Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into ctc_evaluator_py

paddle/operators/math/sequence_padding.cc

@@ -32,7 +32,8 @@ class PaddingLoDTensorFunctor<platform::CPUDeviceContext, T> {
     framework::LoD abs_offset_lod = framework::ToAbsOffset(lod);
 
     auto seq_dims = seq.dims();
-    PADDLE_ENFORCE_EQ(seq_dims[0], abs_offset_lod[level].back(),
+    PADDLE_ENFORCE_EQ(seq_dims[0],
+                      static_cast<int64_t>(abs_offset_lod[level].back()),
                       "The first dimension of LoDTensor seq should be "
                       "equal to the sum of all sequences's length.");
 
@@ -41,32 +42,32 @@ class PaddingLoDTensorFunctor<platform::CPUDeviceContext, T> {
                       "The input padding should be a 3-D Tensor of shape "
                       "[max_sequence_length, num_sequences, sequence_width].");
 
-    const size_t max_sequence_length = MaximumSequenceLength(lod, level);
+    const int64_t max_sequence_length = MaximumSequenceLength(lod, level);
     PADDLE_ENFORCE_EQ(padding_dims[0], max_sequence_length,
                       "The first dimension of Tensor padding should be the "
                       "maximum length of all sequences in LoDTensor seq.");
 
-    const size_t num_sequences = abs_offset_lod[level].size() - 1;
+    const int64_t num_sequences = abs_offset_lod[level].size() - 1;
     PADDLE_ENFORCE_EQ(padding_dims[1], num_sequences,
                       "The second dimension of Tensor padding should be the "
                       "number of sequences in LoDTensor seq.");
 
-    const size_t sequence_width = seq.numel() / seq_dims[0];
+    const int64_t sequence_width = seq.numel() / seq_dims[0];
     PADDLE_ENFORCE_EQ(padding_dims[2], sequence_width,
                       "The third dimension of Tensor padding should be the "
                       "width of sequence in LoDTensor seq.");
 
     const T* seq_data = seq.data<T>();
     T* padding_data = padding.data<T>();
-    for (size_t i = 0; i < max_sequence_length; ++i) {
-      for (size_t j = 0; j < num_sequences; ++j) {
-        size_t start_pos = abs_offset_lod[level][j];
-        size_t sequence_length = abs_offset_lod[level][j + 1] - start_pos;
+    for (int64_t i = 0; i < max_sequence_length; ++i) {
+      for (int64_t j = 0; j < num_sequences; ++j) {
+        int64_t start_pos = abs_offset_lod[level][j];
+        int64_t sequence_length = abs_offset_lod[level][j + 1] - start_pos;
         if (i < sequence_length) {
           // i > 0 => sequence_length > 0
           T scale =
               norm_by_times ? (1.0f / static_cast<T>(sequence_length)) : 1.0f;
-          for (size_t k = 0; k < sequence_width; ++k) {
+          for (int64_t k = 0; k < sequence_width; ++k) {
             padding_data[(i * num_sequences + j) * sequence_width + k] =
                 seq_data[(start_pos + i) * sequence_width + k] * scale;
           }
@@ -93,7 +94,8 @@ class UnpaddingLoDTensorFunctor<platform::CPUDeviceContext, T> {
     framework::LoD abs_offset_lod = framework::ToAbsOffset(lod);
 
     auto seq_dims = seq.dims();
-    PADDLE_ENFORCE_EQ(seq_dims[0], abs_offset_lod[level].back(),
+    PADDLE_ENFORCE_EQ(seq_dims[0],
+                      static_cast<int64_t>(abs_offset_lod[level].back()),
                       "The first dimension of LoDTensor seq should be "
                       "equal to the sum of all sequences's length.");
 
@@ -102,31 +104,31 @@ class UnpaddingLoDTensorFunctor<platform::CPUDeviceContext, T> {
                       "The input padding should be a 3-D Tensor of shape "
                       "[max_sequnece_length, num_sequences, sequence_width].");
 
-    const size_t max_sequence_length = MaximumSequenceLength(lod, level);
+    const int64_t max_sequence_length = MaximumSequenceLength(lod, level);
     PADDLE_ENFORCE_EQ(padding_dims[0], max_sequence_length,
                       "The first dimension of Tensor padding should be "
                       "the maximum length of all sequences in LoDTensor seq.");
 
-    const size_t num_sequences = abs_offset_lod[level].size() - 1;
+    const int64_t num_sequences = abs_offset_lod[level].size() - 1;
     PADDLE_ENFORCE_EQ(padding_dims[1], num_sequences,
                       "The second dimension of Tensor padding should be "
                       "the number of sequences in LoDTensor seq.");
 
