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| | import unittest |
| | import hypothesis.strategies as st |
| | from hypothesis import given, settings |
| | import numpy as np |
| | from caffe2.python import core, workspace, model_helper |
| | import caffe2.python.hypothesis_test_util as hu |
| | import caffe2.python.ideep_test_util as mu |
| |
|
| |
|
| | @unittest.skipIf(not workspace.C.use_mkldnn, "No MKLDNN support.") |
| | class LeakyReluTest(hu.HypothesisTestCase): |
| | def _get_inputs(self, N, C, H, W, order): |
| | input_data = np.random.rand(N, C, H, W).astype(np.float32) - 0.5 |
| |
|
| | |
| | input_data[np.logical_and( |
| | input_data >= 0, input_data <= 0.051)] = 0.051 |
| | input_data[np.logical_and( |
| | input_data <= 0, input_data >= -0.051)] = -0.051 |
| |
|
| | return input_data, |
| |
|
| | def _get_op(self, device_option, alpha, order, inplace=False): |
| | outputs = ['output' if not inplace else "input"] |
| | op = core.CreateOperator( |
| | 'LeakyRelu', |
| | ['input'], |
| | outputs, |
| | alpha=alpha, |
| | device_option=device_option) |
| | return op |
| |
|
| | def _feed_inputs(self, input_blobs, device_option): |
| | names = ['input', 'scale', 'bias'] |
| | for name, blob in zip(names, input_blobs): |
| | self.ws.create_blob(name).feed(blob, device_option=device_option) |
| |
|
| | @given(N=st.integers(2, 3), |
| | C=st.integers(2, 3), |
| | H=st.integers(2, 3), |
| | W=st.integers(2, 3), |
| | alpha=st.floats(0, 1), |
| | seed=st.integers(0, 1000), |
| | **mu.gcs) |
| | @settings(deadline=1000) |
| | def test_leaky_relu_gradients(self, gc, dc, N, C, H, W, alpha, seed): |
| | np.random.seed(seed) |
| |
|
| | op = self._get_op( |
| | device_option=gc, |
| | alpha=alpha, |
| | order='NCHW') |
| | input_blobs = self._get_inputs(N, C, H, W, "NCHW") |
| |
|
| | self.assertDeviceChecks(dc, op, input_blobs, [0]) |
| | self.assertGradientChecks(gc, op, input_blobs, 0, [0]) |
| |
|
| | @given(N=st.integers(2, 10), |
| | C=st.integers(3, 10), |
| | H=st.integers(5, 10), |
| | W=st.integers(7, 10), |
| | alpha=st.floats(0, 1), |
| | seed=st.integers(0, 1000)) |
| | def test_leaky_relu_model_helper_helper(self, N, C, H, W, alpha, seed): |
| | np.random.seed(seed) |
| | order = 'NCHW' |
| | arg_scope = {'order': order} |
| | model = model_helper.ModelHelper(name="test_model", arg_scope=arg_scope) |
| | model.LeakyRelu( |
| | 'input', |
| | 'output', |
| | alpha=alpha) |
| |
|
| | input_blob = np.random.rand(N, C, H, W).astype(np.float32) |
| |
|
| | self.ws.create_blob('input').feed(input_blob) |
| |
|
| | self.ws.create_net(model.param_init_net).run() |
| | self.ws.create_net(model.net).run() |
| |
|
| | output_blob = self.ws.blobs['output'].fetch() |
| |
|
| | assert output_blob.shape == (N, C, H, W) |
| |
|
| |
|
| | if __name__ == "__main__": |
| | unittest.main() |
| |
|
