| import math |
| import sys |
| import errno |
| import os |
| import ctypes |
| import signal |
| import torch |
| import time |
| import traceback |
| import unittest |
| import subprocess |
| from torch import multiprocessing as mp |
| from torch.utils.data import Dataset, TensorDataset, DataLoader, ConcatDataset |
| from torch.utils.data.dataset import random_split |
| from torch.utils.data.dataloader import default_collate, ExceptionWrapper, MANAGER_STATUS_CHECK_INTERVAL |
| from common import TestCase, run_tests, TEST_NUMPY, IS_WINDOWS, NO_MULTIPROCESSING_SPAWN, skipIfRocm |
| |
| # We cannot import TEST_CUDA from common_nn here, because if we do that, |
| # the TEST_CUDNN line from common_nn will be executed multiple times |
| # as well during the execution of this test suite, and it will cause |
| # CUDA OOM error on Windows. |
| TEST_CUDA = torch.cuda.is_available() |
| |
| # We need spawn start method for test_manager_unclean_exit, but |
| # Python 2.7 doesn't allow it. |
| if sys.version_info[0] == 3: |
| # Get a multiprocessing context because some test / third party library will |
| # set start_method when imported, and setting again triggers RuntimeError. |
| mp = mp.get_context(method='spawn') |
| |
| |
| JOIN_TIMEOUT = 17.0 if IS_WINDOWS else 6.5 |
| |
| |
| class TestDatasetRandomSplit(TestCase): |
| def test_lengths_must_equal_datset_size(self): |
| with self.assertRaises(ValueError): |
| random_split([1, 2, 3, 4], [1, 2]) |
| |
| def test_splits_have_correct_size(self): |
| splits = random_split([1, 2, 3, 4, 5, 6], [2, 4]) |
| self.assertEqual(len(splits), 2) |
| self.assertEqual(len(splits[0]), 2) |
| self.assertEqual(len(splits[1]), 4) |
| |
| def test_splits_are_mutually_exclusive(self): |
| data = [5, 2, 3, 4, 1, 6] |
| splits = random_split(data, [2, 4]) |
| all_values = [] |
| all_values.extend(list(splits[0])) |
| all_values.extend(list(splits[1])) |
| data.sort() |
| all_values.sort() |
| self.assertListEqual(data, all_values) |
| |
| |
| class TestTensorDataset(TestCase): |
| |
| def test_len(self): |
| source = TensorDataset(torch.randn(15, 10, 2, 3, 4, 5), torch.randperm(15)) |
| self.assertEqual(len(source), 15) |
| |
| def test_getitem(self): |
| t = torch.randn(15, 10, 2, 3, 4, 5) |
| l = torch.randn(15, 10) |
| source = TensorDataset(t, l) |
| for i in range(15): |
| self.assertEqual(t[i], source[i][0]) |
| self.assertEqual(l[i], source[i][1]) |
| |
| def test_getitem_1d(self): |
| t = torch.randn(15) |
| l = torch.randn(15) |
| source = TensorDataset(t, l) |
| for i in range(15): |
| self.assertEqual(t[i], source[i][0]) |
| self.assertEqual(l[i], source[i][1]) |
| |
| def test_single_tensor(self): |
| t = torch.randn(5, 10) |
| source = TensorDataset(t) |
| self.assertEqual(len(source), 5) |
| for i in range(5): |
| self.assertEqual(t[i], source[i][0]) |
| |
| def test_many_tensors(self): |
| t0 = torch.randn(5, 10, 2, 3, 4, 5) |
| t1 = torch.randn(5, 10) |
| t2 = torch.randn(5, 10, 2, 5) |
| t3 = torch.randn(5, 10, 3, 7) |
| source = TensorDataset(t0, t1, t2, t3) |
| self.assertEqual(len(source), 5) |
| for i in range(5): |
| self.assertEqual(t0[i], source[i][0]) |
| self.assertEqual(t1[i], source[i][1]) |
