Illumination-Estimation-With-Residue-Networks/6_Shading_Resdiual_GAN_Separate_Train/main_full.py at 87151a36bcd1ae62232aa90f453dfee2b54a7d58 · bhushan23/Illumination-Estimation-With-Residue-Networks · GitHub

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#
# Experiment Entry point
# 1. Trains model on Syn Data
# 2. Generates CelebA Data
# 3. Trains on Syn + CelebA Data
#

import torch
torch.manual_seed(100)
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 models import *
from sfs_net_model import SfSNet as sfsnet_pretrained_model
from patch_gan_model import PT_Tutorial_Discriminator

def main():
    ON_SERVER = True

    parser = argparse.ArgumentParser(description='SfSNet - Shading 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)')
    parser.add_argument('--details', type=str, default=None,
                        help='Explaination of the run')
    parser.add_argument('--load_pretrained_model', type=str, default='../pretrained/net_epoch_r5_5.pth',
                        help='Pretrained model path')
    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_sfsnet_gen_20k/',
                        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_20k/',
                        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
    pretrained_model_dict = args.load_pretrained_model


    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-GAN-Separate-Training-Shading-Residual-PreTrained')
    wandb.log({'lr':lr, 'weight decay': wt_decay})

    # Initialize models
    sfs_net_model      = SfsNetPipeline()
    albedo_gen_model = AlbedoGenerationNet()
    # albedo_dis_model = Discriminator()
    albedo_dis_model = PT_Tutorial_Discriminator()

    if use_cuda:
        sfs_net_model = sfs_net_model.cuda()
        albedo_gen_model = albedo_gen_model.cuda()
        albedo_dis_model = albedo_dis_model.cuda()


    if model_dir is not None:
        sfs_net_model.load_state_dict(torch.load(model_dir + 'sfs_net_model.pkl'))
        albedo_gen_model.load_state_dict(torch.load(model_dir + 'albedo_gen_model.pkl'))
        albedo_dis_model.load_state_dict(torch.load(model_dir + 'albedo_dis_model.pkl'))
        sfs_net_model.fix_weights()
        print('Model loaded')
    else:
        sfs_net_model.apply(weights_init)
        albedo_gen_model.apply(weights_init)
        albedo_dis_model.apply(weights_init)
        sfs_net_pretrained_dict = torch.load(pretrained_model_dict)
        sfs_net_state_dict = sfs_net_model.state_dict()
        albedo_gen_state_dict = albedo_gen_model.state_dict()
        load_model_from_pretrained(sfs_net_pretrained_dict, sfs_net_state_dict, albedo_gen_state_dict)
        sfs_net_model.load_state_dict(sfs_net_state_dict)
        albedo_gen_model.load_state_dict(albedo_gen_state_dict)
        sfs_net_model.fix_weights()
        print('Pre-trained model loaded')
        # torch.save(sfs_net_model.state_dict(), log_dir + 'Mix_Training/checkpoints/' + 'sfs_net_model.pkl')

    os.system('mkdir -p {}'.format(args.log_dir))
    with open(args.log_dir+'/details.txt', 'w') as f:
        f.write(args.details)

    # 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
    gan_based_train(sfs_net_model, albedo_gen_model, albedo_dis_model, syn_data, celeba_data=celeba_data, read_first=read_first,\
           batch_size=batch_size, num_epochs=epochs, log_path=log_dir+'GAN_Training/', use_cuda=use_cuda, wandb=wandb, \
           lr=lr, wt_decay=wt_decay)

    # fix the weights of albedo gen model
    albedo_gen_model.fix_weights()
    sfs_net_model.fix_new_weights()

    train(sfs_net_model, albedo_gen_model, albedo_dis_model, syn_data, celeba_data=celeba_data, 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)

    # train_with_shading_loss(sfs_net_model, syn_data, celeba_data=celeba_data, 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()