Update requirements

.gitignore

@@ -1,2 +1,3 @@
 .aider*
 .env
+.venv/

app.py

@@ -1,3 +1,4 @@
+import functools
 import io
 import json
 import logging
@@ -46,7 +47,7 @@ vit_ckpt = "ViT-B-16/openai"
 n_patches_per_img: int = 196
 """Number of patches per image in vit_ckpt."""
 
-max_frequency = 1e-2
+max_frequency = 1e-1
 """Maximum frequency. Any feature that fires more than this is ignored."""
 
 CWD = pathlib.Path(__file__).parent
@@ -93,13 +94,17 @@ def load_model(fpath: str | pathlib.Path, *, device: str = "cpu") -> torch.nn.Mo
 
 
 @beartype.beartype
+@functools.lru_cache(maxsize=512)
 def get_dataset_img(i: int) -> Image.Image:
     return Image.open(requests.get(r2_url + image_fpaths[i], stream=True).raw)
 
 
 @beartype.beartype
 def make_img(
-    img: Image.Image, patches: Float[Tensor, ""], *, upper: float | None = None
+    img: Image.Image,
+    patches: Float[Tensor, " n_patches"],
+    *,
+    upper: float | None = None,
 ) -> Image.Image:
     # Resize to 256x256 and crop to 224x224
     resize_size_px = (512, 512)
@@ -223,16 +228,6 @@ with open(CWD / "data" / "image_labels.json") as fd:
     image_labels = json.load(fd)
 
 
-# TODO:
-# This dataset needs to be the CUB2011 dataset. But that means we need to calculate top_img_i based on CUB2011, not on iNat21 train-mini.
-# examples_dataset = saev.activations.ImageFolder(
-#     "/research/nfs_su_809/workspace/stevens.994/datasets/inat21/train_mini",
-#     transform=v2.Compose([
-#         v2.Resize(size=(512, 512)),
-#         v2.CenterCrop(size=(448, 448)),
-#     ]),
-# )
-
 logger.info("Loaded all datasets.")
 
 #############
@@ -249,6 +244,7 @@ top_img_i = load_tensor(CWD / "data" / "top_img_i.pt")
 top_values = load_tensor(CWD / "data" / "top_values.pt")
 sparsity = load_tensor(CWD / "data" / "sparsity.pt")
 
+
 mask = torch.ones((sae.cfg.d_sae), dtype=bool)
 mask = mask & (sparsity < max_frequency)
 
@@ -285,10 +281,13 @@ def get_sae_examples(
     if not patches:
         return [None] * 12 + [-1] * 3
 
+    logger.info("Getting SAE examples for patches %s.", patches)
+
     img = get_dataset_img(image_i)
     x = vit_transform(img)[None, ...].to(device)
     x_BPD = split_vit.forward_start(x)
-    vit_acts_MD = x_BPD[0, patches].to(device)
+    # Need to add 1 to account for [CLS] token.
+    vit_acts_MD = x_BPD[0, [p + 1 for p in patches]].to(device)
 
     _, f_x_MS, _ = sae(vit_acts_MD)
     f_x_S = f_x_MS.sum(axis=0)
@@ -303,9 +302,8 @@ def get_sae_examples(
             if i_im in seen_i_im:
                 continue
 
-            # example = examples_dataset[i_im]
-            example = None
-            img_patch_pairs.append((example["image"], values_p))
+            example_img = get_dataset_img(i_im)
+            img_patch_pairs.append((example_img, values_p))
             seen_i_im.add(i_im)
 
         # How to scale values.
@@ -492,6 +490,7 @@ with gr.Blocks() as demo:
         inputs=[image_number, patch_numbers],
         outputs=sae_example_images + top_latent_numbers,
         api_name="get-sae-examples",
+        concurrency_limit=16,
     )
 
     pred_dist = gr.Label(label="Pred. Dist.")
@@ -513,6 +512,7 @@ with gr.Blocks() as demo:
         inputs=[image_number, patch_numbers] + latent_numbers + value_sliders,
         outputs=[pred_dist],
         api_name="get-modified",
+        concurrency_limit=16,
     )
 
 

pyproject.toml

@@ -10,6 +10,7 @@ dependencies = [
     "gradio>=5.0.0",
     "jaxtyping>=0.2.36",
     "numpy>=1.26.4",
+    "open-clip-torch>=2.30.0",
     "pillow>=10.4.0",
     "torch>=2.4.0",
     "torchvision>=0.19.0",

