Esempio n. 1
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class test_mean():

	def setup(self):
            self.gp = Gpupy()
            self.rng = np.random.RandomState(0)

	def test_mean(self):
            """Test mean function."""
            a = self.rng.rand(129, 1025).astype(np.float32)

            out_np = a.mean()
            out_gp = self.gp.mean(a)

            assert(np.allclose(out_np, out_gp))

            a = self.rng.rand(1025).astype(np.float32)

            out_np = a.mean()
            out_gp = self.gp.mean(a)

            assert(np.allclose(out_np, out_gp))

	def test_mean(self):
            """Test mean function along axes."""
            a = self.rng.rand(129, 1025).astype(np.float32)

            out_np = a.mean(axis=1)
            out_gp = self.gp.mean(a, axis=1).copy_to_host()

            assert(np.allclose(out_np, out_gp))

            out_np = a.mean(axis=0)
            out_gp = self.gp.mean(a, axis=0).copy_to_host()

            assert(np.allclose(out_np, out_gp))
Esempio n. 2
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class test_T():

    def setup(self):
        self.gp = Gpupy()
        self.rng = np.random.RandomState(0)

    def test_madd(self):
        a = self.rng.rand(129, 1025).astype(np.float32)
        b = self.rng.rand(129, 1025).astype(np.float32)

        out_np = a-b
        out_gp = self.gp.sub(a,b).copy_to_host()

        assert(np.allclose(out_np, out_gp))

    def test_madd_scale(self):
        a = self.rng.rand(129, 1025).astype(np.float32)
        b = self.rng.rand(129, 1025).astype(np.float32)
        alpha = .4
        beta = -1.6

        out_np = (alpha*a-beta*b).astype(np.float32)
        out_gp = self.gp.sub(a,b,alpha = alpha, beta = beta).copy_to_host()
        print out_np
        print out_gp

        assert(np.allclose(out_np, out_gp, atol = 1.e-5))

    def test_vadd(self):
        a = self.rng.rand(1025).astype(np.float32)
        b = self.rng.rand(1025).astype(np.float32)

        out_np = a-b
        out_gp = self.gp.sub(a,b).copy_to_host()

        assert(np.allclose(out_np, out_gp))

    def test_vadd_scale(self):
        a = self.rng.rand(1025).astype(np.float32)
        b = self.rng.rand(1025).astype(np.float32)
        alpha = .4
        beta = -1.6

        out_np = (alpha*a-beta*b).astype(np.float32)
        out_gp = self.gp.sub(a,b,alpha = alpha, beta = beta).copy_to_host()
        print out_np
        print out_gp

        assert(np.allclose(out_np, out_gp, atol = 1.e-5))
Esempio n. 3
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class test_ones():

    def setup(self):
        self.gp = Gpupy()

    def test_ones(self):
        """Create arrays of ones."""
        out_np = np.ones(shape=(129,1025), dtype=np.float32)
        out_gp = self.gp.ones(shape=(129,1025)).copy_to_host()
        assert(np.allclose(out_np, out_gp))

        out_np = np.ones(shape=129, dtype=np.float32)
        out_gp = self.gp.ones(shape=129).copy_to_host()
        assert(np.allclose(out_np, out_gp))

        out_np = np.ones(129, dtype=np.float32)
        out_gp = self.gp.ones(129).copy_to_host()
        assert(np.allclose(out_np, out_gp))
Esempio n. 4
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class test_reshape():

    def setup(self):
        self.gp = Gpupy()

    def test_reshape(self):
        """ Reshapes array.
        """
        a = np.array(np.arange(10), dtype=np.float32, order='F')

        out_np = np.reshape(a, newshape=(2,5), order='F')
        out_gp = self.gp.reshape(a, newshape=(2,5)).copy_to_host()
        assert(np.allclose(out_np, out_gp))

        a = np.array(np.arange(10), dtype=np.float32, order='c')

        out_np = np.reshape(a, newshape=(2,5), order='C')
        out_gp = self.gp.reshape(a, newshape=(2,5), order='C').copy_to_host()
        assert(np.allclose(out_np, out_gp))
Esempio n. 5
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class test_diag():

