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import torch as th
from torch import nn
from torch.utils.data import DataLoader
import numpy as np
from einops import rearrange, repeat, reduce
from scripts.utils.configuration import Configuration
from model.loci import Loci
import time
import lpips
from skimage.metrics import structural_similarity as ssimloss
from skimage.metrics import peak_signal_noise_ratio as psnrloss
def validation_adept(valloader: DataLoader, net: Loci, cfg: Configuration, device):
# memory
mseloss = nn.MSELoss()
avgloss = 0
start_time = time.time()
with th.no_grad():
for i, input in enumerate(valloader):
# get input frame and target frame
tensor = input[0].float().to(device)
background_fix = input[1].to(device)
# apply skip frames
tensor = tensor[:,range(0, tensor.shape[1], cfg.defaults.skip_frames)]
sequence_len = tensor.shape[1]
# initial frame
input = tensor[:,0]
target = th.clip(tensor[:,0], 0, 1)
error_last = None
# placehodlers
mask_cur = None
mask_last = None
rawmask_last = None
position_last = None
gestalt_last = None
priority_last = None
gt_positions_target = None
slots_occlusionfactor = None
# loop through frames
for t_index,t in enumerate(range(-cfg.defaults.teacher_forcing, sequence_len-1)):
# move to next frame
t_run = max(t, 0)
input = tensor[:,t_run]
target = th.clip(tensor[:,t_run+1], 0, 1)
# obtain prediction
(
output_next,
position_next,
gestalt_next,
priority_next,
mask_next,
rawmask_next,
object_next,
background,
slots_occlusionfactor,
output_cur,
position_cur,
gestalt_cur,
priority_cur,
mask_cur,
rawmask_cur,
object_cur,
position_encoder_cur,
slots_bounded,
slots_partially_occluded_cur,
slots_occluded_cur,
slots_partially_occluded_next,
slots_occluded_next,
slots_closed,
output_hidden,
largest_object,
rawmask_hidden,
object_hidden
) = net(
input,
error_last,
mask_last,
rawmask_last,
position_last,
gestalt_last,
priority_last,
background_fix,
slots_occlusionfactor,
reset = (t == -cfg.defaults.teacher_forcing),
evaluate=True,
warmup = (t < 0),
shuffleslots = False,
reset_mask = (t <= 0),
allow_spawn = True,
show_hidden = False,
clean_slots = (t <= 0),
)
# 1. Track error
if t >= 0:
loss = mseloss(output_next, target)
avgloss += loss.item()
# 2. Remember output
mask_last = mask_next.clone()
rawmask_last = rawmask_next.clone()
position_last = position_next.clone()
gestalt_last = gestalt_next.clone()
priority_last = priority_next.clone()
# 3. Error for next frame
# background error
bg_error_cur = th.sqrt(reduce((input - background)**2, 'b c h w -> b 1 h w', 'mean')).detach()
bg_error_next = th.sqrt(reduce((target - background)**2, 'b c h w -> b 1 h w', 'mean')).detach()
# prediction error
error_next = th.sqrt(reduce((target - output_next)**2, 'b c h w -> b 1 h w', 'mean')).detach()
error_next = th.sqrt(error_next) * bg_error_next
error_last = error_next.clone()
print(f"Validation loss: {avgloss / len(valloader.dataset):.2e}, Time: {time.time() - start_time}")
pass
def validation_clevrer(valloader: DataLoader, net: Loci, cfg: Configuration, device):
# memory
mseloss = nn.MSELoss()
lpipsloss = lpips.LPIPS(net='vgg').to(device)
avgloss_mse = 0
avgloss_lpips = 0
avgloss_psnr = 0
avgloss_ssim = 0
start_time = time.time()
burn_in_length = 6
rollout_length = 42
with th.no_grad():
