support export post_process in PicoDet (#5044)

* support export post_process in PicoDet

configs/picodet/_base_/picodet_esnet.yml

 architecture: PicoDet
 pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ESNet_x1_0_pretrained.pdparams
+export_post_process: False    # Whether post-processing is included in the network
 
 PicoDet:
   backbone: ESNet

ppdet/engine/export_utils.py

@@ -165,6 +165,8 @@ def _dump_infer_config(config, path, image_shape, model):
         reader_cfg, dataset_cfg, config['metric'], label_arch, image_shape[1:])
 
     if infer_arch == 'PicoDet':
+        if config.get('export_post_process', False):
+            infer_cfg['arch'] = 'GFL'
         infer_cfg['NMS'] = config['PicoHead']['nms']
         # In order to speed up the prediction, the threshold of nms 
         # is adjusted here, which can be changed in infer_cfg.yml

ppdet/engine/trainer.py

@@ -631,7 +631,8 @@ class Trainer(object):
             im_shape = [image_shape[0], 2]
             scale_factor = [image_shape[0], 2]
 
-        if hasattr(self.model, 'deploy'):
+        export_post_process = self.cfg.get('export_post_process', False)
+        if hasattr(self.model, 'deploy') and not export_post_process:
             self.model.deploy = True
         if hasattr(self.model, 'fuse_norm'):
             self.model.fuse_norm = self.cfg['TestReader'].get('fuse_normalize',
@@ -668,7 +669,7 @@ class Trainer(object):
             pruned_input_spec = input_spec
 
         # TODO: Hard code, delete it when support prune input_spec.
-        if self.cfg.architecture == 'PicoDet':
+        if self.cfg.architecture == 'PicoDet' and not export_post_process:
             pruned_input_spec = [{
                 "image": InputSpec(
                     shape=image_shape, name='image')