jcjohnson

require 'torch'

local utils = require 'utils'
require 'DataLoader'


local cmd = torch.CmdLine()
cmd:option('-train_data_h5', 'data/vg-regions-720.h5', 'path to the h5file containing the preprocessed dataset')
cmd:option('-train_data_json', 'data/vg-regions-720-dicts.json', 'path to the json file containing additional info')
cmd:option('-use_split_indicator', 1)

jcjohnson

require 'torch'

local utils = require 'utils'
require 'DataLoader'


local cmd = torch.CmdLine()
cmd:option('-train_data_h5', 'data/vg-regions-720.h5', 'path to the h5file containing the preprocessed dataset')
cmd:option('-train_data_json', 'data/vg-regions-720-dicts.json', 'path to the json file containing additional info')
cmd:option('-use_split_indicator', 1)

jcjohnson

require 'nn'

print(nn.SpatialReflectionPadding)

local m = nn.SpatialReflectionPadding(2, 2, 2, 2)
print(m)

local x = torch.randn(2, 3, 4, 5)
local y = m:forward(x)

jcjohnson

require 'torch'
require 'nn'
require 'image'
local hdf5 = require 'hdf5'


local cmd = torch.CmdLine()
cmd:option('-image_list', '')
cmd:option('-model', '')
cmd:option('-layer', 30)  -- Last ReLU for VGG-16

jcjohnson

import argparse, os
import numpy as np
from scipy.misc import imread, imresize
from skimage.filters import gaussian
import h5py


parser = argparse.ArgumentParser()
parser.add_argument('--train_dir', default='data/yang-91')
parser.add_argument('--val_dir', default='data/set5')

jcjohnson

import argparse, os
import numpy as np
from scipy.misc import imread, imsave


parser = argparse.ArgumentParser()
parser.add_argument('--template_dir', required=True) # Low-res images
parser.add_argument('--source_dir', required=True)   # Outputs from CNN
parser.add_argument('--output_dir', required=True)
args = parser.parse_args()

jcjohnson

STYLE_WEIGHT=5e2
STYLE_SCALE=1.0

th neural_style.lua \
  -content_image examples/inputs/hoovertowernight.jpg \
  -style_image starry_night_gigapixel.jpg \
  -style_scale $STYLE_SCALE \
  -print_iter 1 \
  -style_weight $STYLE_WEIGHT \
  -image_size 256 \

jcjohnson

def index(x, axis, idxs):
    """
    Inputs:
    - x: torch.Tensor with x.dim() == N
    - axis: Integer with 0 <= axis < N
    - idxs: List of integers, with 0 <= idxs[i] < x.size(axis)

    Returns:
        y: torch.Tensor satisfying
      

jcjohnson

from __future__ import print_function
import argparse
import json


parser = argparse.ArgumentParser()
parser.add_argument('--questions_file', required=True)
parser.add_argument('--answers_file', required=True)

jcjohnson

from __future__ import print_function
import argparse
import json
from collections import defaultdict
import numpy as np


parser = argparse.ArgumentParser()
parser.add_argument('--questions_file', required=True)
parser.add_argument('--answers_file', required=True)