From 3c7129a5434b086a3253a28479d8fa43b32823a9 Mon Sep 17 00:00:00 2001
From: natanielruiz <nruiz9@gatech.edu>
Date: 星期一, 30 十月 2017 06:19:01 +0800
Subject: [PATCH] next
---
code/test_preangles.py | 56 +++++++++++++++++++++-----------------------------------
1 files changed, 21 insertions(+), 35 deletions(-)
diff --git a/code/test_preangles.py b/code/test_preangles.py
index cfee8d1..be9bfda 100644
--- a/code/test_preangles.py
+++ b/code/test_preangles.py
@@ -1,4 +1,9 @@
+import sys, os, argparse
+
import numpy as np
+import cv2
+import matplotlib.pyplot as plt
+
import torch
import torch.nn as nn
from torch.autograd import Variable
@@ -8,15 +13,7 @@
import torchvision
import torch.nn.functional as F
-import cv2
-import matplotlib.pyplot as plt
-import sys
-import os
-import argparse
-
-import datasets
-import hopenet
-import utils
+import datasets, hopenet, utils
def parse_args():
"""Parse input arguments."""
@@ -46,12 +43,8 @@
gpu = args.gpu_id
snapshot_path = args.snapshot
- # 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, 0)
- # ResNet18
- # model = hopenet.Hopenet(torchvision.models.resnet.BasicBlock, [2, 2, 2, 2], 66)
+ # ResNet50 structure
+ model = hopenet.Hopenet(torchvision.models.resnet.Bottleneck, [3, 4, 6, 3], 66)
print 'Loading snapshot.'
# Load snapshot
@@ -64,18 +57,18 @@
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':
- 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)
+ if args.dataset == 'Pose_300W_LP':
+ pose_dataset = datasets.Pose_300W_LP(args.data_dir, args.filename_list, transformations)
+ elif args.dataset == 'Pose_300W_LP_random_ds':
+ 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 == '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 == 'AFLW_aug':
+ pose_dataset = datasets.AFLW_aug(args.data_dir, args.filename_list, transformations)
elif args.dataset == 'AFW':
pose_dataset = datasets.AFW(args.data_dir, args.filename_list, transformations)
else:
@@ -93,9 +86,6 @@
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
@@ -105,6 +95,7 @@
for i, (images, labels, cont_labels, name) in enumerate(test_loader):
images = Variable(images).cuda(gpu)
total += cont_labels.size(0)
+
label_yaw = cont_labels[:,0].float()
label_pitch = cont_labels[:,1].float()
label_roll = cont_labels[:,2].float()
@@ -117,21 +108,16 @@
_, roll_bpred = torch.max(roll.data, 1)
# Continuous predictions
- 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
+ yaw_predicted = angles[:,0].data.cpu()
+ pitch_predicted = angles[:,1].data.cpu()
+ roll_predicted = angles[:,2].data.cpu()
# Mean absolute error
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
+ # Save first image in batch with pose cube or axis.
if args.save_viz:
name = name[0]
if args.dataset == 'BIWI':
--
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