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Utils.py
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Utils.py
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import os
import torch
import torch.optim as optim
import math
from torch.optim import lr_scheduler
from matplotlib import pyplot as plt
import mmcv
import torch.nn.functional as F
from scipy.stats import pearsonr
import datetime
import time
import torch.nn as nn
from torch.nn import CrossEntropyLoss
from collections import defaultdict, deque
import torch.distributed as dist
from typing import List
# from torchvision import transforms
from PIL import Image
from torchvision import transforms as T
from data.imageDataTransform import Compose, RandomHorizontalFlip, RandomVerticalFlip, Resize_and_RandomCrop, RandomCrop, ToTensor
from torchvision.utils import make_grid
from torch.autograd import Variable
from os.path import join
import numpy as np
from torchvision.datasets import ImageFolder
import torchvision.transforms as transforms
def normalizeImg(imgTensor):
if isinstance(imgTensor, list):
imgTensor = torch.stack(imgTensor, dim=0)
if imgTensor.dim() == 2: # single image H x W
imgTensor = imgTensor.unsqueeze(0)
if imgTensor.dim() == 3: # single image
if imgTensor.size(0) == 1: # if single-channel, convert to 3-channel
imgTensor = torch.cat((imgTensor, imgTensor, imgTensor), 0)
imgTensor = imgTensor.unsqueeze(0)
if imgTensor.dim() == 4 and imgTensor.size(1) == 1: # single-channel images
imgTensor = torch.cat((imgTensor, imgTensor, imgTensor), 1)
imgTensor = imgTensor.clone() # avoid modifying tensor in-place
def norm_ip(img, min, max):
img.clamp_(min=min, max=max)
img.add_(-min).div_(max - min + 1e-5)
return img
imgTensorNormalize = norm_ip(imgTensor, float(imgTensor.min()), float(imgTensor.max()))
return imgTensorNormalize
def TensorToPILImage(imageTensor):
imageTensor = normalizeImg(imageTensor)
image = imageTensor.cpu().clone()
image = image.squeeze(0)
image = transforms.ToPILImage()(image)
return image
def imgTensorShow(imageTensor, title):
imageTensor = normalizeImg(imageTensor)
image = imageTensor.cpu().clone()
image = image.squeeze(0)
image = transforms.ToPILImage()(image)
plt.imshow(image)
if title is not None:
plt.title(title)
plt.pause(0.001) # pause a bit so that plots are updated
def imgShow(pilImgFile, imgType='RGB', title=None):
if imgType == 'gray':
plt.imshow(pilImgFile, cmap ='gray')
else:
plt.imshow(pilImgFile)
if title is not None:
plt.title(title)
plt.pause(0.001) # pause a bit so that plots are updated
def show(imgs):
import torchvision.transforms.functional as F
if not isinstance(imgs, list):
imgs = [imgs]
fig, axs = plt.subplots(ncols=len(imgs), squeeze=False)
for i, img in enumerate(imgs):
img = img.detach()
if len(img.shape) == 4:
img = img.squeeze(0)
img = F.to_pil_image(img)
axs[0, i].imshow(np.asarray(img))
axs[0, i].set(xticklabels=[], yticklabels=[], xticks=[], yticks=[])
def is_dist_avail_and_initialized():
if not dist.is_available():
return False
if not dist.is_initialized():
return False
return True
class SmoothedValue(object):
"""
Track a series of values and provide access to smoothed values over a
window or the global series average.
"""
def __init__(self, window_size=20, fmt=None):
if fmt is None:
fmt = "{value:.4f} ({global_avg:.4f})"
self.deque = deque(maxlen=window_size)
self.total = 0.0
self.count = 0
self.fmt = fmt
def update(self, value, n=1):
self.deque.append(value)
self.count += n
self.total += value * n
def synchronize_between_processes(self):
"""
Warning: does not synchronize the deque!
