From 5c5e7f80bf9b560763a5ee35cd5d01ae1ec60a84 Mon Sep 17 00:00:00 2001
From: natanielruiz <nataniel777@hotmail.com>
Date: 星期二, 11 七月 2017 11:21:21 +0800
Subject: [PATCH] next
---
/dev/null | 82 -----------------------------------------
1 files changed, 0 insertions(+), 82 deletions(-)
diff --git a/code/test.py~ b/code/test.py~
deleted file mode 100644
index fa2787b..0000000
--- a/code/test.py~
+++ /dev/null
@@ -1,82 +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 cv2
-import matplotlib.pyplot as plt
-import sys
-import os
-import argparse
-
-from datasets import AFLW2000
-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)
-
- args = parser.parse_args()
-
- return args
-
-if __name__ == '__main__':
- args = parse_args()
-
- cudnn.enabled = True
- batch_size = 1
- gpu = args.gpu_id
- snapshot_path = os.path.join('output/snapshots', args.snapshot + '.pkl')
-
- model = hopenet.Simple_CNN()
-
- 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(302),transforms.CenterCrop(302),transforms.ToTensor()])
-
- pose_dataset = AFLW2000(args.data_dir, args.filename_list,
- transformations)
- test_loader = torch.utils.data.DataLoader(dataset=pose_dataset,
- batch_size=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).
- error = .0
- total = 0
- for i, (images, labels) in enumerate(test_loader):
- images = Variable(images).cuda(gpu)
- labels = Variable(labels).cuda(gpu)
- outputs = model(images)
- _, predicted = torch.max(outputs.data, 1)
- total += labels.size(0)
- # TODO: There are more efficient ways.
- for idx in xrange(len(outputs)):
- error += utils.mse_loss(outputs[idx], labels[idx])
-
-
- print('Test MSE error of the model on the ' + str(total) +
- ' test images: %.4f' % (error / total))
--
Gitblit v1.8.0