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@ -5288,18 +5288,18 @@ class CTCLoss(PrimitiveWithInfer):
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Inputs:
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- **inputs** (Tensor) - The input Tensor must be a `3-D` tensor whose shape is
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:math:`(max_time, batch_size, num_classes)`. `num_classes` must be `num_labels + 1` classes, `num_labels`
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(`max_time`, `batch_size`, `num_classes`). `num_classes` must be `num_labels + 1` classes, `num_labels`
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indicates the number of actual labels. Blank labels are reserved. Default blank label is `num_classes - 1`.
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Data type must be float16, float32 or float64.
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- **labels_indices** (Tensor) - The indices of labels. `labels_indices[i, :] == [b, t]` means `labels_values[i]`
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stores the id for `(batch b, time t)`. The type must be int64 and rank must be 2.
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- **labels_values** (Tensor) - A `1-D` input tensor. The values are associated with the given batch and time.
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The type must be int32. `labels_values[i]` must in the range of `[0, num_classes)`.
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- **sequence_length** (Tensor) - A tensor containing sequence lengths with the shape of :math:`(batch_size)`.
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- **sequence_length** (Tensor) - A tensor containing sequence lengths with the shape of (`batch_size`).
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The type must be int32. Each value in the tensor must not be greater than `max_time`.
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Outputs:
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- **loss** (Tensor) - A tensor containing log-probabilities, the shape is :math:`(batch_size)`. The tensor has
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- **loss** (Tensor) - A tensor containing log-probabilities, the shape is (`batch_size`). The tensor has
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the same type with `inputs`.
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- **gradient** (Tensor) - The gradient of `loss`, has the same type and shape with `inputs`.
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@ -5357,21 +5357,20 @@ class CTCGreedyDecoder(PrimitiveWithInfer):
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Inputs:
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- **inputs** (Tensor) - The input Tensor must be a `3-D` tensor whose shape is
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:math:`(\text{max_time}, \text{batch_size}, \text{num_classes})`. `num_classes` must be
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`num_labels + 1` classes, `num_labels` indicates the number of actual labels. Blank labels are reserved.
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(`max_time`, `batch_size`, `num_classes`). `num_classes` must be `num_labels + 1` classes,
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`num_labels` indicates the number of actual labels. Blank labels are reserved.
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Default blank label is `num_classes - 1`. Data type must be float32 or float64.
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- **sequence_length** (Tensor) - A tensor containing sequence lengths with the shape of
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:math:`(\text{batch_size})`. The type must be int32.
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Each value in the tensor must not greater than `max_time`.
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- **sequence_length** (Tensor) - A tensor containing sequence lengths with the shape of (`batch_size`).
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The type must be int32. Each value in the tensor must not greater than `max_time`.
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Outputs:
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- **decoded_indices** (Tensor) - A tensor with shape of :math:`(\text{total_decoded_outputs}, 2)`.
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- **decoded_indices** (Tensor) - A tensor with shape of (`total_decoded_outputs`, 2).
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Data type is int64.
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- **decoded_values** (Tensor) - A tensor with shape of :math:`(\text{total_decoded_outputs})`,
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- **decoded_values** (Tensor) - A tensor with shape of (`total_decoded_outputs`),
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it stores the decoded classes. Data type is int64.
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- **decoded_shape** (Tensor) - The value of tensor is :math:`[\text{batch_size}, \text{max_decoded_legth}]`.
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- **decoded_shape** (Tensor) - The value of tensor is [`batch_size`, `max_decoded_legth`].
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Data type is int64.
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- **log_probability** (Tensor) - A tensor with shape of :math:`(\text{batch_size}, 1)`,
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- **log_probability** (Tensor) - A tensor with shape of (`batch_size`, 1),
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containing sequence log-probability, has the same type as `inputs`.
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Examples:
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@ -5523,6 +5522,129 @@ class BasicLSTMCell(PrimitiveWithInfer):
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return (c_dtype, mstype.float16, c_dtype, c_dtype, c_dtype, c_dtype, c_dtype)
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class DynamicRNN(PrimitiveWithInfer):
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r"""
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DynamicRNN Operator.
