"""Example code to do convolution.""" import os import numpy as np import tvm import topi from tvm.contrib.pickle_memoize import memoize from topi.util import get_const_tuple def verify_conv2d_nhwc(batch, in_channel, in_size, num_filter, kernel, stride, padding): in_height = in_width = in_size A = tvm.placeholder((batch, in_height, in_width, in_channel), name='A') W = tvm.placeholder((kernel, kernel, in_channel, num_filter), name='W') B = topi.nn.conv2d_nhwc(A, W, stride, padding) a_shape = get_const_tuple(A.shape) w_shape = get_const_tuple(W.shape) dtype = A.dtype @memoize("topi.tests.test_topi_conv2d_nhwc.verify_nhwc") 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 = topi.testing.conv2d_nhwc_python(a_np, w_np, stride, padding) return a_np, w_np, b_np a_np, w_np, b_np = get_ref_data() def check_device(device): if not tvm.module.enabled(device): print("Skip because %s is not enabled" % device) return print("Running on target: %s" % device) with tvm.target.create(device): s = topi.generic.schedule_conv2d_nhwc([B]) ctx = tvm.context(device, 0) a = tvm.nd.array(a_np, ctx) w = tvm.nd.array(w_np, ctx) b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=B.dtype), ctx) func = tvm.build(s, [A, W, B], device) func(a, w, b) np.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5) for device in ['llvm']: check_device(device) def test_conv2d_nhwc(): verify_conv2d_nhwc(1, 256, 32, 256, 3, 1, "SAME") verify_conv2d_nhwc(4, 128, 16, 128, 5, 2, "SAME") verify_conv2d_nhwc(4, 128, 16, 256, 5, 2, "SAME") verify_conv2d_nhwc(1, 256, 32, 256, 3, 1, "VALID") verify_conv2d_nhwc(1, 256, 32, 256, 3, 1, "VALID") verify_conv2d_nhwc(4, 128, 16, 128, 5, 2, "VALID") verify_conv2d_nhwc(4, 128, 16, 256, 5, 2, "VALID") if __name__ == "__main__": test_conv2d_nhwc()