!10596 Add export.py to textcnn (PR to master)

From: @penny369
Reviewed-by: @guoqi1024,@pandoublefeng
Signed-off-by: @guoqi1024

model_zoo/official/cv/xception/train.py

@@ -14,15 +14,13 @@
 # ============================================================================
 """train Xception."""
 import os
-import time
 import argparse
-import numpy as np
 
 from mindspore import context
 from mindspore import Tensor
 from mindspore.nn.optim.momentum import Momentum
 from mindspore.train.model import Model, ParallelMode
-from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, Callback
+from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, TimeMonitor, LossMonitor
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
 from mindspore.communication.management import init, get_rank, get_group_size
 from mindspore.train.loss_scale_manager import FixedLossScaleManager
@@ -37,59 +35,6 @@ from src.loss import CrossEntropySmooth
 
 set_seed(1)
 
-class Monitor(Callback):
-    """
-    Monitor loss and time.
-
-    Args:
-        lr_init (numpy array): train lr
-
-    Returns:
-        None
-
-    Examples:
-        >>> Monitor(lr_init=Tensor([0.05]*100).asnumpy())
-    """
-
-    def __init__(self, lr_init=None):
-        super(Monitor, self).__init__()
-        self.lr_init = lr_init
-        self.lr_init_len = len(lr_init)
-
-    def epoch_begin(self, run_context):
-        self.losses = []
-        self.epoch_time = time.time()
-
-    def epoch_end(self, run_context):
-        cb_params = run_context.original_args()
-
-        epoch_mseconds = (time.time() - self.epoch_time) * 1000
-        per_step_mseconds = epoch_mseconds / cb_params.batch_num
-        print("epoch time: {:5.3f}, per step time: {:5.3f}, avg loss: {:5.3f}".format(epoch_mseconds,
-                                                                                      per_step_mseconds,
-                                                                                      np.mean(self.losses)))
-
-    def step_begin(self, run_context):
-        self.step_time = time.time()
-
-    def step_end(self, run_context):
-        cb_params = run_context.original_args()
-        step_mseconds = (time.time() - self.step_time) * 1000
-        step_loss = cb_params.net_outputs
-
-        if isinstance(step_loss, (tuple, list)) and isinstance(step_loss[0], Tensor):
-            step_loss = step_loss[0]
-        if isinstance(step_loss, Tensor):
-            step_loss = np.mean(step_loss.asnumpy())
-
-        self.losses.append(step_loss)
-        cur_step_in_epoch = (cb_params.cur_step_num - 1) % cb_params.batch_num
-
-        print("epoch: [{:3d}/{:3d}], step:[{:5d}/{:5d}], loss:[{:5.3f}/{:5.3f}], time:[{:5.3f}], lr:[{:5.3f}]".format(
-            cb_params.cur_epoch_num - 1 + config.finish_epoch, cb_params.epoch_num + config.finish_epoch,
-            cur_step_in_epoch, cb_params.batch_num, step_loss,
-            np.mean(self.losses), step_mseconds, self.lr_init[cb_params.cur_step_num - 1]), flush=True)
-
 if __name__ == '__main__':
     parser = argparse.ArgumentParser(description='image classification training')
     parser.add_argument('--is_distributed', action='store_true', default=False, help='distributed training')
@@ -153,7 +98,7 @@ if __name__ == '__main__':
                       amp_level='O3', keep_batchnorm_fp32=True)
 
         # define callbacks
-        cb = [Monitor(lr_init=lr.asnumpy())]
+        cb = [TimeMonitor(), LossMonitor()]
         if config.save_checkpoint:
             save_ckpt_path = os.path.join(config.save_checkpoint_path, 'model_' + str(rank) + '/')
             config_ck = CheckpointConfig(save_checkpoint_steps=config.save_checkpoint_epochs * step_size,

model_zoo/official/nlp/textcnn/README.md

@@ -83,6 +83,7 @@ After installing MindSpore via the official website, you can start training and
         │   ├── config.py            // parameter configuration
         ├── train.py               // training script
         ├── eval.py               //  evaluation script
+        ├── export.py               //  export checkpoint to other format file
 ```
 
 ## [Script Parameters](#contents)
@@ -175,4 +176,4 @@ For more configuration details, please refer the script `config.py`.
 
 # [ModelZoo Homepage](#contents)  
 
- Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).  
+ Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).  

model_zoo/official/nlp/textcnn/export.py

@@ -0,0 +1,56 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+"""
+##############export checkpoint file into air, onnx, mindir models#################
+python export.py
+"""
+import argparse
+import numpy as np
+
+from mindspore import Tensor, load_checkpoint, load_param_into_net, export, context
+
+from src.config import cfg
+from src.textcnn import TextCNN
+from src.dataset import MovieReview
+
+parser = argparse.ArgumentParser(description='TextCNN export')
+parser.add_argument("--device_id", type=int, default=0, help="device id")
+parser.add_argument("--ckpt_file", type=str, required=True, help="checkpoint file path.")
+parser.add_argument("--file_name", type=str, default="textcnn", help="output file name.")
+parser.add_argument('--file_format', type=str, choices=["AIR", "ONNX", "MINDIR"], default='AIR', help='file format')
+parser.add_argument("--device_target", type=str, choices=["Ascend", "GPU", "CPU"], default="Ascend",
+                    help="device target")
+parser.add_argument('--dataset_name', type=str, default='MR', choices=['MR'],
+                    help='dataset name.')
+
+args = parser.parse_args()
+
+context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target, device_id=args.device_id)
+
+if __name__ == '__main__':
+
+    if args.dataset_name == 'MR':
+        instance = MovieReview(root_dir=cfg.data_path, maxlen=cfg.word_len, split=0.9)
+    else:
+        raise ValueError("dataset is not support.")
+
+    net = TextCNN(vocab_len=instance.get_dict_len(), word_len=cfg.word_len,
+                  num_classes=cfg.num_classes, vec_length=cfg.vec_length)
+
+    param_dict = load_checkpoint(args.ckpt_file)
+    load_param_into_net(net, param_dict)
+
+    input_arr = Tensor(np.ones([cfg.batch_size, cfg.word_len], np.int32))
+    export(net, input_arr, file_name=args.file_name, file_format=args.file_format)