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cleanup val script

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vlpr 7 months ago
parent efdde28e76
commit e9c6248949
1 changed files with 4 additions and 14 deletions
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18
src/common/validation.py
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@ -6,19 +6,16 @@ from pathlib import Path
from PIL import Image from PIL import Image
import time import time
# @ray.remote(num_cpus=1, num_gpus=0.3)
def val_image_pair(model, hr_image, lr_image, output_image_path=None, device='cuda'): def val_image_pair(model, hr_image, lr_image, output_image_path=None, device='cuda'):
with torch.inference_mode(): with torch.inference_mode():
start_time = time.perf_counter_ns() start_time = time.perf_counter_ns()
# prepare lr_image # prepare lr_image
lr_image = torch.tensor(lr_image).type(torch.float32).permute(2,0,1) lr_image = torch.tensor(lr_image).type(torch.float32).permute(2,0,1)
lr_image = lr_image.unsqueeze(0).to(torch.device(device)) lr_image = lr_image.unsqueeze(0).to(torch.device(device))
b, c, h, w = lr_image.shape
lr_image = lr_image.reshape(b, c, h, w)
# predict # predict
pred_lr_image = model(lr_image) pred_lr_image = model(lr_image)
# postprocess # postprocess
pred_lr_image = pred_lr_image.squeeze(0).permute(1,2,0).type(torch.uint8) pred_lr_image = pred_lr_image.squeeze(0).permute(1,2,0).type(torch.uint8)
pred_lr_image = pred_lr_image.cpu().numpy() pred_lr_image = pred_lr_image.cpu().numpy()
run_time_ns = time.perf_counter_ns() - start_time run_time_ns = time.perf_counter_ns() - start_time
torch.cuda.empty_cache() torch.cuda.empty_cache()
@ -28,12 +25,11 @@ def val_image_pair(model, hr_image, lr_image, output_image_path=None, device='cu
# metrics # metrics
hr_image = modcrop(hr_image, model.scale) hr_image = modcrop(hr_image, model.scale)
left, right = _rgb2ycbcr(pred_lr_image)[:, :, 0], _rgb2ycbcr(hr_image)[:, :, 0] Y_left, Y_right = _rgb2ycbcr(pred_lr_image)[:, :, 0], _rgb2ycbcr(hr_image)[:, :, 0]
lr_area = np.prod(lr_image.shape[-2:]) lr_area = np.prod(lr_image.shape[-2:])
return PSNR(left, right, model.scale), cal_ssim(left, right), run_time_ns, lr_area return PSNR(Y_left, Y_right, model.scale), cal_ssim(Y_left, Y_right), run_time_ns, lr_area
def valid_steps(model, datasets, config, log_prefix=""): def valid_steps(model, datasets, config, log_prefix=""):
# ray.init(num_cpus=16, num_gpus=1, ignore_reinit_error=True, log_to_driver=False, runtime_env={"working_dir": "../"})
dataset_names = list(datasets.keys()) dataset_names = list(datasets.keys())
results = [] results = []
@ -52,17 +48,11 @@ def valid_steps(model, datasets, config, log_prefix=""):
test_dataset = datasets[dataset_name] test_dataset = datasets[dataset_name]
tasks = [] tasks = []
for hr_image, lr_image, hr_image_path, lr_image_path in test_dataset: for hr_image, lr_image, hr_image_path, lr_image_path in test_dataset:
output_image_path = predictions_path / f'{Path(hr_image_path).stem}_rcnet.png' if config.save_predictions else None output_image_path = predictions_path / f'{Path(hr_image_path).stem}.png' if config.save_predictions else None
task = val_image_pair(model, hr_image, lr_image, output_image_path, device=config.device) task = val_image_pair(model, hr_image, lr_image, output_image_path, device=config.device)
tasks.append(task) tasks.append(task)
total_time = time.time() - start_time total_time = time.time() - start_time
# ready_refs, remaining_refs = ray.wait(tasks, num_returns=1, timeout=None)
# while len(remaining_refs) > 0:
# print(f"\rReady {len(ready_refs)+1}/{len(test_dataset)}", end=" ")
# ready_refs, remaining_refs = ray.wait(tasks, num_returns=len(ready_refs)+1, timeout=None)
# print("\r", end=" ")
# tasks = [ray.get(task) for task in tasks]
for psnr, ssim, run_time_ns, lr_area in tasks: for psnr, ssim, run_time_ns, lr_area in tasks:
psnrs.append(psnr) psnrs.append(psnr)

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