def test_mode_writing():
run_id = "trial_" + datetime.now().strftime("%Y%m%d-%H%M%S%f")
worker = socket.gethostname()
for s in range(0, 10):
fw = FileWriter(trial_dir="/tmp/ts_outputs/" + run_id,
step=s,
worker=worker)
if s % 2 == 0:
fw.write_tensor(
tdata=np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32),
tname="arr",
mode=ModeKeys.TRAIN,
mode_step=s // 2,
)
else:
fw.write_tensor(
tdata=np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32),
tname="arr",
mode=ModeKeys.EVAL,
mode_step=s // 2,
)
fw.close()
write_dummy_collection_file("/tmp/ts_outputs/" + run_id)
files = glob.glob("/tmp/ts_outputs/" + run_id + "/**/*.tfevents",
recursive=True)
global_steps = []
train_steps = []
eval_steps = []
for f in files:
fr = FileReader(fname=f)
for tu in fr.read_tensors():
tensor_name, step, tensor_data, mode, mode_step = tu
if step % 2 == 0:
assert mode == ModeKeys.TRAIN
train_steps.append(step // 2)
else:
assert mode == ModeKeys.EVAL
eval_steps.append(step // 2)
assert mode_step == step // 2
global_steps.append(step)
trial = create_trial("/tmp/ts_outputs/" + run_id)
assert trial.steps() == sorted(global_steps)
assert trial.steps(ModeKeys.TRAIN) == sorted(train_steps)
assert trial.steps(ModeKeys.EVAL) == sorted(eval_steps)
shutil.rmtree("/tmp/ts_outputs/" + run_id)
def test_index():
numpy_tensor = [
np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32),
np.array([[1.0, 2.0, 4.0], [3.0, 4.0, 5.0]], dtype=np.float32),
]
runid = "default"
logdir = "."
step = 0
worker = "worker_0"
run_dir = os.path.join(logdir, runid)
writer = FileWriter(trial_dir=run_dir,
step=step,
worker=worker,
verbose=True)
for i in (0, len(numpy_tensor) - 1):
n = "tensor" + str(i)
writer.write_tensor(tdata=numpy_tensor[i], tname=n)
writer.flush()
writer.close()
efl = TensorFileLocation(step_num=step, worker_name=worker)
eventfile = efl.get_file_location(trial_dir=run_dir)
indexfile = IndexFileLocationUtils.get_index_key_for_step(
run_dir, step, worker)
fo = open(eventfile, "rb")
with open(indexfile) as idx_file:
index_data = json.load(idx_file)
tensor_payload = index_data["tensor_payload"]
i = 0
for tensor in tensor_payload:
start_idx = int(tensor["start_idx"])
fo.seek(start_idx, 0)
length = int(tensor["length"])
line = fo.read(length)
zoo = open("test.txt", "wb")
zoo.write(line)
zoo.close()
testfile_reader = FileReader("./test.txt")
tensor_values = list(testfile_reader.read_tensors())
assert np.allclose(
tensor_values[0][2].all(),
numpy_tensor[i].all()), "indexwriter not working"
i = i + 1
fo.close()
shutil.rmtree(run_dir)
os.remove("test.txt")
def test_mode_data():
run_id = "trial_" + datetime.now().strftime("%Y%m%d-%H%M%S%f")
trial_dir = "/tmp/ts_outputs/" + run_id
c = CollectionManager()
c.add("default")
c.get("default").tensor_names = ["arr_1"]
c.get("default").tensor_names = ["arr_2"]
c.export(trial_dir, DEFAULT_COLLECTIONS_FILE_NAME)
trial = create_trial(trial_dir)
worker = socket.gethostname()
for s in range(0, 10):
fw = FileWriter(trial_dir=trial_dir, step=s, worker=worker)
if s % 2 == 0:
fw.write_tensor(
tdata=np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32),
tname="arr_1",
mode=modes.TRAIN,
mode_step=s // 2,
)
else:
fw.write_tensor(
tdata=np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32),
tname="arr_2",
mode=modes.EVAL,
mode_step=s // 2,
)
fw.close()
assert trial.tensor_names() == ["arr_1", "arr_2"]
assert trial.tensor_names(step=0) == ["arr_1"]
assert trial.tensor_names(step=1) == ["arr_2"]
assert trial.tensor_names(step=0, mode=modes.TRAIN) == ["arr_1"]
assert trial.tensor_names(step=0, mode=modes.EVAL) == ["arr_2"]
assert trial.tensor_names(mode=modes.TRAIN) == ["arr_1"]
assert trial.tensor_names(mode=modes.EVAL) == ["arr_2"]
def test_mode_data():
run_id = "trial_" + datetime.now().strftime("%Y%m%d-%H%M%S%f")
trial_dir = "/tmp/ts_outputs/" + run_id
c = CollectionManager()
c.add("default")
c.get("default").tensor_names = ["arr"]
c.export(trial_dir, DEFAULT_COLLECTIONS_FILE_NAME)
tr = create_trial(trial_dir)
worker = socket.gethostname()
for s in range(0, 10):
fw = FileWriter(trial_dir=trial_dir, step=s, worker=worker)
if s % 2 == 0:
fw.write_tensor(
tdata=np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32),
tname="arr",
mode=modes.TRAIN,
mode_step=s // 2,
)
else:
fw.write_tensor(
tdata=np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32),
tname="arr",
mode=modes.EVAL,
mode_step=s // 2,
)
fw.close()
if s % 2 == 0:
assert tr.has_passed_step(s // 2,
mode=modes.TRAIN) == StepState.AVAILABLE
assert tr.has_passed_step(
s // 2, mode=modes.EVAL) == StepState.NOT_YET_AVAILABLE
else:
assert tr.has_passed_step(s // 2,
mode=modes.EVAL) == StepState.AVAILABLE
assert tr.has_passed_step(s) == StepState.AVAILABLE
assert tr.has_passed_step(s + 1) == StepState.NOT_YET_AVAILABLE
assert tr.has_passed_step(
s + 1, mode=modes.TRAIN) == StepState.NOT_YET_AVAILABLE
assert len(tr.tensor_names()) == 1
assert len(tr.steps()) == 10
assert len(tr.steps(mode=modes.TRAIN)) == 5
assert len(tr.steps(mode=modes.EVAL)) == 5
assert len(tr.modes()) == 2
for i in range(10):
if i % 2 == 0:
assert tr.mode(i) == modes.TRAIN
else:
assert tr.mode(i) == modes.EVAL
assert tr.mode_step(i) == i // 2
for i in range(5):
assert tr.global_step(modes.TRAIN, i) == (i * 2)
assert tr.global_step(modes.EVAL, i) == (i * 2) + 1
assert len(tr.tensor("arr").steps()) == 10
assert len(tr.tensor("arr").steps(mode=modes.TRAIN)) == 5
assert len(tr.tensor("arr").steps(mode=modes.EVAL)) == 5
for i in range(10):
assert tr.tensor("arr").value(i) is not None
if i < 5:
assert tr.tensor("arr").value(i, mode=modes.TRAIN) is not None
assert tr.tensor("arr").value(i, mode=modes.EVAL) is not None
shutil.rmtree("/tmp/ts_outputs/" + run_id)