| # Owner(s): ["module: onnx"] |
| |
| from test_pytorch_common import TestCase, run_tests |
| |
| import torch |
| import torch.onnx |
| from torch.nn import Module |
| |
| import onnx |
| |
| import io |
| import itertools |
| |
| from torch.onnx.symbolic_helper import _export_onnx_opset_version |
| from torch.onnx import producer_name, producer_version |
| |
| |
| def check_onnx_opset_operator(model, ops, opset_version=_export_onnx_opset_version): |
| # check_onnx_components |
| assert ( |
| model.producer_name == producer_name and |
| model.producer_version == producer_version and |
| model.opset_import[0].version == opset_version) |
| |
| # check the schema with the onnx checker |
| onnx.checker.check_model(model) |
| |
| # check target type and attributes |
| graph = model.graph |
| # ops should contain an object for each node |
| # in graph.node, in the right order. |
| # At least the op_name should be specified, |
| # but the op's attributes can optionally be |
| # specified as well |
| assert len(ops) == len(graph.node) |
| for i in range(0, len(ops)): |
| assert graph.node[i].op_type == ops[i]["op_name"] |
| if "attributes" in ops[i] : |
| attributes = ops[i]["attributes"] |
| assert len(attributes) == len(graph.node[i].attribute) |
| for j in range(0, len(attributes)): |
| for attribute_field in attributes[j].keys(): |
| assert attributes[j][attribute_field] == getattr(graph.node[i].attribute[j], attribute_field) |
| |
| |
| def check_onnx_opsets_operator(module, x, ops, opset_versions, training=torch.onnx.TrainingMode.EVAL, |
| input_names=None, dynamic_axes=None): |
| for opset_version in opset_versions: |
| f = io.BytesIO() |
| torch.onnx.export(module, x, f, |
| opset_version=opset_version, |
| training=training, |
| input_names=input_names, |
| dynamic_axes=dynamic_axes) |
| model = onnx.load(io.BytesIO(f.getvalue())) |
| check_onnx_opset_operator(model, ops[opset_version], opset_version) |
| |
| |
| class TestONNXOpset(TestCase): |
| |
| def test_opset_fallback(self): |
| class MyModule(Module): |
| def forward(self, x): |
| return torch.isnan(x) |
| |
| ops = [{"op_name" : "IsNaN"}] |
| ops = {9 : ops, 10 : ops} |
| x = torch.tensor([1.0, float("nan"), 2.0]) |
| check_onnx_opsets_operator(MyModule(), x, ops, opset_versions=[9, 10]) |
| |
| def test_topk(self): |
| class MyModule(Module): |
| def forward(self, x): |
| return torch.topk(x, 3) |
| |
| ops_9 = [{"op_name": "TopK", "attributes": [{"name": "axis", "i": -1, "type": 2}, |
| {"name": "k", "i": 3, "type": 2}]}] |
| ops_10 = [{"op_name": "TopK", "attributes": [{"name": "axis", "i": -1, "type": 2}]}] |
| ops = {9: ops_9, 10: ops_10} |
| x = torch.arange(1., 6., requires_grad=True) |
| check_onnx_opsets_operator(MyModule(), x, ops, opset_versions=[9, 10]) |
| |
| # test with dynamic k |
| class MyModuleDynamic(torch.jit.ScriptModule): |
| @torch.jit.script_method |
| def forward(self, input, k): |
| return torch.topk(input, k) |
| |
| ops_10 = [{"op_name": "Constant", "attributes": [{"name": "value", "type": 4}]}, |
| {"op_name": "Reshape"}, |
| {"op_name": "TopK", "attributes": [{"name": "axis", "i": -1, "type": 2}]}] |
| ops = {10: ops_10} |
| x = torch.arange(1., 6., requires_grad=True) |
| k = torch.tensor(3) |
| module = MyModuleDynamic() |
| check_onnx_opsets_operator(module, [x, k], ops, |
| opset_versions=[10]) |
