text: """Example code to do convolution."""
  
  import numpy as np
  import tvm
  from tvm import autotvm
  from tvm.autotvm.task.space import FallbackConfigEntity
  import topi
  import topi.testing
  from tvm.contrib.pickle_memoize import memoize
  from topi.util import get_const_tuple
  
  
  def verify_conv2d_nchw(batch, in_channel, in_size, num_filter, kernel, stride, padding, dilation=1, add_bias=False, add_relu=False):
      print("Workload: (%d, %d, %d, %d, %d, %d, %d)" % (batch, in_channel, in_size, num_filter, kernel, stride, padding))
  
      in_height = in_width = in_size
  
      A = tvm.placeholder((batch, in_channel, in_height, in_width), name='A')
      W = tvm.placeholder((num_filter, in_channel, kernel, kernel), name='W')
      bias = tvm.placeholder((num_filter, 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_conv2d_nchw.verify_conv2d_nchw")
      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))
          c_np = topi.testing.conv2d_nchw_python(a_np, dw_np, stride, padding)
          if add_bias:
              b_np = np.random.uniform(size=bias_shape).astype(dtype)
              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)
          with tvm.target.create(device):
              dW = topi.nn.dilate(W, (1, 1, dilation, dilation))
              C = topi.nn.conv2d(A, dW, stride, padding, layout='NCHW', out_dtype=dtype)
              if add_bias:
                  C = topi.add(C, bias)
              if add_relu:
                  C = topi.nn.relu(C)
              s = topi.generic.schedule_conv2d_nchw([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, kernel, stride, padding, 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, kernel, stride, padding, dilation))
              func(a, w, c)
          np.testing.assert_allclose(c.asnumpy(), c_np, rtol=1e-5)
  
  
      for device in ['cuda', 'llvm -device=arm_cpu', 'opencl -device=mali']:
          check_device(device)
  
  
  class WinogradFallback(autotvm.FallbackContext):
      def _query_inside(self, target, workload):
          key = (target, workload)
          if key in self.memory:
              return self.memory[key]
          cfg = FallbackConfigEntity()
          cfg.template_key = 'winograd'
          self.memory[key] = cfg
          return cfg
  
  
  def test_conv2d_nchw():
      autotvm.DispatchContext.current.silent = True
  
      with WinogradFallback():
          # resnet 18 workloads
          verify_conv2d_nchw(1, 64, 56, 64, 3, 1, 1)
          verify_conv2d_nchw(1, 128, 28, 128, 3, 1, 1)
          verify_conv2d_nchw(1, 256, 14, 256, 3, 1, 1)
          verify_conv2d_nchw(1, 512, 7, 512, 3, 1, 1)
  
          # batch size = 2
          verify_conv2d_nchw(2, 64, 56, 64, 3, 1, 1)
  
          # relu, bias
          verify_conv2d_nchw(2, 64, 56, 64, 3, 1, 1, add_bias=True)
          verify_conv2d_nchw(2, 64, 56, 64, 3, 1, 1, add_relu=True)
          verify_conv2d_nchw(2, 64, 56, 64, 3, 1, 1, add_relu=True, add_bias=True)
  
          # werid workloads
          verify_conv2d_nchw(1, 1, 1, 1, 3, 1, 1)
          verify_conv2d_nchw(3, 3, 3, 3, 3, 1, 1)
          verify_conv2d_nchw(2, 13, 71, 59, 3, 1, 1)
  
  if __name__ == "__main__":
      test_conv2d_nchw()
  