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validation_stflow_ds.py
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import numpy as np
import torch
import random
import PIL
import os
import torchvision
import torch.nn.functional as F
from torchvision import transforms
import sys
sys.path.append("../../")
from os.path import exists, join
import matplotlib.pyplot as plt
import pdb
def validate(srmodel, stmodel, val_loader, exp_name, logstep, args):
random.seed(0)
torch.manual_seed(0)
np.random.seed(0)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
color = 'inferno' if args.trainset == 'era5' else 'viridis'
state=None
nll_list=[]
srmodel.eval()
stmodel.eval()
with torch.no_grad():
for batch_idx, item in enumerate(val_loader):
x = item[0].to(args.device)
x_past, x_for = x[:,:, :2,...], x[:,:,2:,...]
x_resh = F.interpolate(x[:,0,...], (x_for.shape[3]//args.s,x_for.shape[4]//args.s))
# split time series into lags and prediction window
x_past_lr, x_for_lr = x_resh[:,:2, ...].squeeze(1), x_resh[:,2:,...]
# reshape into correct format for 3D convolutions - but now i dont use them anymore? xD
x_past_lr = x_past_lr.unsqueeze(1).contiguous().float().to(args.device)
x_for_lr = x_for_lr.unsqueeze(1).contiguous().float().to(args.device)
# run forecasting method
z, state, nll_st = stmodel.forward(x=x_for_lr, x_past=x_past_lr, state=state)
# run SR model
x_for_hat_lr, _ , _= stmodel._predict(x_past_lr.cuda(), state)
z, nll_sr = srmodel.forward(x_hr=x_for.squeeze(1), xlr=x_for_hat_lr.squeeze(1))
# Generative loss
nll_list.append(nll_st.mean().detach().cpu().numpy())
if batch_idx == 50:
break
# ---------------------- Evaluate Predictions---------------------- #
# evalutae for different temperatures (just for last batch, perhaps change l8er)
mu0, *_ = stmodel._predict(x_past_lr, state, eps=0)
mu05, *_ = stmodel._predict(x_past_lr, state, eps=0.5)
mu08, *_ = stmodel._predict(x_past_lr, state, eps=0.8)
mu1, *_ = stmodel._predict(x_past_lr, state, eps=1)
# super-resolve
mu0, *_ = srmodel(x_hr=x_for, xlr=mu1.squeeze(1), reverse=True, eps=0)
mu05, *_ = srmodel(x_hr=x_for, xlr=mu1.squeeze(1), reverse=True, eps=0.5)
mu08, *_ = srmodel(x_hr=x_for, xlr=mu1.squeeze(1), reverse=True, eps=0.8)
mu1, *_ = srmodel(x_hr=x_for, xlr=mu1.squeeze(1), reverse=True, eps=1.0)
savedir = "{}/snapshots/validationset_{}/".format(exp_name, args.trainset)
os.makedirs(savedir, exist_ok=True)
# grid_ground_truth = torchvision.utils.make_grid(x_for_lr[0:9, :, :, :].squeeze(1).cpu(), nrow=3)
# plt.figure()
# plt.imshow(grid_ground_truth.permute(1, 2, 0)[:,:,0].contiguous(), cmap=color)
# plt.axis('off')
# plt.title("Frame at t (valid)")
# plt.savefig(savedir + "x_t_step_{}_valid.png".format(logstep), dpi=300)
# # visualize past frames the prediction is based on (context)
# grid_past = torchvision.utils.make_grid(x_past_lr[0:9, -1, :, :].cpu(), nrow=3)
# plt.figure()
# plt.imshow(grid_past.permute(1, 2, 0)[:,:,0].contiguous(), cmap=color)
# plt.axis('off')
# plt.title("Frame at t (valid)")
# plt.savefig(savedir + "_x_t_step_{}_valid.png".format(logstep), dpi=300)
grid_mu0 = torchvision.utils.make_grid(mu0[0:9,:,:,:].squeeze(1).cpu(), nrow=3)
plt.figure()
plt.imshow(grid_mu0.permute(1, 2, 0)[:,:,0].contiguous(), cmap=color)
plt.axis('off')
plt.title("Prediction at t (valid), mu=0")
plt.savefig(savedir + "mu_0_logstep_{}_valid.png".format(logstep), dpi=300)
# grid_mu05 = torchvision.utils.make_grid(mu05[0:9,:,:,:].squeeze(1).cpu(), nrow=3)
# plt.figure()
# plt.imshow(grid_mu0.permute(1, 2, 0)[:,:,0].contiguous(), cmap=color)
# plt.axis('off')
# plt.title("Prediction at t (valid), mu=0.5")
# plt.savefig(savedir + "mu_0.5_logstep_{}_valid.png".format(logstep), dpi=300)
grid_mu08 = torchvision.utils.make_grid(mu08[0:9,:,:,:].squeeze(1).cpu(), nrow=3)
plt.figure()
plt.imshow(grid_mu08.permute(1, 2, 0)[:,:,0].contiguous(), cmap=color)
plt.axis('off')
plt.title("Prediction at t (valid), mu=0.8")
plt.savefig(savedir + "mu_0.8_logstep_{}_valid.png".format(logstep), dpi=300)
# grid_mu1 = torchvision.utils.make_grid(mu1[0:9,:,:,:].squeeze(1).cpu(), nrow=3)
# plt.figure()
# plt.imshow(grid_mu1.permute(1, 2, 0)[:,:,0].contiguous(), cmap=color)
# plt.axis('off')
# plt.title("Prediction at t (valid), mu=1.0")
# plt.savefig(savedir + "mu_1_logstep_{}_valid.png".format(logstep), dpi=300)
abs_err = torch.abs(mu1 - x_for.squeeze(1))
grid_abs_error = torchvision.utils.make_grid(abs_err[0:9,:,:,:].cpu())
plt.figure()
plt.imshow(grid_abs_error.permute(1, 2, 0)[:,:,0].contiguous(), cmap=color)
plt.axis('off')
plt.title("Abs Err at t (valid), mu=1.0")
plt.savefig(savedir + "abs_error_logstep_{}_valid.png".format(logstep), dpi=300)
print("Average Validation Neg. Log Probability Mass:", np.mean(nll_list))
return np.mean(nll_list)