Merge pull request #1 from littletomatodonkey/me/add_pdemo

fix convert weight

ppcls/engine/engine.py

@@ -344,15 +344,15 @@ class Engine(object):
 
             if self.use_dali:
                 self.train_dataloader.reset()
-            metric_msg = ", ".join([
-                self.output_info[key].avg_info for key in self.output_info
-            ])
+            metric_msg = ", ".join(
+                [self.output_info[key].avg_info for key in self.output_info])
             logger.info("[Train][Epoch {}/{}][Avg]{}".format(
                 epoch_id, self.config["Global"]["epochs"], metric_msg))
             self.output_info.clear()
 
             # eval model and save model if possible
-            start_eval_epoch = self.config["Global"].get("start_eval_epoch", 0) - 1
+            start_eval_epoch = self.config["Global"].get("start_eval_epoch",
+                                                         0) - 1
             if self.config["Global"][
                     "eval_during_train"] and epoch_id % self.config["Global"][
                         "eval_interval"] == 0 and epoch_id > start_eval_epoch:
@@ -367,7 +367,8 @@ class Engine(object):
                         self.output_dir,
                         model_name=self.config["Arch"]["name"],
                         prefix="best_model",
-                        loss=self.train_loss_func)
+                        loss=self.train_loss_func,
+                        save_student_model=True)
                 logger.info("[Eval][Epoch {}][best metric: {}]".format(
                     epoch_id, best_metric["metric"]))
                 logger.scaler(

ppcls/utils/convert_weights.py

-# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import os
-import paddle
-
-
-def convert_distill_weights(distill_weights_path, student_weights_path):
-    assert os.path.exists(distill_weights_path), \
-        "Given distill_weights_path {} not exist.".format(distill_weights_path)
-    # Load teacher and student weights 
-    all_params = paddle.load(distill_weights_path)
-    # Extract student weights
-    s_params = {key[len("Student."):]: all_params[key] for key in all_params if "Student." in key}
-    # Save student weights
-    paddle.save(s_params, student_weights_path)

ppcls/utils/save_load.py

@@ -42,6 +42,14 @@ def _mkdir_if_not_exist(path):
                 raise OSError('Failed to mkdir {}'.format(path))
 
 
+def _extract_student_weights(all_params, student_prefix="Student."):
+    s_params = {
+        key[len(student_prefix):]: all_params[key]
+        for key in all_params if student_prefix in key
+    }
+    return s_params
+
+
 def load_dygraph_pretrain(model, path=None):
     if not (os.path.isdir(path) or os.path.exists(path + '.pdparams')):
         raise ValueError("Model pretrain path {}.pdparams does not "
@@ -117,7 +125,7 @@ def init_model(config, net, optimizer=None, loss: paddle.nn.Layer=None):
         else:  # common load
             load_dygraph_pretrain(net, path=pretrained_model)
             logger.info("Finish load pretrained model from {}".format(
-                    pretrained_model))
+                pretrained_model))
 
 
 def save_model(net,
@@ -126,7 +134,8 @@ def save_model(net,
                model_path,
                model_name="",
                prefix='ppcls',
-               loss: paddle.nn.Layer=None):
+               loss: paddle.nn.Layer=None,
+               save_student_model=False):
     """
     save model to the target path
     """
@@ -137,11 +146,18 @@ def save_model(net,
     model_path = os.path.join(model_path, prefix)
 
     params_state_dict = net.state_dict()
-    loss_state_dict = loss.state_dict()
-    keys_inter = set(params_state_dict.keys()) & set(loss_state_dict.keys())
-    assert len(keys_inter) == 0, \
-        f"keys in model and loss state_dict must be unique, but got intersection {keys_inter}"
-    params_state_dict.update(loss_state_dict)
+    if loss is not None:
+        loss_state_dict = loss.state_dict()
+        keys_inter = set(params_state_dict.keys()) & set(loss_state_dict.keys(
+        ))
+        assert len(keys_inter) == 0, \
+            f"keys in model and loss state_dict must be unique, but got intersection {keys_inter}"
+        params_state_dict.update(loss_state_dict)
+
+    if save_student_model:
+        s_params = _extract_student_weights(params_state_dict)
+        if len(s_params) > 0:
+            paddle.save(s_params, model_path + "_student.pdparams")
 
     paddle.save(params_state_dict, model_path + ".pdparams")
     paddle.save([opt.state_dict() for opt in optimizer], model_path + ".pdopt")