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

main.py

@@ -2,6 +2,7 @@ import logging
 from pathlib import Path
 from typing import TYPE_CHECKING
 
+from cv2 import imwrite
 import tqdm
 
 from src.config import presets
@@ -18,6 +19,7 @@ from src.interpolator import (
 
 if TYPE_CHECKING:
     import torch
+    import numpy as np
 
 
 def performing_warning_message(device: "torch.device"):
@@ -53,7 +55,7 @@ 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")
+    logging.info(f"Available VRAM: {vram_available / (1024**3):.2f} GB")
     return device
 
 
@@ -95,10 +97,9 @@ class InterpolationPipeline:
         self.interpolator = init_interpolator(self.model_runner, self.device)
 
     def run(self, video_path: Path, output_video: str):
-        prev_frame_path = None
-        frame_count = 0
+        prev_frames = tuple()
+        interpolated_frames: list["np.ndarray"] = []
         part = 0
-        source_frame_length = 0
         chunk_seconds = 10
         length = self.video_maker.get_video_duration(video_path)
         last_part_seconds = 1 if length % chunk_seconds else 0
@@ -106,41 +107,38 @@ class InterpolationPipeline:
         fps = self.video_maker.get_fps(video_path)
         logging.info(f"Video FPS: {fps}")
         fps *= 2  # Doubling FPS
-        for frame_paths in self.video_maker.video_to_frames_generator(
+        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(frame_paths)}")
-            if prev_frame_path is not None:
-                img1 = prev_frame_path[-1]
-                img2 = frame_paths[0]
-                output_path = self.fs.interpolated_path / f"img_{frame_count:08d}.png"
-                self.interpolator.interpolate(img1, img2, output_path)
-                logging.debug(f"Interpolated image saved to: {output_path}")
-                self._merge_frames_to_video(
-                    self.fs.video_part_path / f"video_{part:08d}.mp4",
-                    fps,
-                    source_frame_length=source_frame_length,
+            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}")
-                frame_count += 1
                 part += 1
             for i in tqdm.tqdm(
-                range(len(frame_paths) - 1),
+                range(len(frames) - 1),
                 desc=f"Processing video frames {part + 1} / {total_parts}",
             ):
-                img1 = frame_paths[i]
-                img2 = frame_paths[i + 1]
-                output_path = self.fs.interpolated_path / f"img_{i:08d}.png"
-                self.interpolator.interpolate(img1, img2, output_path)
-                logging.debug(f"Interpolated image saved to: {output_path}")
-                frame_count += 1
-            source_frame_length = len(frame_paths)
-            prev_frame_path = frame_paths
+                img1 = frames[i]
+                img2 = frames[i + 1]
+                img1_2 = self.interpolator.interpolate(img1, img2)
+                interpolated_frames.append(img1_2)
+            prev_frames = frames
 
-        self._merge_frames_to_video(
-            self.fs.video_part_path / f"video_{part:08d}.mp4",
-            fps,
-            source_frame_length=source_frame_length,
+        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)
@@ -148,32 +146,40 @@ class InterpolationPipeline:
             f"Video interpolation completed. Output saved to: {self.fs.output_path / output_video}"
         )
 
-    def _merge_frames_to_video(
-        self, output_video: Path, fps: float, source_frame_length: int = 0
+    def _save_images(
+        self,
+        source: tuple["np.ndarray", ...],
+        interpolated: list["np.ndarray"],
     ):
-        self._move_frames(source_frame_length)
+        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 _move_frames(self, source_frame_length: int = 0):
-        self.fs.clear_directory(self.fs.moved_path)
-        src_frames = sorted(self.fs.frames_path.glob("*.png"))
-        interpolated_frames = sorted(self.fs.interpolated_path.glob("*.png"))
-        index = 0
-        for i in range(source_frame_length):
-            moved_frame_path = self.fs.moved_path / f"img_{index:08d}.png"
-            src_frames[i].rename(moved_frame_path)
-            index += 1
-            if i < len(interpolated_frames):
-                moved_interpolated_path = self.fs.moved_path / f"img_{index:08d}.png"
-                interpolated_frames[i].rename(moved_interpolated_path)
-                index += 1
-        logging.info(
-            f"Moved {len(src_frames)} source frames and {len(interpolated_frames)} interpolated frames to {self.fs.moved_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(
@@ -220,7 +226,7 @@ def main():
         base_path=Path(args.base_path),
         video_path=Path(args.video_path),
         output_video=args.output,
-        preset=getattr(presets, args.preset.upper())
+        preset=getattr(presets, args.preset.upper()),
     )
 
