diff --git a/dygraph/ppdet/engine/trainer.py b/dygraph/ppdet/engine/trainer.py index 20ea16b7a485d2e57b918996427ec7c37bc0b058..443e564511ed806ac868a32fca61958f159aac47 100644 --- a/dygraph/ppdet/engine/trainer.py +++ b/dygraph/ppdet/engine/trainer.py @@ -165,6 +165,7 @@ class Trainer(object): if not self._weights_loaded: self.load_weights(self.cfg.pretrain_weights) + model = self.model if self._nranks > 1: model = paddle.DataParallel(self.model) else: diff --git a/dygraph/ppdet/metrics/map_utils.py b/dygraph/ppdet/metrics/map_utils.py index 51d06bb2664c41ce0966aa77f297475e7b2c8585..620200be9ded3c2fb0f7af4f5a42a514da1079c9 100644 --- a/dygraph/ppdet/metrics/map_utils.py +++ b/dygraph/ppdet/metrics/map_utils.py @@ -102,7 +102,7 @@ class DetectionMAP(object): self.evaluate_difficult = evaluate_difficult self.reset() - def update(self, bbox, gt_box, gt_label, difficult=None): + def update(self, bbox, score, label, gt_box, gt_label, difficult=None): """ Update metric statics from given prediction and ground truth infomations. @@ -117,13 +117,13 @@ class DetectionMAP(object): # record class score positive visited = [False] * len(gt_label) - for b in bbox: - label, score, xmin, ymin, xmax, ymax = b.tolist() + for b, s, l in zip(bbox, score, label): + xmin, ymin, xmax, ymax = b.tolist() pred = [xmin, ymin, xmax, ymax] max_idx = -1 max_overlap = -1.0 for i, gl in enumerate(gt_label): - if int(gl) == int(label): + if int(gl) == int(l): overlap = jaccard_overlap(pred, gt_box[i], self.is_bbox_normalized) if overlap > max_overlap: @@ -134,12 +134,12 @@ class DetectionMAP(object): if self.evaluate_difficult or \ int(np.array(difficult[max_idx])) == 0: if not visited[max_idx]: - self.class_score_poss[int(label)].append([score, 1.0]) + self.class_score_poss[int(l)].append([s, 1.0]) visited[max_idx] = True else: - self.class_score_poss[int(label)].append([score, 0.0]) + self.class_score_poss[int(l)].append([s, 0.0]) else: - self.class_score_poss[int(label)].append([score, 0.0]) + self.class_score_poss[int(l)].append([s, 0.0]) def reset(self): """ diff --git a/dygraph/ppdet/metrics/metrics.py b/dygraph/ppdet/metrics/metrics.py index 644658cf48c8054578f88db49fc581d93a7ee9d9..6647b83821aecbf24096fb01d0b4419f6fb1d28e 100644 --- a/dygraph/ppdet/metrics/metrics.py +++ b/dygraph/ppdet/metrics/metrics.py @@ -148,6 +148,8 @@ class VOCMetric(Metric): def update(self, inputs, outputs): bboxes = outputs['bbox'].numpy() + scores = outputs['score'].numpy() + labels = outputs['label'].numpy() bbox_lengths = outputs['bbox_num'].numpy() if bboxes.shape == (1, 1) or bboxes is None: @@ -171,9 +173,12 @@ class VOCMetric(Metric): else difficults[i] bbox_num = bbox_lengths[i] bbox = bboxes[bbox_idx:bbox_idx + bbox_num] + score = scores[bbox_idx:bbox_idx + bbox_num] + label = labels[bbox_idx:bbox_idx + bbox_num] gt_box, gt_label, difficult = prune_zero_padding(gt_box, gt_label, difficult) - self.detection_map.update(bbox, gt_box, gt_label, difficult) + self.detection_map.update(bbox, score, label, gt_box, gt_label, + difficult) bbox_idx += bbox_num def accumulate(self): diff --git a/dygraph/ppdet/modeling/architectures/ssd.py b/dygraph/ppdet/modeling/architectures/ssd.py index 55ff07efeee491362d6ed94f476979935aae79d9..4d195191ffa90a5f2f622207ae7c95bd0555e02d 100644 --- a/dygraph/ppdet/modeling/architectures/ssd.py +++ b/dygraph/ppdet/modeling/architectures/ssd.py @@ -54,4 +54,14 @@ class SSD(BaseArch): return {"loss": self._forward()} def