Drop scivision

.flake8

@@ -1,2 +1,7 @@
 [flake8]
 max-line-length=120
+exclude =
+    venv
+    __pycache__
+    tests
+    vectors

.gitignore

@@ -3,3 +3,5 @@
 **/__pycache__/
 vectors/
 *.ipynb
+*.egg-info/
+venv/

.python-version

@@ -0,0 +1 @@
+3.12

README.md

@@ -6,30 +6,50 @@ It's a companion project to an R-shiny based image annotation app that is not ye
 
 ## Installation
 
-### Python environment setup
+### Environment and package installation
 
-Use anaconda or miniconda to create a python environment using the included `environment.yml`
+#### Using pip
+
+Create a fresh virtual environment in the repository root using Python >=3.12 and (e.g.) `venv`: 
 
 ```
-conda env create -f environment.yml
+python -m venv venv
 ```
 
-Please note that this is specifically pinned to python 3.9 due to dependency versions; we make experimental use of the [CEFAS plankton model available through SciVision](https://sci.vision/#/model/resnet50-plankton), which in turn uses an older version of pytorch that isn't packaged above python 3.9.
+Next, install the package using `pip`:
 
-### Object store connection
+```
+python -m pip install .
+```
 
-`.env` contains environment variable names for S3 connection details for the [JASMIN object store](https://github.com/NERC-CEH/object_store_tutorial/). Fill these in with your own credentials. If you're not sure what the `ENDPOINT` should be, please reach out to one of the project contributors listed below. 
+Most likely you are interested in developing and/or experimenting, so you will probably want to install the package in 'editable' mode (`-e`), along with dev tools and jupyter notebook functionality
 
+```
+python -m pip install -e .[all]
+```
 
-### Package installation
+#### Using conda
 
-Get started by cloning this repository and running
+Use anaconda or miniconda to create a python environment using the included `environment.yml`
 
-`pip install -e .`
+```
+conda env create -f environment.yml
+conda activate cyto_ml
+```
+
+Next install this package _without dependencies_:
+
+```
+python -m pip install --no-deps -e .
+```
+
+### Object store connection
+
+`.env` contains environment variable names for S3 connection details for the [JASMIN object store](https://github.com/NERC-CEH/object_store_tutorial/). Fill these in with your own credentials. If you're not sure what the `ENDPOINT` should be, please reach out to one of the project contributors listed below. 
 
 ### Running tests
 
-`python -m pytest` or `py.test`
+`pytest` or `py.test`
 
 ## Contents
 

environment.yml

@@ -1,25 +1,29 @@
-name: cyto_39
+name: cyto_ml
 channels:
   - pytorch
   - conda-forge
-  - defaults
+channel_priority: flexible
 dependencies:
-  - python=3.9
-  - pytorch=1.10.0
-  - mkl=2024.0
-  - chromadb=0.5.3
+  - python=3.12
+  - pytorch
+  - black
+  - chromadb
+  - flake8
   - intake-xarray
-  - scikit-image
-  - scikit-learn
+  - intake=0.7
+  - isort
+  - jupyterlab
+  - jupytext
+  - matplotlib
   - pandas
   - pytest
   - python-dotenv
   - s3fs
-  - jupyterlab
-  - jupytext
+  - scikit-image
+  - scikit-learn
+  - xarray
   - pip
   - streamlit
   - plotly
   - pip:
-    - scivision
-    - git+https://github.com/alan-turing-institute/plankton-cefas-scivision@main
+    - git+https://github.com/jmarshrossney/resnet50-cefas

notebooks/ImageEmbeddings.md

@@ -12,14 +12,13 @@ jupyter:
     name: python3
 ---
 
-Use this with the `cyto_39` environment (the scivision model needs a specific version of `pytorch` that isn't packaged for >3.9, i have raised a Github issue asking if they plan to update it)
+Use this with the `cyto_ml` environment.
 
 `conda env create -f environment.yml`
-`conda activate cyto_39`
+`conda activate cyto_ml`
 
 ```python
 import os
-from scivision import load_pretrained_model, load_dataset
 from dotenv import load_dotenv
 import torch
 import torchvision
@@ -29,58 +28,37 @@ sys.path.append('../')
 from cyto_ml.models.scivision import prepare_image
 from intake_xarray import ImageSource
 load_dotenv()  # sets our object store endpoint and credentials from the .env file
+
+from intake import open_catalog
+
+from resnet50_cefas import load_model
 ```
 
 ```python
-dataset = load_dataset(f"{os.environ.get('ENDPOINT', '')}/metadata/intake.yml")
-model = load_pretrained_model("https://github.com/alan-turing-institute/plankton-cefas-scivision")
+dataset = open_catalog(f"{os.environ.get('ENDPOINT', '')}/metadata/intake.yml")
+model = load_model()
 dataset.test_image().to_dask()
 ```
 
