Python model.generate_model() Examples

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Example #1
Source File: online_test_wo_detector.py    From Real-time-GesRec with MIT License 4 votes vote down vote up 
def load_models(opt):
    opt.resume_path = opt.resume_path_clf
    opt.pretrain_path = opt.pretrain_path_clf
    opt.sample_duration = opt.sample_duration_clf
    opt.model = opt.model_clf
    opt.model_depth = opt.model_depth_clf
    opt.width_mult = opt.width_mult_clf
    opt.modality = opt.modality_clf
    opt.resnet_shortcut = opt.resnet_shortcut_clf
    opt.n_classes = opt.n_classes_clf
    opt.n_finetune_classes = opt.n_finetune_classes_clf
    if opt.root_path != '':
        opt.video_path = os.path.join(opt.root_path, opt.video_path)
        opt.annotation_path = os.path.join(opt.root_path, opt.annotation_path)
        opt.result_path = os.path.join(opt.root_path, opt.result_path)
        if opt.resume_path:
            opt.resume_path = os.path.join(opt.root_path, opt.resume_path)
        if opt.pretrain_path:
            opt.pretrain_path = os.path.join(opt.root_path, opt.pretrain_path)

    opt.scales = [opt.initial_scale]
    for i in range(1, opt.n_scales):
        opt.scales.append(opt.scales[-1] * opt.scale_step)
    opt.arch = '{}'.format(opt.model)
    opt.mean = get_mean(opt.norm_value)
    opt.std = get_std(opt.norm_value)

    print(opt)
    with open(os.path.join(opt.result_path, 'opts_clf.json'), 'w') as opt_file:
        json.dump(vars(opt), opt_file)

    torch.manual_seed(opt.manual_seed)
    classifier, parameters = generate_model(opt)

    if opt.resume_path:
        print('loading checkpoint {}'.format(opt.resume_path))
        checkpoint = torch.load(opt.resume_path)
#        assert opt.arch == checkpoint['arch']

        classifier.load_state_dict(checkpoint['state_dict'])

    print('Model 2 \n', classifier)
    pytorch_total_params = sum(p.numel() for p in classifier.parameters() if
                               p.requires_grad)
    print("Total number of trainable parameters: ", pytorch_total_params)

    return classifier