get_pred(self): - return dict(zip(['bbox', 'bbox_num'], self._forward())) + bbox_pred, bbox_num = self._forward() + label = bbox_pred[:, 0] + score = bbox_pred[:, 1] + bbox = bbox_pred[:, 2:] + output = { + 'bbox': bbox, + 'score': score, + 'label': label, + 'bbox_num': bbox_num + } + return output diff --git a/dygraph/ppdet/modeling/heads/ssd_head.py b/dygraph/ppdet/modeling/heads/ssd_head.py index fb004c498b1ab30b908df9e50d1ca26bc80114d7..3ad8259e953308e7021baeb7f906038c4d7e92b7 100644 --- a/dygraph/ppdet/modeling/heads/ssd_head.py +++ b/dygraph/ppdet/modeling/heads/ssd_head.py @@ -58,7 +58,7 @@ class SSDHead(nn.Layer): __inject__ = ['anchor_generator', 'loss'] def __init__(self, - num_classes=81, + num_classes=80, in_channels=(512, 1024, 512, 256, 256, 256), anchor_generator=AnchorGeneratorSSD().__dict__, kernel_size=3, @@ -67,7 +67,8 @@ class SSDHead(nn.Layer): conv_decay=0., loss='SSDLoss'): super(SSDHead, self).__init__() - self.num_classes = num_classes + # add background class + self.num_classes = num_classes + 1 self.in_channels = in_channels self.anchor_generator = anchor_generator self.loss = loss @@ -106,7 +107,7 @@ class SSDHead(nn.Layer): score_conv_name, nn.Conv2D( in_channels=in_channels[i], - out_channels=num_prior * num_classes, + out_channels=num_prior * self.num_classes, kernel_size=kernel_size, padding=padding)) else: @@ -114,7 +115,7 @@ class SSDHead(nn.Layer): score_conv_name, SepConvLayer( in_channels=in_channels[i], - out_channels=num_prior * num_classes, + out_channels=num_prior * self.num_classes, kernel_size=kernel_size, padding=padding, conv_decay=conv_decay, @@ -129,8 +130,8 @@ class SSDHead(nn.Layer): box_preds = [] cls_scores = [] prior_boxes = [] - for feat, box_conv, score_conv in zip(feats, self.box_convs, - self.score_convs): + for i, (feat, box_conv, score_conv + ) in enumerate(zip(feats, self.box_convs, self.score_convs)): box_pred = box_conv(feat) box_pred = paddle.transpose(box_pred, [0, 2, 3, 1]) box_pred = paddle.reshape(box_pred, [0, -1, 4]) diff --git a/dygraph/ppdet/modeling/losses/ssd_loss.py b/dygraph/ppdet/modeling/losses/ssd_loss.py index 8561a83cb771d949ae56c7e361face894741a5b3..04ba75b64c4eee4b4460e2acefb130a4741be81d 100644 --- a/dygraph/ppdet/modeling/losses/ssd_loss.py +++ b/dygraph/ppdet/modeling/losses/ssd_loss.py @@ -114,7 +114,8 @@ class SSDLoss(nn.Layer): scores = paddle.concat(scores, axis=1) prior_boxes = paddle.concat(anchors, axis=0) gt_label = gt_class.unsqueeze(-1) - batch_size, num_priors, num_classes = scores.shape + batch_size, num_priors = scores.shape[:2] + num_classes = scores.shape[-1] - 1 def _reshape_to_2d(x): return paddle.flatten(x, start_axis=2) @@ -137,7 +138,8 @@ class SSDLoss(nn.Layer): # 2. Compute confidence for mining hard examples # 2.1. Get the target label based on matched indices - target_label, _ = self._label_target_assign(gt_label, matched_indices) + target_label, _ = self._label_target_assign( + gt_label, matched_indices, mismatch_value=num_classes) confidence = _reshape_to_2d(scores) # 2.2. Compute confidence loss. # Reshape confidence to 2D tensor. @@ -173,7 +175,10 @@ class SSDLoss(nn.Layer): encoded_bbox, matched_indices) # 4.3. Assign classification targets target_label, target_conf_weight = self._label_target_assign( - gt_label, matched_indices, neg_mask=neg_mask) + gt_label, + matched_indices, + neg_mask=neg_mask, + mismatch_value=num_classes) # 5. Compute loss. # 5.1 Compute confidence loss.