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utility.py
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import os
import math
import time
import datetime
import glob
import numpy as np
import scipy.misc as misc
from functools import reduce
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import torch
import torch.optim as optim
import torch.optim.lr_scheduler as lrs
from torch.utils import data
#from torchvision import datasets, transforms
import common
from warmup_scheduler import GradualWarmupScheduler
import cv2
class EBSD_Ti64DIC_dataset(data.Dataset):
"""
Custom Dataset compatible with torch.utils.data.DataLoader
"""
def __init__(self, args, root_lr, root_hr, upsample_2d= True, is_Train=True):
#import pdb; pdb.set_trace()
self.args = args
self.root_lr = root_lr
self.root_hr = root_hr
#self.data_lr_files = sorted(glob.glob(f'{root_lr}/*.npy')) if not is_Train else None
self.data_lr_files = sorted(glob.glob(f'{root_lr}/*.npy')) if root_lr is not None else None
self.data_hr_files = sorted(glob.glob(f'{root_hr}/*.npy'))
self.is_Train = is_Train
self.upsample_2D = upsample_2d
def load_file(self, filepath_lr, filepath_hr):
# Load Numpy files
if self.is_Train:
hr = np.load(f'{filepath_hr}')
lr = np.load(f'{filepath_lr}') if filepath_lr is not None else None
#lr, hr = self._get_patch(hr, self.args.patch_size)
lr, hr = self._get_patch(lr, hr, self.args.patch_size)
else:
#hr = np.load(f'{filepath_hr}')
#lr, hr = self._get_patch(hr, patch_size=128)
lr = np.load(f'{filepath_lr}')
hr = np.load(f'{filepath_hr}')
lr, hr = common.set_channel([lr, hr], self.args.n_colors)
lr, hr = common.np2Tensor([lr, hr], self.args.rgb_range)
filename_hr = os.path.basename(filepath_hr)
filename_hr = os.path.splitext(filename_hr)[0]
#filename_lr = os.path.basename(filepath_lr)
#filename_lr = os.path.splitext(filename_lr)[0]
return (lr, hr,filename_hr, filename_hr)
def __getitem__(self, index):
#filepath_lr = self.data_lr_files[index] if not self.is_Train else None
filepath_lr = self.data_lr_files[index] if self.data_lr_files is not None else None
filepath_hr = self.data_hr_files[index]
return self.load_file(filepath_lr, filepath_hr)
def __len__(self):
return len(self.data_hr_files)
def _get_patch(self, lr, hr, patch_size):
scale = self.args.scale
if self.upsample_2D:
lr, hr = common.get_patch( lr, hr, patch_size, scale)
else:
lr, hr = common.get_patch_1D(lr, hr, patch_size, scale)
#lr, hr = common.augment([lr, hr])
#lr = common.add_noise(lr, self.args.noise)
return lr, hr
class EBSD_Ti64DIC_Test_dataset(data.Dataset):
"""
Custom Dataset compatible with torch.utils.data.DataLoader
"""
def __init__(self, args, root_lr, root_hr, is_Train=True):
#import pdb; pdb.set_trace()
self.args = args
self.root_lr = root_lr
self.root_hr = root_hr
self.data_hr_files = sorted(glob.glob(f'{root_hr}/*.npy'))
self.is_Train = is_Train
def load_file(self, filepath_hr):
# Load Numpy files
hr = np.load(f'{filepath_hr}')
#lr = common.downsample(hr, self.args.scale)
hr = hr[0:40,0:40,:]
lr = hr
lr, hr = common.set_channel([lr, hr], self.args.n_colors)
lr, hr = common.np2Tensor([lr, hr], self.args.rgb_range)
filename_hr = os.path.basename(filepath_hr)
filename_hr = os.path.splitext(filename_hr)[0]
return (lr, hr,filename_hr, filename_hr)
def __getitem__(self, index):
filepath_hr = self.data_hr_files[index]
return self.load_file(filepath_hr)
def __len__(self):
return len(self.data_hr_files)
def _get_patch(self, hr):
patch_size = self.args.patch_size
scale = self.args.scale
lr, hr = common.get_patch( hr, patch_size, scale)
#lr, hr = common.augment([lr, hr])
#lr = common.add_noise(lr, self.args.noise)
return lr, hr
class Misorientation_dist:
def __init__(self, args, dist_type = 'rot_dist', act = None, syms_req = True):
syms_type = args.syms_type
print(f'Parameters for Misorientation Distance')
print('+++++++++++++++++++++++++++++++++++++++++')
print(f'dist_type: {dist_type} activation:{act} symmetry type:{syms_type} Symmetry:{syms_req}')
print('+++++++++++++++++++++++++++++++++++++++++++++++++')
from mat_sci_torch_quats.losses import ActAndLoss, Loss
from mat_sci_torch_quats.symmetries import hcp_syms, fcc_syms
if syms_req:
if syms_type == 'HCP':
syms = hcp_syms
elif syms_type == 'FCC':
syms = fcc_syms
else:
syms = None
self.act_loss = ActAndLoss(act, Loss(dist_func=dist_type, syms=syms), quat_dim=-1)
def __call__(self, sr, hr):
loss = self.act_loss(sr, hr)
return loss
class timer():
def __init__(self):
self.acc = 0
self.tic()
def tic(self):
self.t0 = time.time()
def toc(self):
return time.time() - self.t0
def hold(self):
self.acc += self.toc()
def release(self):
ret = self.acc
self.acc = 0
return ret
def reset(self):
self.acc = 0
class checkpoint():
def __init__(self, args):
self.args = args
self.ok = True
self.log = torch.Tensor()
now = datetime.datetime.now().strftime('%Y-%m-%d-%H:%M:%S')
if args.load == '.':
if args.save == '.':
args.save = now
self.dir = '/data/dkjangid/Material_Projects/superresolution/Quaternion_experiments/NMI_model_weights/' + args.save
else:
self.dir = '/data/dkjangid/Material_Projects/superresolution/Quaternion_experiments/NMI_model_weights/' + args.load
if not os.path.exists(self.dir):
args.load = '.'
