I was trying to import a "progress_bar" function from mutils1.py on the Ubuntu terminal such as below:
$ from mutils1 import progress_bar
but I was receiving the below error so far, Could anyone help me to fix it please? thanks.
from mutils1 import progress_bar Traceback (most recent call last): File "", line 1, in ImportError: cannot import name 'progress_bar' from 'mutils1' (/scratch/amirabb/amirabb/DeepFacade-master/mutils1.py)
Here is mutils1.py python code to help you to figure it out.
import os
import sys
import time
import math
import torch
import torch.nn as nn
import torch.nn.init as init
from torch.utils.data import DataLoader
import torch.utils.data as Data
from torchvision import datasets,models,transforms
from torch.utils import data
from PIL import Image
import numpy as np
# import torch
# class EcpDataset(data.Dataset):
# def __init__(self,root,type = 'train',transform = True):
# self.root = root
# self._transform = transform
# self.files = None
# self.labels = None
# self.label_dict = {
# 0:0,
# 29:1,
# 67:2,
# 76:3,
# 128:4,
# 149:5,
# 151:6,
# 217:7,
# 225:8
# }
class EcpDataset(data.Dataset):
def __init__(self,root,type = 'train',transform = True):
self.root = root
self._transform = transform
self.files = None
self.labels = None
self.label_dict = {
0:0,
29:1,
67:2,
76:3,
128:4,
149:5,
151:6,
217:7,
225:8
}
image_filelist = os.listdir(self.root+'/images')
label_filelist = os.listdir(self.root+'/labels')
filterlist = list(map(lambda x:''.join(x.replace('.jpg','_mask.png')),image_filelist))
image_filelist = list(map(lambda x:x.replace('_mask.png','.jpg'), list(set(filterlist)&set(label_filelist))))
self.image_filelist = image_filelist[:80] if type == 'train' else image_filelist[80:]
def __len__(self):
return len(self.image_filelist)
def __getitem__(self,index):
img_path = self.root+'/images/'+self.image_filelist[index]
lbl_path = self.root+'/labels/'+self.image_filelist[index].replace('.jpg','_mask.png')
img = np.array(Image.open(img_path))
img = img.transpose(2,0,1)
lbl = self.process_label(lbl_path)
img = torch.from_numpy(img).float()
# img = img.permute(0,2,3,1)
lbl = torch.from_numpy(lbl).type(torch.LongTensor)
return img,lbl
def process_label(self,lbl_filename):
label = np.array(Image.open(lbl_filename).convert('L'))
relabel = np.zeros([label.shape[0],label.shape[1]])
for i in range(label.shape[0]):
for j in range(label.shape[1]):
# print(label[i,j])
relabel[i,j] = self.label_dict[label[i,j]]
return relabel
def loaddata(imagepath,labelpath,kind = True):
if kind:
x = 60000
else: x = 10000
image = np.fromfile(imagepath,'float').reshape(x,784,3).transpose(0,2,1)
label = np.fromfile(labelpath,'float').reshape(x,784)
images = torch.from_numpy(image).type(torch.FloatTensor)
labels = torch.from_numpy(label).type(torch.FloatTensor)
dataset = Data.TensorDataset(data_tensor=images, target_tensor=labels)
loader = Data.DataLoader(dataset=dataset, batch_size=128, shuffle=True)
return images,labels,loader
def saveimage(grayimage,filename):
# print(grayimage)
grayimage = np.squeeze(grayimage)
nummatrix = np.array([ [255, 255, 0],
[128 ,255, 255]
,[255 ,128 , 0]
,[ 0 ,255, 0]
,[128 ,128 ,128]
,[255 , 0 , 0]
,[128 , 0 ,255]
,[ 0 , 0 ,255]
,[0, 0 ,0]])
numdict = {
0:nummatrix[8],
1:nummatrix[7],
2:nummatrix[6],
3:nummatrix[5],
4:nummatrix[4],
5:nummatrix[3],
6:nummatrix[2],
7:nummatrix[1],
8:nummatrix[0],
}
image = np.array(list(map(lambda x:nummatrix[8-x],grayimage)))
Image.fromarray(np.uint8(image)).save(filename)
def get_mean_and_std(dataset):
dataloader = torch.utils.data.DataLoader(dataset, batch_size=1, shuffle=True, num_workers=2)
mean = torch.zeros(3)
std = torch.zeros(3)
print('==> Computing mean and std..')
