def test_multiple_dataset_loader(self, repo_with_20_samples):
repo = repo_with_20_samples
co = repo.checkout(write=True)
second_aset = co.arraysets['second_aset']
del second_aset['10']
co.commit('deleting')
co.close()
co = repo.checkout()
first_aset = co.arraysets['writtenaset']
second_aset = co.arraysets['second_aset']
with pytest.raises(ValueError):
# emtpy list
make_torch_dataset([])
with pytest.raises(TypeError):
# if more than one dataset, those should be in a list/tuple
make_torch_dataset(first_aset, first_aset)
with pytest.warns(UserWarning, match='Arraysets do not contain equal number of samples'):
torch_dset = make_torch_dataset([first_aset, second_aset])
loader = DataLoader(torch_dset, batch_size=6, drop_last=True)
total_samples = 0
for dset1, dset2 in loader:
total_samples += dset1.shape[0]
assert dset1.shape == (6, 5, 7)
assert dset2.shape == (6, 5, 7)
assert total_samples == 18 # drop last is True
co.close()
def test_field_names(self, repo_with_20_samples):
repo = repo_with_20_samples
co = repo.checkout()
first_aset = co.arraysets['writtenaset']
second_aset = co.arraysets['second_aset']
with pytest.raises(
ValueError): # number of dsets and field_names are different
make_torch_dataset([first_aset, second_aset],
field_names=('input', ))
with pytest.raises(TypeError): # field_names's type is wrong
make_torch_dataset([first_aset, second_aset],
field_names={
'input': '',
'target': ''
})
torch_dset = make_torch_dataset([first_aset, second_aset],
field_names=('input', 'target'))
assert hasattr(torch_dset[1], 'input')
assert hasattr(torch_dset[1], 'target')
if torch.__version__ > '1.0.1':
loader = DataLoader(torch_dset, batch_size=5)
for sample in loader:
assert hasattr(sample, 'input')
assert hasattr(sample, 'target')
co.close()
def test_dataset_loader_fails_with_write_enabled_checkout(self, repo_with_20_samples):
repo = repo_with_20_samples
co = repo.checkout(write=True)
first_aset = co.arraysets['writtenaset']
second_aset = co.arraysets['second_aset']
with pytest.raises(TypeError):
make_torch_dataset([first_aset, second_aset])
co.close()
def test_warns_experimental(self, repo_with_20_samples):
repo = repo_with_20_samples
co = repo.checkout()
first_aset = co.arraysets['writtenaset']
second_aset = co.arraysets['second_aset']
with pytest.warns(UserWarning, match='Dataloaders are experimental'):
make_torch_dataset([first_aset, second_aset])
co.close()
def test_warns_arrayset_sample_size_mismatch(self, repo_with_20_samples):
repo = repo_with_20_samples
co = repo.checkout(write=True)
second_aset = co.arraysets['second_aset']
del second_aset['10']
co.commit('deleting')
co.close()
co = repo.checkout()
first_aset = co.arraysets['writtenaset']
second_aset = co.arraysets['second_aset']
with pytest.warns(UserWarning, match='Arraysets do not contain equal number of samples'):
make_torch_dataset([first_aset, second_aset])
co.close()
def test_with_keys(self, repo_with_20_samples):
repo = repo_with_20_samples
co = repo.checkout()
aset = co.arraysets['writtenaset']
# with keys
keys = ['2', '4', '5', '6', '7', '9', '15', '18', '19']
bad_tensor0 = aset['0']
bad_tensor1 = aset['1']
bad_tensor3 = aset['3']
bad_tensor8 = aset['8']
torch_dset = make_torch_dataset(aset, keys=keys)
loader = DataLoader(torch_dset, batch_size=3)
total_batches = 0
for batch in loader:
assert batch[0].size(0) == 3
total_batches += 1
for sample in batch:
assert not np.allclose(sample, bad_tensor0)
assert not np.allclose(sample, bad_tensor1)
assert not np.allclose(sample, bad_tensor3)
assert not np.allclose(sample, bad_tensor8)
assert total_batches == 3
co.close()
def test_field_names(self, repo_with_20_samples):
repo = repo_with_20_samples
co = repo.checkout()
first_aset = co.arraysets['writtenaset']
second_aset = co.arraysets['second_aset']
with pytest.raises(ValueError): # number of dsets and field_names are different
make_torch_dataset([first_aset, second_aset], field_names=('input',))
with pytest.raises(TypeError): # field_names's type is wrong
make_torch_dataset([first_aset, second_aset], field_names={'input': '', 'target': ''})
torch_dset = make_torch_dataset([first_aset, second_aset], field_names=('input', 'target'))
assert len(torch_dset) == 20
loader = DataLoader(torch_dset, batch_size=5)
for sample in loader:
assert type(sample).__name__ == 'BatchTuple_input_target'
assert sample._fields == ('input', 'target')
co.close()
