re-doing contrib/semseg

samuelstevens · Nov 26, 2024 · 4a68769 · 4a68769
1 parent 63c5684
commit 4a68769
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Showing 23 changed files with 1,140 additions and 1,503 deletions.
diff --git a/contrib/semseg/__main__.py b/contrib/semseg/__main__.py
@@ -0,0 +1 @@
+
diff --git a/contrib/semseg/config.py b/contrib/semseg/config.py
@@ -14,11 +14,11 @@ class Train:
     """Linear layer learning rate."""
     weight_decay: float = 1e-3
     """Weight decay  for AdamW."""
-    n_epochs: int = 10
+    n_epochs: int = 100
     """Number of training epochs for linear layer."""
-    batch_size: int = 32
+    batch_size: int = 1024
     """Training batch size for linear layer."""
-    n_workers: int = 8
+    n_workers: int = 32
     """Number of dataloader workers."""
     train_acts: saev.config.DataLoad = dataclasses.field(
         default_factory=saev.config.DataLoad

diff --git a/contrib/semseg/dashboard.py b/contrib/semseg/dashboard.py
@@ -15,23 +15,21 @@ def __():
     if pkg_root not in sys.path:
         sys.path.append(pkg_root)
 
-    import os
     import csv
-    import einops
-
-    import marimo as mo
-    import sklearn.decomposition
+    import os
 
-    import numpy as np
-    import datasets
+    import altair as alt
     import beartype
-    from PIL import Image, ImageDraw
-    from jaxtyping import jaxtyped, Int
-
-    import torch
+    import datasets
+    import einops
+    import marimo as mo
     import matplotlib.pyplot as plt
+    import numpy as np
     import polars as pl
-    import altair as alt
+    import sklearn.decomposition
+    import torch
+    from jaxtyping import Int, jaxtyped
+    from PIL import Image, ImageDraw
 
     import saev.activations
     import saev.config

diff --git a/contrib/semseg/dashboard2.py b/contrib/semseg/dashboard2.py
@@ -14,17 +14,17 @@ def __():
 
     import random
 
-    import marimo as mo
     import beartype
-
+    import contrib.semantic_seg.training
+    import marimo as mo
     import numpy as np
     import torch
-    from torchvision.transforms import v2
+    from jaxtyping import Int, UInt8, jaxtyped
     from PIL import Image
-    from jaxtyping import jaxtyped, UInt8, Int
+    from torchvision.transforms import v2
 
-    import contrib.semantic_seg.training
     import saev.config
+
     return (
         Image,
         Int,
@@ -45,7 +45,9 @@ def __():
 
 @app.cell
 def __(contrib):
-    ckpt_fpath = "/home/stevens.994/projects/saev-live/checkpoints/faithfulness/model.pt"
+    ckpt_fpath = (
+        "/home/stevens.994/projects/saev-live/checkpoints/faithfulness/model.pt"
+    )
     model = contrib.semantic_seg.training.load(ckpt_fpath)
     model.eval()
     return ckpt_fpath, model
@@ -68,15 +70,12 @@ def __(contrib, saev):
 @app.cell
 def __(v2):
     def make_img_transform():
-        return v2.Compose(
-            [
-                v2.Resize(size=224, interpolation=v2.InterpolationMode.NEAREST),
-                v2.CenterCrop(size=(224, 224)),
-                # v2.ToImage(),
-                # v2.ToDtype(torch.uint8),
-            ]
-        )
-
+        return v2.Compose([
+            v2.Resize(size=224, interpolation=v2.InterpolationMode.NEAREST),
+            v2.CenterCrop(size=(224, 224)),
+            # v2.ToImage(),
+            # v2.ToDtype(torch.uint8),
+        ])
 
     img_transform = make_img_transform()
     return img_transform, make_img_transform
@@ -97,14 +96,14 @@ def make_colors(seed: int = 42) -> UInt8[np.ndarray, "n 3"]:
         colors = np.array(colors, dtype=np.uint8)
         return colors
 
-
     @jaxtyped(typechecker=beartype.beartype)
     def color_map(map: UInt8[np.ndarray, "width height"]) -> Image.Image:
         colored = np.zeros((224, 224, 3), dtype=np.uint8)
         for i, color in enumerate(make_colors()):
             colored[map == i, :] = color
 
         return Image.fromarray(colored)
+
     return color_map, make_colors
 
 
@@ -177,11 +176,17 @@ def mean_iou(
 
         if ignore_class is not None:
             pred_one_hot = torch.cat(
-                (pred_one_hot[..., :ignore_class], pred_one_hot[..., ignore_class + 1 :]),
+                (
+                    pred_one_hot[..., :ignore_class],
+                    pred_one_hot[..., ignore_class + 1 :],
+                ),
                 axis=-1,
             )
             true_one_hot = torch.cat(
-                (true_one_hot[..., :ignore_class], true_one_hot[..., ignore_class + 1 :]),
+                (
+                    true_one_hot[..., :ignore_class],
+                    true_one_hot[..., ignore_class + 1 :],
+                ),
                 axis=-1,
             )
 
