Added ninapro dataset provider.

.vscode/launch.json

@@ -220,7 +220,7 @@
             "request": "launch",
             "program": "${cwd}/scripts/main.py",
             "console": "integratedTerminal",
-            "args": ["--full", "--algos", "darts_space_constant_random_archs", "--datasets", "sphericalcifar100"]
+            "args": ["--full", "--algos", "darts_space_constant_random_archs", "--datasets", "ninapro"]
         },
         {
             "name": "Proxynas-Darts-Space-Full",

archai/datasets/__init__.py

@@ -14,4 +14,5 @@ from .providers.flower102_provider import Flower102Provider
 from .providers.imagenet16120_provider import ImageNet16120Provider
 from .providers.synthetic_cifar10_provider import SyntheticCifar10Provider
 from .providers.intel_image_provider import IntelImageProvider
-from .providers.spherical_cifar100_provider import SphericalCifar100Provider
+from .providers.spherical_cifar100_provider import SphericalCifar100Provider
+from .providers.ninapro_provider import NinaproProvider

archai/datasets/providers/ninapro_provider.py

@@ -0,0 +1,90 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
+from typing import List, Tuple, Union, Optional
+import os
+import gzip
+import pickle
+import numpy as np 
+
+from overrides import overrides, EnforceOverrides
+import torch
+import torch.utils.data as data_utils
+from torch.utils.data.dataset import Dataset
+
+import torchvision
+from torchvision.transforms import transforms
+
+from archai.datasets.dataset_provider import DatasetProvider, register_dataset_provider, TrainTestDatasets
+from archai.common.config import Config
+from archai.common import utils
+
+
+def load_ninapro_data(path, train=True):
+    ''' Modified from 
+    https://github.com/rtu715/NAS-Bench-360/blob/ba7ff6bd0762073d1ce49207b95245c5c742b567/backbone/data_utils/load_data.py#L396 '''
+
+    trainset = load_ninapro(path, 'train')
+    valset = load_ninapro(path, 'val')
+    testset = load_ninapro(path, 'test')
+
+    if train:
+        return trainset, valset, testset
+    else:
+        targets = torch.cat((trainset.targets, valset.targets))
+        trainset = data_utils.ConcatDataset([trainset, valset])
+        trainset.targets = targets # for compatibility with stratified sampler
+
+    return trainset, None, testset
+
+
+def load_ninapro(path, whichset):
+    ''' Modified from 
+    https://github.com/rtu715/NAS-Bench-360/blob/ba7ff6bd0762073d1ce49207b95245c5c742b567/backbone/data_utils/load_data.py#L396 '''
+
+    data_str = 'ninapro_' + whichset + '.npy'
+    label_str = 'label_' + whichset + '.npy'
+
+    data = np.load(os.path.join(path, data_str),
+                             encoding="bytes", allow_pickle=True)
+    labels = np.load(os.path.join(path, label_str), encoding="bytes", allow_pickle=True)
+
+    data = np.transpose(data, (0, 2, 1))
+    data = data[:, None, :, :]
+    data = torch.from_numpy(data.astype(np.float32))
+    labels = torch.from_numpy(labels.astype(np.int64))
+
+    all_data = data_utils.TensorDataset(data, labels)
+    all_data.targets = labels # for compatibility with stratified data sampler
+    return all_data
+
+
+
+class NinaproProvider(DatasetProvider):
+    def __init__(self, conf_dataset:Config):
+        super().__init__(conf_dataset)
+        self._dataroot = utils.full_path(conf_dataset['dataroot'])
+
+    @overrides
+    def get_datasets(self, load_train:bool, load_test:bool,
+                     transform_train, transform_test)->TrainTestDatasets:
+        trainset, testset = None, None
+
+        path_to_data = os.path.join(self._dataroot, 'ninapro')
+
+        # load the dataset but without any validation split
+        trainset, _, testset = load_ninapro_data(path_to_data, train=False)
+
+        return trainset, testset
+
+    @overrides
+    def get_transforms(self)->tuple:
+        # return empty transforms since we have preprocessed data
+        train_transf = []
+        test_transf = []
+
+        train_transform = transforms.Compose(train_transf)
+        test_transform = transforms.Compose(test_transf)
+        return train_transform, test_transform
+
+register_dataset_provider('ninapro', NinaproProvider)

confs/datasets/ninapro.yaml

@@ -0,0 +1,8 @@
+__include__: './dataroot.yaml' # default dataset settings are for cifar
+
+dataset:
+  name: 'ninapro'
+  n_classes: 18
+  channels: 1 # number of channels in image
+  max_batches: -1 # if >= 0 then only these many batches are generated (useful for debugging)
+  storage_name: 'ninapro' # name of folder or tar file to copy from cloud storage