ptrblck

Code to test:

x = torch.randint(2, (1000, 1000))

torch_times = {}
torch_tmp = []
for dim in range(2):
    for _ in range(10):
        t0 = time.perf_counter()
        tu, ti = torch.unique(x, return_inverse=True, dim=dim)

ptrblck

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Dec 21 19:26:32 2018

@author: ptrblck
"""

import torch
import numpy as np

ptrblck

import torch
import torch.nn as nn


batch_size = 10
channels = 3
h, w = 16, 16
x = torch.randn(batch_size, channels, h, w)

pool = nn.AvgPool2d((1, 2), (1, 2))

ptrblck

import torch
import torch.nn as nn
import torch.optim as optim

from torch.utils.data import Dataset, DataLoader


device = 'cpu'

class MyDataset(Dataset):

ptrblck

conda create -n pytorch4 python=3.6
conda activate pytorch4
conda install pytorch=0.4.0 torchvision -c pytorch
pip install requests dominate
conda install opencv pillow scipy

ptrblck

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader

import torchvision.models as models
import torchvision.transforms as transforms


# Use standard model with [batch_size, 3, 224, 224] input

ptrblck

import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F


torch.manual_seed(2809)

ptrblck

import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.utils.data import DataLoader

import torchvision.transforms as transforms
import torchvision.datasets as datasets

import matplotlib.pyplot as plt

ptrblck

import torch
import torch.nn as nn

class UNet_down_block(torch.nn.Module):
    def __init__(self, input_channel, output_channel, down_size):
        super(UNet_down_block, self).__init__()
        self.conv1 = torch.nn.Conv2d(input_channel, output_channel, 3, padding=1)
        self.bn1 = torch.nn.BatchNorm2d(output_channel)
        self.conv2 = torch.nn.Conv2d(output_channel, output_channel, 3, padding=1)

ptrblck

from __future__ import print_function, division
 
import torch
from torch.autograd import Variable
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix
import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler