Toward Devkit Consistency (#8360)

* Accommodate UAPI

* Fix bugs

* Set defaults for use_fd_format

* Restore visualize.py

* Rename variable

* Optimize save_dir

* Fix mistakenly update

* Add format check

configs/slim/quant/mask_rcnn_r50_fpn_1x_qat.yml

@@ -8,9 +8,11 @@ QAT:
     'quantizable_layer_type': ['Conv2D', 'Linear']}
   print_model: True
 
-
 epoch: 5
 
+TrainReader:
+  batch_size: 1
+
 LearningRate:
   base_lr: 0.001
   schedulers:

configs/slim/quant/yolov3_darknet_qat.yml

@@ -10,6 +10,9 @@ QAT:
 
 epoch: 50
 
+TrainReader:
+  batch_size: 8
+
 LearningRate:
   base_lr: 0.0001
   schedulers:

deploy/python/infer.py

@@ -101,8 +101,9 @@ class Detector(object):
                  enable_mkldnn_bfloat16=False,
                  output_dir='output',
                  threshold=0.5,
-                 delete_shuffle_pass=False):
-        self.pred_config = self.set_config(model_dir)
+                 delete_shuffle_pass=False,
+                 use_fd_format=False):
+        self.pred_config = self.set_config(model_dir, use_fd_format=use_fd_format)
         self.predictor, self.config = load_predictor(
             model_dir,
             self.pred_config.arch,
@@ -125,8 +126,8 @@ class Detector(object):
         self.output_dir = output_dir
         self.threshold = threshold
 
-    def set_config(self, model_dir):
-        return PredictConfig(model_dir)
+    def set_config(self, model_dir, use_fd_format):
+        return PredictConfig(model_dir, use_fd_format=use_fd_format)
 
     def preprocess(self, image_list):
         preprocess_ops = []
@@ -560,7 +561,8 @@ class DetectorSOLOv2(Detector):
             enable_mkldnn=False,
             enable_mkldnn_bfloat16=False,
             output_dir='./',
-            threshold=0.5, ):
+            threshold=0.5, 
+            use_fd_format=False):
         super(DetectorSOLOv2, self).__init__(
             model_dir=model_dir,
             device=device,
@@ -574,7 +576,8 @@ class DetectorSOLOv2(Detector):
             enable_mkldnn=enable_mkldnn,
             enable_mkldnn_bfloat16=enable_mkldnn_bfloat16,
             output_dir=output_dir,
-            threshold=threshold, )
+            threshold=threshold, 
+            use_fd_format=use_fd_format)
 
     def predict(self, repeats=1, run_benchmark=False):
         '''
@@ -650,7 +653,8 @@ class DetectorPicoDet(Detector):
             enable_mkldnn=False,
             enable_mkldnn_bfloat16=False,
             output_dir='./',
-            threshold=0.5, ):
+            threshold=0.5, 
+            use_fd_format=False):
         super(DetectorPicoDet, self).__init__(
             model_dir=model_dir,
             device=device,
@@ -664,7 +668,8 @@ class DetectorPicoDet(Detector):
             enable_mkldnn=enable_mkldnn,
             enable_mkldnn_bfloat16=enable_mkldnn_bfloat16,
             output_dir=output_dir,
-            threshold=threshold, )
+            threshold=threshold, 
+            use_fd_format=use_fd_format)
 
     def postprocess(self, inputs, result):
         # postprocess output of predictor
@@ -745,7 +750,8 @@ class DetectorCLRNet(Detector):
             enable_mkldnn=False,
             enable_mkldnn_bfloat16=False,
             output_dir='./',
-            threshold=0.5, ):
+            threshold=0.5, 
+            use_fd_format=False):
         super(DetectorCLRNet, self).__init__(
             model_dir=model_dir,
             device=device,
@@ -759,7 +765,8 @@ class DetectorCLRNet(Detector):
             enable_mkldnn=enable_mkldnn,
             enable_mkldnn_bfloat16=enable_mkldnn_bfloat16,
             output_dir=output_dir,
-            threshold=threshold, )
+            threshold=threshold, 
+            use_fd_format=use_fd_format)
 
         deploy_file = os.path.join(model_dir, 'infer_cfg.yml')
         with open(deploy_file) as f:
@@ -867,9 +874,24 @@ class PredictConfig():
         model_dir (str): root path of model.yml
     """
 
