Refactor image tensor conversion and model inference in interpolator.py and torch.py

main.py

@@ -1,7 +1,199 @@
 import logging
 from pathlib import Path
-from src.runner import run
+from typing import TYPE_CHECKING
+
+from cv2 import imwrite
+import tqdm
+
 from src.config import presets
+from src.utils.fs import FileSystem
+from src.utils.video import VideoMaker
+from src.interpolator import (
+    ImageInterpolator,
+    Anchor,
+    get_device,
+    get_vram_available,
+    ModelRunner,
+)
+
+
+if TYPE_CHECKING:
+    import torch
+    import numpy as np
+
+
+def performing_warning_message(device: "torch.device"):
+    if device.type in ("cpu", "mps"):
+        if device.type == "mps":
+            logging.warning(
+                "Running on Apple Silicon GPU (MPS) may have limited performance. Consider using a CUDA-enabled GPU for better performance."
+            )
+        else:
+            logging.warning(
+                "Running on CPU may be very slow. Consider using a GPU for better performance."
+            )
+    elif device.type == "cuda":
+        pass
+    else:
+        raise Exception(f"Unsupported device type: {device.type}")
+
+
+def init_fs(base_path: Path) -> FileSystem:
+    fs = FileSystem(base_path)
+    fs.clear_directory(fs.frames_path)
+    fs.clear_directory(fs.interpolated_path)
+    fs.clear_directory(fs.moved_path)
+    fs.clear_directory(fs.video_part_path)
+    return fs
+
+
+def init_video_maker() -> VideoMaker:
+    return VideoMaker()
+
+
+def init_device() -> "torch.device":
+    device = get_device()
+    performing_warning_message(device)
+    vram_available = get_vram_available(device)
+    logging.info(f"Available VRAM: {vram_available / (1024**3):.2f} GB")
+    return device
+
+
+def init_anchor(device: "torch.device") -> Anchor:
+    if device.type in ("cpu", "mps"):
+        return Anchor(resolution=8192 * 8192, memory=1, memory_bias=0)
+    elif device.type == "cuda":
+        return Anchor(
+            resolution=1024 * 512, memory=1500 * 1024**2, memory_bias=2500 * 1024**2
+        )
+    else:
+        raise Exception(f"Unsupported device type: {device.type}")
+
+
+def init_model_runner(
+    config: Path, checkpoint_path: Path, device: "torch.device"
+) -> ModelRunner:
+    return ModelRunner(config, checkpoint_path, device)
+
+
+def init_interpolator(
+    model_runner: ModelRunner, device: "torch.device"
+) -> ImageInterpolator:
+    anchor = init_anchor(device)
+    return ImageInterpolator(device, anchor, model_runner)
+
+
+class InterpolationPipeline:
+    def __init__(
+        self,
+        config: Path,
+        checkpoint_path: Path,
+        base_path: Path,
+    ):
+        self.fs = init_fs(base_path)
+        self.video_maker = init_video_maker()
+        self.device = init_device()
+        self.model_runner = init_model_runner(config, checkpoint_path, self.device)
+        self.interpolator = init_interpolator(self.model_runner, self.device)
+
+    def run(self, video_path: Path, output_video: str):
+        prev_frames = tuple()
+        interpolated_frames: list["np.ndarray"] = []
+        part = 0
+        chunk_seconds = 10
+        length = self.video_maker.get_video_duration(video_path)
+        last_part_seconds = 1 if length % chunk_seconds else 0
+        total_parts = int(length // chunk_seconds) + last_part_seconds
+        fps = self.video_maker.get_fps(video_path)
+        logging.info(f"Video FPS: {fps}")
+        fps *= 2  # Doubling FPS
+        width, height = self.video_maker.get_size(video_path)
+        for frames in self.video_maker.video_to_frames_generator(
+            video_path, self.fs.frames_path, chunk_seconds
+        ):
+            logging.info(f"Processing frames: {len(frames)}")
+            if prev_frames:
+                img1 = prev_frames[-1]
+                img2 = frames[0]
+                img1_2 = self.interpolator.interpolate(img1, img2)
+                interpolated_frames.append(img1_2)
+                generator = self._frame_generator(prev_frames, interpolated_frames)
+                part_path = self.fs.video_part_path / f"video_{part:08d}.mp4"
+                self.video_maker.images_to_video_pipeline(
+                    generator, part_path, width, height, fps
+                )
+                interpolated_frames = []
+                logging.info(f"Finished processing part {part:08d}")
+                part += 1
+            for i in tqdm.tqdm(
+                range(len(frames) - 1),
+                desc=f"Processing video frames {part + 1} / {total_parts}",
+            ):
+                img1 = frames[i]
+                img2 = frames[i + 1]
+                img1_2 = self.interpolator.interpolate(img1, img2)
+                interpolated_frames.append(img1_2)
+            prev_frames = frames
+
+        generator = self._frame_generator(prev_frames, interpolated_frames)
+        part_path = self.fs.video_part_path / f"video_{part:08d}.mp4"
+        self.video_maker.images_to_video_pipeline(
+            generator, part_path, width, height, fps
+        )
+        logging.info(f"Finished processing part {part:08d}")
+        self._merge_video_parts(self.fs.output_path / output_video)
+        logging.info(
+            f"Video interpolation completed. Output saved to: {self.fs.output_path / output_video}"
+        )
+
+    def _save_images(
+        self,
+        source: tuple["np.ndarray", ...],
+        interpolated: list["np.ndarray"],
+    ):
+        logging.info("Saving images...")
+        self.fs.clear_directory(self.fs.moved_path)
+        index = 0
+        for i, frame in enumerate(source):
+            name = self.fs.moved_path / f"img_{index:08d}.png"
+            index += 1
+            imwrite(name, frame)
+            if i < len(interpolated):
+                name = self.fs.moved_path / f"img_{index:08d}.png"
+                index += 1
+                imwrite(name, interpolated[i])
+        logging.info("Success...")
+
+    def _merge_frames_to_video(self, output_video: Path, fps: float):
+        self.video_maker.images_to_video(self.fs.moved_path, output_video, fps)
+
+    def _merge_video_parts(self, output_video: Path):
+        self.video_maker.concatenate_videos(self.fs.video_part_path, output_video)
+        self.fs.clear_directory(self.fs.video_part_path)
+
+    def _frame_generator(
+        self,
+        source: tuple["np.ndarray", ...],
+        interpolated: list["np.ndarray"],
+    ):
+        for i, frame in enumerate(source):
+            yield frame
+            if i < len(interpolated):
+                yield interpolated[i]
+
+
+def runner(
+    base_path: Path,
+    video_path: Path,
+    output_video: str,
+    preset: presets.Preset = presets.LARGE,
+):
+    pipeline = InterpolationPipeline(
+        config=preset.config,
+        checkpoint_path=preset.checkpoint,
+        base_path=base_path,
+    )
+    pipeline.run(video_path, output_video)
 
