Collecting Summary Record
Linux Ascend GPU CPU Model Optimization Intermediate Expert
Overview
Scalars, images, computational graphs, and model hyperparameters during training are recorded in files and can be viewed on the web page.
Operation Process
Prepare a training script, specify scalars, images, computational graphs, and model hyperparameters in the training script, record them in the summary log file, and run the training script.
Start MindInsight and specify the summary log file directory using startup parameters. After MindInsight is started, access the visualization page based on the IP address and port number. The default access IP address is
http://127.0.0.1:8080.During the training, when data is written into the summary log file, you can view the data on the web page.
Preparing The Training Script
Currently, MindSpore supports to save scalars, images, computational graph, and model hyperparameters to summary log file and display them on the web page.
MindSpore currently supports three ways to record data into summary log file.
Method one: Automatically collected through SummaryCollector
The Callback mechanism in MindSpore provides a quick and easy way to collect common information, including the calculational graph, loss value, learning rate, parameter weights, etc. It is named ‘SummaryCollector’.
When you write a training script, you just instantiate the SummaryCollector and apply it to either model.train or model.eval. You can automatically collect some common summary data. SummaryCollector detailed usage can reference API document mindspore.train.callback.SummaryCollector.
The sample code is as follows:
import mindspore
import mindspore.nn as nn
from mindspore import context
from mindspore import Tensor
from mindspore.train import Model
from mindspore.common.initializer import TruncatedNormal
import mindspore.ops as ops
from mindspore.train.callback import SummaryCollector
from mindspore.nn.metrics import Accuracy
"""AlexNet initial."""
def conv(in_channels, out_channels, kernel_size, stride=1, padding=0, pad_mode="valid"):
weight = weight_variable()
return nn.Conv2d(in_channels, out_channels,
kernel_size=kernel_size, stride=stride, padding=padding,
weight_init=weight, has_bias=False, pad_mode=pad_mode)
def fc_with_initialize(input_channels, out_channels):
weight = weight_variable()
bias = weight_variable()
return nn.Dense(input_channels, out_channels, weight, bias)
def weight_variable():
return TruncatedNormal(0.02)
class AlexNet(nn.Cell):
def __init__(self, num_classes=10, channel=3):
super(AlexNet, self).__init__()
self.conv1 = conv(channel, 96, 11, stride=4)
self.conv2 = conv(96, 256, 5, pad_mode="same")
self.conv3 = conv(256, 384, 3, pad_mode="same")
self.conv4 = conv(384, 384, 3, pad_mode="same")
self.conv5 = conv(384, 256, 3, pad_mode="same")
self.relu = nn.ReLU()
self.max_pool2d = ops.MaxPool(ksize=3, strides=2)
self.flatten = nn.Flatten()
self.fc1 = fc_with_initialize(6*6*256, 4096)
self.fc2 = fc_with_initialize(4096, 4096)
self.fc3 = fc_with_initialize(4096, num_classes)
def construct(self, x):
x = self.conv1(x)
x = self.relu(x)
x = self.max_pool2d(x)
x = self.conv2(x)
x = self.relu(x)
x = self.max_pool2d(x)
x = self.conv3(x)
x = self.relu(x)
x = self.conv4(x)
x = self.relu(x)
x = self.conv5(x)
x = self.relu(x)
x = self.max_pool2d(x)
x = self.flatten(x)
x = self.fc1(x)
x = self.relu(x)
x = self.fc2(x)
x = self.relu(x)
x = self.fc3(x)
return x
context.set_context(mode=context.GRAPH_MODE)
network = AlexNet(num_classes=10)
loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
lr = Tensor(0.5, mindspore.float32)
opt = nn.Momentum(network.trainable_params(), lr, momentum=0.9)
model = Model(network, loss, opt, metrics={"Accuracy": Accuracy()})
ds_train = create_dataset('./dataset_path')
# Init a SummaryCollector callback instance, and use it in model.train or model.eval
summary_collector = SummaryCollector(summary_dir='./summary_dir', collect_freq=1)
# Note: dataset_sink_mode should be set to False, else you should modify collect freq in SummaryCollector
model.train(epoch=1, train_dataset=ds_train, callbacks=[summary_collector], dataset_sink_mode=False)
ds_eval = create_dataset('./dataset_path')
model.eval(ds_eval, callbacks=[summary_collector])
Method two: Custom collection of network data with summary operators and SummaryCollector
In addition to providing the SummaryCollector that automatically collects some summary data, MindSpore provides summary operators that enable custom collection other data on the network, such as the input of each convolutional layer, or the loss value in the loss function, etc.
