From 4b67b5c8ed5566ec3030d537536282e830d87e40 Mon Sep 17 00:00:00 2001
From: natanielruiz <nruiz9@gatech.edu>
Date: 星期一, 30 十月 2017 07:15:49 +0800
Subject: [PATCH] next
---
code/test_preangles.py | 24 ++++++++++++------------
1 files changed, 12 insertions(+), 12 deletions(-)
diff --git a/code/test_preangles.py b/code/test_preangles.py
index 05f621a..d4a9f5f 100644
--- a/code/test_preangles.py
+++ b/code/test_preangles.py
@@ -36,13 +36,6 @@
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()
@@ -56,7 +49,7 @@
print 'Loading snapshot.'
# Load snapshot
saved_state_dict = torch.load(snapshot_path)
- load_filtered_state_dict(model, saved_state_dict)
+ model.load_state_dict(saved_state_dict)
print 'Loading data.'
@@ -95,6 +88,9 @@
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
@@ -109,7 +105,7 @@
label_pitch = cont_labels[:,1].float()
label_roll = cont_labels[:,2].float()
- yaw, pitch, roll, angles = model(images)
+ yaw, pitch, roll = model(images)
# Binned predictions
_, yaw_bpred = torch.max(yaw.data, 1)
@@ -117,9 +113,13 @@
_, 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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