share train_batch_size from reader to model (#1531)

* share train_batch_size from reader to model

* update floor_divide

* add makedirs for dumping config

configs/anchor_free/cornernet_squeeze_dcn_r50_vd_fpn.yml

@@ -31,7 +31,6 @@ FPN:
   spatial_scale: [0.03125, 0.0625, 0.125]
 
 CornerHead:
-  train_batch_size: 14
   test_batch_size: 1
   ae_threshold: 0.5
   num_dets: 100

configs/anchor_free/cornernet_squeeze_dcn_r50_vd_fpn_mixup_cosine.yml

@@ -31,7 +31,6 @@ FPN:
   spatial_scale: [0.03125, 0.0625, 0.125]
 
 CornerHead:
-  train_batch_size: 14
   test_batch_size: 1
   ae_threshold: 0.5
   num_dets: 100

configs/anchor_free/cornernet_squeeze_hg104.yml

@@ -20,7 +20,6 @@ Hourglass:
   modules: [2, 2, 2, 2, 4]
 
 CornerHead:
-  train_batch_size: 14
   test_batch_size: 1
   ae_threshold: 0.5
   num_dets: 100

configs/anchor_free/cornernet_squeeze_r50_vd_fpn.yml

@@ -30,7 +30,6 @@ FPN:
   spatial_scale: [0.03125, 0.0625, 0.125]
 
 CornerHead:
-  train_batch_size: 14
   test_batch_size: 1
   ae_threshold: 0.5
   num_dets: 100

ppdet/modeling/anchor_heads/corner_head.py

@@ -110,9 +110,10 @@ def nms(heat):
 def _topk(scores, batch_size, height, width, K):
     scores_r = fluid.layers.reshape(scores, [batch_size, -1])
     topk_scores, topk_inds = fluid.layers.topk(scores_r, K)
-    topk_clses = topk_inds / (height * width)
+    topk_inds = fluid.layers.cast(topk_inds, 'int32')
+    topk_clses = topk_inds // (height * width)
     topk_inds = topk_inds % (height * width)
-    topk_ys = fluid.layers.cast(topk_inds / width, 'float32')
+    topk_ys = fluid.layers.cast(topk_inds // width, 'float32')
     topk_xs = fluid.layers.cast(topk_inds % width, 'float32')
     return topk_scores, topk_inds, topk_clses, topk_ys, topk_xs
 
@@ -231,10 +232,10 @@ class CornerHead(object):
         num_dets(int): num of detections, 1000 by default
         top_k(int): choose top_k pair of corners in prediction, 100 by default 
     """
-    __shared__ = ['num_classes', 'stack']
+    __shared__ = ['num_classes', 'stack', 'train_batch_size']
 
     def __init__(self,
-                 train_batch_size,
+                 train_batch_size=14,
                  test_batch_size=1,
                  num_classes=80,
                  stack=2,
@@ -480,12 +481,10 @@ class CornerHead(object):
             cornerpool_lib.right_pool,
             is_test=True,
             name='br_modules_' + str(ind))
-
         tl_heat = self.pred_mod(
             tl_modules, self.num_classes, name='tl_heats_' + str(ind))
         br_heat = self.pred_mod(
             br_modules, self.num_classes, name='br_heats_' + str(ind))
-
         tl_tag = self.pred_mod(tl_modules, 1, name='tl_tags_' + str(ind))
         br_tag = self.pred_mod(br_modules, 1, name='br_tags_' + str(ind))
 

tools/export_model.py

@@ -122,8 +122,11 @@ def parse_reader(reader_cfg, metric, arch):
 
 
 def dump_infer_config(FLAGS, config):
+    arch_state = 0
     cfg_name = os.path.basename(FLAGS.config).split('.')[0]
     save_dir = os.path.join(FLAGS.output_dir, cfg_name)
+    if not os.path.exists(save_dir):
+        os.makedirs(save_dir)
     from ppdet.core.config.yaml_helpers import setup_orderdict
     setup_orderdict()
     infer_cfg = OrderedDict({
@@ -138,7 +141,13 @@ def dump_infer_config(FLAGS, config):
         if arch in infer_arch:
             infer_cfg['arch'] = arch
             infer_cfg['min_subgraph_size'] = min_subgraph_size
+            arch_state = 1
             break
+    if not arch_state:
+        logger.error(
+            'Architecture: {} is not supported for exporting model now'.format(
+                infer_arch))
+        os._exit(0)
 
     if 'Mask' in config['architecture']:
         infer_cfg['mask_resolution'] = config['MaskHead']['resolution']
@@ -223,8 +232,8 @@ def main():
     exe.run(startup_prog)
     checkpoint.load_params(exe, infer_prog, cfg.weights)
 
-    save_infer_model(FLAGS, exe, feed_vars, test_fetches, infer_prog)
     dump_infer_config(FLAGS, cfg)
+    save_infer_model(FLAGS, exe, feed_vars, test_fetches, infer_prog)
 
 
 if __name__ == '__main__':

tools/infer.py

@@ -180,12 +180,17 @@ def main():
         logger.info('Infer iter {}'.format(iter_id))
         if 'TTFNet' in cfg.architecture:
             res['bbox'][1].append([len(res['bbox'][0])])
+        if 'CornerNet' in cfg.architecture:
+            from ppdet.utils.post_process import corner_post_process
+            post_config = getattr(cfg, 'PostProcess', None)
+            corner_post_process(res, post_config, cfg.num_classes)
 
         bbox_results = None
         mask_results = None
         lmk_results = None
         if 'bbox' in res:
             bbox_results = bbox2out([res], clsid2catid, is_bbox_normalized)
+
         if 'mask' in res:
             mask_results = mask2out([res], clsid2catid,
                                     model.mask_head.resolution)