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eval_sampler.py
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import torch
from einops import rearrange
from torch.utils.data import DataLoader
import numpy as np
from absl import app
from absl import flags
from ml_collections.config_flags import config_flags
import os
from tqdm import tqdm
from models.vqgan_3d import Generator as Generator3D
from utils.sampler_utils import retrieve_autoencoder_components_state_dicts, get_latent_loaders, get_sampler, latent_ids_to_onehot
from utils.log_utils import load_model
from utils.visual import to_unNorm, back_to_HU, save_volume
from utils.metrics import MSE
from evaluate.feature_extractor import FeatureExtractor3D
# Commandline arguments
FLAGS = flags.FLAGS
config_flags.DEFINE_config_file("config", None, "Training configuration.", lock_config=False)
config_flags.DEFINE_config_file("ct_config", "configs/default_ct_256_vqgan_config.py", "CT VQGAN training configuration.", lock_config=True)
config_flags.DEFINE_config_file("xray_config", "configs/default_xray_vqgan_config.py", "XRay VQGAN training configuration.", lock_config=True)
flags.mark_flags_as_required(["config"])
# Torch options
torch.backends.cudnn.benchmark = True
torch.backends.cudnn.deterministic = False
device = torch.device('cuda')
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
try:
m.weight.data.normal_(0.0, 0.02)
except:
pass
elif classname.find('BatchNorm') != -1:
m.weight.data.normal_(1.0, 0.02)
m.bias.data.fill_(0)
def reconstruct_from_codes(H, sampler, x, generator):
latents_one_hot = latent_ids_to_onehot(x, H.ct_config.model.latent_shape, H.ct_config.model.codebook_size)
q = sampler.embed(latents_one_hot)
images = generator(q.float())
return images
def evaluate(H, sampler, generator_ct, test_loader, test_real_loader, res_dir):
mse_total_avg = 0.
feature_extractor = FeatureExtractor3D().to(device)
feature_extractor.apply(weights_init).eval()
pbar = tqdm(enumerate(test_loader), total=len(test_loader))
for it, test_data in pbar:
real_data = next(test_real_loader)
test_real = real_data["ct"]
test_x = test_data["ct_codes"].to(device, non_blocking=True)
test_context = test_data["xray_embed"].to(device, non_blocking=True)
test_context = rearrange(test_context, "b () r l c -> b (r l) c")
test_x = rearrange(test_x, "b () l -> b l")
test_real = reconstruct_from_codes(H, sampler, test_x[:H.diffusion.sampling_batch_size], generator_ct)
x_sampled = sampler.sample(context=test_context[:H.diffusion.sampling_batch_size], sample_steps=H.diffusion.sampling_steps, temp=H.diffusion.sampling_temp, train=False)
x_sampled_img = reconstruct_from_codes(H, sampler, x_sampled, generator_ct)
# tensor -> numpy array -> unnormalisation
real_ct, recon_ct = to_unNorm(test_real[0], x_sampled_img[0])
mse = MSE(real_ct, recon_ct, size_average=False)
if H.data.dataset != 'bags':
# back to Hounsfield scale for visualisation
real_ct = back_to_HU(real_ct).astype(np.int32) - 1024
recon_ct = back_to_HU(recon_ct).astype(np.int32) - 1024
else:
real_ct *= 4095
recon_ct *= 4095
mse_total_avg += float(mse)
pbar.set_description(
f"mse: {np.round(mse, 7)}")
# Saving to .raw for visualisation
save_volume(real_ct, os.path.join(res_dir, f"real_ct_{it:04}"))
save_volume(recon_ct, os.path.join(res_dir,
f"recon_ct_{it:04}"))
total_mse = round((mse_total_avg/len(test_loader)), 4)
print(f">>>> Total avg mse: {total_mse}")
def main(argv):
H = FLAGS.config
H.ct_config = FLAGS.ct_config
H.xray_config = FLAGS.xray_config
outputs_dir = f'eval_outputs/{H.run.name}/{str(H.model.load_step)}'
try:
os.makedirs(outputs_dir, exist_ok=True)
except OSError:
pass
# Read latents
latents_filepath = f'logs/{H.run.name}_{H.run.experiment}/train_latents'
assert os.path.exists(latents_filepath), f"Error: Latents path {latents_filepath} not found"
# Load latents
_, test_latent_loader = get_latent_loaders(H)
# Load CT Generator
quanitzer_and_generator_state_dict = retrieve_autoencoder_components_state_dicts(
H.ct_config, ['quantize', 'generator'], remove_component_from_key=True)
ct_embedding_weight = quanitzer_and_generator_state_dict.pop('embedding.weight').to(device)
generator_ct = Generator3D(
H.ct_config.model.emb_dim,
H.ct_config.data.channels,
H.ct_config.model.nf,
H.ct_config.model.ch_mult,
H.ct_config.model.res_blocks,
H.ct_config.data.img_size,
H.ct_config.model.attn_resolutions
)
generator_ct.load_state_dict(quanitzer_and_generator_state_dict, strict=False)
generator_ct = generator_ct.to(device)
# Create and load latent sampler
sampler = get_sampler(H, ct_embedding_weight).to(device)
sampler = load_model(sampler, H.model.name, H.model.load_step, f'{H.run.name}_{H.run.experiment}').to(device)
sampler = sampler.eval()
if H.data.loader == 'bagct':
from utils.dataloader import BagXCT_dataset
test_dataset = BagXCT_dataset(data_dir=H.data.data_dir, train=False,
xray_scale=H.xray_config.data.img_size,
ct_scale=H.ct_config.data.img_size)
else:
from utils.dataloader import XCT_dataset
test_dataset = XCT_dataset(data_dir=H.data.data_dir, train=False,
xray_scale=H.xray_config.data.img_size,
scale=H.ct_config.data.img_size ,
projections=H.data.num_xrays,
load_res=H.data.load_res)
test_loader = DataLoader(test_dataset, batch_size=1, shuffle=False,
num_workers=0, pin_memory=True, drop_last=False)
test_dataloader = iter(test_loader)
evaluate(H, sampler, generator_ct,
test_latent_loader, test_dataloader, outputs_dir)
if __name__ == '__main__':
app.run(main)