FM / ideep

/usr/local/lib64/python3.6/site-packages/caffe2/python/ideep/

/usr/local/lib64/python3.6/site-packages/caffe2/python/ideep

Name	Size	Mode	Actions
__pycache__/	-	0755	rm
adam_op_test.py	3212	0644	edit dl rm
blobs_queue_db_test.py	4045	0644	edit dl rm
channel_shuffle_op_test.py	1286	0644	edit dl rm
concat_split_op_test.py	5532	0644	edit dl rm
convfusion_op_test.py	31919	0644	edit dl rm
conv_op_test.py	5666	0644	edit dl rm
conv_transpose_test.py	2595	0644	edit dl rm
copy_op_test.py	3074	0644	edit dl rm
dropout_op_test.py	2075	0644	edit dl rm
elementwise_sum_op_test.py	6317	0644	edit dl rm
expanddims_squeeze_op_test.py	4285	0644	edit dl rm
fc_op_test.py	11745	0644	edit dl rm
leaky_relu_op_test.py	2854	0644	edit dl rm
LRN_op_test.py	1195	0644	edit dl rm
moment_sgd_op_test.py	1777	0644	edit dl rm
operator_fallback_op_test.py	3450	0644	edit dl rm
order_switch_op_test.py	2299	0644	edit dl rm
pool_op_test.py	4296	0644	edit dl rm
pre_convert_test.py	4115	0644	edit dl rm
relu_op_test.py	3705	0644	edit dl rm
reshape_op_test.py	5919	0644	edit dl rm
shape_op_test.py	2631	0644	edit dl rm
sigmoid_op_test.py	775	0644	edit dl rm
softmax_op_test.py	932	0644	edit dl rm
spatial_bn_op_test.py	5237	0644	edit dl rm
test_ideep_net.py	4094	0644	edit dl rm
transform_ideep_net.py	11683	0644	edit dl rm
transpose_op_test.py	1284	0644	edit dl rm
weightedsum_op_test.py	1560	0644	edit dl rm
__init__.py	0	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/caffe2/python/ideep/leaky_relu_op_test.py (2854B)

@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

# default step size is 0.05
        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()

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 # default step size is 0.05 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()