# 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 torchvision
from cerebras.modelzoo.data.vision.classification.dataset_factory import (
Processor,
)
[docs]class OxfordIIITPetProcessor(Processor):
[docs] def __init__(self, params):
super().__init__(params)
self.allowable_split = ["trainval", "test"]
self.num_classes = 37
def create_dataset(self, use_training_transforms=True, split="trainval"):
self.check_split_valid(split)
transform, target_transform = self.process_transform(
use_training_transforms
)
dataset = torchvision.datasets.OxfordIIITPet(
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, train_split_percent=None, 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
)
trainval_set = torchvision.datasets.OxfordIIITPet(
root=self.data_dir,
split="trainval",
transform=None,
download=False,
)
test_set = torchvision.datasets.OxfordIIITPet(
root=self.data_dir,
split="test",
transform=eval_transform,
download=False,
)
train_percent = train_split_percent or 80 # default is 80%
val_percent = 100 - train_percent
train_set, val_set = self.split_dataset(
trainval_set, [train_percent, val_percent], seed
)
if use_1k_sample:
train_set.truncate_to_idx(800)
val_set.truncate_to_idx(200)
train_set.set_transforms(
transform=train_transform, target_transform=train_target_transform
)
val_set.set_transforms(
transform=eval_transform, target_transform=eval_target_transform
)
return train_set, val_set, test_set