refine ctc align op with padding (#19926)

* refine ctc align op with padding 
* refine api sample code

paddle/fluid/API.spec

@@ -161,7 +161,7 @@ paddle.fluid.layers.sequence_last_step (ArgSpec(args=['input'], varargs=None, ke
 paddle.fluid.layers.sequence_slice (ArgSpec(args=['input', 'offset', 'length', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '39fbc5437be389f6c0c769f82fc1fba2'))
 paddle.fluid.layers.dropout (ArgSpec(args=['x', 'dropout_prob', 'is_test', 'seed', 'name', 'dropout_implementation'], varargs=None, keywords=None, defaults=(False, None, None, 'downgrade_in_infer')), ('document', '558d13133596209190df9a624264f28f'))
 paddle.fluid.layers.split (ArgSpec(args=['input', 'num_or_sections', 'dim', 'name'], varargs=None, keywords=None, defaults=(-1, None)), ('document', '78cf3a7323d1a7697658242e13f63759'))
-paddle.fluid.layers.ctc_greedy_decoder (ArgSpec(args=['input', 'blank', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '2bc3a59efa9d52b628a6255422d9f0e8'))
+paddle.fluid.layers.ctc_greedy_decoder (ArgSpec(args=['input', 'blank', 'input_length', 'padding_value', 'name'], varargs=None, keywords=None, defaults=(None, 0, None)), ('document', '9abb7bb8d267e017620a39a146dc47ea'))
 paddle.fluid.layers.edit_distance (ArgSpec(args=['input', 'label', 'normalized', 'ignored_tokens', 'input_length', 'label_length'], varargs=None, keywords=None, defaults=(True, None, None, None)), ('document', '77cbfb28cd2fc589f589c7013c5086cd'))
 paddle.fluid.layers.l2_normalize (ArgSpec(args=['x', 'axis', 'epsilon', 'name'], varargs=None, keywords=None, defaults=(1e-12, None)), ('document', 'c1df110ea65998984f564c5c10abc54a'))
 paddle.fluid.layers.matmul (ArgSpec(args=['x', 'y', 'transpose_x', 'transpose_y', 'alpha', 'name'], varargs=None, keywords=None, defaults=(False, False, 1.0, None)), ('document', '3720b4a386585094435993deb028b592'))

paddle/fluid/operators/ctc_align_op.cc

@@ -22,15 +22,18 @@ class CTCAlignOp : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("Input"),
+    PADDLE_ENFORCE_EQ(ctx->HasInput("Input"), true,
                       "Input of CTCAlignOp should not be null.");
-    PADDLE_ENFORCE(ctx->HasOutput("Output"),
+    PADDLE_ENFORCE_EQ(ctx->HasOutput("Output"), true,
                       "Output of CTCAlignOp should not be null.");
 
     auto input_dims = ctx->GetInputDim("Input");
 
     // TODO(wanghaoshuang): it is tricky to set the wrong dimension here.
     ctx->SetOutputDim("Output", input_dims);
+    if (ctx->HasInput("InputLength")) {
+      ctx->SetOutputDim("OutputLength", {input_dims[0], 1});
+    }
   }
 
  protected:
@@ -47,7 +50,17 @@ class CTCAlignOpMaker : public framework::OpProtoAndCheckerMaker {
     AddInput("Input",
              "2-D Tensor or LodTensor with  shape "
              "[Lp, 1], where Lp is the sum of all input sequences' length.");
+    AddInput("InputLength",
+             "2-D Tensor with shape [batch_size, 1], "
+             " When Input is padding mode, InputLength is length of every "
+             "sequence in Input.")
+        .AsDispensable();
     AddOutput("Output", "(Tensor, default: Tensor<int>), The align result.");
+    AddOutput("OutputLength",
+              "2-D Tensor with shape [batch_size, 1], "
+              "When Input is padding mode, OutputLength is length of every "
+              "sequence in Output.")
+        .AsDispensable();
     AddAttr<int>("blank",
                  "(int, default: 0), the blank label setted in Connectionist "
                  "Temporal Classification (CTC) op.")
@@ -84,6 +97,9 @@ or Given:
     Input.data = [[0, 1, 2, 2, 0, 4], 
                   [0, 4, 5, 0, 6, 0], 
                   [0, 7, 7, 7, 0, 0]]
+    InputLength.data  = [[6],
+                         [5],
+                         [4]],   
     Input.dims = {3, 6},
     Input.Lod = []
 And:
@@ -94,7 +110,10 @@ And:
 Then:
     Output.data = [[1, 2, 4, 0, 0, 0],
                    [4, 5, 6, 0, 0, 0],
-                   [7, 0, 0, 0, 0, 0]]
+                   [7, 0, 0, 0, 0, 0]],
+    OutputLength.data = [[3],
+                         [3],
+                         [1]],
     Output.dims = {3, 6},
     Output.Lod = []
 )DOC");

