Source code for cerebras.modelzoo.data.vision.classification.data.dtd

# Copyright 2022 Cerebras Systems.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import numpy as np
import torchvision

from cerebras.modelzoo.data.vision.classification.dataset_factory import (
    Processor,
    VisionSubset,
)


[docs]class DTDProcessor(Processor): def __init__(self, params): super().__init__(params) self.allowable_split = ["train", "val", "test"] self.num_classes = 47 def create_dataset(self, use_training_transforms=True, split="train"): self.check_split_valid(split) transform, target_transform = self.process_transform( use_training_transforms ) dataset = torchvision.datasets.DTD( root=self.data_dir, split=split, transform=transform, target_transform=target_transform, download=False, ) return dataset def create_vtab_dataset(self, use_1k_sample=True, seed=42): train_transform, train_target_transform = self.process_transform( use_training_transforms=True ) eval_transform, eval_target_transform = self.process_transform( use_training_transforms=False ) train_set = torchvision.datasets.DTD( root=self.data_dir, split="train", transform=train_transform, target_transform=train_target_transform, download=False, ) val_set = torchvision.datasets.DTD( root=self.data_dir, split="val", transform=eval_transform, target_transform=eval_target_transform, download=False, ) test_set = torchvision.datasets.DTD( root=self.data_dir, split="test", transform=eval_transform, target_transform=eval_target_transform, download=False, ) if use_1k_sample: rng = np.random.default_rng(seed) sample_idx = self.create_shuffled_idx(len(train_set), rng) train_set = VisionSubset(train_set, sample_idx[:800]) sample_idx = self.create_shuffled_idx(len(val_set), rng) val_set = VisionSubset(val_set, sample_idx[:200]) return train_set, val_set, test_set