test_nnpack.py 6.63 KB
Newer Older
1 2
import tvm
import numpy as np
3
import scipy.signal
4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64
from tvm.contrib import nnpack

def test_fully_connected_output():
    n = 1024
    l = 128
    m = 235
    bias = tvm.var('bias', dtype=tvm.float32)
    A = tvm.placeholder((n, l), name='A')
    B = tvm.placeholder((m, l), name='B')
    C = nnpack.fully_connected_output(A, B)
    D = tvm.compute(C.shape, lambda i, j: C[i,j] + bias, name="D")
    s = tvm.create_schedule(D.op)

    def verify(target="llvm"):
        if not tvm.module.enabled(target):
            print("skip because %s is not enabled..." % target)
            return
        if not tvm.get_global_func("tvm.contrib.nnpack.fully_connected_output", True):
            print("skip because extern function is not avalable")
            return
        ctx = tvm.cpu(0)
        f = tvm.build(s, [A, B, D, bias], target)
        a = tvm.nd.array(np.random.uniform(size=(n, l)).astype(A.dtype), ctx)
        b = tvm.nd.array(np.random.uniform(size=(m, l)).astype(B.dtype), ctx)
        d = tvm.nd.array(np.zeros((n, m), dtype=D.dtype), ctx)
        bb = 10.0
        f(a, b, d, bb)
        np.testing.assert_allclose(
            d.asnumpy(), np.dot(a.asnumpy(), b.asnumpy().T) + bb, rtol=1e-5)
    verify()


def test_fully_connected_inference():
    n = 1024
    l = 128
    m = 235
    bias = tvm.var('bias', dtype=tvm.float32)
    A = tvm.placeholder((l, ), name='A')
    B = tvm.placeholder((m, l), name='B')
    C = nnpack.fully_connected_inference(A, B)
    D = tvm.compute(C.shape, lambda i: C[i] + bias, name="D")
    s = tvm.create_schedule(D.op)

    def verify(target="llvm"):
        if not tvm.module.enabled(target):
            print("skip because %s is not enabled..." % target)
            return
        if not tvm.get_global_func("tvm.contrib.nnpack.fully_connected_inference", True):
            print("skip because extern function is not avalable")
            return
        ctx = tvm.cpu(0)
        f = tvm.build(s, [A, B, D, bias], target)
        a = tvm.nd.array(np.random.uniform(size=(l)).astype(A.dtype), ctx)
        b = tvm.nd.array(np.random.uniform(size=(m, l)).astype(B.dtype), ctx)
        d = tvm.nd.array(np.zeros((m, ), dtype=D.dtype), ctx)
        bb = 10.0
        f(a, b, d, bb)
        np.testing.assert_allclose(
            d.asnumpy(), np.dot(a.asnumpy(), b.asnumpy().T) + bb, rtol=1e-5)
    verify()

65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192
def np_conv(na, nw, padding, stride=1):
    batch, in_channel, in_height, in_width = na.shape
    _, num_filter, kernel_h, kernel_w = nw.shape
    if isinstance(stride, int):
        stride_h = stride_w = stride
    else:
        stride_h, stride_w = stride

    if isinstance(padding, int):
        pad_h = pad_w = padding * 2
    else:
        pad_h, pad_w = padding
        pad_h *= 2
        pad_w *= 2

    pad_top = int(np.ceil(float(pad_h) / 2))
    pad_bottom = pad_h - pad_top
    pad_left = int(np.ceil(float(pad_w) / 2))
    pad_right = pad_w - pad_left

    out_channel = num_filter
    out_height = (in_height - kernel_h + pad_h) // stride_h + 1
    out_width = (in_width - kernel_w + pad_w) // stride_w + 1
    nb = np.zeros((batch, out_channel, out_height, out_width))
    for n in range(batch):
        for f in range(out_channel):
            for c in range(in_channel):
                if pad_h > 0:
                    apad = np.zeros((in_height + pad_h, in_width + pad_w))
                    apad[pad_top:-pad_bottom, pad_left:-pad_right] = na[n, c]
                else:
                    apad = na[n, c]
                out = scipy.signal.convolve2d(
                    apad, np.rot90(np.rot90(nw[f, c])), mode='valid')
                nb[n, f] += out[::stride, ::stride]
    return nb

def test_convolution_inference():
    BATCH = 32
    IH = 48
    IW = 48
    IC = 16
    OC = 16
    K = 3
    PAD = 1
    STRIDE = 1