for inputs, targets in dataloader:
for i in range(3):
mean[i] += inputs[:,i,:,:].mean()
std[i] += inputs[:,i,:,:].std()
mean.div_(len(dataset))
std.div_(len(dataset))
return mean, std
def init_params(net):
for m in net.modules():
if isinstance(m, nn.Conv2d):
init.kaiming_normal(m.weight, mode='fan_out')
if m.bias:
init.constant(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d):
init.constant(m.weight, 1)
init.constant(m.bias, 0)
elif isinstance(m, nn.Linear):
init.normal(m.weight, std=1e-3)
if m.bias:
init.constant(m.bias, 0)
class TripletLossFunc(nn.Module):
def __init__(self,anchor,positive,negative,beta):
super(TripletLossFunc,self).__init__()
self.anchor = anchor
self.positive = positive
self.negative = negative
self.beta = beta
def forward(self):
matched = torch.pow(self.anchor-self.positive,2)
mimatched = torch.pow(self.anchor-self.negative,2)
distance = matched-mimatched+self.beta
loss = torch.max(distance,0)
return loss
# _,term_width = os.popen('stty size', 'r').read().split()
# term_width = int(term_width)
term_width = int(80)
TOTAL_BAR_LENGTH = 65.
last_time = time.time()
begin_time = last_time
def progress_bar(current, total, msg=None):
global last_time, begin_time,tot_time
if current == 0:
begin_time = time.time() # Reset for new bar.
cur_len = int(TOTAL_BAR_LENGTH*current/total)
rest_len = int(TOTAL_BAR_LENGTH - cur_len) - 1
sys.stdout.write(' [')
for i in range(cur_len):
sys.stdout.write('=')
sys.stdout.write('>')
for i in range(rest_len):
sys.stdout.write('.')
sys.stdout.write(']')
cur_time = time.time()
step_time = cur_time - last_time
last_time = cur_time
tot_time = cur_time - begin_time
L = []
L.append(' Step: %s' % format_time(step_time))
L.append(' | Tot: %s' % format_time(tot_time))
if msg:
L.append(' | ' + msg)
msg = ''.join(L)
sys.stdout.write(msg)
for i in range(term_width-int(TOTAL_BAR_LENGTH)-len(msg)-3):
sys.stdout.write(' ')
# Go back to the center of the bar.
for i in range(term_width-int(TOTAL_BAR_LENGTH/2)+2):
sys.stdout.write('\b')
sys.stdout.write(' %d/%d ' % (current+1, total))
if current < total-1:
sys.stdout.write('\r')
else:
sys.stdout.write('\n')
sys.stdout.flush()
def format_time(seconds):
days = int(seconds / 3600/24)
seconds = seconds - days*3600*24
hours = int(seconds / 3600)
seconds = seconds - hours*3600
minutes = int(seconds / 60)
seconds = seconds - minutes*60
secondsf = int(seconds)
seconds = seconds - secondsf
millis = int(seconds*1000)
f = ''
i = 1
if days > 0:
f += str(days) + 'D'
i += 1
if hours > 0 and i <= 2:
f += str(hours) + 'h'
i += 1
if minutes > 0 and i <= 2:
f += str(minutes) + 'm'
i += 1
if secondsf > 0 and i <= 2:
f += str(secondsf) + 's'
i += 1
if millis > 0 and i <= 2:
f += str(millis) + 'ms'
i += 1
if f == '':
f = '0ms'
return f
