def step(split, epoch, opt, dataLoader, model, criterion, optimizer=None):
if split == 'train':
model.train()
else:
model.eval()
Loss, Acc, Mpjpe, Loss3D = AverageMeter(), AverageMeter(), AverageMeter(
), AverageMeter()
nIters = len(dataLoader)
bar = Bar('==>', max=nIters)
for i, (input, target2D, target3D, meta) in enumerate(dataLoader):
input_var = torch.autograd.Variable(input).float().cuda()
target2D_var = torch.autograd.Variable(target2D).float().cuda()
target3D_var = torch.autograd.Variable(target3D).float().cuda()
depMap, depth = model(input_var, target2D_var)
depthPridict = depth[opt.nStack - 1]
if opt.DEBUG >= 2:
gt = getPreds(target2D.cpu().numpy()) * 4
pred = getPreds((depMap[opt.nStack - 1].data).cpu().numpy()) * 4
debugger = Debugger()
debugger.addImg(
(input[0].numpy().transpose(1, 2, 0) * 256).astype(np.uint8))
debugger.addPoint2D(pred[0], (255, 0, 0))
debugger.addPoint2D(gt[0], (0, 0, 255))
debugger.showImg()
debugger.saveImg('debug/{}.png'.format(i))
loss = 0
for k in range(opt.nStack):
loss += criterion(depth[k], target3D_var[:, :, 2])
Loss.update(loss.data[0], input.size(0))
#Acc.update(Accuracy((output[opt.nStack - 1].data).cpu().numpy(), (target2D_var.data).cpu().numpy()))
mpjpe, num3D = MPJPE2(target3D.cpu().numpy(),
(depthPridict.data).cpu().numpy(), meta)
Mpjpe.update(mpjpe, num3D)
if split == 'train':
optimizer.zero_grad()
loss.backward()
optimizer.step()
Bar.suffix = '{split} Epoch: [{0}][{1}/{2}]| Total: {total:} | ETA: {eta:} | Loss {loss.avg:.6f} | Mpjpe {Mpjpe.avg:.6f} ({Mpjpe.val:.6f})'.format(
epoch,
i,
nIters,
total=bar.elapsed_td,
eta=bar.eta_td,
loss=Loss,
split=split,
Mpjpe=Mpjpe)
bar.next()
bar.finish()
return Loss.avg, Acc.avg, Mpjpe.avg, Loss3D.avg
def main():
opt = opts().parse()
#if opt.loadModel != 'none':
model = AlexNet(ref.nJoints).cuda()
model.load_state_dict(torch.load("save.model"))
for (i, filename) in enumerate(os.listdir("./testimages/")):
img = cv2.imread("./testimages/" + filename)
c = np.ones(2) * ref.h36mImgSize / 2
s = ref.h36mImgSize * 1.0
img2 = Crop(img, c, s, 0, ref.inputRes) / 256.
input = torch.from_numpy(img2)
input = input.contiguous().view(1, input.size(0), input.size(1),
input.size(2))
print(input.size())
input_var = torch.autograd.Variable(input).float().cuda()
output = model(input_var)
print(output.size())
reg = (output.data).cpu().numpy() #.reshape(pred.shape[0], 1)
four = lambda t: t * 4.57
fourfunc = np.vectorize(four)
reg = fourfunc(reg)
print(reg)
debugger = Debugger()
debugger.addImg(
(input[0].numpy().transpose(1, 2, 0) * 256).astype(np.uint8))
debugger.addPoint2D(reg, (255, 0, 0))
#debugger.addPoint3D(np.concatenate([pred, (reg + 1) / 2. * 256], axis = 1))
debugger.saveImg(path="./result/" + filename)
np.set_printoptions(threshold=np.inf, linewidth=np.inf)
with open("./result/" + filename[:-4] + ".out", 'w') as f:
f.write(np.array2string(reg, separator=', '))
"""
def main():
opt = opts().parse()
if opt.loadModel != 'none':
model = torch.load(opt.loadModel).cuda()
else:
model = torch.load('hgreg-3d.pth').cuda()
img = cv2.imread(opt.demo)
input = torch.from_numpy(img.transpose(2, 0, 1)).float() / 256.
