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module.py
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module.py
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# -*- coding:utf-8 -*-
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
import ast
import os
import argparse
import copy
import numpy as np
import paddle.fluid as fluid
import paddlehub as hub
from paddle.fluid.core import PaddleTensor, AnalysisConfig, create_paddle_predictor
from paddlehub.module.module import moduleinfo, runnable, serving
from stgan_bald.data_feed import reader
from stgan_bald.processor import postprocess, base64_to_cv2, cv2_to_base64, check_dir
def check_attribute_conflict(label_batch):
''' Based on https://github.com/LynnHo/AttGAN-Tensorflow'''
attrs = "Bald,Bangs,Black_Hair,Blond_Hair,Brown_Hair,Bushy_Eyebrows,Eyeglasses,Male,Mouth_Slightly_Open,Mustache,No_Beard,Pale_Skin,Young".split(
',')
def _set(label, value, attr):
if attr in attrs:
label[attrs.index(attr)] = value
attr_id = attrs.index('Bald')
for label in label_batch:
if attrs[attr_id] != 0:
_set(label, 0, 'Bangs')
return label_batch
@moduleinfo(
name="stgan_bald",
version="1.0.0",
summary="Baldness generator",
author="Arrow, 七年期限，Mr.郑先生_",
author_email="1084667371@qq.com，2733821739@qq.com",
type="image/gan")
class StganBald(hub.Module):
def _initialize(self):
self.default_pretrained_model_path = os.path.join(self.directory, "module")
self._set_config()
def _set_config(self):
"""
predictor config setting
"""
self.model_file_path = os.path.join(self.default_pretrained_model_path,
'__model__')
self.params_file_path = os.path.join(self.default_pretrained_model_path,
'__params__')
cpu_config = AnalysisConfig(self.model_file_path, self.params_file_path)
cpu_config.disable_glog_info()
cpu_config.disable_gpu()
self.cpu_predictor = create_paddle_predictor(cpu_config)
try:
_places = os.environ["CUDA_VISIBLE_DEVICES"]
int(_places[0])
use_gpu = True
self.place = fluid.CUDAPlace(0)
except:
use_gpu = False
self.place = fluid.CPUPlace()
if use_gpu:
gpu_config =AnalysisConfig(self.model_file_path, self.params_file_path)
gpu_config.disable_glog_info()
gpu_config.enable_use_gpu(
memory_pool_init_size_mb=1000, device_id=0)
self.gpu_predictor = create_paddle_predictor(gpu_config)
def bald(self,
images=None,
paths=None,
data=None,
use_gpu=False,
org_labels=[[0.,0.,1.,0.,0.,1.,1.,1.,0.,0.,0.,0.,1.]],
target_labels=None,
visualization=True,
output_dir="bald_output"):
"""
API for super resolution.
Args:
images (list(numpy.ndarray)): images data, shape of each is [H, W, C], the color space is BGR.
paths (list[str]): The paths of images.
data (dict): key is 'image', the corresponding value is the path to image.
use_gpu (bool): Whether to use gpu.
visualization (bool): Whether to save image or not.
output_dir (str): The path to store output images.
Returns:
res (list[dict]): each element in the list is a dict, the keys and values are:
save_path (str, optional): the path to save images. (Exists only if visualization is True)
data (numpy.ndarray): data of post processed image.
"""
if use_gpu:
try:
_places = os.environ["CUDA_VISIBLE_DEVICES"]
int(_places[0])
except:
raise RuntimeError(
"Environment Variable CUDA_VISIBLE_DEVICES is not set correctly. If you wanna use gpu, please set CUDA_VISIBLE_DEVICES as cuda_device_id."
)
if data and 'image' in data:
if paths is None:
paths = list()
paths += data['image']
all_data = list()
for yield_data in reader(images, paths, org_labels, target_labels):
all_data.append(yield_data)
total_num = len(all_data)
res = list()
outputs = []
for i in range(total_num):
image_np = all_data[i]['img']
org_label_np = [all_data[i]['org_label']]
target_label_np = [all_data[i]['target_label']]
for j in range(5):
if j % 2 == 0:
label_trg_tmp = copy.deepcopy(target_label_np)
new_i = 0
label_trg_tmp[0][new_i] = 1.0 - label_trg_tmp[0][new_i]
label_trg_tmp = check_attribute_conflict(
label_trg_tmp)
change_num = j * 0.02 + 0.3
label_org_tmp = list(
map(lambda x: ((x * 2) - 1) * change_num, org_label_np))
label_trg_tmp = list(
map(lambda x: ((x * 2) - 1) * change_num, label_trg_tmp))
image = PaddleTensor(image_np.copy())
org_label = PaddleTensor(np.array(label_org_tmp).astype('float32'))
target_label = PaddleTensor(np.array(label_trg_tmp).astype('float32'))
output = self.gpu_predictor.run(
[image, target_label, org_label]
) if use_gpu else self.cpu_predictor.run([image, org_label, target_label])
outputs.append(output)
out = postprocess(
data_out=outputs,
org_im=all_data[i]['org_im'],
org_im_path=all_data[i]['org_im_path'],
output_dir=output_dir,
visualization=visualization)
res.append(out)
return res
@serving
def serving_method(self, images, **kwargs):
"""
Run as a service.
"""
images_decode = [base64_to_cv2(image) for image in images]
results = self.bald(images=images_decode, **kwargs)
output = {}
for key, value in results[0].items():
output[key] = cv2_to_base64(value)
return output