upgrade zoobot to latest version

azure/batch/Dockerfile

@@ -1,18 +1,26 @@
-FROM nvidia/cuda:11.3.1-base-ubuntu20.04
+FROM nvidia/cuda:12.1.1-base-ubuntu22.04
 
-ENV DEBIAN_FRONTEND noninteractive
+ENV DEBIAN_FRONTEND=noninteractive
 
 WORKDIR /usr/src/zoobot
 
+# Install prerequisites and add deadsnakes PPA for Python 3.10
 RUN apt-get update && apt-get -y upgrade && \
     apt-get install --no-install-recommends -y \
+    software-properties-common && \
+    add-apt-repository ppa:deadsnakes/ppa && \
+    apt-get update && apt-get install --no-install-recommends -y \
     build-essential \
-    python3 \
+    python3.10 \
+    python3.10-distutils \
+    python3.10-dev \
     python3-pip \
     git && \
     apt-get clean && rm -rf /var/lib/apt/lists/*
 
-RUN ln -s /usr/bin/python3 /usr/bin/python
+# Link Python 3.10 as default
+RUN ln -sf /usr/bin/python3.10 /usr/bin/python && \
+    ln -sf /usr/bin/python3.10 /usr/bin/python3
 
 # install a newer version of pip
 # as we can't use the use the ubuntu package pip version (20.0.2)
@@ -23,6 +31,7 @@ RUN apt-get remove -y python3-pip
 RUN ln -s /usr/local/bin/pip3 /usr/bin/pip
 RUN ln -s /usr/local/bin/pip3 /usr/bin/pip3
 
-# install our dependencies (see setup.py)
+
+# Install project dependencies (see setup.py)
 COPY setup.py .
-RUN pip install . --extra-index-url https://download.pytorch.org/whl/cu113
+RUN pip install . --extra-index-url https://download.pytorch.org/whl/cu121

azure/batch/scripts/predict_on_catalog.py

@@ -64,15 +64,7 @@ def __getitem__(self, idx):
           # ensure we raise other response errors like 404 and 500 etc
           # Note: we don't retry on errors that aren't in the `status_forcelist`, instead we fast fail!
           response.raise_for_status()
-          url_mime_type = response.headers['content-type']
-          # handle PNG images
-          if url_mime_type == 'image/png':
-              # use PIL image to read the png file buffer
-              image = Image.open(response.raw)
-          else: # but assume all other images are JPEG
-              # HWC PIL image
-              image = Image.fromarray(
-                  galaxy_dataset.decode_jpeg(response.raw.read()))
+          image = Image.open(response.raw)
       except Exception as e:
           # add some logging on the failed url
           logging.critical('Cannot load {}'.format(url))
@@ -98,7 +90,7 @@ def __getitem__(self, idx):
 class PredictionGalaxyDataModule(galaxy_datamodule.GalaxyDataModule):
     # override the setup method to setup our prediction dataset on the prediction catalog
     def setup(self, stage: Optional[str] = None):
-        self.predict_dataset = PredictionGalaxyDataset(catalog=self.predict_catalog, transform=self.transform)
+        self.predict_dataset = PredictionGalaxyDataset(catalog=self.predict_catalog, transform=self.test_transform)
 
 
 def save_predictions_to_json(predictions: np.ndarray, image_ids: List[str], label_cols: List[str], save_loc: str):

azure/batch/scripts/train_model_finetune_on_catalog.py

@@ -6,7 +6,7 @@
 from galaxy_datasets.pytorch.galaxy_datamodule import GalaxyDataModule
 
 from zoobot.pytorch.training import finetune
-from zoobot.shared.schemas import cosmic_dawn_ortho_schema
+from zoobot.shared.schemas import cosmic_dawn_ortho_schema, euclid_ortho_schema
 
 if __name__ == '__main__':
 