	def setup(self):
		self.gp = Gpupy()
		self.rng = np.random.RandomState(0)

	def test_v2m(self):
		a = self.rng.rand(129).astype(np.float32)

		out_np = np.diag(a)
		out_gp = self.gp.diag(a).copy_to_host()
		assert(np.allclose(out_np, out_gp))

	def test_m2v(self):
		a = self.rng.rand(129, 129).astype(np.float32)

		out_np = np.diag(a)
		out_gp = self.gp.diag(a).copy_to_host()
		assert(np.allclose(out_np, out_gp))
Esempio n. 6
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class test_zero_diag:
    def setup(self):
        self.gp = Gpupy()
        self.rng = np.random.RandomState(0)

    def test_zero_diag(self):
        a = self.rng.rand(129, 129).astype(np.float32)

        out_np = a - np.diag(np.diag(a))
        out_gp = self.gp.zero_diag(a).copy_to_host()
        assert np.allclose(out_np, out_gp)
Esempio n. 7
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class test_relu():

    def setup(self):
        self.gp = Gpupy()
        self.rng = np.random.RandomState(0)

    def test_clip_m(self):
        """clip on matrices."""
        a = self.rng.rand(129, 1025).astype(np.float32)
        out_np = np.clip(a,-.1,.1)
        out_gp = self.gp.clip(a,-.1,.1).copy_to_host()
        assert(np.allclose(out_np, out_gp))

        a = self.rng.rand(129, 1025).astype(np.float32)
        out_np = np.clip(a,-.1,.1)
        out_gp = cuda.to_device(a)
        self.gp.clip(a, -.1, .1, out=out_gp)
        assert(np.allclose(out_np, out_gp.copy_to_host()))

    def test_clip_v(self):
        """clip on vectors."""
        a = self.rng.rand(1025).astype(np.float32)
        out_np = np.clip(a,-.1,.1)
        out_gp = self.gp.clip(a,-.1,.1).copy_to_host()
        assert(np.allclose(out_np, out_gp))

        a = self.rng.rand(1025).astype(np.float32)
        out_np = np.clip(a,-.1,.1)
        out_gp = cuda.to_device(a)
        self.gp.clip(a, -.1, .1, out=out_gp)
        assert(np.allclose(out_np, out_gp.copy_to_host()))
Esempio n. 8
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class test_mult():

    def setup(self):
        self.gp = Gpupy()
        self.rng = np.random.RandomState(0)

    def test_matrix_multiply(self):
        a = self.rng.rand(129, 1025).astype(np.float32)
        b = self.rng.rand(129, 1025).astype(np.float32)

        out_np = a*b
        out_gp = self.gp.mult(a,b).copy_to_host()

        assert(np.allclose(out_np, out_gp))

    def test_vector_multiply(self):
        a = self.rng.rand(1025).astype(np.float32)
        b = self.rng.rand(1025).astype(np.float32)

        out_np = a*b
        out_gp = self.gp.mult(a,b).copy_to_host()

        assert(np.allclose(out_np, out_gp))
Esempio n. 9
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class test_scal():

    def setup(self):
        self.gp = Gpupy()
        self.rng = np.random.RandomState(0)

    def test_mscal(self):
        a = self.rng.rand(129, 1025).astype(np.float32)
        alpha = 2.

        out_np = (alpha*a).astype(np.float32)
        out_gp = self.gp.scal(a, alpha).copy_to_host()

        assert(np.allclose(out_np, out_gp))

    def test_vscal(self):
        a = self.rng.rand(1025).astype(np.float32)
        alpha = 2.