 
 def main():
@@ -30,7 +222,7 @@ def main():
         default="global",
     )
     args = parser.parse_args()
-    run(
+    runner(
         base_path=Path(args.base_path),
         video_path=Path(args.video_path),
         output_video=args.output,

pyproject.toml

@@ -5,6 +5,7 @@ description = "Add your description here"
 readme = "README.md"
 requires-python = ">=3.12"
 dependencies = [
+    "imageio>=2.37.3",
     "numpy>=2.4.4",
     "omegaconf>=2.3.0",
     "opencv-python>=4.13.0.92",

src/config/AMT-G.yaml

@@ -10,7 +10,7 @@ save_dir: work_dir
 eval_interval: 1
 
 network:
-  name: AMT-G.Model
+  name: src.networks.AMT-G.Model
   params:
     corr_radius: 3
     corr_lvls: 4

src/config/AMT-L.yaml

@@ -10,7 +10,7 @@ save_dir: work_dir
 eval_interval: 1
 
 network:
-  name: AMT-L.Model
+  name: src.networks.AMT-L.Model
   params:
     corr_radius: 3
     corr_lvls: 4

src/config/AMT-S.yaml

@@ -10,7 +10,7 @@ save_dir: work_dir
 eval_interval: 1
 
 network:
-  name: AMT-S.Model
+  name: src.networks.AMT-S.Model
   params:
     corr_radius: 3
     corr_lvls: 4

src/interpolator.py

@@ -1,13 +1,14 @@
 import logging
 from pathlib import Path
+from typing import Optional
 