input = input.view(1, input.size(0), input.size(1), input.size(2))
input_var = torch.autograd.Variable(input).float().cuda()
output = model(input_var)
pred = getPreds((output[-2].data).cpu().numpy())[0] * 4
reg = (output[-1].data).cpu().numpy().reshape(pred.shape[0], 1)
debugger = Debugger()
debugger.addImg((input[0].numpy().transpose(1, 2, 0)*256).astype(np.uint8))
debugger.addPoint2D(pred, (255, 0, 0))
debugger.addPoint3D(np.concatenate([pred, (reg + 1) / 2. * 256], axis = 1))
debugger.showImg(pause = True)
debugger.show3D()
def check_logic():
preproc = nn.ModuleList([model.conv1_, model.bn1, model.relu, model.r1, model.maxpool, model.r4, model.r5 ])
hg = model.hourglass[0]
lower_hg = getEncoder(hg)
data_loader = torch.utils.data.DataLoader(
H36M(opts, 'val'),
batch_size = 1,
shuffle = False,
num_workers = int(ref.nThreads)
)
for k, (input, target) in enumerate(data_loader):
if(k>nSamples):
break
input_var = torch.autograd.Variable(input).float().cuda()
for mod in preproc:
input_var = mod(input_var)
for mod in lower_hg:
input_var = mod(input_var)
#decode
ups = input_var
upper_hg = nn.ModuleList(getDecoder(hg))
for mod in upper_hg:
ups = mod(ups)
Residual = model.Residual
for j in range(nModules):
ups = Residual[j](ups)
lin_ = model.lin_
ups = lin_[0](ups)
tmpOut = model.tmpOut
ups = tmpOut[0](ups)
pred = eval.getPreds(ups.data.cpu().numpy())*4
gt = eval.getPreds(target.cpu().numpy()) * 4
# init = getPreds(input.numpy()[:, 3:])
debugger = Debugger()
img = (input[0].numpy()[:3].transpose(1, 2, 0)*256).astype(np.uint8).copy()
print(img.shape)
debugger.addImg(img)
debugger.addPoint2D(pred[0], (255, 0, 0))
debugger.addPoint2D(gt[0], (0, 0, 255))
# debugger.addPoint2D(init[0], (0, 255, 0))
debugger.showAllImg(pause = True)
def generate(imageName):
process_image(imageName)
model = torch.load('../model/Stage3/model_10.pth',
map_location=lambda storage, loc: storage)
img = cv2.imread(imageName)
input = torch.from_numpy(img.transpose(2, 0, 1)).float() / 256.
input = input.view(1, input.size(0), input.size(1), input.size(2))
input_var = torch.autograd.Variable(input).float()
output = model(input_var)
print(output[-2].data[0][-2].shape)
pred = getPreds((output[-2].data).cpu().numpy())[0] * 4
reg = (output[-1].data).cpu().numpy().reshape(pred.shape[0], 1)
print(pred, (reg + 1) / 2. * 256)
debugger = Debugger()
debugger.addImg(
(input[0].numpy().transpose(1, 2, 0) * 256).astype(np.uint8))
debugger.addPoint2D(pred, (255, 0, 0))
debugger.addPoint3D(np.concatenate([pred, (reg + 1) / 2. * 256], axis=1))
debugger.showImg(pause=True)
debugger.show3D()
'''
def main():
pickle.load = partial(pickle.load, encoding="latin1")
pickle.Unpickler = partial(pickle.Unpickler, encoding="latin1")
opt = opts().parse()
if opt.loadModel != 'none':
model = torch.load(opt.loadModel).cuda()
else:
model = torch.load('../../tr_models/hgreg-3d.pth').cuda()
#opt.demo has the path to dir containing frames of demo video
all_frames = os.listdir(opt.demo)
n_frames = len(all_frames)
#specifics
dir_name = opt.demo.split('/')[-1]
save_path = '../../output/demo/'+dir_name
try:
os.makedirs(save_path)
except OSError:
pass
for idx, frame in enumerate(all_frames):
print('processing frame {}'.format(idx))
img = cv2.imread(opt.demo+'/'+frame)
input = torch.from_numpy(img.transpose(2, 0, 1)).float() / 256.