    OH = (IH + 2*PAD - K) + 1
    OW = (IW + 2*PAD - K) + 1
    dshape = (IC, IH, IW)
    kshape = (OC, IC, K, K)
    bshape = (OC, )
    oshape = (OC, OH, OW)

    data = tvm.placeholder(dshape, name='data')
    kernel = tvm.placeholder(kshape, name='kernel')
    bias = tvm.placeholder(bshape, name='bias')
    output = nnpack.convolution_inference(data, kernel, bias,
        [PAD, PAD, PAD, PAD], [STRIDE, STRIDE])
    s = tvm.create_schedule(output.op)

    def verify(target="llvm"):
        if not tvm.module.enabled(target):
            print("skip because %s is not enabled..." % target)
            return
        if not tvm.get_global_func("tvm.contrib.nnpack.fully_connected_inference", True):
            print("skip because extern function is not avalable")
            return
        ctx = tvm.cpu(0)
        f = tvm.build(s, [data, kernel, bias, output], target)

        na = np.random.uniform(size=dshape).astype(data.dtype)
        nb = np.random.uniform(size=kshape).astype(kernel.dtype)
        nc = np.zeros(bshape, dtype=bias.dtype)
        ta = tvm.nd.array(na, ctx)
        tb = tvm.nd.array(nb, ctx)
        tc = tvm.nd.array(nc, ctx)
        td = tvm.nd.array(np.zeros(oshape, dtype=output.dtype), ctx)
        f(ta, tb, tc, td)
        nd = np_conv(np.reshape(na, (1, IC, IH, IW)), nb, PAD, STRIDE)
        np.testing.assert_allclose(
            td.asnumpy(), nd.reshape(IC, IH, IW), rtol=1e-5)
    verify()

def test_convolution_output():
    BATCH = 32
    IH = 48
    IW = 48
    IC = 16
    OC = 16
    K = 3
    PAD = 1

    OH = (IH + 2*PAD - K) + 1
    OW = (IW + 2*PAD - K) + 1
    dshape = (BATCH, IC, IH, IW)
    kshape = (OC, IC, K, K)
    bshape = (OC, )
    oshape = (BATCH, OC, OH, OW)

    data = tvm.placeholder(dshape, name='data')
    kernel = tvm.placeholder(kshape, name='kernel')
    bias = tvm.placeholder(bshape, name='bias')
    output = nnpack.convolution_output(data, kernel, bias, [PAD, PAD, PAD, PAD])
    s = tvm.create_schedule(output.op)

    def verify(target="llvm"):
        if not tvm.module.enabled(target):
            print("skip because %s is not enabled..." % target)
            return
        if not tvm.get_global_func("tvm.contrib.nnpack.fully_connected_inference", True):
            print("skip because extern function is not avalable")
            return
        ctx = tvm.cpu(0)
        f = tvm.build(s, [data, kernel, bias, output], target)

        na = np.random.uniform(size=dshape).astype(data.dtype)
        nb = np.random.uniform(size=kshape).astype(kernel.dtype)
        nc = np.zeros(bshape, dtype=bias.dtype)
        ta = tvm.nd.array(na, ctx)
        tb = tvm.nd.array(nb, ctx)
        tc = tvm.nd.array(nc, ctx)
        td = tvm.nd.array(np.zeros(oshape, dtype=output.dtype), ctx)
        f(ta, tb, tc, td)
        nd = np_conv(na, nb, PAD)
        np.testing.assert_allclose(
            td.asnumpy(), nd, rtol=1e-5)
    verify()
193 194

if __name__ == "__main__":
195 196
    import nose
    nose.runmodule()