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Add support of count_include_pad and test end to end test for AveragePool (#17034) #1

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Add support of count_include_pad and test end to end test for AveragePool (#17034) #1

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63 changes: 2 additions & 61 deletions caffe2/onnx/backend.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -343,8 +343,8 @@ Caffe2Backend::get_special_operators() const {
{"Constant", &Caffe2Backend::CreateConstant},
{"ConstantOfShape", &Caffe2Backend::CreateConstantOfShape},
{"Conv", &Caffe2Backend::CreateConvPoolOpBase},
{"AveragePool", &Caffe2Backend::CreatePadPool},
{"GlobalAveragePool", &Caffe2Backend::CreatePadPool},
{"AveragePool", &Caffe2Backend::CreateConvPoolOpBase},
{"GlobalAveragePool", &Caffe2Backend::CreateConvPoolOpBase},
{"GlobalMaxPool", &Caffe2Backend::CreateConvPoolOpBase},
{"MaxPool", &Caffe2Backend::CreateConvPoolOpBase},
{"Reshape", &Caffe2Backend::CreateReshape},
Expand Down Expand Up @@ -545,65 +545,6 @@ Caffe2Ops Caffe2Backend::CreateConvPoolOpBase(
return CommonOnnxNodeToCaffe2Ops(onnx_node, ctx);
}

Caffe2Ops Caffe2Backend::CreatePadPool(
OnnxNode* onnx_node,
const ConversionContext& ctx) {
auto& node = onnx_node->node;
auto& attributes = onnx_node->attributes;
Caffe2Ops ret;
// Pad
bool padding = false;
const std::string pad_name = ctx.opset_version() < 2 ? "paddings" : "pads";
const auto pad_input = dummy_->NewDummyName();
if (attributes.HasAttribute("count_include_pad") &&
attributes.HasAttribute(pad_name)) {
auto count_include_pad = attributes.get<int64_t>("count_include_pad", 0L);
::google::protobuf::RepeatedField<::google::protobuf::int64> pads;
pads =
attributes
.get<::google::protobuf::RepeatedField<::google::protobuf::int64>>(
pad_name);
if (count_include_pad == 1 && pads.size() == 4 &&
!(pads.Get(0) == 0 && pads.Get(1) == 0 && pads.Get(2) == 0 &&
pads.Get(3) == 0)) {
padding = true;
attributes.remove(pad_name);
caffe2::Argument arg_pads;
arg_pads.add_ints(pads.Get(0));
arg_pads.add_ints(pads.Get(1));
arg_pads.add_ints(pads.Get(2));
arg_pads.add_ints(pads.Get(3));
arg_pads.set_name("pads");
auto* c2_op = ret.ops.Add();
BuildOperator(
c2_op, "PadImage", {node.input(0)}, {pad_input}, {arg_pads});
} else if (count_include_pad == 1) {
std::string str;
bool pads_flag = false;
str += "[";
for (const auto& i : pads) {
str += c10::to_string(i) + ",";
pads_flag = pads_flag || i > 0;
}
str += "]";
if (pads_flag == true) {
CAFFE_THROW(
"Caffe2 only supports padding 2D Tensor, whereas padding is ", str);
}
}
}
// Pool
auto c2_ops = Caffe2Backend::CreateConvPoolOpBase(onnx_node, ctx);
auto* pool_op = c2_ops.ops.Mutable(0);
if (padding) {
pool_op->set_input(0, pad_input);
}
auto* c2_op = ret.ops.Add();
c2_op->CopyFrom(*pool_op);

return ret;
}

Caffe2Ops Caffe2Backend::CreateReshape(
OnnxNode* onnx_node,
const ConversionContext& ctx) {
Expand Down
9 changes: 8 additions & 1 deletion test/onnx/test_pytorch_onnx_caffe2.py
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Original file line number Diff line number Diff line change
Expand Up @@ -664,11 +664,18 @@ def test_maxpool2d_single_padding(self):
model = nn.MaxPool2d(5, padding=2)
self.run_model_test(model, train=False, batch_size=BATCH_SIZE)

@unittest.skip("C2 and PyTorch have small difference in padding implementation")
def test_avgpool2d(self):
model = nn.AvgPool2d(5, padding=(2))
self.run_model_test(model, train=False, batch_size=BATCH_SIZE)

def test_avgpool2d_with_count_include_pad_set_false(self):
model = nn.AvgPool2d(7, padding=(2), count_include_pad=False)
self.run_model_test(model, train=False, batch_size=BATCH_SIZE)

def test_avgpool2d_with_count_include_pad_set_true(self):
model = nn.AvgPool2d(7, padding=(2), count_include_pad=True)
self.run_model_test(model, train=False, batch_size=BATCH_SIZE)

def test_avgpool2d_no_padding(self):
model = nn.AvgPool2d(5)
self.run_model_test(model, train=False, batch_size=BATCH_SIZE)
Expand Down