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| | | import sys, os, argparse |
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| | | import numpy as np |
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| | | import cv2 |
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| | | import matplotlib.pyplot as plt |
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| | | import torch |
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| | | import torch.nn as nn |
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| | | from torch.autograd import Variable |
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| | | import torchvision |
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| | | import torch.nn.functional as F |
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| | | |
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| | | import cv2 |
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| | | import matplotlib.pyplot as plt |
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| | | import sys |
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| | | import os |
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| | | import argparse |
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| | | |
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| | | import datasets |
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| | | import hopenet |
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| | | import utils |
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| | | import datasets, hopenet, utils |
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| | | |
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| | | def parse_args(): |
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| | | """Parse input arguments.""" |
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| | |
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| | | gpu = args.gpu_id |
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| | | snapshot_path = args.snapshot |
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| | | |
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| | | # ResNet101 with 3 outputs. |
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| | | # model = hopenet.Hopenet(torchvision.models.resnet.Bottleneck, [3, 4, 23, 3], 66) |
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| | | # ResNet50 |
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| | | model = hopenet.Hopenet(torchvision.models.resnet.Bottleneck, [3, 4, 6, 3], 66, 0) |
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| | | # ResNet18 |
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| | | # model = hopenet.Hopenet(torchvision.models.resnet.BasicBlock, [2, 2, 2, 2], 66) |
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| | | # ResNet50 structure |
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| | | model = hopenet.Hopenet(torchvision.models.resnet.Bottleneck, [3, 4, 6, 3], 66) |
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| | | |
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| | | print 'Loading snapshot.' |
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| | | # Load snapshot |
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| | | |
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| | | print 'Loading data.' |
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| | | |
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| | | # transformations = transforms.Compose([transforms.Scale(224), |
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| | | # transforms.RandomCrop(224), transforms.ToTensor()]) |
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| | | |
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| | | transformations = transforms.Compose([transforms.Scale(224), |
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| | | transforms.RandomCrop(224), transforms.ToTensor(), |
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| | | transforms.CenterCrop(224), transforms.ToTensor(), |
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| | | transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])]) |
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| | | |
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| | | if args.dataset == 'AFLW2000': |
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| | | pose_dataset = datasets.AFLW2000(args.data_dir, args.filename_list, |
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| | | transformations) |
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| | | if args.dataset == 'Pose_300W_LP': |
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| | | pose_dataset = datasets.Pose_300W_LP(args.data_dir, args.filename_list, transformations) |
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| | | elif args.dataset == 'Pose_300W_LP_random_ds': |
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| | | pose_dataset = datasets.Pose_300W_LP_random_ds(args.data_dir, args.filename_list, transformations) |
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| | | elif args.dataset == 'AFLW2000': |
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| | | pose_dataset = datasets.AFLW2000(args.data_dir, args.filename_list, transformations) |
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| | | elif args.dataset == 'BIWI': |
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| | | pose_dataset = datasets.BIWI(args.data_dir, args.filename_list, transformations) |
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| | | elif args.dataset == 'AFLW': |
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| | | pose_dataset = datasets.AFLW(args.data_dir, args.filename_list, transformations) |
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| | | elif args.dataset == 'AFLW_aug': |
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| | | pose_dataset = datasets.AFLW_aug(args.data_dir, args.filename_list, transformations) |
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| | | elif args.dataset == 'AFW': |
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| | | pose_dataset = datasets.AFW(args.data_dir, args.filename_list, transformations) |
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| | | else: |
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| | |
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| | | # Test the Model |
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| | | model.eval() # Change model to 'eval' mode (BN uses moving mean/var). |
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| | | total = 0 |
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| | | n_margins = 20 |
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| | | yaw_correct = np.zeros(n_margins) |
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| | | pitch_correct = np.zeros(n_margins) |
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| | | roll_correct = np.zeros(n_margins) |
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| | | |
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| | | idx_tensor = [idx for idx in xrange(66)] |
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| | | idx_tensor = torch.FloatTensor(idx_tensor).cuda(gpu) |
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| | | |
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| | | yaw_error = .0 |
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| | | pitch_error = .0 |
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| | | |
