tests/python/train/test_autograd.py - master - githubmirror/incubator-mxnet

tests/python/train/test_autograd.py
105 строк4 KB
hankaj
Python string formatting (#21136)
16 сен 2022, 14:01
16 сен 2022, 14:01c8922fe
# Licensed to the Apache Software Foundation (ASF) under one
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# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you 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.

# pylint: skip-file
from __future__ import print_function

import mxnet as mx
from mxnet import gluon
from mxnet.gluon import nn
from mxnet.test_utils import get_mnist_ubyte
import numpy as np
import logging
import os
from mxnet import autograd
logging.basicConfig(level=logging.DEBUG)

@mx.util.use_np
def test_autograd(tmpdir):
    # define network
    def get_net():
        net = nn.Sequential()
        net.add(nn.Dense(128, activation='relu'))
        net.add(nn.Dense(64, activation='relu'))
        net.add(nn.Dense(10))
        return net
    path = str(tmpdir)
    get_mnist_ubyte(path)

    batch_size = 100
    train_data = mx.io.MNISTIter(
            image=os.path.join(path, 'train-images-idx3-ubyte'),
            label=os.path.join(path, 'train-labels-idx1-ubyte'),
            data_shape=(784,),
            label_name='sm_label',
            batch_size=batch_size, shuffle=True, flat=True, silent=False, seed=10)
    val_data = mx.io.MNISTIter(
            image=os.path.join(path, 't10k-images-idx3-ubyte'),
            label=os.path.join(path, 't10k-labels-idx1-ubyte'),
            data_shape=(784,),
            label_name='sm_label',
            batch_size=batch_size, shuffle=True, flat=True, silent=False)

    def score(net, ctx_list):
        metric = gluon.metric.Accuracy()
        val_data.reset()
        for batch in val_data:
            datas = gluon.utils.split_and_load(batch.data[0], ctx_list, batch_axis=0)
            labels = gluon.utils.split_and_load(batch.label[0], ctx_list, batch_axis=0)
            outputs = []
            for x in datas:
                outputs.append(net(x))
            metric.update(labels, outputs)
        return metric.get()[1]

    def train(net, epoch, ctx_list):
        net.initialize(mx.init.Xavier(magnitude=2.24), ctx=ctx_list)
        trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.5})
        metric = gluon.metric.Accuracy()
        loss = gluon.loss.SoftmaxCrossEntropyLoss()

        for i in range(epoch):
            train_data.reset()
            for batch in train_data:
                datas = gluon.utils.split_and_load(batch.data[0], ctx_list, batch_axis=0)
                labels = gluon.utils.split_and_load(batch.label[0], ctx_list, batch_axis=0)
                outputs = []
                with autograd.record():
                    for x, y in zip(datas, labels):
                        z = net(x)
                        L = loss(z, y)
                        L.backward()
                        outputs.append(z)
                trainer.step(batch.data[0].shape[0])
                metric.update(labels, outputs)
            name, acc = metric.get()
            metric.reset()
            print(f'training acc at epoch {i}: {name}={acc}')


    net1 = get_net()
    train(net1, 5, [mx.cpu(0), mx.cpu(1)])
    acc1 = score(net1, [mx.cpu(0)])
    acc2 = score(net1, [mx.cpu(0), mx.cpu(1)])
    assert acc1 > 0.95
    assert abs(acc1 - acc2) < 0.01
    net1.save_parameters('mnist.params')

    net2 = get_net()
    net2.load_parameters('mnist.params', ctx=[mx.cpu(0)])
    acc3 = score(net2, [mx.cpu(0)])
    assert abs(acc3 - acc1) < 0.0001