def check_grads_disabled(self, activations, labels, input_length):
"""
Check if optimization to disable gradients is working
"""
ctc_cost = ctc(activations, labels, input_length)
ctc_function = aesara.function([], [ctc_cost])
for node in ctc_function.maker.fgraph.apply_nodes:
if isinstance(node.op, ConnectionistTemporalClassification):
assert node.op.compute_grad is False
def setup_cpu_op(
self,
activations,
labels,
input_length,
compute_grad=True,
mode=mode_without_gpu,
):
cpu_ctc_cost = ctc(activations, labels, input_length)
outputs = [cpu_ctc_cost]
if compute_grad:
# Symbolic gradient of CTC cost
cpu_ctc_grad = grad(mean(cpu_ctc_cost), activations)
outputs += [cpu_ctc_grad]
return aesara.function([], outputs, mode=mode)
def run_ctc(self, activations, labels, input_length, expected_costs,
expected_grads):
# Create symbolic variables
t_activations = aesara.shared(activations, name="activations")
t_activation_times = aesara.shared(input_length,
name="activation_times")
t_labels = aesara.shared(labels, name="labels")
t_cost = ctc(t_activations, t_labels, t_activation_times)
# Symbolic gradient of CTC cost
t_grad = tt.grad(tt.mean(t_cost), t_activations)
# Compile symbolic functions
train = aesara.function([], [t_cost, t_grad])
cost, grad = train()
utt.assert_allclose(expected_grads / cost.shape[0], grad)
utt.assert_allclose(expected_costs, cost)
self.check_grads_disabled(t_activations, t_labels, t_activation_times)
def wrapper(acts):
# Create auxiliary symbolic variables
t_activation_times = aesara.shared(in_lengths,
name="activation_times")
t_labels = aesara.shared(labels, name="labels")
return ctc(acts, t_labels, t_activation_times)