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https://github.com/pytorch/pytorch.git
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0b66c01462
(2) cnn bugfix and enhancement (3) device checker fix (4) suppress prints from caffe2_python (5) moar tests!
217 lines
7.9 KiB
Python
217 lines
7.9 KiB
Python
import numpy as np
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from pycaffe2 import core, device_checker, gradient_checker, workspace
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from caffe2.proto import caffe2_pb2, caffe2_legacy_pb2
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import sys
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import unittest
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if workspace.has_gpu_support and workspace.NumberOfGPUs() > 0:
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gpu_device_option = caffe2_pb2.DeviceOption()
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gpu_device_option.device_type = caffe2_pb2.CUDA
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cpu_device_option = caffe2_pb2.DeviceOption()
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device_checker = device_checker.DeviceChecker(
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0.01, [gpu_device_option, cpu_device_option])
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gradient_checkers = [
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gradient_checker.GradientChecker(
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0.005, 0.05, gpu_device_option, "gpu_checker_ws"),
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gradient_checker.GradientChecker(
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0.01, 0.05, cpu_device_option, "cpu_checker_ws"),
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]
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else:
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cpu_device_option = caffe2_pb2.DeviceOption()
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device_checker = device_checker.DeviceChecker(
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0.01, [cpu_device_option])
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gradient_checkers = [
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gradient_checker.GradientChecker(
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0.01, 0.05, cpu_device_option, "cpu_checker_ws")
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]
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class TestConvLegacyPooling(unittest.TestCase):
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def setUp(self):
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self.test_configs = [
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# stride, kernel, legacy_pad, size, order
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(1, 1, 1, 7, "NHWC"),
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(1, 1, 2, 7, "NHWC"),
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(1, 3, 1, 7, "NHWC"),
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(1, 3, 2, 7, "NHWC"),
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(1, 5, 1, 14, "NHWC"),
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(1, 5, 2, 14, "NHWC"),
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(2, 7, 1, 24, "NHWC"),
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(2, 7, 2, 24, "NHWC"),
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(1, 1, 1, 7, "NCHW"),
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(1, 1, 2, 7, "NCHW"),
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(1, 3, 1, 7, "NCHW"),
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(1, 3, 2, 7, "NCHW"),
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(1, 5, 1, 14, "NCHW"),
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(1, 5, 2, 14, "NCHW"),
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(2, 7, 1, 24, "NCHW"),
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(2, 7, 2, 24, "NCHW"),
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]
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def testConvolutionLegacyPadding(self):
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for stride, kernel, legacy_pad, size, order in self.test_configs:
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print 'conv', stride, kernel, legacy_pad, size, order
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op = core.CreateOperator("Conv")(
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["X", "w", "b"], ["Y"], stride=stride, kernel=kernel,
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legacy_pad=legacy_pad, order=order)
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if order == "NHWC":
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X = np.random.rand(2, size, size, 3).astype(np.float32) - 0.5
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w = np.random.rand(4, kernel, kernel, 3).astype(np.float32) - 0.5
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else:
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X = np.random.rand(2, 3, size, size).astype(np.float32) - 0.5
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w = np.random.rand(4, 3, kernel, kernel).astype(np.float32) - 0.5
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b = np.random.rand(4).astype(np.float32) - 0.5
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res = device_checker.CheckSimple(op, [X, w, b], [0])
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self.assertTrue(res)
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for checker in gradient_checkers:
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for i in range(3):
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res, grad, grad_estimated = checker.CheckSimple(
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op, [X, w, b], i, [0])
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self.assertTrue(res)
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class TestMaxPoolingLegacyPadding(unittest.TestCase):
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def setUp(self):
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self.test_configs = [
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(2, 3, 2, 12, "NHWC"),
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(2, 3, 2, 16, "NHWC"),
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(1, 3, 2, 8, "NHWC"),
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(1, 3, 2, 14, "NHWC"),
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(2, 3, 2, 14, "NHWC"),
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(1, 3, 2, 7, "NHWC"),
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(2, 3, 2, 12, "NCHW"),
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(2, 3, 2, 16, "NCHW"),
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(1, 3, 2, 8, "NCHW"),
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(1, 3, 2, 14, "NCHW"),
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(2, 3, 2, 14, "NCHW"),
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(1, 3, 2, 7, "NCHW"),
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]
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def testMaxPoolingLegacyPadding(self):
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for stride, kernel, legacy_pad, size, order in self.test_configs:
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print 'MaxPool', stride, kernel, legacy_pad, size, order
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op = core.CreateOperator("MaxPool")(
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["X"], ["Y", "Y_maxid"], stride=stride, kernel=kernel,
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legacy_pad=legacy_pad, order=order)
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# In order to avoid the problem of race conditions, we will do a randperm
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# so that the values will be apart at least 0.01
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if order == "NHWC":
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X = np.random.permutation(1 * size * size * 3).reshape(1, size, size, 3).astype(np.float32) * 0.01
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else:
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X = np.random.permutation(1 * size * size * 3).reshape(1, 3, size, size).astype(np.float32) * 0.01
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res = device_checker.CheckSimple(op, [X], [0])
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self.assertTrue(res)
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for checker in gradient_checkers:
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res, grad, grad_estimated = checker.CheckSimple(op, [X], 0, [0])
