[MOT] fix fairmot (#4614)

* fix Momentum and label list

* fix export of mcfairmot

configs/mot/fairmot/_base_/optimizer_30e_momentum.yml

@@ -12,7 +12,6 @@ LearningRate:
 
 OptimizerBuilder:
   optimizer:
-    momentum: 0.9
     type: Momentum
   regularizer:
     factor: 0.0001

configs/mot/fairmot/fairmot_enhance_dla34_60e_1088x608.yml

@@ -9,6 +9,13 @@ norm_type: sync_bn
 use_ema: true
 ema_decay: 0.9998
 
+# add crowdhuman
+TrainDataset:
+  !MOTDataSet
+    dataset_dir: dataset/mot
+    image_lists: ['mot17.train', 'caltech.all', 'cuhksysu.train', 'prw.train', 'citypersons.train', 'eth.train', 'crowdhuman.train', 'crowdhuman.val']
+    data_fields: ['image', 'gt_bbox', 'gt_class', 'gt_ide']
+
 worker_num: 4
 TrainReader:
   inputs_def:

configs/mot/fairmot/fairmot_hrnetv2_w18_dlafpn_30e_1088x608.yml

@@ -10,7 +10,7 @@ norm_type: sync_bn
 use_ema: true
 ema_decay: 0.9998
 
-# for MOT training
+# add crowdhuman
 TrainDataset:
   !MOTDataSet
     dataset_dir: dataset/mot

configs/mot/fairmot/fairmot_hrnetv2_w18_dlafpn_30e_576x320.yml

@@ -10,7 +10,7 @@ norm_type: sync_bn
 use_ema: true
 ema_decay: 0.9998
 
-# for MOT training
+# add crowdhuman
 TrainDataset:
   !MOTDataSet
     dataset_dir: dataset/mot

configs/mot/fairmot/fairmot_hrnetv2_w18_dlafpn_30e_864x480.yml

@@ -10,7 +10,7 @@ norm_type: sync_bn
 use_ema: true
 ema_decay: 0.9998
 
-# for MOT training
+# add crowdhuman
 TrainDataset:
   !MOTDataSet
     dataset_dir: dataset/mot

ppdet/data/source/mot.py

@@ -154,7 +154,7 @@ class MOTDataSet(DetDataset):
             last_index += v
 
         self.num_identities_dict = defaultdict(int)
-        self.num_identities_dict[0] = int(last_index + 1) # single class
+        self.num_identities_dict[0] = int(last_index + 1)  # single class
         self.num_imgs_each_data = [len(x) for x in self.img_files.values()]
         self.total_imgs = sum(self.num_imgs_each_data)
 
@@ -249,6 +249,7 @@ class MCMOTDataSet(DetDataset):
             └——————labels_with_ids
                         └——————train
     """
+
     def __init__(self,
                  dataset_dir=None,
                  image_lists=[],
@@ -343,22 +344,26 @@ class MCMOTDataSet(DetDataset):
 
         # cname2cid and cid2cname 
         cname2cid = {}
-        if self.label_list:
+        if self.label_list is not None:
             # if use label_list for multi source mix dataset, 
             # please make sure label_list in the first sub_dataset at least.
             sub_dataset = self.image_lists[0].split('.')[0]
             label_path = os.path.join(self.dataset_dir, sub_dataset,
                                       self.label_list)
             if not os.path.exists(label_path):
-                raise ValueError("label_list {} does not exists".format(
-                    label_path))
-            with open(label_path, 'r') as fr:
-                label_id = 0
-                for line in fr.readlines():
-                    cname2cid[line.strip()] = label_id
-                    label_id += 1
+                logger.info(
+                    "Note: label_list {} does not exists, use VisDrone 10 classes labels as default.".
+                    format(label_path))
+                cname2cid = visdrone_mcmot_label()
+            else:
+                with open(label_path, 'r') as fr:
+                    label_id = 0
+                    for line in fr.readlines():
+                        cname2cid[line.strip()] = label_id
+                        label_id += 1
         else:
             cname2cid = visdrone_mcmot_label()
+
         cid2cname = dict([(v, k) for (k, v) in cname2cid.items()])
 
         logger.info('MCMOT dataset summary: ')

ppdet/modeling/post_process.py

@@ -440,13 +440,13 @@ class CenterNetPostProcess(TTFBox):
     def __call__(self, hm, wh, reg, im_shape, scale_factor):
         heat = self._simple_nms(hm)
         scores, inds, topk_clses, ys, xs = self._topk(heat)
-        scores = paddle.tensor.unsqueeze(scores, [1])
-        clses = paddle.tensor.unsqueeze(topk_clses, [1])
+        scores = scores.unsqueeze(1)
+        clses = topk_clses.unsqueeze(1)
 
         reg_t = paddle.transpose(reg, [0, 2, 3, 1])
         # Like TTFBox, batch size is 1.
         # TODO: support batch size > 1
-        reg = paddle.reshape(reg_t, [-1, paddle.shape(reg_t)[-1]])
+        reg = paddle.reshape(reg_t, [-1, reg_t.shape[-1]])
         reg = paddle.gather(reg, inds)
         xs = paddle.cast(xs, 'float32')
         ys = paddle.cast(ys, 'float32')
@@ -454,7 +454,7 @@ class CenterNetPostProcess(TTFBox):
         ys = ys + reg[:, 1:2]
 
         wh_t = paddle.transpose(wh, [0, 2, 3, 1])
-        wh = paddle.reshape(wh_t, [-1, paddle.shape(wh_t)[-1]])
+        wh = paddle.reshape(wh_t, [-1, wh_t.shape[-1]])
         wh = paddle.gather(wh, inds)
 
         if self.regress_ltrb:
@@ -486,8 +486,7 @@ class CenterNetPostProcess(TTFBox):
         scale_x = scale_factor[:, 1:2]
         scale_expand = paddle.concat(
             [scale_x, scale_y, scale_x, scale_y], axis=1)
-        boxes_shape = paddle.shape(bboxes)
-        boxes_shape.stop_gradient = True
+        boxes_shape = bboxes.shape[:]
         scale_expand = paddle.expand(scale_expand, shape=boxes_shape)
         bboxes = paddle.divide(bboxes, scale_expand)
         if self.for_mot: