Add flatten op interface and enhance APIs about detection to support...

Add flatten op interface and enhance APIs about detection to support variable-length image. (#12422)

* add flatten api&enhance detection api

* unify shape_op data type

* update API.spec

paddle/fluid/API.spec

@@ -159,6 +159,7 @@ paddle.fluid.layers.relu ArgSpec(args=['x'], varargs=None, keywords=None, defaul
 paddle.fluid.layers.log ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.crop ArgSpec(args=['x', 'shape', 'offsets', 'name'], varargs=None, keywords=None, defaults=(None, None, None))
 paddle.fluid.layers.rank_loss ArgSpec(args=['label', 'left', 'right', 'name'], varargs=None, keywords=None, defaults=(None,))
+paddle.fluid.layers.flatten ArgSpec(args=['x', 'axis', 'name'], varargs=None, keywords=None, defaults=(1, None))
 paddle.fluid.layers.data ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True))
 paddle.fluid.layers.open_recordio_file ArgSpec(args=['filename', 'shapes', 'lod_levels', 'dtypes', 'pass_num', 'for_parallel'], varargs=None, keywords=None, defaults=(1, True))
 paddle.fluid.layers.open_files ArgSpec(args=['filenames', 'shapes', 'lod_levels', 'dtypes', 'thread_num', 'buffer_size', 'pass_num', 'is_test'], varargs=None, keywords=None, defaults=(None, None, 1, None))

paddle/fluid/operators/shape_op.cc

@@ -38,7 +38,7 @@ class ShapeOpMaker : public framework::OpProtoAndCheckerMaker {
     AddInput("Input", "(Tensor), The input tensor.");
     AddOutput("Out",
               "(Tensor), The shape of input tensor, the data type of the shape"
-              " is int64_t, will be on the same device with the input Tensor.");
+              " is int32_t, will be on the same device with the input Tensor.");
     AddComment(R"DOC(
 Shape Operator
 
@@ -53,5 +53,5 @@ Get the shape of input tensor. Only support CPU input Tensor now.
 namespace ops = paddle::operators;
 REGISTER_OPERATOR(shape, ops::ShapeOp, ops::ShapeOpMaker,
                   paddle::framework::EmptyGradOpMaker);
-REGISTER_OP_CPU_KERNEL(shape, ops::ShapeKernel<int>, ops::ShapeKernel<int64_t>,
+REGISTER_OP_CPU_KERNEL(shape, ops::ShapeKernel<int>, ops::ShapeKernel<int32_t>,
                        ops::ShapeKernel<float>, ops::ShapeKernel<double>);

paddle/fluid/operators/shape_op.cu

@@ -15,6 +15,6 @@ limitations under the License. */
 #include "paddle/fluid/operators/shape_op.h"
 
 REGISTER_OP_CUDA_KERNEL(shape, paddle::operators::ShapeKernel<int>,
-                        paddle::operators::ShapeKernel<int64_t>,
+                        paddle::operators::ShapeKernel<int32_t>,
                         paddle::operators::ShapeKernel<float>,
                         paddle::operators::ShapeKernel<double>);

paddle/fluid/operators/shape_op.h

@@ -27,7 +27,7 @@ class ShapeKernel : public framework::OpKernel<T> {
   void Compute(const framework::ExecutionContext& ctx) const override {
     auto* in_t = ctx.Input<Tensor>("Input");
     auto* out_t = ctx.Output<Tensor>("Out");
-    auto out_data = out_t->mutable_data<int64_t>(platform::CPUPlace());
+    auto out_data = out_t->mutable_data<int32_t>(platform::CPUPlace());
     auto in_dims = in_t->dims();
     for (int i = 0; i < in_dims.size(); ++i) {
       out_data[i] = in_dims[i];

python/paddle/fluid/layers/detection.py

@@ -20,7 +20,9 @@ from .layer_function_generator import autodoc, templatedoc
 from ..layer_helper import LayerHelper
 from . import tensor
 from . import nn
+from . import ops
 import math
+import numpy
 from functools import reduce
 
