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| # Copyright 2021 - Valeo Comfort and Driving Assistance - Oriane Siméoni @ valeo.ai | |
| # | |
| # Licensed 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. | |
| """ | |
| Code adapted from previous method LOST: https://github.com/valeoai/LOST | |
| """ | |
| import os | |
| import time | |
| import torch | |
| import torch.nn as nn | |
| import numpy as np | |
| from tqdm import tqdm | |
| from misc import bbox_iou, get_bbox_from_segmentation_labels | |
| def evaluation_unsupervised_object_discovery( | |
| dataset, | |
| model, | |
| evaluation_mode: str = "single", # choices are ["single", "multi"] | |
| output_dir: str = "outputs", | |
| no_hards: bool = False, | |
| ): | |
| assert evaluation_mode == "single" | |
| sigmoid = nn.Sigmoid() | |
| # ---------------------------------------------------- | |
| # Loop over images | |
| preds_dict = {} | |
| cnt = 0 | |
| corloc = np.zeros(len(dataset.dataloader)) | |
| start_time = time.time() | |
| pbar = tqdm(dataset.dataloader) | |
| for im_id, inp in enumerate(pbar): | |
| # ------------ IMAGE PROCESSING ------------------------------------------- | |
| img = inp[0] | |
| init_image_size = img.shape | |
| # Get the name of the image | |
| im_name = dataset.get_image_name(inp[1]) | |
| # Pass in case of no gt boxes in the image | |
| if im_name is None: | |
| continue | |
| # Padding the image with zeros to fit multiple of patch-size | |
| size_im = ( | |
| img.shape[0], | |
| int(np.ceil(img.shape[1] / model.vit_patch_size) * model.vit_patch_size), | |
| int(np.ceil(img.shape[2] / model.vit_patch_size) * model.vit_patch_size), | |
| ) | |
| paded = torch.zeros(size_im) | |
| paded[:, : img.shape[1], : img.shape[2]] = img | |
| img = paded | |
| # # Move to gpu | |
| img = img.cuda(non_blocking=True) | |
| # Size for transformers | |
| # w_featmap = img.shape[-2] // model.vit_patch_size | |
| # h_featmap = img.shape[-1] // model.vit_patch_size | |
| # ------------ GROUND-TRUTH ------------------------------------------- | |
| gt_bbxs, gt_cls = dataset.extract_gt(inp[1], im_name) | |
| if gt_bbxs is not None: | |
| # Discard images with no gt annotations | |
| # Happens only in the case of VOC07 and VOC12 | |
| if gt_bbxs.shape[0] == 0 and no_hards: | |
| continue | |
| outputs = model(img[None, :, :, :]) | |
| preds = (sigmoid(outputs[0].detach()) > 0.5).float().squeeze().cpu().numpy() | |
| # get bbox | |
| pred = get_bbox_from_segmentation_labels( | |
| segmenter_predictions=preds, | |
| scales=[model.vit_patch_size, model.vit_patch_size], | |
| initial_image_size=init_image_size[1:], | |
| ) | |
| # ------------ Visualizations ------------------------------------------- | |
| # Save the prediction | |
| preds_dict[im_name] = pred | |
| # Compare prediction to GT boxes | |
| ious = bbox_iou(torch.from_numpy(pred), torch.from_numpy(gt_bbxs)) | |
| if torch.any(ious >= 0.5): | |
| corloc[im_id] = 1 | |
| cnt += 1 | |
| if cnt % 50 == 0: | |
| pbar.set_description(f"Peekaboo {int(np.sum(corloc))}/{cnt}") | |
| # Evaluate | |
| print(f"corloc: {100*np.sum(corloc)/cnt:.2f} ({int(np.sum(corloc))}/{cnt})") | |
| result_file = os.path.join(output_dir, "uod_results.txt") | |
| with open(result_file, "w") as f: | |
| f.write("corloc,%.1f,,\n" % (100 * np.sum(corloc) / cnt)) | |
| print("File saved at %s" % result_file) | |