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Upgrades python/hypothesis_test.py to use brew instead of CNNHelperModel

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Summary: Upgrades this file to use brew instead of CNNHelperModel

Reviewed By: harouwu

Differential Revision: D5252089

fbshipit-source-id: 6df4350717c1d42bc4bcc63d255cd422f085ee05
This commit is contained in:
Po-Yen Chou 2017-06-15 14:58:16 -07:00 committed by Facebook Github Bot
parent e9cba7e69f
commit 5ce9cbae70
1 changed files with 15 additions and 11 deletions
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26
caffe2/python/hypothesis_test.py
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@ -1422,8 +1422,9 @@ class TestOperators(hu.HypothesisTestCase):
(["async_dag"] if workspace.has_gpu_support else [])),
do=st.sampled_from(hu.device_options))
def test_dag_net_forking(self, net_type, num_workers, do):
from caffe2.python.cnn import CNNModelHelper
m = CNNModelHelper()
from caffe2.python.model_helper import ModelHelper
from caffe2.python import brew
m = ModelHelper(name="test_model")
n = 10
d = 2
depth = 2
@ -1437,16 +1438,18 @@ class TestOperators(hu.HypothesisTestCase):
mid_1 = "{}_{}_m".format(i + 1, 2 * j)
mid_2 = "{}_{}_m".format(i + 1, 2 * j + 1)
top = "{}_{}".format(i, j)
m.FC(
brew.fc(
m,
bottom_1, mid_1,
dim_in=d, dim_out=d,
weight_init=m.ConstantInit(np.random.randn()),
bias_init=m.ConstantInit(np.random.randn()))
m.FC(
weight_init=('ConstantFill', dict(value=np.random.randn())),
bias_init=('ConstantFill', dict(value=np.random.randn())))
brew.fc(
m,
bottom_2, mid_2,
dim_in=d, dim_out=d,
weight_init=m.ConstantInit(np.random.randn()),
bias_init=m.ConstantInit(np.random.randn()))
weight_init=('ConstantFill', dict(value=np.random.randn())),
bias_init=('ConstantFill', dict(value=np.random.randn())))
m.net.Sum([mid_1, mid_2], top)
m.net.SquaredL2Distance(["0_0", "label"], "xent")
m.net.AveragedLoss("xent", "loss")
@ -1769,16 +1772,17 @@ class TestOperators(hu.HypothesisTestCase):
n=st.integers(1, 5),
d=st.integers(1, 5))
def test_elman_recurrent_network(self, t, n, d):
from caffe2.python import cnn
from caffe2.python import model_helper, brew
np.random.seed(1701)
step_net = cnn.CNNModelHelper(name="Elman")
step_net = model_helper.ModelHelper(name="Elman")
# TODO: name scope external inputs and outputs
step_net.Proto().external_input.extend(
["input_t", "seq_lengths", "timestep",
"hidden_t_prev", "gates_t_w", "gates_t_b"])
step_net.Proto().type = "simple"
step_net.Proto().external_output.extend(["hidden_t", "gates_t"])
step_net.FC("hidden_t_prev", "gates_t", dim_in=d, dim_out=d, axis=2)
brew.fc(step_net,
"hidden_t_prev", "gates_t", dim_in=d, dim_out=d, axis=2)
step_net.net.Sum(["gates_t", "input_t"], ["gates_t"])
step_net.net.Sigmoid(["gates_t"], ["hidden_t"])