def main():
  batch_size=64
  num_classes=1000
  epoch_size=100 
  num_epochs=1
  image_shape=(3,229,229)
  # epoch_size is similar to the idea of steps

  # set fake data
  network='resnet'
  num_layers=50
  dev = mx.gpu(0) if len(get_gpus()) > 0 else mx.cpu()

  net= import_module('symbols.'+network)
  sym= net.get_symbol(num_classes=num_classes,image_shape=image_shape,num_layers=num_layers,dtype=np.float32)
  mod = mx.mod.Module(symbol=sym,context=dev)
  data = [mx.random.uniform(-1.0,1.0,shape=shape,ctx=dev) for _, shape in mod.data_shapes]
  DataIter = mx.io.DataBatch(data,[])

  # get model 
  model_resnet50 = vision.resnet50_v1(pretrained=false)

  # pick optimizer 
  optim = mx.optimizer.SGD();
  
  # run training
  train(model_resnet50,DataIter,optim)
コード例 #2
0
def run_profile_test(config):
    network = config['network']
    batch_size = config['batch_size']
    dev = config['dev']
    dry_run = config['dry_run']
    iteration = config['iteration']
    out_dir = config['out_dir']

    #config dev
    if dev == 'gpu':
        dev_list = [mx.gpu(0)] if len(get_gpus()) > 0 else []
    elif dev == 'cpu':
        dev_list = [mx.cpu()]
    else:
        logging.error('no valid device')


    #clean and create out_dir
    if os.path.isdir(out_dir):
        shutil.rmtree(out_dir)

    os.mkdir(out_dir)
    logging.info('network: {} dev {}'.format(network, dev))
    logging.info('batch size {}, dry_run: {}, iteration {}'.format(batch_size, dry_run, iteration))
    run_profiler(network, batch_size, dev_list[0], iteration, dry_run)
コード例 #3
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    # return num images per second
    return num_batches * batch_size / (time.time() - tic)


if __name__ == '__main__':
    if opt.network == 'all':
        networks = [
            'alexnet', 'vgg-16', 'resnetv1-50', 'resnet-50', 'resnet-152',
            'inception-bn', 'inception-v3', 'inception-v4',
            'inception-resnet-v2', 'mobilenet', 'densenet121', 'squeezenet1.1'
        ]
        logging.info('It may take some time to run all models, '
                     'set --network to run a specific one')
    else:
        networks = [opt.network]
    devs = [mx.gpu(0)] if len(get_gpus()) > 0 else []
    # Enable USE_MKLDNN for better CPU performance
    devs.append(mx.cpu())

    if opt.batch_size == 0:
        batch_sizes = [1, 32, 64, 128, 256]
        logging.info('run batchsize [1, 32, 64, 128, 256] by default, '
                     'set --batch-size to run a specific one')
    else:
        batch_sizes = [opt.batch_size]

    for net in networks:
        logging.info('network: %s', net)
        if net in ['densenet121', 'squeezenet1.1']:
            logging.info('network: %s is converted from gluon modelzoo', net)
            logging.info(
コード例 #4
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    # run
    dry_run = 5                 # use 5 iterations to warm up
    for i in range(dry_run+num_batches):
        if i == dry_run:
            tic = time.time()
        mod.forward(batch, is_train=False)
        for output in mod.get_outputs():
            output.wait_to_read()

    # return num images per second
    return num_batches*batch_size/(time.time() - tic)

if __name__ == '__main__':
    networks = ['alexnet', 'vgg-16', 'inception-bn', 'inception-v3', 'resnet-50', 'resnet-152']
    devs = [mx.gpu(0)] if len(get_gpus()) > 0 else []
    # Enable USE_MKLDNN for better CPU performance
    devs.append(mx.cpu())

    batch_sizes = [1, 2, 4, 8, 16, 32]
    for net in networks:
        logging.info('network: %s', net)
        for d in devs:
            logging.info('device: %s', d)
            logged_fp16_warning = False
            for b in batch_sizes:
                for dtype in ['float32', 'float16']:
                    if d == mx.cpu() and dtype == 'float16':
                        #float16 is not supported on CPU
                        continue
                    elif net in ['inception-bn', 'alexnet'] and dtype == 'float16':
コード例 #5
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                          metrics=acc,
                          **kwargs)
        r = acc.get()[1]
        print('testing %s, acc = %f, speed = %f img/sec' % (m, r, speed))
        assert r > g and r < g + .1


def test_imagenet1k_inception_bn(**kwargs):
    acc = mx.metric.create('acc')
    m = 'imagenet1k-inception-bn'
    g = 0.72
    (speed, ) = score(model=m,
                      data_val='data/val-5k-256.rec',
                      rgb_mean='123.68,116.779,103.939',
                      metrics=acc,
                      **kwargs)
    r = acc.get()[1]
    print('Tested %s acc = %f, speed = %f img/sec' % (m, r, speed))
    assert r > g and r < g + .1


if __name__ == '__main__':
    gpus = get_gpus()
    assert len(gpus) > 0
    batch_size = 16 * len(gpus)
    gpus = ','.join([str(i) for i in gpus])

    download_data()
    test_imagenet1k_resnet(gpus=gpus, batch_size=batch_size)
    test_imagenet1k_inception_bn(gpus=gpus, batch_size=batch_size)
コード例 #6
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ファイル: test_score.py プロジェクト: bittnt/mxnet
def test_imagenet1k_resnet(**kwargs):
    models = ["imagenet1k-resnet-34", "imagenet1k-resnet-50", "imagenet1k-resnet-101", "imagenet1k-resnet-152"]
    accs = [0.72, 0.75, 0.765, 0.76]
    for (m, g) in zip(models, accs):
        acc = mx.metric.create("acc")
        (speed,) = score(model=m, data_val="data/val-5k-256.rec", rgb_mean="0,0,0", metrics=acc, **kwargs)
        r = acc.get()[1]
        print("testing %s, acc = %f, speed = %f img/sec" % (m, r, speed))
        assert r > g and r < g + 0.1


def test_imagenet1k_inception_bn(**kwargs):
    acc = mx.metric.create("acc")
    m = "imagenet1k-inception-bn"
    g = 0.72
    (speed,) = score(model=m, data_val="data/val-5k-256.rec", rgb_mean="123.68,116.779,103.939", metrics=acc, **kwargs)
    r = acc.get()[1]
    print("Tested %s acc = %f, speed = %f img/sec" % (m, r, speed))
    assert r > g and r < g + 0.1


if __name__ == "__main__":
    gpus = get_gpus()
    assert len(gpus) > 0
    batch_size = 16 * len(gpus)
    gpus = ",".join([str(i) for i in gpus])

    download_data()
    test_imagenet1k_resnet(gpus=gpus, batch_size=batch_size)
    test_imagenet1k_inception_bn(gpus=gpus, batch_size=batch_size)