def __init__(self, net, input_nodes):
self._op_shape_inference = {
MaceOp.Conv2D.name: self.infer_shape_conv_pool_shape,
MaceOp.Deconv2D.name: self.infer_shape_deconv,
MaceOp.DepthwiseConv2d.name: self.infer_shape_conv_pool_shape,
MaceOp.Eltwise.name: self.infer_shape_general,
MaceOp.FoldedBatchNorm.name: self.infer_shape_general,
MaceOp.AddN.name: self.infer_shape_general,
MaceOp.Activation.name: self.infer_shape_general,
MaceOp.Pooling.name: self.infer_shape_conv_pool_shape,
MaceOp.Concat.name: self.infer_shape_concat,
MaceOp.Split.name: self.infer_shape_slice,
MaceOp.Softmax.name: self.infer_shape_general,
MaceOp.FullyConnected.name: self.infer_shape_fully_connected,
MaceOp.Crop.name: self.infer_shape_crop,
}
self._net = net
self._output_shape_cache = {}
for input_node in input_nodes:
input_shape = input_node.shape[:]
# transpose input from NCHW to NHWC
Transformer.transpose_shape(input_shape, [0, 3, 1, 2])
self._output_shape_cache[input_node.name] = input_shape
for tensor in net.tensors:
self._output_shape_cache[tensor.name] = list(tensor.dims)
def __init__(self, net, input_nodes):
self._op_shape_inference = {
MaceOp.Conv2D.name: self.infer_shape_conv_pool_shape,
MaceOp.Deconv2D.name: self.infer_shape_deconv,
MaceOp.DepthwiseConv2d.name: self.infer_shape_conv_pool_shape,
MaceOp.DepthwiseDeconv2d.name: self.infer_shape_deconv,
MaceOp.Eltwise.name: self.infer_shape_general,
MaceOp.BatchNorm.name: self.infer_shape_general,
MaceOp.AddN.name: self.infer_shape_general,
MaceOp.Activation.name: self.infer_shape_general,
MaceOp.Pooling.name: self.infer_shape_conv_pool_shape,
MaceOp.Concat.name: self.infer_shape_concat,
MaceOp.Split.name: self.infer_shape_slice,
MaceOp.Softmax.name: self.infer_shape_general,
MaceOp.FullyConnected.name: self.infer_shape_fully_connected,
MaceOp.Crop.name: self.infer_shape_crop,
MaceOp.BiasAdd.name: self.infer_shape_general,
MaceOp.ChannelShuffle.name: self.infer_shape_channel_shuffle,
MaceOp.Transpose.name: self.infer_shape_permute,
MaceOp.PriorBox.name: self.infer_shape_prior_box,
MaceOp.Reshape.name: self.infer_shape_reshape,
MaceOp.ResizeBilinear.name: self.infer_shape_resize_bilinear,
}
self._net = net
self._output_shape_cache = {}
for input_node in input_nodes:
input_shape = input_node.shape[:]
# transpose input from NCHW to NHWC
Transformer.transpose_shape(input_shape, [0, 3, 1, 2])
self._output_shape_cache[input_node.name] = input_shape
for tensor in net.tensors:
self._output_shape_cache[tensor.name] = list(tensor.dims)
def __init__(self, net, input_nodes):
self._op_shape_inference = {
MaceOp.Conv2D.name: self.infer_shape_conv_pool_shape,
MaceOp.DepthwiseConv2d.name: self.infer_shape_conv_pool_shape,
MaceOp.Eltwise.name: self.infer_shape_general,
MaceOp.FoldedBatchNorm.name: self.infer_shape_general,
MaceOp.AddN.name: self.infer_shape_general,
MaceOp.Activation.name: self.infer_shape_general,
MaceOp.Pooling.name: self.infer_shape_conv_pool_shape,
MaceOp.Concat.name: self.infer_shape_concat,
MaceOp.Slice.name: self.infer_shape_slice,
MaceOp.Softmax.name: self.infer_shape_general,
MaceOp.FullyConnected.name: self.infer_shape_fully_connected,
}
self._net = net
self._output_shape_cache = {}
for input_node in input_nodes:
input_shape = input_node.shape[:]
# transpose input from NCHW to NHWC
Transformer.transpose_shape(input_shape, [0, 3, 1, 2])
self._output_shape_cache[input_node.name] = input_shape
for tensor in net.tensors:
self._output_shape_cache[tensor.name] = list(tensor.dims)