重命名 pt2tf 为 pt2pb
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zhutian
Gitee
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from functools import partial
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import tensorflow as tf
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from onnx_tf.common import get_unique_suffix
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from onnx_tf.common import exception
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from onnx_tf.handlers.backend_handler import BackendHandler
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from onnx_tf.handlers.handler import onnx_op
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from onnx_tf.handlers.handler import partial_support
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from onnx_tf.handlers.handler import ps_description
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from .rnn_mixin import RNNMixin
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@onnx_op("RNN")
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@partial_support(True)
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@ps_description("RNN with clip is not supported in Tensorflow.")
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class RNN(RNNMixin, BackendHandler):
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@classmethod
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def args_check(cls, node, **kwargs):
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if "clip" in node.attrs:
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exception.OP_UNSUPPORTED_EXCEPT("RNN with clip", "Tensorflow")
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@classmethod
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def _custom_getter(cls,
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getter,
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name,
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node=None,
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tensor_dict=None,
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is_bidirectional=None,
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*args,
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**kwargs):
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names = name.split("/")
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if is_bidirectional:
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if "fw" in names:
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index = 0
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elif "bw" in names:
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index = 1
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else:
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raise RuntimeError("Can not get {} for bidirectional. "
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"Either fw and bw is not in name scope.".format(
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names[-1]))
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if names[-1] == "kernel":
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if is_bidirectional:
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w = tf.split(tensor_dict[node.inputs[1]], 2)[index]
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r = tf.split(tensor_dict[node.inputs[2]], 2)[index]
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else:
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w = tensor_dict[node.inputs[1]]
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r = tensor_dict[node.inputs[2]]
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new_w = tf.transpose(tf.squeeze(w))
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new_r = tf.transpose(tf.squeeze(r))
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kernel = tf.concat([new_w, new_r], 0)
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return kernel
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if names[-1] == "bias":
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if len(node.inputs) >= 4:
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if is_bidirectional:
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b = tf.split(tensor_dict[node.inputs[3]], 2)[index]
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else:
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b = tensor_dict[node.inputs[3]]
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w_b, r_b = tf.split(tf.squeeze(b), 2)
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w_b = tf.transpose(w_b)
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r_b = tf.transpose(r_b)
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return tf.add(w_b, r_b)
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return getter(name, *args, **kwargs)
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return getter(name, *args, **kwargs)
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@classmethod
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def _common(cls, node, **kwargs):
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tensor_dict = kwargs["tensor_dict"]
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x = tensor_dict[node.inputs[0]]
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input_shape = x.get_shape().as_list()
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input_size = len(node.inputs)
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hidden_size = node.attrs["hidden_size"]
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direction = node.attrs.get("direction", "forward")
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num_directions = 2 if direction == "bidirectional" else 1
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output_sequence = node.attrs.get("output_sequence", 0)
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# TODO(fumihwh): check if prev node is one of RNN
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# process input if it comes from other previous cell
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# which has shape [seq_length, num_directions, batch_size, hidden_size]
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if len(input_shape) == 4 and input_shape[1] == 1:
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x = tf.squeeze(x)
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sequence_length = None
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if input_size >= 5 and node.inputs[4] in tensor_dict:
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sequence_length = tensor_dict[node.inputs[4]]
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cell_kwargs = {}
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tf_activations = [tf.nn.tanh]
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if "activations" in node.attrs:
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activations = list(map(lambda x: x.lower(), node.attrs["activations"]))
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activation_alpha = node.attrs.get("activation_alpha", [None] * 2)
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activation_beta = node.attrs.get("activation_beta", [None] * 2)
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tf_activations = [
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cls.rnn_get_activation(activations[0], activation_alpha[0],
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activation_beta[0])
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]
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if num_directions == 2:
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tf_activations.append(
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cls.rnn_get_activation(activations[1], activation_alpha[1],
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activation_beta[1]))
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# TODO(fumihwh): check if reverse and bidirectional works
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with tf.variable_scope(
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"RNN_" + get_unique_suffix(),
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custom_getter=partial(
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cls._custom_getter,
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node=node,
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tensor_dict=tensor_dict,
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is_bidirectional=num_directions == 2)):
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cell_kwargs["num_units"] = hidden_size
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initial_state = None
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initial_state_bw = None
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if input_size == 6:
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initial_h = tensor_dict.get(node.inputs[5], None)
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if initial_h is not None:
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initial_state = (initial_h[0],)
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if num_directions == 2:
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initial_state_bw = (initial_h[1],)
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rnn_kwargs = {}
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if num_directions == 1:
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rnn_kwargs["initial_state"] = initial_state
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elif num_directions == 2:
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rnn_kwargs["initial_state_fw"] = initial_state
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rnn_kwargs["initial_state_bw"] = initial_state_bw
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rnn_kwargs["sequence_length"] = sequence_length
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rnn_kwargs["time_major"] = True
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rnn_kwargs["dtype"] = tf.float32
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outputs, states = cls.rnn(x, tf.nn.rnn_cell.BasicRNNCell, cell_kwargs,
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rnn_kwargs, tf_activations, direction)
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if num_directions == 1:
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state = states[0]
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h = tf.expand_dims(state, 0)
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output = tf.expand_dims(outputs, 1)
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else:
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state_fw = states[0][0]
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state_bw = states[1][0]
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output_fw = outputs[0]
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output_bw = outputs[1]
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h_fw = tf.expand_dims(state_fw, 0)
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h_bw = tf.expand_dims(state_bw, 0)
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h = tf.concat((h_fw, h_bw), axis=0)
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output_fw = tf.expand_dims(output_fw, 1)
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output_bw = tf.expand_dims(output_bw, 1)
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output = tf.concat((output_fw, output_bw), axis=1)
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return [output, h] if output_sequence == 0 else [h]
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@classmethod
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def version_1(cls, node, **kwargs):
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return cls._common(node, **kwargs)
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@classmethod
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def version_7(cls, node, **kwargs):
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return cls._common(node, **kwargs)
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