| self.assertEqual(t2[i], source[i][2]) |
| self.assertEqual(t3[i], source[i][3]) |
| |
| |
| class TestConcatDataset(TestCase): |
| |
| def test_concat_two_singletons(self): |
| result = ConcatDataset([[0], [1]]) |
| self.assertEqual(2, len(result)) |
| self.assertEqual(0, result[0]) |
| self.assertEqual(1, result[1]) |
| |
| def test_concat_two_non_singletons(self): |
| result = ConcatDataset([[0, 1, 2, 3, 4], |
| [5, 6, 7, 8, 9]]) |
| self.assertEqual(10, len(result)) |
| self.assertEqual(0, result[0]) |
| self.assertEqual(5, result[5]) |
| |
| def test_concat_two_non_singletons_with_empty(self): |
| # Adding an empty dataset somewhere is correctly handled |
| result = ConcatDataset([[0, 1, 2, 3, 4], |
| [], |
| [5, 6, 7, 8, 9]]) |
| self.assertEqual(10, len(result)) |
| self.assertEqual(0, result[0]) |
| self.assertEqual(5, result[5]) |
| |
| def test_concat_raises_index_error(self): |
| result = ConcatDataset([[0, 1, 2, 3, 4], |
| [5, 6, 7, 8, 9]]) |
| with self.assertRaises(IndexError): |
| # this one goes to 11 |
| result[11] |
| |
| def test_add_dataset(self): |
| d1 = TensorDataset(torch.rand(7, 3, 28, 28), torch.rand(7)) |
| d2 = TensorDataset(torch.rand(7, 3, 28, 28), torch.rand(7)) |
| d3 = TensorDataset(torch.rand(7, 3, 28, 28), torch.rand(7)) |
| result = d1 + d2 + d3 |
| self.assertEqual(21, len(result)) |
| self.assertEqual(0, (d1[0][0] - result[0][0]).abs().sum()) |
| self.assertEqual(0, (d2[0][0] - result[7][0]).abs().sum()) |
| self.assertEqual(0, (d3[0][0] - result[14][0]).abs().sum()) |
| |
| |
| # Stores the first encountered exception in .exception. |
| # Inspired by https://stackoverflow.com/a/33599967 |
| class ErrorTrackingProcess(mp.Process): |
| |
| def __init__(self, *args, **kwargs): |
| super(ErrorTrackingProcess, self).__init__(*args, **kwargs) |
| self._pconn, self._cconn = mp.Pipe() |
| self._exception = None |
| |
| def run(self): |
| # Disable stderr printing from os level, and make workers not printing |
| # to stderr. |
| # Can't use sys.stderr.close, otherwise Python `raise` will error with |
| # ValueError: I/O operation on closed file. |
| os.close(sys.stderr.fileno()) |
| try: |
| super(ErrorTrackingProcess, self).run() |
| self._cconn.send(None) |
| except Exception as e: |
| self._cconn.send(ExceptionWrapper(sys.exc_info())) |
| raise |
| |
| @property |
| def exception(self): |
| if self._pconn.poll(): |
| self._exception = self._pconn.recv() |
| if self._exception is None: |
| return None |
| else: |
| return self._exception.exc_type(self._exception.exc_msg) |
| |
| # ESRCH means that os.kill can't finds alive proc |
| def send_signal(self, signum, ignore_ESRCH=False): |
| try: |
| os.kill(self.pid, signum) |
| except OSError as e: |
| if not ignore_ESRCH or e.errno != errno.ESRCH: |
| raise |
| |
| |
| class ErrorDataset(Dataset): |
| |
| def __init__(self, size): |
| self.size = size |
| |
| def __len__(self): |
| return self.size |
| |
| |
| class SegfaultDataset(Dataset): |
| |
| def __init__(self, size): |
| self.size = size |
| |
| def __getitem__(self, idx): |
| return ctypes.string_at(0) |
| |
| def __len__(self): |
| return self.size |
| |
| |
| class SleepDataset(Dataset): |
| |
| def __init__(self, size, sleep_sec): |
| self.size = size |
| self.sleep_sec = sleep_sec |
| self.sleeped = False |
| |
| def __getitem__(self, idx): |
| if not self.sleeped: |
| time.sleep(self.sleep_sec) |
| self.sleeped = True |
| return idx |
| |
| def __len__(self): |
| return self.size |
| |
| |
| class SeedDataset(Dataset): |
| |
| def __init__(self, size): |
| self.size = size |
| |
| def __getitem__(self, idx): |
| return torch.initial_seed() |
| |
| def __len__(self): |
| return self.size |
| |
| |
| # Inspired by https://stackoverflow.com/a/26703365 |
| # This will ensure that each worker at least processes one data |
| class SynchronizedSeedDataset(Dataset): |
| |
| def __init__(self, size, num_workers): |
| assert size >= num_workers |
| self.count = mp.Value('i', 0, lock=True) |
| self.barrier = mp.Semaphore(0) |
| self.num_workers = num_workers |
| self.size = size |
| |
| def __getitem__(self, idx): |
| with self.count.get_lock(): |
| self.count.value += 1 |
| if self.count.value == self.num_workers: |
| self.barrier.release() |
| self.barrier.acquire() |
| self.barrier.release() |
| return torch.initial_seed() |
| |
| def __len__(self): |
| return self.size |
| |
| |
| def _test_timeout(): |
| dataset = SleepDataset(10, 3) |
| dataloader = DataLoader(dataset, batch_size=2, num_workers=2, timeout=1) |
| _ = next(iter(dataloader)) |
| |
| |
| def _test_segfault(): |
| dataset = SegfaultDataset(10) |
| dataloader = DataLoader(dataset, batch_size=2, num_workers=2) |
| _ = next(iter(dataloader)) |
| |
| |
| # test custom init function |
| def init_fn(worker_id): |
| torch.manual_seed(12345) |
| |
| |
| class TestDataLoader(TestCase): |
| |
| def setUp(self): |
| self.data = torch.randn(100, 2, 3, 5) |
| self.labels = torch.randperm(50).repeat(2) |
| self.dataset = TensorDataset(self.data, self.labels) |
| |
| def _test_sequential(self, loader): |
| batch_size = loader.batch_size |
| for i, (sample, target) in enumerate(loader): |
| idx = i * batch_size |
| self.assertEqual(sample, self.data[idx:idx + batch_size]) |
| self.assertEqual(target, self.labels[idx:idx + batch_size]) |
| self.assertEqual(i, math.floor((len(self.dataset) - 1) / batch_size)) |
| |
| def _test_shuffle(self, loader): |
| found_data = {i: 0 for i in range(self.data.size(0))} |
| found_labels = {i: 0 for i in range(self.labels.size(0))} |
| batch_size = loader.batch_size |
| for i, (batch_samples, batch_targets) in enumerate(loader): |
| for sample, target in zip(batch_samples, batch_targets): |
| for data_point_idx, data_point in enumerate(self.data): |
| if data_point.eq(sample).all(): |
| self.assertFalse(found_data[data_point_idx]) |
| found_data[data_point_idx] += 1 |
| break |
| self.assertEqual(target, self.labels[data_point_idx]) |
| found_labels[data_point_idx] += 1 |
| self.assertEqual(sum(found_data.values()), (i + 1) * batch_size) |
| self.assertEqual(sum(found_labels.values()), (i + 1) * batch_size) |
| self.assertEqual(i, math.floor((len(self.dataset) - 1) / batch_size)) |
| |
| def _test_error(self, loader): |
| it = iter(loader) |
| errors = 0 |
| while True: |
| try: |
| next(it) |
| except NotImplementedError: |
| errors += 1 |
| except StopIteration: |
| self.assertEqual(errors, |
| math.ceil(float(len(loader.dataset)) / loader.batch_size)) |
| return |
| |
| def test_invalid_assign_after_init(self): |
| dl = DataLoader(self.dataset) |
| for attr in ('batch_size', 'sampler', 'drop_last'): |
| def fn(): |
| setattr(dl, attr, {}) |
| |
| self.assertRaises(ValueError, fn) |
| |
| def test_sequential(self): |
| self._test_sequential(DataLoader(self.dataset)) |
| |
| def test_sequential_batch(self): |
| self._test_sequential(DataLoader(self.dataset, batch_size=2)) |
| |
| def test_growing_dataset(self): |
| dataset = [torch.ones(4) for _ in range(4)] |
| dataloader_seq = DataLoader(dataset, shuffle=False) |
| dataloader_shuffle = DataLoader(dataset, shuffle=True) |
| dataset.append(torch.ones(4)) |
| self.assertEqual(len(dataloader_seq), 5) |
| self.assertEqual(len(dataloader_shuffle), 5) |
| |
| @unittest.skipIf(not TEST_CUDA, "CUDA unavailable") |
| def test_sequential_pin_memory(self): |
| loader = DataLoader(self.dataset, batch_size=2, pin_memory=True) |
| for input, target in loader: |
| self.assertTrue(input.is_pinned()) |
| self.assertTrue(target.is_pinned()) |
| |
| def test_multiple_dataloaders(self): |
| loader1_it = iter(DataLoader(self.dataset, num_workers=1)) |
| loader2_it = iter(DataLoader(self.dataset, num_workers=2)) |
| next(loader1_it) |
| next(loader1_it) |
| next(loader2_it) |
| next(loader2_it) |
| next(loader1_it) |
| next(loader2_it) |
| |
| @unittest.skip("temporarily disable until flaky failures are fixed") |
| def test_segfault(self): |
| p = ErrorTrackingProcess(target=_test_segfault) |
| p.start() |
| p.join(JOIN_TIMEOUT) |
| try: |
| self.assertFalse(p.is_alive()) |
| self.assertNotEqual(p.exitcode, 0) |
| if IS_WINDOWS: |
| self.assertIsInstance(p.exception, OSError) |
| self.assertRegex(str(p.exception), r'access violation reading ') |
| else: |
| self.assertIsInstance(p.exception, RuntimeError) |
| self.assertRegex(str(p.exception), r'DataLoader worker \(pid \d+\) is killed by signal: ') |
| finally: |
| p.terminate() |
| |
| def test_timeout(self): |
| p = ErrorTrackingProcess(target=_test_timeout) |
| p.start() |
| p.join(JOIN_TIMEOUT) |
| try: |
| self.assertFalse(p.is_alive()) |
| self.assertNotEqual(p.exitcode, 0) |
| self.assertIsInstance(p.exception, RuntimeError) |
| self.assertRegex(str(p.exception), r'DataLoader timed out after \d+ seconds') |
| finally: |
| p.terminate() |
| |
| def test_worker_seed(self): |
| num_workers = 6 |
| dataset = SynchronizedSeedDataset(num_workers, num_workers) |
| dataloader = DataLoader(dataset, batch_size=1, num_workers=num_workers) |
| seeds = set() |
| for batch in dataloader: |
| seeds.add(batch[0]) |
| self.assertEqual(len(seeds), num_workers) |
| |
| def test_worker_init_fn(self): |
| dataset = SeedDataset(4) |
| dataloader = DataLoader(dataset, batch_size=2, num_workers=2, |
| worker_init_fn=init_fn) |
| for batch in dataloader: |
| self.assertEqual(12345, batch[0]) |
| self.assertEqual(12345, batch[1]) |
| |
| def test_shuffle(self): |
| self._test_shuffle(DataLoader(self.dataset, shuffle=True)) |
| |
| def test_shuffle_batch(self): |
| self._test_shuffle(DataLoader(self.dataset, batch_size=2, shuffle=True)) |
| |
