def __init__(self, compute_grad=True):
if not ctc_available():
raise RuntimeError("Baidu CTC is not available and "
"GpuConnectionistTemporalClassification Op "
"can not be constructed.")
self.compute_grad = compute_grad
# Return only the cost. Gradient will be returned by grad()
self.default_output = 0
super().__init__(self.func_file, self.func_name)
import aesara
import aesara.gpuarray
from aesara.gpuarray.ctc import GpuConnectionistTemporalClassification, gpu_ctc
from aesara.gradient import grad
from aesara.tensor.math import mean
from aesara.tensor.nnet.ctc import (
ConnectionistTemporalClassification,
ctc,
ctc_available,
)
from tests import unittest_tools as utt
from tests.gpuarray.config import mode_with_gpu, mode_without_gpu
from tests.tensor.nnet.test_ctc import setup_ctc_case, setup_grad_case, setup_torch_case
@pytest.mark.skipif(not ctc_available(),
reason="Optional library warp-ctc not available")
class TestCTC:
def check_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")
inputs = [t_activations, t_labels, t_activation_times]
# Execute several tests for each test case
self.check_expected_values(t_activations, t_labels, t_activation_times,
expected_costs, expected_grads)
[
[[0.1, 0.6, 0.1, 0.1, 0.1], [0.1, 0.1, 0.6, 0.1, 0.1]],
[[0.6, 0.1, 0.1, 0.1, 0.1], [0.1, 0.1, 0.5, 0.2, 0.1]],
],
dtype=np.float32,
)
activation_times = np.asarray([2, 2], dtype=np.int32)
labels = np.asarray([[1, 2], [1, 2]], dtype=np.int32)
return [activations, labels, activation_times]
@pytest.mark.skipif(
not ctc_available(), reason="Optional library warp-ctc not available"
)
@pytest.mark.skipif(
aesara.config.mode == "FAST_COMPILE" or aesara.config.cxx == "",
reason="We need a c compiler",
)
class TestCTC:
"""
Test Baidu CTC wrapper implementation.
Expected values for costs and gradients are obtained through an external
C implementation, that uses the library directly.
"""
def run_ctc(
self, activations, labels, input_length, expected_costs, expected_grads