152 lines
5.5 KiB
Python
152 lines
5.5 KiB
Python
# !/usr/bin/env/python3
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# Copyright (c) Facebook, Inc. and its affiliates.
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Cifar10 data module."""
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import os
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import pytorch_lightning as pl
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import webdataset as wds
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from torch.utils.data import DataLoader
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from torchvision import transforms
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class CIFAR10DataModule(pl.LightningDataModule): # pylint: disable=too-many-instance-attributes
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"""Data module class."""
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def __init__(self, **kwargs):
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"""Initialization of inherited lightning data module."""
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super(CIFAR10DataModule, self).__init__() # pylint: disable=super-with-arguments
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self.train_dataset = None
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self.valid_dataset = None
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self.test_dataset = None
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self.train_data_loader = None
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self.val_data_loader = None
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self.test_data_loader = None
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self.normalize = transforms.Normalize(mean=[0.5, 0.5, 0.5],
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std=[0.5, 0.5, 0.5])
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self.valid_transform = transforms.Compose([
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transforms.ToTensor(),
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self.normalize,
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])
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self.train_transform = transforms.Compose([
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transforms.RandomResizedCrop(32),
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transforms.RandomHorizontalFlip(),
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transforms.ToTensor(),
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self.normalize,
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])
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self.args = kwargs
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def prepare_data(self):
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"""Implementation of abstract class."""
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@staticmethod
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def get_num_files(input_path):
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"""Gets num files.
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Args:
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input_path : path to input
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"""
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return len(os.listdir(input_path)) - 1
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def setup(self, stage=None):
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"""Downloads the data, parse it and split the data into train, test,
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validation data.
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Args:
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stage: Stage - training or testing
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"""
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data_path = self.args.get("train_glob", "/pvc/output/processing")
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train_base_url = data_path + "/train"
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val_base_url = data_path + "/val"
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test_base_url = data_path + "/test"
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train_count = self.get_num_files(train_base_url)
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val_count = self.get_num_files(val_base_url)
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test_count = self.get_num_files(test_base_url)
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train_url = "{}/{}-{}".format(train_base_url, "train",
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"{0.." + str(train_count) + "}.tar")
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valid_url = "{}/{}-{}".format(val_base_url, "val",
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"{0.." + str(val_count) + "}.tar")
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test_url = "{}/{}-{}".format(test_base_url, "test",
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"{0.." + str(test_count) + "}.tar")
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self.train_dataset = (wds.WebDataset(
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train_url,
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handler=wds.warn_and_continue,
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nodesplitter=wds.shardlists.split_by_node).shuffle(100).decode("pil").rename(
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image="ppm;jpg;jpeg;png",
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info="cls").map_dict(image=self.train_transform).to_tuple(
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"image", "info").batched(40))
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self.valid_dataset = (wds.WebDataset(
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valid_url,
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handler=wds.warn_and_continue,
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nodesplitter=wds.shardlists.split_by_node).shuffle(100).decode("pil").rename(
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image="ppm",
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info="cls").map_dict(image=self.valid_transform).to_tuple(
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"image", "info").batched(20))
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self.test_dataset = (wds.WebDataset(
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test_url,
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handler=wds.warn_and_continue,
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nodesplitter=wds.shardlists.split_by_node).shuffle(100).decode("pil").rename(
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image="ppm",
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info="cls").map_dict(image=self.valid_transform).to_tuple(
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"image", "info").batched(20))
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def create_data_loader(self, dataset, batch_size, num_workers): # pylint: disable=no-self-use
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"""Creates data loader."""
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return DataLoader(dataset,
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batch_size=batch_size,
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num_workers=num_workers)
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def train_dataloader(self):
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"""Train Data loader.
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Returns:
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output - Train data loader for the given input
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"""
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self.train_data_loader = self.create_data_loader(
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self.train_dataset,
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self.args.get("train_batch_size", None),
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self.args.get("train_num_workers", 4),
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)
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return self.train_data_loader
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def val_dataloader(self):
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"""Validation Data Loader.
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Returns:
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output - Validation data loader for the given input
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"""
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self.val_data_loader = self.create_data_loader(
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self.valid_dataset,
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self.args.get("val_batch_size", None),
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self.args.get("val_num_workers", 4),
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)
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return self.val_data_loader
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def test_dataloader(self):
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"""Test Data Loader.
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Returns:
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output - Test data loader for the given input
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"""
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self.test_data_loader = self.create_data_loader(
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self.test_dataset,
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self.args.get("val_batch_size", None),
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self.args.get("val_num_workers", 4),
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)
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return self.test_data_loader
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