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 = 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')
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)))))
# print(x.requires_grad_()) # x = x.requires_grad_() # print(x) # x = [1,2,3,4] # x.reverse() # print(x) # # del x # print(x) import torch from utils.modelIOFuncs import export_vps vp_gt = torch.randn(4) vp_est = torch.randn(4) export_vps(vp_gt=vp_gt, vp_est=vp_est, output_dir='./', batch_num=1) import torch x = torch.zeros(3, 4, 5, 6) y = torch.sum(x, dim=1, keepdim=True) print(x.size()) print(y.size())