def test_lots_of_data_with_multiple_backend(self, repo_with_10000_samples):
repo = repo_with_10000_samples
co = repo.checkout()
aset = co.arraysets['aset']
torch_dset = make_torch_dataset([aset])
loader = DataLoader(torch_dset, batch_size=1000, drop_last=True)
for data in loader:
assert data.aset.shape == (1000, 5, 7)
co.close()
def test_lots_of_data_with_multiple_backend_multiple_worker_dataloader(self, repo_with_10000_samples):
repo = repo_with_10000_samples
co = repo.checkout()
aset = co.arraysets['aset']
torch_dset = make_torch_dataset([aset])
loader = DataLoader(torch_dset, batch_size=1000, drop_last=True, num_workers=2)
for data in loader:
assert type(data).__name__ == 'BatchTuple_aset'
assert data.aset.shape == (1000, 5, 7)
co.close()
def test_lots_of_data_with_multiple_backend(self, repo_300_filled_samples):
repo = repo_300_filled_samples
co = repo.checkout()
aset = co.columns['aset']
torch_dset = make_torch_dataset([aset])
loader = DataLoader(torch_dset, batch_size=10, drop_last=True)
for data in loader:
assert type(data).__name__ == 'BatchTuple_aset'
assert data.aset.shape == (10, 5, 7)
co.close()
def test_local_without_data_fails_data_unavailable(self, written_two_cmt_server_repo, managed_tmpdir):
new_tmpdir = pjoin(managed_tmpdir, 'new')
mkdir(new_tmpdir)
server, _ = written_two_cmt_server_repo
repo = Repository(path=new_tmpdir, exists=False)
repo.clone('name', '[email protected]', server, remove_old=True)
co = repo.checkout()
aset = co.arraysets['writtenaset']
with pytest.raises(FileNotFoundError):
torch_dset = make_torch_dataset(aset, keys=['1', '2'])
co.close()
repo._env._close_environments()
def test_local_without_data_fails_no_common_no_local(
self, written_two_cmt_server_repo, managed_tmpdir):
new_tmpdir = pjoin(managed_tmpdir, 'new')
mkdir(new_tmpdir)
server, _ = written_two_cmt_server_repo
repo = Repository(path=new_tmpdir, exists=False)
repo.clone('name', '[email protected]', server, remove_old=True)
co = repo.checkout()
aset = co.columns['writtenaset']
with pytest.raises(ValueError):
torch_dset = make_torch_dataset(aset)
co.close()
repo._env._close_environments()
def test_two_aset_loader_two_worker_dataloader(self, repo_with_20_samples):
repo = repo_with_20_samples
co = repo.checkout()
first_aset = co.arraysets['writtenaset']
second_aset = co.arraysets['second_aset']
torch_dset = make_torch_dataset([first_aset, second_aset])
loader = DataLoader(torch_dset, batch_size=2, drop_last=True, num_workers=2)
count = 0
for asets_batch in loader:
assert type(asets_batch).__name__ == 'BatchTuple_writtenaset_second_aset'
assert isinstance(asets_batch, tuple)
assert len(asets_batch) == 2
assert asets_batch._fields == ('writtenaset', 'second_aset')
assert asets_batch.writtenaset.shape == (2, 5, 7)
assert asets_batch.second_aset.shape == (2, 5, 7)
assert np.allclose(asets_batch.writtenaset, -asets_batch.second_aset)
count += 1
assert count == 10
def test_with_index_range(self, repo_with_20_samples):
repo = repo_with_20_samples
co = repo.checkout()
aset = co.arraysets['writtenaset']
# with keys
bad_tensor0 = aset['0']
bad_tensor1 = aset['1']
# with index range
index_range = slice(2, 20)
torch_dset = make_torch_dataset(aset, index_range=index_range)
loader = DataLoader(torch_dset, batch_size=3)
total_batches = 0
for batch in loader:
assert batch[0].size(0) == 3
total_batches += 1
for sample in batch:
assert not np.allclose(sample, bad_tensor0)
assert not np.allclose(sample, bad_tensor1)
assert total_batches == 6
co.close()
parser = ArgumentParser()
parser.add_argument('--gpus', type=int, default=None)
parser.add_argument('--lr', type=float, default=1e-3)
parser.add_argument('--batch_size', type=int, default=32)
parser.add_argument('--max_epochs', type=int, default=1)
parser.add_argument('--max_elems', type=int, default=60000)
parser.add_argument('--hangar', action='store_true')
args = parser.parse_args()
repo = Repository(path=Path(__file__).parent / "hangar")
co = repo.checkout()
if args.hangar:
dataset = make_torch_dataset(
[co.columns['digits'], co.columns['label']],
index_range=slice(0, args.max_elems))
else:
dataset = MNIST(os.getcwd(),
download=True,
transform=transforms.ToTensor())
print(len(dataset))
datapoint, label = dataset[0]
print(type(datapoint), type(label))
print("making a loader!")
train_loader = DataLoader(dataset,
batch_size=args.batch_size,
num_workers=16,
shuffle=False)
# init model
