support slice op

examples/oneflow2onnx/models/test_shufflenet.py

+"""
+Copyright 2020 The OneFlow Authors. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+"""
+
+import oneflow as flow
+from oneflow import Tensor
+import oneflow.nn as nn
+from typing import Callable, Any, List
+from oneflow_onnx.oneflow2onnx.util import convert_to_onnx_and_check
+import tempfile
+
+def channel_shuffle(x: Tensor, groups: int) -> Tensor:
+    batchsize, num_channels, height, width = x.size()
+    channels_per_group = num_channels // groups
+
+    # reshape
+    x = flow.reshape(x, [batchsize, groups, channels_per_group, height, width])
+
+    x = flow.transpose(x, 1, 2)
+
+    # flatten
+    x = flow.reshape(x, [batchsize, -1, height, width])
+
+    return x
+
+
+class InvertedResidual(nn.Module):
+    def __init__(self, inp: int, oup: int, stride: int) -> None:
+        super().__init__()
+
+        if not (1 <= stride <= 3):
+            raise ValueError("illegal stride value")
+        self.stride = stride
+
+        branch_features = oup // 2
+        assert (self.stride != 1) or (inp == branch_features << 1)
+
+        if self.stride > 1:
+            self.branch1 = nn.Sequential(
+                self.depthwise_conv(
+                    inp, inp, kernel_size=3, stride=self.stride, padding=1
+                ),
+                nn.BatchNorm2d(inp),
+                nn.Conv2d(
+                    inp, branch_features, kernel_size=1, stride=1, padding=0, bias=False
+                ),
+                nn.BatchNorm2d(branch_features),
+                nn.ReLU(inplace=True),
+            )
+        else:
+            self.branch1 = nn.Sequential()
+
+        self.branch2 = nn.Sequential(
+            nn.Conv2d(
+                inp if (self.stride > 1) else branch_features,
+                branch_features,
+                kernel_size=1,
+                stride=1,
+                padding=0,
+                bias=False,
+            ),
+            nn.BatchNorm2d(branch_features),
+            nn.ReLU(inplace=True),
+            self.depthwise_conv(
+                branch_features,
+                branch_features,
+                kernel_size=3,
+                stride=self.stride,
+                padding=1,
+            ),
+            nn.BatchNorm2d(branch_features),
+            nn.Conv2d(
+                branch_features,
+                branch_features,
+                kernel_size=1,
+                stride=1,
+                padding=0,
+                bias=False,
+            ),
+            nn.BatchNorm2d(branch_features),
+            nn.ReLU(inplace=True),
+        )
+
+    @staticmethod
+    def depthwise_conv(
+        i: int,
+        o: int,
+        kernel_size: int,
+        stride: int = 1,
+        padding: int = 0,
+        bias: bool = False,
+    ) -> nn.Conv2d:
+        return nn.Conv2d(i, o, kernel_size, stride, padding, bias=bias, groups=i)
+
+    def forward(self, x: Tensor) -> Tensor:
+        if self.stride == 1:
+            cnt_at_dim1 = int(x.shape[1] / 2)
+            x1 = x[:, 0:cnt_at_dim1, ::]
+            x2 = x[:, cnt_at_dim1:, ::]
+            out = flow.cat((x1, self.branch2(x2)), dim=1)
+        else:
+            out = flow.cat((self.branch1(x), self.branch2(x)), dim=1)
+
+        out = channel_shuffle(out, 2)
+
+        return out
+
+
+class ShuffleNetV2(nn.Module):
+    def __init__(
+        self,
+        stages_repeats: List[int],
+        stages_out_channels: List[int],
+        num_classes: int = 1000,
+        inverted_residual: Callable[..., nn.Module] = InvertedResidual,
+    ) -> None:
+        super().__init__()
+
+        if len(stages_repeats) != 3:
+            raise ValueError("expected stages_repeats as list of 3 positive ints")
+        if len(stages_out_channels) != 5:
+            raise ValueError("expected stages_out_channels as list of 5 positive ints")
+        self._stage_out_channels = stages_out_channels
+
+        input_channels = 3
+        output_channels = self._stage_out_channels[0]
+        self.conv1 = nn.Sequential(
+            nn.Conv2d(input_channels, output_channels, 3, 2, 1, bias=False),
+            nn.BatchNorm2d(output_channels),
+            nn.ReLU(inplace=True),
+        )
+        input_channels = output_channels
+
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+
+        # Static annotations for mypy
+        self.stage2: nn.Sequential
+        self.stage3: nn.Sequential
+        self.stage4: nn.Sequential
+        stage_names = ["stage{}".format(i) for i in [2, 3, 4]]
+        for name, repeats, output_channels in zip(
+            stage_names, stages_repeats, self._stage_out_channels[1:]
+        ):
+            seq = [inverted_residual(input_channels, output_channels, 2)]
+            for i in range(repeats - 1):
+                seq.append(inverted_residual(output_channels, output_channels, 1))
+            setattr(self, name, nn.Sequential(*seq))
+            input_channels = output_channels
+
+        output_channels = self._stage_out_channels[-1]
+        self.conv5 = nn.Sequential(
+            nn.Conv2d(input_channels, output_channels, 1, 1, 0, bias=False),
+            nn.BatchNorm2d(output_channels),
+            nn.ReLU(inplace=True),
+        )
+
+        self.fc = nn.Linear(output_channels, num_classes)
+
+    def _forward_impl(self, x: Tensor) -> Tensor:
+        x = self.conv1(x)
+        x = self.maxpool(x)
+        x = self.stage2(x)
+        x = self.stage3(x)
+        x = self.stage4(x)
+        x = self.conv5(x)
+        x = x.mean([2, 3])  # globalpool
+        x = self.fc(x)
+        return x
+
+    def forward(self, x: Tensor) -> Tensor:
+        return self._forward_impl(x)
+
+
+def _shufflenetv2(arch: str, *args: Any, **kwargs: Any):
+    model = ShuffleNetV2(*args, **kwargs)
+    return model
+
+
+def shufflenetv2_x0dot5():
+    return ShuffleNetV2([4, 8, 4], [24, 48, 96, 192, 1024])
+
+
+def shufflenetv2_x1():
+    return ShuffleNetV2([4, 8, 4], [24, 116, 232, 464, 1024])
+
+shufflenet = shufflenetv2_x0dot5()
+shufflenet.eval()
+shufflenet = shufflenet.to("cuda")
+
+class shufflenetGraph(flow.nn.Graph):
+    def __init__(self):
+        super().__init__()
+        self.m = shufflenet
+
+    def build(self, x):
+        out = self.m(x)
+        return out
+
+def test_shufflenet():
+    
+    shufflenet_graph = shufflenetGraph()
+    shufflenet_graph._compile(flow.randn(1, 3, 224, 224).to("cuda"))
+
+    with tempfile.TemporaryDirectory() as tmpdirname:
+        flow.save(shufflenet.state_dict(), tmpdirname)
+        convert_to_onnx_and_check(shufflenet_graph, flow_weight_dir=tmpdirname, onnx_model_path="/tmp")
+
+test_shufflenet()

