infer.py

#copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
#
#Licensed under the Apache License, Version 2.0 (the "License");
#you may not use this file except in compliance with the License.
#You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
#Unless required by applicable law or agreed to in writing, software
#distributed under the License is distributed on an "AS IS" BASIS,
#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#See the License for the specific language governing permissions and
#limitations under the License.

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

import argparse
import functools
import os
from PIL import Image
import paddle.fluid as fluid
import paddle
import numpy as np
from scipy.misc import imsave
import glob
from util.config import add_arguments, print_arguments

parser = argparse.ArgumentParser(description=__doc__)
add_arg = functools.partial(add_arguments, argparser=parser)
# yapf: disable
add_arg('model_net',         str,   'cgan',            "The model used")
add_arg('net_G',             str,   "resnet_9block",   "Choose the CycleGAN and Pix2pix generator's network, choose in [resnet_9block|resnet_6block|unet_128|unet_256]")
add_arg('input',             str,   None,              "The images to be infered.")
add_arg('init_model',        str,   None,              "The init model file of directory.")
add_arg('output',            str,   "./infer_result",  "The directory the infer result to be saved to.")
add_arg('input_style',       str,   "A",               "The style of the input, A or B")
add_arg('norm_type',         str,   "batch_norm",      "Which normalization to used")
add_arg('use_gpu',           bool,  True,              "Whether to use GPU to train.")
add_arg('dropout',           bool,  False,             "Whether to use dropout")
add_arg('data_shape',        int,   256,               "The shape of load image")
add_arg('g_base_dims',       int,   64,                "Base channels in CycleGAN generator")
add_arg('c_dim',             int,   13,                "the size of attrs")
add_arg('use_gru',           bool,  False,             "Whether to use GRU")
add_arg('crop_size',         int,   178,               "crop size")
add_arg('image_size',        int,   128,               "image size")
add_arg('selected_attrs',    str,
    "Bald,Bangs,Black_Hair,Blond_Hair,Brown_Hair,Bushy_Eyebrows,Eyeglasses,Male,Mouth_Slightly_Open,Mustache,No_Beard,Pale_Skin,Young",
"the attributes we selected to change")
add_arg('batch_size',        int,   16,                "batch size when test")
add_arg('test_list',       str,   "./data/celeba/test_list_attr_celeba.txt",                "the test list file")
add_arg('dataset_dir',       str,   "./data/celeba/",                "the dataset directory")
add_arg('n_layers',        int,     5,      "default layers in generotor")
add_arg('gru_n_layers',    int,     4,       "default layers of GRU in generotor")
# yapf: enable


def infer(args):
    data_shape = [-1, 3, args.data_shape, args.data_shape]
    input = fluid.layers.data(name='input', shape=data_shape, dtype='float32')
    label_org_ = fluid.layers.data(
        name='label_org_', shape=[args.c_dim], dtype='float32')
    label_trg_ = fluid.layers.data(
        name='label_trg_', shape=[args.c_dim], dtype='float32')

    model_name = 'net_G'
    if args.model_net == 'cyclegan':
        from network.CycleGAN_network import CycleGAN_model
        model = CycleGAN_model()
        if args.input_style == "A":
            fake = model.network_G(input, name="GA", cfg=args)
        elif args.input_style == "B":
            fake = model.network_G(input, name="GB", cfg=args)
        else:
            raise "Input with style [%s] is not supported." % args.input_style
    elif args.model_net == 'Pix2pix':
        from network.Pix2pix_network import Pix2pix_model
        model = Pix2pix_model()
        fake = model.network_G(input, "generator", cfg=args)

    elif args.model_net == 'STGAN':
        from network.STGAN_network import STGAN_model
        model = STGAN_model()
        fake, _ = model.network_G(
            input, label_org_, label_trg_, cfg=args, name='net_G')
    elif args.model_net == 'AttGAN':
        from network.AttGAN_network import AttGAN_model
        model = AttGAN_model()
        fake, _ = model.network_G(
            input, label_org_, label_trg_, cfg=args, name='net_G')
    else:
        raise NotImplementedError("model_net {} is not support".format(
            args.model_net))

