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import torch
import numpy as np
import random
def poly_lr(optimizer, init_lr, curr_iter, max_iter, power=0.9):
lr = init_lr * (1 - float(curr_iter) / max_iter) ** power
for param_group in optimizer.param_groups:
param_group['lr'] = lr
cur_lr = lr
return cur_lr
def clip_gradient(optimizer, grad_clip):
"""
For calibrating misalignment gradient via cliping gradient technique
:param optimizer:
:param grad_clip:
:return:
"""
for group in optimizer.param_groups:
for param in group['params']:
if param.grad is not None:
param.grad.data.clamp_(-grad_clip, grad_clip)
def set_random_seed(seed=42):
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
np.random.seed(seed)
random.seed(seed)
from torch import nn
def bceLoss():
return nn.BCEWithLogitsLoss()
def crossEntropyLoss():
return nn.CrossEntropyLoss()
def mseLoss():
return nn.MSELoss() |