!3051 add loss monitor to lenet

Merge pull request !3051 from chenzhongming/newnew_master

mindspore/train/callback/_loss_monitor.py

@@ -14,7 +14,6 @@
 # ============================================================================
 """LossMonitor Callback class."""
 
-import time
 import numpy as np
 from mindspore.common.tensor import Tensor
 
@@ -32,62 +31,32 @@ class LossMonitor(Callback):
 
     Args:
         per_print_times (int): Print loss every times. Default: 1.
-        lr_init (numpy array): train learning rate. Default: None.
 
     Raises:
         ValueError: If print_step is not int or less than zero.
-
-    Examples:
-        >>> LossMonitor(100, lr_init=Tensor([0.05]*100).asnumpy())
     """
 
-    def __init__(self, per_print_times=1, lr_init=None):
+    def __init__(self, per_print_times=1):
         super(LossMonitor, self).__init__()
         if not isinstance(per_print_times, int) or per_print_times < 0:
             raise ValueError("print_step must be int and >= 0.")
         self._per_print_times = per_print_times
-        self.lr_init = 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)))
-        print("*" * 60)
-
-    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
+        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())
+        if isinstance(loss, (tuple, list)):
+            if isinstance(loss[0], Tensor) and isinstance(loss[0].asnumpy(), np.ndarray):
+                loss = loss[0]
 
-        self.losses.append(step_loss)
-        cur_step_in_epoch = int((cb_params.cur_step_num - 1) % cb_params.batch_num) + 1
+        if isinstance(loss, Tensor) and isinstance(loss.asnumpy(), np.ndarray):
+            loss = np.mean(loss.asnumpy())
 
-        if isinstance(step_loss, float) and (np.isnan(step_loss) or np.isinf(step_loss)):
-            raise ValueError("Epoch: [{:3d}/{:3d}], step: [{:5d}/{:5d}]. "
-                             "Invalid loss, terminating training.".format(
-                                 cb_params.cur_epoch_num - 1, cb_params.epoch_num,
-                                 cur_step_in_epoch, cb_params.batch_num))
+        cur_step_in_epoch = (cb_params.cur_step_num - 1) % cb_params.batch_num + 1
 
+        if isinstance(loss, float) and (np.isnan(loss) or np.isinf(loss)):
+            raise ValueError("epoch: {} step: {}. Invalid loss, terminating training.".format(
+                cb_params.cur_epoch_num, cur_step_in_epoch))
         if self._per_print_times != 0 and cb_params.cur_step_num % self._per_print_times == 0:
-            print("Epoch: [{:3d}/{:3d}], step: [{:5d}/{:5d}], "
-                  "loss: [{:5.4f}], avg los: [{:5.4f}], time: [{:5.4f}ms]".format(
-                      cb_params.cur_epoch_num, cb_params.epoch_num,
-                      cur_step_in_epoch, int(cb_params.batch_num),
-                      step_loss, np.mean(self.losses),
-                      step_mseconds), flush=True)
+            print("epoch: %s step: %s, loss is %s" % (cb_params.cur_epoch_num, cur_step_in_epoch, loss), flush=True)

model_zoo/lenet_quant/src/loss_monitor.py

@@ -0,0 +1,92 @@
+# 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.
+# ============================================================================
+"""LossMonitor Callback class."""
+
+import time
+import numpy as np
+from mindspore.common.tensor import Tensor
+from mindspore.train.callback import Callback
+
+
+class LossMonitor(Callback):
+    """
+    Monitor the loss in training.
+
+    If the loss is NAN or INF, it will terminate training.
+
+    Note:
+        If per_print_times is 0 do not print loss.
+
+    Args:
+        per_print_times (int): Print loss every times. Default: 1.
+        lr_init (numpy array): train learning rate. Default: None.
+
+    Raises:
+        ValueError: If print_step is not int or less than zero.
+
+    Examples:
+        >>> LossMonitor(100, lr_init=Tensor([0.05]*100).asnumpy())
+    """
+
+    def __init__(self, per_print_times=1, lr_init=None):
+        super(LossMonitor, self).__init__()
+        if not isinstance(per_print_times, int) or per_print_times < 0:
+            raise ValueError("print_step must be int and >= 0.")
+        self._per_print_times = per_print_times
+        self.lr_init = 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)))
+        print("*" * 60)
+
+    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 = int((cb_params.cur_step_num - 1) % cb_params.batch_num) + 1
+
+        if isinstance(step_loss, float) and (np.isnan(step_loss) or np.isinf(step_loss)):
+            raise ValueError("Epoch: [{:3d}/{:3d}], step: [{:5d}/{:5d}]. "
+                             "Invalid loss, terminating training.".format(
+                                 cb_params.cur_epoch_num - 1, cb_params.epoch_num,
+                                 cur_step_in_epoch, cb_params.batch_num))
+
+        if self._per_print_times != 0 and cb_params.cur_step_num % self._per_print_times == 0:
+            print("Epoch: [{:3d}/{:3d}], step: [{:5d}/{:5d}], "
+                  "loss: [{:5.4f}], avg loss: [{:5.4f}], time: [{:5.4f}ms]".format(
+                      cb_params.cur_epoch_num, cb_params.epoch_num,
+                      cur_step_in_epoch, int(cb_params.batch_num),
+                      step_loss, np.mean(self.losses),
+                      step_mseconds), flush=True)

model_zoo/lenet_quant/train.py

@@ -22,12 +22,13 @@ import os
 import argparse
 import mindspore.nn as nn
 from mindspore import context
-from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor
+from mindspore.train.callback import ModelCheckpoint, CheckpointConfig
 from mindspore.train import Model
 from mindspore.nn.metrics import Accuracy
 from src.dataset import create_dataset
 from src.config import mnist_cfg as cfg
 from src.lenet_fusion import LeNet5 as LeNet5Fusion
+from src.loss_monitor import LossMonitor
 
 parser = argparse.ArgumentParser(description='MindSpore MNIST Example')
 parser.add_argument('--device_target', type=str, default="Ascend",

model_zoo/lenet_quant/train_quant.py

@@ -23,13 +23,14 @@ import argparse
 import mindspore.nn as nn
 from mindspore import context
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
-from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor
+from mindspore.train.callback import ModelCheckpoint, CheckpointConfig
 from mindspore.train import Model
 from mindspore.nn.metrics import Accuracy
 from mindspore.train.quant import quant
 from src.dataset import create_dataset
 from src.config import mnist_cfg as cfg
 from src.lenet_fusion import LeNet5 as LeNet5Fusion
+from src.loss_monitor import LossMonitor
 
 parser = argparse.ArgumentParser(description='MindSpore MNIST Example')
 parser.add_argument('--device_target', type=str, default="Ascend",