mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-06 12:20:52 +01:00
c016e64914
Summary: TSIA Reviewed By: bwasti Differential Revision: D4334592 fbshipit-source-id: f101887ede2691aef8ca317e5286347c52779774
297 lines
10 KiB
Python
297 lines
10 KiB
Python
# TODO(jiayq): as more and more tests are moving to hypothesis test, we
|
|
# can gradually remove this test script. DO NOT ADD MORE TESTS TO THIS
|
|
# FILE.
|
|
|
|
from __future__ import absolute_import
|
|
from __future__ import division
|
|
from __future__ import print_function
|
|
from __future__ import unicode_literals
|
|
import numpy as np
|
|
from caffe2.python import \
|
|
core, device_checker, gradient_checker, test_util, workspace
|
|
import caffe2.python.hypothesis_test_util as hu
|
|
from hypothesis import assume, given, settings
|
|
import hypothesis.strategies as st
|
|
from caffe2.proto import caffe2_pb2
|
|
|
|
import collections
|
|
import unittest
|
|
|
|
|
|
if workspace.has_gpu_support and workspace.NumCudaDevices() > 0:
|
|
gpu_device_option = caffe2_pb2.DeviceOption()
|
|
gpu_device_option.device_type = caffe2_pb2.CUDA
|
|
cpu_device_option = caffe2_pb2.DeviceOption()
|
|
gpu_device_checker = device_checker.DeviceChecker(
|
|
0.01, [gpu_device_option]
|
|
)
|
|
device_checker = device_checker.DeviceChecker(
|
|
0.01, [gpu_device_option, cpu_device_option]
|
|
)
|
|
gpu_gradient_checkers = [
|
|
gradient_checker.GradientChecker(
|
|
0.005, 0.05, gpu_device_option, "gpu_checker_ws"
|
|
),
|
|
]
|
|
gradient_checkers = [
|
|
gradient_checker.GradientChecker(
|
|
0.005, 0.05, gpu_device_option, "gpu_checker_ws"
|
|
),
|
|
gradient_checker.GradientChecker(
|
|
0.01, 0.05, cpu_device_option, "cpu_checker_ws"
|
|
),
|
|
]
|
|
else:
|
|
cpu_device_option = caffe2_pb2.DeviceOption()
|
|
gpu_device_option = None
|
|
gpu_device_checker = device_checker.DeviceChecker(
|
|
0.01, []
|
|
)
|
|
device_checker = device_checker.DeviceChecker(0.01, [cpu_device_option])
|
|
|
|
gradient_checkers = [
|
|
gradient_checker.GradientChecker(
|
|
0.01, 0.05, cpu_device_option, "cpu_checker_ws"
|
|
)
|
|
]
|
|
gpu_gradient_checkers = []
|
|
|
|
|
|
class TestLRN(test_util.TestCase):
|
|
|
|
def setUp(self):
|
|
self.test_configs = [(6, 10), (3, 13), ]
|
|
|
|
def testLRN(self):
|
|
for input_size, depth in self.test_configs:
|
|
op = core.CreateOperator("LRN",
|
|
["X"],
|
|
["Y", "Y_scale"],
|
|
size=11,
|
|
alpha=0.001,
|
|
beta=0.5,
|
|
bias=2.0,
|
|
order="NHWC"
|
|
)
|
|
X = np.random.rand(2, input_size, input_size,
|
|
depth).astype(np.float32)
|
|
res = device_checker.CheckSimple(op, [X], [0])
|
|
self.assertTrue(res)
|
|
for checker in gradient_checkers:
|
|
res, grad, grad_estimated = checker.CheckSimple(op, [X], 0, [0])
|
|
self.assertTrue(res)
|
|
|
|
|
|
class TestFlatten(test_util.TestCase):
|
|
|
|
def testFlatten(self):
|
|
op = core.CreateOperator("Flatten", ["X"], ["Y"])
|
|
