From 6664c6d52fad58e396861946a3bed7d5afc4d44d Mon Sep 17 00:00:00 2001
From: natanielruiz <nataniel777@hotmail.com>
Date: 星期五, 07 七月 2017 10:53:52 +0800
Subject: [PATCH] Training for hopenet works.
---
code/test_resnet_bins.py | 26 +++++++++++++++-----------
1 files changed, 15 insertions(+), 11 deletions(-)
diff --git a/code/test_resnet_bins.py b/code/test_resnet_bins.py
index 34fc8f5..0a093ee 100644
--- a/code/test_resnet_bins.py
+++ b/code/test_resnet_bins.py
@@ -13,7 +13,7 @@
import os
import argparse
-from datasets import AFLW2000
+import datasets
import hopenet
import utils
@@ -55,9 +55,10 @@
print 'Loading data.'
- transformations = transforms.Compose([transforms.Scale(224),transforms.RandomCrop(224), transforms.ToTensor()])
+ transformations = transforms.Compose([transforms.Scale(224),
+ transforms.RandomCrop(224), transforms.ToTensor()])
- pose_dataset = AFLW2000(args.data_dir, args.filename_list,
+ pose_dataset = datasets.AFLW2000_binned(args.data_dir, args.filename_list,
transformations)
test_loader = torch.utils.data.DataLoader(dataset=pose_dataset,
batch_size=batch_size,
@@ -69,7 +70,9 @@
# Test the Model
model.eval() # Change model to 'eval' mode (BN uses moving mean/var).
- error = .0
+ yaw_correct = 0
+ pitch_correct = 0
+ roll_correct = 0
total = 0
for i, (images, labels, name) in enumerate(test_loader):
images = Variable(images).cuda(gpu)
@@ -78,13 +81,14 @@
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
# TODO: There are more efficient ways.
+ yaw_correct += (outputs[:][0] == labels[:][0])
+ pitch_correct += (outputs[:][])
for idx in xrange(len(outputs)):
- # if abs(outputs[idx].data[1] - labels[idx].data[1]) * 180 / np.pi > 30:
- print name
- print abs(outputs[idx].data - labels[idx].data) * 180 / np.pi, 180 * outputs[idx].data / np.pi, labels[idx].data * 180 / np.pi
- # error += utils.mse_loss(outputs[idx], labels[idx])
- error += abs(outputs[idx].data - labels[idx].data) * 180 / np.pi
+ yaw_correct += (outputs[idx].data[0] == labels[idx].data[0])
+ pitch_correct += (outputs[idx].data[1] == labels[idx].data[1])
+ roll_correct += (outputs[idx].data[2] == labels[idx].data[2])
- print('Test MSE error of the model on the ' + str(total) +
- ' test images: %.4f' % (error / total))
+ print('Test accuracies of the model on the ' + str(total) +
+ ' test images. Yaw: %.4f %%, Pitch: %.4f %%, Roll: %.4f %%' % (yaw_correct / total,
+ pitch_correct / total, roll_correct / total))
--
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