-    def __init__(self, model_dir):
+    def __init__(self, model_dir, use_fd_format=False):
         # parsing Yaml config for Preprocess
-        deploy_file = os.path.join(model_dir, 'infer_cfg.yml')
+        fd_deploy_file = os.path.join(model_dir, 'inference.yml')
+        ppdet_deploy_file = os.path.join(model_dir, 'infer_cfg.yml')
+        if use_fd_format:
+            if not os.path.exists(fd_deploy_file) and os.path.exists(
+                    ppdet_deploy_file):
+                raise RuntimeError(
+                    "Non-FD format model detected. Please set `use_fd_format` to False."
+                )
+            deploy_file = fd_deploy_file
+        else:
+            if not os.path.exists(ppdet_deploy_file) and os.path.exists(
+                    fd_deploy_file):
+                raise RuntimeError(
+                    "FD format model detected. Please set `use_fd_format` to False."
+                )
+            deploy_file = ppdet_deploy_file
         with open(deploy_file) as f:
             yml_conf = yaml.safe_load(f)
         self.check_model(yml_conf)
@@ -1121,6 +1143,9 @@ def print_arguments(args):
 
 
 def main():
+    if FLAGS.use_fd_format:
+        deploy_file = os.path.join(FLAGS.model_dir, 'inference.yml')
+    else:
         deploy_file = os.path.join(FLAGS.model_dir, 'infer_cfg.yml')
     with open(deploy_file) as f:
         yml_conf = yaml.safe_load(f)
@@ -1146,7 +1171,8 @@ def main():
         enable_mkldnn=FLAGS.enable_mkldnn,
         enable_mkldnn_bfloat16=FLAGS.enable_mkldnn_bfloat16,
         threshold=FLAGS.threshold,
-        output_dir=FLAGS.output_dir)
+        output_dir=FLAGS.output_dir,
+        use_fd_format=FLAGS.use_fd_format)
 
     # predict from video file or camera video stream
     if FLAGS.video_file is not None or FLAGS.camera_id != -1:

deploy/python/keypoint_infer.py

@@ -76,7 +76,8 @@ class KeyPointDetector(Detector):
                  enable_mkldnn=False,
                  output_dir='output',
                  threshold=0.5,
-                 use_dark=True):
+                 use_dark=True,
+                 use_fd_format=False):
         super(KeyPointDetector, self).__init__(
             model_dir=model_dir,
             device=device,
@@ -89,11 +90,12 @@ class KeyPointDetector(Detector):
             cpu_threads=cpu_threads,
             enable_mkldnn=enable_mkldnn,
             output_dir=output_dir,
-            threshold=threshold, )
+            threshold=threshold, 
+            use_fd_format=use_fd_format)
         self.use_dark = use_dark
 
-    def set_config(self, model_dir):
-        return PredictConfig_KeyPoint(model_dir)
+    def set_config(self, model_dir, use_fd_format):
+        return PredictConfig_KeyPoint(model_dir, use_fd_format=use_fd_format)
 
     def get_person_from_rect(self, image, results):
         # crop the person result from image
@@ -302,9 +304,24 @@ class PredictConfig_KeyPoint():
         model_dir (str): root path of model.yml
     """
 
-    def __init__(self, model_dir):
+    def __init__(self, model_dir, use_fd_format=False):
         # parsing Yaml config for Preprocess
-        deploy_file = os.path.join(model_dir, 'infer_cfg.yml')
+        fd_deploy_file = os.path.join(model_dir, 'inference.yml')
+        ppdet_deploy_file = os.path.join(model_dir, 'infer_cfg.yml')
+        if use_fd_format:
+            if not os.path.exists(fd_deploy_file) and os.path.exists(
+                    ppdet_deploy_file):
+                raise RuntimeError(
+                    "Non-FD format model detected. Please set `use_fd_format` to False."
+                )
+            deploy_file = fd_deploy_file
+        else:
+            if not os.path.exists(ppdet_deploy_file) and os.path.exists(
+                    fd_deploy_file):
+                raise RuntimeError(
+                    "FD format model detected. Please set `use_fd_format` to False."
+                )
+            deploy_file = ppdet_deploy_file
         with open(deploy_file) as f:
             yml_conf = yaml.safe_load(f)
         self.check_model(yml_conf)
@@ -368,7 +385,8 @@ def main():
         enable_mkldnn=FLAGS.enable_mkldnn,
         threshold=FLAGS.threshold,
         output_dir=FLAGS.output_dir,
-        use_dark=FLAGS.use_dark)
+        use_dark=FLAGS.use_dark,
+        use_fd_format=FLAGS.use_fd_format)
 
     # predict from video file or camera video stream
     if FLAGS.video_file is not None or FLAGS.camera_id != -1:

deploy/python/utils.py

@@ -211,6 +211,7 @@ def argsparser():
         type=str,
         default="shape_range_info.pbtxt",
         help="Path of a dynamic shape file for tensorrt.")
+    parser.add_argument("--use_fd_format", action="store_true")
     return parser
 
