def test():
"""
Function to carry out the testing/validation loop for the Full Network for a single epoch.
:return: None
"""
running_recon_loss = 0.0
model.eval()
for batch_idx, (vp_diff, vid1, vid2) in enumerate(testloader):
vp_diff = vp_diff.to(device)
vid1, vid2 = vid1.to(device), vid2.to(device)
img1, img2 = get_first_frame(vid1), get_first_frame(vid2)
img1, img2 = img1.to(device), img2.to(device)
with torch.no_grad():
gen_v2, rep_v1 = model(vp_diff=vp_diff, vid1=vid1, img2=img2)
# save videos
convert_to_vid(tensor=vid1,
output_dir=output_video_dir,
batch_num=batch_idx + 1,
view=1,
item_type='input')
convert_to_vid(tensor=vid2,
output_dir=output_video_dir,
batch_num=batch_idx + 1,
view=2,
item_type='input')
convert_to_vid(tensor=gen_v2,
output_dir=output_video_dir,
batch_num=batch_idx + 1,
view=2,
item_type='output')
convert_to_vid(tensor=rep_v1[-1],
output_dir=output_video_dir,
batch_num=batch_idx + 1,
view=1,
item_type='rep')
# loss
recon_loss = criterion(gen_v2, vid2)
# reconstruction losses for videos gen from features and same view
# recon3_loss = criterion(recon_v1, vid1)
# recon4_loss = criterion(recon_v2, vid2)
loss = recon_loss
running_recon_loss += recon_loss.item()
if (batch_idx + 1) % 10 == 0:
print('\tBatch {}/{} Recon Loss:{}'.format(
batch_idx + 1, len(testloader), "{0:.5f}".format(recon_loss)))
print('Testing Complete Recon Loss:{}'.format("{0:.5f}".format(
(running_recon_loss / len(testloader)))))
def test():
"""
Function to carry out the testing/validation loop for the Full Network for a single epoch.
:return: None
"""
running_recon_loss = 0.0
running_vp_loss = 0.0
model.eval()
for batch_idx, (vp_diff, vid1, vid2) in enumerate(testloader):
vp_diff = vp_diff.type(torch.FloatTensor).to(device)
vid1, vid2 = vid1.to(device), vid2.to(device)
img1, img2 = get_first_frame(vid1), get_first_frame(vid2)
img1, img2 = img1.to(device), img2.to(device)
with torch.no_grad():
gen_v2, vp_est, est_kp_v2 = model(vp_diff=vp_diff, vid1=vid1, img2=img2)
# save videos
convert_to_vid(tensor=vid1, output_dir=output_video_dir,
batch_num=batch_idx + 1, view=1, item_type='input')
convert_to_vid(tensor=vid2, output_dir=output_video_dir,
batch_num=batch_idx + 1, view=2, item_type='input')
convert_to_vid(tensor=gen_v2, output_dir=output_video_dir,
batch_num=batch_idx + 1, view=2, item_type='output')
convert_to_vid(tensor=est_kp_v2, output_dir=output_video_dir,
batch_num=batch_idx + 1, view=2, item_type='kp')
export_vps(vp_gt=vp_diff, vp_est=vp_est, output_dir=output_video_dir, batch_num=batch_idx + 1)
# loss
recon_loss = criterion(gen_v2, vid2)
vp_loss = criterion(vp_est, vp_diff)
loss = recon_loss + vp_loss
running_recon_loss += recon_loss.item()
running_vp_loss += vp_loss.item()
if (batch_idx + 1) % 10 == 0:
print('\tBatch {}/{} ReconLoss:{} VPLoss:{}'.format(
batch_idx + 1,
len(testloader),
"{0:.5f}".format(recon_loss),
"{0:.5f}".format(vp_loss)))
print('Testing Complete ReconLoss:{} VPLoss:{}'.format(
"{0:.5f}".format((running_recon_loss / len(testloader))),
"{0:.5f}".format((running_vp_loss / len(testloader)))))
def test():
"""
Function to carry out the testing/validation loop for the Full Network for a single epoch.
