import os, sys

import numpy as np

import imageio

import json

import random

import time

import jittor as jt

from jittor import nn

from tqdm import tqdm, trange

import datetime

import matplotlib.pyplot as plt

from nerf_helper.utils import *

from nerf_helper.load_llff import load_llff_data

from nerf_helper.load_deepvoxels import load_dv_data

from nerf_helper.load_blender import load_blender_data

from tensorboardX import SummaryWriter

from jrender_vol.renderPass import render as render

from jrender_vol.camera import *

jt.flags.use_cuda = 1

DEBUG = False

def batchify(fn, chunk):

"""Constructs a version of 'fn' that applies to smaller batches.

"""

if chunk is None:

return fn

def ret(inputs):

arr = []

for i in range(0, inputs.shape[0], chunk):

arr.append(fn(inputs[i:i+chunk]))

return jt.concat(arr, 0)

return ret

def run_network(inputs, viewdirs, fn, embed_fn, embeddirs_fn, netchunk=1024*64):

"""Prepares inputs and applies network 'fn'.

"""

inputs_flat = jt.reshape(inputs, [-1, inputs.shape[-1]])

embedded = embed_fn(inputs_flat)

if viewdirs is not None:

input_dirs = viewdirs[:,None].expand([*inputs.shape[:-1], viewdirs.shape[-1]])

input_dirs_flat = jt.reshape(input_dirs, [-1, input_dirs.shape[-1]])

embedded_dirs = embeddirs_fn(input_dirs_flat)

embedded = jt.concat([embedded, embedded_dirs], -1)

outputs_flat = batchify(fn, netchunk)(embedded)

outputs = jt.reshape(outputs_flat, list(inputs.shape[:-1]) + [outputs_flat.shape[-1]])

return outputs

def render_path(render_poses, hwf, chunk, render_kwargs, gt_imgs=None, savedir=None, render_factor=0, intrinsic = None, expname=""):

H, W, focal = hwf

if render_factor!=0:

# Render downsampled for speed

H = H//render_factor

W = W//render_factor

focal = focal/render_factor

rgbs = []

disps = []

t = time.time()

for i, c2w in enumerate(tqdm(render_poses)):

print(i, time.time() - t)

t = time.time()

rgb, disp, acc, _ = render(H, W, focal, chunk=chunk, c2w=c2w[:3,:4], intrinsic=intrinsic, **render_kwargs)

rgbs.append(rgb.numpy())

disps.append(disp.numpy())

if i==0:

print(rgb.shape, disp.shape)

if savedir is not None:

rgb8 = to8b(rgbs[-1])

filename = os.path.join(savedir, expname + '_r_{:d}.png'.format(i))

imageio.imwrite(filename, rgb8)

del rgb

del disp

del acc

del _

rgbs = np.stack(rgbs, 0)

disps = np.stack(disps, 0)

return rgbs, disps

def create_nerf(args):

"""Instantiate NeRF's MLP model.

"""

input_dims = 3

if args.embed_depth:

input_dims += 1

print ('embed depth')

print ('input dims: {}'.format(input_dims))

embed_fn, input_ch = get_embedder(args.multires, args.i_embed, input_dims)

input_ch_views = 0

embeddirs_fn = None

if args.use_viewdirs:

embeddirs_fn, input_ch_views = get_embedder(args.multires_views, args.i_embed)

output_ch = 5 if args.N_importance > 0 else 4

skips = [4]

model = NeRF(D=args.netdepth, W=args.netwidth,

input_ch=input_ch, output_ch=output_ch, skips=skips,

input_ch_views=input_ch_views, use_viewdirs=args.use_viewdirs)

grad_vars = list(model.parameters())

model_fine = None

if args.N_importance > 0:

model_fine = NeRF(D=args.netdepth_fine, W=args.netwidth_fine,

input_ch=input_ch, output_ch=output_ch, skips=skips,

input_ch_views=input_ch_views, use_viewdirs=args.use_viewdirs)

grad_vars += list(model_fine.parameters())

network_query_fn = lambda inputs, viewdirs, network_fn : run_network(inputs, viewdirs, network_fn,

embed_fn=embed_fn,

embeddirs_fn=embeddirs_fn,

netchunk=args.netchunk)