 

src/config/presets.py

@@ -19,6 +19,6 @@ LARGE = Preset(
 )
 
 GLOBAL = Preset(
-    config=Path("src/config/AMT-g.yaml"),
+    config=Path("src/config/AMT-G.yaml"),
     checkpoint=Path("src/pretrained/amt-g.pth"),
 )

src/interpolator.py

@@ -1,12 +1,12 @@
 import logging
 from pathlib import Path
+from typing import Optional
 
 import torch
 import numpy as np
 from omegaconf import OmegaConf, DictConfig
-from imageio import imread, imwrite
 
-from src.utils.torch import img2tensor, check_dim_and_resize, tensor2img
+from src.utils.torch import img2tensor, tensor2img
 from src.utils.build import build_from_cfg
 from src.utils.padder import InputPadder
 
@@ -40,7 +40,7 @@ class ModelRunner:
         model.load_state_dict(checkpoint["state_dict"])
         model = model.to(get_device())
         model.eval()
-        self.model = model
+        self.model = torch.compile(model)
 
 
 def get_vram_available(device: torch.device) -> int:
@@ -83,7 +83,7 @@ class ImageInterpolator:
             f"Initialized ImageInterpolator with device: {device}, anchor: {anchor}, available VRAM: {self.vram_available} bytes"
         )
 
-    def interpolate(self, image1: Path, image2: Path, output_path: Path):
+    def interpolate(self, image1: np.ndarray, image2: np.ndarray) -> np.ndarray:
         """
         Interpolates between two images and saves the result.
         Args:
@@ -92,38 +92,21 @@ class ImageInterpolator:
             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(imread(image1)).to(self.device)
-        tensor2 = img2tensor(imread(image2)).to(self.device)
+        tensor1 = img2tensor(image1, self.device)
+        tensor2 = img2tensor(image2, self.device)
         logging.debug(
             f"Image shapes after conversion to tensors: {tensor1.shape}, {tensor2.shape}"
         )
-        tensor1, tensor2 = check_dim_and_resize(tensor1, tensor2)
-        logging.debug(f"Image shapes after resizing: {tensor1.shape}, {tensor2.shape}")
-        h, w = tensor1.shape[2], tensor1.shape[3]
-        logging.debug(f"Interpolating images of size: {h}x{w}")
 
-        scale = self.scale(h, w)
-        logging.debug(f"Calculated scale factor: {scale:.2f}")
-        padding = int(16 / scale)
-        logging.debug(f"Calculated padding: {padding} pixels")
-        padder = InputPadder(tensor1.shape, divisor=padding)
-        tensor1_padded, tensor2_padded = padder.pad(tensor1, tensor2)
-        logging.debug(
-            f"Image shapes after padding: {tensor1_padded.shape}, {tensor2_padded.shape}"
-        )
-
-        tensor1_padded = tensor1_padded.to(self.device)
-        tensor2_padded = tensor2_padded.to(self.device)
         logging.debug("Running model inference for interpolation")
         with torch.no_grad():
-            interpolated = self.model_runner.model(
-                tensor1_padded, tensor2_padded, self.embt, scale_factor=scale, eval=True
-            )["imgt_pred"]
+            with torch.amp.autocast(self.device.type):
+                interpolated = self.model_runner.model(
+                    tensor1, tensor2, self.embt
+                )["imgt_pred"]
         logging.debug(f"Interpolated image shape before unpadding: {interpolated.shape}")
-        (interpolated,) = padder.unpad(interpolated)
         logging.debug(f"Interpolated image shape after unpadding: {interpolated.shape}")
-        imwrite(output_path, tensor2img(interpolated.cpu()))
-        logging.debug(f"Saved interpolated image to: {output_path}")
+        return tensor2img(interpolated.cpu())
 
