Пример #1
0
def main():
    global msglogger
    check_pytorch_version()
    args = parser.parse_args()
    if not os.path.exists(args.output_dir):
        os.makedirs(args.output_dir)
    msglogger = apputils.config_pylogger(
        os.path.join(script_dir, 'logging.conf'), args.name, args.output_dir)

    # Log various details about the execution environment.  It is sometimes useful
    # to refer to past experiment executions and this information may be useful.
    apputils.log_execution_env_state(sys.argv, gitroot=module_path)
    msglogger.debug("Distiller: %s", distiller.__version__)

    start_epoch = 0
    best_top1 = 0

    if args.deterministic:
        # Experiment reproducibility is sometimes important.  Pete Warden expounded about this
        # in his blog: https://petewarden.com/2018/03/19/the-machine-learning-reproducibility-crisis/
        # In Pytorch, support for deterministic execution is still a bit clunky.
        if args.workers > 1:
            msglogger.error(
                'ERROR: Setting --deterministic requires setting --workers/-j to 0 or 1'
            )
            exit(1)
        # Use a well-known seed, for repeatability of experiments
        torch.manual_seed(0)
        random.seed(0)
        np.random.seed(0)
        cudnn.deterministic = True
    else:
        # This issue: https://github.com/pytorch/pytorch/issues/3659
        # Implies that cudnn.benchmark should respect cudnn.deterministic, but empirically we see that
        # results are not re-produced when benchmark is set. So enabling only if deterministic mode disabled.
        cudnn.benchmark = True

    if args.gpus is not None:
        try:
            args.gpus = [int(s) for s in args.gpus.split(',')]
        except ValueError:
            msglogger.error(
                'ERROR: Argument --gpus must be a comma-separated list of integers only'
            )
            exit(1)
        available_gpus = torch.cuda.device_count()
        for dev_id in args.gpus:
            if dev_id >= available_gpus:
                msglogger.error(
                    'ERROR: GPU device ID {0} requested, but only {1} devices available'
                    .format(dev_id, available_gpus))
                exit(1)
        # Set default device in case the first one on the list != 0
        torch.cuda.set_device(args.gpus[0])

    # Infer the dataset from the model name
    args.dataset = 'cifar10' if 'cifar' in args.arch else 'imagenet'

    # Create the model
    png_summary = args.summary is not None and args.summary.startswith('png')
    is_parallel = not png_summary and args.summary != 'compute'  # For PNG summary, parallel graphs are illegible
    model = create_model(args.pretrained,
                         args.dataset,
                         args.arch,
                         parallel=is_parallel,
                         device_ids=args.gpus)

    compression_scheduler = None
    # Create a couple of logging backends.  TensorBoardLogger writes log files in a format
    # that can be read by Google's Tensor Board.  PythonLogger writes to the Python logger.
    tflogger = TensorBoardLogger(msglogger.logdir)
    pylogger = PythonLogger(msglogger)

    # We can optionally resume from a checkpoint
    if args.resume:
        model, compression_scheduler, start_epoch = apputils.load_checkpoint(
            model, chkpt_file=args.resume)

        if 'resnet' in args.arch and 'preact' not in args.arch and 'cifar' in args.arch:
            distiller.resnet_cifar_remove_layers(model)
            #model = distiller.resnet_cifar_remove_channels(model, compression_scheduler.zeros_mask_dict)

    # Define loss function (criterion) and optimizer
    criterion = nn.CrossEntropyLoss().cuda()
    optimizer = torch.optim.SGD(model.parameters(),
                                lr=args.lr,
                                momentum=args.momentum,
                                weight_decay=args.weight_decay)
    msglogger.info('Optimizer Type: %s', type(optimizer))
    msglogger.info('Optimizer Args: %s', optimizer.defaults)

    # This sample application can be invoked to produce various summary reports.
    if args.summary:
        which_summary = args.summary
        if which_summary.startswith('png'):
            apputils.draw_img_classifier_to_file(
                model, 'model.png', args.dataset,
                which_summary == 'png_w_params')
        else:
            distiller.model_summary(model, which_summary, args.dataset)
        exit()