X = np.random.rand(2, 3, 4, 5).astype(np.float32)
|
|
res = device_checker.CheckSimple(op, [X], [0])
|
|
self.assertTrue(res)
|
|
for checker in gradient_checkers:
|
|
res, grad, grad_estimated = checker.CheckSimple(op, [X], 0, [0])
|
|
self.assertTrue(res)
|
|
|
|
|
|
class TestConcat(test_util.TestCase):
|
|
|
|
def setUp(self):
|
|
self.test_configs = [
|
|
# input_size, depth1, depth2, depth3, depth4
|
|
(3, 2, 3, 4, 5),
|
|
(4, 5, 4, 3, 2),
|
|
]
|
|
|
|
def testConcatNHWC(self):
|
|
for input_size, d1, d2, d3, d4 in self.test_configs:
|
|
op = core.CreateOperator("Concat",
|
|
["X1", "X2", "X3", "X4"],
|
|
["Y", "Y_dims"],
|
|
order="NHWC"
|
|
)
|
|
Xs = [
|
|
np.random.rand(2, input_size, input_size,
|
|
d1).astype(np.float32),
|
|
np.random.rand(2, input_size, input_size,
|
|
d2).astype(np.float32),
|
|
np.random.rand(2, input_size, input_size,
|
|
d3).astype(np.float32),
|
|
np.random.rand(2, input_size, input_size, d4).astype(np.float32)
|
|
]
|
|
for i in range(4):
|
|
res = device_checker.CheckSimple(op, Xs, [0])
|
|
self.assertTrue(res)
|
|
for checker in gradient_checkers:
|
|
res, grad, grad_estimated = checker.CheckSimple(op, Xs, i,
|
|
[0])
|
|
self.assertTrue(res)
|
|
|
|
def testConcatNCHW(self):
|
|
for input_size, d1, d2, d3, d4 in self.test_configs:
|
|
op = core.CreateOperator("Concat",
|
|
["X1", "X2", "X3", "X4"],
|
|
["Y", "Y_dims"],
|
|
order="NCHW"
|
|
)
|
|
Xs = [
|
|
np.random.rand(2, d1, input_size,
|
|
input_size).astype(np.float32),
|
|
np.random.rand(2, d2, input_size,
|
|
input_size).astype(np.float32),
|
|
np.random.rand(2, d3, input_size,
|
|
input_size).astype(np.float32),
|
|
np.random.rand(2, d4, input_size, input_size).astype(np.float32)
|
|
]
|
|
for i in range(4):
|
|
res = device_checker.CheckSimple(op, Xs, [0])
|
|
self.assertTrue(res)
|
|
for checker in gradient_checkers:
|
|
res, grad, grad_estimated = checker.CheckSimple(op, Xs, i,
|
|
[0])
|
|
self.assertTrue(res)
|
|
|
|
|
|
class TestRelu(test_util.TestCase):
|
|
|
|
def setUp(self):
|
|
self.test_configs = [
|
|
# input size
|
|
# (0, 1),
|
|
(1, 1),
|
|
(2, 1),
|
|
(1, 3, 3, 1),
|
|
(2, 3, 3, 1),
|
|
(1, 5, 5, 3),
|
|
(2, 5, 5, 3),
|
|
]
|
|
|
|
def testRelu(self):
|
|
for input_size in self.test_configs:
|
|
op = core.CreateOperator("Relu", ["X"], ["Y"])
|
|
X = np.random.rand(*input_size).astype(np.float32)
|
|
# go away from the origin point to avoid kink problems
|
|
X += 0.01 * np.sign(X)
|
|
X[X == 0] = 0.01
|
|
res = device_checker.CheckSimple(op, [X], [0])
|
|
self.assertTrue(res)
|
|
for checker in gradient_checkers:
|
|
res, grad, grad_estimated = checker.CheckSimple(op, [X], 0, [0])
|
|
self.assertTrue(res)
|
|
|
|
|
|
class TestTanh(test_util.TestCase):
|
|
|
|
def setUp(self):
|
|
self.test_configs = [
|
|
# (0, 1),
|
|
(1, 1),
|
|
(2, 1),
|
|
(1, 2, 3, 4),
|
|
]
|
|
|
|
def testTanh(self):
|
|
for input_size in self.test_configs:
|
|