input = input.view(1, input.size(0), input.size(1), input.size(2))
input_var = torch.autograd.Variable(input).float().cuda()
output = model(input_var)
pred = getPreds((output[-2].data).cpu().numpy())[0] * 4
reg = (output[-1].data).cpu().numpy().reshape(pred.shape[0], 1)
debugger = Debugger()
debugger.addImg((input[0].numpy().transpose(1, 2, 0)*256).astype(np.uint8))
debugger.addPoint2D(pred, (255, 0, 0))
debugger.addPoint3D(np.concatenate([pred, (reg + 1) / 2. * 256], axis = 1))
# debugger.showImg(pause = True)
debugger.saveImg(path=save_path+'/frame{}.jpg'.format(idx))
debugger.save3D(path=save_path+'/frame_p3d{}.jpg'.format(idx))
print('frame {} done'.format(idx))
def step(split, epoch, opt, dataLoader, model, criterion, optimizer=None):
if split == 'train':
model.train()
else:
model.eval()
Loss, Acc = AverageMeter(), AverageMeter()
preds = []
nIters = len(dataLoader)
bar = Bar('{}'.format(opt.expID), max=nIters)
for i, (input, target, target2, meta) in enumerate(dataLoader):
input_var = torch.autograd.Variable(input).float().cuda()
target_var = torch.autograd.Variable(target).float().cuda()
target_var2 = torch.autograd.Variable(target2).float().cuda()
#print( input_var)
output = model(input_var)
#print(output[-1].size())
if opt.DEBUG >= 2:
gt = getPreds(target.cpu().numpy()) * 4
pred = getPreds((output[opt.nStack - 1].data).cpu().numpy()) * 4
debugger = Debugger()
img = (input[0].numpy().transpose(1, 2, 0) * 256).astype(
np.uint8).copy()
debugger.addImg(img)
debugger.addPoint2D(pred[0], (255, 0, 0))
debugger.addPoint2D(gt[0], (0, 0, 255))
debugger.showAllImg(pause=True)
loss = criterion(output[0], target_var)
for k in range(1, opt.nStack):
loss += criterion(output[k], target_var)
Loss.update(loss.data[0], input.size(0))
Acc.update(
Accuracy((output[opt.nStack - 1].data).cpu().numpy(),
(target_var.data).cpu().numpy()))
if split == 'train':
optimizer.zero_grad()
loss.backward()
optimizer.step()
else:
input_ = input.cpu().numpy()
input_[0] = Flip(input_[0]).copy()
inputFlip_var = torch.autograd.Variable(
torch.from_numpy(input_).view(1, input_.shape[1], ref.inputRes,
ref.inputRes)).float().cuda(
opt.GPU)
outputFlip = model(inputFlip_var)
outputFlip = ShuffleLR(
Flip((outputFlip[opt.nStack -
1].data).cpu().numpy()[0])).reshape(
1, ref.nJoints, 64, 64)
output_ = (
(output[opt.nStack - 1].data).cpu().numpy() + outputFlip) / 2
preds.append(
finalPreds(output_, meta['center'], meta['scale'],
meta['rotate'])[0])
Bar.suffix = '{split} Epoch: [{0}][{1}/{2}]| Total: {total:} | ETA: {eta:} | Loss {loss.avg:.6f} | Acc {Acc.avg:.6f} ({Acc.val:.6f})'.format(
epoch,
i,
nIters,
total=bar.elapsed_td,
eta=bar.eta_td,
loss=Loss,
Acc=Acc,
split=split)
bar.next()
bar.finish()
return {'Loss': Loss.avg, 'Acc': Acc.avg}, preds
class PoseExtractor:
