Illumination-Estimation-With-Residue-Networks/4_Shading_Albedo_Residual/main_base_sfs.py at a8fd929e79ca572c615bcea07e8fe6e507dc781f · bhushan23/Illumination-Estimation-With-Residue-Networks · GitHub

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##
##  To Run SFS NET Model Provided by Author
##

import torch
import torchvision
from torchvision import transforms
from torch.utils.data import DataLoader
import matplotlib.pyplot as plt
import torch.nn as nn
import argparse

import wandb
from data_loading import *
from utils import *
from shading import *
from train import *
from sfs_net_model import *
from models import sfsNetShading, ReconstructImage

def main():
    ON_SERVER = True

    parser = argparse.ArgumentParser(description='SfSNet - Residual')
    parser.add_argument('--batch_size', type=int, default=8, metavar='N',
                        help='input batch size for training (default: 8)')
    parser.add_argument('--epochs', type=int, default=10, metavar='N',
                        help='number of epochs to train (default: 10)')
    parser.add_argument('--lr', type=float, default=0.001, metavar='LR',
                        help='learning rate (default: 0.001)')
    parser.add_argument('--wt_decay', type=float, default=0.0005, metavar='W',
                        help='SGD momentum (default: 0.0005)')
    parser.add_argument('--no_cuda', action='store_true', default=False,
                        help='disables CUDA training')
    parser.add_argument('--seed', type=int, default=1, metavar='S',
                        help='random seed (default: 1)')
    parser.add_argument('--read_first', type=int, default=-1,
                        help='read first n rows (default: -1)')
    if ON_SERVER:
        parser.add_argument('--syn_data', type=str, default='/nfs/bigdisk/bsonawane/sfsnet_data/',
                        help='Synthetic Dataset path')
        parser.add_argument('--celeba_data', type=str, default='/nfs/bigdisk/bsonawane/CelebA-dataset/CelebA_crop_resize_128/',
                        help='CelebA Dataset path')
        parser.add_argument('--log_dir', type=str, default='./results/',
                        help='Log Path')
    else:
        parser.add_argument('--syn_data', type=str, default='../data/sfs-net/',
                        help='Synthetic Dataset path')
        parser.add_argument('--celeba_data', type=str, default='../data/celeba/',
                        help='CelebA Dataset path')
        parser.add_argument('--log_dir', type=str, default='./results/',
                        help='Log Path')
    parser.add_argument('--load_model', type=str, default=None,
                        help='load model from')
    args = parser.parse_args()
    use_cuda = not args.no_cuda and torch.cuda.is_available()

    torch.manual_seed(args.seed)

    # initialization
    syn_data = args.syn_data
    celeba_data = args.celeba_data
    batch_size = args.batch_size
    lr         = args.lr
    wt_decay   = args.wt_decay
    log_dir    = args.log_dir
    epochs     = args.epochs
    model_dir  = args.load_model
    read_first = args.read_first
    if read_first == -1:
        read_first = None

    # Debugging and check working
    # syn_train_csv = syn_data + '/train.csv'
    # train_dataset, _ = get_sfsnet_dataset(syn_dir=syn_data+'train/', read_from_csv=syn_train_csv, read_celeba_csv=None, read_first=read_first, validation_split=5)
    # train_dl  = DataLoader(train_dataset, batch_size=10, shuffle=False)
    # validate_shading_method(train_dl)
    # return

    # Init WandB for logging
    wandb.init(project='SfSNet-CelebA-Baseline-Model')
    wandb.log({'lr':lr, 'weight decay': wt_decay})

    # Initialize models
    shading_model         = sfsNetShading()
    image_recon_model     = ReconstructImage()
    sfs_net_model      = SfSNet_Base_Pipeline(shading_model, image_recon_model)

    if use_cuda:
        sfs_net_model = sfs_net_model.cuda()

    if model_dir is not None:
        sfs_net_model.load_state_dict(torch.load(model_dir + 'sfs_net_model.pkl'))
    else:
        print('Initializing weights')
        sfs_net_model.apply(weights_init)

    wandb.watch(sfs_net_model)
    # 1. Train on Synthetic data
    train(sfs_net_model, syn_data, celeba_data = None, read_first=read_first, \
            batch_size=batch_size, num_epochs=epochs, log_path=log_dir+'Synthetic_Train/', use_cuda=use_cuda, wandb=wandb, \
            lr=lr, wt_decay=wt_decay)

    # 2. Generate Pseudo-Training information for CelebA dataset
    # Load CelebA dataset
    celeba_train_csv = celeba_data + '/train.csv'
    celeba_test_csv = celeba_data + '/test.csv'

    train_dataset, _ = get_celeba_dataset(read_from_csv=celeba_train_csv, read_first=read_first, validation_split=0)
    test_dataset, _ = get_celeba_dataset(read_from_csv=celeba_test_csv, read_first=read_first, validation_split=0)

    celeba_train_dl  = DataLoader(train_dataset, batch_size=1, shuffle=True)
    celeba_test_dl   = DataLoader(test_dataset, batch_size=1, shuffle=True)

    out_celeba_images_dir = celeba_data + 'synthesized_data/'
    out_train_celeba_images_dir = out_celeba_images_dir + 'train/'
    out_test_celeba_images_dir = out_celeba_images_dir + 'test/'

    os.system('mkdir -p {}'.format(out_train_celeba_images_dir))
    os.system('mkdir -p {}'.format(out_test_celeba_images_dir))

    # Dump normal, albedo, shading, face and sh for celeba dataset
    v_total = generate_celeba_synthesize(sfs_net_model, celeba_train_dl, train_epoch_num=epochs, use_cuda=use_cuda,
                                                            out_folder=out_train_celeba_images_dir, wandb=wandb)
    v_total = generate_celeba_synthesize(sfs_net_model, celeba_test_dl, train_epoch_num=epochs, use_cuda=use_cuda,
                                                            out_folder=out_test_celeba_images_dir, wandb=wandb)

    # generate CSV for images generated above
    generate_celeba_synthesize_data_csv(out_train_celeba_images_dir, out_celeba_images_dir + '/train.csv')
    generate_celeba_synthesize_data_csv(out_test_celeba_images_dir, out_celeba_images_dir + '/test.csv')

    # 3. Train on both Synthetic and Real (Celeba) dataset
    train(sfs_net_model, syn_data, celeba_data=out_celeba_images_dir, read_first=read_first,\
            batch_size=batch_size, num_epochs=epochs, log_path=log_dir+'Mix_Training/', use_cuda=use_cuda, wandb=wandb, \
            lr=lr, wt_decay=wt_decay)


if __name__ == '__main__':
    main()