# Create optimizer

optimizer = jt.optim.Adam(params=grad_vars, lr=args.lrate, betas=(0.9, 0.999))

start = 0

basedir = args.basedir

expname = args.expname

##########################

# Load checkpoints

if args.ft_path is not None and args.ft_path!='None':

ckpts = [args.ft_path]

else:

ckpts = [os.path.join(basedir, expname, f) for f in sorted(os.listdir(os.path.join(basedir, expname))) if 'tar' in f]

print('Found ckpts', ckpts)

if len(ckpts) > 0 and not args.no_reload:

ckpt_path = ckpts[-1]

print('Reloading from', ckpt_path)

ckpt = jt.load(ckpt_path)

start = ckpt['global_step']

if 'optimizer_state_dict' in ckpt.keys():

optimizer.load_state_dict(ckpt['optimizer_state_dict'])

# Load model

model.load_state_dict(ckpt['network_fn_state_dict'])

if model_fine is not None:

model_fine.load_state_dict(ckpt['network_fine_state_dict'])

##########################

render_kwargs_train = {

'network_query_fn' : network_query_fn,

'perturb' : args.perturb,

'N_importance' : args.N_importance,

'network_fine' : model_fine,

'N_samples' : args.N_samples,

'network_fn' : model,

'use_viewdirs' : args.use_viewdirs,

'white_bkgd' : args.white_bkgd,

'raw_noise_std' : args.raw_noise_std,

'embed_depth' : args.embed_depth,

}

# NDC only good for LLFF-style forward facing data

if args.dataset_type != 'llff' or args.no_ndc:

print('Not ndc!')

render_kwargs_train['ndc'] = False

render_kwargs_train['lindisp'] = args.lindisp

render_kwargs_test = {k : render_kwargs_train[k] for k in render_kwargs_train}

render_kwargs_test['perturb'] = False

render_kwargs_test['raw_noise_std'] = 0.

return render_kwargs_train, render_kwargs_test, start, grad_vars, optimizer

def config_parser():

gpu = "gpu"+os.environ.get("CUDA_VISIBLE_DEVICES", "0")

import configargparse

parser = configargparse.ArgumentParser()

parser.add_argument('--config', is_config_file=True,

help='config file path')

parser.add_argument("--expname", type=str,

help='experiment name')

parser.add_argument("--basedir", type=str, default='./logs/'+gpu+"/",

help='where to store ckpts and logs')

parser.add_argument("--datadir", type=str, default='./data/llff/fern',

help='input data directory')

# training options

parser.add_argument("--netdepth", type=int, default=8,

help='layers in network')

parser.add_argument("--netwidth", type=int, default=256,

help='channels per layer')

parser.add_argument("--netdepth_fine", type=int, default=8,

help='layers in fine network')

parser.add_argument("--embed_depth", type=bool, default=False,

help='embed distance bwtween pts and camera position, which could help model to converge but may be harmful to generalize')

parser.add_argument("--netwidth_fine", type=int, default=256,

help='channels per layer in fine network')

parser.add_argument("--N_rand", type=int, default=32*32*4,

help='batch size (number of random rays per gradient step)')

parser.add_argument("--lrate", type=float, default=5e-4,

help='learning rate')

parser.add_argument("--lrate_decay", type=int, default=250,

help='exponential learning rate decay (in 1000 steps)')

parser.add_argument("--chunk", type=int, default=1024*8,

help='number of rays processed in parallel, decrease if running out of memory')

parser.add_argument("--netchunk", type=int, default=1024*64,

help='number of pts sent through network in parallel, decrease if running out of memory')

parser.add_argument("--no_batching", action='store_true',

help='only take random rays from 1 image at a time')

parser.add_argument("--no_reload", action='store_true',

help='do not reload weights from saved ckpt')

parser.add_argument("--ft_path", type=str, default=None,

help='specific weights npy file to reload for coarse network')

