text: # Licensed to the Apache Software Foundation (ASF) under one
  # or more contributor license agreements.  See the NOTICE file
  # distributed with this work for additional information
  # regarding copyright ownership.  The ASF licenses this file
  # to you under the Apache License, Version 2.0 (the
  # "License"); you may not use this file except in compliance
  # with the License.  You may obtain a copy of the License at
  #
  #   http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing,
  # software distributed under the License is distributed on an
  # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
  # KIND, either express or implied.  See the License for the
  # specific language governing permissions and limitations
  # under the License.
  import numpy as np
  import tvm
  from tvm import te
  from tvm import autotvm
  from tvm.autotvm.task.space import FallbackConfigEntity
  from tvm.contrib import nnpack
  from tvm.contrib.pickle_memoize import memoize
  from tvm import topi
  import tvm.topi.testing
  from tvm.topi.util import get_const_tuple
  from pytest import skip
  
  
  def verify_conv2d_nchw(batch, in_channel, in_size, num_filter, kernel, stride, padding, dilation=1, add_bias=False, add_relu=False,
          devices=['cuda', 'llvm -device=arm_cpu', 'opencl -device=mali']):
      print("Workload: (%d, %d, %d, %d, %d, %d, %d, %d)" % (batch, in_channel, in_size, num_filter, kernel, stride, padding, dilation))
  
      in_height = in_width = in_size
  
      A = te.placeholder((batch, in_channel, in_height, in_width), name='A')
      W = te.placeholder((num_filter, in_channel, kernel, kernel), name='W')
      bias = te.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 = tvm.topi.testing.dilate_python(w_np, (1, 1, dilation, dilation))
          c_np = tvm.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:
              skip("s is not enabled" % device)
          print("Running on target: %s" % device)
          with tvm.target.create(device):
              C = topi.nn.conv2d(A, W, stride, padding, dilation, 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)
          tvm.testing.assert_allclose(c.asnumpy(), c_np, rtol=1e-4)
  
  
      for device in devices:
          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_nnpack_fp32'
          self.memory[key] = cfg
          return cfg
  
  def test_conv2d_nchw():
      if not tvm.get_global_func("tvm.contrib.nnpack.convolution_inference_without_weight_transform", True):
          skip("extern function is not available")
  
      if not nnpack.is_available():
          skip("nnpack is not available")
  
      devices = ['llvm -device=arm_cpu']
      autotvm.GLOBAL_SCOPE.silent = True
      with WinogradFallback():
          # resnet 18 workloads
          verify_conv2d_nchw(1, 64, 56, 64, 3, 1, 1, devices=devices)
          verify_conv2d_nchw(1, 128, 28, 128, 3, 1, 1, devices=devices)
          verify_conv2d_nchw(1, 256, 14, 256, 3, 1, 1, devices=devices)
          verify_conv2d_nchw(1, 512, 7, 512, 3, 1, 1, devices=devices)
  
          # unet workloads
          verify_conv2d_nchw(1, 3, 192, 12, 3, 1, 1, add_bias=True, devices=devices)
          verify_conv2d_nchw(1, 4, 192, 12, 3, 1, 1, add_bias=True, devices=devices)
          verify_conv2d_nchw(1, 12, 96, 24, 3, 1, 1, add_bias=True, devices=devices)
          verify_conv2d_nchw(1, 24, 48, 48, 3, 1, 1, add_bias=True, devices=devices)
          verify_conv2d_nchw(1, 48, 24, 96, 3, 1, 1, add_bias=True, devices=devices)
          verify_conv2d_nchw(1, 96, 12, 180, 3, 1, 1, add_bias=True, devices=devices)
          verify_conv2d_nchw(1, 180, 6, 220, 3, 1, 1, add_bias=True, devices=devices)
          verify_conv2d_nchw(1, 220, 6, 180, 3, 1, 1, add_bias=True, devices=devices)
          verify_conv2d_nchw(1, 180, 12, 96, 3, 1, 1, add_bias=True, devices=devices)
          verify_conv2d_nchw(1, 96, 24, 48, 3, 1, 1, add_bias=True, devices=devices)
          verify_conv2d_nchw(1, 48, 48, 24, 3, 1, 1, add_bias=True, devices=devices)
          verify_conv2d_nchw(1, 24, 96, 12, 3, 1, 1, add_bias=True, devices=devices)
          verify_conv2d_nchw(1, 12, 192, 1, 3, 1, 1, add_bias=True, devices=devices)
  
          # relu, bias
          verify_conv2d_nchw(1, 64, 56, 64, 3, 1, 1, add_bias=True, devices=devices)
          verify_conv2d_nchw(1, 64, 56, 64, 3, 1, 1, add_relu=True, devices=devices)
          verify_conv2d_nchw(1, 64, 56, 64, 3, 1, 1, add_relu=True, add_bias=True, devices=devices)
  
          # werid workloads
          verify_conv2d_nchw(1, 3, 3, 3, 3, 1, 1, devices=devices)
          verify_conv2d_nchw(1, 13, 71, 59, 3, 1, 1, devices=devices)
      autotvm.GLOBAL_SCOPE.silent = False
  
  
  if __name__ == "__main__":
      import pytest
      pytest.main([__file__])
  