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dataloader.py
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dataloader.py
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# Copyright 2019-2020 Stanislav Pidhorskyi
#
# 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 dareblopy as db
import random
import numpy as np
import torch
import torch.tensor
import torch.utils
import torch.utils.data
import time
import math
cpu = torch.device('cpu')
class TFRecordsDataset:
def __init__(self, cfg, logger, rank=0, world_size=1, buffer_size_mb=200, channels=3, seed=None, train=True, needs_labels=False):
self.cfg = cfg
self.logger = logger
self.rank = rank
self.last_data = ""
if train:
self.part_count = cfg.DATASET.PART_COUNT
self.part_size = cfg.DATASET.SIZE // self.part_count
else:
self.part_count = cfg.DATASET.PART_COUNT_TEST
self.part_size = cfg.DATASET.SIZE_TEST // self.part_count
self.workers = []
self.workers_active = 0
self.iterator = None
self.filenames = {}
self.batch_size = 512
self.features = {}
self.channels = channels
self.seed = seed
self.train = train
self.needs_labels = needs_labels
assert self.part_count % world_size == 0
self.part_count_local = self.part_count // world_size
if train:
path = cfg.DATASET.PATH
else:
path = cfg.DATASET.PATH_TEST
for r in range(2, cfg.DATASET.MAX_RESOLUTION_LEVEL + 1):
files = []
for i in range(self.part_count_local * rank, self.part_count_local * (rank + 1)):
file = path % (r, i)
files.append(file)
self.filenames[r] = files
self.buffer_size_b = 1024 ** 2 * buffer_size_mb
self.current_filenames = []
def reset(self, lod, batch_size):
assert lod in self.filenames.keys()
self.current_filenames = self.filenames[lod]
self.batch_size = batch_size
img_size = 2 ** lod
self.features = {
# 'shape': db.FixedLenFeature([3], db.int64),
'rgb': db.FixedLenFeature([self.channels, img_size, img_size], db.uint8),
'pop': db.FixedLenFeature([1, img_size, img_size], db.uint8)
}
buffer_size = self.buffer_size_b // (self.channels * img_size * img_size)
if self.seed is None:
seed = np.uint64(time.time() * 1000)
else:
seed = self.seed
self.logger.info('!' * 80)
self.logger.info('! Seed is used for to shuffle data in TFRecordsDataset!')
self.logger.info('!' * 80)
self.iterator = db.ParsedTFRecordsDatasetIterator(self.current_filenames, self.features, self.batch_size, buffer_size, seed=seed)
def __iter__(self):
return self.iterator
def __len__(self):
return self.part_count_local * self.part_size
def make_dataloader(cfg, logger, dataset, GPU_batch_size, local_rank, numpy=False):
class BatchCollator(object):
def __init__(self, device=torch.device("cpu")):
self.device = device
self.flip = cfg.DATASET.FLIP_IMAGES
self.numpy = numpy
def __call__(self, batch):
with torch.no_grad():
x, pop = batch
if self.flip:
flips = [(slice(None, None, None), slice(None, None, None), slice(None, None, random.choice([-1, None]))) for _ in range(x.shape[0])]
x = np.array([img[flip] for img, flip in zip(x, flips)])
pop = np.array([img[flip] for img, flip in zip(pop, flips)])
pop_fake_1 = np.rot90(pop, 1, (2, 3)).copy()
pop_fake_2 = np.rot90(pop, 3, (2, 3)).copy()
if self.numpy:
return x, pop, pop_fake_1, pop_fake_2
x = torch.tensor(x, requires_grad=True, device=torch.device(self.device), dtype=torch.float32)
pop = torch.tensor(pop, requires_grad=True, device=torch.device(self.device), dtype=torch.float32)
pop_fake_1 = torch.tensor(pop_fake_1, requires_grad=True, device=torch.device(self.device), dtype=torch.float32)
pop_fake_2 = torch.tensor(pop_fake_2, requires_grad=True, device=torch.device(self.device), dtype=torch.float32)
return x, pop, pop_fake_1, pop_fake_2
batches = db.data_loader(iter(dataset), BatchCollator(local_rank), len(dataset) // GPU_batch_size)
return batches
def make_dataloader_y(cfg, logger, dataset, GPU_batch_size, local_rank):
class BatchCollator(object):
def __init__(self, device=torch.device("cpu")):
self.device = device
self.flip = cfg.DATASET.FLIP_IMAGES
def __call__(self, batch):
with torch.no_grad():
x, y = batch
if self.flip:
flips = [(slice(None, None, None), slice(None, None, None), slice(None, None, random.choice([-1, None]))) for _ in range(x.shape[0])]
x = np.array([img[flip] for img, flip in zip(x, flips)])
x = torch.tensor(x, requires_grad=True, device=torch.device(self.device), dtype=torch.float32)
return x, y
batches = db.data_loader(iter(dataset), BatchCollator(local_rank), len(dataset) // GPU_batch_size)
