Add projects package (#3)

Author: fcogidi

.pre-commit-config.yaml

@@ -58,3 +58,5 @@ repos:
       entry: python3 -m pytest -m "not integration_test"
       pass_filenames: false
       always_run: true
+
+exclude: "projects"

mmlearn/datasets/__init__.py

@@ -5,13 +5,8 @@
 from mmlearn.datasets.imagenet import ImageNet
 from mmlearn.datasets.librispeech import LibriSpeech
 from mmlearn.datasets.llvip import LLVIPDataset
-from mmlearn.datasets.medvqa import MedVQA
-from mmlearn.datasets.mimiciv_cxr import MIMICIVCXR
 from mmlearn.datasets.nihcxr import NIHCXR
 from mmlearn.datasets.nyuv2 import NYUv2Dataset
-from mmlearn.datasets.pmcoa import PMCOA
-from mmlearn.datasets.quilt import Quilt
-from mmlearn.datasets.roco import ROCO
 from mmlearn.datasets.sunrgbd import SUNRGBDDataset
 
 
@@ -21,12 +16,7 @@
     "ImageNet",
     "LibriSpeech",
     "LLVIPDataset",
-    "MedVQA",
-    "MIMICIVCXR",
     "NIHCXR",
     "NYUv2Dataset",
-    "PMCOA",
-    "Quilt",
-    "ROCO",
     "SUNRGBDDataset",
 ]

mmlearn/datasets/processors/__init__.py

@@ -5,18 +5,12 @@
     RandomMaskGenerator,
 )
 from mmlearn.datasets.processors.tokenizers import HFTokenizer
-from mmlearn.datasets.processors.transforms import (
-    MedVQAProcessor,
-    TrimText,
-    med_clip_vision_transform,
-)
+from mmlearn.datasets.processors.transforms import TrimText
 
 
 __all__ = [
     "BlockwiseImagePatchMaskGenerator",
     "HFTokenizer",
-    "MedVQAProcessor",
     "RandomMaskGenerator",
     "TrimText",
-    "med_clip_vision_transform",
 ]

mmlearn/datasets/processors/transforms.py

@@ -1,10 +1,8 @@
 """Custom transforms for datasets."""
 
-from typing import List, Literal, Union
+from typing import List, Union
 
 from hydra_zen import store
-from timm.data.transforms import ResizeKeepRatio
-from torchvision import transforms
 
 
 @store(group="datasets/transforms", provider="mmlearn")
@@ -30,72 +28,3 @@ def __call__(self, sentence: Union[str, List[str]]) -> Union[str, List[str]]:
             sentence[i] = s[: self.trim_size]
 
         return sentence
-
-
-class MedVQAProcessor:
-    """Preprocessor for textual reports of MedVQA datasets."""
-
-    def __call__(self, sentence: Union[str, List[str]]) -> Union[str, List[str]]:
-        """Process the textual captions."""
-        if not isinstance(sentence, (list, str)):
-            raise TypeError(
-                f"Expected sentence to be a string or list of strings, got {type(sentence)}"
-            )
-
-        def _preprocess_sentence(sentence: str) -> str:
-            sentence = sentence.lower()
-            if "? -yes/no" in sentence:
-                sentence = sentence.replace("? -yes/no", "")
-            if "? -open" in sentence:
-                sentence = sentence.replace("? -open", "")
-            if "? - open" in sentence:
-                sentence = sentence.replace("? - open", "")
-            return (
-                sentence.replace(",", "")
-                .replace("?", "")
-                .replace("'s", " 's")
-                .replace("...", "")
-                .replace("x ray", "x-ray")
-                .replace(".", "")
-            )
-
-        if isinstance(sentence, str):
-            return _preprocess_sentence(sentence)
-
-        for i, s in enumerate(sentence):
-            sentence[i] = _preprocess_sentence(s)
-
-        return sentence
-
-
-@store(group="datasets/transforms", provider="mmlearn")  # type: ignore[misc]
-def med_clip_vision_transform(
-    image_crop_size: int = 224, job_type: Literal["train", "eval"] = "train"
-) -> transforms.Compose:
-    """Return transforms for training/evaluating CLIP with medical images.
-
-    Parameters
-    ----------
-    image_crop_size : int, default=224
-        Size of the image crop.
-    job_type : {"train", "eval"}, default="train"
-        Type of the job (training or evaluation) for which the transforms are needed.
-
-    Returns
-    -------
-    transforms.Compose
-        Composed transforms for training CLIP with medical images.
-    """
-    return transforms.Compose(
-        [
-            ResizeKeepRatio(512, interpolation="bicubic"),
-            transforms.RandomCrop(image_crop_size)
-            if job_type == "train"
-            else transforms.CenterCrop(image_crop_size),
-            transforms.ToTensor(),
-            transforms.Normalize(
-                mean=[0.48145466, 0.4578275, 0.40821073],
-                std=[0.26862954, 0.26130258, 0.27577711],
-            ),
-        ]
-    )