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Args:
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cell_type (str): An string identifying the cell type in the op. Default: 'LSTM'.
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Only 'LSTM' is currently supported.
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direction (str): An string identifying the direction in the op. Default: 'UNIDIRECTIONAL'.
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Only 'UNIDIRECTIONAL' is currently supported.
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cell_depth (int): An integer identifying the cell depth in the op. Default: 1.
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use_peephole (bool): An bool identifying if use peephole in the op. Default: False.
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keep_prob (float): An float identifying the keep prob in the op. Default: 1.0.
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cell_clip (float): An float identifying the cell clip in the op. Default: -1.0.
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num_proj (int): An integer identifying the num proj in the op. Default: 0.
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time_major (bool): An bool identifying the time major in the op. Default: True.
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Only `True` is currently supported.
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activation (str): An string identifying the type of activation function in the op. Default: 'tanh'.
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Only 'tanh' is currently supported.
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forget_bias (float): An float identifying the forget bias in the op. Default: 0.0.
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is_training (bool): An bool identifying is training in the op. Default: True.
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Inputs:
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- **x** (Tensor) - Current words. Tensor of shape (`num_step`, `batch_size`, `input_size`).
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The data type must be float16 or float32.
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- **w** (Tensor) - Weight. Tensor of shape (`input_size + hidden_size`, `4 x hidden_size`).
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The data type must be float16 or float32.
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- **b** (Tensor) - Bias. Tensor of shape (`4 x hidden_size`).
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The data type must be float16 or float32.
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- **seq_length (Tensor) - The length of each batch. Tensor of shape (`batch_size`).
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Only `None` is currently supported.
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- **init_h (Tensor) - Hidden state of initial time. Tensor of shape (1, `batch_size`, `hidden_size`).
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- **init_c (Tensor) - Cell state of initial time. Tensor of shape (1, `batch_size`, `hidden_size`).
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Outputs:
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- **y** (Tensor) - A Tensor of shape (`num_step`, `batch_size`, `hidden_size`).
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Has the same type with input `b`.
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- **output_h** (Tensor) - A Tensor of shape (`num_step`, `batch_size`, `hidden_size`).
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With data type of float16.
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- **output_c** (Tensor) - A Tensor of shape (`num_step`, `batch_size`, `hidden_size`).
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Has the same type with input `b`.
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- **i** (Tensor) - A Tensor of shape (`num_step`, `batch_size`, `hidden_size`).
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Has the same type with input `b`.
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- **j** (Tensor) - A Tensor of shape (`num_step`, `batch_size`, `hidden_size`).
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Has the same type with input `b`.
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- **f** (Tensor) - A Tensor of shape (`num_step`, `batch_size`, `hidden_size`).
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Has the same type with input `b`.
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- **o** (Tensor) - A Tensor of shape (`num_step`, `batch_size`, `hidden_size`).
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Has the same type with input `b`.
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- **tanhct** (Tensor) - A Tensor of shape (`num_step`, `batch_size`, `hidden_size`).
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Has the same type with input `b`.