def __init__(self, flag_save_pose_image, flag_save_pose_file):
rospy.loginfo("Initializing 3D Pose Extractor")
self.frameInfo = FrameInfo()
self.bridge = CvBridge()
self.lock = Lock()
self.image_shape = (256, 256, 3)
self.debugger = Debugger()
self.tracking_info_topic = rospy.get_param(
'~tracking_info_topic', '/person_tracker/tracking_info')
self.model_name = rospy.get_param('~pose_model', 'hgreg-3d.pth')
self.model = {}
self.save_pose_image = flag_save_pose_image
self.save_pose_file = flag_save_pose_file
self.publish_person = False
self.initModel()
self.frame_info_pub = rospy.Publisher('~frame_info',
FrameInfo,
queue_size=1)
self.person_pub = rospy.Publisher('~person', Person, queue_size=1)
self.tracking_info_sub = rospy.Subscriber(self.tracking_info_topic,
FrameInfo,
self.trackingInfoCallback,
queue_size=1)
def trackingInfoCallback(self, tracking_info_msg):
begin = time.time()
self.frameInfo.frame_id = tracking_info_msg.frame_id
self.frameInfo.image_frame = tracking_info_msg.image_frame
self.frameInfo.last_frame = tracking_info_msg.last_frame
rospy.loginfo("Frame ID: {}".format(self.frameInfo.frame_id))
persons = tracking_info_msg.persons
numPersons = len(persons)
if numPersons != 0:
for person in persons:
rospy.loginfo("Person {} is detected".format(person.person_id))
try:
# multi-threading for publishing single person
#p = ThreadPool(numPersons)
#p.map(self.poseEstimation, persons)
#p.close()
for person in persons:
self.poseEstimation(person)
self.frame_info_pub.publish(self.frameInfo)
self.frameInfo = FrameInfo()
if tracking_info_msg.last_frame:
rospy.loginfo('Last frame in the video!')
except BaseException as e:
rospy.logerr(e)
else:
rospy.logwarn("No person is detected!")
self.frame_info_pub.publish(self.frameInfo)
if tracking_info_msg.last_frame:
rospy.loginfo('Last frame in the video!')
rospy.loginfo("FPS: {}".format(1 / (time.time() - begin)))
def poseEstimation(self, tracked_person):
person_id = tracked_person.person_id
try:
curImage = self.bridge.imgmsg_to_cv2(self.frameInfo.image_frame)
person_image = curImage[
int(tracked_person.bbox.top):int(tracked_person.bbox.top +
tracked_person.bbox.height),
int(tracked_person.bbox.left):int(tracked_person.bbox.left +
tracked_person.bbox.width)]
except CvBridgeError as e:
rospy.logerr(e)
# Resize input image
rospy.logdebug("person image shape: {}".format(person_image.shape))
if person_image.shape != self.image_shape:
h, w = person_image.shape[0], person_image.shape[1]
center = torch.FloatTensor((w / 2, h / 2))
scale = 1.0 * max(h, w)
res = 256
input_image = Crop(person_image, center, scale, 0, res)
else:
input_image = person_image
# Feed input image to model
rospy.loginfo("feeding image to model")
input = torch.from_numpy(input_image.transpose(2, 0, 1)).float() / 256.
