From 607a859e1b10bdd03da933794f581e5e72774ec0 Mon Sep 17 00:00:00 2001 From: Bugra Tekin Date: Fri, 18 Oct 2019 19:07:57 +0200 Subject: [PATCH] minor code clean up in validation --- valid.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/valid.py b/valid.py index f05ff8b..ec79ca9 100644 --- a/valid.py +++ b/valid.py @@ -88,7 +88,7 @@ def valid(datacfg, modelcfg, weightfile): test_width = model.test_width test_height = model.test_height num_keypoints = model.num_keypoints - num_labels = num_keypoints * 2 + 3 + num_labels = num_keypoints * 2 + 3 # +2 for width, height, +1 for class label # Get the parser for the test dataset valid_dataset = dataset.listDataset(valid_images, @@ -122,7 +122,7 @@ def valid(datacfg, modelcfg, weightfile): # Iterate through all batch elements for box_pr, target in zip([all_boxes], [target[0]]): # For each image, get all the targets (for multiple object pose estimation, there might be more than 1 target per image) - truths = target.view(-1, num_keypoints*2+3) + truths = target.view(-1, num_labels) # Get how many objects are present in the scene num_gts = truths_length(truths) # Iterate through each ground-truth object @@ -134,8 +134,8 @@ def valid(datacfg, modelcfg, weightfile): box_gt.append(truths[k][0]) # Denormalize the corner predictions - corners2D_gt = np.array(np.reshape(box_gt[:18], [9, 2]), dtype='float32') - corners2D_pr = np.array(np.reshape(box_pr[:18], [9, 2]), dtype='float32') + corners2D_gt = np.array(np.reshape(box_gt[:18], [-1, 2]), dtype='float32') + corners2D_pr = np.array(np.reshape(box_pr[:18], [-1, 2]), dtype='float32') corners2D_gt[:, 0] = corners2D_gt[:, 0] * im_width corners2D_gt[:, 1] = corners2D_gt[:, 1] * im_height corners2D_pr[:, 0] = corners2D_pr[:, 0] * im_width