 import torch
 import numpy as np
 from omegaconf import OmegaConf, DictConfig
 
-from .utils.torch import img2tensor, check_dim_and_resize, tensor2img
+from src.utils.torch import img2tensor, tensor2img
-from .utils.build import build_from_cfg
+from src.utils.build import build_from_cfg
-from .utils.padder import InputPadder
+from src.utils.padder import InputPadder
 
 
 class Anchor:
@@ -29,7 +30,6 @@ class ModelRunner:
             ckpt_path (Path): Path to model checkpoint in .pth format
             device (torch.device): Device to load the model on
         """
-        torch.set_float32_matmul_precision("high")
         omega_config = OmegaConf.load(config)
         network_config: DictConfig = omega_config.network
         logging.info(
@@ -40,7 +40,7 @@ class ModelRunner:
         model.load_state_dict(checkpoint["state_dict"])
         model = model.to(get_device())
         model.eval()
-        self.model = torch.compile(model, mode="max-autotune")
+        self.model = torch.compile(model)
 
 
 def get_vram_available(device: torch.device) -> int:
@@ -77,33 +77,38 @@ class ImageInterpolator:
         self.device = device
         self.anchor = anchor
         self.vram_available = get_vram_available(device)
-        self._scale = None
-        self._padder = None
         self.embt = torch.tensor(1 / 2).float().view(1, 1, 1, 1).to(device)
         self.model_runner = model_runner
         logging.debug(
             f"Initialized ImageInterpolator with device: {device}, anchor: {anchor}, available VRAM: {self.vram_available} bytes"
         )
 
-    def interpolate(self, image1: torch.Tensor, image2: torch.Tensor) -> torch.Tensor:
+    def interpolate(self, image1: np.ndarray, image2: np.ndarray) -> np.ndarray:
-        interpolated = self.model_runner.model(
+        """
-            image1, image2, self.embt, scale_factor=self._scale, eval=True
+        Interpolates between two images and saves the result.
-        )["imgt_pred"]
+        Args:
-        if not self._padder:
+            image1 (Path): Path to the first input image (only png and jpg formats are supported)
-            raise NotImplemented("Padder not implemented")
+            image2 (Path): Path to the second input image (only png and jpg formats are supported)
-        return self._padder.unpad(interpolated)[0]
+            output_path (Path): Path to save the interpolated image (only png and jpg formats are supported)
+        """
+        logging.debug(f"Reading images: {image1} and {image2}")
+        tensor1 = img2tensor(image1, self.device)
+        tensor2 = img2tensor(image2, self.device)
+        logging.debug(
+            f"Image shapes after conversion to tensors: {tensor1.shape}, {tensor2.shape}"
+        )
 
-    def make_tensor(self, img: np.ndarray) -> torch.Tensor:
+        logging.debug("Running model inference for interpolation")
-        tensor = img2tensor(img).to(self.device)
+        with torch.no_grad():
-        h, w = tensor.shape[2], tensor.shape[3]
+            with torch.amp.autocast(self.device.type):
-        scale = self.scale(h, w)
+                interpolated = self.model_runner.model(
-        padding = int(16 / scale)
+                    tensor1, tensor2, self.embt
-        if self._padder is None:
+                )["imgt_pred"]
-            self._padder = InputPadder(tensor.shape, padding)
+        logging.debug(f"Interpolated image shape before unpadding: {interpolated.shape}")
-        return self._padder.pad(tensor)[0]
+        logging.debug(f"Interpolated image shape after unpadding: {interpolated.shape}")
+        return tensor2img(interpolated.cpu())
 
     def scale(self, height: int, width: int) -> float:
-        if self._scale is None:
         scale = (
             self.anchor.resolution
             / (height * width)
@@ -114,9 +119,7 @@ class ImageInterpolator:
         scale = 1 if scale > 1 else scale
         scale = 1 / np.floor(1 / np.sqrt(scale) * 16) * 16
         if scale < 1:
-                logging.debug(
+            logging.info(
                 f"Due to the limited VRAM, the video will be scaled by {scale:.2f}"
             )
-            self._scale = float(scale)
+        return scale
-            logging.info(f"Calculated scale factor: {self._scale:.2f}")
-        return self._scale

