from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import argparse
from functools import partial
import json
import traceback


import imlib as im
import numpy as np
import pylib
import tensorflow as tf
import tflib as tl

import data
import models

import os


# ==============================================================================
# =                                    param                                   =
# ==============================================================================

parser = argparse.ArgumentParser()
parser.add_argument('--experiment_name', help='experiment_name')
parser.add_argument('--gpu', type=str, default='all', help='gpu')
parser.add_argument('--dataroot', type=str, default='/data/Datasets/CelebA/Img')
# if assigned, only given images will be tested.
parser.add_argument('--img', type=int, nargs='+', default=None, help='e.g., --img 182638 202599')
# for multiple attributes
parser.add_argument('--test_att', type=str, default='Bangs')
args_ = parser.parse_args()
with open('./output/%s/setting.txt' % args_.experiment_name) as f:
    args = json.load(f)

# model
atts = args['atts']
img_size = args['img_size']

dataroot = args_.dataroot
img = args_.img
print('Using selected images:', img)

gpu = args_.gpu
if gpu != 'all':
    os.environ['CUDA_VISIBLE_DEVICES'] = gpu

# others
use_cropped_img = args['use_cropped_img']
experiment_name = args_.experiment_name


# ==============================================================================
# =                                   graphs                                   =
# ==============================================================================

# data
sess = tl.session()
te_data = data.Celeba(dataroot, atts, img_size, 1, part='test', sess=sess, crop=not use_cropped_img, im_no=img)
# models
G = partial(models.G)
D = partial(models.D)

# inputs
xa_sample = tf.placeholder(tf.float32, shape=[1, img_size, img_size, 3])
_b_sample = tf.placeholder(tf.float32, shape=[1, ])
raw_b_sample = tf.placeholder(tf.float32, shape=[1, ])

# sample
test_label = _b_sample
x_sample, mask_sample = G(xa_sample,  test_label, is_training=False)

# ==============================================================================
# =                                    test                                    =
# ==============================================================================

# initialization
ckpt_dir = './output/%s/checkpoints' % experiment_name
tl.load_checkpoint(ckpt_dir, sess)

# test
try:
    for idx, batch in enumerate(te_data):
        xa_sample_ipt = batch[0]
        a_sample_ipt = batch[1]
        b_sample_ipt_list = [a_sample_ipt.copy() for _ in range(1)]
        # test_single_attributes
        tmp = np.array(a_sample_ipt, copy=True)
        tmp[:] = 1 - tmp[:]   # inverse attribute
        b_sample_ipt_list.append(tmp)

        x_sample_opt_list = [xa_sample_ipt, np.full((1, img_size, img_size // 10, 3), -1.0)]
        mask_sample_opt_list = [np.full((1, img_size, img_size // 10, 1), -1.0)]
        for i, b_sample_ipt in enumerate(b_sample_ipt_list):
            x_sample_opt_list.append(sess.run(x_sample, feed_dict={xa_sample: xa_sample_ipt,
                                                                   _b_sample: b_sample_ipt,}))
            mask_sample_opt_list.append(sess.run(mask_sample, feed_dict={xa_sample: xa_sample_ipt,
                                                                         _b_sample: b_sample_ipt}))
        sample = np.concatenate(x_sample_opt_list, 2)
        masks = np.concatenate(mask_sample_opt_list, 2)

        save_folder = 'sample_testing'
        save_dir = './output/%s/%s' % (experiment_name, save_folder)
        pylib.mkdir(save_dir)
        im.imwrite(sample.squeeze(0), '%s/%06d%s.png' % (save_dir,
                                                         idx + 182638 if img is None else img[idx],
                                                         '_%s' % ''))
        im.imwrite(masks.squeeze(0), '%s/Mask%06d%s.png' % (save_dir,
                                                            idx + 182638 if img is None else img[idx],
                                                            '_%s' % ''))

        print('%06d.png done!' % (idx + 182638 if img is None else img[idx]))
except:
    traceback.print_exc()
finally:
    sess.close()
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import argparse
from functools import partial
import json
import traceback


import imlib as im
import numpy as np
import pylib
import tensorflow as tf
import tflib as tl