| |
| def test_maxpool(self): |
| module = torch.nn.MaxPool1d(2, stride=1) |
| |
| ops_9 = [{"op_name" : "MaxPool", |
| "attributes" : |
| [{"name": "kernel_shape", "ints": [2], "type": 7}, |
| {"name": "pads", "ints": [0, 0], "type": 7}, |
| {"name": "strides", "ints": [1], "type": 7}]}] |
| ops_10 = [{"op_name" : "MaxPool", |
| "attributes" : |
| [{"name": "ceil_mode", "i": 0, "type": 2}, |
| {"name": "kernel_shape", "ints": [2], "type": 7}, |
| {"name": "pads", "ints": [0, 0], "type": 7}, |
| {"name": "strides", "ints": [1], "type": 7}]}] |
| ops = {9 : ops_9, 10 : ops_10} |
| x = torch.randn(20, 16, 50) |
| check_onnx_opsets_operator(module, x, ops, opset_versions=[9, 10]) |
| |
| # add test with dilations |
| module = torch.nn.MaxPool1d(2, stride=1, dilation=2) |
| |
| ops_10 = [{"op_name" : "MaxPool", |
| "attributes" : |
| [{"name": "ceil_mode", "i": 0, "type": 2}, |
| {"name": "dilations", "ints": [2], "type": 7}, |
| {"name": "kernel_shape", "ints": [2], "type": 7}, |
| {"name": "pads", "ints": [0, 0], "type": 7}, |
| {"name": "strides", "ints": [1], "type": 7}]}] |
| ops = {10 : ops_10} |
| x = torch.randn(20, 16, 50) |
| check_onnx_opsets_operator(module, x, ops, opset_versions=[10]) |
| |
| def test_upsample(self): |
| class MyModule(Module): |
| def __init__(self): |
| super(MyModule, self).__init__() |
| |
| def forward(self, x): |
| size = [v * 2 for v in x.size()[2:]] |
| size = [int(i) for i in size] |
| return torch.nn.functional.interpolate(x, size=size, mode="nearest") |
| |
| module = MyModule() |
| ops8 = [{"op_name" : "Upsample", "attributes" : [{"name": "mode", "s": ("nearest").encode(), "type": 3}, |
| {"name": "scales", "floats": [1.0, 1.0, 2.0, 2.0], "type": 6}]}] |
| ops9 = [{"op_name" : "Constant"}, |
| {"op_name" : "Upsample", "attributes" : [{"name": "mode", "s": ("nearest").encode(), "type": 3}]}] |
| ops = {8 : ops8, 9 : ops9} |
| x = torch.randn(2, 2, 2, 2) |
| check_onnx_opsets_operator(module, x, ops, opset_versions=[8, 9]) |
| |
| def test_cast_constant(self): |
| class MyModule(Module): |
| def __init__(self): |
| super(MyModule, self).__init__() |
| |
| def forward(self, x): |
| return x - 1 |
| |
| module = MyModule() |
| ops_8 = [{"op_name" : "Constant"}, |
| {"op_name" : "Cast", "attributes": [{"name": "to", "i": 7, "type": 2}]}, |
| {"op_name" : "Sub"}] |
| ops_9 = [{"op_name" : "Constant"}, {"op_name" : "Sub"}] |
| ops = {8 : ops_8, 9 : ops_9} |
| x = torch.ones(5, 6, dtype=torch.long) |
| check_onnx_opsets_operator(module, x, ops, opset_versions=[8, 9]) |
| |
| def test_slice(self): |
| class MyModule(Module): |
| def forward(self, x): |
| return x[0:1] |
| |
| ops_9 = [{"op_name" : "Slice", |
| "attributes" : |
| [{"name": "axes", "ints": [0], "type": 7}, |
| {"name": "ends", "ints": [1], "type": 7}, |
| {"name": "starts", "ints": [0], "type": 7}]}] |
| ops_10 = [{"op_name" : "Constant"}, |
| {"op_name" : "Constant"}, |
| {"op_name" : "Constant"}, |
| {"op_name" : "Constant"}, |
| {"op_name" : "Slice", |
| "attributes" : []}] |
| ops = {9 : ops_9, 10 : ops_10} |
| x = torch.randn(3) |
| check_onnx_opsets_operator(MyModule(), x, ops, opset_versions=[9, 10]) |
| |
| class DynamicSliceModel(torch.jit.ScriptModule): |
| @torch.jit.script_method |
| def forward(self, x): |
| return x[1:x.size(0)] |
| |
| module = DynamicSliceModel() |
