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--- |
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annotations_creators: [] |
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language: |
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- en |
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language_creators: |
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- found |
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license: |
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- mit |
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multilinguality: |
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- monolingual |
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pretty_name: laion-aesthetics-12m-umap |
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size_categories: [] |
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source_datasets: [] |
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tags: |
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- laion |
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- stable-diffuson |
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- text2img |
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task_categories: [] |
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task_ids: [] |
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--- |
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# LAION-Aesthetics :: CLIP → UMAP |
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This dataset is a CLIP (text) → UMAP embedding of the [LAION-Aesthetics dataset](https://laion.ai/blog/laion-aesthetics/) - specifically the [`improved_aesthetics_6plus` version](https://huggingface.co/datasets/ChristophSchuhmann/improved_aesthetics_6plus), which filters the full dataset to images with scores of > 6 under the "aesthetic" filtering model. |
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Thanks LAION for this amazing corpus! |
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--- |
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The dataset here includes coordinates for 3x separate UMAP fits using different values for the `n_neighbors` parameter - `10`, `30`, and `60` - which are broken out as separate columns with different suffixes: |
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- `n_neighbors=10` → (`x_nn10`, `y_nn10`) |
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- `n_neighbors=30` → (`x_nn30`, `y_nn30`) |
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- `n_neighbors=60` → (`x_nn60`, `y_nn60`) |
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### `nn10` |
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### `nn30` |
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### `nn60` |
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(The version from [Twitter](https://twitter.com/clured/status/1565399157606580224).) |
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## Pipeline |
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The script for producing this can be found here: |
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https://github.com/davidmcclure/loam-viz/blob/laion/laion.py |
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And is very simple - just using the `openai/clip-vit-base-patch32` model out-of-the-box to encode the text captions: |
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```python |
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@app.command() |
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def clip( |
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src: str, |
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dst: str, |
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text_col: str = 'TEXT', |
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limit: Optional[int] = typer.Option(None), |
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batch_size: int = typer.Option(512), |
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): |
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"""Embed with CLIP.""" |
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df = pd.read_parquet(src) |
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if limit: |
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df = df.head(limit) |
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tokenizer = CLIPTokenizerFast.from_pretrained('openai/clip-vit-base-patch32') |
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model = CLIPTextModel.from_pretrained('openai/clip-vit-base-patch32') |
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model = model.to(device) |
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texts = df[text_col].tolist() |
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embeds = [] |
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for batch in chunked_iter(tqdm(texts), batch_size): |
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enc = tokenizer( |
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batch, |
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return_tensors='pt', |
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padding=True, |
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truncation=True, |
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) |
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enc = enc.to(device) |
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with torch.no_grad(): |
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res = model(**enc) |
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embeds.append(res.pooler_output.to('cpu')) |
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embeds = torch.cat(embeds).numpy() |
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np.save(dst, embeds) |
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print(embeds.shape) |
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``` |
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Then using `cuml.GaussianRandomProjection` to do an initial squeeze to 64d (which gets the embedding tensor small enough to fit onto a single GPU for the UMAP) - |
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```python |
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@app.command() |
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def random_projection(src: str, dst: str, dim: int = 64): |
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"""Random projection on an embedding matrix.""" |
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rmm.reinitialize(managed_memory=True) |
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embeds = np.load(src) |
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rp = cuml.GaussianRandomProjection(n_components=dim) |
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embeds = rp.fit_transform(embeds) |
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np.save(dst, embeds) |
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print(embeds.shape) |
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``` |
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And then `cuml.UMAP` to get from 64d -> 2d - |
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```python |
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@app.command() |
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def umap( |
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df_src: str, |
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embeds_src: str, |
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dst: str, |
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n_neighbors: int = typer.Option(30), |
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n_epochs: int = typer.Option(1000), |
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negative_sample_rate: int = typer.Option(20), |
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): |
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"""UMAP to 2d.""" |
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rmm.reinitialize(managed_memory=True) |
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df = pd.read_parquet(df_src) |
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embeds = np.load(embeds_src) |
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embeds = embeds.astype('float16') |
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print(embeds.shape) |
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print(embeds.dtype) |
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reducer = cuml.UMAP( |
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n_neighbors=n_neighbors, |
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n_epochs=n_epochs, |
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negative_sample_rate=negative_sample_rate, |
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verbose=True, |
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) |
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x = reducer.fit_transform(embeds) |
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df['x'] = x[:,0] |
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df['y'] = x[:,1] |
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df.to_parquet(dst) |
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print(df) |
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``` |