paddle/fluid/operators/ctc_align_op.cu

@@ -43,17 +43,15 @@ __global__ void MergeAndDelCudaKernel(const int64_t num_token, const T* tokens,
 }
 
 template <typename T>
-__global__ void PaddingMergeAndDelCudaKernel(const int64_t num_token,
-                                             const T* tokens, const int blank,
-                                             const int merge_repeated,
-                                             const int padding_value,
-                                             const int64_t batch_size,
-                                             T* output) {
+__global__ void PaddingMergeAndDelCudaKernel(
+    const int64_t num_token, const T* tokens, const T* tokens_length,
+    const int blank, const int merge_repeated, const int padding_value,
+    const int64_t batch_size, T* output, T* output_length) {
   int ind = blockIdx.x * blockDim.x + threadIdx.x;
   if (ind >= batch_size) return;
   int output_idx = ind * num_token;
   T prev_token = -1;
-  for (int i = ind * num_token; i < ind * num_token + num_token; i++) {
+  for (int i = ind * num_token; i < ind * num_token + tokens_length[ind]; i++) {
     if ((unsigned)tokens[i] != blank &&
         !(merge_repeated && tokens[i] == prev_token)) {
       output[output_idx] = tokens[i];
@@ -61,6 +59,7 @@ __global__ void PaddingMergeAndDelCudaKernel(const int64_t num_token,
     }
     prev_token = tokens[i];
   }
+  output_length[ind] = output_idx - ind * num_token;
   for (int i = output_idx; i < ind * num_token + num_token; i++) {
     output[i] = padding_value;
   }
@@ -86,10 +85,15 @@ class CTCAlignOpCUDAKernel : public framework::OpKernel<T> {
       auto input_dims = input->dims();
       T* output_data = output->mutable_data<T>({input_dims[0], input_dims[1]},
                                                ctx.GetPlace());
+      auto* input_length = ctx.Input<LoDTensor>("InputLength");
+      const T* input_length_data = input_length->data<T>();
+      auto* output_length = ctx.Output<LoDTensor>("OutputLength");
+      T* output_length_data =
+          output_length->mutable_data<T>({input_dims[0], 1}, ctx.GetPlace());
       PaddingMergeAndDelCudaKernel<
           T><<<32, (input_dims[0] + 32 - 1) / 32, 0, stream>>>(
-          input_dims[1], tokens, blank, merge_repeated, padding_value,
-          input_dims[0], output_data);
+          input_dims[1], tokens, input_length_data, blank, merge_repeated,
+          padding_value, input_dims[0], output_data, output_length_data);
     } else {
       const size_t level = 0;
       auto input_lod = framework::ToAbsOffset(input->lod());