op = core.CreateOperator("Tanh", ["X"], ["Y"])
|
|
X = np.random.rand(*input_size).astype(np.float32) - 0.5
|
|
res = device_checker.CheckSimple(op, [X], [0])
|
|
self.assertTrue(res)
|
|
for checker in gradient_checkers:
|
|
res, grad, grad_estimated = checker.CheckSimple(op, [X], 0, [0])
|
|
self.assertTrue(res)
|
|
|
|
|
|
class TestExp(test_util.TestCase):
|
|
|
|
def setUp(self):
|
|
self.test_configs = [
|
|
# (0, 1),
|
|
(1, 1),
|
|
(2, 1),
|
|
(1, 2, 3, 4),
|
|
]
|
|
|
|
def testExp(self):
|
|
for input_size in self.test_configs:
|
|
op = core.CreateOperator("Exp", ["X"], ["Y"])
|
|
X = np.random.rand(*input_size).astype(np.float32) - 0.5
|
|
res = device_checker.CheckSimple(op, [X], [0])
|
|
self.assertTrue(res)
|
|
for checker in gradient_checkers:
|
|
res, grad, grad_estimated = checker.CheckSimple(op, [X], 0, [0])
|
|
self.assertTrue(res)
|
|
|
|
|
|
class TestSigmoid(test_util.TestCase):
|
|
|
|
def setUp(self):
|
|
self.test_configs = [
|
|
# (0, 1),
|
|
(1, 1),
|
|
(2, 1),
|
|
(1, 2, 3, 4),
|
|
]
|
|
|
|
def testSigmoid(self):
|
|
for input_size in self.test_configs:
|
|
op = core.CreateOperator("Sigmoid", ["X"], ["Y"])
|
|
X = np.random.rand(*input_size).astype(np.float32) - 0.5
|
|
res = device_checker.CheckSimple(op, [X], [0])
|
|
self.assertTrue(res)
|
|
for checker in gradient_checkers:
|
|
res, grad, grad_estimated = checker.CheckSimple(op, [X], 0, [0])
|
|
self.assertTrue(res)
|
|
|
|
|
|
class TestSum(test_util.TestCase):
|
|
|
|
def setUp(self):
|
|
self.test_configs = [
|
|
# ((0, 1), False),
|
|
((1, 2, 3, 4), True),
|
|
((1, 2, 3, 4), False)]
|
|
|
|
def testSum(self):
|
|
for (input_size, in_place) in self.test_configs:
|
|
op = core.CreateOperator("Sum", ["X1", "X2"],
|
|
["Y" if not in_place else "X1"])
|
|
X1 = np.random.rand(*input_size).astype(np.float32) - 0.5
|
|
X2 = np.random.rand(*input_size).astype(np.float32) - 0.5
|
|
res = device_checker.CheckSimple(op, [X1, X2], [0])
|
|
self.assertTrue(res)
|
|
for checker in gradient_checkers:
|
|
res, grad, grad_estimated = checker.CheckSimple(
|
|
op, [X1, X2], 0, [0])
|
|
self.assertTrue(res)
|
|
|
|
|
|
class TestMakeTwoClass(test_util.TestCase):
|
|
|
|
def setUp(self):
|
|
self.test_configs = [
|
|
# input size
|
|
# (0, 1),
|
|
(1,),
|
|
(7,),
|
|
(1, 3),
|
|
(2, 5),
|
|
]
|
|
|
|
def testMakeTwoClass(self):
|
|
for input_size in self.test_configs:
|
|
op = core.CreateOperator("MakeTwoClass", ["X"], ["Y"])
|
|
X = np.random.rand(*input_size).astype(np.float32)
|
|
# step a little to avoid gradient problems
|
|
X[X < 0.01] += 0.01
|
|
X[X > 0.99] -= 0.01
|
|
res = device_checker.CheckSimple(op, [X], [0])
|
|
self.assertTrue(res)
|
|
for checker in gradient_checkers:
|
|
res, grad, grad_estimated = checker.CheckSimple(op, [X], 0, [0])
|
|
self.assertTrue(res)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
workspace.GlobalInit(["python"])
|
|
unittest.main()
|