projects/__init__.py

@@ -0,0 +1,33 @@
+## Benchmarking CLIP-style Methods on Medical Data
+Prior to running any experiments under this project, please install the required dependencies by running the following command:
+```bash
+pip install -r requirements.txt
+```
+**NOTE**: It is assumed that the requirements for the `mmlearn` package have already been installed in a virtual environment. 
+If not, please refer to the README file in the `mmlearn` package for installation instructions.
+
+Also, please make sure to set the following environment variables:
+```bash
+export MIMICIVCXR_ROOT_DIR=/path/to/mimic-cxr/data
+export PMCOA_ROOT_DIR=/path/to/pmc_oa/data
+export QUILT_ROOT_DIR=/path/to/quilt/data
+export ROCO_ROOT_DIR=/path/to/roco/data
+```
+
+If you are running an experiment with the MedVQA dataset, please also set the following environment variables:
+```bash
+export PATHVQA_ROOT_DIR=/path/to/pathvqa/data
+export VQARAD_ROOT_DIR=/path/to/vqarad/data
+```
+
+To run an experiment, use the following command:
+
+**To Run Locally**:
+```bash
+mmlearn_run 'hydra.searchpath=[pkg://projects.med_benchmarking.configs]' +experiment=baseline experiment_name=test
+```
+
+**To Run on a SLURM Cluster**:
+```bash
+mmlearn_run --multirun hydra.launcher.mem_gb=32 hydra.launcher.qos=your_qos hydra.launcher.partition=your_partition hydra.launcher.gres=gpu:4 hydra.launcher.cpus_per_task=8 hydra.launcher.tasks_per_node=4 hydra.launcher.nodes=1 hydra.launcher.stderr_to_stdout=true hydra.launcher.timeout_min=60 '+hydra.launcher.additional_parameters={export: ALL}' 'hydra.searchpath=[pkg://projects.med_benchmarking.configs]' +experiment=baseline experiment_name=test
+```

projects/med_benchmarking/README.md

@@ -0,0 +1,82 @@
+import os
+from typing import Literal
+
+from hydra_zen import builds, store
+from omegaconf import MISSING
+from timm.data.transforms import ResizeKeepRatio
+from torchvision import transforms
+
+from mmlearn.conf import external_store
+from projects.med_benchmarking.datasets.medvqa import MedVQA, MedVQAProcessor
+from projects.med_benchmarking.datasets.mimiciv_cxr import MIMICIVCXR
+from projects.med_benchmarking.datasets.pmcoa import PMCOA
+from projects.med_benchmarking.datasets.quilt import Quilt
+from projects.med_benchmarking.datasets.roco import ROCO
+
+
+_MedVQAConf = builds(
+    MedVQA,
+    split="train",
+    encoder={"image_size": 224, "feat_dim": 512, "images_filename": "images_clip.pkl"},
+    autoencoder={
+        "available": True,
+        "image_size": 128,
+        "feat_dim": 64,
+        "images_filename": "images128x128.pkl",
+    },
+    num_ans_candidates=MISSING,
+)
+_PathVQAConf = builds(
+    MedVQA,
+    root_dir=os.getenv("PATHVQA_ROOT_DIR", MISSING),
+    num_ans_candidates=3974,
+    autoencoder={"available": False},
+    builds_bases=(_MedVQAConf,),
+)
+_VQARADConf = builds(
+    MedVQA,
+    root_dir=os.getenv("VQARAD_ROOT_DIR", MISSING),
+    num_ans_candidates=458,
+    autoencoder={"available": False},
+    builds_bases=(_MedVQAConf,),
+)
+external_store(_MedVQAConf, name="MedVQA", group="datasets")
+external_store(_PathVQAConf, name="PathVQA", group="datasets")
+external_store(_VQARADConf, name="VQARAD", group="datasets")
+
+external_store(MedVQAProcessor, name="MedVQAProcessor", group="datasets/transforms")
+
+
+@external_store(group="datasets/transforms")
+def med_clip_vision_transform(
+    image_crop_size: int = 224, job_type: Literal["train", "eval"] = "train"
+) -> transforms.Compose:
+    """Return transforms for training/evaluating CLIP with medical images.
+
+    Parameters
+    ----------
+    image_crop_size : int, default=224
+        Size of the image crop.
+    job_type : {"train", "eval"}, default="train"
+        Type of the job (training or evaluation) for which the transforms are needed.
+
+    Returns
+    -------
+    transforms.Compose
+        Composed transforms for training CLIP with medical images.
+    """
+    return transforms.Compose(
+        [
+            ResizeKeepRatio(
+                512 if job_type == "train" else image_crop_size, interpolation="bicubic"
+            ),
+            transforms.RandomCrop(image_crop_size)
+            if job_type == "train"
+            else transforms.CenterCrop(image_crop_size),
+            transforms.ToTensor(),
+            transforms.Normalize(
+                mean=[0.48145466, 0.4578275, 0.40821073],
+                std=[0.26862954, 0.26130258, 0.27577711],
+            ),
+        ]
+    )

