluid.initializer import Normal, Xavier
 from paddle.fluid.regularizer import L2Decay
 from paddle.fluid.dygraph.nn import Conv2D, Pool2D, Linear
 from ppdet.core.workspace import register
+from ..backbone.name_adapter import NameAdapter
+from ..backbone.resnet import Blocks
 
 
 @register
-class TwoFCHead(Layer):
+class Res5Feat(Layer):
+    def __init__(self, feat_in=1024, feat_out=512):
+        super(Res5Feat, self).__init__()
+        na = NameAdapter(self)
+        self.res5_conv = []
+        self.res5 = self.add_sublayer(
+            'res5_roi_feat',
+            Blocks(
+                feat_in, feat_out, count=3, name_adapter=na, stage_num=5))
+        self.feat_out = feat_out * 4
+
+    def forward(self, roi_feat, stage=0):
+        y = self.res5(roi_feat)
+        return y
+
+
+@register
+class TwoFCFeat(Layer):
 
     __shared__ = ['num_stages']
 
     def __init__(self, in_dim=256, mlp_dim=1024, resolution=7, num_stages=1):
-        super(TwoFCHead, self).__init__()
+        super(TwoFCFeat, self).__init__()
         self.in_dim = in_dim
         self.mlp_dim = mlp_dim
         self.num_stages = num_stages
@@ -94,6 +113,7 @@ class BBoxHead(Layer):
         self.bbox_score_list = []
         self.bbox_delta_list = []
         self.with_pool = with_pool
+
         for stage in range(num_stages):
             score_name = 'bbox_score_{}'.format(stage)
             delta_name = 'bbox_delta_{}'.format(stage)
@@ -132,11 +152,14 @@ class BBoxHead(Layer):
     def forward(self, body_feats, rois, spatial_scale, stage=0):
         bbox_feat = self.bbox_feat(body_feats, rois, spatial_scale, stage)
         if self.with_pool:
-            bbox_feat = fluid.layers.pool2d(
+            bbox_feat_ = fluid.layers.pool2d(
                 bbox_feat, pool_type='avg', global_pooling=True)
+            bbox_feat_ = fluid.layers.squeeze(bbox_feat_, axes=[2, 3])
+        else:
+            bbox_feat_ = bbox_feat
         bbox_head_out = []
-        scores = self.bbox_score_list[stage](bbox_feat)
-        deltas = self.bbox_delta_list[stage](bbox_feat)
+        scores = self.bbox_score_list[stage](bbox_feat_)
+        deltas = self.bbox_delta_list[stage](bbox_feat_)
         bbox_head_out.append((scores, deltas))
         return bbox_feat, bbox_head_out
 

ppdet/utils/checkpoint.py

@@ -59,8 +59,8 @@ def load_dygraph_ckpt(model,
     assert os.path.exists(ckpt), "Path {} does not exist.".format(ckpt)
     if load_static_weights:
         pre_state_dict = fluid.load_program_state(ckpt)
-        param_state_dict = {}
         model_dict = model.state_dict()
+        param_state_dict = {}
         for key in model_dict.keys():
             weight_name = model_dict[key].name
             if weight_name in pre_state_dict.keys():

ppdet/utils/eval_utils.py

@@ -11,7 +11,7 @@ def json_eval_results(metric, json_directory=None, dataset=None):
     """
     assert metric == 'COCO'
     from ppdet.utils.coco_eval import cocoapi_eval
-    anno_file = dataset.get_anno()
+    anno_file = os.path.join(dataset['dataset_dir'], dataset['anno_path'])
     json_file_list = ['proposal.json', 'bbox.json', 'mask.json']
     if json_directory:
         assert os.path.exists(
@@ -36,10 +36,10 @@ def coco_eval_results(outs_res=None, include_mask=False, dataset=None):
     from pycocotools.coco import COCO
     from pycocotools.cocoeval import COCOeval
     from ppdet.py_op.post_process import get_det_res, get_seg_res
-    anno_file = os.path.join(dataset.dataset_dir, dataset.anno_path)
+    anno_file = os.path.join(dataset['dataset_dir'], dataset['anno_path'])
     cocoGt = COCO(anno_file)
     catid = {
-        i + dataset.with_background: v
+        i + 1 if dataset['with_background'] else 0: v
         for i, v in enumerate(cocoGt.getCatIds())
     }
 

tools/eval.py

@@ -41,11 +41,6 @@ def parse_args():
 
 def run(FLAGS, cfg, place):
 
-    if FLAGS.use_gpu:
-        devices_num = 1
-    else:
-        devices_num = int(os.environ.get('CPU_NUM', 1))
-
     # Data 
     eval_loader, _ = create('EvalReader')(cfg['worker_num'], place)
 

tools/train.py

@@ -166,7 +166,7 @@ def run(FLAGS, cfg, place):
                     logger.info(strs)
 
         # Save Stage 
-        if fluid.dygraph.parallel.Env().local_rank == 0:
+        if ParallelEnv().local_rank == 0:
             cfg_name = os.path.basename(FLAGS.config).split('.')[0]
             save_name = str(e_id + 1) if e_id + 1 != int(
                 cfg.epoch) else "model_final"