input = input.view(1, input.size(0), input.size(1), input.size(2))
input_var = torch.autograd.Variable(input).float().cuda()
# lock when using model to estimate pose
self.lock.acquire()
try:
output = self.model(input_var)
finally:
self.lock.release()
rospy.logdebug("got output from model")
# Get 2D pose
rospy.logdebug("Rendering 2D pose")
pose2D = getPreds((output[-2].data).cpu().numpy())[0] * 4
# Get 3D pose
rospy.logdebug("Rendering 3D pose")
reg = (output[-1].data).cpu().numpy().reshape(pose2D.shape[0], 1)
pose3D = np.concatenate([pose2D, (reg + 1) / 2. * 256], axis=1)
rospy.logdebug("pose 3d shape: {}".format(pose3D.shape))
for pose in pose3D:
joint = Point()
joint.x = pose[0]
joint.y = pose[1]
joint.z = pose[2]
tracked_person.person_pose.append(joint)
# publish person
if self.publish_person:
self.person_pub.publish(tracked_person)
self.lock.acquire()
try:
self.frameInfo.persons.append(tracked_person)
finally:
self.lock.release()
rospy.logdebug("pose3D: \n {}".format(pose3D))
# Save pose image
if self.save_pose_image:
cv2.imwrite(
pkg_path + '/scripts/debug/original/ogImg_' +
str(self.frame_id) + '.png', self.cv_image)
cv2.imwrite(
pkg_path + '/scripts/debug/input/inputImg_' +
str(self.frame_id) + '.png', input_image)
self.debugger.addImg(input_image, imgId=self.frame_id)
self.debugger.addPoint2D(pose2D, (255, 0, 0), imgId=self.frame_id)
self.debugger.saveImg(pkg_path + '/scripts/debug/pose/poseImg_' +
str(self.frame_id) + '.png',
imgId=self.frame_id)
if self.save_pose_file:
file_name = pkg_path + '/pose_file/pose_{:04d}.txt'.format(
self.frame_id)
with file(file_name, 'w') as outfile:
np.savetxt(outfile, pose3D, fmt='%-7.2f')
rospy.loginfo("Person {} processing finished".format(person_id))
# Initialize model
def initModel(self):
rospy.loginfo("=====> Loading and Initializing Model")
model_path = rospkg.RosPack().get_path(
'pose_3d_ros') + '/models/' + self.model_name
self.model = torch.load(model_path).cuda()
img = np.zeros((256, 256, 3))
input = torch.from_numpy(img.transpose(2, 0, 1)).float() / 256.
input = input.view(1, input.size(0), input.size(1), input.size(2))
input_var = torch.autograd.Variable(input).float().cuda()
output = self.model(input_var)
rospy.loginfo("Model Initialization Done")
def step(split, epoch, opt, dataLoader, model, criterion, optimizer=None):
if split == 'train':
model.train()
else:
model.eval()
Loss, Acc = AverageMeter(), AverageMeter()
nIters = len(dataLoader)
# bar = Bar('==>', max=nIters)
start_time = time.time()
for i, (input, target) in enumerate(dataLoader):
input_var = torch.autograd.Variable(input).float().cuda()
target_var = torch.autograd.Variable(
target.cuda(async=True)).float().cuda()
if (model.hgType == 'vae'):
output, latentspace = model(input_var)
else:
output = model(input_var)
if opt.DEBUG >= 2:
gt = getPreds(target.cpu().numpy()) * 4
pred = getPreds((output[opt.nStack - 1].data).cpu().numpy()) * 4
# init = getPreds(input.numpy()[:, 3:])
debugger = Debugger()
img = (input[0].numpy()[:3].transpose(1, 2, 0) * 256).astype(
np.uint8).copy()
debugger.addImg(img)
debugger.addPoint2D(pred[0], (255, 0, 0))
debugger.addPoint2D(gt[0], (0, 0, 255))
# debugger.addPoint2D(init[0], (0, 255, 0))
debugger.showAllImg(pause=True)
