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130 lines
4.8 KiB
130 lines
4.8 KiB
import inspect
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import paddle
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class Sequential(paddle.nn.LayerDict):
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"""A sequence of modules with potentially inferring shape on construction.
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If layers are passed with names, these can be referenced with dot notation.
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Arguments
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---------
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input_shape : iterable
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A list or tuple of ints or None, representing the expected shape of an
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input tensor. None represents a variable-length dimension. If no
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``input_shape`` is passed, no shape inference will be performed.
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*layers, **named_layers
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The inputs are treated as a list of layers to be
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applied in sequence. The output shape of each layer is used to
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infer the shape of the following layer. If a tuple is returned,
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only the shape of the first element is used to determine input
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shape of the next layer (e.g. RNN returns output, hidden).
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Example
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-------
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>>> inputs = paddle.rand(10, 40, 50)
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>>> model = Sequential(input_shape=inputs.shape)
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>>> model.append(Linear, n_neurons=100, layer_name="layer1")
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>>> model.append(Linear, n_neurons=200, layer_name="layer2")
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>>> outputs = model(inputs)
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>>> outputs.shape
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paddle.shape([10, 40, 200])
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>>> outputs = model.layer1(inputs)
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>>> outputs.shape
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paddle.shape([10, 40, 100])
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"""
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def __init__(self, *layers, input_shape=None, **named_layers):
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super().__init__()
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# Make sure either layers or input_shape is passed
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if not layers and input_shape is None and not named_layers:
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raise ValueError("Must pass either layers or input shape")
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# Keep track of what layers need "lengths" passed
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self.length_layers = []
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# Replace None dimensions with arbitrary value
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self.input_shape = input_shape
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if input_shape and None in input_shape:
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self.input_shape = list(input_shape)
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for i, dim in enumerate(self.input_shape):
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# To reduce size of dummy tensors, use 1 for batch dim
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if i == 0 and dim is None:
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dim = 1
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# Use 64 as nice round arbitrary value, big enough that
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# halving this dimension a few times doesn't reach 1
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self.input_shape[i] = dim or 256
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# Append non-named layers
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for layer in layers:
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self.append(layer)
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# Append named layers
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for name, layer in named_layers.items():
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self.append(layer, layer_name=name)
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def append(self, layer, *args, layer_name=None, **kwargs):
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"""Add a layer to the list of layers, inferring shape if necessary.
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Arguments
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---------
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layer : A paddle.nn.Module class or object
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If the layer is a class, it should accept an argument called
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``input_shape`` which will be inferred and passed. If the layer
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is a module object, it is added as-is.
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layer_name : str
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The name of the layer, for reference. If the name is in use,
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``_{count}`` will be appended.
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*args, **kwargs
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These are passed to the layer if it is constructed.
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"""
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# Compute layer_name
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if layer_name is None:
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layer_name = str(len(self))
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elif layer_name in self:
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index = 0
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while f"{layer_name}_{index}" in self:
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index += 1
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layer_name = f"{layer_name}_{index}"
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# Check if it needs to be constructed with input shape
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if self.input_shape:
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argspec = inspect.getfullargspec(layer)
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if "input_shape" in argspec.args + argspec.kwonlyargs:
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input_shape = self.get_output_shape()
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layer = layer(*args, input_shape=input_shape, **kwargs)
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# Finally, append the layer.
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try:
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self[layer_name] = layer
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# self.add_module(layer_name, layer)
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except TypeError:
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raise ValueError(
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"Must pass `input_shape` at initialization and use "
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"modules that take `input_shape` to infer shape when "
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"using `append()`.")
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def get_output_shape(self):
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"""Returns expected shape of the output.
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Computed by passing dummy input constructed with the
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``self.input_shape`` attribute.
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"""
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with paddle.no_grad():
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dummy_input = paddle.zeros(self.input_shape)
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dummy_output = self(dummy_input)
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return dummy_output.shape
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def forward(self, x):
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"""Applies layers in sequence, passing only the first element of tuples.
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Arguments
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---------
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x : paddle.Tensor
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The input tensor to run through the network.
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"""
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for layer in self.values():
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x = layer(x)
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if isinstance(x, tuple):
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x = x[0]
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return x
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