        out_np = (alpha*a).astype(np.float32)
        out_gp = self.gp.scal(a, alpha).copy_to_host()

        assert(np.allclose(out_np, out_gp))
Esempio n. 10
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class test_zeros():

    def setup(self):
        self.gp = Gpupy()

    def test_zeros(self):
        """Create array of zeros."""
        out_np = np.zeros(shape=(129,1025), dtype=np.float32)
        out_gp = self.gp.zeros(shape=(129,1025)).copy_to_host()
        assert(np.allclose(out_np, out_gp))

        out_np = np.zeros(shape=(129,1025), dtype=np.float32)
        out_gp = cuda.to_device(np.ones(shape=(129,1025), dtype=np.float32))
        self.gp.zeros(shape=(129,1025), out=out_gp)
        assert(np.allclose(out_np, out_gp.copy_to_host()))

        out_np = np.zeros(shape=129, dtype=np.float32)
        out_gp = self.gp.zeros(shape=129).copy_to_host()
        assert(np.allclose(out_np, out_gp))

        out_np = np.zeros(129, dtype=np.float32)
        out_gp = cuda.to_device(np.ones(129, dtype=np.float32))
        self.gp.zeros(shape=129, out=out_gp)
        assert(np.allclose(out_np, out_gp.copy_to_host()))
Esempio n. 11
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 def setup(self):
     self.gp = Gpupy()
Esempio n. 12
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 def setup(self):
     self.gp = Gpupy()
     self.rng = np.random.RandomState(0)
Esempio n. 13
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class test_add():

    def setup(self):
        self.gp = Gpupy()
        self.rng = np.random.RandomState(0)

    def test_madd(self):
        """Add two matrices."""
        a = self.rng.rand(129, 1025).astype(np.float32)
        b = self.rng.rand(129, 1025).astype(np.float32)

        out_np = a+b
        out_gp = self.gp.add(a,b).copy_to_host()
        assert(np.allclose(out_np, out_gp))

        out_gp = self.gp.zeros(shape=(129, 1025))
        self.gp.add(a,b, out=out_gp)
        assert(np.allclose(out_np, out_gp.copy_to_host()))

    def test_madd_scale(self):
        a = self.rng.rand(129, 1025).astype(np.float32)
        b = self.rng.rand(129, 1025).astype(np.float32)
        alpha = .4
        beta = -1.6

        out_np = (alpha*a+beta*b).astype(np.float32)
        out_gp = self.gp.add(a, b, alpha = alpha, beta = beta).copy_to_host()
        assert(np.allclose(out_np, out_gp, atol = 1.e-5))

        out_gp = self.gp.zeros(shape=(129, 1025))
        self.gp.add(a,b, alpha = alpha, beta = beta, out=out_gp)
        assert(np.allclose(out_np, out_gp.copy_to_host(), atol=1.e-5))

    def test_vadd(self):
        a = self.rng.rand(1025).astype(np.float32)
        b = self.rng.rand(1025).astype(np.float32)

        out_np = a+b
        out_gp = self.gp.add(a,b).copy_to_host()
        assert(np.allclose(out_np, out_gp))

        out_gp = self.gp.zeros(shape=1025)
        self.gp.add(a,b, out=out_gp)
        assert(np.allclose(out_np, out_gp.copy_to_host()))

    def test_vadd_scale(self):
        a = self.rng.rand(1025).astype(np.float32)
        b = self.rng.rand(1025).astype(np.float32)
        alpha = .4
        beta = -1.6

        out_np = (alpha*a+beta*b).astype(np.float32)
        out_gp = self.gp.add(a, b, alpha = alpha, beta = beta).copy_to_host()
        assert(np.allclose(out_np, out_gp, atol=1.e-5))

        out_gp = self.gp.zeros(shape=1025)
        self.gp.add(a, b, alpha = alpha, beta = beta, out=out_gp)
        assert(np.allclose(out_np, out_gp.copy_to_host()))

    def test_ms_scale(self):
        a = self.rng.rand(129,1025).astype(np.float32)
        b = 1.
        alpha = .4
        beta = -1.6

        out_np = (alpha*a+beta*b).astype(np.float32)
        out_gp = self.gp.add(a,b,alpha = alpha, beta = beta).copy_to_host()
        assert(np.allclose(out_np, out_gp, atol = 1.e-5))

        out_gp = self.gp.zeros(shape=(129, 1025))
        self.gp.add(a, b, alpha = alpha, beta = beta, out=out_gp)
        assert(np.allclose(out_np, out_gp.copy_to_host(), atol = 1.e-5))

    def test_ms(self):
        a = self.rng.rand(129,1025).astype(np.float32)
        b = 1.

