kolergy

#!/usr/freeware/bin/python
## #!/home/QtPalmtop/bin/python

#-------------------------------------------------------------------------------------------
#-------------------------------------------------------------------------------------------
# Ustructured NN
# Kolergy
# Written in Python.  See http://www.python.org/
#-------------------------------------------------------------------------------------------
#-------------------------------------------------------------------------------------------

hagerty

with tf.device(gpu):
# Generator
    x8 = tf.placeholder(tf.float32, shape=[None, FLAGS.ws, FLAGS.ws, 8])
    x3 = tf.placeholder(tf.float32, shape=[None, scale * FLAGS.ws, scale * FLAGS.ws, 3])
    label_distance = tf.placeholder(tf.float32, shape=[None, FLAGS.ws, FLAGS.ws, 1])
    for i in range(layers):
        alpha[i] = tf.Variable(0.9, name='alpha_' + str(i))
        beta[i] = tf.maximum( 0.0 , tf.minimum ( 1.0 , alpha[i] ), name='beta_'+str(i))
        bi[i] = tf.Variable(tf.constant(0.0,shape=[FLAGS.filters]), name='bi_'+str(i))
        bo[i] = tf.Variable(tf.constant(0.0,shape=[FLAGS.filters]), name='bo_'+str(i))

ndronen

#!/usr/bin/env python
"""
A quick, partial implementation of ENet (https://arxiv.org/abs/1606.02147) using PyTorch.
The original Torch ENet implementation can process a 480x360 image in ~12 ms (on a P2 AWS
instance).  TensorFlow takes ~35 ms.  The PyTorch implementation takes ~25 ms, an improvement
over TensorFlow, but worse than the original Torch.
"""

from __future__ import absolute_import

tonyfast

peterbell10

#!/bin/bash

# Activate the build environment
eval "$(conda shell.bash hook)"
conda activate pytorch-dev

cd ~/git/pytorch

# Enable ccache
export CCACHE_COMPRESS=true

n2cholas

'''
To run the CPU benchmark: `CUDA_VISIBLE_DEVICES="" python benchmark.py --name cpu`
To run the GPU benchmark: `CUDA_VISIBLE_DEVICES=0 python benchmark.py --name cuda`
To run the distributed benchmark: `python -u -m torch.distributed.launch --nproc_per_node=2 --use_env  benchmark.py --name dist`
'''


import argparse
import time
import math

lirnli

schwehr

andrewjjenkins

# Portions Copyright 2016 The Kubernetes Authors All rights reserved.
# Portions Copyright 2018 AspenMesh
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software

ericspod

import torch
import warnings
import threading
import numpy as np

from ignite.engine.engine import Engine, Events

def ensure_tuple(vals):
    """
    Returns a tuple containing just `vals` if it is not a list or tuple, or `vals` converted to a tuple otherwise.