# 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):
[docs] 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