From 5483d8fec0814e9cc9f5e6fdbb69810f74c76ac9 Mon Sep 17 00:00:00 2001
From: natanielruiz <nruiz9@gatech.edu>
Date: 星期一, 30 十月 2017 07:09:27 +0800
Subject: [PATCH] next
---
code/test_preangles.py | 25 ++++++++++++++++++++++---
1 files changed, 22 insertions(+), 3 deletions(-)
diff --git a/code/test_preangles.py b/code/test_preangles.py
index be9bfda..9cdc8e3 100644
--- a/code/test_preangles.py
+++ b/code/test_preangles.py
@@ -36,6 +36,13 @@
return args
+def load_filtered_state_dict(model, snapshot):
+ # By user apaszke from discuss.pytorch.org
+ model_dict = model.state_dict()
+ snapshot = {k: v for k, v in snapshot.items() if k in model_dict}
+ model_dict.update(snapshot)
+ model.load_state_dict(model_dict)
+
if __name__ == '__main__':
args = parse_args()
@@ -50,6 +57,7 @@
# Load snapshot
saved_state_dict = torch.load(snapshot_path)
model.load_state_dict(saved_state_dict)
+ # load_filtered_state_dict(model, saved_state_dict)
print 'Loading data.'
@@ -63,6 +71,8 @@
pose_dataset = datasets.Pose_300W_LP_random_ds(args.data_dir, args.filename_list, transformations)
elif 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':
@@ -86,11 +96,16 @@
model.eval() # Change model to 'eval' mode (BN uses moving mean/var).
total = 0
+ idx_tensor = [idx for idx in xrange(66)]
+ idx_tensor = torch.FloatTensor(idx_tensor).cuda(gpu)
+
yaw_error = .0
pitch_error = .0
roll_error = .0
l1loss = torch.nn.L1Loss(size_average=False)
+
+
for i, (images, labels, cont_labels, name) in enumerate(test_loader):
images = Variable(images).cuda(gpu)
@@ -108,9 +123,13 @@
_, roll_bpred = torch.max(roll.data, 1)
# Continuous predictions
- yaw_predicted = angles[:,0].data.cpu()
- pitch_predicted = angles[:,1].data.cpu()
- roll_predicted = angles[:,2].data.cpu()
+ yaw_predicted = utils.softmax_temperature(yaw.data, 1)
+ 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() * 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))
--
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