for i, input in enumerate(valloader):
# get input frame and target frame
tensor = input[0].float().to(device)
background_fix = input[1].to(device)
# apply skip frames
tensor = tensor[:,range(0, tensor.shape[1], cfg.defaults.skip_frames)]
sequence_len = tensor.shape[1]
# initial frame
input = tensor[:,0]
target = th.clip(tensor[:,0], 0, 1)
error_last = None
# placehodlers
mask_cur = None
mask_last = None
rawmask_last = None
position_last = None
gestalt_last = None
priority_last = None
gt_positions_target = None
slots_occlusionfactor = None
# loop through frames
for t_index,t in enumerate(range(-cfg.defaults.teacher_forcing, min(burn_in_length + rollout_length-1, sequence_len-1))):
# move to next frame
t_run = max(t, 0)
input = tensor[:,t_run]
target = th.clip(tensor[:,t_run+1], 0, 1)
if t_run >= burn_in_length:
blackout = th.tensor((np.random.rand(valloader.batch_size) < 0.2)[:,None,None,None]).float().to(device)
input = blackout * (input * 0) + (1-blackout) * input
error_last = blackout * (error_last * 0) + (1-blackout) * error_last
# obtain prediction
(
output_next,
position_next,
gestalt_next,
priority_next,
mask_next,
rawmask_next,
object_next,
background,
slots_occlusionfactor,
output_cur,
position_cur,
gestalt_cur,
priority_cur,
mask_cur,
rawmask_cur,
object_cur,
position_encoder_cur,
slots_bounded,
slots_partially_occluded_cur,
slots_occluded_cur,
slots_partially_occluded_next,
slots_occluded_next,
slots_closed,
output_hidden,
largest_object,
rawmask_hidden,
object_hidden
) = net(
input,
error_last,
mask_last,
rawmask_last,
position_last,
gestalt_last,
priority_last,
background_fix,
slots_occlusionfactor,
reset = (t == -cfg.defaults.teacher_forcing),
evaluate=True,
warmup = (t < 0),
shuffleslots = False,
reset_mask = (t <= 0),
allow_spawn = True,
show_hidden = False,
clean_slots = (t <= 0),
)
# 1. Track error
if t >= 0:
loss_mse = mseloss(output_next, target)
loss_ssim = np.sum([ssimloss(output_next[i].cpu().numpy(), target[i].cpu().numpy(), channel_axis=0,gaussian_weights=True,sigma=1.5,use_sample_covariance=False,data_range=1) for i in range(output_next.shape[0])]),
loss_psnr = np.sum([psnrloss(output_next[i].cpu().numpy(), target[i].cpu().numpy(), data_range=1) for i in range(output_next.shape[0])]),
loss_lpips = th.sum(lpipsloss(output_next*2-1, target*2-1))
avgloss_mse += loss_mse.item()
avgloss_ssim += loss_ssim[0].item()
avgloss_psnr += loss_psnr[0].item()
avgloss_lpips += loss_lpips.item()
# 2. Remember output
mask_last = mask_next.clone()
rawmask_last = rawmask_next.clone()
position_last = position_next.clone()
gestalt_last = gestalt_next.clone()
priority_last = priority_next.clone()
# 3. Error for next frame
# background error
bg_error_next = th.sqrt(reduce((target - background)**2, 'b c h w -> b 1 h w', 'mean')).detach()
# prediction error
error_next = th.sqrt(reduce((target - output_next)**2, 'b c h w -> b 1 h w', 'mean')).detach()
error_next = th.sqrt(error_next) * bg_error_next
error_last = error_next.clone()
print(f"MSE loss: {avgloss_mse / len(valloader.dataset):.2e}, LPIPS loss: {avgloss_lpips / len(valloader.dataset):.2e}, PSNR loss: {avgloss_psnr / len(valloader.dataset):.2e}, SSIM loss: {avgloss_ssim / len(valloader.dataset):.2e}, Time: {time.time() - start_time}")
pass
|