 __all__ = [
@@ -264,10 +266,11 @@ def detection_output(loc,
         prior_box_var=prior_box_var,
         target_box=loc,
         code_type='decode_center_size')
-    old_shape = scores.shape
-    scores = nn.reshape(x=scores, shape=(-1, old_shape[-1]))
+    compile_shape = scores.shape
+    run_shape = ops.shape(scores)
+    scores = nn.flatten(x=scores, axis=2)
     scores = nn.softmax(input=scores)
-    scores = nn.reshape(x=scores, shape=old_shape)
+    scores = nn.reshape(x=scores, shape=compile_shape, actual_shape=run_shape)
     scores = nn.transpose(scores, perm=[0, 2, 1])
     scores.stop_gradient = True
     nmsed_outs = helper.create_tmp_variable(dtype=decoded_box.dtype)
@@ -677,9 +680,10 @@ def ssd_loss(location,
         raise ValueError("Only support mining_type == max_negative now.")
 
     num, num_prior, num_class = confidence.shape
+    conf_shape = ops.shape(confidence)
 
     def __reshape_to_2d(var):
-        return nn.reshape(x=var, shape=[-1, var.shape[-1]])
+        return nn.flatten(x=var, axis=2)
 
     # 1. Find matched boundding box by prior box.
     #   1.1 Compute IOU similarity between ground-truth boxes and prior boxes.
@@ -690,7 +694,8 @@ def ssd_loss(location,
 
     # 2. Compute confidence for mining hard examples
     # 2.1. Get the target label based on matched indices
-    gt_label = nn.reshape(x=gt_label, shape=gt_label.shape + (1, ))
+    gt_label = nn.reshape(
+        x=gt_label, shape=(len(gt_label.shape) - 1) * (0, ) + (-1, 1))
     gt_label.stop_gradient = True
     target_label, _ = target_assign(
         gt_label, matched_indices, mismatch_value=background_label)
@@ -701,9 +706,12 @@ def ssd_loss(location,
     target_label = __reshape_to_2d(target_label)
     target_label.stop_gradient = True
     conf_loss = nn.softmax_with_cross_entropy(confidence, target_label)
-
     # 3. Mining hard examples
-    conf_loss = nn.reshape(x=conf_loss, shape=(num, num_prior))
+    conf_loss = nn.reshape(
+        x=conf_loss,
+        shape=(num, num_prior),
+        actual_shape=ops.slice(
+            conf_shape, axes=[0], starts=[0], ends=[2]))
     conf_loss.stop_gradient = True
     neg_indices = helper.create_tmp_variable(dtype='int32')
     dtype = matched_indices.dtype
@@ -772,7 +780,11 @@ def ssd_loss(location,
     # 5.3 Compute overall weighted loss.
     loss = conf_loss_weight * conf_loss + loc_loss_weight * loc_loss
     # reshape to [N, Np], N is the batch size and Np is the prior box number.
-    loss = nn.reshape(x=loss, shape=[-1, num_prior])
+    loss = nn.reshape(
+        x=loss,
+        shape=(num, num_prior),
+        actual_shape=ops.slice(
+            conf_shape, axes=[0], starts=[0], ends=[2]))
     loss = nn.reduce_sum(loss, dim=1, keep_dim=True)
     if normalize:
         normalizer = nn.reduce_sum(target_loc_weight)
@@ -1005,13 +1017,7 @@ def multi_box_head(inputs,
     """
 
     def _reshape_with_axis_(input, axis=1):
-        if not (axis > 0 and axis < len(input.shape)):
-            raise ValueError("The axis should be smaller than "
-                             "the arity of input and bigger than 0.")
-        new_shape = [
-            -1, reduce(lambda x, y: x * y, input.shape[axis:len(input.shape)])
-        ]
-        out = nn.reshape(x=input, shape=new_shape)
+        out = nn.flatten(x=input, axis=axis)
         return out
 
     def _is_list_or_tuple_(data):
@@ -1101,11 +1107,13 @@ def multi_box_head(inputs,
             stride=stride)
 
         mbox_loc = nn.transpose(mbox_loc, perm=[0, 2, 3, 1])
-        new_shape = [
+        compile_shape = [
             mbox_loc.shape[0],
             mbox_loc.shape[1] * mbox_loc.shape[2] * mbox_loc.shape[3] / 4, 4
         ]
-        mbox_loc_flatten = nn.reshape(mbox_loc, shape=new_shape)
+        run_shape = tensor.assign(numpy.array([0, -1, 4]).astype("int32"))
+        mbox_loc_flatten = nn.reshape(
+            mbox_loc, shape=compile_shape, actual_shape=run_shape)
         mbox_locs.append(mbox_loc_flatten)
 
