[Feat] Support saving and loading models in different formats

README.md

@@ -1 +1 @@
-README_CN.md
+README_CN.md

deploy/python/collect_dynamic_shape.py

@@ -16,6 +16,7 @@
 import os
 
 import numpy as np
+import paddle
 from paddle.inference import create_predictor
 from paddle.inference import Config as PredictConfig
 
@@ -29,22 +30,20 @@
 
 def parse_args():
     parser = argparse.ArgumentParser(description='Test')
-    parser.add_argument(
-        "--config",
-        help="The deploy config generated by exporting model.",
-        type=str,
-        required=True)
+    parser.add_argument("--config",
+                        help="The deploy config generated by exporting model.",
+                        type=str,
+                        required=True)
     parser.add_argument(
         '--image_path',
         help='The directory or path or file list of the images to be predicted.',
         type=str,
         required=True)
 
-    parser.add_argument(
-        '--dynamic_shape_path',
-        type=str,
-        default="./dynamic_shape.pbtxt",
-        help='The path to save dynamic shape.')
+    parser.add_argument('--dynamic_shape_path',
+                        type=str,
+                        default="./dynamic_shape.pbtxt",
+                        help='The path to save dynamic shape.')
 
     return parser.parse_args()
 
@@ -62,7 +61,10 @@ def collect_dynamic_shape(args):
 
     # prepare config
     cfg = DeployConfig(args.config)
-    pred_cfg = PredictConfig(cfg.model, cfg.params)
+    if paddle.__version__.split('.')[0] == '2':
+        pred_cfg = PredictConfig(cfg.model, cfg.params)
+    else:
+        pred_cfg = PredictConfig(cfg.model_dir, cfg.model_prefix)
     pred_cfg.enable_use_gpu(1000, 0)
     pred_cfg.collect_shape_range_info(args.dynamic_shape_path)
 

deploy/python/infer.py

@@ -16,6 +16,7 @@
 import os
 
 import numpy as np
+import paddle
 from paddle.inference import create_predictor, PrecisionType
 from paddle.inference import Config as PredictConfig
 
@@ -52,7 +53,10 @@ def auto_tune(args, imgs, img_nums):
     num = min(len(imgs), img_nums)
 
     cfg = DeployConfig(args.cfg)
-    pred_cfg = PredictConfig(cfg.model, cfg.params)
+    if paddle.__version__.split('.')[0] == '2':
+        pred_cfg = PredictConfig(cfg.model, cfg.params)
+    else:
+        pred_cfg = PredictConfig(cfg.model_dir, cfg.model_prefix)
     pred_cfg.enable_use_gpu(100, 0)
     if not args.print_detail:
         pred_cfg.disable_glog_info()
@@ -139,7 +143,11 @@ def __init__(self, args):
                 logger=logger)
 
     def _init_base_config(self):
-        self.pred_cfg = PredictConfig(self.cfg.model, self.cfg.params)
+        if paddle.__version__.split('.')[0] == '2':
+            self.pred_cfg = PredictConfig(self.cfg.model, self.cfg.params)
+        else:
+            self.pred_cfg = PredictConfig(self.cfg.model_dir,
+                                          self.cfg.model_prefix)
         if not self.args.print_detail:
             self.pred_cfg.disable_glog_info()
         self.pred_cfg.enable_memory_optim()

deploy/python/infer_dataset.py

@@ -32,13 +32,12 @@
 
 def parse_args():
     parser = argparse.ArgumentParser(description='Model Infer')
-    parser.add_argument(
-        "--config",
-        dest="cfg",
-        help="The config file.",
-        default=None,
-        type=str,
-        required=True)
+    parser.add_argument("--config",
+                        dest="cfg",
+                        help="The config file.",
+                        default=None,
+                        type=str,
+                        required=True)
 
     parser.add_argument(
         '--dataset_type',
@@ -53,11 +52,10 @@ def parse_args():
         type=str,
         default=None,
         required=True)
-    parser.add_argument(
-        '--dataset_mode',
-        help='The dataset mode, such as train, val.',
-        type=str,
-        default="val")
+    parser.add_argument('--dataset_mode',
+                        help='The dataset mode, such as train, val.',
+                        type=str,
+                        default="val")
     parser.add_argument(
         '--resize_width',
         help='Set the resize width to acclerate the test. In default, it is 0, '
@@ -70,11 +68,10 @@ def parse_args():
         'which means use the origin height.',
         type=int,
         default=0)
-    parser.add_argument(
-        '--batch_size',
-        help='Mini batch size of one gpu or cpu.',
-        type=int,
-        default=1)
+    parser.add_argument('--batch_size',
+                        help='Mini batch size of one gpu or cpu.',
+                        type=int,
+                        default=1)
 
