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import numpy as np
import matplotlib.pyplot as plt
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
from l2cs import select_device, natural_keys, gazeto3d, angular, getArch, L2CS, Gaze360, Mpiigaze
def parse_args():
"""Parse input arguments."""
parser = argparse.ArgumentParser(
description='Gaze estimation using L2CSNet .')
# Gaze360
parser.add_argument(
'--gaze360image_dir', dest='gaze360image_dir', help='Directory path for gaze images.',
default='datasets/Gaze360/Image', type=str)
parser.add_argument(
'--gaze360label_dir', dest='gaze360label_dir', help='Directory path for gaze labels.',
default='datasets/Gaze360/Label/test.label', type=str)
# mpiigaze
parser.add_argument(
'--gazeMpiimage_dir', dest='gazeMpiimage_dir', help='Directory path for gaze images.',
default='datasets/MPIIFaceGaze/Image', type=str)
parser.add_argument(
'--gazeMpiilabel_dir', dest='gazeMpiilabel_dir', help='Directory path for gaze labels.',
default='datasets/MPIIFaceGaze/Label', type=str)
# Important args -------------------------------------------------------------------------------------------------------
# ----------------------------------------------------------------------------------------------------------------------
parser.add_argument(
'--dataset', dest='dataset', help='gaze360, mpiigaze',
default= "gaze360", type=str)
parser.add_argument(
'--snapshot', dest='snapshot', help='Path to the folder contains models.',
default='output/snapshots/L2CS-gaze360-_loader-180-4-lr', type=str)
parser.add_argument(
'--evalpath', dest='evalpath', help='path for the output evaluating gaze test.',
default="evaluation/L2CS-gaze360-_loader-180-4-lr", type=str)
parser.add_argument(
'--gpu',dest='gpu_id', help='GPU device id to use [0]',
default="0", type=str)
parser.add_argument(
'--batch_size', dest='batch_size', help='Batch size.',
default=100, type=int)
parser.add_argument(
'--arch', dest='arch', help='Network architecture, can be: ResNet18, ResNet34, [ResNet50], ''ResNet101, ResNet152, Squeezenet_1_0, Squeezenet_1_1, MobileNetV2',
default='ResNet50', type=str)
# ---------------------------------------------------------------------------------------------------------------------
# Important args ------------------------------------------------------------------------------------------------------
args = parser.parse_args()
return args
def getArch(arch,bins):
# Base network structure
if arch == 'ResNet18':
model = L2CS( torchvision.models.resnet.BasicBlock,[2, 2, 2, 2], bins)
elif arch == 'ResNet34':
model = L2CS( torchvision.models.resnet.BasicBlock,[3, 4, 6, 3], bins)
elif arch == 'ResNet101':
model = L2CS( torchvision.models.resnet.Bottleneck,[3, 4, 23, 3], bins)
elif arch == 'ResNet152':
model = L2CS( torchvision.models.resnet.Bottleneck,[3, 8, 36, 3], bins)
else:
if arch != 'ResNet50':
print('Invalid value for architecture is passed! '
'The default value of ResNet50 will be used instead!')
model = L2CS( torchvision.models.resnet.Bottleneck, [3, 4, 6, 3], bins)
return model
if __name__ == '__main__':
args = parse_args()
cudnn.enabled = True
gpu = select_device(args.gpu_id, batch_size=args.batch_size)
batch_size=args.batch_size
arch=args.arch
data_set=args.dataset
evalpath =args.evalpath
snapshot_path = args.snapshot
bins=args.bins
angle=args.angle
bin_width=args.bin_width
transformations = transforms.Compose([
transforms.Resize(448),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]
)
])
if data_set=="gaze360":
gaze_dataset=Gaze360(args.gaze360label_dir,args.gaze360image_dir, transformations, 180, 4, train=False)
test_loader = torch.utils.data.DataLoader(
dataset=gaze_dataset,
batch_size=batch_size,
shuffle=False,
num_workers=4,
pin_memory=True)
if not os.path.exists(evalpath):
os.makedirs(evalpath)
# list all epochs for testing
folder = os.listdir(snapshot_path)
folder.sort(key=natural_keys)
softmax = nn.Softmax(dim=1)
with open(os.path.join(evalpath,data_set+".log"), 'w') as outfile:
configuration = f"\ntest configuration = gpu_id={gpu}, batch_size={batch_size}, model_arch={arch}\nStart testing dataset={data_set}----------------------------------------\n"
print(configuration)
outfile.write(configuration)
epoch_list=[]
avg_yaw=[]
avg_pitch=[]
avg_MAE=[]
for epochs in folder:
# Base network structure
model=getArch(arch, 90)
saved_state_dict = torch.load(os.path.join(snapshot_path, epochs))
model.load_state_dict(saved_state_dict)
model.cuda(gpu)