    # prepare environment
    place = fluid.CPUPlace()
    if args.use_gpu:
        place = fluid.CUDAPlace(0)
    exe = fluid.Executor(place)
    exe.run(fluid.default_startup_program())
    for var in fluid.default_main_program().global_block().all_parameters():
        print(var.name)
    print(args.init_model + '/' + model_name)
    fluid.io.load_persistables(exe, args.init_model + "/" + model_name)
    print('load params done')

    if not os.path.exists(args.output):
        os.makedirs(args.output)

    if args.model_net == 'AttGAN' or args.model_net == 'STGAN':
        test_reader = celeba_reader_creator(
            image_dir=args.dataset_dir,
            list_filename=args.test_list,
            batch_size=args.batch_size,
            drop_last=False,
            args=args)
        reader_test = test_reader.get_test_reader(
            args, shuffle=False, return_name=True)
        for data in zip(reader_test()):
            real_img, label_org, name = data[0]
            print("read {}".format(name))
            label_trg = copy.deepcopy(label_org)
            tensor_img = fluid.LoDTensor()
            tensor_label_org = fluid.LoDTensor()
            tensor_label_trg = fluid.LoDTensor()
            tensor_label_org_ = fluid.LoDTensor()
            tensor_label_trg_ = fluid.LoDTensor()
            tensor_img.set(real_img, place)
            tensor_label_org.set(label_org, place)
            real_img_temp = np.squeeze(real_img).transpose([0, 2, 3, 1])
            images = [real_img_temp]
            for i in range(args.c_dim):
                label_trg_tmp = copy.deepcopy(label_trg)
                for j in range(args.batch_size):
                    label_trg_tmp[j][i] = 1.0 - label_trg_tmp[j][i]
                label_trg_ = map(lambda x: ((x * 2) - 1) * 0.5, label_trg_tmp)
                for j in range(args.batch_size):
                    label_trg_[j][i] = label_trg_[j][i] * 2.0
                tensor_label_org_.set(label_org, place)
                tensor_label_trg.set(label_trg, place)
                tensor_label_trg_.set(label_trg_, place)
                out = exe.run(feed={
                    "input": tensor_img,
                    "label_org_": tensor_label_org_,
                    "label_trg_": tensor_label_trg_
                },
                              fetch_list=fake.name)
                fake_temp = np.squeeze(out[0]).transpose([0, 2, 3, 1])
                images.append(fake_temp)
            images_concat = np.concatenate(images, 1)
            images_concat = np.concatenate(images_concat, 1)
            imsave(args.output + "/fake_img_" + name[0], (
                (images_concat + 1) * 127.5).astype(np.uint8))

    elif args.model_net == 'Pix2pix' or args.model_net == 'cyclegan':
        for file in glob.glob(args.input):
            print("read {}".format(file))
            image_name = os.path.basename(file)
            image = Image.open(file).convert('RGB')
            image = image.resize((256, 256), Image.BICUBIC)
            image = np.array(image).transpose([2, 0, 1]).astype('float32')
            image = image / 255.0
            image = (image - 0.5) / 0.5
            data = image[np.newaxis, :]
            tensor = fluid.LoDTensor()
            tensor.set(data, place)

            fake_temp = exe.run(fetch_list=[fake.name], feed={"input": tensor})
            fake_temp = np.squeeze(fake_temp[0]).transpose([1, 2, 0])
            input_temp = np.squeeze(data).transpose([1, 2, 0])

            imsave(args.output + "/fake_" + image_name, (
                (fake_temp + 1) * 127.5).astype(np.uint8))
    else:
        raise NotImplementedError("model_net {} is not support".format(
            args.model_net))


if __name__ == "__main__":
    args = parser.parse_args()
    print_arguments(args)
    infer(args)