| x = torch.rand(1, 2) |
| ops_10 = [{"op_name" : "Shape"}, |
| {"op_name" : "Constant"}, |
| {"op_name" : "Gather", |
| "attributes" : [{"name" : "axis", "i" : 0, "type" : 2}]}, |
| {"op_name" : "Unsqueeze", |
| "attributes" : [{"name" : "axes", "i" : 0, "type" : 7}]}, |
| {"op_name": "Constant"}, |
| {"op_name" : "Slice", |
| "attributes" : []}] |
| ops = {10 : ops_10} |
| check_onnx_opsets_operator(module, x, ops, opset_versions=[10], |
| input_names=['x'], dynamic_axes={"x": [0, 1]}) |
| |
| ops_10 = [{"op_name" : "Constant"}, |
| {"op_name" : "Constant"}, |
| {"op_name" : "Constant"}, |
| {"op_name" : "Constant"}, |
| {"op_name" : "Slice", |
| "attributes" : []}] |
| ops = {10 : ops_10} |
| check_onnx_opsets_operator(module, x, ops, opset_versions=[10]) |
| |
| def test_flip(self): |
| class MyModule(Module): |
| def forward(self, x): |
| return torch.flip(x, dims=[0]) |
| |
| ops_10 = [{"op_name" : "Constant"}, |
| {"op_name" : "Constant"}, |
| {"op_name" : "Constant"}, |
| {"op_name" : "Constant"}, |
| {"op_name" : "Slice", |
| "attributes" : []}] |
| ops = {10 : ops_10} |
| import numpy |
| x = torch.tensor(numpy.arange(6.0).reshape(2, 3)) |
| check_onnx_opsets_operator(MyModule(), x, ops, opset_versions=[10]) |
| |
| def test_dropout(self): |
| class MyModule(Module): |
| def __init__(self): |
| super(MyModule, self).__init__() |
| self.dropout = torch.nn.Dropout(0.5) |
| |
| def forward(self, x): |
| return self.dropout(x) |
| |
| x = torch.randn(1, 2, 3) |
| |
| # we should only export the onnx Dropout op in training mode; test both modes |
| |
| # test training mode |
| ops = [{"op_name" : "Dropout", "attributes" : [{"name" : "ratio", "f" : 0.5, "type" : 1}]}] |
| ops = {9 : ops, 10 : ops} |
| check_onnx_opsets_operator(MyModule(), x, ops, opset_versions=[9, 10], training=torch.onnx.TrainingMode.TRAINING) |
| |
| # test eval mode |
| ops = [{"op_name" : "Identity"}] |
| ops = {9 : ops, 10 : ops} |
| check_onnx_opsets_operator(MyModule(), x, ops, opset_versions=[9, 10], training=torch.onnx.TrainingMode.EVAL) |
| |
| def test_full(self): |
| class MyModule(Module): |
| def forward(self, x): |
| return torch.full((3, 4), x) |
| |
| ops = [{"op_name" : "Constant"}, |
| {"op_name" : "ConstantOfShape"}, |
| {"op_name" : "Add"}] |
| ops = {9 : ops, 10 : ops} |
| x = torch.tensor(12.) |
| check_onnx_opsets_operator(MyModule(), x, ops, opset_versions=[9, 10]) |
| |
| def test_interpolate(self): |
| class MyModel(torch.nn.Module): |
| def forward(self, x): |
| size = [v * 2 for v in x.size()[2:]] |
| return torch.nn.functional.interpolate(x, |
| size=size, |
| mode="nearest") |
| ops_9 = [{"op_name" : "Shape"}, |
| {"op_name" : "Constant"}, |
| {"op_name" : "Gather"}, |
| {"op_name" : "Shape"}, |
| {"op_name" : "Constant"}, |
| {"op_name" : "Gather"}, |
| {"op_name" : "Constant"}, |
| {"op_name" : "Mul"}, |
| {"op_name" : "Constant"}, |
| {"op_name" : "Mul"}, |
| {"op_name" : "Unsqueeze"}, |
| {"op_name" : "Unsqueeze"}, |
| {"op_name" : "Concat"}, |
| {"op_name" : "Cast"}, |
| {"op_name" : "Shape"}, |
| {"op_name" : "Slice"}, |
| {"op_name" : "Cast"}, |
| {"op_name" : "Div"}, |
| {"op_name" : "Constant"}, |
| {"op_name" : "Concat"}, |
| {"op_name" : "Upsample", |
| "attributes" : |
| [{"name": "mode", "s": ("nearest").encode(), "type": 3}]}] |
| ops_10 = [{"op_name" : "Shape"}, |
| {"op_name" : "Constant"}, |