| def test_sequential_workers(self): |
| self._test_sequential(DataLoader(self.dataset, num_workers=4)) |
| |
| def test_seqential_batch_workers(self): |
| self._test_sequential(DataLoader(self.dataset, batch_size=2, num_workers=4)) |
| |
| def test_shuffle_workers(self): |
| self._test_shuffle(DataLoader(self.dataset, shuffle=True, num_workers=4)) |
| |
| def test_shuffle_batch_workers(self): |
| self._test_shuffle(DataLoader(self.dataset, batch_size=2, shuffle=True, num_workers=4)) |
| |
| def _test_batch_sampler(self, **kwargs): |
| # [(0, 1), (2, 3, 4), (5, 6), (7, 8, 9), ...] |
| batches = [] |
| for i in range(0, 100, 5): |
| batches.append(tuple(range(i, i + 2))) |
| batches.append(tuple(range(i + 2, i + 5))) |
| |
| dl = DataLoader(self.dataset, batch_sampler=batches, **kwargs) |
| self.assertEqual(len(dl), 40) |
| for i, (input, _target) in enumerate(dl): |
| if i % 2 == 0: |
| offset = i * 5 // 2 |
| self.assertEqual(len(input), 2) |
| self.assertEqual(input, self.data[offset:offset + 2]) |
| else: |
| offset = i * 5 // 2 |
| self.assertEqual(len(input), 3) |
| self.assertEqual(input, self.data[offset:offset + 3]) |
| |
| def test_RandomSampler(self): |
| |
| from collections import Counter |
| from torch.utils.data import RandomSampler |
| |
| def sample_stat(sampler, num_samples): |
| counts = Counter(sampler) |
| count_repeated = sum(val > 1 for val in counts.values()) |
| return (count_repeated, min(counts.keys()), max(counts.keys())) |
| |
| # test sample with replacement |
| n = len(self.dataset) + 1 # ensure at least one sample is drawn more than once |
| sampler_with_replacement = RandomSampler(self.dataset, replacement=True, num_samples=n) |
| count_repeated, minval, maxval = sample_stat(sampler_with_replacement, n) |
| self.assertTrue(count_repeated > 0) |
| self.assertTrue(minval >= 0) |
| self.assertTrue(maxval < len(self.dataset)) |
| |
| # test sample without replacement |
| sampler_without_replacement = RandomSampler(self.dataset) |
| count_repeated, minval, maxval = sample_stat(sampler_without_replacement, len(self.dataset)) |
| self.assertTrue(count_repeated == 0) |
| self.assertTrue(minval == 0) |
| self.assertTrue(maxval == len(self.dataset) - 1) |
| |
| # raise error when replacement=False and num_samples is not None |
| self.assertRaises(ValueError, lambda: RandomSampler(self.dataset, num_samples=len(self.dataset))) |
| |
| self.assertRaises(ValueError, lambda: RandomSampler(self.dataset, num_samples=0)) |
| |
| @unittest.skipIf(NO_MULTIPROCESSING_SPAWN, "Disabled for environments that \ |
| don't support multiprocessing with spawn start method") |
| def test_batch_sampler(self): |
| self._test_batch_sampler() |
| self._test_batch_sampler(num_workers=4) |
| |
| @unittest.skipIf(not TEST_CUDA, "CUDA unavailable") |
| def test_shuffle_pin_memory(self): |
| loader = DataLoader(self.dataset, batch_size=2, shuffle=True, num_workers=4, pin_memory=True) |
| for input, target in loader: |
| self.assertTrue(input.is_pinned()) |
| self.assertTrue(target.is_pinned()) |
| |
| @unittest.skipIf(not TEST_NUMPY, "numpy unavailable") |
| def test_numpy(self): |
| import numpy as np |
| |
| class TestDataset(torch.utils.data.Dataset): |
