Summary Record

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.nn as nn
from mindspore import context
from mindspore import Tensor
from mindspore.train import Model
from mindspore.common.initializer import TruncatedNormal
from mindspore.ops import operations as P
from mindspore.train.callback import SummaryCollector

"""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)  # 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 = P.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(is_grad=False, sparse=True, reduction="mean")
lr = Tensor(0.1)
opt = nn.Momentum(network.trainable_params(), lr, momentum=0.9)
model = Model(network, loss, opt)
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, 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. 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
from mindspore.ops import operations as P
from mindspore.ops import functional as F
from mindspore.nn import Optimizer


class CrossEntropyLoss(nn.Cell):
    """Loss function definition."""
    def __init__(self):
        super(CrossEntropyLoss, self).__init__()
        self.cross_entropy = P.SoftmaxCrossEntropyWithLogits()
        self.mean = P.ReduceMean()
        self.one_hot = P.OneHot()
        self.on_value = Tensor(1.0, mstype.float32)
        self.off_value = Tensor(0.0, mstype.float32)

        # Init ScalarSummary
        self.sm_scalar = P.ScalarSummary()

    def construct(self, logits, label):
        label = self.one_hot(label, F.shape(logits)[1], self.on_value, self.off_value)
        loss = self.cross_entropy(logits, label)[0]
        loss = self.mean(loss, (-1,))

        # Record loss
        self.sm_scalar("loss", loss)
        return loss


class MyOptimizer(Optimizer):
    """Optimizer definition."""
    def __init__(self, learning_rate, params, ......):
        ......
        # Initialize ScalarSummary
        self.sm_scalar = P.ScalarSummary()
        self.histogram_summary = P.HistogramSummary()
        self.weight_names = [param.name for param in self.parameters]

    def construct(self, grads):
        ......
        # Record learning rate here
        self.sm_scalar("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.sm_image = P.ImageSummary()
        # Init TensorSummary
        self.sm_tensor = P.TensorSummary()

    def construct(self, data):
        # Record image by Summary operator
        self.sm_image("image", data)
        # Record tensor by Summary operator
        self.sm_tensor("tensor", data)
        ......
        return out

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=None)

train_ds = create_mindrecord_dataset_for_training()

summary_collector = SummaryCollector(summary_dir='./summary_dir', collect_freq=1)
model.train(epoch=2, 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.

Notices

1. To limit time of listing summaries, MindInsight lists at most 999 summary items.

2. Multiple SummaryRecord instances can not be used at the same time. (SummaryRecord is used in SummaryCollector)

   If you use two or more instances of SummaryCollector in the callback list of ‘model.train’ or ‘model.eval’, it is seen as using multiple SummaryRecord instances at the same time, and it will cause recoding data fail.

   If the custom callback use SummaryRecord, it can not be used with SummaryCollector at the same time.

   Right code:

   ...示例
   summary_collector = SummaryCollector('./summary_dir')
   model.train(epoch=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(epoch=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(epoch=2, train_dataset, callbacks=[confusion_callback, summary_collector])