From f415df3448622f30c3d1eb680596871672b38dac Mon Sep 17 00:00:00 2001
From: natanielruiz <nataniel777@hotmail.com>
Date: 星期四, 26 十月 2017 02:51:37 +0800
Subject: [PATCH] after fg
---
code/test_preangles.py | 60 +++++++++++++++++++++++++-----------------------------------
1 files changed, 25 insertions(+), 35 deletions(-)
diff --git a/code/test_preangles.py b/code/test_preangles.py
index 7cf8ebb..cfee8d1 100644
--- a/code/test_preangles.py
+++ b/code/test_preangles.py
@@ -60,9 +60,6 @@
print 'Loading data.'
- # transformations = transforms.Compose([transforms.Scale(224),
- # transforms.RandomCrop(224), transforms.ToTensor()])
-
transformations = transforms.Compose([transforms.Scale(224),
transforms.CenterCrop(224), transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])
@@ -70,10 +67,15 @@
if args.dataset == 'AFLW2000':
pose_dataset = datasets.AFLW2000(args.data_dir, args.filename_list,
transformations)
+ elif args.dataset == 'AFLW2000_ds':
+ pose_dataset = datasets.AFLW2000_ds(args.data_dir, args.filename_list,
+ transformations)
elif args.dataset == 'BIWI':
pose_dataset = datasets.BIWI(args.data_dir, args.filename_list, transformations)
elif args.dataset == 'AFLW':
pose_dataset = datasets.AFLW(args.data_dir, args.filename_list, transformations)
+ elif args.dataset == 'Pose_300W_LP':
+ pose_dataset = datasets.Pose_300W_LP(args.data_dir, args.filename_list, transformations)
elif args.dataset == 'AFW':
pose_dataset = datasets.AFW(args.data_dir, args.filename_list, transformations)
else:
@@ -90,10 +92,6 @@
# Test the Model
model.eval() # Change model to 'eval' mode (BN uses moving mean/var).
total = 0
- n_margins = 20
- yaw_correct = np.zeros(n_margins)
- pitch_correct = np.zeros(n_margins)
- roll_correct = np.zeros(n_margins)
idx_tensor = [idx for idx in xrange(66)]
idx_tensor = torch.FloatTensor(idx_tensor).cuda(gpu)
@@ -104,12 +102,12 @@
l1loss = torch.nn.L1Loss(size_average=False)
- for i, (images, labels, name) in enumerate(test_loader):
+ for i, (images, labels, cont_labels, name) in enumerate(test_loader):
images = Variable(images).cuda(gpu)
- total += labels.size(0)
- label_yaw = labels[:,0].float()
- label_pitch = labels[:,1].float()
- label_roll = labels[:,2].float()
+ total += cont_labels.size(0)
+ label_yaw = cont_labels[:,0].float()
+ label_pitch = cont_labels[:,1].float()
+ label_roll = cont_labels[:,2].float()
yaw, pitch, roll, angles = model(images)
@@ -123,38 +121,30 @@
pitch_predicted = utils.softmax_temperature(pitch.data, 1)
roll_predicted = utils.softmax_temperature(roll.data, 1)
- yaw_predicted = torch.sum(yaw_predicted * idx_tensor, 1).cpu()
- pitch_predicted = torch.sum(pitch_predicted * idx_tensor, 1).cpu()
- roll_predicted = torch.sum(roll_predicted * idx_tensor, 1).cpu()
+ yaw_predicted = torch.sum(yaw_predicted * idx_tensor, 1).cpu() * 3 - 99
+ pitch_predicted = torch.sum(pitch_predicted * idx_tensor, 1).cpu() * 3 - 99
+ roll_predicted = torch.sum(roll_predicted * idx_tensor, 1).cpu() * 3 - 99
# Mean absolute error
- yaw_error += torch.sum(torch.abs(yaw_predicted - label_yaw) * 3)
- pitch_error += torch.sum(torch.abs(pitch_predicted - label_pitch) * 3)
- roll_error += torch.sum(torch.abs(roll_predicted - label_roll) * 3)
-
- # Binned Accuracy
- # for er in xrange(n_margins):
- # yaw_bpred[er] += (label_yaw[0] in range(yaw_bpred[0,0] - er, yaw_bpred[0,0] + er + 1))
- # pitch_bpred[er] += (label_pitch[0] in range(pitch_bpred[0,0] - er, pitch_bpred[0,0] + er + 1))
- # roll_bpred[er] += (label_roll[0] in range(roll_bpred[0,0] - er, roll_bpred[0,0] + er + 1))
-
- # print label_yaw[0], yaw_bpred[0,0]
+ yaw_error += torch.sum(torch.abs(yaw_predicted - label_yaw))
+ pitch_error += torch.sum(torch.abs(pitch_predicted - label_pitch))
+ roll_error += torch.sum(torch.abs(roll_predicted - label_roll))
# Save images with pose cube.
# TODO: fix for larger batch size
if args.save_viz:
name = name[0]
- cv2_img = cv2.imread(os.path.join(args.data_dir, name + '.jpg'))
- #print os.path.join('output/images', name + '.jpg')
- #print label_yaw[0] * 3 - 99, label_pitch[0] * 3 - 99, label_roll[0] * 3 - 99
- #print yaw_predicted * 3 - 99, pitch_predicted * 3 - 99, roll_predicted * 3 - 99
- utils.plot_pose_cube(cv2_img, yaw_predicted[0] * 3 - 99, pitch_predicted[0] * 3 - 99, roll_predicted[0] * 3 - 99)
+ if args.dataset == 'BIWI':
+ cv2_img = cv2.imread(os.path.join(args.data_dir, name + '_rgb.png'))
+ else:
+ cv2_img = cv2.imread(os.path.join(args.data_dir, name + '.jpg'))
+ if args.batch_size == 1:
+ error_string = 'y %.2f, p %.2f, r %.2f' % (torch.sum(torch.abs(yaw_predicted - label_yaw)), torch.sum(torch.abs(pitch_predicted - label_pitch)), torch.sum(torch.abs(roll_predicted - label_roll)))
+ cv2.putText(cv2_img, error_string, (30, cv2_img.shape[0]- 30), fontFace=1, fontScale=1, color=(0,0,255), thickness=2)
+ # utils.plot_pose_cube(cv2_img, yaw_predicted[0], pitch_predicted[0], roll_predicted[0], size=100)
+ utils.draw_axis(cv2_img, yaw_predicted[0], pitch_predicted[0], roll_predicted[0], tdx = 200, tdy= 200, size=100)
cv2.imwrite(os.path.join('output/images', name + '.jpg'), cv2_img)
print('Test error in degrees of the model on the ' + str(total) +
' test images. Yaw: %.4f, Pitch: %.4f, Roll: %.4f' % (yaw_error / total,
pitch_error / total, roll_error / total))
-
- # Binned accuracy
- # for idx in xrange(len(yaw_correct)):
- # print yaw_correct[idx] / total, pitch_correct[idx] / total, roll_correct[idx] / total
--
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