-The scivision wrapper depends on this being an xarray Dataset with settable attributes, rather than a DataArray
-
-Setting exif_tags: True (Dataset) or False (DataArray) is what controls this
-https://docs.xarray.dev/en/stable/generated/xarray.DataArray.to_dataset.html 
-
-https://github.com/alan-turing-institute/scivision/blob/07fb74e5231bc1d56cf39df38c19ef40e3265e4c/src/scivision/io/reader.py#L183
-https://github.com/intake/intake/blob/29c8878aa7bf6e93185e2c9639f8739445dff22b/intake/__init__.py#L101
-
-But now we're dependent on image height and width metadata being set in the EXIF tags to use the `predict` interface, this is set in the model description through `scivision`, this is brittle
-
-https://github.com/alan-turing-institute/plankton-cefas-scivision/blob/main/resnet50_cefas/model.py#L71
-
-
-
-A quick look at the example dataset that comes with the model, for reference
-
-
-In this case we don't want to use the `predict` interface anyway (one of N class labels) - we want the features that go into the last fully-connected layer (as described here https://stackoverflow.com/a/52548419)
-
 ```python
-network = torch.nn.Sequential(*(list(model._plumbing.model.pretrained_model.children())[:-1]))
+network = load_model(strip_final_layer=True)
 ```
 
 ```python
 imgs = dataset.test_image().to_dask()
-i= imgs.to_numpy()
-i.shape
-
+imgs.to_numpy().shape
 ```
 
-https://github.com/alan-turing-institute/plankton-cefas-scivision/blob/main/resnet50_cefas/data.py 
-
-
-
 Pass the image through our truncated network and get some embeddings out
 
 ```python
-o = torch.stack([torchvision.transforms.ToTensor()(i)])
+o = prepare_image(imgs)
 feats = network(o)
 feats.shape
 ```
 
 ```python
-embeddings = list(feats[0].squeeze(1).squeeze(1).detach().numpy().astype(float))
+embeddings = feats[0].tolist()
 ```
 
 ```python
@@ -129,7 +107,7 @@ index
 
 ```python
 def flat_embeddings(features: torch.Tensor):
-    return list(features[0].squeeze(1).squeeze(1).detach().numpy().astype(float))
+    return features[0].tolist()
 ```
 
 ```python
@@ -158,6 +136,8 @@ This scales ok at 8000 or so images
 collection.count()
 ```
 
+This is _really_ slow - joe
+
 ```python
 res = index.apply(file_embeddings, axis=1)
 ```

pyproject.toml

@@ -1,12 +1,39 @@
+[build-system]
+requires = ["setuptools >= 61.0"]
+build-backend = "setuptools.build_meta"
+
 [project]
 name = "cyto_ml"
-version = "0.1"
+version = "0.2.0"
+requires-python = ">=3.12"
 description = "This package supports the processing and analysis of plankton sample data"
 readme = "README.md"
-requires-python = "==3.9.*"
+dependencies = [
+    "chromadb",
+    "intake==0.7.0",
+    "intake-xarray",
+    "pandas",
+    "python-dotenv",
+    "s3fs",
+    "scikit-image", # secretly required by intake-xarray as default reader
+    "torch",
+    "xarray",
+    "resnet50-cefas@git+https://github.com/jmarshrossney/resnet50-cefas",
+]
 
-[tool.setuptools]
-py-modules = []
+[project.optional-dependencies]
+jupyter = ["jupyterlab", "jupytext", "matplotlib"]
+dev = ["pytest", "black", "flake8", "isort"]
+all = ["cyto_ml[jupyter,dev]"]
 
 [tool.jupytext]
 formats = "ipynb,md"
+
+[tool.pytest.ini_options]
+filterwarnings = [
+    "ignore::DeprecationWarning",
+]
+
+[tool.black]
+target-version = ["py312"]
+line-length = 88

scripts/intake_metadata.py

@@ -7,6 +7,7 @@
 Via https://gallery.pangeo.io/repos/pangeo-data/pangeo-tutorial-gallery/intake.html#Build-an-intake-catalog
 
 """
+
 import os
 from cyto_ml.data.intake import intake_yaml
 from cyto_ml.data.s3 import s3_endpoint, image_index

cyto_ml/data/vectorstore.py → src/cyto_ml/data/vectorstore.py

@@ -9,7 +9,7 @@
 
 logging.basicConfig(level=logging.INFO)
 # TODO make this sensibly configurable, not confusingly hardcoded
-STORE = os.path.join(os.path.abspath(os.path.dirname(__file__)), "../../vectors")
+STORE = os.path.join(os.path.abspath(os.path.dirname(__file__)), "../../../vectors")
 
 client = chromadb.PersistentClient(
     path=STORE,

src/cyto_ml/models/scivision.py

@@ -0,0 +1,36 @@
+import torch
+from torchvision.transforms.v2.functional import to_image, to_dtype
+from xarray import DataArray
+
+
+def prepare_image(image: DataArray):
+    """
+    Take an xarray of image data and prepare it to pass through the model
+    a) Converts the image data to a PyTorch tensor
+    b) Accepts a single image or batch (no need for torch.stack)
+    """
+    # Computes the DataArray and returns a numpy array
+    image_numpy = image.to_numpy()
+
+    # Convert the image data to a PyTorch tensor
+    tensor_image = to_dtype(
+        to_image(image_numpy),  # permutes HWC -> CHW
+        torch.float32,
+        scale=True,  # rescales [0, 255] -> [0, 1]
+    )
+    assert torch.all((tensor_image >= 0.0) & (tensor_image <= 1.0))
+
+    if tensor_image.dim() == 3:
+        # Single image, add a batch dimension
+        tensor_image = tensor_image.unsqueeze(0)
+
+    assert tensor_image.dim() == 4
+
+    return tensor_image
+
+
+def flat_embeddings(features: torch.Tensor):
+    """Utility function that takes the features returned by the model in truncate_model
+    And flattens them into a list suitable for storing in a vector database"""
+    # TODO: this only returns the 0th tensor in the batch...why?
+    return features[0].detach().tolist()