# rendering options

parser.add_argument("--N_samples", type=int, default=64,

help='number of coarse samples per ray')

parser.add_argument("--N_importance", type=int, default=0,

help='number of additional fine samples per ray')

parser.add_argument("--perturb", type=float, default=1.,

help='set to 0. for no jitter, 1. for jitter')

parser.add_argument("--use_viewdirs", action='store_true',

help='use full 5D input instead of 3D')

parser.add_argument("--i_embed", type=int, default=0,

help='set 0 for default positional encoding, -1 for none')

parser.add_argument("--multires", type=int, default=10,

help='log2 of max freq for positional encoding (3D location)')

parser.add_argument("--multires_views", type=int, default=4,

help='log2 of max freq for positional encoding (2D direction)')

parser.add_argument("--raw_noise_std", type=float, default=0.,

help='std dev of noise added to regularize sigma_a output, 1e0 recommended')

parser.add_argument("--render_only", action='store_true',

help='do not optimize, reload weights and render out render_poses path')

parser.add_argument("--render_test", action='store_true',

help='render the test set instead of render_poses path')

parser.add_argument("--render_factor", type=int, default=0,

help='downsampling factor to speed up rendering, set 4 or 8 for fast preview')

# training options

parser.add_argument("--precrop_iters", type=int, default=0,

help='number of steps to train on central crops')

parser.add_argument("--precrop_frac", type=float,

default=.5, help='fraction of img taken for central crops')

# dataset options

parser.add_argument("--dataset_type", type=str, default='llff',

help='options: llff / blender / deepvoxels')

parser.add_argument("--testskip", type=int, default=8,

help='will load 1/N images from test/val sets, useful for large datasets like deepvoxels')

parser.add_argument("--faketestskip", type=int, default=1,

help='will load 1/N images from test/val sets, useful for large datasets like deepvoxels')

parser.add_argument("--valid_ratio", type=float, default=-1.0,

help='importance sampling for training pixel selection, help to convergence, useful only for rgba images')

## deepvoxels flags

parser.add_argument("--shape", type=str, default='greek',

help='options : armchair / cube / greek / vase')

## blender flags

parser.add_argument("--white_bkgd", action='store_true',

help='set to render synthetic data on a white bkgd (always use for dvoxels)')

parser.add_argument("--half_res", action='store_true',

help='load blender synthetic data at 400x400 instead of 800x800')

parser.add_argument("--near", type=float, default=2.,

help='set near distance')

parser.add_argument("--far", type=float, default=6.,

help='set far distance')

parser.add_argument("--do_intrinsic", action='store_true',

help='use intrinsic matrix')

parser.add_argument("--blender_factor", type=int, default=1,

help='downsample factor for blender images')

parser.add_argument("--do_pose_normalization", type=bool, default=False,

help='normalize all poses, useful for 360 scenes')

parser.add_argument("--target_radius", type=float, default=1.0,

help='radius of the sphere for normalization, used only if do_pose_normalization is set true')

## llff flags

parser.add_argument("--factor", type=int, default=8,

help='downsample factor for LLFF images')

parser.add_argument("--no_ndc", action='store_true',

help='do not use normalized device coordinates (set for non-forward facing scenes)')

parser.add_argument("--lindisp", action='store_true',

help='sampling linearly in disparity rather than depth')

parser.add_argument("--spherify", action='store_true',

help='set for spherical 360 scenes')

parser.add_argument("--llffhold", type=int, default=8,

help='will take every 1/N images as LLFF test set, paper uses 8')

# logging/saving options

parser.add_argument("--N_iters", type=int, default=51000,

help='number of iterations')

parser.add_argument("--i_print", type=int, default=100,

help='frequency of console printout and metric loggin')

parser.add_argument("--i_img", type=int, default=50000,

help='frequency of tensorboard image logging')

parser.add_argument("--i_weights", type=int, default=10000,

help='frequency of weight ckpt saving')

parser.add_argument("--i_testset", type=int, default=50000,

help='frequency of testset saving')

parser.add_argument("--i_tottest", type=int, default=400000,

help='frequency of testset saving')

parser.add_argument("--i_video", type=int, default=50000,

help='frequency of render_poses video saving')

return parser

def train():

parser = config_parser()

args = parser.parse_args()

# Load data

intrinsic = None

if args.dataset_type == 'llff':

images, poses, bds, render_poses, i_test = load_llff_data(args.datadir, args.factor,

recenter=True, bd_factor=.75,

spherify=args.spherify)

hwf = poses[0,:3,-1]

poses = poses[:,:3,:4]

print('Loaded llff', images.shape, render_poses.shape, hwf, args.datadir)

if not isinstance(i_test, list):

i_test = [i_test]

if args.llffhold > 0:

print('Auto LLFF holdout,', args.llffhold)

i_test = np.arange(images.shape[0])[::args.llffhold]

i_val = i_test

i_train = np.array([i for i in np.arange(int(images.shape[0])) if

(i not in i_test and i not in i_val)])

print('DEFINING BOUNDS')

if args.no_ndc:

near = np.ndarray.min(bds) * .9

far = np.ndarray.max(bds) * 1.