requirements.txt

@@ -38,9 +38,11 @@ fsspec==2024.12.0
     #   gradio-client
     #   huggingface-hub
     #   torch
-gradio==5.9.1
+ftfy==6.3.1
+    # via open-clip-torch
+gradio==5.10.0
     # via saev-image-classification (pyproject.toml)
-gradio-client==1.5.2
+gradio-client==1.5.3
     # via gradio
 h11==0.14.0
     # via
@@ -57,6 +59,8 @@ huggingface-hub==0.27.1
     # via
     #   gradio
     #   gradio-client
+    #   open-clip-torch
+    #   timm
 idna==3.10
     # via
     #   anyio
@@ -118,6 +122,8 @@ nvidia-nvjitlink-cu12==12.4.127
     #   torch
 nvidia-nvtx-cu12==12.4.127
     # via torch
+open-clip-torch==2.30.0
+    # via saev-image-classification (pyproject.toml)
 orjson==3.10.13
     # via gradio
 packaging==24.2
@@ -152,6 +158,9 @@ pyyaml==6.0.2
     # via
     #   gradio
     #   huggingface-hub
+    #   timm
+regex==2024.11.6
+    # via open-clip-torch
 requests==2.32.3
     # via huggingface-hub
 rich==13.9.4
@@ -160,6 +169,10 @@ ruff==0.8.6
     # via gradio
 safehttpx==0.1.6
     # via gradio
+safetensors==0.5.0
+    # via
+    #   open-clip-torch
+    #   timm
 semantic-version==2.10.0
     # via gradio
 setuptools==75.7.0
@@ -176,16 +189,25 @@ starlette==0.41.3
     #   gradio
 sympy==1.13.1
     # via torch
+timm==1.0.12
+    # via open-clip-torch
 tomlkit==0.13.2
     # via gradio
 torch==2.5.1
     # via
     #   saev-image-classification (pyproject.toml)
+    #   open-clip-torch
+    #   timm
     #   torchvision
 torchvision==0.20.1
-    # via saev-image-classification (pyproject.toml)
+    # via
+    #   saev-image-classification (pyproject.toml)
+    #   open-clip-torch
+    #   timm
 tqdm==4.67.1
-    # via huggingface-hub
+    # via
+    #   huggingface-hub
+    #   open-clip-torch
 triton==3.1.0
     # via torch
 typer==0.15.1
@@ -207,5 +229,7 @@ urllib3==2.3.0
     # via requests
 uvicorn==0.34.0
     # via gradio
+wcwidth==0.2.13
+    # via ftfy
 websockets==14.1
     # via gradio-client

uv.lock

@@ -162,6 +162,18 @@ wheels = [
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 ]
 
+[[package]]
+name = "ftfy"
+version = "6.3.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "wcwidth" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/a5/d3/8650919bc3c7c6e90ee3fa7fd618bf373cbbe55dff043bd67353dbb20cd8/ftfy-6.3.1.tar.gz", hash = "sha256:9b3c3d90f84fb267fe64d375a07b7f8912d817cf86009ae134aa03e1819506ec", size = 308927 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/ab/6e/81d47999aebc1b155f81eca4477a616a70f238a2549848c38983f3c22a82/ftfy-6.3.1-py3-none-any.whl", hash = "sha256:7c70eb532015cd2f9adb53f101fb6c7945988d023a085d127d1573dc49dd0083", size = 44821 },
+]
+
 [[package]]
 name = "gradio"
 version = "5.3.0"
@@ -493,6 +505,25 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/87/20/199b8713428322a2f22b722c62b8cc278cc53dffa9705d744484b5035ee9/nvidia_nvtx_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl", hash = "sha256:781e950d9b9f60d8241ccea575b32f5105a5baf4c2351cab5256a24869f12a1a", size = 99144 },
 ]
 
+[[package]]
+name = "open-clip-torch"
+version = "2.30.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "ftfy" },
+    { name = "huggingface-hub" },
+    { name = "regex" },
+    { name = "safetensors" },
+    { name = "timm" },
+    { name = "torch" },
+    { name = "torchvision" },
+    { name = "tqdm" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/28/71/133f3eb549d61a937e488805046baaee9eda4acfa8f8cbf01f43f64d2654/open_clip_torch-2.30.0.tar.gz", hash = "sha256:9a635e542a4fb83b268ec8ba2585698e2d5badcb1a517d26dcb49dff1a64c49f", size = 1485046 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/be/86/6ba3921b9fc0c83fd1838b1fb197973245994258586887876625eda732f8/open_clip_torch-2.30.0-py3-none-any.whl", hash = "sha256:68343092181a03a6a0b3ba8a3529856e40299d4c06bc83082ce73e0ba438187a", size = 1514664 },
+]
+
 [[package]]
 name = "orjson"
 version = "3.10.13"
@@ -723,6 +754,44 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/fa/de/02b54f42487e3d3c6efb3f89428677074ca7bf43aae402517bc7cca949f3/PyYAML-6.0.2-cp313-cp313-win_amd64.whl", hash = "sha256:8388ee1976c416731879ac16da0aff3f63b286ffdd57cdeb95f3f2e085687563", size = 156446 },
 ]
 
+[[package]]
+name = "regex"
+version = "2024.11.6"
+source = { registry = "https://pypi.org/simple" }
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+wheels = [
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