else:
self.log = torch.load(self.dir + '/psnr_log.pt')
print('Continue from epoch {}...'.format(len(self.log)))
if args.reset:
os.system('rm -rf ' + self.dir)
args.load = '.'
def _make_dir(path):
if not os.path.exists(path):
os.makedirs(path)
_make_dir(self.dir)
_make_dir(self.dir + '/model')
_make_dir(self.dir + '/results')
open_type = 'a' if os.path.exists(self.dir + '/log.txt') else 'w'
self.log_file = open(self.dir + '/log.txt', open_type)
with open(self.dir + '/config.txt', open_type) as f:
f.write(now + '\n\n')
for arg in vars(args):
f.write('{}: {}\n'.format(arg, getattr(args, arg)))
f.write('\n')
def save(self, trainer, epoch, is_best=False):
trainer.model.save(self.dir, epoch, is_best=is_best)
trainer.loss.save(self.dir)
trainer.loss.plot_loss(self.dir, epoch)
#self.plot_psnr(epoch)
#torch.save(self.log, os.path.join(self.dir, 'psnr_log.pt'))
torch.save(
trainer.optimizer.state_dict(),
os.path.join(self.dir, 'optimizer.pt')
)
def add_log(self, log):
self.log = torch.cat([self.log, log])
def write_log(self, log, refresh=False):
print(log)
self.log_file.write(log + '\n')
if refresh:
self.log_file.close()
self.log_file = open(self.dir + '/log.txt', 'a')
def done(self):
self.log_file.close()
def plot_psnr(self, epoch):
axis = np.linspace(1, epoch, epoch)
label = 'SR on {}'.format(self.args.data_test)
fig = plt.figure()
plt.title(label)
for idx_scale, scale in enumerate(self.args.scale):
plt.plot(
axis,
self.log[:, idx_scale].numpy(),
label='Scale {}'.format(scale)
)
plt.legend()
plt.xlabel('Epochs')
plt.ylabel('PSNR')
plt.grid(True)
plt.savefig('{}/test_{}.pdf'.format(self.dir, self.args.data_test))
plt.close(fig)
def plot_val_loss(self, total_val_loss_all, epoch_list):
#axis = np.linspace(0, epoch, epoch+1)
axis = epoch_list
label = 'Misorientation Loss on Validation Data'
fig = plt.figure()
plt.title(label)
plt.plot(
axis,
total_val_loss_all,
label = label
)
plt.legend()
plt.xlabel('Epochs')
plt.ylabel('MisOrientation Error')
plt.grid(True)
plt.savefig('{}/val_loss.pdf'.format(self.dir))
plt.close(fig)
def save_results(self, filenames, save_list, postfix, scale, epoch, dataset='val'):
results_dir = f'{self.dir}/results'
if not os.path.exists(results_dir):
os.mkdir(results_dir)
kwargs_imshow = {'vmin': -1, 'vmax': 1}
if not self.args.scalar_first:
channels = ['q1', 'q2', 'q3', 'q0']
else:
channels = ['q0', 'q1', 'q2', 'q3']
#import pdb; pdb.set_trace()
for idx, file_name in enumerate(filenames):
#base_name = os.path.basename(file_path)
#file_name = os.path.splitext(base_name)[0][:-3]
if not os.path.exists(f'{self.dir}/results/{dataset}_{epoch}'):
os.mkdir(f'{self.dir}/results/{dataset}_{epoch}')
filename = '{}/results/{}_{}/{}_x{}'.format(self.dir, dataset, epoch, file_name, scale)
if self.args.n_colors !=3:
for ch_num, channel in enumerate(channels):
fig, axes = plt.subplots((len(postfix)+1)//3,3, figsize=(14,12), constrained_layout = True)
#fig.tight_layout(pad=2.0)
fig.suptitle(f'{dataset} data: Filename:{file_name}_{channel}' , fontweight ="bold")
for a, img, title in zip(axes.reshape(-1), save_list, postfix):
img_arr = img[idx].cpu().numpy()
if ch_num == 0: # save only one time
np.save(f'{filename}_{title}.npy', img_arr)
img_numpy = img_arr[:,:, ch_num]
im = a.imshow(img_numpy, **kwargs_imshow, cmap='jet')
a.set_title(title, fontweight="bold")
