delldu commented on a change in pull request #8443:
URL: https://github.com/apache/tvm/pull/8443#discussion_r670288455
##########
File path: src/relay/op/nn/pad.cc
##########
@@ -272,5 +272,86 @@ RELAY_REGISTER_OP("nn.mirror_pad")
.add_type_rel("MirrorPad", MirrorPadRel)
.set_attr<TOpPattern>("TOpPattern", kInjective);
+Array<te::Tensor> Im2colCompute(const Attrs& attrs, const Array<te::Tensor>&
inputs,
+ const Type& out_type) {
+ const auto* param = attrs.as<Im2colAttrs>();
+ ICHECK(param != nullptr);
+
+ return Array<te::Tensor>{
+ topi::im2col(inputs[0], param->kernel_size, param->dilation,
param->padding, param->stride)};
+}
+
+bool Im2colRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
+ const TypeReporter& reporter) {
+ // types: [input, output]
+ ICHECK_EQ(types.size(), 2) << "Expects two types, one for the input and
another for the output";
+
+ const auto* input = types[0].as<TensorTypeNode>();
+ if (input == nullptr) return false;
+
+ if (input->shape.size() != 4) {
+ reporter->GetDiagCtx().EmitFatal(Diagnostic::Error(reporter->GetSpan())
+ << "Im2lossRel: input data should be 4
dimensions, NxCxHxW.");
+ return false;
+ }
+
+ const Im2colAttrs* param = attrs.as<Im2colAttrs>();
+ if (param == nullptr) return false;
+
+ // Calculate output shape
+ auto kernel_h = tvm::cast(tvm::DataType::Int(32), param->kernel_size[0]);
+ auto kernel_w = tvm::cast(tvm::DataType::Int(32), param->kernel_size[1]);
+ auto dilation_h = tvm::cast(tvm::DataType::Int(32), param->dilation[0]);
+ auto dilation_w = tvm::cast(tvm::DataType::Int(32), param->dilation[1]);
+ auto padding_h = tvm::cast(tvm::DataType::Int(32), param->padding[0]);
+ auto padding_w = tvm::cast(tvm::DataType::Int(32), param->padding[1]);
+ auto stride_h = tvm::cast(tvm::DataType::Int(32), param->stride[0]);
+ auto stride_w = tvm::cast(tvm::DataType::Int(32), param->stride[1]);
Review comment:
Sorry, let me paste the source code.
```
bool Im2colRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
const TypeReporter& reporter) {
// types: [input, output]
ICHECK_EQ(types.size(), 2) << "Expects two types, one for the input and
another for the output";
const auto* input = types[0].as<TensorTypeNode>();
if (input == nullptr) return false;
if (input->shape.size() != 4) {
reporter->GetDiagCtx().EmitFatal(Diagnostic::Error(reporter->GetSpan())
<< "Im2lossRel: input data should be 4
dimensions, NxCxHxW.");
return false;
}
const Im2colAttrs* param = attrs.as<Im2colAttrs>();
if (param == nullptr) return false;
ICHECK_EQ(param->kernel_size.size(), 2) << "Expects two parameters for
kernel height and width";
ICHECK_EQ(param->dilation.size(), 2) << "Expects two parameters for
dilation height and width";
ICHECK_EQ(param->padding.size(), 2) << "Expects two parameters for padding
height and width";
ICHECK_EQ(param->stride.size(), 2) << "Expects two parameters for stride
height and width";
// Calculate output shape
auto kernel_h = tvm::cast(tvm::DataType::Int(32), param->kernel_size[0]);
auto kernel_w = tvm::cast(tvm::DataType::Int(32), param->kernel_size[1]);
auto dilation_h = tvm::cast(tvm::DataType::Int(32), param->dilation[0]);
auto dilation_w = tvm::cast(tvm::DataType::Int(32), param->dilation[1]);
auto padding_h = tvm::cast(tvm::DataType::Int(32), param->padding[0]);
auto padding_w = tvm::cast(tvm::DataType::Int(32), param->padding[1]);
auto stride_h = tvm::cast(tvm::DataType::Int(32), param->stride[0]);
auto stride_w = tvm::cast(tvm::DataType::Int(32), param->stride[1]);
auto dilated_kernel_h = (kernel_h - 1) * dilation_h + 1;
auto dilated_kernel_w = (kernel_w - 1) * dilation_w + 1;
// Output size after padding
auto output_h = (input->shape[2] + 2 * padding_h - dilated_kernel_h) /
stride_h + 1;
auto output_w = (input->shape[3] + 2 * padding_w - dilated_kernel_w) /
stride_w + 1;
tvm::Array<tvm::PrimExpr> output_shape;
output_shape.push_back(input->shape[0]); // N
output_shape.push_back(input->shape[1] * kernel_h * kernel_w); // K
output_shape.push_back(output_h * output_w); // L
// assign output type
reporter->Assign(types[1], TensorType(output_shape, input->dtype));
return true;
}
```
1) RELAY_REGISTER_OP need Im2col with interface: bool Im2colRel(const
Array<Type>& **types**, int num_inputs, const Attrs& **attrs**, const
TypeReporter& **reporter**);
2) We must parse attrs for checking they are valid or not;
3) We must calculate output shape depend on parse results.
Unfortunately, **attrs** are not integer, so we have to do such stupid
things.
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