paddle/fluid/operators/ctc_align_op.h

@@ -41,11 +41,17 @@ class CTCAlignKernel : public framework::OpKernel<T> {
     if (input->lod().empty()) {
       size_t padding_value =
           static_cast<size_t>(ctx.Attr<int>("padding_value"));
+      auto* input_length = ctx.Input<LoDTensor>("InputLength");
+      const T* input_length_data = input_length->data<T>();
+
+      auto* output_length = ctx.Output<LoDTensor>("OutputLength");
+      T* output_length_data = output_length->mutable_data<T>(ctx.GetPlace());
+
       for (size_t batch_id = 0; batch_id < (unsigned)input_dims[0];
            batch_id++) {
         T prev_token = -1;
         size_t output_idx = 0;
-        for (size_t i = 0; i < (unsigned)input_dims[1]; i++) {
+        for (size_t i = 0; i < (unsigned)input_length_data[batch_id]; i++) {
           size_t input_ind = batch_id * input_dims[1] + i;
           if ((unsigned)input_data[input_ind] != blank &&
               !(merge_repeated && input_data[input_ind] == prev_token)) {
@@ -55,6 +61,7 @@ class CTCAlignKernel : public framework::OpKernel<T> {
           }
           prev_token = input_data[input_ind];
         }
+        output_length_data[batch_id] = output_idx;
         for (size_t j = output_idx; j < (unsigned)input_dims[1]; j++)
           output_data[batch_id * input_dims[1] + j] = padding_value;
       }

python/paddle/fluid/layers/nn.py

@@ -5851,7 +5851,11 @@ def edit_distance(input,
     return edit_distance_out, sequence_num
 
 
-def ctc_greedy_decoder(input, blank, name=None):
+def ctc_greedy_decoder(input,
+                       blank,
+                       input_length=None,
+                       padding_value=0,
+                       name=None):
     """
     This op is used to decode sequences by greedy policy by below steps:
 
@@ -5865,6 +5869,7 @@ def ctc_greedy_decoder(input, blank, name=None):
     .. code-block:: text
 
         Given:
+        for lod mode:
 
         input.data = [[0.6, 0.1, 0.3, 0.1],
                       [0.3, 0.2, 0.4, 0.1],
@@ -5893,38 +5898,83 @@ def ctc_greedy_decoder(input, blank, name=None):
 
         output.lod = [[2, 1]]
 
+        for padding mode:
+
+         input.data = [[[0.6, 0.1, 0.3, 0.1],
+                        [0.3, 0.2, 0.4, 0.1],
+                        [0.1, 0.5, 0.1, 0.3],
+                        [0.5, 0.1, 0.3, 0.1]],
+
+                       [[0.5, 0.1, 0.3, 0.1],
+                        [0.2, 0.2, 0.2, 0.4],
+                        [0.2, 0.2, 0.1, 0.5],
+                        [0.5, 0.1, 0.3, 0.1]]]
+
+        input_length.data = [[4], [4]]
+        input.shape = [2, 4, 4]
+
+        step1: Apply argmax to first input sequence which is input.data[0:4]. Then we get:
+               [[0], [2], [1], [0]], for input.data[4:8] is [[0], [3], [3], [0]], shape is [2,4,1]
+        step2: Change the argmax result to use padding mode, then argmax result is 
+                [[0, 2, 1, 0], [0, 3, 3, 0]], shape is [2, 4], lod is [], input_length is [[4], [4]]
+        step3: Apply ctc_align to padding argmax result, padding_value is 0
+
+        Finally:
+        output.data = [[2, 1, 0, 0],
+                       [3, 0, 0, 0]]
+        output_length.data = [[2], [1]]
+
+
+
 
     Args:
 
         input(Variable): (LoDTensor<float>), the probabilities of
-                         variable-length sequences, which is a 2-D Tensor with
-                         LoD information. It's shape is [Lp, num_classes + 1],
+                         variable-length sequences. When in lod mode, it is a 2-D Tensor with
+                         LoD information. It's shape is [Lp, num_classes + 1] 
                          where Lp is the sum of all input sequences' length and
-                         num_classes is the true number of classes. (not
-                         including the blank label).
+                         num_classes is the true number of classes. When in padding mode,
+                         it is a 3-D Tensor with padding, It's shape is [batch_size, N, num_classes + 1].
+                         (not including the blank label).
         blank(int): the blank label index of Connectionist Temporal
                     Classification (CTC) loss, which is in thehalf-opened
                     interval [0, num_classes + 1).
-        name (str): The name of this layer. It is optional.
+        input_length(Variable, optional): (LoDTensor<int>), shape is [batch_size, 1], when in lod mode, input_length
+                                 is None.
+        padding_value(int): padding value.
+        name (str, optional): The name of this layer. It is optional.
 