return batches
class TFRecordsDatasetImageNet:
def __init__(self, cfg, logger, rank=0, world_size=1, buffer_size_mb=200, channels=3, seed=None, train=True, needs_labels=False):
self.cfg = cfg
self.logger = logger
self.rank = rank
self.last_data = ""
self.part_count = cfg.DATASET.PART_COUNT
if train:
self.part_size = cfg.DATASET.SIZE // cfg.DATASET.PART_COUNT
else:
self.part_size = cfg.DATASET.SIZE_TEST // cfg.DATASET.PART_COUNT
self.workers = []
self.workers_active = 0
self.iterator = None
self.filenames = {}
self.batch_size = 512
self.features = {}
self.channels = channels
self.seed = seed
self.train = train
self.needs_labels = needs_labels
assert self.part_count % world_size == 0
self.part_count_local = cfg.DATASET.PART_COUNT // world_size
if train:
path = cfg.DATASET.PATH
else:
path = cfg.DATASET.PATH_TEST
for r in range(2, cfg.DATASET.MAX_RESOLUTION_LEVEL + 1):
files = []
for i in range(self.part_count_local * rank, self.part_count_local * (rank + 1)):
file = path % (r, i)
files.append(file)
self.filenames[r] = files
self.buffer_size_b = 1024 ** 2 * buffer_size_mb
self.current_filenames = []
def reset(self, lod, batch_size):
assert lod in self.filenames.keys()
self.current_filenames = self.filenames[lod]
self.batch_size = batch_size
if self.train:
img_size = 2 ** lod + 2 ** (lod - 3)
else:
img_size = 2 ** lod
if self.needs_labels:
self.features = {
'data': db.FixedLenFeature([3, img_size, img_size], db.uint8),
'label': db.FixedLenFeature([], db.int64)
}
else:
self.features = {
'data': db.FixedLenFeature([3, img_size, img_size], db.uint8)
}
buffer_size = self.buffer_size_b // (self.channels * img_size * img_size)
if self.seed is None:
seed = np.uint64(time.time() * 1000)
else:
seed = self.seed
self.logger.info('!' * 80)
self.logger.info('! Seed is used for to shuffle data in TFRecordsDataset!')
self.logger.info('!' * 80)
self.iterator = db.ParsedTFRecordsDatasetIterator(self.current_filenames, self.features, self.batch_size, buffer_size, seed=seed)
def __iter__(self):
return self.iterator
def __len__(self):
return self.part_count_local * self.part_size
def make_imagenet_dataloader(cfg, logger, dataset, GPU_batch_size, target_size, local_rank, do_random_crops=True):
class BatchCollator(object):
def __init__(self, device=torch.device("cpu")):
self.device = device
self.flip = cfg.DATASET.FLIP_IMAGES
self.size = target_size
p = math.log2(target_size)
self.source_size = 2 ** p + 2 ** (p - 3)
self.do_random_crops = do_random_crops
def __call__(self, batch):
with torch.no_grad():
x, = batch
if self.do_random_crops:
images = []
for im in x:
deltax = self.source_size - target_size
deltay = self.source_size - target_size
offx = np.random.randint(deltax + 1)
offy = np.random.randint(deltay + 1)
im = im[:, offy:offy + self.size, offx:offx + self.size]
images.append(im)
x = np.stack(images)
if self.flip:
flips = [(slice(None, None, None), slice(None, None, None), slice(None, None, random.choice([-1, None]))) for _ in range(x.shape[0])]
x = np.array([img[flip] for img, flip in zip(x, flips)])
x = torch.tensor(x, requires_grad=True, device=torch.device(self.device), dtype=torch.float32)
return x
batches = db.data_loader(iter(dataset), BatchCollator(local_rank), len(dataset) // GPU_batch_size)
return batches
def make_imagenet_dataloader_y(cfg, logger, dataset, GPU_batch_size, target_size, local_rank, do_random_crops=True):
class BatchCollator(object):
def __init__(self, device=torch.device("cpu")):
self.device = device
self.flip = cfg.DATASET.FLIP_IMAGES
self.size = target_size
p = math.log2(target_size)
self.source_size = 2 ** p + 2 ** (p - 3)
self.do_random_crops = do_random_crops
def __call__(self, batch):
with torch.no_grad():
x, y = batch
if self.do_random_crops:
images = []
for im in x:
deltax = self.source_size - target_size
deltay = self.source_size - target_size
offx = np.random.randint(deltax + 1)
offy = np.random.randint(deltay + 1)
im = im[:, offy:offy+self.size, offx:offx+self.size]
images.append(im)
x = np.stack(images)
if self.flip:
flips = [(slice(None, None, None), slice(None, None, None), slice(None, None, random.choice([-1, None]))) for _ in range(x.shape[0])]
x = np.array([img[flip] for img, flip in zip(x, flips)])
x = torch.tensor(x, requires_grad=True, device=torch.device(self.device), dtype=torch.float32)
return x, y
batches = db.data_loader(iter(dataset), BatchCollator(local_rank), len(dataset) // GPU_batch_size)
return batches