else:

near = 0.

far = 1.

print('NEAR FAR', near, far)

elif args.dataset_type == 'blender':

testskip = args.testskip

faketestskip = args.faketestskip

if jt.mpi and jt.mpi.local_rank()!=0:

testskip = faketestskip

faketestskip = 1

if args.do_intrinsic:

images, poses, intrinsic, render_poses, hwf, i_split = load_blender_data(args.datadir, args.half_res, args.testskip,

args.blender_factor, args.do_pose_normalization, args.target_radius, True)

else:

images, poses, render_poses, hwf, i_split = load_blender_data(args.datadir, args.half_res, args.testskip,

args.blender_factor, args.do_pose_normalization, args.target_radius)

print('Loaded blender', images.shape, render_poses.shape, hwf, args.datadir)

i_train, i_val, i_test = i_split

i_test_tot = i_test

i_test = i_test[::args.faketestskip]

near = args.near

far = args.far

print(args.do_intrinsic)

print("hwf", hwf)

print("near", near)

print("far", far)

if args.do_pose_normalization:

print ("using normalized pose, radius {:.2f}".format(args.target_radius))

if images.shape[-1] == 4:

masks = images[..., -1:]

else:

masks = np.ones_like(images[..., :1])

if args.white_bkgd:

images = images[...,:3]*images[...,-1:] + (1.-images[...,-1:])

else:

images = images[...,:3]

elif args.dataset_type == 'deepvoxels':

images, poses, render_poses, hwf, i_split = load_dv_data(scene=args.shape,

basedir=args.datadir,

testskip=args.testskip)

print('Loaded deepvoxels', images.shape, render_poses.shape, hwf, args.datadir)

i_train, i_val, i_test = i_split

hemi_R = np.mean(np.linalg.norm(poses[:,:3,-1], axis=-1))

near = hemi_R-1.

far = hemi_R+1.

else:

print('Unknown dataset type', args.dataset_type, 'exiting')

return

# Cast intrinsics to right types

H, W, focal = hwf

H, W = int(H), int(W)

hwf = [H, W, focal]

render_poses = np.array(poses[i_test])

# Create log dir and copy the config file

basedir = args.basedir

expname = args.expname

os.makedirs(os.path.join(basedir, expname), exist_ok=True)

f = os.path.join(basedir, expname, 'args.txt')

with open(f, 'w') as file:

for arg in sorted(vars(args)):

attr = getattr(args, arg)

file.write('{} = {}\n'.format(arg, attr))

if args.config is not None:

f = os.path.join(basedir, expname, 'config.txt')

with open(f, 'w') as file:

file.write(open(args.config, 'r').read())

# Create nerf model

render_kwargs_train, render_kwargs_test, start, grad_vars, optimizer = create_nerf(args)

global_step = start

bds_dict = {

'near' : near,

'far' : far,

}

render_kwargs_train.update(bds_dict)

render_kwargs_test.update(bds_dict)

# Move testing data to GPU

render_poses = jt.array(render_poses)

# Short circuit if only rendering out from trained model

if args.render_only:

print('RENDER ONLY')

with jt.no_grad():

testsavedir = os.path.join(basedir, expname, 'renderonly_{}_{:06d}'.format('test' if args.render_test else 'path', start))

os.makedirs(testsavedir, exist_ok=True)

print('test poses shape', render_poses.shape)

rgbs, _ = render_path(render_poses, hwf, args.chunk, render_kwargs_test, savedir=testsavedir, render_factor=args.render_factor)

print('Done rendering', testsavedir)

imageio.mimwrite(os.path.join(testsavedir, 'video.mp4'), to8b(rgbs), fps=30, quality=8)

return

# Prepare raybatch tensor if batching random rays

accumulation_steps = 1

N_rand = args.N_rand//accumulation_steps

use_batching = not args.no_batching

if use_batching:

