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test_crossstore.py
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import time, math, os, sys, random
import cv2 as cv
import torch.nn as nn
import torch.nn.functional as F
import torch
import numpy as np
from torch.autograd import Variable
import argparse
from utils import *
from network_integrate import *
from tensorboardX import SummaryWriter
def main(args):
net = torch.load('checkpoint/scaling_wIntegral_wExposure_best-model.pth')['net']
eps = 1.0/255.0
# data_dir_hdr = os.path.join(args.data_dir, "hdr160320")
data_dir_ldr = os.path.join(args.data_dir, "imgs_101000055")
data_dir_ldr = os.path.join(args.data_dir, "multi-stores_160320")
log_dir = os.path.join(args.output_dir, "logs")
im_dir = os.path.join(args.output_dir, "im")
frames = [name for name in sorted(os.listdir(data_dir_ldr)) if os.path.isfile(os.path.join(data_dir_ldr, name))]
random.seed('111')
random.shuffle(frames)
frames_test = frames
testing_samples = len(frames_test)
print("\n\nData to be used:")
print("\t%d testing HDRs" % testing_samples)
test_ldr_paths = []
test_hdr_paths = []
test_exposure = []
test_label = []
# brackets = list(range(50,1050,50))
# for filename in frames_test:
# filename = filename.strip()
# namelist = filename.split('_')
# for bb in brackets:
# test_hdr_paths.append(f'{data_dir_hdr}/{filename}')
# ldr_name = f'{filename[:-4]}_{bb}.png'
# test_ldr_paths.append(f'{data_dir_ldr}/{ldr_name}')
# test_exposure.append(bb)
# test_label.append(float(namelist[3][:-4]))
for filename in frames_test:
filename = filename.strip()
namelist = filename.split('_')
ldr_name = filename
test_ldr_paths.append(f'{data_dir_ldr}/{ldr_name}')
test_hdr_paths.append(f'{data_dir_ldr}/{ldr_name}')
bb = namelist[6]
test_exposure.append(bb)
test_label.append(float(namelist[5]))
test_dataset = LDR2HDR2Illuminance(test_ldr_paths, test_hdr_paths, test_exposure, test_label, args)
test_dataloader = torch.utils.data.DataLoader(test_dataset, batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers, pin_memory=True, drop_last=True)
dphi = 2 * np.pi/args.width
dtheta = np.pi/args.height
phi = torch.zeros((args.height//2, args.width))
theta = torch.zeros((args.height//2, args.width))
for y in range(args.height//2):
for x in range(args.width):
phi[y,x] = (x / args.width) * 2 * np.pi
theta[y,x] = (y / args.height) * np.pi
phi = phi.unsqueeze(0).repeat(args.batch_size,1,1).cuda()
theta = theta.unsqueeze(0).repeat(args.batch_size,1,1).cuda()
criterion_hdr = nn.MSELoss().cuda()
criterion_reg = nn.MSELoss().cuda()
test_loss = 0
test_loss_reg = 0
test_loss_hdr = 0
test_count = 0
test_correct_025 = 0
test_correct_010 = 0
for _, batch_data in enumerate(test_dataloader):
net.eval()
with torch.no_grad():
ldr_test = batch_data['ldr'].cuda()
hdr_test = batch_data['hdr'].cuda()
label_test = batch_data['label'].cuda() / 1000.0
exposure_test = torch.LongTensor(batch_data['exposure']).reshape(ldr_test.shape[0],1)
exposure_test = torch.zeros(args.batch_size,20).scatter_(1,exposure_test/50-1,1)
exposure_test = exposure_test.cuda()
hdr_pred_test, label_pred_test = net(ldr_test, exposure_test)
# img = 179 * (hdr_pred_test[:,0,:,:] * 0.2126 + hdr_pred_test[:,1,:,:] * 0.7152 + hdr_pred_test[:,2,:,:] * 0.0722)
# output = img[:,0:args.height//2,:].mul(torch.sin(theta)).mul(torch.cos(theta)) * dphi * dtheta
#
# label_pred_test = output.sum([1,2]) / 1000.0
# for i in range(16):
# cv.imwrite(f'test{i}.hdr', hdr_pred_test[i,:,:,:].cpu().numpy().transpose(1,2,0)[:,:,::-1])
# cv.imwrite(f'gt{i}.hdr', hdr_test[i,:,:,:].cpu().numpy().transpose(1,2,0)[:,:,::-1])
label_pred_test = label_pred_test.squeeze()
label_pred_test = label_pred_test * 1.1964
# loss_hdr_test = criterion_hdr(torch.log(hdr_pred_test+eps), torch.log(hdr_test+eps))
# test_loss_hdr += loss_hdr_test.item()
loss_reg_test = criterion_reg(label_pred_test, label_test.float())
test_loss_reg += loss_reg_test.item()