Summary operators currently supported:
ScalarSummary: Record a scalar data.
TensorSummary: Record a tensor data.
ImageSummary: Record a image data.
HistogramSummary: Convert tensor data into histogram data records.
The recording method is shown in the following steps.
Step 1: Call the summary operator in the construct function of the derived class that inherits nn.Cell to collect image or scalar data.
For example, when a network is defined, image data is recorded in construct of the network. When the loss function is defined, the loss value is recorded in construct of the loss function.
Record the dynamic learning rate in construct of the optimizer when defining the optimizer.
The sample code is as follows:
from mindspore import context, Tensor, nn
from mindspore.common import dtype as mstype
import mindspore.ops as ops
from mindspore.nn import Optimizer
class CrossEntropyLoss(nn.Cell):
"""Loss function definition."""
def __init__(self):
super(CrossEntropyLoss, self).__init__()
self.cross_entropy = ops.SoftmaxCrossEntropyWithLogits()
self.mean = ops.ReduceMean()
self.one_hot = ops.OneHot()
self.on_value = Tensor(1.0, mstype.float32)
self.off_value = Tensor(0.0, mstype.float32)
# Init ScalarSummary
self.scalar_summary = ops.ScalarSummary()
def construct(self, logits, label):
label = self.one_hot(label, ops.shape(logits)[1], self.on_value, self.off_value)
loss = self.cross_entropy(logits, label)[0]
loss = self.mean(loss, (-1,))
# Record loss
self.scalar_summary("loss", loss)
return loss
class MyOptimizer(Optimizer):
"""Optimizer definition."""
def __init__(self, learning_rate, params, ......):
......
# Initialize ScalarSummary
self.scalar_summary = ops.ScalarSummary()
self.histogram_summary = ops.HistogramSummary()
self.weight_names = [param.name for param in self.parameters]
def construct(self, grads):
......
# Record learning rate here
self.scalar_summary("learning_rate", learning_rate)
# Record weight
self.histogram_summary(self.weight_names[0], self.paramters[0])
# Record gradient
self.histogram_summary(self.weight_names[0] + ".gradient", grads[0])
......
class Net(nn.Cell):
"""Net definition."""
def __init__(self):
super(Net, self).__init__()
......
# Init ImageSummary
self.image_summary = ops.ImageSummary()
# Init TensorSummary
self.tensor_summary = ops.TensorSummary()
def construct(self, data):
# Record image by Summary operator
self.image_summary("image", data)
# Record tensor by Summary operator
self.tensor_summary("tensor", data)
......
return out
In the same Summary operator, the name given to the data must not be repeated, otherwise the data collection and presentation will have unexpected behavior. For example, if two
ScalarSummaryoperators are used to collect scalar data, two scalars cannot be given the same name.
Step 2: In the training script, instantiate the SummaryCollector and apply it to model.train.
The sample code is as follows:
from mindspore import Model, nn, context
from mindspore.train.callback import SummaryCollector
......
context.set_context(mode=context.GRAPH_MODE)
net = Net()
loss_fn = CrossEntropyLoss()
optim = MyOptimizer(learning_rate=0.01, params=network.trainable_params())
model = Model(net, loss_fn=loss_fn, optimizer=optim, metrics={"Accuracy": Accuracy()})
train_ds = create_mindrecord_dataset_for_training()
summary_collector = SummaryCollector(summary_dir='./summary_dir', collect_freq=1)
model.train(epoch=2, train_dataset=train_ds, callbacks=[summary_collector])
Method three: Custom callback recording data
MindSpore supports custom callback and support to record data into summary log file in custom callback, and display the data by the web page.
The following pseudocode is shown in the CNN network, where developers can use the network output with the original tag and the prediction tag to generate the image of the confusion matrix.