src/networks/AMT-G.py

@@ -2,10 +2,10 @@ from typing import Optional
 
 import torch
 import torch.nn as nn
-from .blocks.raft import coords_grid, BasicUpdateBlock, BidirCorrBlock
+from src.networks.blocks.raft import coords_grid, BasicUpdateBlock, BidirCorrBlock
-from .blocks.feat_enc import LargeEncoder
+from src.networks.blocks.feat_enc import LargeEncoder
-from .blocks.ifrnet import resize, Encoder, InitDecoder, IntermediateDecoder
+from src.networks.blocks.ifrnet import resize, Encoder, InitDecoder, IntermediateDecoder
-from .blocks.multi_flow import multi_flow_combine, MultiFlowDecoder
+from src.networks.blocks.multi_flow import multi_flow_combine, MultiFlowDecoder
 
 
 class Model(nn.Module):
@@ -177,11 +177,14 @@ class Model(nn.Module):
         )
 
         if scale_factor != 1.0:
-            factor = 1.0 / scale_factor
+            up_flow0_1 = resize(up_flow0_1, scale_factor=(1.0 / scale_factor)) * (
-            up_flow0_1 = resize(up_flow0_1, factor) * factor
+                1.0 / scale_factor
-            up_flow1_1 = resize(up_flow1_1, factor) * factor
+            )
-            mask = resize(mask, factor)
+            up_flow1_1 = resize(up_flow1_1, scale_factor=(1.0 / scale_factor)) * (
-            img_res = resize(img_res, factor)
+                1.0 / scale_factor
+            )
+            mask = resize(mask, scale_factor=(1.0 / scale_factor))
+            img_res = resize(img_res, scale_factor=(1.0 / scale_factor))
 
         # Merge multiple predictions
         imgt_pred = multi_flow_combine(

src/networks/AMT-L.py

@@ -1,10 +1,10 @@
 import torch
 import torch.nn as nn
-from .blocks.raft import coords_grid, BasicUpdateBlock, BidirCorrBlock
+from src.networks.blocks.raft import coords_grid, BasicUpdateBlock, BidirCorrBlock
-from .blocks.feat_enc import BasicEncoder
+from src.networks.blocks.feat_enc import BasicEncoder
-from .blocks.ifrnet import resize, Encoder, InitDecoder, IntermediateDecoder
+from src.networks.blocks.ifrnet import resize, Encoder, InitDecoder, IntermediateDecoder
 
-from .blocks.multi_flow import multi_flow_combine, MultiFlowDecoder
+from src.networks.blocks.multi_flow import multi_flow_combine, MultiFlowDecoder
 
 
 class Model(nn.Module):
@@ -142,11 +142,14 @@ class Model(nn.Module):
         )
 
         if scale_factor != 1.0:
-            factor = 1.0 / scale_factor
+            up_flow0_1 = resize(up_flow0_1, scale_factor=(1.0 / scale_factor)) * (
-            up_flow0_1 = resize(up_flow0_1, factor) * factor
+                1.0 / scale_factor
-            up_flow1_1 = resize(up_flow1_1, factor) * factor
+            )
-            mask = resize(mask, factor)
+            up_flow1_1 = resize(up_flow1_1, scale_factor=(1.0 / scale_factor)) * (
-            img_res = resize(img_res, factor)
+                1.0 / scale_factor
+            )
+            mask = resize(mask, scale_factor=(1.0 / scale_factor))
+            img_res = resize(img_res, scale_factor=(1.0 / scale_factor))
 
         # Merge multiple predictions
         imgt_pred = multi_flow_combine(

src/networks/AMT-S.py

@@ -1,9 +1,9 @@
 import torch
 import torch.nn as nn
-from .blocks.raft import coords_grid, SmallUpdateBlock, BidirCorrBlock
+from src.networks.blocks.raft import coords_grid, SmallUpdateBlock, BidirCorrBlock
-from .blocks.feat_enc import SmallEncoder
+from src.networks.blocks.feat_enc import SmallEncoder
-from .blocks.ifrnet import resize, Encoder, InitDecoder, IntermediateDecoder
+from src.networks.blocks.ifrnet import resize, Encoder, InitDecoder, IntermediateDecoder
-from .blocks.multi_flow import multi_flow_combine, MultiFlowDecoder
+from src.networks.blocks.multi_flow import multi_flow_combine, MultiFlowDecoder
 
 
 class Model(nn.Module):
@@ -140,11 +140,14 @@ class Model(nn.Module):
         )
 
         if scale_factor != 1.0:
-            factor = 1.0 / scale_factor
+            up_flow0_1 = resize(up_flow0_1, scale_factor=(1.0 / scale_factor)) * (
-            up_flow0_1 = resize(up_flow0_1, factor) * factor
+                1.0 / scale_factor
-            up_flow1_1 = resize(up_flow1_1, factor) * factor
+            )
-            mask = resize(mask, factor)
+            up_flow1_1 = resize(up_flow1_1, scale_factor=(1.0 / scale_factor)) * (
-            img_res = resize(img_res, factor)
+                1.0 / scale_factor
+            )
+            mask = resize(mask, scale_factor=(1.0 / scale_factor))
+            img_res = resize(img_res, scale_factor=(1.0 / scale_factor))
 
         # Merge multiple predictions
         imgt_pred = multi_flow_combine(

src/networks/IFRNet.py

@@ -1,7 +1,7 @@
 import torch
 import torch.nn as nn
-from ..utils.flow_utils import warp
+from src.utils.flow_utils import warp
-from .blocks.ifrnet import convrelu, resize, ResBlock
+from src.networks.blocks.ifrnet import convrelu, resize, ResBlock
 
 
 class Encoder(nn.Module):

src/networks/blocks/ifrnet.py

@@ -1,7 +1,7 @@
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-from ...utils.flow_utils import warp
+from src.utils.flow_utils import warp
 
 
 def resize(x, scale_factor):

src/networks/blocks/multi_flow.py

@@ -1,7 +1,7 @@
 import torch
 import torch.nn as nn
-from ...utils.flow_utils import warp
+from src.utils.flow_utils import warp
-from .ifrnet import convrelu, resize, ResBlock
+from src.networks.blocks.ifrnet import convrelu, resize, ResBlock
 