| def __getitem__(self, i): |
| return np.ones((2, 3, 4)) * i |
| |
| def __len__(self): |
| return 1000 |
| |
| loader = DataLoader(TestDataset(), batch_size=12) |
| batch = next(iter(loader)) |
| self.assertIsInstance(batch, torch.DoubleTensor) |
| self.assertEqual(batch.size(), torch.Size([12, 2, 3, 4])) |
| |
| def test_error(self): |
| self._test_error(DataLoader(ErrorDataset(100), batch_size=2, shuffle=True)) |
| |
| @unittest.skipIf(NO_MULTIPROCESSING_SPAWN, "Disabled for environments that \ |
| don't support multiprocessing with spawn start method") |
| def test_error_workers(self): |
| self._test_error(DataLoader(ErrorDataset(41), batch_size=2, shuffle=True, num_workers=4)) |
| |
| @unittest.skipIf(IS_WINDOWS, "FIXME: stuck test") |
| @unittest.skipIf(not TEST_CUDA, "CUDA unavailable") |
| def test_partial_workers(self): |
| r"""Check that workers exit even if the iterator is not exhausted.""" |
| for pin_memory in (True, False): |
| loader = iter(DataLoader(self.dataset, batch_size=2, num_workers=4, pin_memory=pin_memory)) |
| workers = loader.workers |
| if pin_memory: |
| pin_memory_thread = loader.pin_memory_thread |
| for i, sample in enumerate(loader): |
| if i == 10: |
| break |
| del loader |
| for w in workers: |
| w.join(JOIN_TIMEOUT) |
| self.assertFalse(w.is_alive(), 'subprocess not terminated') |
| if pin_memory: |
| pin_memory_thread.join(JOIN_TIMEOUT) |
| self.assertFalse(pin_memory_thread.is_alive()) |
| |
| @staticmethod |
| def _main_process(dataset, worker_pids, main_exit_event, raise_error): |
| loader = iter(DataLoader(dataset, batch_size=2, num_workers=4, pin_memory=True)) |
| workers = loader.workers |
| for i in range(len(workers)): |
| worker_pids[i] = int(workers[i].pid) |
| for i, sample in enumerate(loader): |
| if i == 3: |
| # Simulate an exit of the manager process |
| main_exit_event.set() |
| if raise_error: |
| raise RuntimeError('Error') |
| else: |
| if IS_WINDOWS: |
| os.system('taskkill /PID ' + str(os.getpid()) + ' /F') |
| else: |
| os.kill(os.getpid(), signal.SIGKILL) |
| |
| @staticmethod |
| def _is_process_alive(pid, pname): |
| # There is a chance of a terminated child process's pid being reused by a new unrelated process, |
| # but since we are looping this check very frequently, we will know that the child process dies |
| # before the new unrelated process starts. |
| if IS_WINDOWS: |
| command = 'tasklist | find "{}" /i'.format(pid) |
| else: |
| command = 'ps -p {} -o comm='.format(pid) |
| p = subprocess.Popen(command, stdout=subprocess.PIPE, shell=True) |
| (output, err) = p.communicate() |
| p_status = p.wait() |
| output = output.decode('utf-8') |
| return pname in output |
| |
| @unittest.skipIf(NO_MULTIPROCESSING_SPAWN, "Disabled for environments that \ |
| don't support multiprocessing with spawn start method") |
| @unittest.skipIf(sys.version_info[0] == 2, |
| "spawn start method is not supported in Python 2, \ |
| but we need it for creating another process with CUDA") |
| @unittest.skipIf(not TEST_CUDA, "CUDA unavailable") |
| @skipIfRocm |
| def test_main_process_unclean_exit(self): |
| r'''There might be ConnectionResetError or leaked semaphore warning (due to dirty process exit), \ |
