!7087 Resume training - NASNet

Merge pull request !7087 from Mina/resume-nasnet

model_zoo/official/cv/nasnet/train.py

@@ -1,115 +1,131 @@
-# 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.
-# ============================================================================
-"""train imagenet."""
-import argparse
-import os
-
-from mindspore import Tensor
-from mindspore import context
-from mindspore.context import ParallelMode
-from mindspore.communication.management import init, get_rank, get_group_size
-from mindspore.nn.optim.rmsprop import RMSProp
-from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
-from mindspore.train.model import Model
-from mindspore.train.serialization import load_checkpoint, load_param_into_net
-from mindspore.common import set_seed
-
-from src.config import nasnet_a_mobile_config_gpu as cfg
-from src.dataset import create_dataset
-from src.nasnet_a_mobile import NASNetAMobile, CrossEntropy
-from src.lr_generator import get_lr
-
-
-set_seed(cfg.random_seed)
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser(description='image classification training')
-    parser.add_argument('--dataset_path', type=str, default='', help='Dataset path')
-    parser.add_argument('--resume', type=str, default='', help='resume training with existed checkpoint')
-    parser.add_argument('--is_distributed', action='store_true', default=False,
-                        help='distributed training')
-    parser.add_argument('--platform', type=str, default='GPU', choices=('Ascend', 'GPU'), help='run platform')
-    args_opt = parser.parse_args()
-
-    context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.platform, save_graphs=False)
-    if os.getenv('DEVICE_ID', "not_set").isdigit():
-        context.set_context(device_id=int(os.getenv('DEVICE_ID')))
-
-    # init distributed
-    if args_opt.is_distributed:
-        if args_opt.platform == "Ascend":
-            init()
-        else:
-            init("nccl")
-        cfg.rank = get_rank()
-        cfg.group_size = get_group_size()
-        parallel_mode = ParallelMode.DATA_PARALLEL
-        context.set_auto_parallel_context(parallel_mode=parallel_mode, device_num=cfg.group_size,
-                                          gradients_mean=True)
-    else:
-        cfg.rank = 0
-        cfg.group_size = 1
-
-    # dataloader
-    dataset = create_dataset(args_opt.dataset_path, cfg, True)
-    batches_per_epoch = dataset.get_dataset_size()
-
-    # network
-    net = NASNetAMobile(cfg.num_classes)
-    if args_opt.resume:
-        ckpt = load_checkpoint(args_opt.resume)
-        load_param_into_net(net, ckpt)
-
-    #loss
-    loss = CrossEntropy(smooth_factor=cfg.label_smooth_factor, num_classes=cfg.num_classes, factor=cfg.aux_factor)
-
-    # learning rate schedule
-    lr = get_lr(lr_init=cfg.lr_init, lr_decay_rate=cfg.lr_decay_rate,
-                num_epoch_per_decay=cfg.num_epoch_per_decay, total_epochs=cfg.epoch_size,
-                steps_per_epoch=batches_per_epoch, is_stair=True)
-    lr = Tensor(lr)
-
-    # optimizer
-    decayed_params = []
-    no_decayed_params = []
-    for param in net.trainable_params():
-        if 'beta' not in param.name and 'gamma' not in param.name and 'bias' not in param.name:
-            decayed_params.append(param)
-        else:
-            no_decayed_params.append(param)
-    group_params = [{'params': decayed_params, 'weight_decay': cfg.weight_decay},
-                    {'params': no_decayed_params},
-                    {'order_params': net.trainable_params()}]
-    optimizer = RMSProp(group_params, lr, decay=cfg.rmsprop_decay, weight_decay=cfg.weight_decay,
-                        momentum=cfg.momentum, epsilon=cfg.opt_eps, loss_scale=cfg.loss_scale)
-
-    model = Model(net, loss_fn=loss, optimizer=optimizer)
-
-    print("============== Starting Training ==============")
-    loss_cb = LossMonitor(per_print_times=batches_per_epoch)
-    time_cb = TimeMonitor(data_size=batches_per_epoch)
-    callbacks = [loss_cb, time_cb]
-    config_ck = CheckpointConfig(save_checkpoint_steps=batches_per_epoch, keep_checkpoint_max=cfg.keep_checkpoint_max)
-    save_ckpt_path = os.path.join(cfg.ckpt_path, 'ckpt_' + str(cfg.rank) + '/')
-    ckpoint_cb = ModelCheckpoint(prefix=f"nasnet-a-mobile-rank{cfg.rank}", directory=save_ckpt_path, config=config_ck)
-    if args_opt.is_distributed & cfg.is_save_on_master:
-        if cfg.rank == 0:
-            callbacks.append(ckpoint_cb)
-        model.train(cfg.epoch_size, dataset, callbacks=callbacks, dataset_sink_mode=True)
-    else:
-        callbacks.append(ckpoint_cb)
-        model.train(cfg.epoch_size, dataset, callbacks=callbacks, dataset_sink_mode=True)
-    print("train success")
+# 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.
+# ============================================================================
+"""train imagenet."""
+import argparse
+import os
+
+from mindspore import Tensor
+from mindspore import context
+from mindspore.context import ParallelMode
+from mindspore.communication.management import init, get_rank, get_group_size
+from mindspore.nn.optim.rmsprop import RMSProp
+from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
+from mindspore.train.model import Model
+from mindspore.train.serialization import load_checkpoint, load_param_into_net
+from mindspore.common import set_seed
+
+from src.config import nasnet_a_mobile_config_gpu as cfg
+from src.dataset import create_dataset
+from src.nasnet_a_mobile import NASNetAMobile, CrossEntropy
+from src.lr_generator import get_lr
+
+
+set_seed(cfg.random_seed)
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='image classification training')
+    parser.add_argument('--dataset_path', type=str, default='', help='Dataset path')
+    parser.add_argument('--resume', type=str, default='', help='resume training with existed checkpoint')
+    parser.add_argument('--is_distributed', action='store_true', default=False,
+                        help='distributed training')
+    parser.add_argument('--platform', type=str, default='GPU', choices=('Ascend', 'GPU'), help='run platform')
+    args_opt = parser.parse_args()
+
+    context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.platform, save_graphs=False)
+    if os.getenv('DEVICE_ID', "not_set").isdigit():
+        context.set_context(device_id=int(os.getenv('DEVICE_ID')))
+
+    # init distributed
+    if args_opt.is_distributed:
+        if args_opt.platform == "Ascend":
+            init()
+        else:
+            init("nccl")
+        cfg.rank = get_rank()
+        cfg.group_size = get_group_size()
+        parallel_mode = ParallelMode.DATA_PARALLEL
+        context.set_auto_parallel_context(parallel_mode=parallel_mode, device_num=cfg.group_size,
+                                          gradients_mean=True)
+    else:
+        cfg.rank = 0
+        cfg.group_size = 1
+
+    # dataloader
+    dataset = create_dataset(args_opt.dataset_path, cfg, True)
+    batches_per_epoch = dataset.get_dataset_size()
+
+    # network
+    net = NASNetAMobile(cfg.num_classes)
+    if args_opt.resume:
+        ckpt = load_checkpoint(args_opt.resume)
+        load_param_into_net(net, ckpt)
+
+    #loss
+    loss = CrossEntropy(smooth_factor=cfg.label_smooth_factor, num_classes=cfg.num_classes, factor=cfg.aux_factor)
+
+    # learning rate schedule
+    lr = get_lr(lr_init=cfg.lr_init, lr_decay_rate=cfg.lr_decay_rate,
+                num_epoch_per_decay=cfg.num_epoch_per_decay, total_epochs=cfg.epoch_size,
+                steps_per_epoch=batches_per_epoch, is_stair=True)
+    if args_opt.resume:
+        name_dir = os.path.basename(args_opt.resume)
+        name, ext = name_dir.split(".")
+        split_result = name.split("_")
+        resume = split_result[-2].split("-")
+        resume_epoch = int(resume[-1])
+        step_num_in_epoch = int(split_result[-1])
+        assert step_num_in_epoch == ds_train.get_dataset_size()\
+        , "This script only supports resuming at the end of epoch"
+        lr = lr[(ds_train.get_dataset_size() * (resume_epoch - 1) + step_num_in_epoch):]
+    lr = Tensor(lr, mstype.float32)
+
+    # optimizer
+    decayed_params = []
+    no_decayed_params = []
+    for param in net.trainable_params():
+        if 'beta' not in param.name and 'gamma' not in param.name and 'bias' not in param.name:
+            decayed_params.append(param)
+        else:
+            no_decayed_params.append(param)
+    group_params = [{'params': decayed_params, 'weight_decay': cfg.weight_decay},
+                    {'params': no_decayed_params},
+                    {'order_params': net.trainable_params()}]
+    optimizer = RMSProp(group_params, lr, decay=cfg.rmsprop_decay, weight_decay=cfg.weight_decay,
+                        momentum=cfg.momentum, epsilon=cfg.opt_eps, loss_scale=cfg.loss_scale)
+
+    model = Model(net, loss_fn=loss, optimizer=optimizer)
+
+    print("============== Starting Training ==============")
+    loss_cb = LossMonitor(per_print_times=batches_per_epoch)
+    time_cb = TimeMonitor(data_size=batches_per_epoch)
+    callbacks = [loss_cb, time_cb]
+    config_ck = CheckpointConfig(save_checkpoint_steps=batches_per_epoch, keep_checkpoint_max=cfg.keep_checkpoint_max)
+    save_ckpt_path = os.path.join(cfg.ckpt_path, 'ckpt_' + str(cfg.rank) + '/')
+    ckpoint_cb = ModelCheckpoint(prefix=f"nasnet-a-mobile-rank{cfg.rank}", directory=save_ckpt_path, config=config_ck)
+    if args_opt.is_distributed & cfg.is_save_on_master:
+        if cfg.rank == 0:
+            callbacks.append(ckpoint_cb)
+        if args_opt.resume:
+            model.train(cfg.epoch_size - resume_epoch, dataset, callbacks=callbacks, dataset_sink_mode=True)
+        else:
+            model.train(cfg.epoch_size, dataset, callbacks=callbacks, dataset_sink_mode=True)
+    else:
+        callbacks.append(ckpoint_cb)
+        if args_opt.resume:
+            model.train(cfg.epoch_size - resume_epoch, dataset, callbacks=callbacks, dataset_sink_mode=True)
+        else:
+            model.train(cfg.epoch_size, dataset, callbacks=callbacks, dataset_sink_mode=True)
+    print("train success")