 
 def multi_flow_combine(

src/runner.py

@@ -1,179 +0,0 @@
-import logging
-from pathlib import Path
-from typing import TYPE_CHECKING
-
-from cv2 import imwrite
-import tqdm
-import torch
-
-from .config import presets
-from .utils.fs import FileSystem
-from .utils.video import VideoMaker
-from .utils.torch import tensor2img
-from .interpolator import (
-    ImageInterpolator,
-    Anchor,
-    get_device,
-    get_vram_available,
-    ModelRunner,
-)
-
-
-if TYPE_CHECKING:
-    import torch
-    import numpy as np
-
-
-def performing_warning_message(device: "torch.device"):
-    if device.type in ("cpu", "mps"):
-        if device.type == "mps":
-            logging.warning(
-                "Running on Apple Silicon GPU (MPS) may have limited performance. Consider using a CUDA-enabled GPU for better performance."
-            )
-        else:
-            logging.warning(
-                "Running on CPU may be very slow. Consider using a GPU for better performance."
-            )
-    elif device.type == "cuda":
-        pass
-    else:
-        raise Exception(f"Unsupported device type: {device.type}")
-
-
-def init_fs(base_path: Path) -> FileSystem:
-    fs = FileSystem(base_path)
-    fs.clear_directory(fs.frames_path)
-    fs.clear_directory(fs.interpolated_path)
-    fs.clear_directory(fs.moved_path)
-    fs.clear_directory(fs.video_part_path)
-    return fs
-
-
-def init_video_maker() -> VideoMaker:
-    return VideoMaker()
-
-
-def init_device() -> "torch.device":
-    device = get_device()
-    performing_warning_message(device)
-    vram_available = get_vram_available(device)
-    logging.info(f"Available VRAM: {vram_available / (1024**3):.2f} GB")
-    return device
-
-
-def init_anchor(device: "torch.device") -> Anchor:
-    if device.type in ("cpu", "mps"):
-        return Anchor(resolution=8192 * 8192, memory=1, memory_bias=0)
-    elif device.type == "cuda":
-        # return Anchor(
-        #     resolution=1024 * 512, memory=1500 * 1024**2, memory_bias=2500 * 1024**2
-        # )
-        return Anchor(
-            resolution=1280 * 720, memory=6500 * 1024**2, memory_bias=7500 * 1024**2
-        )
-    else:
-        raise Exception(f"Unsupported device type: {device.type}")
-
-
-def init_model_runner(
-    config: Path, checkpoint_path: Path, device: "torch.device"
-) -> ModelRunner:
-    return ModelRunner(config, checkpoint_path, device)
-
-
-def init_interpolator(
-    model_runner: ModelRunner, device: "torch.device"
-) -> ImageInterpolator:
-    anchor = init_anchor(device)
-    return ImageInterpolator(device, anchor, model_runner)
-
-
-class InterpolationPipeline:
-    def __init__(
-        self,
-        config: Path,
-        checkpoint_path: Path,
-        base_path: Path,
-    ):
-        self.fs = init_fs(base_path)
-        self.video_maker = init_video_maker()
-        self.device = init_device()
-        self.model_runner = init_model_runner(config, checkpoint_path, self.device)
-        self.interpolator = init_interpolator(self.model_runner, self.device)
-
-    def run(self, video_path: Path, output_video: str):
-        prev_frames: tuple["np.ndarray", ...] = tuple()
-        interpolated_frames: list["np.ndarray"] = []
-        part = 0
-        chunk_seconds = 10
-        length = self.video_maker.get_video_duration(video_path)
-        last_part_seconds = 1 if length % chunk_seconds else 0
-        total_parts = int(length // chunk_seconds) + last_part_seconds
-        fps = self.video_maker.get_fps(video_path)
-        logging.info(f"Video FPS: {fps}")
-        fps *= 2  # Doubling FPS
-        width, height = self.video_maker.get_size(video_path)
-        with torch.autocast(self.device.type, torch.float16):
-            with torch.no_grad():
-                prev_tensor = None
-                for idx, frames in enumerate(
-                    self.video_maker.video_to_frames_generator(
-                        video_path, self.fs.frames_path, chunk_seconds
-                    )
-                ):
-                    interpolated_frames: list["np.ndarray"] = []
-                    for frame in tqdm.tqdm(frames):
-                        tensor = self.interpolator.make_tensor(frame)
-                        if prev_tensor is None:
-                            prev_tensor = tensor
-                            continue
-                        interpolated_frames.append(
-                            tensor2img(
-                                self.interpolator.interpolate(
-                                    prev_tensor, tensor
-                                )
-                            )
-                        )
-                        prev_tensor = tensor
-
-                    generator = self._frame_generator(frames, interpolated_frames)
-                    part_path = self.fs.video_part_path / f"video_{idx:08d}.mp4"
-                    self.video_maker.images_to_video_pipeline(
-                        generator, part_path, width, height, fps
-                    )
-                self._merge_video_parts(self.fs.output_path / output_video)
-
-    def _merge_video_parts(self, output_video: Path):
-        self.video_maker.concatenate_videos(self.fs.video_part_path, output_video)
-        self.fs.clear_directory(self.fs.video_part_path)
-
-    def _frame_generator(
-        self,
-        source: tuple["np.ndarray", ...],
-        interpolated: list["np.ndarray"],
-    ):
-        if len(source) == len(interpolated):
-            first = interpolated
-            second = source
-        else:
-            first = source
-            second = interpolated
-
-        for i, frame in enumerate(first):
-            yield frame
-            if i < len(second):
-                yield second[i]
-
-
-def run(
-    base_path: Path,
-    video_path: Path,
-    output_video: str,
-    preset: presets.Preset = presets.LARGE,
-):
-    pipeline = InterpolationPipeline(
-        config=preset.config,
-        checkpoint_path=preset.checkpoint,
-        base_path=base_path,
-    )
-    pipeline.run(video_path, output_video)

src/utils/build.py

@@ -1,19 +1,16 @@
+import importlib
 
 from typing import TYPE_CHECKING
-from ..networks import AMT_G, AMT_L, AMT_S
 
 if TYPE_CHECKING:
     from omegaconf import DictConfig
 
 
-def build_from_cfg(config: "DictConfig"):
+def base_build_fn(module: str, cls: str, params: dict):
-    packages = {
+    return getattr(importlib.import_module(module, package=None), cls)(**params)
-        "AMT-G": AMT_G,
-        "AMT-L": AMT_L,
-        "AMT-S": AMT_S
-    }
 
