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| # coding:utf-8 | |
| import os | |
| import argparse | |
| import numpy as np | |
| from torch.utils.data import DataLoader | |
| from MaskLoader import MaskLoader | |
| from utils import ( | |
| IOUMetric, | |
| transform, | |
| inverse_transform, | |
| direct_sigmoid, | |
| inverse_sigmoid | |
| ) | |
| VALUE_MAX = 0.05 | |
| VALUE_MIN = 0.01 | |
| def parse_args(): | |
| parser = argparse.ArgumentParser(description='Evaluation for PCA Mask Encoding.') | |
| parser.add_argument('--root', default='datasets', type=str) | |
| parser.add_argument('--dataset', default='coco_2017_val', type=str) | |
| parser.add_argument('--matrix', default='./projects/LME/coco_2017_train_class_agnosticTrue_whitenTrue_sigmoidTrue_128.npz', type=str) | |
| # mask encoding params. | |
| parser.add_argument('--mask_size', default=28, type=int) | |
| parser.add_argument('--n_components', default=128, type=int) | |
| parser.add_argument('--class_agnostic', default=True, type=bool) | |
| parser.add_argument('--whiten', default=True, type=bool) | |
| parser.add_argument('--sigmoid', default=True, type=bool) | |
| parser.add_argument('--batch-size', default=1024, type=int) | |
| args = parser.parse_args() | |
| return args | |
| if __name__ == "__main__": | |
| args = parse_args() | |
| # parse args. | |
| mask_size = args.mask_size | |
| n_components = args.n_components | |
| class_agnostic = args.class_agnostic | |
| whiten = args.whiten | |
| sigmoid = args.sigmoid | |
| dataset_root = './datasets/' | |
| matrix_path = args.matrix #os.path.join(dataset_root, ) | |
| # load matrix. | |
| print("Loading matrix parameters: {}".format(matrix_path)) | |
| parameters = np.load(matrix_path) | |
| components_c = parameters['components_c'] | |
| mean_c = parameters['mean_c'] | |
| ratio_c = parameters['ratio_c'] | |
| explained_variance_c = parameters['explained_variance_c'] | |
| if class_agnostic: | |
| components_c = np.squeeze(components_c) | |
| mean_c = np.squeeze(mean_c) | |
| explained_variance_c = np.squeeze(explained_variance_c) | |
| assert n_components == components_c.shape[0], \ | |
| print("The n_components in component_ must equal to the supposed shape.") | |
| else: | |
| # TODO: We have not achieve the function in class-specific. | |
| raise NotImplementedError | |
| # build data loader. | |
| mask_data = MaskLoader(root=dataset_root, dataset=args.dataset, size=mask_size) | |
| mask_loader = DataLoader(mask_data, batch_size=args.batch_size, shuffle=False, num_workers=4) | |
| size_data = len(mask_loader) | |
| # evaluation. | |
| IoUevaluate = IOUMetric(2) | |
| print("Start Eva ...") | |
| for i, masks in enumerate(mask_loader): | |
| print("Eva [{} / {}]".format(i, size_data)) | |
| # generate the reconstruction mask. | |
| masks = masks.view(masks.shape[0], -1).numpy() | |
| masks = masks.astype(np.float32) | |
| # pre-process. | |
| if sigmoid: | |
| value_random = VALUE_MAX * np.random.rand(masks.shape[0], masks.shape[1]) | |
| value_random = np.maximum(value_random, VALUE_MIN) | |
| masks_random = np.where(masks > value_random, 1 - value_random, value_random) | |
| masks_random = inverse_sigmoid(masks_random) | |
| else: | |
| masks_random = masks | |
| # --> encode --> decode. | |
| mask_rc = transform(masks_random, components_=components_c, explained_variance_=explained_variance_c, | |
| mean_=mean_c, whiten=whiten) | |
| mask_rc = inverse_transform(mask_rc, components_=components_c, explained_variance_=explained_variance_c, | |
| mean_=mean_c, whiten=whiten) | |
| # post-process. | |
| if sigmoid: | |
| mask_rc = direct_sigmoid(mask_rc) | |
| # eva. | |
| mask_rc = np.where(mask_rc >= 0.5, 1, 0) | |
| IoUevaluate.add_batch(mask_rc, masks) | |
| _, _, _, mean_iu, _ = IoUevaluate.evaluate() | |
| print("The mIoU for {}: {}".format(args.matrix, mean_iu)) | |