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,
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_total_loss = 0.0
running_con1app_loss = 0.0
running_con2app_loss = 0.0
running_con1kp_loss = 0.0
running_con2kp_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():
output_v1, output_v2, app_v1, app_v2, app_v1_est, app_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(vid1, output_video_dir, batch_idx + 1, 1, 'input')
convert_to_vid(vid2, output_video_dir, batch_idx + 1, 2, 'input')
convert_to_vid(output_v1, output_video_dir, batch_idx + 1, 1,
'output')
convert_to_vid(output_v2, output_video_dir, batch_idx + 1, 2,
'output')
# loss
con1app_loss = criterion(app_v1, app_v1_est)
con2app_loss = criterion(app_v2, app_v2_est)
con1kp_loss = criterion(kp_v1, kp_v1_est)
con2kp_loss = criterion(kp_v2, kp_v2_est)
recon1_loss = criterion(output_v1, vid1)
recon2_loss = criterion(output_v2, vid2)
loss = con1app_loss + con2app_loss + con1kp_loss + con2kp_loss + recon1_loss + recon2_loss
running_total_loss += loss.item()
running_con1app_loss += con1app_loss.item()
running_con2app_loss += con2app_loss.item()
running_con1kp_loss += con1kp_loss.item()
running_con2kp_loss += con2kp_loss.item()
running_recon1_loss += recon1_loss.item()
running_recon2_loss += recon2_loss.item()
if (batch_idx + 1) % 10 == 0:
print(
'\tBatch {}/{} Loss:{} con1app:{} con2app:{} con1kp:{} con2kp:{} recon1:{} recon2:{}'
.format(batch_idx + 1, len(testloader), "{0:.5f}".format(loss),
"{0:.5f}".format(con1app_loss),
"{0:.5f}".format(con2app_loss),
"{0:.5f}".format(con1kp_loss),
"{0:.5f}".format(con2kp_loss),
"{0:.5f}".format(recon1_loss),
"{0:.5f}".format(recon2_loss)))
print(
'Testing Complete Loss:{} con1app:{} con2app:{} con1kp:{} con2kp:{} recon1:{} recon2:{}'
.format("{0:.5f}".format(
(running_total_loss / len(testloader))), "{0:.5f}".format(
(running_con1app_loss / len(testloader))), "{0:.5f}".format(
(running_con2app_loss / len(testloader))),
"{0:.5f}".format(
(running_con1kp_loss / len(testloader))), "{0:.5f}".format(
(running_con2kp_loss / len(testloader))),
"{0:.5f}".format(
(running_recon1_loss / len(testloader))), "{0:.5f}".format(
(running_recon2_loss / len(testloader)))))