ay27

# mainly from:
# 1. https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#env-vars
# 2. https://github.com/NVIDIA/DeepLearningExamples/issues/57
# 3. https://docs.nvidia.com/deeplearning/frameworks/tensorflow-user-guide/index.html#variablesaddtf

def is_using_hvd():
    env_vars = ["OMPI_COMM_WORLD_RANK", "OMPI_COMM_WORLD_SIZE"]

    if all([var in os.environ for var in env_vars]):
        return True

alsrgv

from __future__ import print_function

import argparse
import torch.backends.cudnn as cudnn
import torch.nn.functional as F
import torch.optim as optim
import torch.utils.data.distributed
from torchvision import models
import horovod.torch as hvd
import timeit

alper111

import torch
import torchvision

class VGGPerceptualLoss(torch.nn.Module):
    def __init__(self, resize=True):
        super(VGGPerceptualLoss, self).__init__()
        blocks = []
        blocks.append(torchvision.models.vgg16(pretrained=True).features[:4].eval())
        blocks.append(torchvision.models.vgg16(pretrained=True).features[4:9].eval())
        blocks.append(torchvision.models.vgg16(pretrained=True).features[9:16].eval())

wangg12

import os
import torch
import argparse
from maskrcnn_benchmark.config import cfg
from maskrcnn_benchmark.utils.c2_model_loading import load_c2_format


def removekey(d, listofkeys):
    r = dict(d)
    for key in listofkeys:

bfarzin

hbredin

ctmakro

# if input image is in range 0..1, please first multiply img by 255
# assume image is ndarray of shape [height, width, channels] where channels can be 1, 3 or 4
def imshow(img):
    import cv2
    import IPython
    _,ret = cv2.imencode('.jpg', img) 
    i = IPython.display.Image(data=ret)
    IPython.display.display(i)

madhawav

import cv2
import time

person_cascade = cv2.CascadeClassifier(
    os.path.join('/path/to/haarcascade_fullbody.xml'))
cap = cv2.VideoCapture("/path/to/test/video")
while True:
    r, frame = cap.read()
    if r:
        start_time = time.time()

tarlen5

"""
author: Timothy C. Arlen
date: 28 Feb 2018

Calculate Mean Average Precision (mAP) for a set of bounding boxes corresponding to specific
image Ids. Usage:

> python calculate_mean_ap.py

Will display a plot of precision vs recall curves at 10 distinct IoU thresholds as well as output

mehdidc

tmux cheatsheet

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