"""
if not is_dist_avail_and_initialized():
return
t = torch.tensor([self.count, self.total], dtype=torch.float64, device='cuda')
dist.barrier()
dist.all_reduce(t)
t = t.tolist()
self.count = int(t[0])
self.total = t[1]
@property
def median(self):
d = torch.tensor(list(self.deque))
return d.median().item()
@property
def avg(self):
d = torch.tensor(list(self.deque), dtype=torch.float32)
return d.mean().item()
@property
def global_avg(self):
return self.total / self.count
@property
def max(self):
return max(self.deque)
@property
def value(self):
return self.deque[-1]
def __str__(self):
return self.fmt.format(
median=self.median,
avg=self.avg,
global_avg=self.global_avg,
max=self.max,
value=self.value)
class MetricLogger(object):
def __init__(self, delimiter="\t"):
self.meters = defaultdict(SmoothedValue)
self.delimiter = delimiter
def update(self, **kwargs):
for k, v in kwargs.items():
if isinstance(v, torch.Tensor):
v = v.item()
assert isinstance(v, (float, int))
self.meters[k].update(v)
def __getattr__(self, attr):
if attr in self.meters:
return self.meters[attr]
if attr in self.__dict__:
return self.__dict__[attr]
raise AttributeError("'{}' object has no attribute '{}'".format(
type(self).__name__, attr))
def __str__(self):
loss_str = []
for name, meter in self.meters.items():
loss_str.append(
"{}: {}".format(name, str(meter))
)
return self.delimiter.join(loss_str)
def synchronize_between_processes(self):
for meter in self.meters.values():
meter.synchronize_between_processes()
def add_meter(self, name, meter):
self.meters[name] = meter
def log_every(self, iterable, print_freq, header=None):
i = 0
if not header:
header = ''
start_time = time.time()
end = time.time()
iter_time = SmoothedValue(fmt='{avg:.4f}')
data_time = SmoothedValue(fmt='{avg:.4f}')
space_fmt = ':' + str(len(str(len(iterable)))) + 'd'
if torch.cuda.is_available():
log_msg = self.delimiter.join([
header,
'[{0' + space_fmt + '}/{1}]',
'eta: {eta}',
'{meters}',
'time: {time}',
'data: {data}',
'max mem: {memory:.0f}'
])
else:
log_msg = self.delimiter.join([
header,
'[{0' + space_fmt + '}/{1}]',
'eta: {eta}',
'{meters}',
'time: {time}',
'data: {data}'
])
MB = 1024.0 * 1024.0
for obj in iterable:
data_time.update(time.time() - end)
yield obj
iter_time.update(time.time() - end)
if i % print_freq == 0:
eta_seconds = iter_time.global_avg * (len(iterable) - i)
eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
if torch.cuda.is_available():
print(log_msg.format(
i, len(iterable), eta=eta_string,
meters=str(self),
time=str(iter_time), data=str(data_time),
memory=torch.cuda.max_memory_allocated() / MB))
else:
print(log_msg.format(
i, len(iterable), eta=eta_string,
meters=str(self),
time=str(iter_time), data=str(data_time)))
i += 1
end = time.time()
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
print('{} Total time: {}'.format(header, total_time_str))
def create_optimizer(config, model):
if config.optimizer == 'Adam':
optimizer = optim.Adam(model.parameters(), lr=config.baseLR, betas=(0.9, 0.999))
elif config.optimizer == 'SGD':
optimizer = optim.SGD(model.parameters(), lr=config.baseLR, momentum=0.9, nesterov=True)
else:
raise ValueError("No such optimizer: {}".format(config.optimizer))
return optimizer
def create_lr_scheduler(optimizer, config):
if config.lr_scheduler == 'cosineAnnealing':
scheduler = lr_scheduler.CosineAnnealingLR(optimizer, T_max=config.warmup_epochs, eta_min=config.min_lr)
elif config.lr_scheduler == 'step':
# For each step_size epoch, the learning rate is multiplied by gamma to perform the attenuation operation
scheduler = lr_scheduler.StepLR(optimizer, step_size=config.warmup_epochs, gamma=0.9)
elif config.lr_scheduler == 'multistep':
if config.steps is None: return None
if isinstance(config.steps, int): config.steps = [config.steps]
scheduler = lr_scheduler.MultiStepLR(optimizer,
milestones=config.steps,
gamma=config.gamma)
elif config.lr_scheduler == 'exp-warmup':
lr_lambda = lr_scheduler.exp_warmup(config.rampup_length,
config.rampdown_length,
config.epochs)
scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lr_lambda)
elif config.lr_scheduler == 'consine':
# cosine
lf = lambda x: ((1 + math.cos(x * math.pi / config.warmup_epochs)) / 2) * (1 - config.lrf) + config.lrf
scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
elif config.lr_scheduler == 'none':
scheduler = None
else:
raise ValueError("No such scheduler: {}".format(config.lr_scheduler))
return scheduler
def createLossFunc(config):
if config.lossName == 'crossEntropyLoss':
return CrossEntropyLoss()
elif config.lossName == 'focalLoss':
return FocalLoss(class_num=config.classNumber, gamma=config.focalLossGamma)
else:
raise ValueError("No such LossFunction: {}".format(config.lossName))
def get_mean_std(data: List) -> dict:
dataMean = np.mean(data)
dataStd = np.std(data)
dataMedian = np.median(data)
return {'mean': dataMean, 'std': dataStd, 'median': dataMedian}
def get_PearsonCorrelation(data1: List, data2: List):
pearsonCorrelationCoefficient, pValue = pearsonr(data1, data2)
return pearsonCorrelationCoefficient, pValue
def create_lr_scheduler_step(optimizer,
num_step: int,
epochs: int,
warmup=True,
warmup_epochs=1,
warmup_factor=1e-3):
assert num_step > 0 and epochs > 0
if warmup is False:
warmup_epochs = 0
def f(x):
"""
Returns a learning rate multiplier based on the number of steps
"""
if warmup is True and x <= (warmup_epochs * num_step):
alpha = float(x) / (warmup_epochs * num_step)
# During the warmup process, the lr multiplication factor changes from warmup_factor -> 1
return warmup_factor * (1 - alpha) + alpha
else:
return (1 - (x - warmup_epochs * num_step) / ((epochs - warmup_epochs) * num_step)) ** 0.9
return torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=f)
def saveDataAs_npz(fileName: str, data: List):
# for key, value in data:
np.savez(fileName, data=data)
def read_npz_data(fileName: str):
data = np.load(fileName, allow_pickle=True)
return data
class FocalLoss(nn.Module):
r"""
This criterion is a implemenation of Focal Loss, which is proposed in
Focal Loss for Dense Object Detection.
