From 84249ad41225cfd0a4213bdbec93fe2af2a97d0c Mon Sep 17 00:00:00 2001 From: KaiyangZhou Date: Sat, 3 Aug 2019 23:01:21 +0100 Subject: [PATCH] [update visactmap] change channel order --- torchreid/engine/engine.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/torchreid/engine/engine.py b/torchreid/engine/engine.py index 88a10bb..817be43 100644 --- a/torchreid/engine/engine.py +++ b/torchreid/engine/engine.py @@ -50,7 +50,7 @@ class Engine(object): def run(self, save_dir='log', max_epoch=0, start_epoch=0, fixbase_epoch=0, open_layers=None, start_eval=0, eval_freq=-1, test_only=False, print_freq=10, - dist_metric='euclidean', normalize_feature=False, visrank=False, visrank_topk=20, + dist_metric='euclidean', normalize_feature=False, visrank=False, visrank_topk=10, use_metric_cuhk03=False, ranks=[1, 5, 10, 20], rerank=False, visactmap=False): r"""A unified pipeline for training and evaluating a model. @@ -75,7 +75,7 @@ class Engine(object): visrank (bool, optional): visualizes ranked results. Default is False. It is recommended to enable ``visrank`` when ``test_only`` is True. The ranked images will be saved to "save_dir/visrank_dataset", e.g. "save_dir/visrank_market1501". - visrank_topk (int, optional): top-k ranked images to be visualized. Default is 20. + visrank_topk (int, optional): top-k ranked images to be visualized. Default is 10. use_metric_cuhk03 (bool, optional): use single-gallery-shot setting for cuhk03. Default is False. This should be enabled when using cuhk03 classic split. ranks (list, optional): cmc ranks to be computed. Default is [1, 5, 10, 20]. @@ -163,7 +163,7 @@ class Engine(object): raise NotImplementedError def test(self, epoch, testloader, dist_metric='euclidean', normalize_feature=False, - visrank=False, visrank_topk=20, save_dir='', use_metric_cuhk03=False, + visrank=False, visrank_topk=10, save_dir='', use_metric_cuhk03=False, ranks=[1, 5, 10, 20], rerank=False): r"""Tests model on target datasets. @@ -205,7 +205,7 @@ class Engine(object): @torch.no_grad() def _evaluate(self, epoch, dataset_name='', queryloader=None, galleryloader=None, dist_metric='euclidean', normalize_feature=False, visrank=False, - visrank_topk=20, save_dir='', use_metric_cuhk03=False, ranks=[1, 5, 10, 20], + visrank_topk=10, save_dir='', use_metric_cuhk03=False, ranks=[1, 5, 10, 20], rerank=False): batch_time = AverageMeter() @@ -374,7 +374,7 @@ class Engine(object): # save images in a single figure (add white spacing between images) # from left to right: original image, activation map, overlapped image grid_img = 255 * np.ones((height, 3*width+2*GRID_SPACING, 3), dtype=np.uint8) - grid_img[:, :width, :] = img_np + grid_img[:, :width, :] = img_np[:, :, ::-1] grid_img[:, width+GRID_SPACING: 2*width+GRID_SPACING, :] = am grid_img[:, 2*width+2*GRID_SPACING:, :] = overlapped cv2.imwrite(osp.join(actmap_dir, imname+'.jpg'), grid_img)