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basic_function.py
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basic_function.py
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import os
import sys
import time
import shutil
import random
import argparse
import numpy as np
import torchnet as tnt
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torchvision.utils as vutils
import torchvision.models as models
import torch.backends.cudnn as cudnn
from torch.autograd import Variable, Function
from torch.utils import data
from tensorboardX import SummaryWriter
from PIL import Image
def ColorMapping(seg):
colormap = torch.Tensor([[0,0,0], [128,0,0], [0,128,0], [128,128,0], [0,0,128], [128,0,128],
[0,128,128], [128,128,128], [64,0,0], [192,0,0], [64,128,0], [192,128,0],
[64,0,128], [192,0,128], [64,128,128], [192,128,128], [0, 64,0], [128, 64, 0],
[0,192,0], [128,192,0], [0,64,128]])/255.0#total 21 labels
seg = torch.matmul(seg.transpose(1,2).transpose(2,3), colormap).transpose(3,2).transpose(2,1)
return seg
def get_flip_transfer(h, w):
transfer = torch.zeros(h*w, h*w)
diag_matrix=torch.flip(torch.eye(w),[0])
for i in range(h):
transfer[i*w:(i+1)*w,i*w:(i+1)*w]=diag_matrix
return transfer
def param_restore(model, param_dict):
new_params = model.state_dict().copy()
for i in param_dict:
i_parts = i.split('.')
#print(i)
if not i_parts[0]=='fc':
new_params[i] = param_dict[i]
model.load_state_dict(new_params)
return model
def param_restore_all(model, param_dict):
new_params = model.state_dict().copy()
for i in param_dict:
i_parts = i.split('.')
i_parts.pop(0)
new_params['.'.join(i_parts)] = param_dict[i]
model.load_state_dict(new_params)
return model
def BGR2RGB(img):
out = torch.zeros(img.size())
out[:,0,:,:] = img[:,2,:,:]
out[:,1,:,:] = img[:,1,:,:]
out[:,2,:,:] = img[:,0,:,:]
return out
def map_decode(seg, num_label=21):
seg[seg==255]=0 #######################
labels = range(num_label)
batch_size = seg.size(0)
out = torch.zeros(seg.size()).repeat(1,num_label,1,1)
for i, label in enumerate(labels):
#out_slice = out[:,i:i+1,:,:]1
out[:,i:i+1,:,:] = seg==label
return out
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
def BatchInverse(tensor):
batch_size = tensor.size()[0]
tensor_inverse = []
for i in range(batch_size):
tensor_inverse += [torch.inverse(tensor[i]).unsqueeze(0)]
return torch.cat(tensor_inverse, 0)
def save_checkpoint(state, date, is_best, filename='checkpoint.pth.tar'):
"""Saves checkpoint to disk"""
directory = date
if not os.path.exists(directory):
os.makedirs(directory)
filename = os.path.join(directory, filename)
torch.save(state, filename)
if is_best:
shutil.copyfile(filename, os.path.join(directory, 'model_best.pth.tar'))
def register_checks(model):
def check_grad(module, grad_input, grad_output):
# print(module) you can add this to see that the hook is called
for gi in grad_input:
if gi is not None:
if torch.any(torch.isnan(gi)):
print('NaN gradient in ' + type(module).__name__)
if torch.any(torch.isinf(gi)):
print('Inf gradient in ' + type(module).__name__)
model.apply(lambda module: module.register_backward_hook(check_grad))
def get_mask_pallete(npimg, dataset='pascal_voc'):
"""Get image color pallete for visualizing masks"""
# recovery boundary
if dataset == 'pascal_voc':
npimg[npimg==21] = 255
npimg[npimg==-1] = 255
colorpallete = vocpallete
elif dataset == 'ade20k':
colorpallete = adepallete
elif dataset == 'cityscapes':
colorpallete = citypallete
out_img = Image.fromarray(npimg.squeeze().astype('uint8'))
out_img.putpalette(colorpallete)
return out_img
def _get_voc_pallete(num_cls):
n = num_cls
pallete = [0]*(n*3)
for j in range(0,n):
lab = j
pallete[j*3+0] = 0
pallete[j*3+1] = 0
pallete[j*3+2] = 0
i = 0
while (lab > 0):
pallete[j*3+0] |= (((lab >> 0) & 1) << (7-i))
pallete[j*3+1] |= (((lab >> 1) & 1) << (7-i))
pallete[j*3+2] |= (((lab >> 2) & 1) << (7-i))
i = i + 1
lab >>= 3
return pallete
vocpallete = _get_voc_pallete(256)
