tests/python/topi/python/test_topi_conv3d_ncdhw.py

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"""Example code to do convolution."""

import numpy as np
import tvm
from tvm import te
from tvm import autotvm
from tvm import topi
import tvm.testing
import tvm.topi.testing
from tvm.contrib.pickle_memoize import memoize
from tvm.topi.nn.utils import get_pad_tuple3d
from tvm.topi.utils import get_const_tuple

_conv3d_ncdhw_implement = {
    "generic": (topi.nn.conv3d_ncdhw, topi.generic.schedule_conv3d_ncdhw),
    "cpu": (topi.x86.conv3d_ncdhw, topi.x86.schedule_conv3d_ncdhw),
    "gpu": (topi.cuda.conv3d_ncdhw, topi.cuda.schedule_conv3d_ncdhw),
}


def verify_conv3d_ncdhw(
    batch,
    in_channel,
    in_size,
    num_filter,
    kernel,
    stride,
    padding,
    dilation=1,
    groups=1,
    add_bias=False,
    add_relu=False,
):
    if isinstance(kernel, (tuple, list)):
        if len(kernel) == 3:
            kernel_d = kernel[0]
            kernel_h = kernel[1]
            kernel_w = kernel[2]
        else:
            raise ValueError("Size of kernel can only be 3")
    elif isinstance(kernel, int):
        kernel_d = kernel_h = kernel_w = kernel
    else:
        raise ValueError("Unknown kernel option %s" % kernel)
    pad_front, pad_top, pad_left, pad_back, pad_bottom, pad_right = get_pad_tuple3d(
        padding, (kernel_d, kernel_h, kernel_w)
    )
    padding_sum = pad_front + pad_back + pad_top + pad_left + pad_bottom + pad_right
    print(
        "Workload: (%d, %d, %d, %d, %d, %d, %d, %d, %d, %d)"
        % (
            batch,
            in_channel,
            in_size,
            num_filter,
            kernel_d,
            kernel_h,
            kernel_w,
            stride,
            padding_sum,
            dilation,
        )
    )

    in_depth = in_height = in_width = in_size

    A = te.placeholder((batch, in_channel, in_depth, in_height, in_width), name="A")
    W = te.placeholder((num_filter, in_channel // groups, kernel_d, kernel_h, kernel_w), name="W")
    bias = te.placeholder((num_filter, 1, 1, 1), name="bias")

    a_shape = get_const_tuple(A.shape)
    w_shape = get_const_tuple(W.shape)
    bias_shape = get_const_tuple(bias.shape)
    dtype = A.dtype

    @memoize("topi.tests.test_topi_conv3d_ncdhw.verify_conv3d_ncdhw")
    def get_ref_data():
        a_np = np.random.uniform(size=a_shape).astype(dtype)
        w_np = np.random.uniform(size=w_shape).astype(dtype)
        b_np = np.random.uniform(size=bias_shape).astype(dtype)
        dw_np = tvm.topi.testing.dilate_python(w_np, (1, 1, dilation, dilation, dilation))
        c_np = tvm.topi.testing.conv3d_ncdhw_python(a_np, dw_np, stride, padding, groups)
        if add_bias:
            c_np += b_np
        if add_relu:
            c_np = np.maximum(c_np, 0)
        return a_np, w_np, b_np, c_np

    a_np, w_np, b_np, c_np = get_ref_data()

    def check_target(target, dev):
        print("Running on target: %s" % target)
        fcompute, fschedule = tvm.topi.testing.dispatch(target, _conv3d_ncdhw_implement)
        with tvm.target.Target(target):
            C = fcompute(
                A,
                W,
                (stride, stride, stride),
                padding,
                (dilation, dilation, dilation),
                groups,
                dtype,
            )
            if add_bias:
                C = topi.add(C, bias)
            if add_relu:
                C = topi.nn.relu(C)
            s = fschedule([C])

