1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
|
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()
|
