forked from pytorch/pytorch
-
Notifications
You must be signed in to change notification settings - Fork 1
/
brew_test.py
328 lines (270 loc) · 11.5 KB
/
brew_test.py
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
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
from caffe2.python import brew, core, scope, workspace
from caffe2.python.modeling.parameter_info import ParameterTags
from caffe2.python.model_helper import ModelHelper
from caffe2.python.cnn import CNNModelHelper
import unittest
import numpy as np
class BrewTest(unittest.TestCase):
def setUp(self):
def myhelper(model, val=-1):
return val
if not brew.has_helper(myhelper):
brew.Register(myhelper)
self.myhelper = myhelper
def myhelper2(model, val=-1):
return val
if not brew.has_helper(myhelper2):
brew.Register(myhelper2)
self.myhelper2 = myhelper2
self.model = ModelHelper(name="test_model")
def test_dropout(self):
p = 0.2
X = np.ones((100, 100)).astype(np.float32) - p
workspace.FeedBlob("x", X)
model = ModelHelper(name="test_model")
brew.dropout(model, "x", "out", is_test=False)
workspace.RunNetOnce(model.param_init_net)
workspace.RunNetOnce(model.net)
out = workspace.FetchBlob("out")
self.assertLess(abs(out.mean() - (1 - p)), 0.05)
def test_fc(self):
m, n, k = (15, 15, 15)
X = np.random.rand(m, k).astype(np.float32) - 0.5
workspace.FeedBlob("x", X)
model = ModelHelper(name="test_model")
brew.fc(model, "x", "out_1", k, n)
model.Validate()
workspace.RunNetOnce(model.param_init_net)
workspace.RunNetOnce(model.net)
def test_relu(self):
Xpos = np.ones((5, 5)).astype(np.float32) - 0.5
Xneg = np.ones((5, 5)).astype(np.float32) - 1.5
workspace.FeedBlob("xpos", Xpos)
workspace.FeedBlob("xneg", Xneg)
model = ModelHelper(name="test_model")
brew.relu(model, "xpos", "out_xpos")
brew.relu(model, "xneg", "out_xneg")
model.Validate()
workspace.RunNetOnce(model.param_init_net)
workspace.RunNetOnce(model.net)
pos = workspace.FetchBlob("out_xpos")
self.assertAlmostEqual(pos.mean(), 0.5)
neg = workspace.FetchBlob("out_xneg")
self.assertAlmostEqual(neg.mean(), 0)
def test_tanh(self):
X = np.ones((5, 5)).astype(np.float32) - 0.5
workspace.FeedBlob("x", X)
model = ModelHelper(name="test_model")
brew.tanh(model, "x", "out_tanh")
model.Validate()
workspace.RunNetOnce(model.param_init_net)
workspace.RunNetOnce(model.net)
out = workspace.FetchBlob("out_tanh")
self.assertAlmostEqual(out.mean(), np.tanh(0.5), places=5)
def test_validate(self):
model = ModelHelper(name="test_model")
model.params.append("aaa")
model.params.append("bbb")
self.assertEqual(model._Validate(), [])
model.params.append("xxx")
model.params.append("bbb")
self.assertEqual(model._Validate(), ["bbb"])
def test_arg_scope(self):
myhelper = self.myhelper
myhelper2 = self.myhelper2
n = 15
with brew.arg_scope([myhelper], val=n):
res = brew.myhelper(self.model)
self.assertEqual(n, res)
with brew.arg_scope([myhelper, myhelper2], val=n):
res1 = brew.myhelper(self.model)
res2 = brew.myhelper2(self.model)
self.assertEqual([n, n], [res1, res2])
def test_arg_scope_single(self):
X = np.random.rand(64, 3, 32, 32).astype(np.float32) - 0.5
workspace.FeedBlob("x", X)
model = ModelHelper(name="test_model")
with brew.arg_scope(
brew.conv,
stride=2,
