def _testForwardCase(self,
use_cudnn=False,
data_format="NHWC",
dtype=dtypes.float32):
for seed in range(5):
p = self._genParams(use_cudnn, data_format, dtype, seed=seed)
input_data, filter_data, strides, padding, _ = p
with test_util.deterministic_ops():
result_a = nn_impl.depthwise_conv2d_v2(input_data, filter_data, strides,
padding, data_format)
result_b = nn_impl.depthwise_conv2d_v2(input_data, filter_data, strides,
padding, data_format)
self.assertAllEqual(result_a, result_b)
def Gradients(upstream_gradients):
with backprop.GradientTape() as tape:
tape.watch(input_data)
tape.watch(filter_data)
op_output = nn_impl.depthwise_conv2d_v2(
input_data, filter_data, strides, padding, data_format)
gradient_injector_output = op_output * upstream_gradients
return tape.gradient(gradient_injector_output,
[input_data, filter_data])