Lint

Signed-off-by: Krishna Murthy <[email protected]>

Author: Krishna Murthy

eval_nerf.py

@@ -9,8 +9,15 @@
 import yaml
 from tqdm import tqdm
 
-from nerf import (CfgNode, get_ray_bundle, load_blender_data, load_llff_data,
-                  models, get_embedding_function, run_one_iter_of_nerf)
+from nerf import (
+    CfgNode,
+    get_ray_bundle,
+    load_blender_data,
+    load_llff_data,
+    models,
+    get_embedding_function,
+    run_one_iter_of_nerf,
+)
 
 
 def cast_to_image(tensor, dataset_type):
@@ -85,7 +92,7 @@ def main():
     encode_position_fn = get_embedding_function(
         num_encoding_functions=cfg.models.coarse.num_encoding_fn_xyz,
         include_input=cfg.models.coarse.include_input_xyz,
-        log_sampling=cfg.models.coarse.log_sampling_xyz
+        log_sampling=cfg.models.coarse.log_sampling_xyz,
     )
 
     encode_direction_fn = None
@@ -174,7 +181,9 @@ def main():
         times_per_image.append(time.time() - start)
         if configargs.savedir:
             savefile = os.path.join(configargs.savedir, f"{i:04d}.png")
-            imageio.imwrite(savefile, cast_to_image(rgb[..., :3], cfg.dataset.type.lower()))
+            imageio.imwrite(
+                savefile, cast_to_image(rgb[..., :3], cfg.dataset.type.lower())
+            )
             if configargs.save_disparity_image:
                 savefile = os.path.join(configargs.savedir, "disparity", f"{i:04d}.png")
                 imageio.imwrite(savefile, cast_to_disparity_image(disp))

nerf/models.py

@@ -136,7 +136,7 @@ def __init__(
         use_viewdirs=True,
     ):
         super(PaperNeRFModel, self).__init__()
-        
+
         include_input_xyz = 3 if include_input_xyz else 0
         include_input_dir = 3 if include_input_dir else 0
         self.dim_xyz = include_input_xyz + 2 * 3 * num_encoding_fn_xyz
@@ -161,7 +161,7 @@ def __init__(
         self.relu = torch.nn.functional.relu
 
     def forward(self, x):
-        xyz, dirs = x[..., :self.dim_xyz], x[..., self.dim_xyz:]
+        xyz, dirs = x[..., : self.dim_xyz], x[..., self.dim_xyz :]
         for i in range(8):
             if i == 4:
                 x = self.layers_xyz[i](torch.cat((xyz, x), -1))

nerf/nerf_helpers.py

@@ -130,14 +130,20 @@ def positional_encoding(
     encoding = [tensor] if include_input else []
     frequency_bands = None
     if log_sampling:
-        frequency_bands = 2. ** torch.linspace(
-            0., num_encoding_functions - 1, num_encoding_functions,
-            dtype=tensor.dtype, device=tensor.device,
+        frequency_bands = 2.0 ** torch.linspace(
+            0.0,
+            num_encoding_functions - 1,
+            num_encoding_functions,
+            dtype=tensor.dtype,
+            device=tensor.device,
         )
     else:
         frequency_bands = torch.linspace(
-            2. ** 0., 2. ** (num_encoding_functions - 1), num_encoding_functions,
-            dtype=tensor.dtype, device=tensor.device
+            2.0 ** 0.0,
+            2.0 ** (num_encoding_functions - 1),
+            num_encoding_functions,
+            dtype=tensor.dtype,
+            device=tensor.device,
         )
 
     for freq in frequency_bands:

nerf/train_utils.py

@@ -180,13 +180,16 @@ def run_one_iter_of_nerf(
         for batch in batches
     ]
     synthesized_images = list(zip(*pred))
-    synthesized_images = [torch.cat(image, dim=0) if image[0] is not None else (None) for image in synthesized_images]
+    synthesized_images = [
+        torch.cat(image, dim=0) if image[0] is not None else (None)
+        for image in synthesized_images
+    ]
     if mode == "validation":
         synthesized_images = [
             image.view(shape) if image is not None else None
             for (image, shape) in zip(synthesized_images, restore_shapes)
         ]
-    
+
         # Returns rgb_coarse, disp_coarse, acc_coarse, rgb_fine, disp_fine, acc_fine
         # (assuming both the coarse and fine networks are used).
         if model_fine:

tiny_nerf.py

@@ -6,8 +6,7 @@
 import torch
 from tqdm import tqdm, trange
 
-from nerf import (cumprod_exclusive, get_minibatches, get_ray_bundle,
-                  positional_encoding)
+from nerf import cumprod_exclusive, get_minibatches, get_ray_bundle, positional_encoding
 
 
 def compute_query_points_from_rays(

train_nerf.py

@@ -10,9 +10,9 @@
 from torch.utils.tensorboard import SummaryWriter
 from tqdm import tqdm, trange
 
-from nerf import (CfgNode, get_ray_bundle, img2mse, load_blender_data,
-                  load_llff_data, meshgrid_xy, models, mse2psnr,
-                  get_embedding_function, run_one_iter_of_nerf)
+from nerf import (CfgNode, get_embedding_function, get_ray_bundle, img2mse,
+                  load_blender_data, load_llff_data, meshgrid_xy, models,
+                  mse2psnr, run_one_iter_of_nerf)
 
 
 def main():
@@ -63,7 +63,7 @@ def main():
             H, W = int(H), int(W)
             hwf = [H, W, focal]
             if cfg.nerf.train.white_background:
-                images = images[..., :3] * images[..., -1:] + (1. - images[..., -1:])
+                images = images[..., :3] * images[..., -1:] + (1.0 - images[..., -1:])
         elif cfg.dataset.type.lower() == "llff":
             images, poses, bds, render_poses, i_test = load_llff_data(
                 cfg.dataset.basedir, factor=cfg.dataset.downsample_factor
@@ -104,7 +104,7 @@ def main():
         include_input=cfg.models.coarse.include_input_xyz,
         log_sampling=cfg.models.coarse.log_sampling_xyz,
     )
-    
+
     encode_direction_fn = None
     if cfg.models.coarse.use_viewdirs:
         encode_direction_fn = get_embedding_function(
@@ -250,7 +250,7 @@ def main():
                 rgb_fine[..., :3], target_ray_values[..., :3]
             )
         # loss = torch.nn.functional.mse_loss(rgb_pred[..., :3], target_s[..., :3])
-        loss = 0.
+        loss = 0.0
         # if fine_loss is not None:
         #     loss = fine_loss
         # else:
@@ -337,7 +337,7 @@ def main():
                     )
                     target_ray_values = img_target
                 coarse_loss = img2mse(rgb_coarse[..., :3], target_ray_values[..., :3])
-                loss, fine_loss = 0., 0.
+                loss, fine_loss = 0.0, 0.0
                 if rgb_fine is not None:
                     fine_loss = img2mse(rgb_fine[..., :3], target_ray_values[..., :3])
                     loss = fine_loss