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test_batch.py
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test_batch.py
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"""
Copyright (C) 2018 NVIDIA Corporation. All rights reserved.
Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
"""
from __future__ import print_function
from utils import get_config, get_data_loader_folder, pytorch03_to_pytorch04, load_inception, line_interpolate
from trainer import MUNIT_Trainer, UNIT_Trainer
from torch import nn
from scipy.stats import entropy
import torch.nn.functional as F
import argparse
from torch.autograd import Variable
from data import ImageFolder
import numpy as np
import torchvision.utils as vutils
try:
from itertools import izip as zip
except ImportError: # will be 3.x series
pass
import sys
import torch
import os
parser = argparse.ArgumentParser()
parser.add_argument('--config', type=str, default='configs/edges2handbags_folder', help='Path to the config file.')
parser.add_argument('--input_folder', type=str, help="input image folder")
parser.add_argument('--output_folder', type=str, help="output image folder")
parser.add_argument('--checkpoint', type=str, help="checkpoint of autoencoders")
parser.add_argument('--a2b', type=int, help="1 for a2b and 0 for b2a", default=1)
parser.add_argument('--seed', type=int, default=0, help="random seed")
parser.add_argument('--num_style',type=int, default=10, help="number of styles to sample")
parser.add_argument('--num_style_start',type=int, default=0, help="starting style sample index (zero index based)")
parser.add_argument('--synchronized', action='store_true', help="whether use synchronized style code or not")
parser.add_argument('--output_only', action='store_true', help="whether only save the output images or also save the input images")
parser.add_argument('--output_path', type=str, default='.', help="path for logs, checkpoints, and VGG model weight")
parser.add_argument('--file_extension', type=str, default='png', help="jpg or png (default: png)")
parser.add_argument('--video', action='store_true', help="use as video, and use interpolation")
parser.add_argument('--seeds', type=str, default='0,1', help="random seeds for interpolation")
parser.add_argument('--trainer', type=str, default='MUNIT', help="MUNIT|UNIT")
parser.add_argument('--compute_IS', action='store_true', help="whether to compute Inception Score or not")
parser.add_argument('--compute_CIS', action='store_true', help="whether to compute Conditional Inception Score or not")
parser.add_argument('--inception_a', type=str, default='.', help="path to the pretrained inception network for domain A")
parser.add_argument('--inception_b', type=str, default='.', help="path to the pretrained inception network for domain B")
opts = parser.parse_args()
torch.manual_seed(opts.seed)
torch.cuda.manual_seed(opts.seed)
# Load experiment setting
config = get_config(opts.config)
input_dim = config['input_dim_a'] if opts.a2b else config['input_dim_b']
# Load the inception networks if we need to compute IS or CIIS
if opts.compute_IS or opts.compute_IS:
inception = load_inception(opts.inception_b) if opts.a2b else load_inception(opts.inception_a)
# freeze the inception models and set eval mode
inception.eval()
for param in inception.parameters():
param.requires_grad = False
inception_up = nn.Upsample(size=(299, 299), mode='bilinear')
# Setup model and data loader
image_names = ImageFolder(opts.input_folder, transform=None, return_paths=True)
data_loader = get_data_loader_folder(opts.input_folder, 1, False, new_size=config['new_size'], crop=False)
config['vgg_model_path'] = opts.output_path
if opts.trainer == 'MUNIT':
style_dim = config['gen']['style_dim']
trainer = MUNIT_Trainer(config)
elif opts.trainer == 'UNIT':
trainer = UNIT_Trainer(config)
else:
sys.exit("Only support MUNIT|UNIT")
try:
state_dict = torch.load(opts.checkpoint)
trainer.gen_a.load_state_dict(state_dict['a'])
trainer.gen_b.load_state_dict(state_dict['b'])
except:
state_dict = pytorch03_to_pytorch04(torch.load(opts.checkpoint), opts.trainer)
trainer.gen_a.load_state_dict(state_dict['a'])
trainer.gen_b.load_state_dict(state_dict['b'])
trainer.cuda()
trainer.eval()
encode = trainer.gen_a.encode if opts.a2b else trainer.gen_b.encode # encode function
decode = trainer.gen_b.decode if opts.a2b else trainer.gen_a.decode # decode function
if opts.compute_IS:
IS = []
all_preds = []
if opts.compute_CIS:
CIS = []
if opts.trainer == 'MUNIT':
# Start testing
if(opts.video):
style_count = opts.num_style_start + opts.num_style
style = Variable(torch.randn(style_count, style_dim, 1, 1).cuda())
seed_range = [int(seed) for seed in opts.seeds.split(',')]
styles = []
for s in seed_range:
styles.append(style[s].unsqueeze(0))
print(len(image_names))
steps = int(len(image_names)/(len(seed_range)-1))
# print(steps)
style_interp = line_interpolate(styles,steps)
for i, (images, names) in enumerate(zip(data_loader, image_names)):
print(names[1])
images = Variable(images.cuda())
content, _ = encode(images)
s = style_interp[i]
outputs = decode(content, s)
outputs = (outputs + 1) / 2.