    # Load the datasets: the dataset to load is inferred from the model name passed
    # in args.arch.  The default dataset is ImageNet, but if args.arch contains the
    # substring "_cifar", then cifar10 is used.
    train_loader, val_loader, test_loader, _ = apputils.load_data(
        args.dataset, os.path.expanduser(args.data), args.batch_size,
        args.workers, args.validation_size, args.deterministic)
    msglogger.info('Dataset sizes:\n\ttraining=%d\n\tvalidation=%d\n\ttest=%d',
                   len(train_loader.sampler), len(val_loader.sampler),
                   len(test_loader.sampler))

    activations_sparsity = None
    if args.activation_stats:
        # If your model has ReLU layers, then those layers have sparse activations.
        # ActivationSparsityCollector will collect information about this sparsity.
        # WARNING! Enabling activation sparsity collection will significantly slow down training!
        activations_sparsity = ActivationSparsityCollector(model)

    if args.sensitivity is not None:
        # This sample application can be invoked to execute Sensitivity Analysis on your
        # model.  The ouptut is saved to CSV and PNG.
        msglogger.info("Running sensitivity tests")
        test_fnc = partial(test,
                           test_loader=test_loader,
                           criterion=criterion,
                           loggers=[pylogger],
                           print_freq=args.print_freq)
        which_params = [
            param_name for param_name, _ in model.named_parameters()
        ]
        sensitivity = distiller.perform_sensitivity_analysis(
            model,
            net_params=which_params,
            sparsities=np.arange(0.0, 0.50, 0.05)
            if args.sensitivity == 'filter' else np.arange(0.0, 0.95, 0.05),
            test_func=test_fnc,
            group=args.sensitivity)
        distiller.sensitivities_to_png(sensitivity, 'sensitivity.png')
        distiller.sensitivities_to_csv(sensitivity, 'sensitivity.csv')
        exit()

    if args.evaluate:
        # This sample application can be invoked to evaluate the accuracy of your model on
        # the test dataset.
        # You can optionally quantize the model to 8-bit integer before evaluation.
        # For example:
        # python3 compress_classifier.py --arch resnet20_cifar  ../data.cifar10 -p=50 --resume=checkpoint.pth.tar --evaluate
        if args.quantize:
            model.cpu()
            quantizer = quantization.SymmetricLinearQuantizer(model, 8, 8)
            quantizer.prepare_model()
            model.cuda()
        top1, _, _ = test(test_loader, model, criterion, [pylogger],
                          args.print_freq)
        if args.quantize:
            checkpoint_name = 'quantized'
            apputils.save_checkpoint(0,
                                     args.arch,
                                     model,
                                     optimizer=None,
                                     best_top1=top1,
                                     name='_'.split(args.name, checkpoint_name)
                                     if args.name else checkpoint_name,
                                     dir=msglogger.logdir)
        exit()

    if args.compress:
        # The main use-case for this sample application is CNN compression. Compression
        # requires a compression schedule configuration file in YAML.
        compression_scheduler = distiller.file_config(model, optimizer,
                                                      args.compress)

    for epoch in range(start_epoch, start_epoch + args.epochs):
        # This is the main training loop.
        msglogger.info('\n')
        if compression_scheduler:
            compression_scheduler.on_epoch_begin(epoch)

        # Train for one epoch
        train(train_loader,
              model,
              criterion,
              optimizer,
              epoch,
              compression_scheduler,
              loggers=[tflogger, pylogger],
              print_freq=args.print_freq,
              log_params_hist=args.log_params_histograms)
        distiller.log_weights_sparsity(model,
                                       epoch,
                                       loggers=[tflogger, pylogger])
        if args.activation_stats:
            distiller.log_activation_sparsity(epoch,
                                              loggers=[tflogger, pylogger],
                                              collector=activations_sparsity)

        # evaluate on validation set
        top1, top5, vloss = validate(val_loader, model, criterion, [pylogger],
                                     args.print_freq, epoch)
        stats = ('Peformance/Validation/',
                 OrderedDict([('Loss', vloss), ('Top1', top1),
                              ('Top5', top5)]))
        distiller.log_training_progress(stats,
                                        None,
                                        epoch,
                                        steps_completed=0,
                                        total_steps=1,
                                        log_freq=1,
                                        loggers=[tflogger])

        if compression_scheduler:
            compression_scheduler.on_epoch_end(epoch)