     parser.add_argument(
         '--device',
@@ -88,51 +85,45 @@ def parse_args():
         type=eval,
         choices=[True, False],
         help='Whether to use Nvidia TensorRT to accelerate prediction.')
-    parser.add_argument(
-        "--precision",
-        default="fp32",
-        type=str,
-        choices=["fp32", "fp16", "int8"],
-        help='The tensorrt precision.')
+    parser.add_argument("--precision",
+                        default="fp32",
+                        type=str,
+                        choices=["fp32", "fp16", "int8"],
+                        help='The tensorrt precision.')
     parser.add_argument(
         '--enable_auto_tune',
         default=False,
         type=eval,
         choices=[True, False],
-        help='Whether to enable tuned dynamic shape. We uses some images to collect '
+        help=
+        'Whether to enable tuned dynamic shape. We uses some images to collect '
         'the dynamic shape for trt sub graph, which avoids setting dynamic shape manually.'
     )
-    parser.add_argument(
-        '--auto_tuned_shape_file',
-        type=str,
-        default="auto_tune_tmp.pbtxt",
-        help='The temp file to save tuned dynamic shape.')
-    parser.add_argument(
-        '--min_subgraph_size',
-        default=3,
-        type=int,
-        help='The min subgraph size in tensorrt prediction.')
-
-    parser.add_argument(
-        '--cpu_threads',
-        default=10,
-        type=int,
-        help='Number of threads to predict when using cpu.')
-    parser.add_argument(
-        '--enable_mkldnn',
-        default=False,
-        type=eval,
-        choices=[True, False],
-        help='Enable to use mkldnn to speed up when using cpu.')
-
-    parser.add_argument(
-        '--with_argmax',
-        help='Perform argmax operation on the predict result.',
-        action='store_true')
-    parser.add_argument(
-        '--print_detail',
-        help='Print GLOG information of Paddle Inference.',
-        action='store_true')
+    parser.add_argument('--auto_tuned_shape_file',
+                        type=str,
+                        default="auto_tune_tmp.pbtxt",
+                        help='The temp file to save tuned dynamic shape.')
+    parser.add_argument('--min_subgraph_size',
+                        default=3,
+                        type=int,
+                        help='The min subgraph size in tensorrt prediction.')
+
+    parser.add_argument('--cpu_threads',
+                        default=10,
+                        type=int,
+                        help='Number of threads to predict when using cpu.')
+    parser.add_argument('--enable_mkldnn',
+                        default=False,
+                        type=eval,
+                        choices=[True, False],
+                        help='Enable to use mkldnn to speed up when using cpu.')
+
+    parser.add_argument('--with_argmax',
+                        help='Perform argmax operation on the predict result.',
+                        action='store_true')
+    parser.add_argument('--print_detail',
+                        help='Print GLOG information of Paddle Inference.',
+                        action='store_true')
 
     return parser.parse_args()
 
@@ -152,10 +143,11 @@ def get_dataset(args):
         with codecs.open(args.cfg, 'r', 'utf-8') as file:
             dic = yaml.load(file, Loader=yaml.FullLoader)
         transforms_dic = dic['Deploy']['transforms']
-        transforms_dic.insert(0, {
-            "type": "Resize",
-            'target_size': [args.resize_width, args.resize_height]
-        })
+        transforms_dic.insert(
+            0, {
+                "type": "Resize",
+                'target_size': [args.resize_width, args.resize_height]
+            })
         transforms = DeployConfig.load_transforms(transforms_dic).transforms
 
     kwargs = {
@@ -191,7 +183,10 @@ def auto_tune(args, dataset, img_nums):
     num = min(len(dataset), img_nums)
 
     cfg = DeployConfig(args.cfg)
-    pred_cfg = PredictConfig(cfg.model, cfg.params)
+    if paddle.__version__.split('.')[0] == '2':
+        pred_cfg = PredictConfig(cfg.model, cfg.params)
+    else:
+        pred_cfg = PredictConfig(cfg.model_dir, cfg.model_prefix)
     pred_cfg.enable_use_gpu(100, 0)
     if not args.print_detail:
         pred_cfg.disable_glog_info()
@@ -224,6 +219,7 @@ def auto_tune(args, dataset, img_nums):
 
 
 class DatasetPredictor(Predictor):
+
     def __init__(self, args):
         super().__init__(args)
 

paddleseg/deploy/infer.py

@@ -21,25 +21,45 @@
 
 
 class DeployConfig:
+
     def __init__(self, path):
         with codecs.open(path, 'r', 'utf-8') as file:
             self.dic = yaml.load(file, Loader=yaml.FullLoader)
 
-        self._transforms = self.load_transforms(self.dic['Deploy'][
-            'transforms'])
+        self._transforms = self.load_transforms(
+            self.dic['Deploy']['transforms'])
         self._dir = os.path.dirname(path)
+        self._is_old_format = 'model_prefix' not in self.dic['Deploy']
 
     @property
     def transforms(self):
         return self._transforms
 
     @property
     def model(self):
-        return os.path.join(self._dir, self.dic['Deploy']['model'])
+        if self._is_old_format:
+            return os.path.join(self._dir, self.dic['Deploy']['model'])
+        else:
+            return os.path.join(self._dir,
+                                self.dic['Deploy']['model_prefix'] + '.pdmodel')
 
     @property
     def params(self):
-        return os.path.join(self._dir, self.dic['Deploy']['params'])
+        if self._is_old_format:
+            return os.path.join(self._dir, self.dic['Deploy']['params'])
+        else:
+            return os.path.join(
+                self._dir, self.dic['Deploy']['model_prefix'] + '.pdiparams')
+
+    @property
+    def model_dir(self):
+        return self._dir
+
+    @property
+    def model_prefix(self):
+        if self._is_old_format:
+            return self.dic['Deploy']['model'][:-8]
+        return self.dic['Deploy']['model_prefix']
 
     @staticmethod
     def load_transforms(t_list):

paddleseg/models/backbones/efficientformerv2.py

@@ -203,7 +203,7 @@ def forward_tokens(self, x):
     def forward(self, x):
         x = self.patch_embed(x)
         x = self.forward_tokens(x)
-        if self.mode is not 'multi_scale':
+        if self.mode != 'multi_scale':
             x = [
                 paddle.concat(
                     [

tools/export.py

@@ -97,8 +97,7 @@ def main(args):
     # TODO add test config
     deploy_info = {
         'Deploy': {
-            'model': save_name + '.pdmodel',
-            'params': save_name + '.pdiparams',
+            'model_prefix': save_name,
             'transforms': transforms,
             'input_shape': shape,
             'output_op': args.output_op,