-    const size_t sequence_width = seq.numel() / seq_dims[0];
+    const int64_t sequence_width = seq.numel() / seq_dims[0];
     PADDLE_ENFORCE_EQ(padding_dims[2], sequence_width,
                       "The third dimension of Tensor padding should be the "
                       "width of sequence in LoDTensor seq.");
 
     const T* padding_data = padding.data<T>();
     T* seq_data = seq.data<T>();
-    for (size_t i = 0; i < num_sequences; ++i) {
-      size_t start_pos = abs_offset_lod[level][i];
-      size_t sequence_length = abs_offset_lod[level][i + 1] - start_pos;
-      for (size_t j = 0; j < sequence_length; ++j) {
+    for (int64_t i = 0; i < num_sequences; ++i) {
+      int64_t start_pos = abs_offset_lod[level][i];
+      int64_t sequence_length = abs_offset_lod[level][i + 1] - start_pos;
+      for (int64_t j = 0; j < sequence_length; ++j) {
         // sequence_width > j > 0
         T scale =
             norm_by_times ? (1.0f / static_cast<T>(sequence_length)) : 1.0f;
-        for (size_t k = 0; k < sequence_width; ++k) {
+        for (int64_t k = 0; k < sequence_width; ++k) {
           seq_data[(start_pos + j) * sequence_width + k] =
               padding_data[(j * num_sequences + i) * sequence_width + k] *
               scale;

paddle/operators/math/sequence_padding.cu

@@ -71,7 +71,8 @@ class PaddingLoDTensorFunctor<platform::CUDADeviceContext, T> {
     framework::LoD abs_offset_lod = framework::ToAbsOffset(lod);
 
     auto seq_dims = seq.dims();
-    PADDLE_ENFORCE_EQ(seq_dims[0], abs_offset_lod[level].back(),
+    PADDLE_ENFORCE_EQ(seq_dims[0],
+                      static_cast<int64_t>(abs_offset_lod[level].back()),
                       "The first dimension of LoDTensor seq should be "
                       "equal to the sum of all sequences's length.");
 
@@ -80,17 +81,17 @@ class PaddingLoDTensorFunctor<platform::CUDADeviceContext, T> {
                       "The input padding should be a 3-D Tensor of shape "
                       "[max_sequence_length, num_sequences, sequence_width].");
 
-    size_t max_sequence_length = MaximumSequenceLength(lod, level);
+    int64_t max_sequence_length = MaximumSequenceLength(lod, level);
     PADDLE_ENFORCE_EQ(padding_dims[0], max_sequence_length,
                       "The first dimension of Tensor padding should be the "
                       "maximum length of all sequences in LoDTensor seq.");
 
-    const size_t num_sequences = abs_offset_lod[level].size() - 1;
+    const int64_t num_sequences = abs_offset_lod[level].size() - 1;
     PADDLE_ENFORCE_EQ(padding_dims[1], num_sequences,
                       "The second dimension of Tensor padding should be the "
                       "number of sequences in LoDTensor seq.");
 
-    const size_t sequence_width = seq.numel() / seq_dims[0];
+    const int64_t sequence_width = seq.numel() / seq_dims[0];
     PADDLE_ENFORCE_EQ(padding_dims[2], sequence_width,
                       "The third dimension of Tensor padding should be the "
                       "width of sequence in LoDTensor seq.");
@@ -101,7 +102,7 @@ class PaddingLoDTensorFunctor<platform::CUDADeviceContext, T> {
       return;
     }
 
-    const size_t kBlockSize = 512;
+    const int64_t kBlockSize = 512;
 
     /* At least use 32 threads to copy sequence_width elements,
      * and at least 8 elements for each thread.
@@ -143,7 +144,8 @@ class UnpaddingLoDTensorFunctor<platform::CUDADeviceContext, T> {
     framework::LoD abs_offset_lod = framework::ToAbsOffset(lod);
 
     auto seq_dims = seq.dims();
-    PADDLE_ENFORCE_EQ(seq_dims[0], abs_offset_lod[level].back(),
+    PADDLE_ENFORCE_EQ(seq_dims[0],
+                      static_cast<int64_t>(abs_offset_lod[level].back()),
                       "The first dimension of LoDTensor seq should be "
                       "equal to the sum of all sequences's length.");
 
@@ -152,17 +154,17 @@ class UnpaddingLoDTensorFunctor<platform::CUDADeviceContext, T> {
                       "The input padding should be a 3-D Tensor of shape "
                       "[max_sequnece_length, num_sequences, sequence_width].");
 
-    size_t max_sequence_length = MaximumSequenceLength(lod, level);
+    int64_t max_sequence_length = MaximumSequenceLength(lod, level);
     PADDLE_ENFORCE_EQ(padding_dims[0], max_sequence_length,
                       "The first dimension of Tensor padding should be "
                       "the maximum length of all sequences in LoDTensor seq.");
 
-    const size_t num_sequences = abs_offset_lod[level].size() - 1;
+    const int64_t num_sequences = abs_offset_lod[level].size() - 1;
     PADDLE_ENFORCE_EQ(padding_dims[1], num_sequences,
                       "The second dimension of Tensor padding should be "
                       "the number of sequences in LoDTensor seq.");
 