cbar =fig.colorbar(im, ax = axes.ravel().tolist(), shrink=0.95)
cbar.set_ticklabels(np.arange(0,1,0.2))
cbar.set_ticklabels([-1 , 0, 1])
plt.savefig(f'{filename}_{channel}.png')
plt.close()
else:
img_numpy = (np.transpose(img_arr, (1,2,0)) + 1 / 2.0) * 255.0
img_numpy = np.clip(img_numpy, 0, 255).astype(np.uint8)
#import pdb; pdb.set_trace()
#fig.delaxes(axes[2,1])
def quantize(img, rgb_range):
pixel_range = 255 / rgb_range
return img.mul(pixel_range).clamp(0, 255).round().div(pixel_range)
def calculate_psnr(img1, img2):
# img1 and img2 have range [0, 255]
img1 = img1.astype(np.float64)
img2 = img2.astype(np.float64)
mse = np.mean((img1 - img2) ** 2)
if mse == 0:
return float("inf")
return 20 * math.log10(255.0 / math.sqrt(mse))
def ssim(img1, img2):
C1 = (0.01 * 255) ** 2
C2 = (0.03 * 255) ** 2
img1 = img1.astype(np.float64)
img2 = img2.astype(np.float64)
kernel = cv2.getGaussianKernel(11, 1.5)
window = np.outer(kernel, kernel.transpose())
mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5] # valid
mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5]
mu1_sq = mu1 ** 2
mu2_sq = mu2 ** 2
mu1_mu2 = mu1 * mu2
sigma1_sq = cv2.filter2D(img1 ** 2, -1, window)[5:-5, 5:-5] - mu1_sq
sigma2_sq = cv2.filter2D(img2 ** 2, -1, window)[5:-5, 5:-5] - mu2_sq
sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2
ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2))
return ssim_map.mean()
def calculate_ssim(img1, img2):
"""calculate SSIM
the same outputs as MATLAB's
img1, img2: [0, 255]
"""
if not img1.shape == img2.shape:
raise ValueError("Input images must have the same dimensions.")
if img1.ndim == 2:
return ssim(img1, img2)
elif img1.ndim == 3:
if img1.shape[2] == 3:
ssims = []
for i in range(3):
ssims.append(ssim(img1, img2))
return np.array(ssims).mean()
elif img1.shape[2] == 1:
return ssim(np.squeeze(img1), np.squeeze(img2))
else:
raise ValueError("Wrong input image dimensions.")
def calc_psnr_quat(dist, rgb_range):
dist = dist / rgb_range
#rmse = (dist**2).mean().sqrt()
#ps = 20*torch.log10(1/rmse)
ps = 20*torch.log10(1/dist)
return ps
def make_optimizer(args, my_model):
trainable = filter(lambda x: x.requires_grad, my_model.parameters())
if args.optimizer == 'SGD':
optimizer_function = optim.SGD
kwargs = {'momentum': args.momentum}
elif args.optimizer == 'ADAM':
optimizer_function = optim.Adam
kwargs = {
'betas': (args.beta1, args.beta2),
'eps': args.epsilon
}
elif args.optimizer == 'RMSprop':
optimizer_function = optim.RMSprop
kwargs = {'eps': args.epsilon}
kwargs['lr'] = args.lr
kwargs['weight_decay'] = args.weight_decay
return optimizer_function(trainable, **kwargs)
def make_warmup_scheduler(args, my_optimizer):
warmup_epochs = 3
scheduler_cosine = optim.lr_scheduler.CosineAnnealingLR(my_optimizer, args.epochs-warmup_epochs, eta_min=args.optim_lr_min)
scheduler = GradualWarmupScheduler(my_optimizer, multiplier=1, total_epoch=warmup_epochs, after_scheduler=scheduler_cosine)
return scheduler
def make_scheduler(args, my_optimizer):
if args.decay_type == 'step':
scheduler = lrs.StepLR(
my_optimizer,
step_size=args.lr_decay,
gamma=args.gamma
)
elif args.decay_type.find('step') >= 0:
milestones = args.decay_type.split('_')
milestones.pop(0)
milestones = list(map(lambda x: int(x), milestones))
scheduler = lrs.MultiStepLR(
my_optimizer,
milestones=milestones,
gamma=args.gamma
)
return scheduler