pad=2,
weight_init=('XavierFill', {}),
bias_init=('ConstantFill', {})
):
brew.conv(
model=model,
blob_in="x",
blob_out="out",
dim_in=3,
dim_out=64,
kernel=3,
)
model.Validate()
workspace.RunNetOnce(model.param_init_net)
workspace.RunNetOnce(model.net)
out = workspace.FetchBlob("out")
self.assertEqual(out.shape, (64, 64, 17, 17))
def test_arg_scope_nested(self):
myhelper = self.myhelper
n = 16
with brew.arg_scope([myhelper], val=-3), \
brew.arg_scope([myhelper], val=-2):
with brew.arg_scope([myhelper], val=n):
res = brew.myhelper(self.model)
self.assertEqual(n, res)
res = brew.myhelper(self.model)
self.assertEqual(res, -2)
res = brew.myhelper(self.model, val=15)
self.model.Validate()
self.assertEqual(res, 15)
def test_double_register(self):
myhelper = self.myhelper
with self.assertRaises(AttributeError):
brew.Register(myhelper)
def test_has_helper(self):
self.assertTrue(brew.has_helper(brew.conv))
self.assertTrue(brew.has_helper("conv"))
def myhelper3():
pass
self.assertFalse(brew.has_helper(myhelper3))
def test_model_helper(self):
X = np.random.rand(64, 32, 32, 3).astype(np.float32) - 0.5
workspace.FeedBlob("x", X)
my_arg_scope = {'order': 'NHWC'}
model = ModelHelper(name="test_model", arg_scope=my_arg_scope)
with brew.arg_scope(
brew.conv,
stride=2,
pad=2,
weight_init=('XavierFill', {}),
bias_init=('ConstantFill', {})
):
brew.conv(
model=model,
blob_in="x",
blob_out="out",
dim_in=3,
dim_out=64,
kernel=[8, 3]
)
model.Validate()
workspace.RunNetOnce(model.param_init_net)
workspace.RunNetOnce(model.net)
out = workspace.FetchBlob("out")
self.assertEqual(out.shape, (64, 15, 17, 64))
def test_cnn_model_helper_deprecated(self):
X = np.random.rand(64, 32, 32, 3).astype(np.float32) - 0.5
workspace.FeedBlob("x", X)
# CNNModelHelper is going to be deprecated soon. This test is only
# covering some CNNModelHelper logic
model = CNNModelHelper(name="test_model", order='NHWC')
self.assertEqual(model.arg_scope['order'], 'NHWC')
def test_get_params(self):
def param(x):
return core.ScopedBlobReference(x)
def to_str_list(x):
return sorted([str(p) for p in x])
model = ModelHelper(name="test_model")
model.AddParameter(param("a"))
model.AddParameter(param("b"), tags=ParameterTags.COMPUTED_PARAM)
with scope.NameScope("c"):
model.AddParameter(param("a"))
model.AddParameter(param("d"), tags=ParameterTags.COMPUTED_PARAM)
self.assertEqual(to_str_list(model.GetParams()), ['c/a'])
self.assertEqual(to_str_list(model.GetComputedParams()), ['c/d'])
self.assertEqual(to_str_list(model.GetAllParams()), ['c/a', 'c/d'])
# Get AllParams from the global Scope
self.assertEqual(to_str_list(model.GetAllParams('')), [
'a', 'b', 'c/a', 'c/d'])
self.assertEqual(to_str_list(model.GetParams()), ['a', 'c/a'])
self.assertEqual(to_str_list(model.GetComputedParams()), ['b', 'c/d'])
self.assertEqual(to_str_list(model.GetAllParams()),
['a', 'b', 'c/a', 'c/d'])
self.assertEqual(to_str_list(model.GetAllParams('')),
['a', 'b', 'c/a', 'c/d'])
# Get AllParams from the scope 'c'
self.assertEqual(to_str_list(model.GetAllParams('c')), ['c/a', 'c/d'])
self.assertEqual(to_str_list(model.GetAllParams('c/')), ['c/a', 'c/d'])
def test_param_consistence(self):
model = ModelHelper(name='test_mode')
