def infer_step(self, inputs):
"""
Run one inference step.
"""
if self.do_generation:
if self.generator.num_samples:
inputs = {
name: repeat_array_or_tensor(array_or_tensor, self.place,
self.generator.num_samples)
for name, array_or_tensor in inputs.items()
}
if self.mem_efficient:
predictions = []
for idx in range(0, len(inputs["data_id"]), self.batch_size):
part_inputs = {
name:
slice_array_or_tensor(array_or_tensor, self.place, idx,
idx + self.batch_size)
for name, array_or_tensor in inputs.items()
}
part_outputs = self._run_generation(part_inputs)
predictions.extend(part_outputs)
else:
predictions = self._run_generation(inputs)
return predictions
else:
return self._execute(self.infer_program,
self._get_feed(inputs, is_infer=True),
self.infer_fetch_dict)
def infer_step(self, inputs):
"""
Run one inference step.
"""
if self.do_generation:
batch_size = len(inputs["data_id"])
new_bsz = batch_size * self.latent_type_size
inputs = {
name: repeat_array_or_tensor(array_or_tensor, self.place, self.latent_type_size)
for name, array_or_tensor in inputs.items()
}
# Add latent_id
inputs["latent_id"] = np.array(
[i for i in range(self.latent_type_size) for _ in range(batch_size)],
dtype="int64"
).reshape([-1, 1])
return super(Plato, self).infer_step(inputs)
else:
return self._execute(
self.infer_program,
self._get_feed(inputs, is_infer=True),
self.infer_fetch_dict)
