From ba9154f7776b99247d9bfb10120beb294082c21f Mon Sep 17 00:00:00 2001
From: natanielruiz <nruiz9@gatech.edu>
Date: 星期一, 30 十月 2017 06:21:14 +0800
Subject: [PATCH] Next
---
/dev/null | 151 --------------------------------------------------
1 files changed, 0 insertions(+), 151 deletions(-)
diff --git a/code/test_AFW_preangles.py b/code/test_AFW_preangles.py
deleted file mode 100644
index 3754706..0000000
--- a/code/test_AFW_preangles.py
+++ /dev/null
@@ -1,151 +0,0 @@
-import numpy as np
-import torch
-import torch.nn as nn
-from torch.autograd import Variable
-from torch.utils.data import DataLoader
-from torchvision import transforms
-import torch.backends.cudnn as cudnn
-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
-
-def parse_args():
- """Parse input arguments."""
- parser = argparse.ArgumentParser(description='Head pose estimation using the Hopenet network.')
- parser.add_argument('--gpu', dest='gpu_id', help='GPU device id to use [0]',
- default=0, type=int)
- parser.add_argument('--data_dir', dest='data_dir', help='Directory path for data.',
- default='', type=str)
- parser.add_argument('--filename_list', dest='filename_list', help='Path to text file containing relative paths for every example.',
- default='', type=str)
- parser.add_argument('--snapshot', dest='snapshot', help='Name of model snapshot.',
- default='', type=str)
- parser.add_argument('--batch_size', dest='batch_size', help='Batch size.',
- default=1, type=int)
- parser.add_argument('--save_viz', dest='save_viz', help='Save images with pose cube.',
- default=False, type=bool)
- parser.add_argument('--margin', dest='margin', help='Accuracy margin.', default=22.5,
- type=float)
-
- args = parser.parse_args()
-
- return args
-
-if __name__ == '__main__':
- args = parse_args()
-
- cudnn.enabled = True
- 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)
-
- print 'Loading snapshot.'
- # Load snapshot
- saved_state_dict = torch.load(snapshot_path)
- model.load_state_dict(saved_state_dict)
-
- print 'Loading data.'
-
- transformations = transforms.Compose([transforms.Scale(224),
- transforms.CenterCrop(224), transforms.ToTensor()])
-
- pose_dataset = datasets.AFW(args.data_dir, args.filename_list,
- transformations)
- test_loader = torch.utils.data.DataLoader(dataset=pose_dataset,
- batch_size=args.batch_size,
- num_workers=2)
-
- model.cuda(gpu)
-
- print 'Ready to test network.'
-
- # Test the Model
- model.eval() # Change model to 'eval' mode (BN uses moving mean/var).
- total = 0
- n_margins = 20
- yaw_correct = np.zeros(n_margins)
- pitch_correct = np.zeros(n_margins)
- roll_correct = np.zeros(n_margins)
-
- 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)
-
- yaw_correct = .0
- yaw_margin = args.margin
-
- for i, (images, labels, cont_labels, name) in enumerate(test_loader):
- images = Variable(images).cuda(gpu)
- total += labels.size(0)
- label_yaw = cont_labels[:,0]
- label_pitch = cont_labels[:,1].float()
- label_roll = cont_labels[:,2].float()
-
- yaw, pitch, roll, angles = model(images)
-
- # Binned predictions
- _, yaw_bpred = torch.max(yaw.data, 1)
- _, pitch_bpred = torch.max(pitch.data, 1)
- _, roll_bpred = torch.max(roll.data, 1)
-
- # Continuous predictions
- yaw_predicted = utils.softmax_temperature(yaw.data, 0.1)
- 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))
- pitch_error += torch.sum(torch.abs(pitch_predicted - label_pitch))
- roll_error += torch.sum(torch.abs(roll_predicted - label_roll))
-
- # Yaw accuracy
- yaw_tensor_error = torch.abs(yaw_predicted - label_yaw).numpy()
-
- yaw_correct += np.where(yaw_tensor_error <= yaw_margin)[0].shape[0]
-
- if yaw_tensor_error[0] > yaw_margin:
- print name[0] + ' ' + str(yaw_predicted[0]) + ' ' + str(label_yaw[0]) + ' ' + str(yaw_tensor_error[0])
-
- # Save images with pose cube.
- # TODO: fix for larger batch size
- if args.save_viz:
- name = name[0]
- cv2_img = cv2.imread(os.path.join(args.data_dir, name + '.jpg'))
- #print os.path.join('output/images', name + '.jpg')
- #print label_yaw[0] * 3 - 99, label_pitch[0] * 3 - 99, label_roll[0] * 3 - 99
- #print yaw_predicted * 3 - 99, pitch_predicted * 3 - 99, roll_predicted * 3 - 99
- utils.plot_pose_cube(cv2_img, yaw_predicted[0], pitch_predicted[0], roll_predicted[0])
- cv2.imwrite(os.path.join('output/images', name + '.jpg'), cv2_img)
-
- print('Test error in degrees of the model on the ' + str(total) +
- ' test images. Yaw: %.4f, Pitch: %.4f, Roll: %.4f' % (yaw_error / total,
- pitch_error / total, roll_error / total))
- print ('Yaw accuracy (<= ' + str(yaw_margin) + ' degrees) is %.4f' % (yaw_correct / total))
-
- # Binned accuracy
- # for idx in xrange(len(yaw_correct)):
- # print yaw_correct[idx] / total, pitch_correct[idx] / total, roll_correct[idx] / total
--
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