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infer.py
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infer.py
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import os
import json
from datetime import datetime
from statistics import mean
import argparse
import itertools
import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix
from sklearn.utils.multiclass import unique_labels
from scipy.io import savemat
import torch
import torch.optim as optim
from torch.autograd import Variable
import torch.nn.functional as F
from models import Generator, Discriminator, SqueezeNet
from datasets import GANDataset, GazeDataset
from utils import ReplayBuffer, LambdaLR, Logger, gan2gaze, gaze2gan
parser = argparse.ArgumentParser('Options for running inference using GazeNet/GazeNet++ in PyTorch...')
parser.add_argument('--dataset-root-path', type=str, default=None, help='path to dataset')
parser.add_argument('--split', type=str, default='val', help='split to evaluate (train/val/test)')
parser.add_argument('--version', type=str, default=None, help='which version of SqueezeNet to load (1_0/1_1)')
parser.add_argument('--output-dir', type=str, default=None, help='output directory for model and logs')
parser.add_argument('--snapshot-dir', type=str, default=None, help='directory with pre-trained model snapshots')
parser.add_argument('--batch-size', type=int, default=1, metavar='N', help='batch size for training')
parser.add_argument('--log-schedule', type=int, default=10, metavar='N', help='number of iterations to print/save log after')
parser.add_argument('--seed', type=int, default=1, help='set seed to some constant value to reproduce experiments')
parser.add_argument('--no-cuda', action='store_true', default=False, help='do not use cuda for training')
parser.add_argument('--size', type=int, default=256, help='size of the data crop (squared assumed)')
parser.add_argument('--nc', type=int, default=1, help='number of channels of data')
args = parser.parse_args()
# check args
if args.dataset_root_path is None:
assert False, 'Path to dataset not provided!'
if all(args.version != x for x in ['1_0', '1_1']):
assert False, 'Model version not recognized!'
# Output class labels
activity_classes = ['Eyes Closed', 'Forward', 'Shoulder', 'Left Mirror', 'Lap', 'Speedometer', 'Radio', 'Rearview', 'Right Mirror']
args.num_classes = len(activity_classes)
# setup args
args.cuda = not args.no_cuda and torch.cuda.is_available()
if args.output_dir is None:
args.output_dir = datetime.now().strftime("%Y-%m-%d-%H:%M")
args.output_dir = os.path.join('.', 'experiments', 'inference', args.output_dir)
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
else:
assert False, 'Output directory already exists!'
# store config in output directory
with open(os.path.join(args.output_dir, 'config.json'), 'w') as f:
json.dump(vars(args), f)
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
def plot_confusion_matrix(y_true, y_pred, classes, normalize=True, title=None, cmap=plt.cm.Blues):
"""
This function prints and plots the confusion matrix.
Normalization can be applied by setting `normalize=True`.
"""
if not title:
if normalize:
title = 'Normalized confusion matrix'
else:
title = 'Confusion matrix, without normalization'
# Compute confusion matrix
cm = confusion_matrix(y_true, y_pred)
# Only use the labels that appear in the data
#classes = classes[unique_labels(y_true, y_pred)]
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
fig, ax = plt.subplots()
im = ax.imshow(cm, interpolation='nearest', cmap=cmap)
ax.figure.colorbar(im, ax=ax)
# We want to show all ticks...
ax.set(xticks=np.arange(cm.shape[1]),
yticks=np.arange(cm.shape[0]),
# ... and label them with the respective list entries
xticklabels=classes, yticklabels=classes,
title=title,
ylabel='True label',
xlabel='Predicted label')
# Rotate the tick labels and set their alignment.
plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
rotation_mode="anchor")
# Loop over data dimensions and create text annotations.
fmt = '.2f' if normalize else 'd'
thresh = cm.max() / 2.
for i in range(cm.shape[0]):
for j in range(cm.shape[1]):
ax.text(j, i, format(cm[i, j], fmt),
ha="center", va="center",
color="white" if cm[i, j] > thresh else "black")
fig.tight_layout()
fig.savefig(os.path.join(args.output_dir, 'confusion_matrix.jpg'))
return
kwargs = {'batch_size': args.batch_size, 'shuffle': False, 'num_workers': 6}
test_loader = torch.utils.data.DataLoader(GazeDataset(args.dataset_root_path, args.split, False), **kwargs)
# validation function
def test(netG_B2A, netGaze):
correct = 0
if netG_B2A is not None:
netG_B2A.eval()
netGaze.eval()
pred_all = np.array([], dtype='int64')
target_all = np.array([], dtype='int64')
for idx, (data, target) in enumerate(test_loader):
if args.cuda:
data, target = data[:, :args.nc, :, :].cuda(), target.cuda()
data, target = Variable(data), Variable(target)
# do the forward pass
if netG_B2A is not None:
data = gaze2gan(data, test_loader.dataset.mean, test_loader.dataset.std)
fake_data = netG_B2A(data)
fake_data = gan2gaze(fake_data, test_loader.dataset.mean, test_loader.dataset.std)
scores = netGaze(fake_data.expand(-1, 3, -1, -1))[0]
else:
scores = netGaze(data.expand(-1, 3, -1, -1))[0]
scores = scores.view(-1, args.num_classes)
pred = scores.data.max(1)[1] # got the indices of the maximum, match them
correct += pred.eq(target.data).cpu().sum()
print('Done with image {} out {}...'.format(min(args.batch_size*(idx+1), len(test_loader.dataset)), len(test_loader.dataset)))
pred_all = np.append(pred_all, pred.cpu().numpy())
target_all = np.append(target_all, target.cpu().numpy())
print("------------------------\nPredicted {} out of {}".format(correct, len(test_loader.dataset)))
test_accuracy = 100.0*float(correct)/len(test_loader.dataset)
print("Test accuracy = {:.2f}%\n------------------------".format(test_accuracy))
with open(os.path.join(args.output_dir, "logs.txt"), "a") as f:
f.write("\n------------------------\nPredicted {} out of {}\n".format(correct, len(test_loader.dataset)))
f.write("Test accuracy = {:.2f}%\n------------------------\n".format(test_accuracy))
plot_confusion_matrix(target_all, pred_all, activity_classes)
return test_accuracy
if __name__ == '__main__':
# networks
netG_B2A = Generator(args.nc, args.nc)
netGaze = SqueezeNet(args.version)
if args.snapshot_dir is not None:
if os.path.exists(os.path.join(args.snapshot_dir, 'netG_B2A.pth')):
netG_B2A.load_state_dict(torch.load(os.path.join(args.snapshot_dir, 'netG_B2A.pth')), strict=False)
if args.cuda:
netG_B2A.cuda()
else:
netG_B2A = None
if os.path.exists(os.path.join(args.snapshot_dir, 'netGaze.pth')):
netGaze.load_state_dict(torch.load(os.path.join(args.snapshot_dir, 'netGaze.pth')), strict=False)
if os.path.exists(os.path.join(args.snapshot_dir, 'netGaze_wo.pth')):
netGaze.load_state_dict(torch.load(os.path.join(args.snapshot_dir, 'netGaze_wo.pth')), strict=False)
if args.cuda:
netGaze.cuda()
else:
assert False, 'No model snapshot provided!'
test_acc = test(netG_B2A, netGaze)
savemat(os.path.join(args.output_dir, 'accuracy.mat'), {'acc': test_acc})
plt.close('all')