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Examples:
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>>> x = Tensor(np.random.rand(2, 16, 64).astype(np.float16))
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>>> w = Tensor(np.random.rand(96, 128).astype(np.float16))
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>>> b = Tensor(np.random.rand(128).astype(np.float16))
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>>> init_h = Tensor(np.random.rand(1, 16, 32).astype(np.float16))
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>>> init_c = Tensor(np.random.rand(1, 16, 32).astype(np.float16))
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>>> dynamic_rnn = P.DynamicRNN()
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>>> output = lstm(x, w, b, None, init_h, init_c)
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"""
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@prim_attr_register
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def __init__(self,
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cell_type='LSTM',
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direction='UNIDIRECTIONAL',
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cell_depth=1,
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use_peephole=False,
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keep_prob=1.0,
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cell_clip=-1.0,
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num_proj=0,
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time_major=True,
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activation='tanh',
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forget_bias=0.0,
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is_training=True):
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self.forget_bias = validator.check_value_type("forget_bias", forget_bias, [float], self.name)
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self.cell_depth = validator.check_value_type("cell_depth", cell_depth, [int], self.name)
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self.keep_prob = validator.check_value_type("keep_prob", keep_prob, [float], self.name)
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self.cell_clip = validator.check_value_type("cell_clip", cell_clip, [float], self.name)
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self.num_proj = validator.check_value_type("num_proj", num_proj, [int], self.name)
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self.forget_bias = validator.check_value_type("forget_bias", forget_bias, [float], self.name)
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self.use_peephole = validator.check_value_type("use_peephole", use_peephole, [bool], self.name)
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self.time_major = validator.check_value_type("time_major", time_major, [bool], self.name)
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self.is_training = validator.check_value_type("is_training", is_training, [bool], self.name)
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self.cell_type = validator.check_string("cell_type", cell_type, ['LSTM'], self.name)
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self.direction = validator.check_string("direction", direction, ['UNIDIRECTIONAL'], self.name)
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self.activation = validator.check_string("activation", activation, ['tanh'], self.name)
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self.add_prim_attr("io_format", "ND")
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def infer_shape(self, x_shape, w_shape, b_shape, seq_shape, h_shape, c_shape):
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validator.check_integer("x_shape", len(x_shape), 3, Rel.EQ, self.name)
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validator.check_integer("w rank", len(w_shape), 2, Rel.EQ, self.name)
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validator.check_integer("b rank", len(b_shape), 1, Rel.EQ, self.name)
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validator.check_integer("h_shape", len(h_shape), 3, Rel.EQ, self.name)
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validator.check_integer("c_shape", len(c_shape), 3, Rel.EQ, self.name)
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if seq_shape is not None:
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raise ValueError(f"For {self.name}, seq_shape should be None.")
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num_step, batch_size, input_size = x_shape
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hidden_size = w_shape[-1] // 4
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validator.check("b_shape[-1]", b_shape[-1], "w_shape[-1]", w_shape[-1], Rel.EQ, self.name)
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if w_shape[-1] % 4 != 0:
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raise ValueError(f"For {self.name}, w_shape[-1] should multiple of 4.")
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validator.check("w_shape[0]", w_shape[0], "input_size + hidden_size",
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input_size + hidden_size, Rel.EQ, self.name)
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validator.check("b_shape[0]", b_shape[0], "w_shape[1]", w_shape[1], Rel.EQ, self.name)
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validator.check_integer("h_shape[0]", h_shape[0], 1, Rel.EQ, self.name)
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validator.check("h_shape[1]", h_shape[1], "batch_size", batch_size, Rel.EQ, self.name)
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validator.check("h_shape[2]", h_shape[2], "hidden_size", hidden_size, Rel.EQ, self.name)
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validator.check("c_shape", c_shape, "h_shape", h_shape, Rel.EQ, self.name)
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y_shape = (num_step, batch_size, hidden_size)
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return y_shape, y_shape, y_shape, y_shape, y_shape, y_shape, y_shape, y_shape
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def infer_dtype(self, x_dtype, w_dtype, b_dtype, seq_dtype, h_dtype, c_dtype):
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validator.check_tensor_type_same({"x dtype": x_dtype}, (mstype.float32, mstype.float16), self.name)
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validator.check_tensor_type_same({"w dtype": w_dtype}, (mstype.float32, mstype.float16), self.name)
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validator.check_tensor_type_same({"b dtype": b_dtype}, (mstype.float32, mstype.float16), self.name)
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validator.check_tensor_type_same({"h dtype": h_dtype}, (mstype.float32, mstype.float16), self.name)
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validator.check_tensor_type_same({"c dtype": c_dtype}, (mstype.float32, mstype.float16), self.name)
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return b_dtype, x_dtype, b_dtype, b_dtype, b_dtype, b_dtype, b_dtype, b_dtype
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class InTopK(PrimitiveWithInfer):
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r"""
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Whether the targets are in the top `k` predictions.
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mindspore-ci-bot
4 years ago
committed by
Gitee