        # remember best top1 and save checkpoint
        is_best = top1 > best_top1
        best_top1 = max(top1, best_top1)
        apputils.save_checkpoint(epoch, args.arch, model, optimizer,
                                 compression_scheduler, best_top1, is_best,
                                 args.name, msglogger.logdir)

    # Finally run results on the test set
    test(test_loader, model, criterion, [pylogger], args.print_freq)
def main():
    global msglogger
    check_pytorch_version()
    args = parser.parse_args()
    if not os.path.exists(args.output_dir):
        os.makedirs(args.output_dir)
    msglogger = apputils.config_pylogger(os.path.join(script_dir, 'logging.conf'), args.name, args.output_dir)

    # Log various details about the execution environment.  It is sometimes useful
    # to refer to past experiment executions and this information may be useful.
    apputils.log_execution_env_state(sys.argv, gitroot=module_path)
    msglogger.debug("Distiller: %s", distiller.__version__)

    start_epoch = 0
    best_top1 = 0
    best_epoch = 0

    if args.deterministic:
        # Experiment reproducibility is sometimes important.  Pete Warden expounded about this
        # in his blog: https://petewarden.com/2018/03/19/the-machine-learning-reproducibility-crisis/
        # In Pytorch, support for deterministic execution is still a bit clunky.
        if args.workers > 1:
            msglogger.error('ERROR: Setting --deterministic requires setting --workers/-j to 0 or 1')
            exit(1)
        # Use a well-known seed, for repeatability of experiments
        torch.manual_seed(0)
        random.seed(0)
        np.random.seed(0)
        cudnn.deterministic = True
    else:
        # This issue: https://github.com/pytorch/pytorch/issues/3659
        # Implies that cudnn.benchmark should respect cudnn.deterministic, but empirically we see that
        # results are not re-produced when benchmark is set. So enabling only if deterministic mode disabled.
        cudnn.benchmark = True

    if args.gpus is not None:
        try:
            args.gpus = [int(s) for s in args.gpus.split(',')]
        except ValueError:
            msglogger.error('ERROR: Argument --gpus must be a comma-separated list of integers only')
            exit(1)
        available_gpus = torch.cuda.device_count()
        for dev_id in args.gpus:
            if dev_id >= available_gpus:
                msglogger.error('ERROR: GPU device ID {0} requested, but only {1} devices available'
                                .format(dev_id, available_gpus))
                exit(1)
        # Set default device in case the first one on the list != 0
        torch.cuda.set_device(args.gpus[0])

    # Infer the dataset from the model name
    args.dataset = 'cifar10' if 'cifar' in args.arch else 'imagenet'
    args.num_classes = 10 if args.dataset == 'cifar10' else 1000

    if args.earlyexit_thresholds:
        args.num_exits = len(args.earlyexit_thresholds) + 1
        args.loss_exits = [0] * args.num_exits
        args.losses_exits = []
        args.exiterrors = []

    # Create the model
    model = create_model(args.pretrained, args.dataset, args.arch, device_ids=args.gpus)
    compression_scheduler = None
    # Create a couple of logging backends.  TensorBoardLogger writes log files in a format
    # that can be read by Google's Tensor Board.  PythonLogger writes to the Python logger.
    tflogger = TensorBoardLogger(msglogger.logdir)
    pylogger = PythonLogger(msglogger)

    # capture thresholds for early-exit training
    if args.earlyexit_thresholds:
        msglogger.info('=> using early-exit threshold values of %s', args.earlyexit_thresholds)

    # We can optionally resume from a checkpoint
    if args.resume:
        model, compression_scheduler, start_epoch = apputils.load_checkpoint(
            model, chkpt_file=args.resume)

    # Define loss function (criterion) and optimizer
    criterion = nn.CrossEntropyLoss().cuda()
    optimizer = torch.optim.SGD(model.parameters(), lr=args.lr,
                                momentum=args.momentum,
                                weight_decay=args.weight_decay)
    msglogger.info('Optimizer Type: %s', type(optimizer))
    msglogger.info('Optimizer Args: %s', optimizer.defaults)

    if args.ADC:
        return automated_deep_compression(model, criterion, pylogger, args)

    # This sample application can be invoked to produce various summary reports.
    if args.summary:
        return summarize_model(model, args.dataset, which_summary=args.summary)

