Delete test_samples function and its dependencies

test.py

@@ -1,86 +0,0 @@
-import os
-import sys
-import glob
-import argparse
-import numpy as np
-
-import torch
-import torch.nn.functional as F
-from torchvision import transforms
-from PIL import Image
-import utils.utils as utils
-
-
-def test_samples(args, model, intrins=None, device="cpu"):
- img_paths = glob.glob("./samples/img/*.png") + glob.glob("./samples/img/*.jpg")
- img_paths.sort()
-
- # normalize
- normalize = transforms.Normalize(
- mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
- )
-
- with torch.no_grad():
- for img_path in img_paths:
- print(img_path)
- ext = os.path.splitext(img_path)[1]
- img = Image.open(img_path).convert("RGB")
- img = np.array(img).astype(np.float32) / 255.0
- img = torch.from_numpy(img).permute(2, 0, 1).unsqueeze(0).to(device)
- _, _, orig_H, orig_W = img.shape
-
- # zero-pad the input image so that both the width and height are multiples of 32
- l, r, t, b = utils.pad_input(orig_H, orig_W)
- img = F.pad(img, (l, r, t, b), mode="constant", value=0.0)
- img = normalize(img)
-
- intrins_path = img_path.replace(ext, ".txt")
- if os.path.exists(intrins_path):
- # NOTE: camera intrinsics should be given as a txt file
- # it should contain the values of fx, fy, cx, cy
- intrins = utils.get_intrins_from_txt(
- intrins_path, device=device
- ).unsqueeze(0)
- else:
- # NOTE: if intrins is not given, we just assume that the principal point is at the center
- # and that the field-of-view is 60 degrees (feel free to modify this assumption)
- intrins = utils.get_intrins_from_fov(
- new_fov=60.0, H=orig_H, W=orig_W, device=device
- ).unsqueeze(0)
-
- intrins[:, 0, 2] += l
- intrins[:, 1, 2] += t
-
- pred_norm = model(img, intrins=intrins)[-1]
- pred_norm = pred_norm[:, :, t : t + orig_H, l : l + orig_W]
-
- # save to output folder
- # NOTE: by saving the prediction as uint8 png format, you lose a lot of precision
- # if you want to use the predicted normals for downstream tasks, we recommend saving them as float32 NPY files
- pred_norm_np = (
- pred_norm.cpu().detach().numpy()[0, :, :, :].transpose(1, 2, 0)
- ) # (H, W, 3)
- pred_norm_np = ((pred_norm_np + 1.0) / 2.0 * 255.0).astype(np.uint8)
- target_path = img_path.replace("/img/", "/output/").replace(ext, ".png")
- im = Image.fromarray(pred_norm_np)
- im.save(target_path)
-
-
-if __name__ == "__main__":
- parser = argparse.ArgumentParser()
- parser.add_argument("--ckpt", default="dsine", type=str, help="model checkpoint")
- parser.add_argument("--mode", default="samples", type=str, help="{samples}")
- args = parser.parse_args()
-
- # define model
- device = torch.device("cpu")
-
- from models.dsine import DSINE
-
- model = DSINE().to(device)
- model.pixel_coords = model.pixel_coords.to(device)
- model = utils.load_checkpoint("./checkpoints/%s.pt" % args.ckpt, model)
- model.eval()
-
- if args.mode == "samples":
- test_samples(args, model, intrins=None, device=device)