def shape(input, name=None, out_type=dtypes.float32):
"""
Returns the shape of a tensor.
This operation returns a 1-D integer tensor representing the shape of `input`.
For example:
```python
# 't' is [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]]
shape(t) ==> [2, 2, 3]
```
Args:
input: A `Tensor`.
name: A name for the operation (optional).
out_type: (Enforce) The specified output type of the operation.
Now only support tf.float32.
Returns:
A `Tensor` of type `out_type`.
"""
return ops.Shape(input, name=None)
def Setup(self, bottom):
super(BilinearResizeLayer, self).Setup(bottom)
input = bottom[0] if isinstance(bottom, list) else bottom
if isinstance(bottom, list) and len(bottom) > 1:
dshape = ops.Shape(bottom[1])
self._param['dsize'] = (dshape[2], dshape[3])
return ops.BilinearResize(input, **self._param)
def ones_like(model, dtype=None, **kwargs):
"""Initialize a tensor with ones, refer the shape of another tensor.
The values can be access only after the run of graph.
If dtype is ``None``, use ``config.floatX``.
Parameters
----------
model : Tensor
The tensor to refer shape.
dtype : str
The data type of Tensor.
Returns
-------
Tensor
The initialized tensor.
"""
if dtype is None: dtype = config.floatX
else:
raise TypeError("Unsupported data type: {}".format(dtype))
return ops.Fill(shape=ops.Shape(model), value=1)
def shape(input, name=None, out_type=dtypes.float32):
return ops.Shape(input, name=None)