import data
import models

import os


# ==============================================================================
# =                                    param                                   =
# ==============================================================================

parser = argparse.ArgumentParser()
parser.add_argument('--experiment_name', help='experiment_name')
parser.add_argument('--gpu', type=str, default='all', help='gpu')
parser.add_argument('--dataroot', type=str, default='/data/Datasets/CelebA/Img')
# if assigned, only given images will be tested.
parser.add_argument('--img', type=int, nargs='+', default=None, help='e.g., --img 182638 202599')
# for multiple attributes
parser.add_argument('--test_att', type=str, default='Bangs')
args_ = parser.parse_args()
with open('./output/%s/setting.txt' % args_.experiment_name) as f:
    args = json.load(f)

# model
atts = args['atts']
img_size = args['img_size']

dataroot = args_.dataroot
img = args_.img
print('Using selected images:', img)

gpu = args_.gpu
if gpu != 'all':
    os.environ['CUDA_VISIBLE_DEVICES'] = gpu

# others
use_cropped_img = args['use_cropped_img']
experiment_name = args_.experiment_name


# ==============================================================================
# =                                   graphs                                   =
# ==============================================================================

# data
sess = tl.session()
te_data = data.Celeba(dataroot, atts, img_size, 1, part='test', sess=sess, crop=not use_cropped_img, im_no=img)
# models
G = partial(models.G)
D = partial(models.D)

# inputs
xa_sample = tf.placeholder(tf.float32, shape=[1, img_size, img_size, 3])
_b_sample = tf.placeholder(tf.float32, shape=[1, ])
raw_b_sample = tf.placeholder(tf.float32, shape=[1, ])

# sample
test_label = _b_sample
x_sample, mask_sample = G(xa_sample,  test_label, is_training=False)

# ==============================================================================
# =                                    test                                    =
# ==============================================================================

# initialization
ckpt_dir = './output/%s/checkpoints' % experiment_name
tl.load_checkpoint(ckpt_dir, sess)

# test
try:
    for idx, batch in enumerate(te_data):
        xa_sample_ipt = batch[0]
        a_sample_ipt = batch[1]
        b_sample_ipt_list = [a_sample_ipt.copy() for _ in range(1)]
        # test_single_attributes
        tmp = np.array(a_sample_ipt, copy=True)
        tmp[:] = 1 - tmp[:]   # inverse attribute
        b_sample_ipt_list.append(tmp)

        x_sample_opt_list = [xa_sample_ipt, np.full((1, img_size, img_size // 10, 3), -1.0)]
        mask_sample_opt_list = [np.full((1, img_size, img_size // 10, 1), -1.0)]
        for i, b_sample_ipt in enumerate(b_sample_ipt_list):
            x_sample_opt_list.append(sess.run(x_sample, feed_dict={xa_sample: xa_sample_ipt,
                                                                   _b_sample: b_sample_ipt,}))
            mask_sample_opt_list.append(sess.run(mask_sample, feed_dict={xa_sample: xa_sample_ipt,
                                                                         _b_sample: b_sample_ipt}))
        sample = np.concatenate(x_sample_opt_list, 2)
        masks = np.concatenate(mask_sample_opt_list, 2)

        save_folder = 'sample_testing'
        save_dir = './output/%s/%s' % (experiment_name, save_folder)
        pylib.mkdir(save_dir)
        im.imwrite(sample.squeeze(0), '%s/%06d%s.png' % (save_dir,
                                                         idx + 182638 if img is None else img[idx],
                                                         '_%s' % ''))
        im.imwrite(masks.squeeze(0), '%s/Mask%06d%s.png' % (save_dir,
                                                            idx + 182638 if img is None else img[idx],
                                                            '_%s' % ''))

        print('%06d.png done!' % (idx + 182638 if img is None else img[idx]))
except:
    traceback.print_exc()
finally:
    sess.close()
# ============================================================================== # =                                    param                                   = # ============================================================================== # if assigned, only given images will be tested. # for multiple attributes # model # others # ============================================================================== # =                                   graphs                                   = # ============================================================================== # data # models # inputs # sample # ============================================================================== # =                                    test                                    = # ============================================================================== # initialization # test # test_single_attributes # inverse attribute