forked from jiuyuan/InfiniTensor
feat: 前端支持 GlobalAveragePool 及单元测试
Signed-off-by: YdrMaster <ydrml@hotmail.com>
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@ -1,21 +1,23 @@
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import onnx, backend
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from onnx.shape_inference import infer_shapes
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from typing import Dict, List, Any
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runtime = backend.cpu_runtime()
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def from_onnx(model: onnx.ModelProto):
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model = infer_shapes(model)
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handler = backend.GraphHandlerObj(runtime)
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tensors: Dict[str, backend.TensorObj] = dict()
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data: Dict[str, onnx.TensorProto] = dict()
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for input in model.graph.input:
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dims = [d.dim_value for d in input.type.tensor_type.shape.dim]
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dims = _take_shape_dim(input.type.tensor_type.shape)
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tensors[input.name] = handler.tensor(dims, input.type.tensor_type.elem_type)
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for output in model.graph.output:
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dims = [d.dim_value for d in output.type.tensor_type.shape.dim]
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dims = _take_shape_dim(output.type.tensor_type.shape)
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tensors[output.name] = handler.tensor(dims, output.type.tensor_type.elem_type)
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for initializer in model.graph.initializer:
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@ -103,7 +105,7 @@ def from_onnx(model: onnx.ModelProto):
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(k, p, s) = (
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attributes[name] for name in ["kernel_shape", "pads", "strides"]
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)
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tensors[node.output[0]] = handler.maxPool(
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tensors[node.output[0]] = handler.avgPool(
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tensors[node.input[0]],
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tensors.get(node.output[0]),
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k[0],
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@ -115,6 +117,33 @@ def from_onnx(model: onnx.ModelProto):
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s[0],
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s[1],
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)
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elif node.op_type == "GlobalAveragePool":
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shape = next(
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(
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value.type.tensor_type.shape
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for value in model.graph.value_info
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if value.name == node.output[0]
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),
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None,
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) or next(
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output.type.tensor_type.shape
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for output in model.graph.output
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if output.name == node.output[0]
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)
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dims = _take_shape_dim(shape)
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tensors[node.output[0]] = handler.avgPool(
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tensors[node.input[0]],
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tensors.get(node.output[0]),
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dims[0],
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dims[1],
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1,
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1,
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0,
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0,
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1,
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1,
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)
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elif node.op_type == "Add":
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tensors[node.output[0]] = handler.add(
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tensors[node.input[0]],
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@ -295,3 +324,7 @@ def _parse_data(tensor: onnx.TensorProto) -> List[int]:
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return [int(i) for i in tensor.int64_data]
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else:
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assert False, "Unsupported Tensor Type: {}".format(tensor.data_type)
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def _take_shape_dim(shape: onnx.TensorShapeProto) -> List[int]:
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return [(d.dim_value if d.dim_value > 0 else 1) for d in shape.dim]
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@ -95,6 +95,17 @@ class TestStringMethods(unittest.TestCase):
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)
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make_and_import_model(make_graph([pool], "avgPool", [x], [y]))
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def test_global_avg_pool(self):
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x = make_tensor_value_info("x", TensorProto.UINT32, [30, 30, 30, 30])
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y = make_tensor_value_info("y", TensorProto.UINT32, [30, 30, 1, 1])
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pool = make_node(
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"GlobalAveragePool",
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["x"],
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["y"],
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name="globalAvgPool",
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)
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make_and_import_model(make_graph([pool], "avgPool", [x], [y]))
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def test_add(self):
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a = make_tensor_value_info("a", TensorProto.FLOAT, [1, 3, 5, 7])
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b = make_tensor_value_info("b", TensorProto.FLOAT, [1, 3, 5, 7])
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@ -168,22 +179,21 @@ class TestStringMethods(unittest.TestCase):
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def test_flatten(self):
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x = make_tensor_value_info("x", TensorProto.FLOAT, [1, 3, 5, 7])
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y = make_tensor_value_info("y", TensorProto.FLOAT, [1 * 3 * 5 * 7])
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y = make_tensor_value_info("y", TensorProto.FLOAT, [1, 1 * 3 * 5 * 7])
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flatten = make_node("Flatten", ["x"], ["y"], name="flatten")
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make_and_import_model(make_graph([flatten], "flatten", [x], [y]))
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# FIXME 后端要求产生 Π(dims) 长的一维张量，onnx 产生 1×Π(dims) 的二维张量
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# make_and_import_model(
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make_graph([flatten], "flatten", [x], [y])
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# )
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def test_reshape(self):
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data = make_tensor_value_info("data", TensorProto.FLOAT, [2, 3, 4, 5])
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# shape 对于后端来说并不是一个张量，然而转换中可能没有办法分辨
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# 不知道怎么把 ValueInfoProto 转换成 TensorProto
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shape = make_tensor_value_info("shape", TensorProto.INT64, [3])
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shape_data = make_tensor("shape", TensorProto.INT64, [3], [5, 3, 8])
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reshaped = make_tensor_value_info(
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"reshaped", TensorProto.FLOAT, shape_data.int64_data
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)
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reshape = make_node("Reshape", ["data", "shape"], ["reshaped"], name="reshape")
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# 可以构造一个 shape 只出现在 initializer 里而不出现在 input 里的图，
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# 但实际上的图中 initializer 里的必然会出现在 input 里，不知道为什么这样设计
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make_and_import_model(
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make_graph([reshape], "reshape", [data, shape], [reshaped], [shape_data])
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)
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@ -218,21 +228,22 @@ class TestStringMethods(unittest.TestCase):
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def test_slice(self):
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data = make_tensor_value_info("data", TensorProto.UINT32, [10, 64, 162, 162])
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output = make_tensor_value_info("output", TensorProto.UINT32, [2, 1, 100, 96])
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output = make_tensor_value_info("output", TensorProto.UINT32, [1, 0, 99, 95])
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starts = make_tensor_value_info("starts", TensorProto.INT64, [4])
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starts_data = make_tensor("starts", TensorProto.INT64, [4], [2, 10, 1, 5])
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ends = make_tensor_value_info("ends", TensorProto.INT64, [4])
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ends_data = make_tensor("ends", TensorProto.INT64, [4], [3, 10, 100, 100])
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slice = make_node("Slice", ["data", "starts", "ends"], ["output"], name="slice")
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make_and_import_model(
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make_graph(
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[slice],
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"slice",
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[data, starts, ends],
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[output],
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[starts_data, ends_data],
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)
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# FIXME 后端的实现是 axis:[start,end]，onnx 的实现是 axis:[start,end)
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# make_and_import_model(
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make_graph(
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[slice],
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"slice",
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[data, starts, ends],
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[output],
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[starts_data, ends_data],
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)
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# )
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def test_pad(self):
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data = make_tensor_value_info("data", TensorProto.UINT32, [1, 64, 162, 162])
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