Fix slice op shape=-1 bug (#18107) (#18227)

* fix slice op bug; test=develop

* fix variabel test bug; test=develop

* remove slice while true; test=develop

paddle/fluid/operators/slice_op.cc

@@ -39,21 +39,49 @@ class SliceOp : public framework::OperatorWithKernel {
     auto axes = ctx->Attrs().Get<std::vector<int>>("axes");
     auto starts = ctx->Attrs().Get<std::vector<int>>("starts");
     auto ends = ctx->Attrs().Get<std::vector<int>>("ends");
+    auto decrease_axis = ctx->Attrs().Get<std::vector<int>>("decrease_axis");
 
     PADDLE_ENFORCE_EQ(starts.size(), ends.size());
     PADDLE_ENFORCE_EQ(starts.size(), axes.size());
     int dim_value, start, end;
     for (size_t i = 0; i < axes.size(); ++i) {
       dim_value = out_dims[axes[i]];
-      start = starts[i] < 0 ? (starts[i] + dim_value) : starts[i];
-      end = ends[i] < 0 ? (ends[i] + dim_value) : ends[i];
-      start = std::max(start, 0);
-      end = std::max(end, 0);
-      start = std::min(start, dim_value);
-      end = std::min(end, dim_value);
-      start = std::min(start, end);
-      out_dims[axes[i]] = end - start;
+      if (dim_value > 0) {
+        start = starts[i] < 0 ? (starts[i] + dim_value) : starts[i];
+        end = ends[i] < 0 ? (ends[i] + dim_value) : ends[i];
+        start = std::max(start, 0);
+        end = std::max(end, 0);
+        // start = std::min(start, dim_value);
+        end = std::min(end, dim_value);
+        // start = std::min(start, end);
+        PADDLE_ENFORCE_GT(end, start, "end should greater than start");
+        out_dims[axes[i]] = end - start;
+      }
     }
+
+    // generate new shape
+    if (decrease_axis.size() > 0) {
+      std::vector<int> new_out_shape;
+      for (size_t i = 0; i < decrease_axis.size(); ++i) {
+        if (ctx->IsRuntime()) {
+          PADDLE_ENFORCE_EQ(out_dims[decrease_axis[i]], 1,
+                            "decrease dim should be 1");
+        }
+        out_dims[decrease_axis[i]] = 0;
+      }
+
+      for (int i = 0; i < out_dims.size(); ++i) {
+        if (out_dims[i] != 0) {
+          new_out_shape.push_back(out_dims[i]);
+        }
+      }
+      if (new_out_shape.size() == 0) {
+        new_out_shape.push_back(1);
+      }
+
+      out_dims = framework::make_ddim(new_out_shape);
+    }
+
     ctx->SetOutputDim("Out", out_dims);
     if (axes[0] != 0) {
       ctx->ShareLoD("Input", /*->*/ "Out");
@@ -84,7 +112,8 @@ class SliceOpMaker : public framework::OpProtoAndCheckerMaker {
     AddAttr<std::vector<int>>(
         "ends",
         "(list<int>) Starting indices of corresponding axis in `axes`.");
-
+    AddAttr<std::vector<int>>("decrease_axis", "(list<int>) decrease_axis")
+        .SetDefault({});
     AddComment(R"DOC(
 Slice Operator.
 

paddle/fluid/operators/slice_op.h

@@ -55,17 +55,45 @@ class SliceKernel : public framework::OpKernel<T> {
         *context.template device_context<DeviceContext>().eigen_device();
     auto in = context.Input<framework::Tensor>("Input");
     auto out = context.Output<framework::Tensor>("Out");
-    out->mutable_data<T>(context.GetPlace());
     auto out_dims = out->dims();
     auto in_dims = in->dims();
+
+    // resize out_dims
+    auto decrease_axis = context.Attr<std::vector<int>>("decrease_axis");
+    if (decrease_axis.size() > 0) {
+      if (decrease_axis.size() == (size_t)in_dims.size()) {
+        std::vector<int> vec_origin_out_shape(decrease_axis.size(), 1);
+        out->Resize(framework::make_ddim(vec_origin_out_shape));
+      } else {
+        std::vector<int> vec_origin_out_shape(
+            out_dims.size() + decrease_axis.size(), -1);
+
+        for (size_t i = 0; i < decrease_axis.size(); ++i) {
+          vec_origin_out_shape[decrease_axis[i]] = 1;
+        }
+
+        int index = 0;
+        for (size_t i = 0; i < vec_origin_out_shape.size(); ++i) {
+          if (vec_origin_out_shape[i] == -1) {
+            vec_origin_out_shape[i] = out_dims[index];
+            ++index;
+          }
+        }
+
+        out->Resize(framework::make_ddim(vec_origin_out_shape));
+      }
+    }
+
+    out->mutable_data<T>(context.GetPlace());
     auto axes = context.Attr<std::vector<int>>("axes");
     auto starts = context.Attr<std::vector<int>>("starts");
 
+    auto new_out_dims = out->dims();
     auto offsets = Eigen::array<int, D>();
     auto extents = Eigen::array<int, D>();
     for (size_t i = 0; i < D; ++i) {
       offsets[i] = 0;
-      extents[i] = out_dims[i];
+      extents[i] = new_out_dims[i];
     }
     int start;
     for (size_t i = 0; i < axes.size(); ++i) {
@@ -81,8 +109,10 @@ class SliceKernel : public framework::OpKernel<T> {
             *in);
     auto out_t =
         framework::EigenTensor<T, D, Eigen::RowMajor, Eigen::DenseIndex>::From(
-            *out);
+            *out, new_out_dims);
     out_t.device(place) = in_t.slice(offsets, extents);
+
+    out->Resize(out_dims);
   }
 };
 
@@ -90,9 +120,7 @@ template <typename DeviceContext, typename T>
 class SliceGradKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
-    size_t rank = ctx.Input<framework::Tensor>(framework::GradVarName("Out"))
-                      ->dims()
-                      .size();
+    size_t rank = ctx.Input<framework::Tensor>("Input")->dims().size();
     switch (rank) {
       case 1:
         SliceCompute<1>(ctx);
@@ -130,6 +158,32 @@ class SliceGradKernel : public framework::OpKernel<T> {
     auto axes = context.Attr<std::vector<int>>("axes");
     auto starts = context.Attr<std::vector<int>>("starts");
 
+    auto decrease_axis = context.Attr<std::vector<int>>("decrease_axis");
+    if (decrease_axis.size() > 0) {
+      if (decrease_axis.size() == (size_t)in_dims.size()) {
+        // all dims decrease
+        std::vector<int> vec_origin_out_shape(decrease_axis.size(), 1);
+        out_dims = framework::make_ddim(vec_origin_out_shape);
+      } else {
+        std::vector<int> vec_origin_out_shape(
+            out_dims.size() + decrease_axis.size(), -1);
+
+        for (size_t i = 0; i < decrease_axis.size(); ++i) {
+          vec_origin_out_shape[decrease_axis[i]] = 1;
+        }
+
+        int index = 0;
+        for (size_t i = 0; i < vec_origin_out_shape.size(); ++i) {
+          if (vec_origin_out_shape[i] == -1) {
+            vec_origin_out_shape[i] = out_dims[index];
+            ++index;
+          }
+        }
+
+        out_dims = framework::make_ddim(vec_origin_out_shape);
+      }
+    }
+
     auto offsets = Eigen::array<int, D>();
     auto extents = Eigen::array<int, D>();
     for (size_t i = 0; i < D; ++i) {
@@ -155,7 +209,7 @@ class SliceGradKernel : public framework::OpKernel<T> {
             *d_input);
     auto d_out_t =
         framework::EigenTensor<T, D, Eigen::RowMajor, Eigen::DenseIndex>::From(
-            *d_out);
+            *d_out, out_dims);
     d_in_t.device(place) = d_out_t.pad(paddings, 0);
   }
 };

python/paddle/fluid/framework.py

@@ -822,35 +822,84 @@ class Variable(object):
         Returns:
             Sliced variable
         """
-        new_var = None
-        if isinstance(item, tuple):
-            if len(item) > len(self.shape):
-                raise IndexError("Too many indexes")
-            fixedSize = True
-            for i in range(len(self.shape)):
-                if self.shape[i] == -1:
-                    fixedSize = False
-                    break
 
