shuuchen

import torch
from torch import nn
from torch.nn.modules.distance import PairwiseDistance


class TripletLoss(nn.Module):
    
    def __init__(self, margin=5.0):
        super(TripletLoss, self).__init__()
        self.margin = margin

shuuchen

class TripletNetwork(SiameseNetwork):
    def __init__(self):
        super().__init__()
        
    def forward(self, input1, input2, input3):
        output1 = self.forward_once(input1)
        output2 = self.forward_once(input2)
        output3 = self.forward_once(input3)
        return output1, output2, output3

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class SiameseNetwork(nn.Module):
    def __init__(self):
        super(SiameseNetwork, self).__init__()
        
        self.cnn1 = nn.Sequential(
            nn.ReflectionPad2d(1),
            nn.Conv2d(1, 4, kernel_size=3),
            nn.ReLU(inplace=True),
            nn.BatchNorm2d(4),
            

shuuchen

from collections import defaultdict
from heapq import *

def dijkstra(edges, f, t):
    g = defaultdict(list)

    for l, r, c in edges:
        g[l].append((c, r))

    q, seen, mins = [(0, f, ())], set(), {f: 0}

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class UT:
    def __init__(self, n):
        self.ps = list(range(n))
        self.rs = [0] * n

    def find(self, x):
        if self.ps[x] != x:
            self.ps[x] = self.find(self.ps[x])
        return self.ps[x]

shuuchen

This post shows how to deal with the following error:
 
  (base) [dushu@ip-172-20-149-210 ~]$ nvidia-smi
  NVIDIA-SMI has failed because it couldn't communicate with the NVIDIA driver. 
  Make sure that the latest NVIDIA driver is installed and running.

Solution
  Just install the latest nvidia driver.

Process

shuuchen

from collections import defaultdict

class TrieNode:
    def __init__(self):
        self.isWord = False
        self.children = defaultdict(TrieNode)


class Trie:
    def __init__(self):

shuuchen

import torchvision.transforms.functional as F
import numpy as np
from PIL import Image
import os
from matplotlib import pyplot as plt

# input images
d = '../data/homes/test_input/'
img0 = Image.open(os.path.join(d, '352.jpg'))
img1 = Image.open(os.path.join(d, '95.jpg'))

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class ImageFolderWithPaths(datasets.ImageFolder):
    """Custom dataset that includes image file paths. Extends
    torchvision.datasets.ImageFolder
    """

    # override the __getitem__ method. this is the method dataloader calls
    def __getitem__(self, index):
        # this is what ImageFolder normally returns 
        original_tuple = super(ImageFolderWithPaths, self).__getitem__(index)
        # the image file path

shuuchen

C:\Users\to>python
Python 3.6.4 |Anaconda custom (64-bit)| (default, Jan 16 2018, 10:22:32) [MSC v.1900 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import os
>>> import sys
>>> sys.executable
'C:\\Users\\to\\Anaconda3\\python.exe'