examples/oneflow2onnx/nodes/test_slice.py

+"""
+Copyright 2020 The OneFlow Authors. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+"""
+import tempfile
+import oneflow as flow
+from oneflow_onnx.oneflow2onnx.util import convert_to_onnx_and_check
+
+class Slice(flow.nn.Module):
+    def __init__(self) -> None:
+        super(Slice, self).__init__()
+    
+    def forward(self, x: flow.Tensor) -> flow.Tensor:
+        return x[:, :1, :, :]
+
+slice = Slice()
+class sliceOpGraph(flow.nn.Graph):
+    def __init__(self):
+        super().__init__()
+        self.m = slice
+
+    def build(self, x):
+        out = self.m(x)
+        return out
+
+
+def test_slice():
+    
+    slice_graph = sliceOpGraph()
+    slice_graph._compile(flow.randn(1, 3, 224, 224))
+    # print(slice_graph._full_graph_proto)
+
+    with tempfile.TemporaryDirectory() as tmpdirname:
+        flow.save(slice.state_dict(), tmpdirname)
+        convert_to_onnx_and_check(slice_graph, flow_weight_dir=tmpdirname, onnx_model_path="/tmp")
+
+test_slice()

oneflow_onnx/oneflow2onnx/handlers/array.py

@@ -207,6 +207,29 @@ class Concat:
         cls.Version_1(ctx, node, **kwargs)
 
 
+@flow_op("slice", "Slice")
+class Slice:
+    @classmethod
+    def Version_1(cls, ctx, node, **kwargs):
+        starts = ctx.MakeConst(oneflow._oneflow_internal.UniqueStr("start"), np.array(node.attrs["start"]).astype(np.int64))
+        node.input_tensor_names.append(starts.output_tensor_names[0])
+        ends = ctx.MakeConst(oneflow._oneflow_internal.UniqueStr("stop"), np.array(node.attrs["stop"]).astype(np.int64))
+        node.input_tensor_names.append(ends.output_tensor_names[0])
+        slice_axes = []
+        input_shape = ctx.get_shape(node.input_tensor_names[0])
+        for i in range(len(input_shape)):
+            slice_axes.append(i)
+        axes = ctx.MakeConst(oneflow._oneflow_internal.UniqueStr("axes"), np.array(slice_axes).astype(np.int64))
+        node.input_tensor_names.append(axes.output_tensor_names[0])
+        steps = ctx.MakeConst(oneflow._oneflow_internal.UniqueStr("steps"), np.array(node.attrs["step"]).astype(np.int64))
+        node.input_tensor_names.append(steps.output_tensor_names[0])
+
+
+    @classmethod
+    def Version_11(cls, ctx, node, **kwargs):
+        cls.Version_1(ctx, node, **kwargs)
+        
+
 @flow_op("gather_nd", onnx_op="GatherND", flow_ibns=["params", "indices"])
 class GatherND:
     @classmethod