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mgan.py
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import argparse
import os
import shutil
import warnings
import numpy as np
import torch
import torch.nn.functional as F
from torch.utils.tensorboard import SummaryWriter
from tqdm import tqdm, trange
from forward_model import GaussianCompressiveSensing
from model.began import Generator128
from utils import (get_z_vector, load_target_image, load_trained_net,
psnr_from_mse)
warnings.filterwarnings("ignore")
def _mgan_recover(x,
gen,
n_cuts,
forward_model,
optimizer_type='sgd',
mode='zero',
limit=1,
z_lr=1,
n_steps=2000,
z_number=20,
run_dir=None,
run_name=None,
disable_tqdm=False,
**kwargs):
"""
Args:
x - input image, torch tensor (C x H x W)
gen - generator, already loaded with checkpoint weights
forward_model - corrupts the image
n_cuts - the intermediate layer to combine z vectors
n_steps - number of optimization steps during recovery
run_name - use None for no logging
"""
z1_dim, _ = gen.input_shapes[0]
_, z2_dim = gen.input_shapes[n_cuts]
if (isinstance(forward_model, GaussianCompressiveSensing)):
n_pixel_bora = 64 * 64 * 3
n_pixel = np.prod(x.shape)
noise = torch.randn(1, forward_model.n_measure, device=x.device)
noise *= 0.1 * torch.sqrt(torch.tensor(n_pixel / forward_model.n_measure / n_pixel_bora))
z1 = torch.nn.Parameter(get_z_vector((z_number, *z1_dim), mode=mode, limit=limit, device=x.device))
alpha = torch.nn.Parameter(
get_z_vector((z_number, gen.input_shapes[n_cuts][0][0]), mode=mode, limit=limit, device=x.device))
params = [z1, alpha]
if len(z2_dim) > 0:
z2 = torch.nn.Parameter(get_z_vector((1, *z2_dim), mode=mode, limit=limit, device=x.device))
params.append(z2)
else:
z2 = None
if optimizer_type == 'sgd':
optimizer_z = torch.optim.SGD(params, lr=z_lr)
scheduler_z = None
save_img_every_n = 50
elif optimizer_type == 'adam':
optimizer_z = torch.optim.Adam(params, lr=z_lr)
scheduler_z = None
# scheduler_z = torch.optim.lr_scheduler.CosineAnnealingLR(
# optimizer_z, n_steps, 0.05 * z_lr)
save_img_every_n = 50
else:
raise NotImplementedError()
if run_name is not None:
logdir = os.path.join('recovery_tensorboard_logs', run_dir, run_name)
if os.path.exists(logdir):
print("Overwriting pre-existing logs!")
shutil.rmtree(logdir)
writer = SummaryWriter(logdir)
# Save original and distorted image
if run_name is not None:
writer.add_image("Original/Clamp", x.clamp(0, 1))
if forward_model.viewable:
writer.add_image("Distorted/Clamp", forward_model(x.unsqueeze(0).clamp(0, 1)).squeeze(0))
# Recover image under forward model
x = x.expand(1, *x.shape)
y_observed = forward_model(x)
if (isinstance(forward_model, GaussianCompressiveSensing)):
y_observed += noise
for j in trange(n_steps, leave=False, desc='Recovery', disable=disable_tqdm):
optimizer_z.zero_grad()
F_l = gen.forward(z1, None, n_cuts=0, end=n_cuts, **kwargs)
F_l_2 = (F_l * alpha[:, :, None, None]).sum(0, keepdim=True)
x_hats = gen.forward(F_l_2, z2, n_cuts=n_cuts, end=None, **kwargs)
if gen.rescale:
x_hats = (x_hats + 1) / 2
train_mse = F.mse_loss(forward_model(x_hats), y_observed)
train_mse.backward()
optimizer_z.step()
train_mse_clamped = F.mse_loss(forward_model(x_hats.detach().clamp(0, 1)), y_observed)
orig_mse_clamped = F.mse_loss(x_hats.detach().clamp(0, 1), x)
if run_name is not None and j == 0:
writer.add_image('Start', x_hats.clamp(0, 1).squeeze(0))
if run_name is not None:
writer.add_scalar('TRAIN_MSE', train_mse_clamped, j + 1)
writer.add_scalar('ORIG_MSE', orig_mse_clamped, j + 1)
writer.add_scalar('ORIG_PSNR', psnr_from_mse(orig_mse_clamped), j + 1)
if j % save_img_every_n == 0:
writer.add_image('Recovered', x_hats.clamp(0, 1).squeeze(0), j + 1)
if scheduler_z is not None:
scheduler_z.step()
if run_name is not None:
writer.add_image('Final', x_hats.clamp(0, 1).squeeze(0))
return x_hats.squeeze(0), forward_model(x)[0], train_mse_clamped
def mgan_recover(x,
gen,
n_cuts,
forward_model,
optimizer_type='sgd',
mode='zero',
limit=1,
z_lr=1,
n_steps=2000,
z_number=20,
restarts=1,
run_dir=None,
run_name=None,
disable_tqdm=False,
**kwargs):
best_psnr = -float("inf")
best_return_val = None
for i in trange(restarts, desc='Restarts', leave=False, disable=disable_tqdm):
if run_name is not None:
current_run_name = f'{run_name}_{i}'
else:
current_run_name = None
return_val = _mgan_recover(x=x,
gen=gen,
n_cuts=n_cuts,
forward_model=forward_model,
optimizer_type=optimizer_type,
mode=mode,
limit=limit,
z_lr=z_lr,
n_steps=n_steps,
z_number=z_number,
run_dir=run_dir,
run_name=current_run_name,
disable_tqdm=disable_tqdm,
**kwargs)
p = psnr_from_mse(return_val[2])
if p > best_psnr:
best_psnr = p
best_return_val = return_val
return best_return_val
if __name__ == '__main__':
DEVICE = 'cuda:0' if torch.cuda.is_available() else 'cpu'
a = argparse.ArgumentParser()
a.add_argument('--img_dir', required=True)
a.add_argument('--n_cuts', type=int, required=True)
a.add_argument('--disable_tqdm', default=False)
args = a.parse_args()
gen = Generator128(64)
gen = load_trained_net(gen, ('./checkpoints/celeba_began.withskips.bs32.cosine.min=0.25'
'.n_cuts=0/gen_ckpt.49.pt'))
gen = gen.eval().to(DEVICE)
img_size = 128
img_shape = (3, img_size, img_size)
forward_model = GaussianCompressiveSensing(n_measure=20000, img_shape=img_shape)
# forward_model = NoOp()
for img_name in tqdm(os.listdir(args.img_dir), desc='Images', leave=True, disable=args.disable_tqdm):
orig_img = load_target_image(os.path.join(args.img_dir, img_name), img_size).to(DEVICE)
img_basename, _ = os.path.splitext(img_name)
x_hat, x_degraded, _ = mgan_recover(orig_img,
gen,
n_cuts=args.n_cuts,
forward_model=forward_model,
run_dir='mgan_prior',
run_name=img_basename)