# loss_test = loss_hdr_test + loss_reg_test
# test_loss += loss_test.item()
test_count += label_test.shape[0]
# print(label_pred_test)
# print(label_test)
test_correct_025 += ( (abs(label_pred_test-label_test) / label_test) <= 0.25 ).sum().item()
test_correct_010 += ( (abs(label_pred_test-label_test) / label_test) <= 0.10 ).sum().item()
print('25% Accuracy', ( (abs(label_pred_test-label_test) / label_test) <= 0.25 ).sum().item()/label_test.shape[0])
print('10% Accuracy', ( (abs(label_pred_test-label_test) / label_test) <= 0.10 ).sum().item()/label_test.shape[0])
print('\ntest: total loss: {0:.2f} hdr loss: {1:.2f} reg loss: {2:.2f} 25%Accuracy {3:.4f} 10%Accuracy {4:.4f}'.format(
# test_loss / test_count,
# test_loss_hdr / test_count,
test_loss_reg / test_count,
test_correct_025 / test_count,
test_loss_reg / test_count,
test_correct_025 / test_count,
test_correct_010 / test_count))
#####################################################################################################################
# path = 'dataset/HDR/data/2020-8-yue/2020-08-03/afternoon_kitchen_0803'
# batch_img = []
# batch_exposure = []
# bracket = list(range(50,1050,50))
# for i in range(1,17):
# print(f'{path}/{i}.jpg')
# img = cv.imread(f'{path}/{i}.jpg',-1)[:,:,::-1]
## gt = cv.imread('dataset/hdr/0706_126_0_1330.hdr',-1)[:,:,::-1]
# img = cv.resize(img, (args.width, args.height), interpolation=cv.INTER_CUBIC)
# img = img.transpose(2,0,1) / 255.0
# img = torch.Tensor(img)
# batch_img.append(img.unsqueeze(0))
## exposure = bracket[i]
## batch_exposure.append(exposure)
#
# batch_img = torch.cat(batch_img, dim=0).cuda()
## batch_exposure = torch.Tensor(batch_exposure).unsqueeze(1).cuda()
# print(batch_img.shape)
## print(batch_exposure.shape)
## batch_img = torch.Tensor(batch_img).unsqueeze(0).cuda()
#
#
# with torch.no_grad():
# hdr_pred, label_pred = net(batch_img, None)
#
# for i in range(16):
# print(hdr_pred[i+1,:,:,:].shape)
# cv.imwrite(f'{path}/hdr_pred_{i+1}.hdr', hdr_pred[i,:,:,:].cpu().numpy().transpose(1,2,0)[:,:,::-1])
#
#
## cv.imwrite('ldr.jpg', img.cpu().squeeze(0).numpy().transpose(1,2,0) * 255.0)
## cv.imwrite('gt.hdr', gt)
#
# import matplotlib.pyplot as plt
# plt.imsave('out_new.png', out1.squeeze(0).squeeze(0).cpu().numpy())
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("--network", help="network architecture to use", default='deep', choices=['original', 'deep'])
parser.add_argument("--height", default=160, type=int, help="The height of input image")
parser.add_argument("--width", default=320, type=int, help="The width of input image")
parser.add_argument("--data_dir",default="dataset", help='Path to processed dataset')
parser.add_argument("--output_dir", default="training_output", help='Path to output directory, for weights and intermediate results')
parser.add_argument("--rand_data", default=True, help='Random shuffling of training data')
parser.add_argument("--batch_size", default=8, help='Batch size for training')
parser.add_argument("--num_epochs", default=100, help='Number of training epochs')
parser.add_argument("--lr", default=1e-4, help='Learning rate of HDR reconstruction network')
# parser.add_argument("--lr_reg", default=5e-3, help='Learning rate of spherical regression network')
parser.add_argument("--num_workers", default=4, help='Number of workers')
parser.add_argument("--hdr", default=True, help='Whether or not include illuminance loss')
parser.add_argument("--reg", default=False, help='Whether or not include illuminance loss')
parser.add_argument("--bandwidth", default=30, type=int, help="the bandwidth of the S2 signal", required=False)
parser.add_argument("--local_rank", default=0, type=int, help="local_rank")
parser.add_argument("--adding_exposure", default=False, type=bool, help="Whether or not encode exposure information")
parser.add_argument("--restore", default=False, type=bool, help="")
args = parser.parse_args()
main(args)