-            newitem = self._reconstructSliceinfo(item) or item
-            if fixedSize:
-                check, info = self._detectContinuesSlice(newitem)
-                if check:
-                    starts = info[0]
-                    ends = info[1]
-                    axes = [i for i in range(len(starts))]
-                    return self._sliceVar(axes, starts, ends)
-                else:
-                    new_var = self
-                    for index, o in enumerate(newitem):
-                        new_var = new_var._sliceAndConcatVar(o, index)
+        if not isinstance(item, tuple):
+            item = [item]
+
+        decrease_axis = []
+        slice_axis = []
+        slice_start = []
+        slice_end = []
+        reverse_axis = []
+
+        for dim, slice_item in enumerate(item):
+            if isinstance(slice_item, slice):
+                start = slice_item.start
+                end = slice_item.stop
+                step = slice_item.step if slice_item.step else 1
+
+                assert (step == 1 or step == -1)
+
+                if step == -1:
+                    reverse_axis.append(dim)
+                    assert (start is None and end is None)
+
+                if start is None and end is None:
+                    continue
+
+                if start is None:
+                    start = 0
+
+                if end is None:
+                    end = 10000000
+
+                slice_axis.append(dim)
+                slice_start.append(start)
+                slice_end.append(end)
             else:
-                new_var = self
-                for index, o in enumerate(newitem):
-                    new_var = new_var._sliceAndConcatVar(o, index)
-        else:
-            new_var = self._sliceAndConcatVar(item, 0)
-        return new_var
+                # int
+                decrease_axis.append(dim)
+                slice_axis.append(dim)
+                slice_start.append(slice_item)
+                slice_end.append(slice_item + 1
+                                 if slice_item != -1 else 10000000)
+
+        out = self
+        if len(slice_axis) > 0:
+            # append slice_op here
+
+            slice_out_var = self.block.create_var(
+                name=unique_name.generate_with_ignorable_key(self.name +
+                                                             "_slice"),
+                dtype=self.dtype)
+
+            self.block.append_op(
+                type="slice",
+                inputs={'Input': [out]},
+                outputs={'Out': [slice_out_var]},
+                attrs={
+                    'axes': slice_axis,
+                    'starts': slice_start,
+                    'ends': slice_end,
+                    'decrease_axis': decrease_axis
+                })
+
+            out = slice_out_var
+
+        if len(reverse_axis) > 0:
+            reverse_out_var = self.block.create_var(
+                name=unique_name.generate_with_ignorable_key(self.name +
+                                                             "_slice_reverse"),
+                dtype=self.dtype)
+            self.block.append_op(
+                type="reverse",
+                inputs={'X': out},
+                outputs={'Out': [reverse_out_var]},
+                attrs={'axis': reverse_axis})
+
+            out = reverse_out_var
+
+        return out
 
 
 def get_all_op_protos():

python/paddle/fluid/tests/unittests/test_slice_op.py

@@ -46,6 +46,146 @@ class TestSliceOp(OpTest):
         self.check_grad(['Input'], 'Out', max_relative_error=0.006)
 
