fix the logging in construct part and clean content expression

mindocr/models/necks/rnn.py

@@ -3,10 +3,7 @@
 import mindspore as ms
 from mindspore import Tensor, nn, ops
 
-from mindocr.utils.logger import Logger
-
 __all__ = ['RNNEncoder']
-_logger = Logger("mindocr")
 
 
 # TODO: check mindspore nn LSTM diff in performance and precision from paddle/pytorch
@@ -53,14 +50,12 @@ def construct(self, features):
             Tensor: Encoded features . Shape :math:`(W, N, 2*C)` where
         """
         x = features[0]
-        assert x.shape[2] == 1, f'Feature height must be 1, but got {x.shape[2]} from x.shape {x.shape}'
         x = ops.squeeze(x, axis=2)  # [N, C, W]
         x = ops.transpose(x, (2, 0, 1))  # [W, N, C]
 
         if self.hx is None:
             x, hx_n = self.seq_encoder(x)
         else:
-            _logger.info('using self.hx')
             x, hx_n = self.seq_encoder(x, self.hx)  # the results are the same
 
         return x

mindocr/models/utils/rnn_cells.py

@@ -2,7 +2,6 @@
 """
 import mindspore.ops as P
 from mindspore.nn.layer.rnn_cells import RNNCellBase
-from mindspore.ops.primitive import constexpr
 
 __all__ = ["GRUCell"]
 
@@ -26,75 +25,14 @@ def _gru_cell(inputs, hidden, w_ih, w_hh, b_ih, b_hh):
     return hy
 
 
-@constexpr
-def _check_batch_size_equal(batch_size_x, batch_size_hx, cls_name):
-    if batch_size_x != batch_size_hx:
-        raise ValueError(f"For '{cls_name}' batch size of x and hx must be equal, but got {batch_size_x} of x "
-                         f"and {batch_size_hx} of hx.")
-
-
 class GRUCell(RNNCellBase):
-    r"""
-    A GRU(Gated Recurrent Unit) cell.
-
-    .. math::
-
-        \begin{array}{ll}
-        r = \sigma(W_{ir} x + b_{ir} + W_{hr} h + b_{hr}) \\
-        z = \sigma(W_{iz} x + b_{iz} + W_{hz} h + b_{hz}) \\
-        n = \tanh(W_{in} x + b_{in} + r * (W_{hn} h + b_{hn})) \\
-        h' = (1 - z) * n + z * h
-        \end{array}
-
-    Here :math:`\sigma` is the sigmoid function, and :math:`*` is the Hadamard product. :math:`W, b`
-    are learnable weights between the output and the input in the formula. For instance,
-    :math:`W_{ir}, b_{ir}` are the weight and bias used to transform from input :math:`x` to :math:`r`.
-    Details can be found in paper
-    `Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
-    <https://aclanthology.org/D14-1179.pdf>`_.
-
-    The LSTMCell can be simplified in NN layer, the following formula:
-
-    .. math::
-        h^{'},c^{'} = LSTMCell(x, (h_0, c_0))
-
-    Args:
-        input_size (int): Number of features of input.
-        hidden_size (int):  Number of features of hidden layer.
-        has_bias (bool): Whether the cell has bias `b_in` and `b_hn`. Default: True.
-
-    Inputs:
-        - **x** (Tensor) - Tensor of shape (batch_size, `input_size`).
-        - **hx** (Tensor) - Tensor of data type mindspore.float32 and shape (batch_size, `hidden_size`).
-          Data type of `hx` must be the same as `x`.
-
-    Outputs:
-        - **hx'** (Tensor) - Tensor of shape (batch_size, `hidden_size`).
-
-    Raises:
-        TypeError: If `input_size`, `hidden_size` is not an int.
-        TypeError: If `has_bias` is not a bool.
-
-    Supported Platforms:
-        ``Ascend`` ``GPU`` ``CPU``
-
-    Examples:
-        >>> net = nn.GRUCell(10, 16)
-        >>> x = Tensor(np.ones([5, 3, 10]).astype(np.float32))
-        >>> hx = Tensor(np.ones([3, 16]).astype(np.float32))
-        >>> output = []
-        >>> for i in range(5):
-        ...     hx = net(x[i], hx)
-        ...     output.append(hx)
-        >>> print(output[0].shape)
-        (3, 16)
+    """A Modified version of RU(Gated Recurrent Unit) cell, based on mindspore.nn.layer.rnn_cells.GRUCell
+    It adds a type cast protection of all initial variable
     """
     def __init__(self, input_size: int, hidden_size: int, has_bias: bool = True):
         super().__init__(input_size, hidden_size, has_bias, num_chunks=3)
 
     def construct(self, x, hx):
-        _check_batch_size_equal(x.shape[0], hx.shape[0], self.cls_name)
-
         # FIX: make sure the weight and bias dtype is same as the data type from x
         # prevent the input type inconsistent error from P.MatMul operator
         weight_ih = P.cast(self.weight_ih, x.dtype)