From 58f2d9306bb38692b0e30d416ed683ff1fbbb778 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Sun, 2 Sep 2018 12:40:29 +0200 Subject: [PATCH] updates --- README.md | 2 +- data/coco_training_loss.png | Bin 248165 -> 283324 bytes test.py | 9 +-------- utils/utils.py | 4 ++-- 4 files changed, 4 insertions(+), 11 deletions(-) diff --git a/README.md b/README.md index 018e95e3..7ba50a85 100755 --- a/README.md +++ b/README.md @@ -3,7 +3,7 @@ # Introduction This directory contains software developed by Ultralytics LLC, and **is freely available for redistribution under the GPL-3.0 license**. For more information on Ultralytics projects please visit: -http://www.ultralytics.com   +http://www.ultralytics.com # Description diff --git a/data/coco_training_loss.png b/data/coco_training_loss.png index 570ca737d9c412ca6c5ad58097754043f25bea2b..9e4b534e11575feb0ebd856be524526e2f9c6425 100644 GIT binary patch literal 283324 zcmce;c|4ST+dpo`zAGeKrd1+j%|1d%mMB~HF!p_CFey}$60)m=%9cI5sFZ!*4P_r& z)(pn?GiJK(`?>G?d7kU}{qei5SIV3-bAHaxc`WbaeY}tJfsU3cJq-s92?+_kx|*^s z2?-4u2?_Z{Dhlw5)aRPl;4g@$uId$%{C2K+@C&t@nvo|7$%#++KMK`}w$Q}Q%07);i~+E!Xu`6}__;5WHbc3xg?(n3PMzP^IK zqJpj-2%$?-Qc^;~B0?e}7r{F&diuL~S^8ac@jOlVl0V<0Y~y*y!`{uy-qi($|6WTg zS8p%5Q>XAJ`s+X9I-TAAI+2Sf@v6Y}3He#N30)Et7Wz*g^0K%6$MXKmr|?GzA3MA% z!pUU8)<`RP*jReGdfaq%b&_|s^m21@^>VVeg8A5Zc;c5SA_)E$fDteGA8$CkY+0dy z{f2*Bi7ft<(%K&OHefICS1f-?R_NbeBfei&2)`Zwv^|8qKYSHzzB~;$)1MnDPh-B75Q5ARwXQG-#RIeF$_N8aa4;?S72A>0O}fO%Cm|R4^t{2o!qfs>Ww3SO&E2V1Ff5OY_E!r%vcv zIj0xz3u=dl%Js6fikwHv3b)_Dh<8gY5AN)o#}%^otG%W=X(=Ah$KB 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pytorch format model.to(device).eval() -# Get PyTorch dataloader +# Get dataloader # dataset = load_images_with_labels(test_path) # dataloader = torch.utils.data.DataLoader(dataset, batch_size=opt.batch_size, shuffle=False, num_workers=opt.n_cpu) dataloader = load_images_and_labels(test_path, batch_size=opt.batch_size, img_size=opt.img_size) -Tensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor - n_gt = 0 correct = 0 @@ -87,11 +85,6 @@ for batch_i, (imgs, targets) in enumerate(dataloader): correct.extend([0 for _ in range(len(detections))]) else: # Extract target boxes as (x1, y1, x2, y2) - # target_boxes = torch.FloatTensor(annotations[:, 1:].shape) - # target_boxes[:, 0] = (annotations[:, 1] - annotations[:, 3] / 2) - # target_boxes[:, 1] = (annotations[:, 2] - annotations[:, 4] / 2) - # target_boxes[:, 2] = (annotations[:, 1] + annotations[:, 3] / 2) - # target_boxes[:, 3] = (annotations[:, 2] + annotations[:, 4] / 2) target_boxes = xywh2xyxy(annotations[:,1:5]) target_boxes *= opt.img_size diff --git a/utils/utils.py b/utils/utils.py index b3c09d3e..249a855f 100755 --- a/utils/utils.py +++ b/utils/utils.py @@ -368,10 +368,10 @@ def plotResults(): plt.figure(figsize=(16, 8)) s = ['X', 'Y', 'Width', 'Height', 'Objectness', 'Classification', 'Total Loss', 'Precision', 'Recall'] for f in ('/Users/glennjocher/Downloads/results.txt', - ''): + '/Users/glennjocher/Downloads/resultsBCE2.txt'): results = np.loadtxt(f, usecols=[2, 3, 4, 5, 6, 7, 8, 9, 10]).T for i in range(9): plt.subplot(2, 5, i + 1) plt.plot(results[i, :], marker='.', label=f) plt.title(s[i]) - plt.legend() + plt.legend(cocococosadfc)