     Returns:
-        Variable: CTC greedy decode result which is a 2-D tensor with shape [Lp, 1]. \
+        output(Variable): For lod mode, CTC greedy decode result which is a 2-D tensor with shape [Lp, 1]. \
                   'Lp' is the sum if all output sequences' length. If all the sequences \
                   in result were empty, the result LoDTensor will be [-1] with  \
-                  LoD [[]] and dims [1, 1].
+                  LoD [[]] and dims [1, 1]. For padding mode, CTC greedy decode result is a 2-D tensor \
+                  with shape [batch_size, N], output length's shape is [batch_size, 1] which is length \
+                  of every sequence in output.
+        output_length(Variable, optional): length of each sequence of output for padding mode.
 
     Examples:
         .. code-block:: python
 
+            # for lod mode
             import paddle.fluid as fluid
             x = fluid.layers.data(name='x', shape=[8], dtype='float32')
             cost = fluid.layers.ctc_greedy_decoder(input=x, blank=0)
+
+            # for padding mode
+            x_pad = fluid.layers.data(name='x_pad', shape=[4,8], dtype='float32')
+            x_pad_len = fluid.layers.data(name='x_pad_len', shape=[1], dtype='int64')
+            out, out_len = fluid.layers.ctc_greedy_decoder(input=x_pad, blank=0,
+                            input_length=x_pad_len)
+
     """
     helper = LayerHelper("ctc_greedy_decoder", **locals())
     _, topk_indices = topk(input, k=1)
 
     # ctc align op
     ctc_out = helper.create_variable_for_type_inference(dtype="int64")
+
+    if input_length is None:
         helper.append_op(
             type="ctc_align",
             inputs={"Input": [topk_indices]},
@@ -5932,6 +5982,22 @@ def ctc_greedy_decoder(input, blank, name=None):
             attrs={"merge_repeated": True,
                    "blank": blank})
         return ctc_out
+    else:
+        ctc_out_len = helper.create_variable_for_type_inference(dtype="int64")
+        ctc_input = squeeze(topk_indices, [2])
+
+        helper.append_op(
+            type="ctc_align",
+            inputs={"Input": [ctc_input],
+                    "InputLength": [input_length]},
+            outputs={"Output": [ctc_out],
+                     "OutputLength": [ctc_out_len]},
+            attrs={
+                "merge_repeated": True,
+                "blank": blank,
+                "padding_value": padding_value
+            })
+        return ctc_out, ctc_out_len
 
 
 def warpctc(input,

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

@@ -19,10 +19,11 @@ import unittest
 import numpy as np
 from op_test import OpTest
 from test_softmax_op import stable_softmax
+import paddle.fluid as fluid
 
 
-def CTCAlign(input, lod, blank, merge_repeated, padding=0):
-    if lod is not None and len(lod) > 0:
+def CTCAlign(input, lod, blank, merge_repeated, padding=0, input_length=None):
+    if input_length is None:
         lod0 = lod[0]
         result = []
         cur_offset = 0
@@ -38,23 +39,28 @@ def CTCAlign(input, lod, blank, merge_repeated, padding=0):
         result = np.array(result).reshape([len(result), 1]).astype("int32")
         if len(result) == 0:
             result = np.array([-1])
+        return result
     else:
         result = [[] for i in range(len(input))]
+        output_length = []
         for i in range(len(input)):
             prev_token = -1
-            for j in range(len(input[i])):
+            for j in range(input_length[i][0]):
                 token = input[i][j]
                 if (token != blank) and not (merge_repeated and
                                              token == prev_token):
                     result[i].append(token)
                 prev_token = token
             start = len(result[i])
+            output_length.append([start])
             for j in range(start, len(input[i])):
                 result[i].append(padding)
         result = np.array(result).reshape(
             [len(input), len(input[0])]).astype("int32")
+        output_length = np.array(output_length).reshape(
+            [len(input), 1]).astype("int32")
 