#debugger.saveImg('debug/{}.png'.format(i))
loss = criterion(output[0], target_var)
for k in range(1, opt.nStack):
loss += criterion(output[k], target_var)
if (model.hgType == 'vae'):
for k in range(0, opt.nStack):
loss += ref.vaeloss_wt * _compute_kl(latentspace[k])
Loss.update(loss.data[0], input.size(0))
Acc.update(
Accuracy((output[opt.nStack - 1].data).cpu().numpy(),
(target_var.data).cpu().numpy()))
if split == 'train':
optimizer.zero_grad()
loss.backward()
optimizer.step()
# Bar.suffix = '{split} Epoch: [{0}][{1}/{2}]| Total: {total:} | ETA: {eta:} | Loss {loss.avg:.6f} | Acc {Acc.avg:.6f} ({Acc.val:.6f})'.format(epoch, i, nIters, total=bar.elapsed_td, eta=bar.eta_td, loss=Loss, Acc=Acc, split = split)
# bar.next()
curr_time = time.time()
print(
'{split} Epoch: [{0}][{1}/{2}]| Total: {total:f} | ETA: {eta:f} | Loss {loss.avg:.6f} | Acc {Acc.avg:.6f} ({Acc.val:.6f})'
.format(epoch,
i,
nIters,
total=curr_time - start_time,
eta=(curr_time - start_time) * (nIters - i + 1) / (i + 1),
loss=Loss,
Acc=Acc,
split=split))
# bar.finish()
return Loss.avg, Acc.avg
scale = 1.0 * h / mheight
new_im = image.resize((int(w / scale), int(h / scale)),
Image.ANTIALIAS)
new_im.save(filename)
new_im.close()
#opt = opts().parse()
imageName = './images/test3.jpg'
#process_image(imageName)
model = torch.load('../model/Stage3/model_10.pth',
map_location=lambda storage, loc: storage)
img = cv2.imread(imageName)
print(type(np.array(img)))
input = torch.from_numpy(img.transpose(2, 0, 1)).float() / 256.
input = input.view(1, input.size(0), input.size(1), input.size(2))
input_var = torch.autograd.Variable(input).float()
output = model(input_var)
pred = getPreds((output[-2].data).cpu().numpy())[0] * 4
reg = (output[-1].data).cpu().numpy().reshape(pred.shape[0], 1)
print(pred, (reg + 1) / 2. * 256)
debugger = Debugger()
debugger.addImg((input[0].numpy().transpose(1, 2, 0) * 256).astype(np.uint8))
debugger.addPoint2D(pred, (255, 0, 0))
debugger.addPoint3D(np.concatenate([pred, (reg + 1) / 2. * 256], axis=1))
debugger.showImg(pause=True)
debugger.show3D()
def step(split, epoch, opt, dataLoader, model, criterion, optimizer=None):
if split == 'train':
model.train()
else:
model.eval()
Loss, Acc = AverageMeter(), AverageMeter()
preds = []
nIters = len(dataLoader)
bar = Bar('{}'.format(opt.expID), max=nIters)
for i, (input, targets, action, meta) in enumerate(dataLoader):
input_var = torch.autograd.Variable(input).float().cuda(opt.GPU)
target_var = []
for t in range(len(targets)):
target_var.append(
torch.autograd.Variable(targets[t]).float().cuda(opt.GPU))
z = []
for k in range(opt.numNoise):
noise = torch.autograd.Variable(
torch.randn((input_var.shape[0], 1, 64, 64))).cuda(opt.GPU)
z.append(noise)
output, samples = model(input_var, z, action)
pred_sample = maximumExpectedUtility(samples, criterion)
target = maximumExpectedUtility(target_var, criterion)
if opt.DEBUG >= 2:
gt = getPreds(target.cpu().numpy()) * 4
pred = getPreds((pred_sample.data).cpu().numpy()) * 4
debugger = Debugger()
img = (input[0].numpy().transpose(1, 2, 0) * 256).astype(
np.uint8).copy()
debugger.addImg(img)
debugger.addPoint2D(pred[0], (255, 0, 0))
debugger.addPoint2D(gt[0], (0, 0, 255))
debugger.showAllImg(pause=True)