        out_np = (a+b).astype(np.float32)
        out_gp = self.gp.add(a,b).copy_to_host()
        assert(np.allclose(out_np, out_gp, atol = 1.e-5))

        out_gp = self.gp.zeros(shape=(129, 1025))
        self.gp.add(a,b, out=out_gp)
        assert(np.allclose(out_np, out_gp.copy_to_host(), atol = 1.e-5))

    def test_vs_scale(self):
        a = self.rng.rand(1025).astype(np.float32)
        b = 1.
        alpha = .4
        beta = -1.6

        out_np = (alpha*a+beta*b).astype(np.float32)
        out_gp = self.gp.add(a,b,alpha = alpha, beta = beta).copy_to_host()
        assert(np.allclose(out_np, out_gp, atol = 1.e-5))

        out_gp = self.gp.zeros(shape=1025)
        self.gp.add(a, b, alpha = alpha, beta = beta,  out=out_gp)
        assert(np.allclose(out_np, out_gp.copy_to_host(), atol = 1.e-5))

    def test_vs(self):
        """ Add a vector and a scalar."""
        a = self.rng.rand(1025).astype(np.float32)
        b = 1.

        out_np = (a+b).astype(np.float32)
        out_gp = self.gp.add(a,b).copy_to_host()

        assert(np.allclose(out_np, out_gp, atol = 1.e-5))
        out_gp = self.gp.zeros(shape=1025)
        self.gp.add(a,b, out=out_gp)
        assert(np.allclose(out_np, out_gp.copy_to_host()))

    def test_mv_scale(self):
        """ Add a matrix to a vector and scale the result."""
        a = self.rng.rand(129,1025).astype(np.float32)
        b = self.rng.rand(1025).astype(np.float32)
        alpha = .4
        beta = -1.6

        out_np = (alpha*a+beta*b).astype(np.float32)
        out_gp = self.gp.add(a, b, alpha = alpha, beta = beta).copy_to_host()
        assert(np.allclose(out_np, out_gp, atol = 1.e-5))

        out_gp = self.gp.zeros(shape=(129, 1025))
        self.gp.add(a, b, alpha = alpha, beta = beta, out=out_gp)
        assert(np.allclose(out_np, out_gp.copy_to_host(), atol = 1.e-5))

    def test_mv(self):
        """ Add a matrix to a vector."""
        a = self.rng.rand(129,1025).astype(np.float32)
        b = self.rng.rand(1025).astype(np.float32)

        out_np = (a+b).astype(np.float32)
        out_gp = self.gp.add(a,b).copy_to_host()
        assert(np.allclose(out_np, out_gp, atol = 1.e-5))

        out_gp = self.gp.zeros(shape=(129, 1025))
        self.gp.add(a,b, out=out_gp)
        assert(np.allclose(out_np, out_gp.copy_to_host()))

    def test_ma_scale(self):
        """ Add a matrix to a vector with np.newaxis and scale the result."""
        a = self.rng.rand(1025, 1025).astype(np.float32)
        b = self.rng.rand(1025).astype(np.float32)
        alpha = .4
        beta = -1.6

        out_np = (alpha*a+beta*b[np.newaxis,:]).astype(np.float32)
        out_gp = self.gp.add(a,b[np.newaxis,:],alpha = alpha, beta = beta).copy_to_host()
        assert(np.allclose(out_np, out_gp, atol=1.e-5))

        out_gp = self.gp.zeros(shape=(1025, 1025))
        self.gp.add(a, b[np.newaxis,:], alpha = alpha, beta = beta, out=out_gp)
        assert(np.allclose(out_np, out_gp.copy_to_host(), atol=1.e-5))

