From 43416c4717d2430c3e11f042294d12b781fee2e1 Mon Sep 17 00:00:00 2001
From: natanielruiz <nataniel777@hotmail.com>
Date: 星期三, 27 九月 2017 04:09:30 +0800
Subject: [PATCH] Failed lstm experiment
---
code/batch_testing_preangles.py | 20 ++++++++++----------
1 files changed, 10 insertions(+), 10 deletions(-)
diff --git a/code/batch_testing_preangles.py b/code/batch_testing_preangles.py
index 11390b0..f9b8ac3 100644
--- a/code/batch_testing_preangles.py
+++ b/code/batch_testing_preangles.py
@@ -50,7 +50,7 @@
# ResNet101 with 3 outputs.
# model = hopenet.Hopenet(torchvision.models.resnet.Bottleneck, [3, 4, 23, 3], 66)
# ResNet50
- model = hopenet.Hopenet(torchvision.models.resnet.Bottleneck, [3, 4, 6, 3], 66)
+ model = hopenet.Hopenet(torchvision.models.resnet.Bottleneck, [3, 4, 6, 3], 66, 0)
# ResNet18
# model = hopenet.Hopenet(torchvision.models.resnet.BasicBlock, [2, 2, 2, 2], 66)
@@ -61,7 +61,7 @@
print 'Loading data.'
transformations = transforms.Compose([transforms.Scale(224),
- transforms.RandomCrop(224), transforms.ToTensor(),
+ transforms.CenterCrop(224), transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])
if args.dataset == 'AFLW2000':
@@ -114,12 +114,12 @@
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)
@@ -138,9 +138,9 @@
roll_predicted = torch.sum(roll_predicted * idx_tensor, 1).cpu()
# 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)
+ yaw_error += torch.sum(torch.abs(yaw_predicted * 3 - 99 - label_yaw))
+ pitch_error += torch.sum(torch.abs(pitch_predicted * 3 - 99 - label_pitch))
+ roll_error += torch.sum(torch.abs(roll_predicted * 3 - 99 - label_roll))
if args.save_viz:
name = name[0]
--
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