def test_liberate(writer_stock):
with pytest.raises(PermissionError):
StockRoom(enable_write=True)
runner = CliRunner()
res = runner.invoke(cli.liberate)
assert res.exit_code == 0
stock = StockRoom(enable_write=True)
stock.close()
def test_init_repo():
runner = CliRunner()
with runner.isolated_filesystem():
with pytest.raises(RuntimeError):
StockRoom()
res = runner.invoke(cli.init, ['--username', 'test', '--email', '*****@*****.**'])
assert res.exit_code == 0
StockRoom()
def test_commit(repo_with_aset):
runner = CliRunner()
stock = StockRoom()
stock.tag['key'] = 'value'
res = runner.invoke(cli.commit, [])
assert 'Error: Require commit message\n' in res.stdout
res = runner.invoke(cli.commit, ['-m', 'test commit'])
assert res.exit_code == 0
assert 'Commit message:\ntest commit\nCommit Successful. Digest' in res.stdout
stock._repo.hangar_repository._env._close_environments()
def test_commit(repo_with_col):
runner = CliRunner()
stock = StockRoom(enable_write=True)
stock.experiment["key"] = "value"
stock.close()
res = runner.invoke(cli.commit, [])
assert "Error: Require commit message\n" in res.stdout
res = runner.invoke(cli.commit, ["-m", "test commit"])
assert res.exit_code == 0
assert "Commit message:\ntest commit" in res.stdout
assert "Commit Successful. Digest" in res.stdout
stock._repo._env._close_environments()
def test_commit(repo_with_col):
runner = CliRunner()
stock = StockRoom(write=True)
stock.experiment['key'] = 'value'
stock.close()
res = runner.invoke(cli.commit, [])
assert 'Error: Require commit message\n' in res.stdout
res = runner.invoke(cli.commit, ['-m', 'test commit'])
assert res.exit_code == 0
assert 'Commit message:\ntest commit' in res.stdout
assert 'Commit Successful. Digest' in res.stdout
stock._repo._env._close_environments()
def test_opening_two_instances(self, writer_stock):
with pytest.raises(PermissionError):
StockRoom(write=True)
arr = np.arange(20).reshape(4, 5)
oldarr = arr * randint(1, 100)
col1 = writer_stock.data['ndcol']
col1[1] = oldarr
writer_stock.commit('added data')
stock2 = StockRoom()
col2 = stock2.data['ndcol']
assert np.allclose(col2[1], oldarr)
stock2._repo._env._close_environments()
def test_init_repo():
runner = CliRunner()
with runner.isolated_filesystem():
with pytest.raises(RuntimeError):
stock = StockRoom()
res = runner.invoke(cli.init,
['--name', 'test', '--email', '*****@*****.**'])
assert 'Error: stock init should execute only in a git repository' in res.output
cwd = Path.cwd()
cwd.joinpath('.git').mkdir(exist_ok=True)
res = runner.invoke(cli.init,
['--name', 'test', '--email', '*****@*****.**'])
assert res.exit_code == 0
stock = StockRoom()
def reader_stock(writer_stock):
arr = np.arange(20).reshape(4, 5)
col = writer_stock.data["ndcol"]
col[1] = arr
writer_stock.commit("added first data point")
writer_stock.close()
stock_obj = StockRoom()
yield stock_obj
stock_obj._repo._env._close_environments()
def test_import_cifar(repo, torchvision_datasets, dataset, splits, columns):
runner = CliRunner()
res = runner.invoke(cli.import_data, [f"torchvision.{dataset}"])
assert res.exit_code == 0
keys = [
f"{dataset}-{split}-{column}" for split in splits for column in columns
]
stock = StockRoom()
assert stock.data.keys() == tuple(keys)
assert stock.data[f"{dataset}-train-image"][0].shape == (3, 32, 32)
assert stock.data[f"{dataset}-test-label"][0].shape == tuple()
assert stock.data[f"{dataset}-train-image"][0].dtype == np.float32
def test_import_mnist(repo, torchvision_datasets, dataset, splits, columns):
runner = CliRunner()
res = runner.invoke(cli.import_data, [f"torchvision.{dataset}"])
assert res.exit_code == 0
keys = [
f"{dataset}-{split}-{column}" for split in splits for column in columns
]
keys = sorted(keys)
stock = StockRoom()
data_keys = sorted(list(stock.data.keys()))
assert data_keys == keys
assert stock.data[f"{dataset}-train-image"][0].shape == (28, 28)
assert stock.data[f"{dataset}-test-label"][0].shape == tuple()
assert stock.data[f"{dataset}-train-image"][0].dtype == np.float32
def test_import_voc(repo, torchvision_datasets, dataset, splits, columns):
runner = CliRunner()
res = runner.invoke(cli.import_data, [f"torchvision.{dataset}"])
assert res.exit_code == 0
keys = [
f"{dataset}-{split}-{column}" for split in splits for column in columns
]
stock = StockRoom()
assert sorted(stock.data.keys()) == sorted(keys)
assert stock.data[f"{dataset}-train-image"][0].shape == (3, 500, 500)
assert stock.data[f"{dataset}-train-image"][0].dtype == np.uint8
assert stock.data[f"{dataset}-val-image"][0].shape == (3, 500, 500)
assert stock.data[f"{dataset}-trainval-image"][0].shape == (3, 500, 500)
if dataset == "voc_segmentation":
assert stock.data[f"{dataset}-train-segment"][0].shape == (500, 500)
elif dataset == "voc_detection":
assert stock.data[f"{dataset}-train-names"][0][0] == "testname"
assert stock.data[f"{dataset}-train-boxes"][0][0].shape == (2, 2)
def writer_stock(repo_with_col):
stock_obj = StockRoom(write=True)
yield stock_obj
stock_obj._repo._env._close_environments()
def stock(repo_with_aset):
stock_obj = StockRoom()
yield stock_obj
stock_obj._repo.hangar_repository._env._close_environments()
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = x.view(-1, 16 * 5 * 5)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
stock = StockRoom()
imgcol = stock.data['cifar10-train-image']
lblcol = stock.data['cifar10-train-label']
# imshow(imgcol[11])
lr = 0.001
momentum = 0.9
check_every = 500
net = Net()
dset = make_torch_dataset([imgcol, lblcol])
dloader = DataLoader(dset, batch_size=64)
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=lr, momentum=momentum)
for epoch in range(2):
running_loss = 0.0