# For random ray batching

print('get rays')

rays = np.stack([get_rays_np(H, W, focal, p) for p in poses[:,:3,:4]], 0) # [N, ro+rd, H, W, 3]

print('done, concats')

rays_rgb = np.concatenate([rays, images[:,None]], 1) # [N, ro+rd+rgb, H, W, 3]

rays_rgb = np.transpose(rays_rgb, [0,2,3,1,4]) # [N, H, W, ro+rd+rgb, 3]

rays_rgb = np.stack([rays_rgb[i] for i in i_train], 0) # train images only

rays_rgb = np.reshape(rays_rgb, [-1,3,3]) # [(N-1)*H*W, ro+rd+rgb, 3]

rays_rgb = rays_rgb.astype(np.float32)

print('shuffle rays')

np.random.shuffle(rays_rgb)

print('done')

i_batch = 0

# Move training data to GPU

images = jt.array(images.astype(np.float32))

poses = jt.array(poses)

masks = jt.array(masks)

if use_batching:

rays_rgb = jt.array(rays_rgb)

N_iters = args.N_iters

print('Begin')

print('TRAIN views are', i_train)

print('TEST views are', i_test)

print('VAL views are', i_val)

# Summary writers

# writer = SummaryWriter(os.path.join(basedir, 'summaries', expname))

if not jt.mpi or jt.mpi.local_rank()==0:

date = str(datetime.datetime.now())

date = date[:date.rfind(":")].replace("-", "")\

.replace(":", "")\

.replace(" ", "_")

gpu_idx = os.environ.get("CUDA_VISIBLE_DEVICES", "0")

log_dir = os.path.join("./logs", "summaries", "log_" + date +"_gpu" + gpu_idx)

if not os.path.exists(log_dir):

os.makedirs(log_dir)

writer = SummaryWriter(log_dir=log_dir)

start = start + 1

for i in trange(start, N_iters):

# jt.display_memory_info()

time0 = time.time()

# Sample random ray batch

if use_batching:

# Random over all images

batch = rays_rgb[i_batch:i_batch+N_rand] # [B, 2+1, 3*?]

batch = jt.transpose(batch, (1, 0, 2))

batch_rays, target_s = batch[:2], batch[2]

i_batch += N_rand

if i_batch >= rays_rgb.shape[0]:

print("Shuffle data after an epoch!")

rand_idx = jt.randperm(rays_rgb.shape[0])

rays_rgb = rays_rgb[rand_idx]

i_batch = 0

else:

# Random from one image

np.random.seed(i)

img_i = np.random.choice(i_train)

target = images[img_i]#.squeeze(0)

pose = poses[img_i, :3,:4]#.squeeze(0)

mask = masks[img_i]

if N_rand is not None:

rays_o, rays_d = pinhole_get_rays(H, W, focal, pose, intrinsic) # (H, W, 3), (H, W, 3)

if i < args.precrop_iters:

dH = int(H//2 * args.precrop_frac)

dW = int(W//2 * args.precrop_frac)

coords = jt.stack(

jt.meshgrid(

jt.linspace(H//2 - dH, H//2 + dH - 1, 2*dH),

jt.linspace(W//2 - dW, W//2 + dW - 1, 2*dW)

), -1)

center_mask = mask[coords[:, :, 0], coords[:, :, 0]]

mask = jt.reshape(center_mask, (2*dH, 2*dW, 1))

if i == start:

print(f"[Config] Center cropping of size {2*dH} x {2*dW} is enabled until iter {args.precrop_iters}")

else:

coords = jt.stack(jt.meshgrid(jt.linspace(0, H-1, H), jt.linspace(0, W-1, W)), -1) # (H, W, 2)

valid_coords_mask = jt.nonzero(mask > 0.)

invalid_coords_mask = jt.nonzero(mask == 0.)

if valid_coords_mask.shape[0] > invalid_coords_mask.shape[0] or args.valid_ratio < 0:

coords = jt.reshape(coords, [-1,2]) # (H * W, 2)

select_inds = np.random.choice(coords.shape[0], size=[N_rand], replace=False) # (N_rand,)

select_coords = coords[select_inds].int() # (N_rand, 2)

else:

sample_size = np.min([int(N_rand * args.valid_ratio), valid_coords_mask.shape[0]])

select_valid_inds = np.random.choice(valid_coords_mask.shape[0], size=[sample_size], replace=False)

select_invalid_inds = np.random.choice(invalid_coords_mask.shape[0], size=[N_rand - sample_size], replace=False)

select_coords_valid = valid_coords_mask[select_valid_inds]

select_coords_invalid = invalid_coords_mask[select_invalid_inds]

select_coords = jt.concat([select_coords_valid, select_coords_invalid], dim=0).int()

rays_o = rays_o[select_coords[:, 0], select_coords[:, 1]] # (N_rand, 3)

rays_d = rays_d[select_coords[:, 0], select_coords[:, 1]] # (N_rand, 3)

batch_rays = jt.stack([rays_o, rays_d], 0)

target_s = target[select_coords[:, 0], select_coords[:, 1]] # (N_rand, 3)

