chore: add cli animate arguments (#69)

Useful both for CLI usage as for content creation (e.g. Colab notebook
with examples).

README.md

@@ -63,12 +63,12 @@ You can easily benchmark different models and datasets against each other. Here
 ### Embeddings Generation
 
 To build embeddings, run the CLI command `tti-eval build`.
-This commands allows you to interactively select the model and dataset combinations on which to build the embeddings.
+This command allows to interactively select the model and dataset combinations on which to build the embeddings.
 
 Alternatively, you can choose known (model, dataset) pairs using the `--model-dataset` option. For example:
 
 ```
-tti-eval build --model-dataset clip/plants
+tti-eval build --model-dataset clip/Alzheimer-MRI --model-dataset bioclip/Alzheimer-MRI
 ```
 
 ### Model Evaluation
@@ -79,16 +79,24 @@ This command enables interactive selection of model and dataset combinations for
 Alternatively, you can specify known (model, dataset) pairs using the `--model-dataset` option. For example:
 
 ```
-tti-eval evaluate --model-dataset clip/plants
+tti-eval evaluate --model-dataset clip/Alzheimer-MRI --model-dataset bioclip/Alzheimer-MRI
 ```
 
 ### Embeddings Animation
 
 To create 2D animations of the embeddings, use the CLI command `tti-eval animate`.
 This command allows to visualise the reduction of embeddings from two models on the same dataset.
 
+You have the option to interactively select two models and a dataset for visualization.
+Alternatively, you can specify the models and dataset as arguments. For example:
+
+```
+tti-eval animate clip bioclip Alzheimer-MRI
+```
+
 The animations will be saved at the location specified by the environment variable `TTI_EVAL_OUTPUT_PATH`.
 By default, this path corresponds to the repository directory.
+To interactively explore the animation in a temporary session, use the `--interactive` flag.
 
 <div align="center">
     <img width="600" src="https://storage.googleapis.com/docs-media.encord.com/static/img/text-to-image-eval/embeddings.gif">
@@ -377,7 +385,7 @@ To contribute by adding model sources, follow these steps:
 
 ## Known Issues
 
-1. `autofaiss`: The project depends on the [autofaiss][autofaiss] library which can give some trouble on windows. Please reach out or raise an issue with as many system and version details as possible if you encounter it.
+1. `autofaiss`: The project depends on the [autofaiss][autofaiss] library which can give some trouble on Windows. Please reach out or raise an issue with as many system and version details as possible if you encounter it.
 
 [Falah/Alzheimer_MRI]: https://huggingface.co/datasets/Falah/Alzheimer_MRI
 [trpakov/chest-xray-classification]: https://huggingface.co/datasets/trpakov/chest-xray-classification

tti_eval/cli/main.py

@@ -1,7 +1,8 @@
 from typing import Annotated, Optional
 
 import matplotlib.pyplot as plt
-from typer import Option, Typer
+import typer
+from typer import Argument, Option, Typer
 
 from tti_eval.common import Split
 from tti_eval.compute import compute_embeddings_from_definition
@@ -114,18 +115,33 @@ def evaluate_embeddings(
 """,
 )
 def animate_embeddings(
+    from_model: Annotated[Optional[str], Argument(help="Title of the model in the left side of the animation.")] = None,
+    to_model: Annotated[Optional[str], Argument(help="Title of the model in the right side of the animation.")] = None,
+    dataset: Annotated[Optional[str], Argument(help="Title of the dataset where the embeddings were computed.")] = None,
     interactive: Annotated[bool, Option(help="Interactive plot instead of animation.")] = False,
     reduction: Annotated[str, Option(help="Reduction type [pca, tsne, umap (default)].")] = "umap",
 ):
-    from tti_eval.plotting.animation import build_animation, save_animation_to_file
+    from tti_eval.plotting.animation import EmbeddingDefinition, build_animation, save_animation_to_file
 
-    defs = select_existing_embedding_definitions(by_dataset=True, count=2)
-    res = build_animation(defs[0], defs[1], interactive=interactive, reduction=reduction)
+    all_none_input_args = from_model is None and to_model is None and dataset is None
+    all_str_input_args = from_model is not None and to_model is not None and dataset is not None
 
+    if not all_str_input_args and not all_none_input_args:
+        typer.echo("Some arguments were provided. Please either provide all arguments or ignore them entirely.")
+        raise typer.Abort()
+
+    if all_none_input_args:
+        defs = select_existing_embedding_definitions(by_dataset=True, count=2)
+        from_def, to_def = defs[0], defs[1]
+    else:
+        from_def = EmbeddingDefinition(model=from_model, dataset=dataset)
+        to_def = EmbeddingDefinition(model=to_model, dataset=dataset)
+
+    res = build_animation(from_def, to_def, interactive=interactive, reduction=reduction)
     if res is None:
         plt.show()
     else:
-        save_animation_to_file(res, *defs)
+        save_animation_to_file(res, from_def, to_def)
 
 
 @cli.command("list", help="List models and datasets. By default, only cached pairs are listed.")

tti_eval/plotting/reduction.py

@@ -35,7 +35,9 @@ def get_reduction(
             return reduction
 
         elif not embedding_def.embedding_path(split).is_file():
-            raise ValueError(f"{embedding_def} does not have embeddings stored ({embedding_def.embedding_path(split)})")
+            raise ValueError(
+                f"{repr(embedding_def)} does not have embeddings stored ({embedding_def.embedding_path(split)})"
+            )
 
         image_embeddings: EmbeddingArray = np.load(embedding_def.embedding_path(split))["image_embeddings"]
         reduction = cls.reduce(image_embeddings)