 

ppdet/engine/callbacks.py

@@ -160,8 +160,7 @@ class Checkpointer(Callback):
     def __init__(self, model):
         super(Checkpointer, self).__init__(model)
         self.best_ap = -1000.
-        self.save_dir = os.path.join(self.model.cfg.save_dir,
-                                     self.model.cfg.filename)
+        self.save_dir = self.model.cfg.save_dir
         if hasattr(self.model.model, 'student_model'):
             self.weight = self.model.model.student_model
         else:
@@ -323,8 +322,7 @@ class WandbCallback(Callback):
             raise e
 
         self.wandb_params = model.cfg.get('wandb', None)
-        self.save_dir = os.path.join(self.model.cfg.save_dir,
-                                     self.model.cfg.filename)
+        self.save_dir = self.model.cfg.save_dir
         if self.wandb_params is None:
             self.wandb_params = {}
         for k, v in model.cfg.items():

ppdet/engine/trainer.py

@@ -1096,7 +1096,10 @@ class Trainer(object):
     def _get_infer_cfg_and_input_spec(self,
                                       save_dir,
                                       prune_input=True,
-                                      kl_quant=False):
+                                      kl_quant=False,
+                                      yaml_name=None):
+        if yaml_name is None:
+            yaml_name = 'infer_cfg.yml'
         image_shape = None
         im_shape = [None, 2]
         scale_factor = [None, 2]
@@ -1147,7 +1150,7 @@ class Trainer(object):
 
         # Save infer cfg
         _dump_infer_config(self.cfg,
-                           os.path.join(save_dir, 'infer_cfg.yml'), image_shape,
+                           os.path.join(save_dir, yaml_name), image_shape,
                            self.model)
 
         input_spec = [{
@@ -1203,7 +1206,7 @@ class Trainer(object):
 
         return static_model, pruned_input_spec
 
-    def export(self, output_dir='output_inference'):
+    def export(self, output_dir='output_inference', for_fd=False):
         if hasattr(self.model, 'aux_neck'):
             self.model.__delattr__('aux_neck')
         if hasattr(self.model, 'aux_head'):
@@ -1211,23 +1214,31 @@ class Trainer(object):
         self.model.eval()
 
         model_name = os.path.splitext(os.path.split(self.cfg.filename)[-1])[0]
+        if for_fd:
+            save_dir = output_dir
+            save_name = 'inference'
+            yaml_name = 'inference.yml'
+        else:
             save_dir = os.path.join(output_dir, model_name)
+            save_name = 'model'
+            yaml_name = None
+
         if not os.path.exists(save_dir):
             os.makedirs(save_dir)
 
         static_model, pruned_input_spec = self._get_infer_cfg_and_input_spec(
-            save_dir)
+            save_dir, yaml_name=yaml_name)
 
         # dy2st and save model
         if 'slim' not in self.cfg or 'QAT' not in self.cfg['slim_type']:
             paddle.jit.save(
                 static_model,
-                os.path.join(save_dir, 'model'),
+                os.path.join(save_dir, save_name),
                 input_spec=pruned_input_spec)
         else:
             self.cfg.slim.save_quantized_model(
                 self.model,
-                os.path.join(save_dir, 'model'),
+                os.path.join(save_dir, save_name),
                 input_spec=pruned_input_spec)
         logger.info("Export model and saved in {}".format(save_dir))
 

ppdet/utils/checkpoint.py

@@ -300,17 +300,27 @@ def save_model(model,
     """
     if paddle.distributed.get_rank() != 0:
         return
+        
+    save_dir = os.path.normpath(save_dir)
     if not os.path.exists(save_dir):
         os.makedirs(save_dir)
+
+    if save_name == "best_model":
+        best_model_path = os.path.join(save_dir, 'best_model')
+        if not os.path.exists(best_model_path):
+            os.makedirs(best_model_path)
+
     save_path = os.path.join(save_dir, save_name)
     # save model
     if isinstance(model, nn.Layer):
         paddle.save(model.state_dict(), save_path + ".pdparams")
+        best_model = model.state_dict()
     else:
         assert isinstance(model,
                           dict), 'model is not a instance of nn.layer or dict'
         if ema_model is None:
             paddle.save(model, save_path + ".pdparams")
+            best_model = model
         else:
             assert isinstance(ema_model,
                               dict), ("ema_model is not a instance of dict, "
@@ -318,6 +328,11 @@ def save_model(model,
             # Exchange model and ema_model to save
             paddle.save(ema_model, save_path + ".pdparams")
             paddle.save(model, save_path + ".pdema")
+            best_model = ema_model
+
+    if save_name == 'best_model':
+        best_model_path = os.path.join(best_model_path, 'model')
+        paddle.save(best_model, best_model_path + ".pdparams")
     # save optimizer
     state_dict = optimizer.state_dict()
     state_dict['last_epoch'] = last_epoch

tools/export_model.py

@@ -56,6 +56,7 @@ def parse_args():
         default=None,
         type=str,
         help="Configuration file of slim method.")
+    parser.add_argument("--for_fd", action='store_true')
     args = parser.parse_args()
     return args
 
@@ -76,9 +77,10 @@ def run(FLAGS, cfg):
             trainer.load_weights(cfg.weights)
 
     # export model
-    trainer.export(FLAGS.output_dir)
+    trainer.export(FLAGS.output_dir, for_fd=FLAGS.for_fd)
 
     if FLAGS.export_serving_model:
+        assert not FLAGS.for_fd
         from paddle_serving_client.io import inference_model_to_serving
         model_name = os.path.splitext(os.path.split(cfg.filename)[-1])[0]