+
+def build_from_cfg(config: "DictConfig"):
     module, cls = config["name"].rsplit(".", 1)
     params: dict = config.get("params", {})
-    return getattr(packages[module], cls)(**params)
+    return base_build_fn(module, cls, params)
-

src/utils/padder.py

@@ -21,6 +21,9 @@ class InputPadder:
         ]
 
     def pad(self, *inputs: "torch.Tensor"):
+        if len(inputs) == 1:
+            return F.pad(inputs[0], self._pad, mode="replicate")
+        else:
             return [F.pad(x, self._pad, mode="replicate") for x in inputs]
 
     def unpad(self, *inputs: "torch.Tensor"):

src/utils/torch.py

@@ -5,23 +5,26 @@ import numpy as np
 
 
 def tensor2img(tensor: torch.Tensor):
-    return (
+    tensor = (
-        (tensor * 255.0)
+        tensor.mul(255.0)
-        .detach()
+        .clamp_(0, 255)
+        .to(torch.uint8)
         .squeeze(0)
         .permute(1, 2, 0)
-        .cpu()
-        .numpy()
-        .clip(0, 255)
-        .astype(np.uint8)
     )
 
+    return tensor.cpu().numpy()
 
-def img2tensor(img: np.ndarray) -> torch.Tensor:
+
+def img2tensor(img: np.ndarray, device: torch.device) -> torch.Tensor:
     logging.debug(f"Converting image of shape {img.shape} to tensor")
     if img.shape[-1] > 3:
         img = img[:, :, :3]
-    return torch.tensor(img).permute(2, 0, 1).unsqueeze(0) / 255.0
+    tensor = torch.from_numpy(img).permute(2, 0, 1).unsqueeze(0)
+    if device.type != "cuda":
+        return tensor.float() / 255.0
+    
+    return tensor.cuda(non_blocking=True).float().div_(255.0)
 
 
 def check_dim_and_resize(*args: torch.Tensor) -> list[torch.Tensor]:

uv.lock

@@ -1,5 +1,5 @@
 version = 1
-revision = 3
+revision = 2
 requires-python = ">=3.12"
 
 [[package]]
@@ -7,6 +7,7 @@ name = "amt-apple"
 version = "0.1.0"
 source = { virtual = "." }
 dependencies = [
+    { name = "imageio" },
     { name = "numpy" },
     { name = "omegaconf" },
     { name = "opencv-python" },
@@ -16,6 +17,7 @@ dependencies = [
 
 [package.metadata]
 requires-dist = [
+    { name = "imageio", specifier = ">=2.37.3" },
     { name = "numpy", specifier = ">=2.4.4" },
     { name = "omegaconf", specifier = ">=2.3.0" },
     { name = "opencv-python", specifier = ">=4.13.0.92" },
@@ -125,6 +127,19 @@ wheels = [
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 ]
 
+[[package]]
+name = "imageio"
+version = "2.37.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy" },
+    { name = "pillow" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/b1/84/93bcd1300216ea50811cee96873b84a1bebf8d0489ffaf7f2a3756bab866/imageio-2.37.3.tar.gz", hash = "sha256:bbb37efbfc4c400fcd534b367b91fcd66d5da639aaa138034431a1c5e0a41451", size = 389673, upload-time = "2026-03-09T11:31:12.573Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/49/fa/391e437a34e55095173dca5f24070d89cbc233ff85bf1c29c93248c6588d/imageio-2.37.3-py3-none-any.whl", hash = "sha256:46f5bb8522cd421c0f5ae104d8268f569d856b29eb1a13b92829d1970f32c9f0", size = 317646, upload-time = "2026-03-09T11:31:10.771Z" },
+]
+
 [[package]]
 name = "jinja2"
 version = "3.1.6"
@@ -459,6 +474,75 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/e9/a5/1be1516390333ff9be3a9cb648c9f33df79d5096e5884b5df71a588af463/opencv_python-4.13.0.92-cp37-abi3-win_amd64.whl", hash = "sha256:423d934c9fafb91aad38edf26efb46da91ffbc05f3f59c4b0c72e699720706f5", size = 40212062, upload-time = "2026-02-05T07:02:12.724Z" },
 ]
 
+[[package]]
+name = "pillow"
+version = "12.1.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/1f/42/5c74462b4fd957fcd7b13b04fb3205ff8349236ea74c7c375766d6c82288/pillow-12.1.1.tar.gz", hash = "sha256:9ad8fa5937ab05218e2b6a4cff30295ad35afd2f83ac592e68c0d871bb0fdbc4", size = 46980264, upload-time = "2026-02-11T04:23:07.146Z" }
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