    # Load the datasets: the dataset to load is inferred from the model name passed
    # in args.arch.  The default dataset is ImageNet, but if args.arch contains the
    # substring "_cifar", then cifar10 is used.
    train_loader, val_loader, test_loader, _ = apputils.load_data(
        args.dataset, os.path.expanduser(args.data), args.batch_size,
        args.workers, args.validation_size, args.deterministic)
    msglogger.info('Dataset sizes:\n\ttraining=%d\n\tvalidation=%d\n\ttest=%d',
                   len(train_loader.sampler), len(val_loader.sampler), len(test_loader.sampler))

    activations_sparsity = None
    if args.activation_stats:
        # If your model has ReLU layers, then those layers have sparse activations.
        # ActivationSparsityCollector will collect information about this sparsity.
        # WARNING! Enabling activation sparsity collection will significantly slow down training!
        activations_sparsity = ActivationSparsityCollector(model)

    if args.sensitivity is not None:
        return sensitivity_analysis(model, criterion, test_loader, pylogger, args)

    if args.evaluate:
        return evaluate_model(model, criterion, test_loader, pylogger, args)

    if args.compress:
        # The main use-case for this sample application is CNN compression. Compression
        # requires a compression schedule configuration file in YAML.
        compression_scheduler = distiller.file_config(model, optimizer, args.compress)
        # Model is re-transferred to GPU in case parameters were added (e.g. PACTQuantizer)
        model.cuda()
    else:
        compression_scheduler = distiller.CompressionScheduler(model)

    args.kd_policy = None
    if args.kd_teacher:
        teacher = create_model(args.kd_pretrained, args.dataset, args.kd_teacher, device_ids=args.gpus)
        if args.kd_resume:
            teacher, _, _ = apputils.load_checkpoint(teacher, chkpt_file=args.kd_resume)
        dlw = distiller.DistillationLossWeights(args.kd_distill_wt, args.kd_student_wt, args.kd_teacher_wt)
        args.kd_policy = distiller.KnowledgeDistillationPolicy(model, teacher, args.kd_temp, dlw)
        compression_scheduler.add_policy(args.kd_policy, starting_epoch=args.kd_start_epoch, ending_epoch=args.epochs,
                                         frequency=1)

        msglogger.info('\nStudent-Teacher knowledge distillation enabled:')
        msglogger.info('\tTeacher Model: %s', args.kd_teacher)
        msglogger.info('\tTemperature: %s', args.kd_temp)
        msglogger.info('\tLoss Weights (distillation | student | teacher): %s',
                       ' | '.join(['{:.2f}'.format(val) for val in dlw]))
        msglogger.info('\tStarting from Epoch: %s', args.kd_start_epoch)

    for epoch in range(start_epoch, start_epoch + args.epochs):
        # This is the main training loop.
        msglogger.info('\n')
        if compression_scheduler:
            compression_scheduler.on_epoch_begin(epoch)

        # Train for one epoch
        train(train_loader, model, criterion, optimizer, epoch, compression_scheduler,
              loggers=[tflogger, pylogger], args=args)
        distiller.log_weights_sparsity(model, epoch, loggers=[tflogger, pylogger])
        if args.activation_stats:
            distiller.log_activation_sparsity(epoch, loggers=[tflogger, pylogger],
                                              collector=activations_sparsity)

        # evaluate on validation set
        top1, top5, vloss = validate(val_loader, model, criterion, [pylogger], args, epoch)
        stats = ('Peformance/Validation/',
                 OrderedDict([('Loss', vloss),
                              ('Top1', top1),
                              ('Top5', top5)]))
        distiller.log_training_progress(stats, None, epoch, steps_completed=0, total_steps=1, log_freq=1,
                                        loggers=[tflogger])

        if compression_scheduler:
            compression_scheduler.on_epoch_end(epoch, optimizer)

        # remember best top1 and save checkpoint
        is_best = top1 > best_top1
        if is_best:
            best_epoch = epoch
            best_top1 = top1
        msglogger.info('==> Best Top1: %.3f   On Epoch: %d\n', best_top1, best_epoch)
        apputils.save_checkpoint(epoch, args.arch, model, optimizer, compression_scheduler, best_top1, is_best,
                                 args.name, msglogger.logdir)

    # Finally run results on the test set
    test(test_loader, model, criterion, [pylogger], args=args)