rishab-sharma

wget --load-cookies /tmp/cookies.txt "https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=FILEID' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\1\n/p')&id=FILEID" -O FILENAME && rm -rf /tmp/cookies.txt

rishab-sharma

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

# Code from Hang Zhao (@hangzhaomit)
class InnerProd(nn.Module):
    def __init__(self, fc_dim):
        super(InnerProd, self).__init__()
        self.scale = nn.Parameter(torch.ones(fc_dim))
        self.bias = nn.Parameter(torch.zeros(1))

rishab-sharma

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

# Code from Hang Zhao (@hangzhaomit)
class Unet(nn.Module):
    def __init__(self, fc_dim=64, num_downs=5, ngf=64, use_dropout=False):
        super(Unet, self).__init__()

        # construct unet structure

rishab-sharma

import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
from functools import partial

class DilatedResnet18MaxPool(nn.Module):
    def __init__(self, fc_dim=64, conv_size=3):
        super(DilatedResnet18MaxPool, self).__init__()

rishab-sharma

import cv2
import numpy as np
from glob import glob
import requests 
import os

def get_video(video_url):
  r = requests.get(video_url, stream = True) 
  with open('./vid.mp4', 'wb') as f: 
    for chunk in r.iter_content(chunk_size = 1024*1024): 

rishab-sharma

export IMAGE_FAMILY="pytorch-latest-gpu"
export ZONE="us-west1-b"
export INSTANCE_NAME="human-seg"
export INSTANCE_TYPE="n1-standard-8"
gcloud compute instances create $INSTANCE_NAME \
 --zone=$ZONE \
 --image-family=$IMAGE_FAMILY \
 --machine-type=$INSTANCE_TYPE \
 --image-project=deeplearning-platform-release \
 --maintenance-policy=TERMINATE \

rishab-sharma

# coding: utf-8

__author__ = 'rishab-sharma'

import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
import keras
from keras.preprocessing import image
from keras.models import Model
from keras.layers import Dense, GlobalAveragePooling2D , Dropout

rishab-sharma

'''Visualization of the filters of VGG16, via gradient ascent in input space.
This script can run on CPU in a few minutes.
Results example: http://i.imgur.com/4nj4KjN.jpg
'''
from __future__ import print_function

import numpy as np
import time
from keras.preprocessing.image import save_img
from keras.applications import vgg16