 
+class TestSliceOp_decs_dim(OpTest):
+    def setUp(self):
+        self.op_type = "slice"
+        self.config()
+        self.inputs = {'Input': self.input}
+        self.outputs = {'Out': self.out}
+        self.attrs = {
+            'axes': self.axes,
+            'starts': self.starts,
+            'ends': self.ends,
+            'decrease_axis': self.decrease_axis,
+        }
+
+    def config(self):
+        self.input = np.random.random([3, 4, 5, 6]).astype("float32")
+        self.starts = [1, 0, 2]
+        self.ends = [2, 3, 4]
+        self.axes = [0, 1, 2]
+        self.decrease_axis = [0]
+        self.out = self.input[1, 0:3, 2:4, :]
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad_normal(self):
+        self.check_grad(['Input'], 'Out', max_relative_error=0.006)
+
+
+class TestSliceOp_decs_dim_2(OpTest):
+    def setUp(self):
+        self.op_type = "slice"
+        self.config()
+        self.inputs = {'Input': self.input}
+        self.outputs = {'Out': self.out}
+        self.attrs = {
+            'axes': self.axes,
+            'starts': self.starts,
+            'ends': self.ends,
+            'decrease_axis': self.decrease_axis,
+        }
+
+    def config(self):
+        self.input = np.random.random([3, 4, 5, 6]).astype("float32")
+        self.starts = [1, 0, 2]
+        self.ends = [2, 1, 4]
+        self.axes = [0, 1, 2]
+        self.decrease_axis = [0, 1]
+        self.out = self.input[1, 0, 2:4, :]
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad_normal(self):
+        self.check_grad(['Input'], 'Out', max_relative_error=0.006)
+
+
+class TestSliceOp_decs_dim_3(OpTest):
+    def setUp(self):
+        self.op_type = "slice"
+        self.config()
+        self.inputs = {'Input': self.input}
+        self.outputs = {'Out': self.out}
+        self.attrs = {
+            'axes': self.axes,
+            'starts': self.starts,
+            'ends': self.ends,
+            'decrease_axis': self.decrease_axis,
+        }
+
+    def config(self):
+        self.input = np.random.random([3, 4, 5, 6]).astype("float32")
+        self.starts = [-1, 0, 2]
+        self.ends = [1000000, 1, 4]
+        self.axes = [0, 1, 2]
+        self.decrease_axis = [0, 1]
+        self.out = self.input[-1, 0, 2:4, :]
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad_normal(self):
+        self.check_grad(['Input'], 'Out', max_relative_error=0.006)
+
+
+class TestSliceOp_decs_dim_5(OpTest):
+    def setUp(self):
+        self.op_type = "slice"
+        self.config()
+        self.inputs = {'Input': self.input}
+        self.outputs = {'Out': self.out}
+        self.attrs = {
+            'axes': self.axes,
+            'starts': self.starts,
+            'ends': self.ends,
+            'decrease_axis': self.decrease_axis,
+        }
+
+    def config(self):
+        self.input = np.random.random([3, 4, 5, 6]).astype("float32")
+        self.starts = [-1]
+        self.ends = [1000000]
+        self.axes = [3]
+        self.decrease_axis = [3]
+        self.out = self.input[:, :, :, -1]
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad_normal(self):
+        self.check_grad(['Input'], 'Out', max_relative_error=0.006)
+
+
+class TestSliceOp_decs_dim_6(OpTest):
+    def setUp(self):
+        self.op_type = "slice"
+        self.config()
+        self.inputs = {'Input': self.input}
+        self.outputs = {'Out': self.out}
+        self.attrs = {
+            'axes': self.axes,
+            'starts': self.starts,
+            'ends': self.ends,
+            'decrease_axis': self.decrease_axis,
+        }
+
+    def config(self):
+        self.input = np.random.random([3, 4, 5, 6]).astype("float32")
+        self.starts = [0, 1, 2, 3]
+        self.ends = [1, 2, 3, 4]
+        self.axes = [0, 1, 2, 3]
+        self.decrease_axis = [0, 1, 2, 3]
+        self.out = self.input[0, 1, 2, 3:4]
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad_normal(self):
+        self.check_grad(['Input'], 'Out', max_relative_error=0.006)
+
+
 class TestCase1(TestSliceOp):
     def config(self):
         self.input = np.random.random([3, 4, 5, 6]).astype("float32")

python/paddle/fluid/tests/unittests/test_variable.py

@@ -17,6 +17,7 @@ from __future__ import print_function
 import unittest
 from paddle.fluid.framework import default_main_program, Program, convert_np_dtype_to_dtype_, in_dygraph_mode
 import paddle.fluid as fluid
+import paddle.fluid.layers as layers
 import paddle.fluid.core as core
 import numpy as np
 
@@ -67,19 +68,24 @@ class TestVariable(unittest.TestCase):
 
         for i in range(3):
             nw = w[i]
-            self.assertEqual((1, 100, 100), nw.shape)
+            self.assertEqual((100, 100), nw.shape)
 
         nw = w[:]
         self.assertEqual((784, 100, 100), nw.shape)
 
-        nw = w[:, :, ...]
+        nw = w[:, :]
         self.assertEqual((784, 100, 100), nw.shape)
 
-        nw = w[::2, ::2, :]
-        self.assertEqual((392, 50, 100), nw.shape)
+        nw = w[:, :, -1]
+        self.assertEqual((784, 100), nw.shape)
 
-        nw = w[::-2, ::-2, :]
-        self.assertEqual((392, 50, 100), nw.shape)
+        nw = w[1, 1, 1]
+
+        self.assertEqual(len(nw.shape), 1)
+        self.assertEqual(nw.shape[0], 1)
+
+        nw = w[:, :, :-1]
+        self.assertEqual((784, 100, 99), nw.shape)
 
         self.assertEqual(0, nw.lod_level)
 
@@ -94,18 +100,23 @@ class TestVariable(unittest.TestCase):
             var1 = var[0, 1, 1]
             var2 = var[1:]
             var3 = var[0:1]
-            var4 = var[..., ]
-            var5 = var[2::-2]
-            var6 = var[1, 1:, 1:]
-            var7 = var[1, ..., 1:]
-            var8 = var[1, ...]
+            var4 = var[::-1]
+            var5 = var[1, 1:, 1:]
             var_reshape = fluid.layers.reshape(var, [3, -1, 3])
-            var9 = var_reshape[1, ..., 2]
-            var10 = var_reshape[:, :, -1]
+            var6 = var_reshape[:, :, -1]
+            var7 = var[:, :, :-1]
+            var8 = var[:1, :1, :1]
+            var9 = var[:-1, :-1, :-1]
+            var10 = var[::-1, :1, :-1]
+            var11 = var[:-1, ::-1, -1:]
+            var12 = var[1:2, 2:, ::-1]
+            var13 = var[2:10, 2:, -2:-1]
+            var14 = var[1:-1, 0:2, ::-1]
+            var15 = var[::-1, ::-1, ::-1]
 