        a = tvm.nd.array(a_np, dev)
        w = tvm.nd.array(w_np, dev)
        b = tvm.nd.array(b_np, dev)
        c = tvm.nd.array(np.zeros(get_const_tuple(C.shape), dtype=C.dtype), dev)
        if add_bias:
            func = tvm.build(
                s,
                [A, W, bias, C],
                target,
                name="relu_%d_%d_%d_%d_%d_%d_%d_%d_%d_%d_%d"
                % (
                    batch,
                    in_channel,
                    in_size,
                    num_filter,
                    kernel_d,
                    kernel_h,
                    kernel_w,
                    stride,
                    padding_sum,
                    dilation,
                    groups,
                ),
            )
            func(a, w, b, c)
        else:
            func = tvm.build(
                s,
                [A, W, C],
                target,
                name="relu_%d_%d_%d_%d_%d_%d_%d_%d_%d_%d_%d"
                % (
                    batch,
                    in_channel,
                    in_size,
                    num_filter,
                    kernel_d,
                    kernel_h,
                    kernel_w,
                    stride,
                    padding_sum,
                    dilation,
                    groups,
                ),
            )
            func(a, w, c)
        tvm.testing.assert_allclose(c.numpy(), c_np, rtol=1e-4, atol=1e-6)

    for target, dev in tvm.testing.enabled_targets():
        with autotvm.tophub.context(target):  # load tophub pre-tuned parameters
            check_target(target, dev)


@tvm.testing.uses_gpu
def test_conv3d_ncdhw():
    # 3DCNN  workloads
    verify_conv3d_ncdhw(1, 32, 32, 5, 1, 1, 0)
    verify_conv3d_ncdhw(1, 32, 32, 1, 1, 1, 0)
    verify_conv3d_ncdhw(1, 32, 32, 5, 1, 1, 1)
    verify_conv3d_ncdhw(1, 32, 32, 1, 1, 1, 1)

    # bias, relu
    verify_conv3d_ncdhw(1, 64, 56, 3, 1, 1, 1, add_relu=True)
    verify_conv3d_ncdhw(1, 64, 56, 3, 1, 1, 1, add_bias=True)
    verify_conv3d_ncdhw(1, 64, 56, 3, 1, 1, 1, add_bias=True, add_relu=True)

    # dilation = 2
    verify_conv3d_ncdhw(1, 64, 56, 3, 3, 1, 1, dilation=2)

    # batch size
    verify_conv3d_ncdhw(4, 64, 56, 5, 3, 1, 1)

    # weird workloads
    verify_conv3d_ncdhw(2, 2, 2, 2, 2, 2, 2)
    verify_conv3d_ncdhw(3, 3, 3, 3, 3, 3, 3)

    # Asymmetric padding
    verify_conv3d_ncdhw(1, 32, 32, 5, 1, 1, (0, 0, 0, 1, 1, 1))
    verify_conv3d_ncdhw(1, 32, 32, 1, 1, 1, (2, 1, 2, 1, 2, 1))
    verify_conv3d_ncdhw(1, 64, 56, 3, 3, 1, (2, 2, 2, 1, 1, 1), dilation=2)
    verify_conv3d_ncdhw(1, 32, 32, 5, 1, 1, (0, 1, 1))
    verify_conv3d_ncdhw(1, 32, 32, 1, 1, 1, (2, 1, 0))
    verify_conv3d_ncdhw(1, 32, 32, 1, 3, 1, "VALID")
    verify_conv3d_ncdhw(1, 32, 32, 5, 1, 1, "VALID")

    # DHW kernel layout
    verify_conv3d_ncdhw(1, 32, 56, 16, (3, 5, 7), 2, (1, 2, 3))
    verify_conv3d_ncdhw(1, 3, 56, 16, (3, 7, 7), 2, (1, 2, 3, 0, 3, 2))
    verify_conv3d_ncdhw(1, 3, 56, 16, (3, 3, 7), 2, (1, 2, 3))
    verify_conv3d_ncdhw(1, 3, 56, 16, (3, 7, 3), 2, (1, 3, 1))

    # grouped workloads
    verify_conv3d_ncdhw(1, 32, 32, 8, 1, 1, 0, groups=4)
    verify_conv3d_ncdhw(1, 32, 32, 4, 1, 1, 0, groups=4)
    verify_conv3d_ncdhw(1, 32, 32, 8, 1, 1, 1, groups=4)
    verify_conv3d_ncdhw(1, 32, 32, 4, 1, 1, 1, groups=4)


if __name__ == "__main__":
    test_conv3d_ncdhw()