loss = DiscoLoss(output, samples, target_var, criterion)
Loss.update(loss.item(), input.size(0))
Acc.update(
Accuracy((pred_sample.data).cpu().numpy(),
(target.data).cpu().numpy()))
if split == 'train':
optimizer.zero_grad()
loss.backward()
optimizer.step()
else:
input_ = input.cpu().numpy()
input_[0] = Flip(input_[0]).copy()
inputFlip_var = torch.autograd.Variable(
torch.from_numpy(input_).view(1, input_.shape[1], ref.inputRes,
ref.inputRes)).float().cuda(
opt.GPU)
_, samplesFlip = model(inputFlip_var, z, action)
pred_sample_flip = maximumExpectedUtility(samplesFlip, criterion)
outputFlip = ShuffleLR(
Flip((pred_sample_flip.data).cpu().numpy()[0])).reshape(
1, ref.nJoints, ref.outputRes, ref.outputRes)
output_ = old_div(((pred_sample.data).cpu().numpy() + outputFlip),
2)
preds.append(
finalPreds(output_, meta['center'], meta['scale'],
meta['rotate'])[0])
Bar.suffix = '{split} Epoch: [{0}][{1}/{2}]| Total: {total:} | ETA: {eta:} | Loss {loss.avg:.6f} | Acc {Acc.avg:.6f} ({Acc.val:.6f})'.format(
epoch,
i,
nIters,
total=bar.elapsed_td,
eta=bar.eta_td,
loss=Loss,
Acc=Acc,
split=split)
bar.next()
bar.finish()
return {'Loss': Loss.avg, 'Acc': Acc.avg}, preds
def step(split, epoch, opt, dataLoader, model, criterion, optimizer=None):
if split == 'train':
model.train()
else:
model.eval()
Loss, Acc, Mpjpe, Loss3D = AverageMeter(), AverageMeter(), AverageMeter(
), AverageMeter()
nIters = len(dataLoader)
bar = Bar('==>', max=nIters)
for i, (input, target2D, target3D, meta) in enumerate(dataLoader):
input_var = torch.autograd.Variable(input).float().cuda()
target2D_var = torch.autograd.Variable(target2D).float().cuda()
target3D_var = torch.autograd.Variable(target3D).float().cuda()
output = model(input_var)
reg = output[opt.nStack]
if opt.DEBUG >= 2:
gt = getPreds(target2D.cpu().numpy()) * 4
pred = getPreds((output[opt.nStack - 1].data).cpu().numpy()) * 4
debugger = Debugger()
debugger.addImg(
(input[0].numpy().transpose(1, 2, 0) * 256).astype(np.uint8))
debugger.addPoint2D(pred[0], (255, 0, 0))
debugger.addPoint2D(gt[0], (0, 0, 255))
debugger.showImg()
debugger.saveImg('debug/{}.png'.format(i))
# FusioCriterion is an Autograd funciton which can be called only once in the forward pass. So it is defined again in every iteration.
# Don't ask why.
loss = FusionCriterion(opt.regWeight, opt.varWeight)(reg, target3D_var)
Loss3D.update(loss.data[0], input.size(0))
for k in range(opt.nStack):
loss += criterion(output[k], target2D_var)
Loss.update(loss.data[0], input.size(0))
Acc.update(
Accuracy((output[opt.nStack - 1].data).cpu().numpy(),
(target2D_var.data).cpu().numpy()))
mpjpe, num3D = MPJPE((output[opt.nStack - 1].data).cpu().numpy(),
(reg.data).cpu().numpy(), meta)
if num3D > 0:
Mpjpe.update(mpjpe, num3D)
if split == 'train':
optimizer.zero_grad()
loss.backward()
optimizer.step()
Bar.suffix = '{split} Epoch: [{0}][{1}/{2}]| Total: {total:} | ETA: {eta:} | Loss {loss.avg:.6f} | Loss3D {loss3d.avg:.6f} | Acc {Acc.avg:.6f} | Mpjpe {Mpjpe.avg:.6f} ({Mpjpe.val:.6f})'.format(
epoch,
i,
nIters,
total=bar.elapsed_td,
eta=bar.eta_td,
loss=Loss,
Acc=Acc,
split=split,
Mpjpe=Mpjpe,
loss3d=Loss3D)
bar.next()
bar.finish()
return Loss.avg, Acc.avg, Mpjpe.avg, Loss3D.avg