-    const size_t sequence_width = seq.numel() / seq_dims[0];
+    const int64_t sequence_width = seq.numel() / seq_dims[0];
     PADDLE_ENFORCE_EQ(padding_dims[2], sequence_width,
                       "The third dimension of Tensor padding should be the "
                       "width of sequence in LoDTensor seq.");
@@ -173,7 +175,7 @@ class UnpaddingLoDTensorFunctor<platform::CUDADeviceContext, T> {
       return;
     }
 
-    const size_t kBlockSize = 512;
+    const int64_t kBlockSize = 512;
 
     /* At least use 32 threads to copy sequence_width elements,
      * and at least 8 elements for each thread.

python/paddle/v2/fluid/layers/nn.py

@@ -22,38 +22,14 @@ from ..param_attr import ParamAttr
 from tensor import concat
 
 __all__ = [
-    'fc',
-    'embedding',
-    'dynamic_lstm',
-    'gru_unit',
-    'linear_chain_crf',
-    'crf_decoding',
-    'cos_sim',
-    'cross_entropy',
-    'square_error_cost',
-    'accuracy',
-    'chunk_eval',
-    'sequence_conv',
-    'conv2d',
-    'sequence_pool',
-    'pool2d',
-    'batch_norm',
-    'beam_search_decode',
-    'conv2d_transpose',
-    'sequence_expand',
-    'lstm_unit',
-    'reduce_sum',
-    'reduce_mean',
-    'reduce_max',
-    'reduce_min',
-    'sequence_first_step',
-    'sequence_last_step',
-    'dropout',
-    'split',
-    'ctc_greedy_decoder',
-    'edit_distance_error',
-    'l2_normalize',
-    'matmul',
+    'fc', 'embedding', 'dynamic_lstm', 'gru_unit', 'linear_chain_crf',
+    'crf_decoding', 'cos_sim', 'cross_entropy', 'square_error_cost', 'accuracy',
+    'chunk_eval', 'sequence_conv', 'conv2d', 'sequence_pool', 'pool2d',
+    'batch_norm', 'beam_search_decode', 'conv2d_transpose', 'sequence_expand',
+    'lstm_unit', 'reduce_sum', 'reduce_mean', 'reduce_max', 'reduce_min',
+    'sequence_first_step', 'sequence_last_step', 'dropout', 'split',
+    'ctc_greedy_decoder', 'edit_distance_error', 'l2_normalize', 'matmul',
+    'warpctc'
 ]
 
 
@@ -1903,3 +1879,56 @@ def ctc_greedy_decoder(input, blank, name=None):
         attrs={"merge_repeated": True,
                "blank": blank})
     return ctc_out
+
+
+def warpctc(input, label, blank=0, norm_by_times=False, **kwargs):
+    """
+    An operator integrating the open source Warp-CTC library
+    (https://github.com/baidu-research/warp-ctc)
+    to compute Connectionist Temporal Classification (CTC) loss.
+    It can be aliased as softmax with CTC, since a native softmax activation is
+    interated to the Warp-CTC library, to to normlize values for each row of the
+    input tensor.
+
+    Args:
+       input(Variable): (LodTensor, default: LoDTensor<float>),
+         the unscaled probabilities of variable-length sequences,
+         which 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).
+       label(Variable): (LodTensor, default: LoDTensor<int>), the ground truth
+         of variable-length sequence, which is a 2-D Tensor with LoD
+         information. It is of the shape [Lg, 1], where Lg is th sum of
+         all labels' length.
+       blank: (int, default: 0), the blank label index of Connectionist
+         Temporal Classification (CTC) loss, which is in the
+         half-opened interval [0, num_classes + 1).
+       norm_by_times: (bool, default: false), whether to normalize
+       the gradients by the number of time-step,which is also the
+       sequence's length. There is no need to normalize the gradients
+       if warpctc layer was follewed by a mean_op.
+
+    Returns:
+        Variable: The Connectionist Temporal Classification (CTC) loss,
+        which is a 2-D Tensor of the shape [batch_size, 1].
+
+    Examples:
+        .. code-block:: python
+            y = layers.data(name='y', shape=[11, 8], dtype='float32', lod_level=1)
+            y_predict = layers.data(name='y_predict', shape=[11, 1], dtype='float32')
+            cost = layers.warpctc(input=y_predict, label=y)
+
+    """
+    helper = LayerHelper('warpctc', **kwargs)
+    loss_out = helper.create_tmp_variable(dtype=input.dtype)
+    grad_out = helper.create_tmp_variable(dtype=input.dtype)
+    helper.append_op(
+        type='warpctc',
+        inputs={'Logits': [input],
+                'Label': [label]},
+        outputs={'WarpCTCGrad': [grad_out],
+                 'Loss': [loss_out]},
+        attrs={'blank': blank,
+               'norm_by_times': norm_by_times})
+    return loss_out