cnv = brew.conv(model, 'data', 'cnv', 32, 32, 4)
step_model = ModelHelper(name='step_model', param_model=model)
a = brew.fc(step_model, cnv, 'a', 100, 200)
brew.fc(model, a, 'b', 200, 5)
# test the _parameters_info is shared between model and step_model
self.assertEqual(model._parameters_info, step_model._parameters_info)
def test_cond(self):
workspace.FeedBlob("cond", np.array(True))
workspace.FeedBlob("then_value", np.array(1))
workspace.FeedBlob("else_value", np.array(2))
then_model = ModelHelper(name="then_test_model")
then_model.net.Copy("then_value", "output_blob")
else_model = ModelHelper(name="else_test_model")
else_model.net.Copy("else_value", "output_blob")
model = ModelHelper(name="test_model")
brew.cond(
model=model,
cond_blob="cond",
external_blobs=["then_value", "else_value", "output_blob"],
then_model=then_model,
else_model=else_model)
workspace.RunNetOnce(model.param_init_net)
workspace.RunNetOnce(model.net)
output_value = workspace.FetchBlob("output_blob")
self.assertEqual(output_value, 1)
workspace.FeedBlob("cond", np.array(False))
workspace.RunNetOnce(model.param_init_net)
workspace.RunNetOnce(model.net)
output_value = workspace.FetchBlob("output_blob")
self.assertEqual(output_value, 2)
def test_loop(self):
workspace.FeedBlob("cond", np.array(True))
workspace.FeedBlob("ONE", np.array(1))
workspace.FeedBlob("TWO", np.array(2))
workspace.FeedBlob("TEN", np.array(10))
workspace.FeedBlob("counter", np.array(0))
workspace.FeedBlob("output_blob", np.array(0))
loop_model = ModelHelper(name="loop_test_model")
loop_model.net.Add(["output_blob", "TWO"], "output_blob")
cond_model = ModelHelper(name="cond_test_model")
cond_model.net.Add(["counter", "ONE"], "counter")
comp_res = cond_model.net.LT(["counter", "TEN"])
cond_model.net.Copy(comp_res, "cond")
model = ModelHelper(name="test_model")
brew.loop(
model=model,
cond_blob="cond",
external_blobs=["cond", "ONE", "TWO", "TEN", "counter", "output_blob"],
loop_model=loop_model,
cond_model=cond_model)
workspace.RunNetOnce(model.param_init_net)
workspace.RunNetOnce(model.net)
output_value = workspace.FetchBlob("output_blob")
self.assertEqual(output_value, 18)
@unittest.skipIf(not workspace.has_gpu_support, "No gpu support.")
class BrewGPUTest(unittest.TestCase):
def test_relu(self):
Xpos = np.ones((5, 5)).astype(np.float32) - 0.5
Xneg = np.ones((5, 5)).astype(np.float32) - 1.5
workspace.FeedBlob("xpos", Xpos)
workspace.FeedBlob("xneg", Xneg)
model = ModelHelper(name="test_model")
brew.relu(model, "xpos", "out_xpos", use_cudnn=True)
brew.relu(model, "xneg", "out_xneg", use_cudnn=True)
model.Validate()
workspace.RunNetOnce(model.param_init_net)
workspace.RunNetOnce(model.net)
pos = workspace.FetchBlob("out_xpos")
self.assertAlmostEqual(pos.mean(), 0.5)
neg = workspace.FetchBlob("out_xneg")
self.assertAlmostEqual(neg.mean(), 0)
def test_tanh(self):
X = np.ones((5, 5)).astype(np.float32) - 0.5
workspace.FeedBlob("x", X)
model = ModelHelper(name="test_model")
brew.tanh(model, "x", "out_tanh", use_cudnn=True)
model.Validate()
workspace.RunNetOnce(model.param_init_net)
workspace.RunNetOnce(model.net)
out = workspace.FetchBlob("out_tanh")
self.assertAlmostEqual(out.mean(), np.tanh(0.5), places=5)