Pooling¶
Computes a per-channel pooling using a sampling window on the input tensor into an output tensor. The supported sampling window shapes are 2-D or 3-D.
Attributes¶
pooling_type
Pooling operation can be one of:
MAX
For each output element, return the maximum value found in its corresponding sampling window.AVERAGE
For each output element, return the average of the values in its corresponding sampling window.MAX_AVERAGE_BLEND
For each output element, return the weighted sum ofMAX
andAVG
pooling, whereblend_factor
is the blending factor. \(pooling(\text{MAX_AVERAGE_BLEND})=(1-\text{blend_factor}) \cdot pooling(MAX) + \text{blend_factor} \cdot pooling(AVERAGE)\).
blend_factor
A parameter used when the pooling type is set to MAX_AVERAGE_BLEND
.
padding_mode
Controls the padding mode, can be one of:
padding_mode
The padding mode. The padding mode can be one of the following:
EXPLICIT_ROUND_DOWN
Use explicit padding, rounding the output size down.\(O = \lfloor\frac{M - F}{S}\rfloor + 1\)EXPLICIT_ROUND_UP
Use explicit padding, rounding the output size up.\(O = \lceil\frac{M - F}{S}\rceil + 1\)SAME_UPPER
UseSAME
padding, with \(\text{pre-padding} \leq \text{post-padding}\).\(\begin{gather}O = \lceil\frac{I}{S}\rceil \\ P = \lfloor\frac{I-1}{S}\rfloor \cdot S + F -I \\ B = \lfloor\frac{P}{2}\rfloor \\ A = P - B \end{gather}\)SAME_LOWER
UseSAME
padding, with \(\text{pre-padding} \geq \text{post-padding}\).\(\begin{gather}O = \lceil\frac{I}{S}\rceil \\ P = \lfloor\frac{I-1}{S}\rfloor \cdot S + F -I \\ A = \lfloor\frac{P}{2}\rfloor \\ B = P - A \end{gather}\)
average_count_excludes_padding
When setting this parameter, the average pooling calculation ignores the padded input.
Inputs¶
input: tensor of type T
Outputs¶
output: tensor of type T
Data Types¶
T: int8
, float16
, float32
Shape Information¶
Input tensor must be a tensor with rank \(r\geq3\).
Output tensor rank is same as the input tensor rank. If the input’s shape is \([a_0,...,a_n]\), the stride is \(s\), the padding is \(p\), and the sampling window shape is \([r_0,..,r_m]\) where \(m=2\) or \(m=3\):
DLA Support¶
DLA FP16 and DLA INT8 are supported for 2D pooling for max
pooling, and for the inclusive padding mode of average pooling.
Examples¶
Pooling
in1 = network.add_input("input1", dtype=trt.float32, shape=(1, 1, 5, 5))
layer = network.add_pooling_nd(in1, trt.PoolingType.MAX, trt.tensorrt.DimsHW(3, 3))
network.mark_output(layer.get_output(0))
inputs[in1.name] = np.array(
[
[
[
[-10.0, -9.0, -8.0, -7.0, -6.0],
[-5.0, -4.0, -3.0, -2.0, -1.0],
[0.0, 1.0, 2.0, 3.0, 4.0],
[5.0, 6.0, 7.0, 8.0, 9.0],
[10.0, 11.0, 12.0, 13.0, 14.0],
]
]
]
)
np.reshape(np.arange(-10, 15, dtype=np.float32), newshape=(1, 1, 5, 5))
outputs[layer.get_output(0).name] = layer.get_output(0).shape
expected[layer.get_output(0).name] = np.array([[[[2.0, 3.0, 4.0], [7.0, 8.0, 9.0], [12.0, 13.0, 14.0]]]])
in1 = network.add_input("input1", dtype=trt.float32, shape=(1, 1, 5, 5))
layer = network.add_pooling_nd(in1, trt.PoolingType.AVERAGE, trt.tensorrt.DimsHW(3, 3))
layer.post_padding = (1, 1)
layer.pre_padding = (1, 1)
layer.average_count_excludes_padding = True
network.mark_output(layer.get_output(0))
inputs[in1.name] = np.array(
[
[
[
[-10.0, -9.0, -8.0, -7.0, -6.0],
[-5.0, -4.0, -3.0, -2.0, -1.0],
[0.0, 1.0, 2.0, 3.0, 4.0],
[5.0, 6.0, 7.0, 8.0, 9.0],
[10.0, 11.0, 12.0, 13.0, 14.0],
]
]
]
)
np.reshape(np.arange(-10, 15, dtype=np.float32), newshape=(1, 1, 5, 5))
outputs[layer.get_output(0).name] = layer.get_output(0).shape
expected[layer.get_output(0).name] = np.array(
[
[
[
[-7.0, -6.5, -5.5, -4.5, -4.0],
[-4.5, -4.0, -3.0, -2.0, -1.5],
[0.5, 1.0, 2.0, 3.0, 3.5],
[5.5, 6.0, 7.0, 8.0, 8.5],
[8.0, 8.5, 9.5, 10.5, 11.0],
]
]
]
)
C++ API¶
For more information about the C++ IPoolingLayer operator, refer to the C++ IPoolingLayer documentation.
Python API¶
For more information about the Python IPoolingLayer operator, refer to the Python IPoolingLayer documentation.