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| | | l1loss = torch.nn.L1Loss(size_average=False) |
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| | | |
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| | | for i, (images, labels, name) in enumerate(test_loader): |
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| | | for i, (images, labels, cont_labels, name) in enumerate(test_loader): |
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| | | images = Variable(images).cuda(gpu) |
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| | | total += labels.size(0) |
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| | | label_yaw = labels[:,0].float() |
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| | | label_pitch = labels[:,1].float() |
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| | | label_roll = labels[:,2].float() |
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| | | total += cont_labels.size(0) |
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| | | label_yaw = cont_labels[:,0].float() |
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| | | label_pitch = cont_labels[:,1].float() |
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| | | label_roll = cont_labels[:,2].float() |
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| | | |
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| | | yaw, pitch, roll, angles = model(images) |
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| | | |
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| | | pitch_predicted = utils.softmax_temperature(pitch.data, 1) |
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| | | roll_predicted = utils.softmax_temperature(roll.data, 1) |
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| | | |
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| | | yaw_predicted = torch.sum(yaw_predicted * idx_tensor, 1).cpu() |
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| | | pitch_predicted = torch.sum(pitch_predicted * idx_tensor, 1).cpu() |
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| | | roll_predicted = torch.sum(roll_predicted * idx_tensor, 1).cpu() |
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| | | yaw_predicted = torch.sum(yaw_predicted * idx_tensor, 1).cpu() * 3 - 99 |
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| | | pitch_predicted = torch.sum(pitch_predicted * idx_tensor, 1).cpu() * 3 - 99 |
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| | | roll_predicted = torch.sum(roll_predicted * idx_tensor, 1).cpu() * 3 - 99 |
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| | | |
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| | | # Mean absolute error |
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| | | yaw_error += torch.sum(torch.abs(yaw_predicted - label_yaw) * 3) |
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| | | pitch_error += torch.sum(torch.abs(pitch_predicted - label_pitch) * 3) |
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| | | roll_error += torch.sum(torch.abs(roll_predicted - label_roll) * 3) |
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| | | |
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| | | # Binned Accuracy |
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| | | # for er in xrange(n_margins): |
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| | | # yaw_bpred[er] += (label_yaw[0] in range(yaw_bpred[0,0] - er, yaw_bpred[0,0] + er + 1)) |
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| | | # pitch_bpred[er] += (label_pitch[0] in range(pitch_bpred[0,0] - er, pitch_bpred[0,0] + er + 1)) |
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| | | # roll_bpred[er] += (label_roll[0] in range(roll_bpred[0,0] - er, roll_bpred[0,0] + er + 1)) |
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| | | # print label_yaw[0], yaw_bpred[0,0] |
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| | | yaw_error += torch.sum(torch.abs(yaw_predicted - label_yaw)) |
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| | | pitch_error += torch.sum(torch.abs(pitch_predicted - label_pitch)) |
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| | | roll_error += torch.sum(torch.abs(roll_predicted - label_roll)) |
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| | | |
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| | | # Save images with pose cube. |
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| | | # TODO: fix for larger batch size |
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| | | if args.save_viz: |
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| | | name = name[0] |
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| | | cv2_img = cv2.imread(os.path.join(args.data_dir, name + '.jpg')) |
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| | | #print os.path.join('output/images', name + '.jpg') |
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| | | #print label_yaw[0] * 3 - 99, label_pitch[0] * 3 - 99, label_roll[0] * 3 - 99 |
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| | | #print yaw_predicted * 3 - 99, pitch_predicted * 3 - 99, roll_predicted * 3 - 99 |
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| | | utils.plot_pose_cube(cv2_img, yaw_predicted[0] * 3 - 99, pitch_predicted[0] * 3 - 99, roll_predicted[0] * 3 - 99) |
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| | | if args.dataset == 'BIWI': |
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| | | cv2_img = cv2.imread(os.path.join(args.data_dir, name + '_rgb.png')) |
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| | | else: |
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| | | cv2_img = cv2.imread(os.path.join(args.data_dir, name + '.jpg')) |
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| | | if args.batch_size == 1: |
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| | | error_string = 'y %.2f, p %.2f, r %.2f' % (torch.sum(torch.abs(yaw_predicted - label_yaw)), torch.sum(torch.abs(pitch_predicted - label_pitch)), torch.sum(torch.abs(roll_predicted - label_roll))) |
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| | | cv2.putText(cv2_img, error_string, (30, cv2_img.shape[0]- 30), fontFace=1, fontScale=1, color=(0,0,255), thickness=2) |
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| | | # utils.plot_pose_cube(cv2_img, yaw_predicted[0], pitch_predicted[0], roll_predicted[0], size=100) |
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| | | utils.draw_axis(cv2_img, yaw_predicted[0], pitch_predicted[0], roll_predicted[0], tdx = 200, tdy= 200, size=100) |
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| | | cv2.imwrite(os.path.join('output/images', name + '.jpg'), cv2_img) |
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| | | |
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| | | print('Test error in degrees of the model on the ' + str(total) + |
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| | | ' test images. Yaw: %.4f, Pitch: %.4f, Roll: %.4f' % (yaw_error / total, |
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| | | pitch_error / total, roll_error / total)) |
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| | | |
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| | | # Binned accuracy |
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| | | # for idx in xrange(len(yaw_correct)): |
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| | | # print yaw_correct[idx] / total, pitch_correct[idx] / total, roll_correct[idx] / total |
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