adepallete = [0,0,0,120,120,120,180,120,120,6,230,230,80,50,50,4,200,3,120,120,80,140,140,140,204,5,255,230,230,230,4,250,7,224,5,255,235,255,7,150,5,61,120,120,70,8,255,51,255,6,82,143,255,140,204,255,4,255,51,7,204,70,3,0,102,200,61,230,250,255,6,51,11,102,255,255,7,71,255,9,224,9,7,230,220,220,220,255,9,92,112,9,255,8,255,214,7,255,224,255,184,6,10,255,71,255,41,10,7,255,255,224,255,8,102,8,255,255,61,6,255,194,7,255,122,8,0,255,20,255,8,41,255,5,153,6,51,255,235,12,255,160,150,20,0,163,255,140,140,140,250,10,15,20,255,0,31,255,0,255,31,0,255,224,0,153,255,0,0,0,255,255,71,0,0,235,255,0,173,255,31,0,255,11,200,200,255,82,0,0,255,245,0,61,255,0,255,112,0,255,133,255,0,0,255,163,0,255,102,0,194,255,0,0,143,255,51,255,0,0,82,255,0,255,41,0,255,173,10,0,255,173,255,0,0,255,153,255,92,0,255,0,255,255,0,245,255,0,102,255,173,0,255,0,20,255,184,184,0,31,255,0,255,61,0,71,255,255,0,204,0,255,194,0,255,82,0,10,255,0,112,255,51,0,255,0,194,255,0,122,255,0,255,163,255,153,0,0,255,10,255,112,0,143,255,0,82,0,255,163,255,0,255,235,0,8,184,170,133,0,255,0,255,92,184,0,255,255,0,31,0,184,255,0,214,255,255,0,112,92,255,0,0,224,255,112,224,255,70,184,160,163,0,255,153,0,255,71,255,0,255,0,163,255,204,0,255,0,143,0,255,235,133,255,0,255,0,235,245,0,255,255,0,122,255,245,0,10,190,212,214,255,0,0,204,255,20,0,255,255,255,0,0,153,255,0,41,255,0,255,204,41,0,255,41,255,0,173,0,255,0,245,255,71,0,255,122,0,255,0,255,184,0,92,255,184,255,0,0,133,255,255,214,0,25,194,194,102,255,0,92,0,255]
citypallete = [
128,64,128,244,35,232,70,70,70,102,102,156,190,153,153,153,153,153,250,170,30,220,220,0,107,142,35,152,251,152,70,130,180,220,20,60,255,0,0,0,0,142,0,0,70,0,60,100,0,80,100,0,0,230,119,11,32,128,192,0,0,64,128,128,64,128,0,192,128,128,192,128,64,64,0,192,64,0,64,192,0,192,192,0,64,64,128,192,64,128,64,192,128,192,192,128,0,0,64,128,0,64,0,128,64,128,128,64,0,0,192,128,0,192,0,128,192,128,128,192,64,0,64,192,0,64,64,128,64,192,128,64,64,0,192,192,0,192,64,128,192,192,128,192,0,64,64,128,64,64,0,192,64,128,192,64,0,64,192,128,64,192,0,192,192,128,192,192,64,64,64,192,64,64,64,192,64,192,192,64,64,64,192,192,64,192,64,192,192,192,192,192,32,0,0,160,0,0,32,128,0,160,128,0,32,0,128,160,0,128,32,128,128,160,128,128,96,0,0,224,0,0,96,128,0,224,128,0,96,0,128,224,0,128,96,128,128,224,128,128,32,64,0,160,64,0,32,192,0,160,192,0,32,64,128,160,64,128,32,192,128,160,192,128,96,64,0,224,64,0,96,192,0,224,192,0,96,64,128,224,64,128,96,192,128,224,192,128,32,0,64,160,0,64,32,128,64,160,128,64,32,0,192,160,0,192,32,128,192,160,128,192,96,0,64,224,0,64,96,128,64,224,128,64,96,0,192,224,0,192,96,128,192,224,128,192,32,64,64,160,64,64,32,192,64,160,192,64,32,64,192,160,64,192,32,192,192,160,192,192,96,64,64,224,64,64,96,192,64,224,192,64,96,64,192,224,64,192,96,192,192,224,192,192,0,32,0,128,32,0,0,160,0,128,160,0,0,32,128,128,32,128,0,160,128,128,160,128,64,32,0,192,32,0,64,160,0,192,160,0,64,32,128,192,32,128,64,160,128,192,160,128,0,96,0,128,96,0,0,224,0,128,224,0,0,96,128,128,96,128,0,224,128,128,224,128,64,96,0,192,96,0,64,224,0,192,224,0,64,96,128,192,96,128,64,224,128,192,224,128,0,32,64,128,32,64,0,160,64,128,160,64,0,32,192,128,32,192,0,160,192,128,160,192,64,32,64,192,32,64,64,160,64,192,160,64,64,32,192,192,32,192,64,160,192,192,160,192,0,96,64,128,96,64,0,224,64,128,224,64,0,96,192,128,96,192,0,224,192,128,224,192,64,96,64,192,96,64,64,224,64,192,224,64,64,96,192,192,96,192,64,224,192,192,224,192,32,32,0,160,32,0,32,160,0,160,160,0,32,32,128,160,32,128,32,160,128,160,160,128,96,32,0,224,32,0,96,160,0,224,160,0,96,32,128,224,32,128,96,160,128,224,160,128,32,96,0,160,96,0,32,224,0,160,224,0,32,96,128,160,96,128,32,224,128,160,224,128,96,96,0,224,96,0,96,224,0,224,224,0,96,96,128,224,96,128,96,224,128,224,224,128,32,32,64,160,32,64,32,160,64,160,160,64,32,32,192,160,32,192,32,160,192,160,160,192,96,32,64,224,32,64,96,160,64,224,160,64,96,32,192,224,32,192,96,160,192,224,160,192,32,96,64,160,96,64,32,224,64,160,224,64,32,96,192,160,96,192,32,224,192,160,224,192,96,96,64,224,96,64,96,224,64,224,224,64,96,96,192,224,96,192,96,224,192,0,0,0]