@@ -41,11 +41,7 @@
 
     schema_dict = {
         'cosmic_dawn': cosmic_dawn_ortho_schema,
-        'euclid': {
-            'label_cols': ['smooth-or-featured-euclid_smooth', 'smooth-or-featured-euclid_featured-or-disk', 'smooth-or-featured-euclid_problem', 'disk-edge-on-euclid_yes', 'disk-edge-on-euclid_no', 'has-spiral-arms-euclid_yes', 'has-spiral-arms-euclid_no', 'bar-euclid_strong', 'bar-euclid_weak', 'bar-euclid_no', 'bulge-size-euclid_dominant', 'bulge-size-euclid_large', 'bulge-size-euclid_moderate', 'bulge-size-euclid_small', 'bulge-size-euclid_none', 'how-rounded-euclid_round', 'how-rounded-euclid_in-between', 'how-rounded-euclid_cigar-shaped', 'edge-on-bulge-euclid_boxy', 'edge-on-bulge-euclid_none', 'edge-on-bulge-euclid_rounded', 'spiral-winding-euclid_tight', 'spiral-winding-euclid_medium', 'spiral-winding-euclid_loose', 'spiral-arm-count-euclid_1', 'spiral-arm-count-euclid_2', 'spiral-arm-count-euclid_3', 'spiral-arm-count-euclid_4', 'spiral-arm-count-euclid_more-than-4', 'spiral-arm-count-euclid_cant-tell', 'merging-euclid_none', 'merging-euclid_minor-disturbance', 'merging-euclid_major-disturbance', 'merging-euclid_merger', 'clumps-euclid_yes', 'clumps-euclid_no', 'problem-euclid_star', 'problem-euclid_artifact', 'problem-euclid_zoom', 'artifact-euclid_satellite', 'artifact-euclid_scattered', 'artifact-euclid_diffraction', 'artifact-euclid_ray', 'artifact-euclid_saturation', 'artifact-euclid_other', 'artifact-euclid_ghost'],
-            'questions': ['smooth-or-featured-euclid', 'indices 0 to 2', 'asked after None', 'disk-edge-on-euclid', 'indices 3 to 4', 'asked after smooth-or-featured-euclid_featured-or-disk', 'index 1', 'has-spiral-arms-euclid', 'indices 5 to 6', 'asked after disk-edge-on-euclid_no', 'index 4', 'bar-euclid', 'indices 7 to 9', 'asked after disk-edge-on-euclid_no', 'index 4', 'bulge-size-euclid', 'indices 10 to 14', 'asked after disk-edge-on-euclid_no', 'index 4', 'how-rounded-euclid', 'indices 15 to 17',' asked after smooth-or-featured-euclid_smooth', 'index 0', 'edge-on-bulge-euclid', 'indices 18 to 20', 'asked after disk-edge-on-euclid_yes', 'index 3', 'spiral-winding-euclid', 'indices 21 to 23', 'asked after has-spiral-arms-euclid_yes', 'index 5', 'spiral-arm-count-euclid', 'indices 24 to 29', 'asked after has-spiral-arms-euclid_yes', 'index 5', 'merging-euclid', 'indices 30 to 33', 'asked after None', 'clumps-euclid', 'indices 34 to 35', 'asked after disk-edge-on-euclid_no', 'index 4', 'problem-euclid', 'indices 36 to 38', 'asked after smooth-or-featured-euclid_problem', 'index 2', 'artifact-euclid', 'indices 39 to 45', 'asked after problem-euclid_artifact', 'index 37'],
-            'question_answer_pairs': {'smooth-or-featured-euclid': ['_smooth', '_featured-or-disk', '_problem'], 'disk-edge-on-euclid': ['_yes', '_no'], 'has-spiral-arms-euclid': ['_yes', '_no'], 'bar-euclid': ['_strong', '_weak', '_no'], 'bulge-size-euclid': ['_dominant', '_large', '_moderate', '_small', '_none'], 'how-rounded-euclid': ['_round', '_in-between', '_cigar-shaped'], 'edge-on-bulge-euclid': ['_boxy', '_none', '_rounded'], 'spiral-winding-euclid': ['_tight', '_medium', '_loose'], 'spiral-arm-count-euclid': ['_1', '_2', '_3', '_4', '_more-than-4', '_cant-tell'], 'merging-euclid': ['_none', '_minor-disturbance', '_major-disturbance', '_merger'], 'clumps-euclid': ['_yes', '_no'], 'problem-euclid': ['_star', '_artifact', '_zoom'], 'artifact-euclid': ['_satellite', '_scattered', '_diffraction', '_ray', '_saturation', '_other', '_ghost']}
-        }
+        'euclid': euclid_ortho_schema
     }
     schema = schema_dict.get(args.schema, cosmic_dawn_ortho_schema)
     # setup the error reporting tool - https://app.honeybadger.io/projects/
@@ -105,15 +101,12 @@
     else:
         logger = None
 
+
     # load the model from checkpoint
     model = finetune.FinetuneableZoobotTree(
-        checkpoint_loc=args.checkpoint,
         # params specific to tree finetuning
         schema=schema,
-        # params for superclass i.e. any finetuning
-        encoder_dim=args.encoder_dim,
-        n_layers=args.n_layers,
-        prog_bar=args.progress_bar
+        zoobot_checkpoint_loc=args.checkpoint
     )
 
     trainer = finetune.get_trainer(

azure/batch/setup.py

@@ -16,9 +16,9 @@
         "Environment :: GPU :: NVIDIA CUDA"
     ],
     packages=setuptools.find_packages(),
-    python_requires=">=3.7",  # tf 2.8.0 requires Python 3.7 and above
+    python_requires=">=3.9",  # tf 2.8.0 requires Python 3.7 and above
     install_requires=[
-        'zoobot[pytorch_cu113] >= 1.0', # the big cheese - bring in the zoobot!
+        'zoobot[pytorch-cu121] >= 2.0.0', # the big cheese - bring in the zoobot!
         'requests >= 2.28.1', # used to download prediction images from a remote URL
         'honeybadger' # used for error reporting
     ]