     def scale(self, height: int, width: int) -> float:
         scale = (

src/utils/torch.py

@@ -5,23 +5,26 @@ import numpy as np
 
 
 def tensor2img(tensor: torch.Tensor):
-    return (
-        (tensor * 255.0)
-        .detach()
+    tensor = (
+        tensor.mul(255.0)
+        .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]:

src/utils/video.py

@@ -2,9 +2,10 @@ import os
 import logging
 import subprocess
 from pathlib import Path
+from typing import Generator, Iterable
 
 import cv2
-from typing import Generator
+import numpy as np
 
 
 class VideoMaker:
@@ -35,7 +36,7 @@ class VideoMaker:
         with open(file, "w") as f:
             for video in videos:
                 f.write(f"file '{video}'\n")
-        cmd = f"ffmpeg -f concat -safe 0 -i {file} -c copy {output_path}"
+        cmd = f"ffmpeg -y -f concat -safe 0 -i {file} -c copy {output_path}"
         logging.info(f"Running command: {cmd}")
         result = self.run_command(cmd)
         if result != 0:
@@ -66,7 +67,13 @@ class VideoMaker:
 
     def run_command(self, cmd: str) -> int:
         try:
-            subprocess.run(cmd, shell=True, check=True, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
+            subprocess.run(
+                cmd,
+                shell=True,
+                check=True,
+                stdout=subprocess.DEVNULL,
+                stderr=subprocess.DEVNULL,
+            )
             return 0
         except subprocess.CalledProcessError as e:
             logging.error(f"Command failed with error: {e}")
@@ -74,7 +81,7 @@ class VideoMaker:
 
     def video_to_frames_generator(
         self, video_path: Path, output_dir: Path, chunk_seconds: int = 10
-    ) -> Generator[tuple[Path, ...], None, None]:
+    ) -> Generator[tuple[np.ndarray, ...], None, None]:
         """Extracts frames from a video and saves them to disk, yielding paths to the saved frames."""
 
         cap = cv2.VideoCapture(str(video_path))
@@ -85,21 +92,56 @@ class VideoMaker:
         fps = cap.get(cv2.CAP_PROP_FPS)
         frames_per_chunk = int(fps * chunk_seconds)
 
-        frame_index = 0
-
         while True:
             paths = []
-
             for _ in range(frames_per_chunk):
                 ret, frame = cap.read()
                 if not ret:
                     cap.release()
                     return
-
-                frame_path = output_dir / f"img_{frame_index:08d}.png"
-                cv2.imwrite(str(frame_path), frame)
-
-                paths.append(frame_path)
-                frame_index += 1
-
+                paths.append(frame)
             yield tuple(paths)
+
+    def images_to_video_pipeline(
+        self,
+        frames: Iterable[np.ndarray],
+        output_path: Path,
+        width: int,
+        height: int,
+        fps: float,
+    ):
+        pipeline = subprocess.Popen(
+            [
+                "ffmpeg",
+                "-y",
+                "-f", "rawvideo",
+                "-vcodec", "rawvideo",
+                "-pix_fmt", "bgr24",
+                "-s", f"{width}x{height}",
+                "-r", str(fps),
+                "-i", "-",
+                "-an",
+                "-vcodec", "libx264",
+                "-pix_fmt", "yuv420p",
+                str(output_path),
+            ],
+            stdin=subprocess.PIPE,
+            stderr=subprocess.DEVNULL
+        )
+        if pipeline.stdin is None:
+            raise Exception("STDIN closed")
+        for frame in frames:
+            pipeline.stdin.write(frame.tobytes())
+
+        pipeline.stdin.close()
+        pipeline.wait()
+
+    def get_size(self, video_path: Path) -> tuple[int, int]:
+        cap = cv2.VideoCapture(str(video_path))
+        if not cap.isOpened():
+            raise ValueError(f"Cannot open video: {video_path}")
+        width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+        height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+        
+        cap.release()
+        return width, height