def test_eq(self):
nd = np.array
x1 = provider.TensorDatum(step=12, wall_time=0.25, numpy=nd([1.0, 2.0]))
x2 = provider.TensorDatum(step=12, wall_time=0.25, numpy=nd([1.0, 2.0]))
x3 = provider.TensorDatum(
step=23, wall_time=3.25, numpy=nd([-0.5, -2.5])
)
self.assertEqual(x1, x2)
self.assertNotEqual(x1, x3)
self.assertNotEqual(x1, object())
def test_eq_with_rank0_tensor(self):
x1 = provider.TensorDatum(step=12,
wall_time=0.25,
numpy=np.array([1.25]))
x2 = provider.TensorDatum(step=12,
wall_time=0.25,
numpy=np.array([1.25]))
x3 = provider.TensorDatum(step=23,
wall_time=3.25,
numpy=np.array([1.25]))
self.assertEqual(x1, x2)
self.assertNotEqual(x1, x3)
self.assertNotEqual(x1, object())
def read_tensors(
self,
ctx,
*,
experiment_id,
plugin_name,
downsample=None,
run_tag_filter=None,
):
with timing.log_latency("build request"):
req = data_provider_pb2.ReadTensorsRequest()
req.experiment_id = experiment_id
req.plugin_filter.plugin_name = plugin_name
_populate_rtf(run_tag_filter, req.run_tag_filter)
req.downsample.num_points = downsample
with timing.log_latency("_stub.ReadTensors"):
with _translate_grpc_error():
res = self._stub.ReadTensors(req)
with timing.log_latency("build result"):
result = {}
for run_entry in res.runs:
tags = {}
result[run_entry.run_name] = tags
for tag_entry in run_entry.tags:
series = []
tags[tag_entry.tag_name] = series
d = tag_entry.data
for (step, wt, value) in zip(d.step, d.wall_time, d.value):
point = provider.TensorDatum(
step=step,
wall_time=wt,
numpy=tensor_util.make_ndarray(value),
)
series.append(point)
return result
def _convert_tensor_event(event):
"""Helper for `read_tensors`."""
return provider.TensorDatum(
step=event.step,
wall_time=event.wall_time,
numpy=tensor_util.make_ndarray(event.tensor_proto),
)
def test_read_tensors(self):
res = data_provider_pb2.ReadTensorsResponse()
run = res.runs.add(run_name="test")
tag = run.tags.add(tag_name="weights")
tag.data.step.extend([0, 1, 2])
tag.data.wall_time.extend([1234.0, 1235.0, 1236.0])
tag.data.value.append(tensor_util.make_tensor_proto([0.0, 0.0, 42.0]))
tag.data.value.append(tensor_util.make_tensor_proto([1.0, 1.0, 43.0]))
tag.data.value.append(tensor_util.make_tensor_proto([2.0, 2.0, 44.0]))
self.stub.ReadTensors.return_value = res
actual = self.provider.read_tensors(
self.ctx,
experiment_id="123",
plugin_name="histograms",
run_tag_filter=provider.RunTagFilter(runs=["test", "nope"]),
downsample=3,
)
expected = {
"test": {
"weights": [
provider.TensorDatum(
step=0,
wall_time=1234.0,
numpy=np.array([0.0, 0.0, 42.0]),
),
provider.TensorDatum(
step=1,
wall_time=1235.0,
numpy=np.array([1.0, 1.0, 43.0]),
),
provider.TensorDatum(
step=2,
wall_time=1236.0,
numpy=np.array([2.0, 2.0, 44.0]),
),
],
},
}
self.assertEqual(actual, expected)
req = data_provider_pb2.ReadTensorsRequest()
req.experiment_id = "123"
req.plugin_filter.plugin_name = "histograms"
req.run_tag_filter.runs.names.extend(["nope", "test"]) # sorted
req.downsample.num_points = 3
self.stub.ReadTensors.assert_called_once_with(req)
def test_repr(self):
x = provider.TensorDatum(step=123,
wall_time=234.5,
numpy=np.array(-0.25))
repr_ = repr(x)
self.assertIn(repr(x.step), repr_)
self.assertIn(repr(x.wall_time), repr_)
self.assertIn(repr(x.numpy), repr_)
def test_hash(self):
x = provider.TensorDatum(step=12,
wall_time=0.25,
numpy=np.array([1.25]))
with six.assertRaisesRegex(self, TypeError, "unhashable type"):
hash(x)
def convert_tensor_event(event):
return provider.TensorDatum(
step=event.step,
wall_time=event.wall_time,
numpy=tensor_util.make_ndarray(event.tensor_proto),
)