projects/med_benchmarking/configs/__init__.py

@@ -69,7 +69,7 @@ task:
     eps: 1.0e-6
   lr_scheduler:
     scheduler:
-      T_max: 537_775
+      T_max: 107_555 # make sure to change this if max_epochs or accumulate_grad_batches is changed
     extras:
       interval: step
   loss:
@@ -80,15 +80,15 @@ task:
         task_specs:
           - query_modality: text
             target_modality: rgb
-            top_k: [200]
+            top_k: [10, 200]
           - query_modality: rgb
             target_modality: text
-            top_k: [200]
+            top_k: [10, 200]
       run_on_validation: false
       run_on_test: true
 
 trainer:
-  max_epochs: 100
+  max_epochs: 20
   precision: 16-mixed
   deterministic: False
   benchmark: True

configs/experiment/med_contrastive_pretraining.yaml renamed to projects/med_benchmarking/configs/experiment/baseline.yaml

@@ -3,11 +3,10 @@
 import json
 import os
 import warnings
-from typing import Any, Callable, Dict, Literal, Optional
+from typing import Any, Callable, Dict, List, Literal, Optional, Union
 
 import numpy as np
 import torch
-from hydra_zen import MISSING, builds, store
 from PIL import Image
 from torch.utils.data import Dataset
 from torchvision.transforms import CenterCrop, Compose, Grayscale, Resize, ToTensor
@@ -205,32 +204,37 @@ def __len__(self) -> int:
         return len(self.entries)
 
 
-_MedVQAConf = builds(
-    MedVQA,
-    split="train",
-    encoder={"image_size": 224, "feat_dim": 512, "images_filename": "images_clip.pkl"},
-    autoencoder={
-        "available": True,
-        "image_size": 128,
-        "feat_dim": 64,
-        "images_filename": "images128x128.pkl",
-    },
-    num_ans_candidates=MISSING,
-)
-_PathVQAConf = builds(
-    MedVQA,
-    root_dir=os.getenv("PATHVQA_ROOT_DIR", MISSING),
-    num_ans_candidates=3974,
-    autoencoder={"available": False},
-    builds_bases=(_MedVQAConf,),
-)
-_VQARADConf = builds(
-    MedVQA,
-    root_dir=os.getenv("VQARAD_ROOT_DIR", MISSING),
-    num_ans_candidates=458,
-    autoencoder={"available": False},
-    builds_bases=(_MedVQAConf,),
-)
-store(_MedVQAConf, name="MedVQA", group="datasets", provider="mmlearn")
-store(_PathVQAConf, name="PathVQA", group="datasets", provider="mmlearn")
-store(_VQARADConf, name="VQARAD", group="datasets", provider="mmlearn")
+class MedVQAProcessor:
+    """Preprocessor for textual reports of MedVQA datasets."""
+
+    def __call__(self, sentence: Union[str, List[str]]) -> Union[str, List[str]]:
+        """Process the textual captions."""
+        if not isinstance(sentence, (list, str)):
+            raise TypeError(
+                f"Expected sentence to be a string or list of strings, got {type(sentence)}"
+            )
+
+        def _preprocess_sentence(sentence: str) -> str:
+            sentence = sentence.lower()
+            if "? -yes/no" in sentence:
+                sentence = sentence.replace("? -yes/no", "")
+            if "? -open" in sentence:
+                sentence = sentence.replace("? -open", "")
+            if "? - open" in sentence:
+                sentence = sentence.replace("? - open", "")
+            return (
+                sentence.replace(",", "")
+                .replace("?", "")
+                .replace("'s", " 's")
+                .replace("...", "")
+                .replace("x ray", "x-ray")
+                .replace(".", "")
+            )
+
+        if isinstance(sentence, str):
+            return _preprocess_sentence(sentence)
+
+        for i, s in enumerate(sentence):
+            sentence[i] = _preprocess_sentence(s)
+
+        return sentence