        out_np = (alpha*a+beta*b[:,np.newaxis]).astype(np.float32)
        out_gp = self.gp.add(a, b[:,np.newaxis], alpha = alpha, beta = beta).copy_to_host()
        assert(np.allclose(out_np, out_gp, atol=1.e-5))

        out_gp = self.gp.zeros(shape=(1025, 1025))
        self.gp.add(a, b[:,np.newaxis], alpha = alpha, beta = beta, out=out_gp)
        assert(np.allclose(out_np, out_gp.copy_to_host(), atol=1.e-5))

    def test_ma(self):
        """Add a matrix to a vector with a np.newaxis."""
        a = self.rng.rand(1025, 1025).astype(np.float32)
        b = self.rng.rand(1025).astype(np.float32)

        out_np = (a+b[np.newaxis,:]).astype(np.float32)
        out_gp = self.gp.add(a,b[np.newaxis,:]).copy_to_host()
        assert(np.allclose(out_np, out_gp, atol = 1.e-5))

        out_gp = self.gp.zeros(shape=(1025, 1025))
        self.gp.add(a, b[np.newaxis,:], out=out_gp)
        assert(np.allclose(out_np, out_gp.copy_to_host()))

        out_np = (a+b[:,np.newaxis]).astype(np.float32)
        out_gp = self.gp.add(a,b[:,np.newaxis]).copy_to_host()
        assert(np.allclose(out_np, out_gp, atol = 1.e-5))

        out_gp = self.gp.zeros(shape=(1025, 1025))
        self.gp.add(a,b[:,np.newaxis], out=out_gp)
        assert(np.allclose(out_np, out_gp.copy_to_host()))
Esempio n. 14
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class test_relu():

    def setup(self):
        self.gp = Gpupy()
        self.rng = np.random.RandomState(0)

    def test_relu_m(self):
        """relu on matrices."""
        a = self.rng.rand(129, 1025).astype(np.float32)
        t = .5
        out_np = copy.deepcopy(a)
        out_np[out_np<t] = 0.
        out_gp = self.gp.relu(a, thresh=t).copy_to_host()
        assert(np.allclose(out_np, out_gp))

        a = self.rng.rand(129, 1025).astype(np.float32)
        val = .5
        out_np = copy.deepcopy(a)
        out_np[out_np<t] = val
        out_gp = self.gp.relu(a, thresh=t, set_val=val).copy_to_host()
        assert(np.allclose(out_np, out_gp))

        a = self.rng.rand(129, 1025).astype(np.float32)
        val = .5
        out_np = copy.deepcopy(a)
        out_np[out_np<t] = val
        out_gp = cuda.to_device(a)
        self.gp.relu(a, thresh=t, set_val=val, out=out_gp)
        assert(np.allclose(out_np, out_gp.copy_to_host()))

    def test_relu_m_v(self):
        """relu on matrices with vectors for thresholds."""
        a = self.rng.rand(129, 1025).astype(np.float32)
        t = self.rng.rand(1025).astype(np.float32)
        out_np = copy.deepcopy(a)
        out_np[out_np<t] = 0.
        out_gp = self.gp.relu(a, thresh=t).copy_to_host()
        assert(np.allclose(out_np, out_gp))

        a = self.rng.rand(129, 1025).astype(np.float32)
        t = self.rng.rand(129).astype(np.float32)
        val = .5
        out_np = copy.deepcopy(a)
        out_np[out_np<t] = val
        out_gp = self.gp.relu(a, thresh=t, set_val=val).copy_to_host()
        assert(np.allclose(out_np, out_gp))

        a = self.rng.rand(129, 1025).astype(np.float32)
        t = self.rng.rand(129).astype(np.float32)
        val = .5
        out_np = copy.deepcopy(a)
        out_np[out_np<t] = val
        out_gp = cuda.to_device(a)
        self.gp.relu(a, thresh=t, set_val=val, out=out_gp)
        assert(np.allclose(out_np, out_gp.copy_to_host()))

