Initial commit

.github/workflows/docs.yml

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+name: Sphinx Code Documentation GitHub Pages Deploy Action
+on:
+  push:
+    branches:
+      - master
+jobs:
+  deploy:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+      - uses: ammaraskar/sphinx-action@master
+        with:
+          docs-folder: "docs/"
+          pre-build-command: |
+            make install
+            make install-dev
+            make doc-layout
+            make doc
+      - name: Deploy to GitHub Pages
+        uses: JamesIves/github-pages-deploy-action@v4.3.3
+        with:
+          github_token: ${{ secrets.GITHUB_TOKEN }}
+          branch: gh-pages
+          folder: docs/build/html/

.gitignore

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+/notebooks
+/wandb
+__pycache__
+datasets/utils/2mnist_10digits
+run_slurm.sh
+test_slurm.sh
+/logs
+datasets/__pycache__
+venv/
+.python-version

BDD_OIA/.gitignore

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+/models
+/output
+Dockerfile
+__pycache__
+/out
+/notebooks/40-epochs.csv
+/data
+/logs
+/wandb

BDD_OIA/BDD/config.py

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+# General
+BASE_DIR = ""
+N_ATTRIBUTES = 21
+N_CLASSES = 5  # number of bird species
+
+# Training
+UPWEIGHT_RATIO = 9.0
+MIN_LR = 0.0001
+LR_DECAY_SIZE = 0.1

BDD_OIA/BDD/data_processing.py

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+"""
+Make train, val, test datasets based on train_test_split.txt, and by sampling val_ratio of the official train data to make a validation set 
+Each dataset is a list of metadata, each includes official image id, full image path, class label, attribute labels, attribute certainty scores, and attribute labels calibrated for uncertainty
+"""
+
+import argparse
+import os
+import pickle
+import random
+import sys
+from collections import defaultdict as ddict
+from os import listdir
+from os.path import isdir, isfile, join
+
+
+def extract_data(data_dir):
+    cwd = os.getcwd()
+    data_path = join(cwd, data_dir + "/images")
+    print(data_path)
+    val_ratio = 0.2
+
+    path_to_id_map = dict()  # map from full image path to image id
+    with open(data_path.replace("images", "images.txt"), "r") as f:
+        for line in f:
+            items = line.strip().split()
+            path_to_id_map[join(data_path, items[1])] = int(items[0])
+
+    attribute_labels_all = ddict(
+        list
+    )  # map from image id to a list of attribute labels
+    attribute_certainties_all = ddict(
+        list
+    )  # map from image id to a list of attribute certainties
+    attribute_uncertain_labels_all = ddict(
+        list
+    )  # map from image id to a list of attribute labels calibrated for uncertainty
+    # 1 = not visible, 2 = guessing, 3 = probably, 4 = definitely
+    uncertainty_map = {
+        1: {
+            1: 0,
+            2: 0.5,
+            3: 0.75,
+            4: 1,
+        },  # calibrate main label based on uncertainty label
+        0: {1: 0, 2: 0.5, 3: 0.25, 4: 0},
+    }
+    with open(
+        join(
+            cwd, data_dir + "/attributes/image_attribute_labels.txt"
+        ),
+        "r",
+    ) as f:
+        for line in f:
+            (
+                file_idx,
+                attribute_idx,
+                attribute_label,
+                attribute_certainty,
+            ) = line.strip().split()[:4]
+            attribute_label = int(attribute_label)
+            attribute_certainty = int(attribute_certainty)
+            uncertain_label = uncertainty_map[attribute_label][
+                attribute_certainty
+            ]
+            attribute_labels_all[int(file_idx)].append(
+                attribute_label
+            )
+            attribute_uncertain_labels_all[int(file_idx)].append(
+                uncertain_label
+            )
+            attribute_certainties_all[int(file_idx)].append(
+                attribute_certainty
+            )
+
+    is_train_test = dict()  # map from image id to 0 / 1 (1 = train)
+    with open(
+        join(cwd, data_dir + "/train_test_split.txt"), "r"
+    ) as f:
+        for line in f:
+            idx, is_train = line.strip().split()
+            is_train_test[int(idx)] = int(is_train)
+    print(
+        "Number of train images from official train test split:",
+        sum(list(is_train_test.values())),
+    )
+
+    train_val_data, test_data = [], []
+    train_data, val_data = [], []
+    folder_list = [
+        f for f in listdir(data_path) if isdir(join(data_path, f))
+    ]
+    folder_list.sort()  # sort by class index
+    for i, folder in enumerate(folder_list):
+        folder_path = join(data_path, folder)
+        classfile_list = [
+            cf
+            for cf in listdir(folder_path)
+            if (isfile(join(folder_path, cf)) and cf[0] != ".")
+        ]
+        # classfile_list.sort()
+        for cf in classfile_list:
+            img_id = path_to_id_map[join(folder_path, cf)]
+            img_path = join(folder_path, cf)
+            metadata = {
+                "id": img_id,
+                "img_path": img_path,
+                "class_label": i,
+                "attribute_label": attribute_labels_all[img_id],
+                "attribute_certainty": attribute_certainties_all[
+                    img_id
+                ],
+                "uncertain_attribute_label": attribute_uncertain_labels_all[
+                    img_id
+                ],
+            }
+            if is_train_test[img_id]:
+                train_val_data.append(metadata)
+                if val_files is not None:
+                    if img_path in val_files:
+                        val_data.append(metadata)
+                    else:
+                        train_data.append(metadata)
+            else:
+                test_data.append(metadata)
+
+    random.shuffle(train_val_data)
+    split = int(val_ratio * len(train_val_data))
+    train_data = train_val_data[split:]
+    val_data = train_val_data[:split]
+    print("Size of train set:", len(train_data))
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Dataset preparation"
+    )
+    parser.add_argument(
+        "--save_dir", "-d", help="Where to save the new datasets"
+    )
+    parser.add_argument(
+        "--data_dir", help="Where to load the datasets"
+    )
+    args = parser.parse_args()
+    print(args.data_dir)
+    train_data, val_data, test_data = extract_data(args.data_dir)
+
+    for dataset in ["train", "val", "test"]:
+        print("Processing %s set" % dataset)
+        f = open(args.save_dir + dataset + ".pkl", "wb")
+        if "train" in dataset:
+            pickle.dump(train_data, f)
+        elif "val" in dataset:
+            pickle.dump(val_data, f)
+        else:
+            pickle.dump(test_data, f)
+        f.close()