##### Core optimization loop #####

rgb, disp, acc, extras = render(H, W, focal, chunk=args.chunk, rays=batch_rays,

verbose=i < 10, retraw=True,

**render_kwargs_train)

img_loss = img2mse(rgb, target_s)

trans = extras['raw'][...,-1]

loss = img_loss

psnr = mse2psnr(img_loss)

if 'rgb0' in extras:

img_loss0 = img2mse(extras['rgb0'], target_s)

loss = loss + img_loss0

psnr0 = mse2psnr(img_loss0)

optimizer.backward(loss / accumulation_steps)

if i % accumulation_steps == 0:

optimizer.step()

### update learning rate ###

decay_rate = 0.1

decay_steps = args.lrate_decay * accumulation_steps * 1000

new_lrate = args.lrate * (decay_rate ** (global_step / decay_steps))

for param_group in optimizer.param_groups:

param_group['lr'] = new_lrate

################################

dt = time.time()-time0

# Rest is logging

if (i+1)%args.i_weights==0 and (not jt.mpi or jt.mpi.local_rank()==0):

print(i)

path = os.path.join(basedir, expname, '{:06d}.tar'.format(i))

jt.save({

'global_step': global_step,

'network_fn_state_dict': render_kwargs_train['network_fn'].state_dict(),

'network_fine_state_dict': render_kwargs_train['network_fine'].state_dict(),

}, path)

print('Saved checkpoints at', path)

if i%args.i_video==0 and i > 0:

# Turn on testing mode

with jt.no_grad():

rgbs, disps = render_path(render_poses, hwf, args.chunk, render_kwargs_test, intrinsic = intrinsic)

if not jt.mpi or jt.mpi.local_rank()==0:

print('Done, saving', rgbs.shape, disps.shape)

moviebase = os.path.join(basedir, expname, '{}_spiral_{:06d}_'.format(expname, i))

print('movie base ', moviebase)

imageio.mimwrite(moviebase + 'rgb.mp4', to8b(rgbs), fps=30, quality=8)

imageio.mimwrite(moviebase + 'disp.mp4', to8b(disps / np.max(disps)), fps=30, quality=8)

if i%args.i_print==0:

tqdm.write(f"[TRAIN] Iter: {i} Loss: {loss.item()} PSNR: {psnr.item()}")

if i%args.i_img==0:

img_i=np.random.choice(i_val)

target = images[img_i]

pose = poses[img_i, :3,:4]

with jt.no_grad():

rgb, disp, acc, extras = render(H, W, focal, chunk=args.chunk, c2w=pose, intrinsic=intrinsic,

**render_kwargs_test)

psnr = mse2psnr(img2mse(rgb, target))

rgb = rgb.numpy()

disp = disp.numpy()

acc = acc.numpy()

if not jt.mpi or jt.mpi.local_rank()==0:

writer.add_image('test/rgb', to8b(rgb), global_step, dataformats="HWC")

writer.add_image('test/target', target.numpy(), global_step, dataformats="HWC")

writer.add_scalar('test/psnr', psnr.item(), global_step)

jt.clean_graph()

jt.sync_all()

jt.gc()

if i%args.i_testset==0 and i > 0:

si_test = i_test_tot if i%args.i_tottest==0 else i_test

testsavedir = os.path.join(basedir, expname, 'testset_{:06d}'.format(i))

os.makedirs(testsavedir, exist_ok=True)

print('test poses shape', poses[si_test].shape)

with jt.no_grad():

rgbs, disps = render_path(jt.array(poses[si_test]), hwf, args.chunk, render_kwargs_test, savedir=testsavedir, intrinsic = intrinsic, expname = expname)

jt.gc()

global_step += 1

if __name__=='__main__':

train()