-    return result
+    return result, output_length
 
 
 class TestCTCAlignOp(OpTest):
@@ -114,13 +120,18 @@ class TestCTCAlignPaddingOp(OpTest):
         self.input = np.array([[0, 2, 4, 4, 0, 6, 3, 6, 6, 0, 0],
                                [1, 1, 3, 0, 0, 4, 5, 6, 0, 0, 0]]).reshape(
                                    [2, 11]).astype("int32")
+        self.input_length = np.array([[9], [8]]).reshape([2, 1]).astype("int32")
 
     def setUp(self):
         self.config()
-        output = CTCAlign(self.input, self.input_lod, self.blank,
-                          self.merge_repeated, self.padding_value)
-        self.inputs = {"Input": (self.input, self.input_lod), }
-        self.outputs = {"Output": output}
+        output, output_length = CTCAlign(self.input, self.input_lod, self.blank,
+                                         self.merge_repeated,
+                                         self.padding_value, self.input_length)
+        self.inputs = {
+            "Input": (self.input, self.input_lod),
+            "InputLength": self.input_length
+        }
+        self.outputs = {"Output": output, "OutputLength": output_length}
         self.attrs = {
             "blank": self.blank,
             "merge_repeated": self.merge_repeated,
@@ -129,7 +140,6 @@ class TestCTCAlignPaddingOp(OpTest):
 
     def test_check_output(self):
         self.check_output()
-        pass
 
 
 class TestCTCAlignOpCase3(TestCTCAlignPaddingOp):
@@ -142,6 +152,8 @@ class TestCTCAlignOpCase3(TestCTCAlignPaddingOp):
         self.input = np.array([[0, 1, 2, 2, 0, 4], [0, 4, 5, 0, 6, 0],
                                [0, 7, 7, 7, 0, 0]]).reshape(
                                    [3, 6]).astype("int32")
+        self.input_length = np.array([[6], [5],
+                                      [4]]).reshape([3, 1]).astype("int32")
 
 
 class TestCTCAlignOpCase4(TestCTCAlignPaddingOp):
@@ -158,6 +170,8 @@ class TestCTCAlignOpCase4(TestCTCAlignPaddingOp):
         self.input = np.array([[0, 1, 2, 2, 0, 4], [0, 4, 5, 0, 6, 0],
                                [0, 7, 7, 7, 0, 0]]).reshape(
                                    [3, 6]).astype("int32")
+        self.input_length = np.array([[6], [5],
+                                      [4]]).reshape([3, 1]).astype("int32")
 
 
 class TestCTCAlignOpCase5(TestCTCAlignPaddingOp):
@@ -170,6 +184,37 @@ class TestCTCAlignOpCase5(TestCTCAlignPaddingOp):
         self.input = np.array([[0, 1, 2, 2, 0, 4], [0, 4, 5, 0, 6, 0],
                                [0, 7, 1, 7, 0, 0]]).reshape(
                                    [3, 6]).astype("int32")
+        self.input_length = np.array([[6], [5],
+                                      [4]]).reshape([3, 1]).astype("int32")
+
+
+class TestCTCAlignOpApi(unittest.TestCase):
+    def test_api(self):
+        x = fluid.layers.data('x', shape=[4], dtype='float32')
+        y = fluid.layers.ctc_greedy_decoder(x, blank=0)
+
+        x_pad = fluid.layers.data('x_pad', shape=[4, 4], dtype='float32')
+        x_pad_len = fluid.layers.data('x_pad_len', shape=[1], dtype='int64')
+        y_pad, y_pad_len = fluid.layers.ctc_greedy_decoder(
+            x_pad, blank=0, input_length=x_pad_len)
+
+        place = fluid.CPUPlace()
+        x_tensor = fluid.create_lod_tensor(
+            np.random.rand(8, 4).astype("float32"), [[4, 4]], place)
+
+        x_pad_tensor = np.random.rand(2, 4, 4).astype("float32")
+        x_pad_len_tensor = np.array([[4], [4]]).reshape([2, 1]).astype("int64")
+
+        exe = fluid.Executor(place)
+
+        exe.run(fluid.default_startup_program())
+        ret = exe.run(feed={
+            'x': x_tensor,
+            'x_pad': x_pad_tensor,
+            'x_pad_len': x_pad_len_tensor
+        },
+                      fetch_list=[y, y_pad, y_pad_len],
+                      return_numpy=False)
 
 
 if __name__ == "__main__":