IntenseWebs/openvino
3
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

MaxPool-8 python API (#7170)

Browse Source
This commit is contained in:
Tomasz Dołbniak
2021-09-09 11:45:47 +02:00
committed by GitHub
parent aa106ad270
commit f5089912e4
5 changed files with 289 additions and 32 deletions
Download Patch File Download Diff File Expand all files Collapse all files

5
inference-engine/ie_bridges/python/sample/ngraph_function_creation_sample/ngraph_function_creation_sample.py
View File

@@ -11,6 +11,7 @@ from functools import reduce
import cv2
import ngraph
from ngraph.opset1 import max_pool
import numpy as np
from openvino.inference_engine import IECore, IENetwork
@@ -85,7 +86,7 @@ def create_ngraph_function(args: argparse.Namespace) -> ngraph.impl.Function:
add_1_node = ngraph.add(conv_1_node, add_1_kernel)
# maxpool 1
maxpool_1_node = ngraph.max_pool(add_1_node, [2, 2], padding_begin, padding_end, [2, 2], 'ceil', None)
maxpool_1_node = max_pool(add_1_node, [2, 2], padding_begin, padding_end, [2, 2], 'ceil')
# convolution 2
conv_2_kernel_shape, conv_2_kernel_length = shape_and_length([50, 20, 5, 5])
@@ -104,7 +105,7 @@ def create_ngraph_function(args: argparse.Namespace) -> ngraph.impl.Function:
add_2_node = ngraph.add(conv_2_node, add_2_kernel)
# maxpool 2
maxpool_2_node = ngraph.max_pool(add_2_node, [2, 2], padding_begin, padding_end, [2, 2], 'ceil', None)
maxpool_2_node = max_pool(add_2_node, [2, 2], padding_begin, padding_end, [2, 2], 'ceil')
# reshape 1
reshape_1_dims, reshape_1_length = shape_and_length([2])

2
ngraph/python/src/ngraph/opset8/__init__.py
View File

@@ -84,7 +84,7 @@ from ngraph.opset4.ops import lstm_cell
from ngraph.opset1.ops import lstm_sequence
from ngraph.opset1.ops import matmul
from ngraph.opset8.ops import matrix_nms
from ngraph.opset1.ops import max_pool
from ngraph.opset8.ops import max_pool
from ngraph.opset1.ops import maximum
from ngraph.opset1.ops import minimum
from ngraph.opset4.ops import mish

55
ngraph/python/src/ngraph/opset8/ops.py
View File

@@ -276,3 +276,58 @@ def gather(
"batch_dims": batch_dims
}
return _get_node_factory_opset8().create("Gather", inputs, attributes)
@nameable_op
def max_pool(
data: NodeInput,
strides: List[int],
dilations: List[int],
pads_begin: List[int],
pads_end: List[int],
kernel_shape: TensorShape,
rounding_type: str = "floor",
auto_pad: Optional[str] = None,
index_element_type: Optional[str] = "i64",
axis: Optional[int] = 0,
name: Optional[str] = None,
) -> Node:
"""Perform max pooling operation and return both values and indices of the selected elements.
@param data: The node providing input data.
@param strides: The distance (in pixels) to slide the filter on the feature map
over the axes.
@param dilations: The dilation of filter elements(distance between elements).
@param pads_begin: The number of pixels to add at the beginning along each axis.
@param pads_end: The number of pixels to add at the end along each axis.
@param kernel_shape: The pooling operation kernel shape.
@param rounding_type: Determines used rounding schema when computing output shape.
Acceptable values are: ['floor', 'ceil']. Defaults to 'floor'.
@param auto_pad: Determines how the padding is calculated. Acceptable values:
[None, 'same_upper', 'same_lower', 'valid']. Defaults to None.
@param index_element_type: The data type used for the indices output of this operator.
Defaults to i64.
@param axis: The first dimension in the data shape used to determine the maximum
returned index value. The value is the product of all dimensions
starting at the provided axis. Defaults to 0.
@param name: The optional name for the created output node.
@return The new node performing max pooling operation.
"""
if auto_pad is None:
auto_pad = "explicit"
return _get_node_factory_opset8().create(
"MaxPool",
[as_node(data)],
{
"strides": strides,
"dilations": dilations,
"pads_begin": pads_begin,
"pads_end": pads_end,
"kernel": kernel_shape,
"rounding_type": rounding_type.upper(),
"auto_pad": auto_pad.upper(),
"index_element_type": index_element_type,
"axis": axis,
},
)

