/usr/local/lib64/python3.6/site-packages/caffe2/python
Edit: /usr/local/lib64/python3.6/site-packages/caffe2/python/brew_test.py (11739B)
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)