| but they are all safe to ignore''' |
| |
| # `raise_error` controls if the main process is KILL-ed by OS or just |
| # simply raises an error. Both cases are interesting because |
| # 1. In case of it is KILL-ed by OS, the workers need to automatically |
| # discover that their parent is dead and exit gracefully. |
| # 2. In case of it raises an error itself, the parent process needs to |
| # take care of exiting the worker and then exits itself gracefully. |
| for raise_error in (True, False): |
| worker_pids = mp.Array('i', [0] * 4) |
| |
| main_exit_event = mp.Event() |
| p = mp.Process(target=TestDataLoader._main_process, |
| args=(self.dataset, worker_pids, main_exit_event, raise_error)) |
| p.start() |
| worker_pids[-1] = p.pid |
| |
| main_exit_event.wait() |
| |
| exit_status = [False] * len(worker_pids) |
| start_time = time.time() |
| pname = 'python' |
| while True: |
| for i in range(len(worker_pids)): |
| pid = worker_pids[i] |
| if not exit_status[i]: |
| if not TestDataLoader._is_process_alive(pid, pname): |
| exit_status[i] = True |
| if all(exit_status): |
| break |
| else: |
| if time.time() - start_time > MANAGER_STATUS_CHECK_INTERVAL + JOIN_TIMEOUT: |
| self.fail('subprocess not terminated') |
| time.sleep(1) |
| p.join(MANAGER_STATUS_CHECK_INTERVAL + JOIN_TIMEOUT - (time.time() - start_time)) |
| self.assertFalse(p.is_alive(), 'main process not terminated') |
| |
| def test_len(self): |
| def check_len(dl, expected): |
| self.assertEqual(len(dl), expected) |
| n = 0 |
| for sample in dl: |
| n += 1 |
| self.assertEqual(n, expected) |
| check_len(self.dataset, 100) |
| check_len(DataLoader(self.dataset, batch_size=2), 50) |
| check_len(DataLoader(self.dataset, batch_size=3), 34) |
| |
| @unittest.skipIf(not TEST_NUMPY, "numpy unavailable") |
| def test_numpy_scalars(self): |
| import numpy as np |
| |
| class ScalarDataset(torch.utils.data.Dataset): |
| def __init__(self, dtype): |
| self.dtype = dtype |
| |
| def __getitem__(self, i): |
| return self.dtype() |
| |
| def __len__(self): |
| return 4 |
| |
| dtypes = { |
| np.float64: torch.DoubleTensor, |
| np.float32: torch.FloatTensor, |
| np.float16: torch.HalfTensor, |
| np.int64: torch.LongTensor, |
| np.int32: torch.IntTensor, |
| np.int16: torch.ShortTensor, |
| np.int8: torch.CharTensor, |
| np.uint8: torch.ByteTensor, |
| } |
| for dt, tt in dtypes.items(): |
| dset = ScalarDataset(dt) |
| loader = DataLoader(dset, batch_size=2) |
| batch = next(iter(loader)) |
| self.assertIsInstance(batch, tt) |
| |
| @unittest.skipIf(not TEST_NUMPY, "numpy unavailable") |
| def test_default_collate_bad_numpy_types(self): |
| import numpy as np |
| |
| # Should be a no-op |
| arr = np.array(['a', 'b', 'c']) |
| default_collate(arr) |
| |
| arr = np.array([[['a', 'b', 'c']]]) |
| self.assertRaises(TypeError, lambda: default_collate(arr)) |
| |
| arr = np.array([object(), object(), object()]) |
| self.assertRaises(TypeError, lambda: default_collate(arr)) |
| |
| arr = np.array([[[object(), object(), object()]]]) |
| self.assertRaises(TypeError, lambda: default_collate(arr)) |
| |
| |
| class StringDataset(Dataset): |
| def __init__(self): |
| self.s = '12345' |
| |