model.eval()
total = 0
idx_tensor = [idx for idx in range(90)]
idx_tensor = torch.FloatTensor(idx_tensor).cuda(gpu)
avg_error = .0
with torch.no_grad():
for j, (images, labels, cont_labels, name) in enumerate(test_loader):
images = Variable(images).cuda(gpu)
total += cont_labels.size(0)
label_pitch = cont_labels[:,0].float()*np.pi/180
label_yaw = cont_labels[:,1].float()*np.pi/180
gaze_pitch, gaze_yaw = model(images)
# Binned predictions
_, pitch_bpred = torch.max(gaze_pitch.data, 1)
_, yaw_bpred = torch.max(gaze_yaw.data, 1)
# Continuous predictions
pitch_predicted = softmax(gaze_pitch)
yaw_predicted = softmax(gaze_yaw)
# mapping from binned (0 to 28) to angels (-180 to 180)
pitch_predicted = torch.sum(pitch_predicted * idx_tensor, 1).cpu() * 4 - 180
yaw_predicted = torch.sum(yaw_predicted * idx_tensor, 1).cpu() * 4 - 180
pitch_predicted = pitch_predicted*np.pi/180
yaw_predicted = yaw_predicted*np.pi/180
for p,y,pl,yl in zip(pitch_predicted,yaw_predicted,label_pitch,label_yaw):
avg_error += angular(gazeto3d([p,y]), gazeto3d([pl,yl]))
x = ''.join(filter(lambda i: i.isdigit(), epochs))
epoch_list.append(x)
avg_MAE.append(avg_error/total)
loger = f"[{epochs}---{args.dataset}] Total Num:{total},MAE:{avg_error/total}\n"
outfile.write(loger)
print(loger)
fig = plt.figure(figsize=(14, 8))
plt.xlabel('epoch')
plt.ylabel('avg')
plt.title('Gaze angular error')
plt.legend()
plt.plot(epoch_list, avg_MAE, color='k', label='mae')
fig.savefig(os.path.join(evalpath,data_set+".png"), format='png')
plt.show()
elif data_set=="mpiigaze":
model_used=getArch(arch, bins)
for fold in range(15):
folder = os.listdir(args.gazeMpiilabel_dir)
folder.sort()
testlabelpathombined = [os.path.join(args.gazeMpiilabel_dir, j) for j in folder]
gaze_dataset=Mpiigaze(testlabelpathombined,args.gazeMpiimage_dir, transformations, False, angle, fold)
test_loader = torch.utils.data.DataLoader(
dataset=gaze_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=4,
pin_memory=True)
if not os.path.exists(os.path.join(evalpath, f"fold"+str(fold))):
os.makedirs(os.path.join(evalpath, f"fold"+str(fold)))
# list all epochs for testing
folder = os.listdir(os.path.join(snapshot_path,"fold"+str(fold)))
folder.sort(key=natural_keys)
softmax = nn.Softmax(dim=1)
with open(os.path.join(evalpath, os.path.join("fold"+str(fold), data_set+".log")), 'w') as outfile:
configuration = f"\ntest configuration equal gpu_id={gpu}, batch_size={batch_size}, model_arch={arch}\nStart testing dataset={data_set}, fold={fold}---------------------------------------\n"
print(configuration)
outfile.write(configuration)
epoch_list=[]
avg_MAE=[]
for epochs in folder:
model=model_used
saved_state_dict = torch.load(os.path.join(snapshot_path+"/fold"+str(fold),epochs))
model= nn.DataParallel(model,device_ids=[0])
model.load_state_dict(saved_state_dict)
model.cuda(gpu)
model.eval()
total = 0
idx_tensor = [idx for idx in range(28)]
idx_tensor = torch.FloatTensor(idx_tensor).cuda(gpu)
avg_error = .0
with torch.no_grad():
for j, (images, labels, cont_labels, name) in enumerate(test_loader):
images = Variable(images).cuda(gpu)
total += cont_labels.size(0)
label_pitch = cont_labels[:,0].float()*np.pi/180
label_yaw = cont_labels[:,1].float()*np.pi/180
gaze_pitch, gaze_yaw = model(images)
# Binned predictions
_, pitch_bpred = torch.max(gaze_pitch.data, 1)
_, yaw_bpred = torch.max(gaze_yaw.data, 1)
# Continuous predictions
pitch_predicted = softmax(gaze_pitch)
yaw_predicted = softmax(gaze_yaw)
# mapping from binned (0 to 28) to angels (-42 to 42)
pitch_predicted = \
torch.sum(pitch_predicted * idx_tensor, 1).cpu() * 3 - 42
yaw_predicted = \
torch.sum(yaw_predicted * idx_tensor, 1).cpu() * 3 - 42
pitch_predicted = pitch_predicted*np.pi/180
yaw_predicted = yaw_predicted*np.pi/180
for p,y,pl,yl in zip(pitch_predicted, yaw_predicted, label_pitch, label_yaw):
avg_error += angular(gazeto3d([p,y]), gazeto3d([pl,yl]))
x = ''.join(filter(lambda i: i.isdigit(), epochs))
epoch_list.append(x)
avg_MAE.append(avg_error/ total)
loger = f"[{epochs}---{args.dataset}] Total Num:{total},MAE:{avg_error/total} \n"
outfile.write(loger)
print(loger)
fig = plt.figure(figsize=(14, 8))
plt.xlabel('epoch')
plt.ylabel('avg')
plt.title('Gaze angular error')
plt.legend()
plt.plot(epoch_list, avg_MAE, color='k', label='mae')
fig.savefig(os.path.join(evalpath, os.path.join("fold"+str(fold), data_set+".png")), format='png')
# plt.show()
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