path = os.path.join(opts.output_folder, 'output{:09d}.{}'.format(i,opts.file_extension))
vutils.save_image(outputs.data, path, padding=0, normalize=True)
else:
style_fixed = Variable(torch.randn(opts.num_style, style_dim, 1, 1).cuda(), volatile=True)
for i, (images, names) in enumerate(zip(data_loader, image_names)):
if opts.compute_CIS:
cur_preds = []
print(names[1])
images = Variable(images.cuda(), volatile=True)
content, _ = encode(images)
style = style_fixed if opts.synchronized else Variable(torch.randn(opts.num_style, style_dim, 1, 1).cuda(), volatile=True)
style_count = opts.num_style_start + opts.num_style
for j in range(opts.num_style_start,style_count):
s = style[j].unsqueeze(0)
outputs = decode(content, s)
outputs = (outputs + 1) / 2.
if opts.compute_IS or opts.compute_CIS:
pred = F.softmax(inception(inception_up(outputs)), dim=1).cpu().data.numpy() # get the predicted class distribution
if opts.compute_IS:
all_preds.append(pred)
if opts.compute_CIS:
cur_preds.append(pred)
# path = os.path.join(opts.output_folder, 'input{:03d}_output{:03d}.jpg'.format(i, j))
basename = os.path.basename(names[1]).split('.')[0]
filename = '{}.{}'.format(basename,opts.file_extension)
path = os.path.join(opts.output_folder+"_%02d"%j,filename)
if not os.path.exists(os.path.dirname(path)):
os.makedirs(os.path.dirname(path))
vutils.save_image(outputs.data, path, padding=0, normalize=True)
if opts.compute_CIS:
cur_preds = np.concatenate(cur_preds, 0)
py = np.sum(cur_preds, axis=0) # prior is computed from outputs given a specific input
for j in range(cur_preds.shape[0]):
pyx = cur_preds[j, :]
CIS.append(entropy(pyx, py))
if not opts.output_only:
# also save input images
vutils.save_image(images.data, os.path.join(opts.output_folder, 'input{:03d}.jpg'.format(i)), padding=0, normalize=True)
if opts.compute_IS:
all_preds = np.concatenate(all_preds, 0)
py = np.sum(all_preds, axis=0) # prior is computed from all outputs
for j in range(all_preds.shape[0]):
pyx = all_preds[j, :]
IS.append(entropy(pyx, py))
if opts.compute_IS:
print("Inception Score: {}".format(np.exp(np.mean(IS))))
if opts.compute_CIS:
print("conditional Inception Score: {}".format(np.exp(np.mean(CIS))))
elif opts.trainer == 'UNIT':
# Start testing
for i, (images, names) in enumerate(zip(data_loader, image_names)):
print(names[1])
images = Variable(images.cuda(), volatile=True)
content, _ = encode(images)
outputs = decode(content)
outputs = (outputs + 1) / 2.
# path = os.path.join(opts.output_folder, 'input{:03d}_output{:03d}.jpg'.format(i, j))
basename = os.path.basename(names[1])
path = os.path.join(opts.output_folder,basename)
if not os.path.exists(os.path.dirname(path)):
os.makedirs(os.path.dirname(path))
vutils.save_image(outputs.data, path, padding=0, normalize=True)
if not opts.output_only:
# also save input images
vutils.save_image(images.data, os.path.join(opts.output_folder, 'input{:03d}.jpg'.format(i)), padding=0, normalize=True)
else:
pass