| {"op_name" : "Gather"}, |
| {"op_name" : "Shape"}, |
| {"op_name" : "Constant"}, |
| {"op_name" : "Gather"}, |
| {"op_name" : "Constant"}, |
| {"op_name" : "Mul"}, |
| {"op_name" : "Constant"}, |
| {"op_name" : "Mul"}, |
| {"op_name" : "Unsqueeze"}, |
| {"op_name" : "Unsqueeze"}, |
| {"op_name" : "Concat"}, |
| {"op_name" : "Cast"}, |
| {"op_name" : "Shape"}, |
| {"op_name" : "Constant"}, |
| {"op_name" : "Constant"}, |
| {"op_name" : "Constant"}, |
| {"op_name" : "Slice"}, |
| {"op_name" : "Cast"}, |
| {"op_name" : "Div"}, |
| {"op_name" : "Constant"}, |
| {"op_name" : "Concat"}, |
| {"op_name" : "Resize", |
| "attributes" : |
| [{"name": "mode", "s": ("nearest").encode(), "type": 3}]}] |
| |
| ops = {9 : ops_9, 10 : ops_10} |
| x = torch.randn(1, 2, 3, 4, requires_grad=True) |
| check_onnx_opsets_operator(MyModel(), x, ops, opset_versions=[9, 10], |
| input_names=["x"], dynamic_axes={"x": [0, 1, 2, 3]}) |
| |
| ops_9 = [{"op_name" : "Shape"}, |
| {"op_name" : "Slice"}, |
| {"op_name" : "Cast"}, |
| {"op_name" : "Div"}, |
| {"op_name" : "Constant"}, |
| {"op_name" : "Concat"}, |
| {"op_name" : "Upsample", |
| "attributes" : |
| [{"name": "mode", "s": ("nearest").encode(), "type": 3}]}] |
| ops_10 = [{"op_name" : "Shape"}, |
| {"op_name" : "Constant"}, |
| {"op_name" : "Constant"}, |
| {"op_name" : "Constant"}, |
| {"op_name" : "Slice"}, |
| {"op_name" : "Cast"}, |
| {"op_name" : "Div"}, |
| {"op_name" : "Constant"}, |
| {"op_name" : "Concat"}, |
| {"op_name" : "Resize"}] |
| |
| ops = {9 : ops_9, 10 : ops_10} |
| x = torch.randn(1, 2, 3, 4, requires_grad=True) |
| check_onnx_opsets_operator(MyModel(), x, ops, opset_versions=[9, 10]) |
| |
| class MyDynamicModel(torch.nn.Module): |
| def forward(self, x): |
| size = [v * 2 for v in x.size()[2:]] |
| # work around for now: turn the dynamic sizes into constant |
| size = [int(i) for i in size] |
| return torch.nn.functional.interpolate(x, |
| size=size, |
| mode="nearest") |
| ops_9 = [{"op_name" : "Constant"}, |
| {"op_name" : "Upsample", |
| "attributes" : |
| [{"name": "mode", "s": ("nearest").encode(), "type": 3}]}] |
| ops_10 = [{"op_name" : "Constant"}, |
| {"op_name" : "Resize", |
| "attributes" : |
| [{"name": "mode", "s": ("nearest").encode(), "type": 3}]}] |
| ops = {9 : ops_9, 10 : ops_10} |
| x = torch.randn(20, 16, 50) |
| check_onnx_opsets_operator(MyDynamicModel(), x, ops, opset_versions=[9, 10]) |
| |
| def test_grid_sample(self): |
| n, c, h_in, w_in, h_out, w_out = 1, 1, 3, 2, 2, 4 |
| ops = {16: [{"op_name": "GridSample"}]} |
| |
| class MyModule(Module): |
| def forward(self, x, grid, mode, padding_mode, align_corers): |
| return torch.nn.functional.grid_sample(x, grid, mode, padding_mode, align_corners) |
| |
| for mode, padding_mode, align_corners in itertools.product( |
| ("bilinear", "nearest", "bicubic"), |
| ("zeros", "border", "reflection"), |
| (True, False), |
| ): |
| |
| args = ( |
| torch.randn(n, c, h_in, w_in), # x |
| torch.randn(n, h_out, w_out, 2), # grid, |
| mode, |
| padding_mode, |
| align_corners, |
| ) |
| check_onnx_opsets_operator(MyModule(), args, ops, opset_versions=[16], training=torch.onnx.TrainingMode.TRAINING) |
| check_onnx_opsets_operator(MyModule(), args, ops, opset_versions=[16], training=torch.onnx.TrainingMode.EVAL) |
| |
| |
| if __name__ == "__main__": |
| run_tests() |