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self.assertTrue(res)
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class TestAveragePoolingLegacyPadding(unittest.TestCase):
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def setUp(self):
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self.test_configs = [
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(1, 7, 1, 7, "NHWC"),
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(1, 7, 2, 7, "NHWC"),
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(1, 7, 1, 7, "NCHW"),
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(1, 7, 2, 7, "NCHW"),
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]
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def testAveragePoolingLegacyPadding(self):
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for stride, kernel, legacy_pad, size, order in self.test_configs:
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print 'AveragePool', stride, kernel, legacy_pad, size, order
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op = core.CreateOperator("AveragePool")(
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["X"], ["Y"], stride=stride, kernel=kernel,
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legacy_pad=legacy_pad, order=order)
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if order == "NHWC":
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X = np.random.rand(2, size, size, 3).astype(np.float32)
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else:
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X = np.random.rand(2, 3, size, size).astype(np.float32)
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res = device_checker.CheckSimple(op, [X], [0])
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self.assertTrue(res)
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for checker in gradient_checkers:
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res, grad, grad_estimated = checker.CheckSimple(op, [X], 0, [0])
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self.assertTrue(res)
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class TestLRN(unittest.TestCase):
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def setUp(self):
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self.test_configs = [
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(6, 10),
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(3, 13),
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]
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def testLRN(self):
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for input_size, depth in self.test_configs:
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op = core.CreateOperator("LRN")(
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["X"], ["Y", "Y_scale"], size=11, alpha=0.001, beta=0.5, bias=2.0, order="NHWC")
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X = np.random.rand(2, input_size, input_size, depth).astype(np.float32)
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res = device_checker.CheckSimple(op, [X], [0])
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self.assertTrue(res)
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for checker in gradient_checkers:
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res, grad, grad_estimated = checker.CheckSimple(op, [X], 0, [0])
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self.assertTrue(res)
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class TestDepthConcat(unittest.TestCase):
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def setUp(self):
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self.test_configs = [
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# input_size, depth1, depth2, depth3, depth4
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(3, 2, 3, 4, 5),
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(4, 5, 4, 3, 2),
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]
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def testDepthConcatNHWC(self):
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for input_size, d1, d2, d3, d4 in self.test_configs:
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op = core.CreateOperator("DepthConcat")(
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["X1", "X2", "X3", "X4"], ["Y", "Y_dims"], order="NHWC")
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Xs = [np.random.rand(2, input_size, input_size, d1).astype(np.float32),
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np.random.rand(2, input_size, input_size, d2).astype(np.float32),
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np.random.rand(2, input_size, input_size, d3).astype(np.float32),
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np.random.rand(2, input_size, input_size, d4).astype(np.float32)]
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for i in range(4):
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res = device_checker.CheckSimple(op, Xs, [0])
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self.assertTrue(res)
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for checker in gradient_checkers:
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res, grad, grad_estimated = checker.CheckSimple(op, Xs, i, [0])
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self.assertTrue(res)
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def testDepthConcatNCHW(self):
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for input_size, d1, d2, d3, d4 in self.test_configs:
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op = core.CreateOperator("DepthConcat")(
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["X1", "X2", "X3", "X4"], ["Y", "Y_dims"], order="NCHW")
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Xs = [np.random.rand(2, d1, input_size, input_size).astype(np.float32),
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np.random.rand(2, d2, input_size, input_size).astype(np.float32),
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np.random.rand(2, d3, input_size, input_size).astype(np.float32),
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np.random.rand(2, d4, input_size, input_size).astype(np.float32)]
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for i in range(4):
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res = device_checker.CheckSimple(op, Xs, [0])
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self.assertTrue(res)
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for checker in gradient_checkers:
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res, grad, grad_estimated = checker.CheckSimple(op, Xs, i, [0])
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self.assertTrue(res)
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class TestRelu(unittest.TestCase):
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def setUp(self):
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self.test_configs = [
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# input size
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(1, 1),
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(2, 1),
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(1, 3, 3, 1),
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(2, 3, 3, 1),
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(1, 5, 5, 3),
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(2, 5, 5, 3),
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]
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def testRelu(self):
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for input_size in self.test_configs:
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op = core.CreateOperator("Relu")(["X"], ["Y"])
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X = np.random.rand(*input_size).astype(np.float32)
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# go away from the origin point to avoid kink problems
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X += 0.01 * np.sign(X)
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X[X==0] = 0.01
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res = device_checker.CheckSimple(op, [X], [0])
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self.assertTrue(res)
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for checker in gradient_checkers:
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res, grad, grad_estimated = checker.CheckSimple(op, [X], 0, [0])
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self.assertTrue(res)
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if __name__ == '__main__':
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unittest.main() |