@@ -193,7 +198,6 @@ def mean_iou(
         # Handle division by zero
         return ((intersection + eps) / (union + eps)).mean().item()
 
-
     mean_iou(y_pred, y_true, 151)
     return (mean_iou,)
 
@@ -281,7 +285,6 @@ def intersect_and_union(
 
         return area_intersect, area_union, area_pred_label, area_label
 
-
     @jaxtyped(typechecker=beartype.beartype)
     def total_intersect_and_union(
         results,
@@ -321,8 +324,15 @@ def total_intersect_and_union(
         total_area_pred_label = np.zeros((num_labels,), dtype=np.float64)
         total_area_label = np.zeros((num_labels,), dtype=np.float64)
         for result, gt_seg_map in zip(results, gt_seg_maps):
-            area_intersect, area_union, area_pred_label, area_label = intersect_and_union(
-                result, gt_seg_map, num_labels, ignore_index, label_map, reduce_labels
+            area_intersect, area_union, area_pred_label, area_label = (
+                intersect_and_union(
+                    result,
+                    gt_seg_map,
+                    num_labels,
+                    ignore_index,
+                    label_map,
+                    reduce_labels,
+                )
             )
             total_area_intersect += area_intersect
             total_area_union += area_union
@@ -335,7 +345,6 @@ def total_intersect_and_union(
             total_area_label,
         )
 
-
     @jaxtyped(typechecker=beartype.beartype)
     def mean_iou(
         results,
@@ -376,10 +385,13 @@ def mean_iou(
             - *per_category_iou* (`ndarray` of shape `(num_labels,)`):
                 Per category IoU.
         """
-        total_area_intersect, total_area_union, total_area_pred_label, total_area_label = (
-            total_intersect_and_union(
-                results, gt_seg_maps, num_labels, ignore_index, label_map, reduce_labels
-            )
+        (
+            total_area_intersect,
+            total_area_union,
+            total_area_pred_label,
+            total_area_label,
+        ) = total_intersect_and_union(
+            results, gt_seg_maps, num_labels, ignore_index, label_map, reduce_labels
         )
 
         # compute metrics
@@ -396,20 +408,20 @@ def mean_iou(
         metrics["per_category_accuracy"] = acc
 
         if nan_to_num is not None:
-            metrics = dict(
-                {
-                    metric: np.nan_to_num(metric_value, nan=nan_to_num)
-                    for metric, metric_value in metrics.items()
-                }
-            )
+            metrics = dict({
+                metric: np.nan_to_num(metric_value, nan=nan_to_num)
+                for metric, metric_value in metrics.items()
+            })
 
         return metrics
+
     return intersect_and_union, mean_iou, total_intersect_and_union
 
 
 @app.cell
 def __():
     import tensordict
+
     return (tensordict,)
 
 

diff --git a/contrib/semseg/training.py b/contrib/semseg/training.py
@@ -1 +1,40 @@
+import beartype
+import torch
 
+import saev.config
+
+from . import config
+
+n_classes = 151
+
+
+@beartype.beartype
+def main(cfg: config.Train):
+    train_dataset = Dataset(cfg.train_acts, cfg.train_imgs)
+    val_dataset = Dataset(cfg.val_acts, cfg.val_imgs)
+
+    model = torch.nn.Linear(train_dataset.d_vit, n_classes)
+    optim = torch.optim.AdamW(
+        model.parameters, lr=cfg.learning_rate, weight_decay=cfg.weight_decay
+    )
+
+    for epoch in range(cfg.n_epochs):
+        model.train()
+        for batch in train_dataloader:
+            breakpoint()
+
+        model.eval()
+        for batch in val_dataloader:
+            breakpoint()
+
+
+@beartype.beartype
+class Dataset(torch.utils.data.Dataset):
+    def __init__(
+        self, acts_cfg: saev.config.DataLoad, imgs_cfg: saev.config.Ade20kDataset
+    ):
+        breakpoint()
+
+    @property
+    def d_vit(self) -> int:
+        breakpoint()
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@@ -0,0 +1,64 @@
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+<h2 class="section-title" id="header-submodules">Sub-modules</h2>
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+<dt><code class="name"><a title="contrib.semseg" href="semseg/index.html">contrib.semseg</a></code></dt>
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