It is then recorded into the summary log file through the SummaryRecord module.
SummaryRecord detailed usage can reference API document mindspore.train.summary.SummaryRecord.
The sample code is as follows:
from mindspore.train.callback import Callback
from mindspore.train.summary import SummaryRecord
class ConfusionMatrixCallback(Callback):
def __init__(self, summary_dir):
self._summary_dir = summary_dir
def __enter__(self):
# init you summary record in here, when the train script run, it will be inited before training
self.summary_record = SummaryRecord(summary_dir)
def __exit__(self, *exc_args):
# Note: you must close the summary record, it will release the process pool resource
# else your training script will not exit from training.
self.summary_record.close()
return self
def step_end(self, run_context):
cb_params = run_context.run_context.original_args()
# create a confusion matric image, and record it to summary file
confusion_martrix = create_confusion_matrix(cb_params)
self.summary_record.add_value('image', 'confusion_matrix', confusion_matric)
self.summary_record.record(cb_params.cur_step)
# init you train script
...
confusion_martrix = ConfusionMartrixCallback(summary_dir='./summary_dir')
model.train(cnn_network, callbacks=[confusion_martrix])
The above three ways, support the record computational graph, loss value and other data. In addition, MindSpore also supports the saving of computational graph for other phases of training, through
the save_graphs option of context.set_context in the training script is set to True to record computational graphs of other phases, including the computational graph after operator fusion.
In the saved files, ms_output_after_hwopt.pb is the computational graph after operator fusion, which can be viewed on the web page.
Run MindInsight
After completing the data collection in the tutorial above, you can start MindInsight to visualize the collected data. When start MindInsight, you need to specify the summary log file directory with the --summary-base-dir parameter.
The specified summary log file directory can be the output directory of a training or the parent directory of the output directory of multiple training.
The output directory structure for a training is as follows
└─summary_dir
events.out.events.summary.1596869898.hostname_MS
events.out.events.summary.1596869898.hostname_lineage
Start command:
mindinsight start --summary-base-dir ./summary_dir
The output directory structure of multiple training is as follows:
└─summary
├─summary_dir1
│ events.out.events.summary.1596869898.hostname_MS
│ events.out.events.summary.1596869898.hostname_lineage
│
└─summary_dir2
events.out.events.summary.1596869998.hostname_MS
events.out.events.summary.1596869998.hostname_lineage
Start command:
mindinsight start --summary-base-dir ./summary
After successful startup, the visual page can be viewed by visiting the http://127.0.0.1:8080 address through the browser.
Stop MindInsight command:
mindinsight stop
For more parameter Settings, see the MindInsight related commands page.
Notices
To limit time of listing summaries, MindInsight lists at most 999 summary items.
Multiple
SummaryRecordinstances can not be used at the same time. (SummaryRecordis used inSummaryCollector)If you use two or more instances of
SummaryCollectorin the callback list of ‘model.train’ or ‘model.eval’, it is seen as using multipleSummaryRecordinstances at the same time, and it will cause recoding data fail.If the custom callback use
SummaryRecord, it can not be used withSummaryCollectorat the same time.Right code:
... summary_collector = SummaryCollector('./summary_dir') model.train(2, train_dataset, callbacks=[summary_collector]) ... model.eval(dataset， callbacks=[summary_collector])Wrong code:
... summary_collector1 = SummaryCollector('./summary_dir1') summary_collector2 = SummaryCollector('./summary_dir2') model.train(2, train_dataset, callbacks=[summary_collector1, summary_collector2])
Wrong code:
... # Note: the 'ConfusionMatrixCallback' is user-defined, and it uses SummaryRecord to record data. confusion_callback = ConfusionMatrixCallback('./summary_dir1') summary_collector = SummaryCollector('./summary_dir2') model.train(2, train_dataset, callbacks=[confusion_callback, summary_collector])
In each Summary log file directory, only one training data should be placed. If a summary log directory contains summary data from multiple training, MindInsight will overlay the summary data from these training when visualizing the data, which may not be consistent with the expected visualizations.
Currently,
SummaryCollectorandSummaryRecorddo not support scenarios with GPU multi-card running.