[GitHub] [incubator-tvm] optima2005 commented on a change in pull request #4476: Implement 1d deconvolution

[GitBox <gi...@apache.org>]

optima2005 commented on a change in pull request #4476: Implement 1d deconvolution
URL: https://github.com/apache/incubator-tvm/pull/4476#discussion_r357496355
 
 

 ##########
 File path: src/relay/op/nn/convolution.cc
 ##########
 @@ -328,6 +328,160 @@ v            (batch_size, channels, out_height, out_width) if `layout` is `NCHW`
 .add_type_rel("Conv2DTranspose", Conv2DTransposeRel);
 
 
+// relay.nn.conv1d_transpose
+TVM_REGISTER_NODE_TYPE(Conv1DTransposeAttrs);
+
+bool Conv1DTransposeRel(const Array<Type>& types,
+                        int num_inputs,
+                        const Attrs& attrs,
+                        const TypeReporter& reporter) {
+  CHECK_EQ(types.size(), 3);
+  const auto* data = types[0].as<TensorTypeNode>();
+  const auto* weight = types[1].as<TensorTypeNode>();
+  if (data == nullptr) return false;
+
+  static const Layout kNCW("NCW");
+  static const Layout kOIW("OIW");
+
+  const Conv1DTransposeAttrs* param = attrs.as<Conv1DTransposeAttrs>();
+  CHECK(param != nullptr);
+  const Layout in_layout(param->data_layout);
+  const Layout kernel_layout(param->kernel_layout);
+
+  const auto trans_in_layout = BijectiveLayoutNode::make(in_layout, kNCW);
+  CHECK(trans_in_layout.defined())
+    << "Conv only support input layouts that are convertible from NCW."
+    << " But got " << in_layout;
+
+  const auto trans_kernel_layout = BijectiveLayoutNode::make(kernel_layout, kOIW);
+  CHECK(trans_kernel_layout.defined())
+    << "Conv only support kernel layouts that are convertible from OIW."
+    << " But got "<< kernel_layout;
+
+  Layout out_layout(param->out_layout == "" ? param->data_layout : param->out_layout);
+  const auto trans_out_layout = BijectiveLayoutNode::make(out_layout, kNCW);
+  CHECK(trans_out_layout.defined())
+    << "Conv only support output layouts that are convertible from NCW."
+    << " But got " << out_layout;
+
+  IndexExpr channels, dilated_ksize_y, dilated_ksize_x;
+
+  auto dshape_ncw = trans_in_layout.ForwardShape(data->shape);
+
+  // infer weight if the kernel_size and channels are defined
+  if (param->kernel_size.defined() && param->channels.defined()) {
+    CHECK_EQ(param->kernel_size.size(), 1);
+    CHECK_EQ(param->dilation.size(), 1);
+
+    Array<IndexExpr> wshape({dshape_ncw[1],
+            indexdiv(param->channels, param->groups),
+            param->kernel_size[0]});
+
+    wshape = trans_kernel_layout.BackwardShape(wshape);
+    dilated_ksize_x = 1 + (param->kernel_size[0] - 1) * param->dilation[0];
+    channels = param->channels;
+
+    // assign result to reporter
+    reporter->Assign(types[1], TensorTypeNode::make(wshape, data->dtype));
+  } else {
+    // use weight to infer the conv shape.
+    if (weight == nullptr) return false;
+    auto wshape = trans_kernel_layout.ForwardShape(weight->shape);
+    if (param->kernel_size.defined()) {
+      CHECK_EQ(param->kernel_size.size(), 1);
+      // check the size
+      CHECK(reporter->AssertEQ(param->kernel_size[0], wshape[2]))
+          << "Conv1D: shape of weight is inconsistent with kernel_size, "
+          << " kernel_size=" << param->kernel_size
+          << " wshape=" << Array<IndexExpr>(wshape);
+    }
+    if (param->channels.defined()) {
+      CHECK(reporter->AssertEQ(param->channels, wshape[1]))
+          << "Conv1D: shape of weight is inconsistent with channels, "
+          << " channels=" << param->channels
+          << " wshape=" << Array<IndexExpr>(wshape);
+    }
+    CHECK(reporter->AssertEQ(indexdiv(dshape_ncw[1], param->groups), wshape[0]));
+    channels = wshape[1];
+    dilated_ksize_x = 1 + (wshape[2] - 1) * param->dilation[0];
+  }
+  // dilation
+  Array<IndexExpr> oshape({dshape_ncw[0], channels, 0});
+  oshape.Set(2, (param->strides[0] * (dshape_ncw[2] - 1) + dilated_ksize_x -
+                 2 * param->padding[0] + param->output_padding[0]));
 
 Review comment:
   Please use asymmetric padding. (head_padding + tail_padding) see #4511 

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