:return: None
"""
running_total_loss = 0.0
running_con1_loss = 0.0
running_con2_loss = 0.0
running_con3_loss = 0.0
running_con4_loss = 0.0
running_recon1_loss = 0.0
running_recon2_loss = 0.0
model.eval()
for batch_idx, (vp_diff, vid1, vid2) in enumerate(testloader):
vp_diff = vp_diff.to(device)
vid1, vid2 = vid1.to(device), vid2.to(device)
img1, img2 = get_first_frame(vid1), get_first_frame(vid2)
img1, img2 = img1.to(device), img2.to(device)
with torch.no_grad():
gen_v1, gen_v2, rep_v1, rep_v2, rep_v1_est, rep_v2_est, kp_v1, kp_v2, kp_v1_est, kp_v2_est = model(
vp_diff=vp_diff, vid1=vid1, vid2=vid2, img1=img1, img2=img2)
# save videos
convert_to_vid(tensor=vid1,
output_dir=output_video_dir,
batch_num=batch_idx + 1,
view=1,
item_type='input')
convert_to_vid(tensor=vid2,
output_dir=output_video_dir,
batch_num=batch_idx + 1,
view=2,
item_type='input')
convert_to_vid(tensor=gen_v1,
output_dir=output_video_dir,
batch_num=batch_idx + 1,
view=1,
item_type='output')
convert_to_vid(tensor=gen_v2,
output_dir=output_video_dir,
batch_num=batch_idx + 1,
view=2,
item_type='output')
convert_to_vid(tensor=rep_v1,
output_dir=output_video_dir,
batch_num=batch_idx + 1,
view=1,
item_type='rep')
convert_to_vid(tensor=rep_v2,
output_dir=output_video_dir,
batch_num=batch_idx + 1,
view=2,
item_type='rep')
convert_to_vid(tensor=rep_v1_est,
output_dir=output_video_dir,
batch_num=batch_idx + 1,
view=1,
item_type='rep_est')
convert_to_vid(tensor=rep_v2_est,
output_dir=output_video_dir,
batch_num=batch_idx + 1,
view=2,
item_type='rep_est')
convert_to_vid(tensor=kp_v1,
output_dir=output_video_dir,
batch_num=batch_idx + 1,
view=1,
item_type='kp')
convert_to_vid(tensor=kp_v2,
output_dir=output_video_dir,
batch_num=batch_idx + 1,
view=2,
item_type='kp')
convert_to_vid(tensor=kp_v1_est,
output_dir=output_video_dir,
batch_num=batch_idx + 1,
view=1,
item_type='kp_est')
convert_to_vid(tensor=kp_v2_est,
output_dir=output_video_dir,
batch_num=batch_idx + 1,
view=2,
item_type='kp_est')
# loss
# consistency losses between video features
con1_loss = criterion(rep_v1, rep_v1_est)
con2_loss = criterion(rep_v2, rep_v2_est)
con3_loss = criterion(kp_v1, kp_v1_est)
con4_loss = criterion(kp_v2, kp_v2_est)
# reconstruction losses for videos gen from new view
recon1_loss = criterion(gen_v1, vid1)
recon2_loss = criterion(gen_v2, vid2)
loss = CON_LOSS_W * (con1_loss + con2_loss + con3_loss +
con4_loss) + recon1_loss + recon2_loss
running_total_loss += loss.item()
running_con1_loss += con1_loss.item()
running_con2_loss += con2_loss.item()
running_con3_loss += con3_loss.item()
running_con4_loss += con4_loss.item()
running_recon1_loss += recon1_loss.item()
running_recon2_loss += recon2_loss.item()
if (batch_idx + 1) % 10 == 0:
print(
'\tBatch {}/{} Loss:{} con1:{} con2:{} con3:{} con4:{} recon1:{} recon2:{}'
.format(batch_idx + 1, len(testloader), "{0:.5f}".format(loss),
"{0:.5f}".format(con1_loss),
"{0:.5f}".format(con2_loss),
"{0:.5f}".format(con3_loss),
"{0:.5f}".format(con4_loss),
"{0:.5f}".format(recon1_loss),
"{0:.5f}".format(recon2_loss)))
print(
'Testing Complete Loss:{} con1:{} con2:{} con3:{} con4:{} recon1:{} recon2:{}'
.format("{0:.5f}".format(
(running_total_loss / len(testloader))), "{0:.5f}".format(
(running_con1_loss / len(testloader))), "{0:.5f}".format(
(running_con2_loss / len(testloader))), "{0:.5f}".format(
(running_con3_loss / len(testloader))),
"{0:.5f}".format((running_con4_loss / len(testloader))),
"{0:.5f}".format(
(running_recon1_loss / len(testloader))), "{0:.5f}".format(
(running_recon2_loss / len(testloader)))))
