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"""Test code for 3d convolution with winograd."""

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

from common import get_all_backend

_conv3d_ncdhw_implement = {
    "gpu": (topi.cuda.conv3d_ncdhw_winograd, topi.cuda.schedule_conv3d_ncdhw_winograd),
}


def verify_conv3d_ncdhw(batch,
                        in_channel,
                        in_size,
                        num_filter,
                        depth_kernel,
                        space_kernel,
                        stride,
                        padding,
                        dilation=1,
                        add_bias=False,
                        add_relu=False):
    pad_front, pad_top, pad_left, pad_back, pad_bottom, pad_right = get_pad_tuple3d(
        padding, (depth_kernel, space_kernel, space_kernel))
    padding_sum = pad_front + pad_back + pad_top + pad_left + pad_bottom + pad_right
    print("Workload: (%d, %d, %d, %d, %d, %d, %d, %d)" %
          (batch, in_channel, in_size, num_filter, space_kernel, 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, depth_kernel, space_kernel, space_kernel), 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 = topi.testing.dilate_python(w_np, (1, 1, dilation, dilation, dilation))
        c_np = topi.testing.conv3d_ncdhw_python(a_np, dw_np, stride, padding)
        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_device(device):
        ctx = tvm.context(device, 0)
        if not ctx.exist:
            print("Skip because %s is not enabled" % device)
            return
        print("Running on target: %s" % device)
        fcompute, fschedule = topi.testing.dispatch(device, _conv3d_ncdhw_implement)
        with tvm.target.create(device):
            C = fcompute(A, W, (stride, stride, stride), padding, (dilation, dilation, dilation),
                         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, ctx)
        w = tvm.nd.array(w_np, ctx)
        b = tvm.nd.array(b_np, ctx)
        c = tvm.nd.array(np.zeros(get_const_tuple(C.shape), dtype=C.dtype), ctx)
        if add_bias:
            func = tvm.build(
                s, [A, W, bias, C],
                device,
                name="relu_%d_%d_%d_%d_%d_%d_%d_%d" %
                (batch, in_channel, in_size, num_filter, space_kernel, stride, padding_sum, dilation))
            func(a, w, b, c)
        else:
            func = tvm.build(
                s, [A, W, C],
                device,
                name="relu_%d_%d_%d_%d_%d_%d_%d_%d" %
                (batch, in_channel, in_size, num_filter, space_kernel, stride, padding_sum, dilation))
            func(a, w, c)
        tvm.testing.assert_allclose(c.asnumpy(), c_np, rtol=1e-4)

    for device in ["cuda"]:
        with autotvm.tophub.context(device):  # load tophub pre-tuned parameters
            check_device(device)


def test_conv3d_ncdhw():
    # Try without depth transformation
    #3DCNN  workloads
    verify_conv3d_ncdhw(1, 61, 20, 120, 3, 3, 1, 0)
    verify_conv3d_ncdhw(1, 61, 20, 120, 1, 3, 1, 0)
    verify_conv3d_ncdhw(1, 61, 20, 120, 5, 3, 1, 0)
    verify_conv3d_ncdhw(1, 61, 20, 120, 5, 5, 1, 2)
    verify_conv3d_ncdhw(1, 61, 20, 120, 1, 5, 1, 2)
    verify_conv3d_ncdhw(1, 61, 20, 120, 7, 7, 1, 3)
    verify_conv3d_ncdhw(1, 128, 12, 256, 3, 3, 1, 1)
    verify_conv3d_ncdhw(1, 64, 12, 128, 3, 3, 1, 1)

    # bias, relu
    verify_conv3d_ncdhw(1, 64, 12, 128, 3, 3, 1, 1, add_relu=True)
    verify_conv3d_ncdhw(1, 64, 12, 128, 3, 3, 1, 1, add_relu=True, add_bias=True)
    verify_conv3d_ncdhw(1, 64, 12, 128, 1, 3, 1, 1, add_relu=True, add_bias=True)

    # dilation = 2
    verify_conv3d_ncdhw(1, 16, 12, 16, 3, 3, 1, "VALID", dilation=2)
    verify_conv3d_ncdhw(1, 16, 12, 16, 1, 3, 1, "VALID", dilation=2)

    # batch size
    verify_conv3d_ncdhw(4, 32, 12, 64, 3, 3, 1, 1)
    verify_conv3d_ncdhw(4, 32, 12, 64, 1, 3, 1, 1)

    # weird workloads
    verify_conv3d_ncdhw(2, 2, 2, 2, 3, 3, 1, 2)
    verify_conv3d_ncdhw(3, 3, 3, 3, 3, 3, 1, 3)


if __name__ == "__main__":
    test_conv3d_ncdhw()