Loss(x, class) = - \alpha (1-softmax(x)[class])^gamma \log(softmax(x)[class])
The losses are averaged across observations for each minibatch.
Args:
alpha(1D Tensor, Variable) : the scalar factor for this criterion
gamma(float, double) : gamma > 0; reduces the relative loss for well-classified examples (p > .5),
putting more focus on hard, misclassified examples
size_average(bool): By default, the losses are averaged over observations for each minibatch.
However, if the field size_average is set to False, the losses are
instead summed for each minibatch.
"""
def __init__(self, class_num, alpha=None, gamma=2, size_average=True):
super(FocalLoss, self).__init__()
if alpha is None:
self.alpha = Variable(torch.ones(class_num, 1))
else:
if isinstance(alpha, Variable):
self.alpha = alpha
else:
self.alpha = Variable(alpha)
self.gamma = gamma
self.class_num = class_num
self.size_average = size_average
def forward(self, inputs, targets):
eps = 1e-7
device = inputs.device
N = inputs.size(0)
C = inputs.size(1)
P = F.softmax(inputs, dim=1)
class_mask = inputs.data.new(N, C).fill_(0)
class_mask = Variable(class_mask)
ids = targets.view(-1, 1)
# print(f"ids:{ids}")
class_mask.scatter_(1, ids.data, 1.)
# print(class_mask)
if inputs.is_cuda and not self.alpha.is_cuda:
self.alpha = self.alpha.cuda()
alpha = self.alpha[ids.data.view(-1)]
alpha = alpha.to(device)
# print(f"alpha:{alpha}")
probs = (P * class_mask).sum(1).view(-1, 1)
probs = probs.to(device)
# print(f"probs:{probs}, probs + eps:{probs + eps}")
log_p = torch.log(probs + eps)
log_p = log_p.to(device)
# log_p = probs.log()
# print('probs size= {}'.format(probs.size()))
# print(probs)
batch_loss = -alpha * (torch.pow((1 - probs), self.gamma)) * log_p
# print('-----bacth_loss------')
# print(batch_loss)
if self.size_average:
loss = batch_loss.mean()
else:
loss = batch_loss.sum()
return loss
class Get_mean_std_for_dataset:
def __init__(self, dataRootPath:str):
self.means = [0, 0, 0]
self.stdevs = [0, 0, 0]
self.transform = transforms.Compose([transforms.ToTensor()])
# Datasets are already stored in different folders by category
self.dataset = ImageFolder(dataRootPath, self.transform)
def get_mean_std(self, mean_std_path=None):
"""
Calculate the mean and standard deviation of a dataset
:param mean_std_path: The file path where the calculated mean and standard deviation are stored
"""
num_imgs = len(self.dataset)
for data in self.dataset:
img = data[0]
for i in range(3):
# calculate for every channel
self.means[i] += img[i, :, :].mean()
self.stdevs[i] += img[i, :, :].std()
self.means = np.asarray(self.means) / num_imgs
self.stdevs = np.asarray(self.stdevs) / num_imgs
# Write the resulting mean and standard deviation to a file, which can then be read from
"""
with open(mean_std_path, 'wb') as f:
pickle.dump(self.means, f)
pickle.dump(self.stdevs, f)
print('pickle done')
"""
return self.means, self.stdevs