remove fixed hack api

deepspeech/__init__.py

@@ -30,24 +30,13 @@ logger = Log(__name__).getlog()
 logger.warn = logger.warning
 
 ########### hcak paddle #############
-paddle.bool = 'bool'
-paddle.float16 = 'float16'
 paddle.half = 'float16'
-paddle.float32 = 'float32'
 paddle.float = 'float32'
-paddle.float64 = 'float64'
 paddle.double = 'float64'
-paddle.int8 = 'int8'
-paddle.int16 = 'int16'
 paddle.short = 'int16'
-paddle.int32 = 'int32'
 paddle.int = 'int32'
-paddle.int64 = 'int64'
 paddle.long = 'int64'
-paddle.uint8 = 'uint8'
 paddle.uint16 = 'uint16'
-paddle.complex64 = 'complex64'
-paddle.complex128 = 'complex128'
 paddle.cdouble = 'complex128'
 
 
@@ -403,45 +392,7 @@ if not hasattr(paddle.nn.functional, 'glu'):
 #         return x * 0.5 * (1.0 + paddle.erf(x / math.sqrt(2.0)))
 
 
-# hack loss
-def ctc_loss(logits,
-             labels,
-             input_lengths,
-             label_lengths,
-             blank=0,
-             reduction='mean',
-             norm_by_times=True):
-    #logger.info("my ctc loss with norm by times")
-    ## https://github.com/PaddlePaddle/Paddle/blob/f5ca2db2cc/paddle/fluid/operators/warpctc_op.h#L403
-    loss_out = paddle.fluid.layers.warpctc(logits, labels, blank, norm_by_times,
-                                           input_lengths, label_lengths)
-
-    loss_out = paddle.fluid.layers.squeeze(loss_out, [-1])
-    assert reduction in ['mean', 'sum', 'none']
-    if reduction == 'mean':
-        loss_out = paddle.mean(loss_out / label_lengths)
-    elif reduction == 'sum':
-        loss_out = paddle.sum(loss_out)
-    return loss_out
-
-
-logger.warn(
-    "override ctc_loss of paddle.nn.functional if exists, remove this when fixed!"
-)
-F.ctc_loss = ctc_loss
-
 ########### hcak paddle.nn #############
-if not hasattr(paddle.nn, 'Module'):
-    logger.warn("register user Module to paddle.nn, remove this when fixed!")
-    setattr(paddle.nn, 'Module', paddle.nn.Layer)
-
-# maybe cause assert isinstance(sublayer, core.Layer)
-if not hasattr(paddle.nn, 'ModuleList'):
-    logger.warn(
-        "register user ModuleList to paddle.nn, remove this when fixed!")
-    setattr(paddle.nn, 'ModuleList', paddle.nn.LayerList)
-
-
 class GLU(nn.Layer):
     """Gated Linear Units (GLU) Layer"""
 

deepspeech/modules/loss.py

@@ -48,7 +48,8 @@ class CTCLoss(nn.Layer):
         logits = logits.transpose([1, 0, 2])
         # (TODO:Hui Zhang) ctc loss does not support int64 labels
         ys_pad = ys_pad.astype(paddle.int32)
-        loss = self.loss(logits, ys_pad, hlens, ys_lens)
+        loss = self.loss(
+            logits, ys_pad, hlens, ys_lens, norm_by_times=self.batch_average)
         if self.batch_average:
             # Batch-size average
             loss = loss / B