| def __len__(self): |
| return len(self.s) |
| |
| def __getitem__(self, ndx): |
| return (self.s[ndx], ndx) |
| |
| |
| class TestStringDataLoader(TestCase): |
| def setUp(self): |
| self.dataset = StringDataset() |
| |
| @unittest.skipIf(not TEST_CUDA, "CUDA unavailable") |
| def test_shuffle_pin_memory(self): |
| loader = DataLoader(self.dataset, batch_size=2, shuffle=True, num_workers=4, pin_memory=True) |
| for batch_ndx, (s, n) in enumerate(loader): |
| self.assertIsInstance(s[0], str) |
| self.assertTrue(n.is_pinned()) |
| |
| |
| class DictDataset(Dataset): |
| def __len__(self): |
| return 4 |
| |
| def __getitem__(self, ndx): |
| return { |
| 'a_tensor': torch.Tensor(4, 2).fill_(ndx), |
| 'another_dict': { |
| 'a_number': ndx, |
| }, |
| } |
| |
| |
| class TestDictDataLoader(TestCase): |
| def setUp(self): |
| self.dataset = DictDataset() |
| |
| def test_sequential_batch(self): |
| loader = DataLoader(self.dataset, batch_size=2, shuffle=False) |
| batch_size = loader.batch_size |
| for i, sample in enumerate(loader): |
| idx = i * batch_size |
| self.assertEqual(set(sample.keys()), {'a_tensor', 'another_dict'}) |
| self.assertEqual(set(sample['another_dict'].keys()), {'a_number'}) |
| |
| t = sample['a_tensor'] |
| self.assertEqual(t.size(), torch.Size([batch_size, 4, 2])) |
| self.assertTrue((t[0] == idx).all()) |
| self.assertTrue((t[1] == idx + 1).all()) |
| |
| n = sample['another_dict']['a_number'] |
| self.assertEqual(n.size(), torch.Size([batch_size])) |
| self.assertEqual(n[0], idx) |
| self.assertEqual(n[1], idx + 1) |
| |
| @unittest.skipIf(not TEST_CUDA, "CUDA unavailable") |
| def test_pin_memory(self): |
| loader = DataLoader(self.dataset, batch_size=2, pin_memory=True) |
| for batch_ndx, sample in enumerate(loader): |
| self.assertTrue(sample['a_tensor'].is_pinned()) |
| self.assertTrue(sample['another_dict']['a_number'].is_pinned()) |
| |
| |
| class TestWorkerQueueDataset(Dataset): |
| def __init__(self, data): |
| self.data = data |
| self.worker_id = None |
| |
| def worker_init_fn(self, worker_id): |
| self.worker_id = worker_id |
| |
| def __getitem__(self, item): |
| return self.worker_id, self.data[item] |
| |
| def __len__(self): |
| return len(self.data) |
| |
| |
| class TestIndividualWorkerQueue(TestCase): |
| def setUp(self): |
| self.dataset = TestWorkerQueueDataset([i for i in range(128)]) |
| |
| def _run_ind_worker_queue_test(self, batch_size, num_workers): |
| loader = DataLoader( |
| self.dataset, batch_size=batch_size, shuffle=False, num_workers=num_workers, |
| worker_init_fn=self.dataset.worker_init_fn |
| ) |
| current_worker_idx = 0 |
| for i, (worker_ids, sample) in enumerate(loader): |
| self.assertEqual(worker_ids.tolist(), [current_worker_idx] * batch_size) |
| self.assertEqual(sample.tolist(), [j for j in range(i * batch_size, (i + 1) * batch_size)]) |
| current_worker_idx += 1 |
| if current_worker_idx == num_workers: |
| current_worker_idx = 0 |
| |
| def test_ind_worker_queue(self): |
| for batch_size in (8, 16, 32, 64): |
| for num_workers in range(1, 6): |
| self._run_ind_worker_queue_test(batch_size=batch_size, num_workers=num_workers) |
| |
| |
| if __name__ == '__main__': |
| run_tests() |