    def test_relu_m_v(self):
        """relu on matrices with newaxis vectors for thresholds."""
        a = self.rng.rand(129, 1025).astype(np.float32)
        t = self.rng.rand(1025).astype(np.float32)
        out_np = copy.deepcopy(a)
        out_np[out_np<t[np.newaxis,:]] = 0.
        out_gp = self.gp.relu(a, thresh=t[np.newaxis,:]).copy_to_host()
        assert(np.allclose(out_np, out_gp))

        a = self.rng.rand(129, 1025).astype(np.float32)
        t = self.rng.rand(129).astype(np.float32)
        val = .5
        out_np = copy.deepcopy(a)
        out_np[out_np<t[:,np.newaxis]] = val
        out_gp = self.gp.relu(a, thresh=t[:,np.newaxis], set_val=val).copy_to_host()
        assert(np.allclose(out_np, out_gp))

        a = self.rng.rand(129, 1025).astype(np.float32)
        t = self.rng.rand(129).astype(np.float32)
        val = .5
        out_np = copy.deepcopy(a)
        out_np[out_np<t[:,np.newaxis]] = val
        out_gp = cuda.to_device(a)
        self.gp.relu(a, thresh=t[:,np.newaxis], set_val=val, out=out_gp)
        assert(np.allclose(out_np, out_gp.copy_to_host()))

    def test_relu_v(self):
        """relu on vectors"""
        a = self.rng.rand(129).astype(np.float32)
        t = .5
        out_np = copy.deepcopy(a)
        out_np[out_np<t] = 0.
        out_gp = self.gp.relu(a, thresh=t).copy_to_host()
        assert(np.allclose(out_np, out_gp))

        a = self.rng.rand(129).astype(np.float32)
        val = .5
        out_np = copy.deepcopy(a)
        out_np[out_np<t] = val
        out_gp = self.gp.relu(a, thresh=t, set_val=val).copy_to_host()
        assert(np.allclose(out_np, out_gp))

        a = self.rng.rand(129).astype(np.float32)
        val = .5
        out_np = copy.deepcopy(a)
        out_np[out_np<t] = val
        out_gp = cuda.to_device(a)
        self.gp.relu(a, thresh=t, set_val=val, out=out_gp)
        assert(np.allclose(out_np, out_gp.copy_to_host()))
Esempio n. 15
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class test_dot():

    def setup(self):
        self.gp = Gpupy()
        self.rng = np.random.RandomState(0)

    def test_gemm(self):
        """ Test matrix-matrix dot product."""
        a = self.rng.rand(129, 1025).astype(np.float32)
        b = self.rng.rand(1025, 257).astype(np.float32)

        out_np = np.dot(a, b)
        out_gp = self.gp.dot(a, b).copy_to_host()
        assert(np.allclose(out_np, out_gp))

        out_gp = self.gp.zeros(shape=(129, 257))
        self.gp.dot(a, b, out=out_gp)
        assert(np.allclose(out_np, out_gp.copy_to_host()))

    def test_gemv(self):		
        a = self.rng.rand(129, 1025).astype(np.float32)
        b = self.rng.rand(1025).astype(np.float32)

        out_np = np.dot(a, b)
        out_gp = self.gp.dot(a, b).copy_to_host()
        assert(np.allclose(out_np, out_gp))

        out_gp = self.gp.zeros(shape=129)
        self.gp.dot(a, b, out=out_gp)
        assert(np.allclose(out_np, out_gp.copy_to_host()))

    def test_gevm(self):		
        a = self.rng.rand(129, 1025).astype(np.float32)
        b = self.rng.rand(129).astype(np.float32)

        out_np = np.dot(b,a)
        out_gp = self.gp.dot(b,a).copy_to_host()
        assert(np.allclose(out_np, out_gp))

        out_gp = self.gp.zeros(shape=1025)
        self.gp.dot(b, a, out=out_gp)
        assert(np.allclose(out_np, out_gp.copy_to_host()))

    def test_vvdot(self):		
        a = self.rng.rand(129).astype(np.float32)
        b = self.rng.rand(129).astype(np.float32)

        out_np = np.dot(a,b)
        out_gp = self.gp.dot(a,b)
        assert(np.allclose(out_np, out_gp))