# 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 EuroSATProcessor(Processor):
"""RGB version of the EuroSAT Dataset (num channel = 3)"""
[docs] def __init__(self, params):
super().__init__(params)
self.allowable_split = ["train"]
self.num_classes = 10
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.EuroSAT(
root=self.data_dir,
transform=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
)
dataset = torchvision.datasets.EuroSAT(
root=self.data_dir,
transform=None,
download=False,
)
# EuroSAT only comes with a training set. Therefore, the training,
# validation, and test sets are split out of the original training set.
# By default, 60% is used as a new training split, 20% is used for
# validation, and 20% is used for testing.
split_percent = [60, 20, 20]
train_set, val_set, test_set = self.split_dataset(
dataset, split_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
)
test_set.set_transforms(
transform=eval_transform, target_transform=eval_target_transform
)
return train_set, val_set, test_set