main.py

@@ -4,9 +4,10 @@ from typing import TYPE_CHECKING
 
 import tqdm
 
+from src.config import presets
 from src.utils.fs import FileSystem
 from src.utils.video import VideoMaker
-from interpolator import (
+from src.interpolator import (
     ImageInterpolator,
     Anchor,
     get_device,
@@ -125,7 +126,7 @@ class InterpolationPipeline:
                 part += 1
             for i in tqdm.tqdm(
                 range(len(frame_paths) - 1),
-                desc=f"Processing video frames {part} / {total_parts}",
+                desc=f"Processing video frames {part + 1} / {total_parts}",
             ):
                 img1 = frame_paths[i]
                 img2 = frame_paths[i + 1]
@@ -175,14 +176,16 @@ class InterpolationPipeline:
         )
 
 
-def main():
-    config = Path("src/config/AMT-G.yaml")
-    checkpoint_path = Path("src/pretrained/amt-g.pth")
+def main(preset: presets.Preset = presets.LARGE):
     base_path = Path("output")
     video_path = Path("example/video.mp4")
     output_video = "interpolated_video.mp4"
 
-    pipeline = InterpolationPipeline(config, checkpoint_path, base_path)
+    pipeline = InterpolationPipeline(
+        config=preset.config,
+        checkpoint_path=preset.checkpoint,
+        base_path=base_path,
+    )
     pipeline.run(video_path, output_video)
 
 

src/config/AMT-L.yaml

@@ -0,0 +1,62 @@
+exp_name: floloss1e-2_300epoch_bs24_lr2e-4
+seed: 2023
+epochs: 300
+distributed: true
+lr: 2e-4
+lr_min: 2e-5
+weight_decay: 0.0
+resume_state: null
+save_dir: work_dir
+eval_interval: 1
+
+network:
+  name: src.networks.AMT-L.Model
+  params:
+    corr_radius: 3
+    corr_lvls: 4
+    num_flows: 5
+data:
+  train: 
+    name: datasets.vimeo_datasets.Vimeo90K_Train_Dataset
+    params: 
+      dataset_dir: data/vimeo_triplet
+  val:
+    name: datasets.vimeo_datasets.Vimeo90K_Test_Dataset
+    params: 
+      dataset_dir: data/vimeo_triplet
+  train_loader:
+    batch_size: 24
+    num_workers: 12
+  val_loader:
+    batch_size: 24
+    num_workers: 3
+
+logger:
+  use_wandb: true  
+  resume_id: null
+
+losses:
+  - {
+    name: losses.loss.CharbonnierLoss,
+    nickname: l_rec,
+    params: {
+      loss_weight: 1.0,
+      keys: [imgt_pred, imgt]
+    }
+  }
+  - {
+    name: losses.loss.TernaryLoss,
+    nickname: l_ter,
+    params: {
+      loss_weight: 1.0,
+      keys: [imgt_pred, imgt]
+    }
+  }
+  - {
+    name: losses.loss.MultipleFlowLoss,
+    nickname: l_flo,
+    params: {
+      loss_weight: 0.002,
+      keys: [flow0_pred, flow1_pred, flow]
+    }
+  }

src/config/presets.py

@@ -0,0 +1,24 @@
+from pathlib import Path
+from dataclasses import dataclass
+
+
+@dataclass(frozen=True)
+class Preset:
+    config: Path
+    checkpoint: Path
+
+
+SMALL = Preset(
+    config=Path("src/config/AMT-S.yaml"),
+    checkpoint=Path("src/pretrained/amt-s.pth"),
+)
+
+LARGE = Preset(
+    config=Path("src/config/AMT-L.yaml"),
+    checkpoint=Path("src/pretrained/amt-l.pth"),
+)
+
+GLOBAL = Preset(
+    config=Path("src/config/AMT-g.yaml"),
+    checkpoint=Path("src/pretrained/amt-g.pth"),
+)