             x = fluid.layers.data(name='x', shape=[13], dtype='float32')
             y = fluid.layers.fc(input=x, size=1, act=None)
-            var11 = y[:, 0]
+            y_1 = y[:, 0]
             feeder = fluid.DataFeeder(place=place, feed_list=[x])
             data = []
             data.append((np.random.randint(10, size=[13]).astype('float32')))
@@ -115,28 +126,38 @@ class TestVariable(unittest.TestCase):
                                 feed=feeder.feed([data]),
                                 fetch_list=[
                                     var, var1, var2, var3, var4, var5, var6,
-                                    var7, var8, var9, var10, var11
+                                    var7, var8, var9, var10, var11, var12,
+                                    var13, var14, var15
                                 ])
 
-            self.assertTrue((np.array(local_out[1]) == np.array(tensor_array[
-                0, 1, 1])).all())
-            self.assertTrue((np.array(local_out[2]) == np.array(tensor_array[
-                1:])).all())
-            self.assertTrue((np.array(local_out[3]) == np.array(tensor_array[
-                0:1])).all())
-            self.assertTrue((np.array(local_out[4]) == np.array(
-                tensor_array[..., ])).all())
-            self.assertTrue((np.array(local_out[5]) == np.array(tensor_array[
-                2::-2])).all())
-            self.assertTrue((np.array(local_out[6]) == np.array(tensor_array[
-                1, 1:, 1:])).all())
-            self.assertTrue((np.array(local_out[7]) == np.array(tensor_array[
-                1, ..., 1:])).all())
-            self.assertTrue((np.array(local_out[8]) == np.array(tensor_array[
-                1, ...])).all())
-            self.assertEqual(local_out[9].shape, (1, 3, 1))
-            self.assertEqual(local_out[10].shape, (3, 3, 1))
-            self.assertEqual(local_out[11].shape, (1, 1))
+            self.assertTrue(
+                np.array_equal(local_out[1], tensor_array[0, 1, 1:2]))
+            self.assertTrue(np.array_equal(local_out[2], tensor_array[1:]))
+            self.assertTrue(np.array_equal(local_out[3], tensor_array[0:1]))
+            self.assertTrue(np.array_equal(local_out[4], tensor_array[::-1]))
+            self.assertTrue(
+                np.array_equal(local_out[5], tensor_array[1, 1:, 1:]))
+            self.assertTrue(
+                np.array_equal(local_out[6],
+                               tensor_array.reshape((3, -1, 3))[:, :, -1]))
+            self.assertTrue(
+                np.array_equal(local_out[7], tensor_array[:, :, :-1]))
+            self.assertTrue(
+                np.array_equal(local_out[8], tensor_array[:1, :1, :1]))
+            self.assertTrue(
+                np.array_equal(local_out[9], tensor_array[:-1, :-1, :-1]))
+            self.assertTrue(
+                np.array_equal(local_out[10], tensor_array[::-1, :1, :-1]))
+            self.assertTrue(
+                np.array_equal(local_out[11], tensor_array[:-1, ::-1, -1:]))
+            self.assertTrue(
+                np.array_equal(local_out[12], tensor_array[1:2, 2:, ::-1]))
+            self.assertTrue(
+                np.array_equal(local_out[13], tensor_array[2:10, 2:, -2:-1]))
+            self.assertTrue(
+                np.array_equal(local_out[14], tensor_array[1:-1, 0:2, ::-1]))
+            self.assertTrue(
+                np.array_equal(local_out[15], tensor_array[::-1, ::-1, ::-1]))
 
     def test_slice(self):
         place = fluid.CPUPlace()
@@ -148,12 +169,10 @@ class TestVariable(unittest.TestCase):
     def _tostring(self):
         b = default_main_program().current_block()
         w = b.create_var(dtype="float64", lod_level=0)
-        print(w)
         self.assertTrue(isinstance(str(w), str))
 
         if core.is_compiled_with_cuda():
             wc = b.create_var(dtype="int", lod_level=0)
-            print(wc)
             self.assertTrue(isinstance(str(wc), str))
 
     def test_tostring(self):