         # get conf
@@ -1117,11 +1125,15 @@ def multi_box_head(inputs,
             padding=pad,
             stride=stride)
         conf_loc = nn.transpose(conf_loc, perm=[0, 2, 3, 1])
-        new_shape = [
+        new_shape = [0, -1, num_classes]
+        compile_shape = [
             conf_loc.shape[0], conf_loc.shape[1] * conf_loc.shape[2] *
             conf_loc.shape[3] / num_classes, num_classes
         ]
-        conf_loc_flatten = nn.reshape(conf_loc, shape=new_shape)
+        run_shape = tensor.assign(
+            numpy.array([0, -1, num_classes]).astype("int32"))
+        conf_loc_flatten = nn.reshape(
+            conf_loc, shape=compile_shape, actual_shape=run_shape)
         mbox_confs.append(conf_loc_flatten)
 
     if len(box_results) == 1:

python/paddle/fluid/layers/nn.py

@@ -112,6 +112,7 @@ __all__ = [
     'log',
     'crop',
     'rank_loss',
+    'flatten',
 ]
 
 
@@ -5361,3 +5362,70 @@ def rank_loss(label, left, right, name=None):
                 "Right": right},
         outputs={'Out': out})
     return out
+
+
+def flatten(x, axis=1, name=None):
+    """
+    **Flatten layer**
+    Flattens the input tensor into a 2D matrix.
+
+    Examples:
+    Case 1:
+      Given
+        X.shape = (3, 100, 100, 4)
+      and
+        axis = 2
+      We get:
+        Out.shape = (3 * 100, 4 * 100)
+    
+    Case 2:
+      Given
+        X.shape = (3, 100, 100, 4)
+      and
+        axis = 0
+      We get:
+        Out.shape = (1, 3 * 100 * 100 * 4)
+
+    Args:
+        x (Variable): A tensor of rank >= axis.
+        axis (int): Indicate up to which input dimensions (exclusive) should 
+                    be flattened to the outer dimension of the output. 
+                    The value for axis must be in the range [0, R], where R
+                    is the rank of the input tensor. When axis = 0, the shape
+                    of the output tensor is (1, (d_0 X d_1 ... d_n), where the
+                    shape of the input tensor is (d_0, d_1, ... d_n).
+        name(str|None): A name for this layer(optional). If set None, the layer
+                        will be named automatically.
+
+    Returns:
+        Variable: A 2D tensor with the contents of the input tensor, with input
+                  dimensions up to axis flattened to the outer dimension of
+                  the output and remaining input dimensions flattened into the
+                  inner dimension of the output.
+
+    Raises:
+        ValueError: If x is not a variable.
+        ValueError: If axis is not in range [0, rank(x)]. 
+
+    Examples:
+
+        .. code-block:: python
+
+            x = fluid.layers.data(name="x", shape=[4, 4, 3], dtype="float32")
+            out = fluid.layers.flatten(x=x, axis=2)
+    """
+    helper = LayerHelper('flatten', **locals())
+
+    if not (isinstance(x, Variable)):
+        raise ValueError("The input x should be a Variable")
+
+    if not (isinstance(axis, int)) or axis > len(x.shape) or axis < 0:
+        raise ValueError("The axis should be a int, and in range [0, rank(x)]")
+
+    out = helper.create_tmp_variable(x.dtype)
+    helper.append_op(
+        type='flatten',
+        inputs={"X": x},
+        outputs={'Out': out},
+        attrs={"axis": axis})
+    return out

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

@@ -465,6 +465,17 @@ class TestBook(unittest.TestCase):
             self.assertIsNotNone(out)
         print(str(program))
 
+    def test_flatten(self):
+        program = Program()
+        with program_guard(program):
+            x = layers.data(
+                name='x',
+                append_batch_size=False,
+                shape=[4, 4, 3],
+                dtype="float32")
+            out = layers.flatten(x, axis=1, name="flatten")
+            self.assertIsNotNone(out)
+
     def test_shape(self):
         program = Program()
         with program_guard(program):