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)