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DGP_untrain.py
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DGP_untrain.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
import faulthandler
faulthandler.enable()
import os
import sys
# from collections import OrderedDict
import tensorflow as tf
import torch
import torchvision.utils as vutils
import utils
from models import QDGP_G
# from torchsummary import summary
sys.path.append("./")
from str2bool import str2bool
## QNN啥都不需要输入，输入是分层噪声与类别标签
def add_qdgp_parser(parser):
parser.add_argument(
'--dist', action='store_true', default=False,
help='Train with distributed implementation (default: %(default)s)')
parser.add_argument(
'--exp_path', type=str, default='',
help='Experiment path (default: %(default)s)')
parser.add_argument(
'--root_dir', type=str, default='',
help='Root path of dataset (default: %(default)s)')
parser.add_argument(
'--list_file', type=str, default='',
help='List file of the dataset (default: %(default)s)')
parser.add_argument(
'--resolution', type=int, default=128,
help='Resolution to resize the input image (default: %(default)s)')
parser.add_argument(
'--dgp_mode', type=str, default='reconstruct',
help='DGP mode (default: %(default)s)')
parser.add_argument(
'--random_G', action='store_true', default=False,
help='Use randomly initialized generator? (default: %(default)s)')
parser.add_argument(
'--update_G', action='store_true', default=True,
help='Finetune Generator? (default: %(default)s)')
parser.add_argument(
'--update_embed', action='store_true', default=True,
help='Finetune class embedding? (default: %(default)s)')
parser.add_argument(
'--save_G', action='store_true', default=False,
help='Save fine-tuned generator and latent vector? (default: %(default)s)')
parser.add_argument(
'--print_interval', type=int, default=500, nargs='+',
help='Number of iterations to print training loss (default: %(default)s)')
parser.add_argument(
'--save_interval', type=int, default=None, nargs='+',
help='Number of iterations to save image')
parser.add_argument(
'--lr_ratio', type=float, default=[1.0, 1.0, 1.0, 1.0], nargs='+',
help='Decreasing ratio for learning rate in blocks (default: %(default)s)')
parser.add_argument(
'--select_num', type=int, default=500,
help='Number of image pool to select from (default: %(default)s)')
parser.add_argument(
'--iterations', type=int, default=[2000, 2000, 1000, 1000], nargs='+',
help='Training iterations for all stages')
parser.add_argument(
'--G_lrs', type=float, default=[1e-5, 1e-3, 1e-4, 1e-4], nargs='+',
help='Learning rate steps of Generator')
parser.add_argument(
'--sample_std', type=float, default=0.3,
help='sampling standard deviation')
parser.add_argument(
'--z_lrs', type=float, default=[1e-3, 1e-3, 1e-4, 1e-4], nargs='+',
help='Learning rate steps of latent code z')
parser.add_argument(
'--warm_up', type=int, default=100,
help='Number of warmup iterations (default: %(default)s)')
parser.add_argument( ## 这个必须
'--use_in', type=str2bool, default=[False, False, False, False, False], nargs='+',
help='Whether to use instance normalization in generator')
return parser
def add_example_parser(parser):
parser.add_argument(
'--image_path', type=str, default='',
help='Path of the image to be processed (default: %(default)s)')
parser.add_argument(
'--bucket_path', type=str, default='',
help='Path of the experimental bucket to be processed (default: %(default)s)'
)
parser.add_argument(
'--pattern_path', type=str, default='',
help='Path of the patterns to be processed (default: %(default)s)'
)
parser.add_argument(
'--class', type=int, default=-1,
help='class index of the image (default: %(default)s)')
parser.add_argument('--dims', type=int, default=128, metavar='D',
help="dimension of the bucket data")
parser.add_argument('--object', type=str, default="2",
help="image object")
return parser
# prepare arguments and save in config
parser = utils.prepare_parser()
parser = add_qdgp_parser(parser)
parser = add_example_parser(parser)
config = vars(parser.parse_args())
utils.dgp_update_config(config)
print("dims:", config["dims"], flush=True)
print("object:", config["object"], flush=True)
# set random seed
utils.seed_rng(config['seed'])
source_dir = os.path.dirname(os.path.abspath(__file__))
config['exp_path'] = source_dir
# config['exp_path'] = '/home/xtl/Documents/PythonFiles/SPI_QML/untrain_QuGe/deep-generative-prior'
#
# if not os.path.exists('{}/images'.format(config['exp_path'])):
# os.makedirs('{}/images'.format(config['exp_path']))
# if not os.path.exists('{}/images_sheet'.format(config['exp_path'])):
# os.makedirs('{}/images_sheet'.format(config['exp_path']))
# initialize DGP model
# print(qdgp.G, flush=True) # good, no problem
# target image path (original)
config['image_path'] = source_dir + '/data_zhai/shot/{}.png'.format(config['object'])
config['bucket_path'] = source_dir + '/data_zhai/128/128/buckets/{}.npy'.format(config['object'])
config['pattern_path'] = source_dir + '/data_zhai/128/128/randomP/randomP.npy'
# prepare the target image
# img = utils.get_img(config['image_path'], config['resolution']).cuda()
bucket_target = torch.Tensor(utils.get_bucket_128(config['bucket_path'], config['dims'])).cuda()
# config['image_path'] = source_dir + '/data_zhai/shot/{}.png'.format(config['object'])
#
# config['bucket_path'] = source_dir + '/data_zhai/bucket_randomP/64/{}.npy'.format(config['object'])
# config['pattern_path'] = source_dir + '/data_zhai/randomP/randomP.npy'
# # prepare the target image
# img = utils.get_img(config['image_path'], config['resolution']).cuda()
# bucket_target = torch.Tensor(utils.get_bucket(config['bucket_path'], config['dims'])).cuda()
patterns = torch.Tensor(utils.get_pattern(config['pattern_path'], config['dims'])).cuda()
category = torch.Tensor([config['class']]).long().cuda() # 默认是-1
import numpy as np
def test_code(orignal_image, patterns, data_array):
orignal_image = torch.Tensor(orignal_image).cuda()
# patterns = torch.Tensor(patterns, dtype=torch.float32) # with shape (1024*3, 64, 64)
I_orginal = torch.Tensor([torch.sum(torch.multiply(orignal_image, patt)) for patt in patterns]).cuda()
# I_orginal = torch.unsqueeze(I_orginal, dim=1)
print(I_orginal, flush=True)
print(torch.squeeze(data_array), flush=True)
test_result = I_orginal - torch.Tensor(np.squeeze(data_array)).cuda()
print(test_result, flush=True)
# import cv2
# orignal = cv2.imread(config['image_path'], 0)/255.0
#
# test_code(orignal, patterns, bucket_target)
# initialize DGP model
dgp = QDGP_G(config)
dgp.set_target(bucket_target, category)
# we need to ensure the shapes of the tensor
# prepare initial latent vector
dgp.select_z(patterns, select_y=True if config['class'] < 0 else False)
# start reconstruction
# 代码是有问题的。如何衔接起来。
loss_dict = dgp.run(patterns, bucket_target) # 这一步已经优化完成了
# save_imgs = img.clone().cpu()