54
ngraph/python/tests/test_ngraph/test_ops.py
View File

@@ -552,10 +552,24 @@ def test_max_pool():
window_shape = [3]
strides = [1] * len(window_shape)
dilations = [1] * len(window_shape)
pads_begin = [0] * len(window_shape)
pads_end = [0] * len(window_shape)
rounding_type = "floor"
auto_pad = "explicit"
idx_elem_type = "i32"
model = ng.max_pool(A, strides, pads_begin, pads_end, window_shape)
model = ng.max_pool(
A,
strides,
dilations,
pads_begin,
pads_end,
window_shape,
rounding_type,
auto_pad,
idx_elem_type,
)
function = Function([model], parameter_list, "test")
runtime = get_runtime()
@@ -570,7 +584,17 @@ def test_max_pool():
pads_begin = [0] * len(window_shape)
pads_end = [0] * len(window_shape)
model = ng.max_pool(A, strides, pads_begin, pads_end, window_shape)
model = ng.max_pool(
A,
strides,
dilations,
pads_begin,
pads_end,
window_shape,
rounding_type,
auto_pad,
idx_elem_type,
)
function = Function([model], parameter_list, "test")
size = 4
@@ -590,10 +614,21 @@ def test_max_pool():
window_shape = [3, 3]
strides = [1, 1]
dilations = [1, 1]
pads_begin = [0, 0]
pads_end = [0, 0]
model = ng.max_pool(A, strides, pads_begin, pads_end, window_shape)
model = ng.max_pool(
A,
strides,
dilations,
pads_begin,
pads_end,
window_shape,
rounding_type,
auto_pad,
idx_elem_type,
)
function = Function([model], parameter_list, "test")
computation = runtime.computation(function, *parameter_list)
@@ -604,10 +639,21 @@ def test_max_pool():
# test 2d with strides
strides = [2, 2]
dilations = [1, 1]
pads_begin = [0, 0]
pads_end = [0, 0]
model = ng.max_pool(A, strides, pads_begin, pads_end, window_shape)
model = ng.max_pool(
A,
strides,
dilations,
pads_begin,
pads_end,
window_shape,
rounding_type,
auto_pad,
idx_elem_type,
)
function = Function([model], parameter_list, "test")
computation = runtime.computation(function, *parameter_list)
result = computation(input_arr)[0]

205
ngraph/python/tests/test_ngraph/test_pooling.py
View File

@@ -85,17 +85,35 @@ def test_max_pool_basic():
# [12.5, 13.5, 14.5, 15.5]]]], dtype=float32)
data = np.arange(0.5, 16, dtype=np.float32).reshape((1, 1, 4, 4))
strides = [1, 1]
dilations = [1, 1]
pads_begin = [0, 0]
pads_end = [0, 0]
kernel_shape = [2, 2]
rounding_type = "floor"
auto_pad = None
index_et = "i32"
data_node = ng.parameter(data.shape, name="A", dtype=np.float32)
avgpool_node = ng.max_pool(data_node, strides, pads_begin, pads_end, kernel_shape)
comp = rt.computation(avgpool_node, data_node)
maxpool_node = ng.max_pool(
data_node,
strides,
dilations,
pads_begin,
pads_end,
kernel_shape,
rounding_type,
auto_pad,
index_et,
)
comp = rt.computation(maxpool_node, data_node)
result = comp(data)
expected = np.array([[[[5.5, 6.5, 7.5], [9.5, 10.5, 11.5], [13.5, 14.5, 15.5]]]], dtype=np.float32)
assert np.allclose(result, expected)
expected = np.array(
[[[[5.5, 6.5, 7.5], [9.5, 10.5, 11.5], [13.5, 14.5, 15.5]]]], dtype=np.float32
)
expected_idx = np.array([[[[5, 6, 7], [9, 10, 11], [13, 14, 15]]]], dtype=np.int32)
assert np.allclose(result[0], expected)
assert np.allclose(result[1], expected_idx)
def test_max_pool_strides():
@@ -107,20 +125,36 @@ def test_max_pool_strides():
# [12.5, 13.5, 14.5, 15.5]]]], dtype=float32)
data = np.arange(0.5, 16, dtype=np.float32).reshape((1, 1, 4, 4))
strides = [2, 1]
dilations = [1, 1]
pads_begin = [0, 0]
pads_end = [0, 0]
kernel_shape = [2, 2]
rounding_type = "floor"
auto_pad = None
index_et = "i32"
data_node = ng.parameter(data.shape, name="A", dtype=np.float32)
avgpool_node = ng.max_pool(data_node, strides, pads_begin, pads_end, kernel_shape)
comp = rt.computation(avgpool_node, data_node)
maxpool_node = ng.max_pool(
data_node,
strides,
dilations,
pads_begin,
pads_end,
kernel_shape,
rounding_type,
auto_pad,
index_et,
)
comp = rt.computation(maxpool_node, data_node)
result = comp(data)
expected = np.array([[[[5.5, 6.5, 7.5], [13.5, 14.5, 15.5]]]], dtype=np.float32)
assert np.allclose(result, expected)
expected_idx = np.array([[[[5, 6, 7], [13, 14, 15]]]], dtype=np.int32)
assert np.allclose(result[0], expected)
assert np.allclose(result[1], expected_idx)
def test_max_pool_kernel_shape1d():
def test_max_pool_kernel_shape1x1():
rt = get_runtime()
# array([[[[ 0.5, 1.5, 2.5, 3.5],
@@ -129,19 +163,34 @@ def test_max_pool_kernel_shape1d():
# [12.5, 13.5, 14.5, 15.5]]]], dtype=float32)
data = np.arange(0.5, 16, dtype=np.float32).reshape((1, 1, 4, 4))
strides = [1, 1]
dilations = [1, 1]
pads_begin = [0, 0]
pads_end = [0, 0]
kernel_shape = [1, 1]
rounding_type = "floor"
auto_pad = None
index_et = "i32"
data_node = ng.parameter(data.shape, name="A", dtype=np.float32)
avgpool_node = ng.max_pool(data_node, strides, pads_begin, pads_end, kernel_shape)
comp = rt.computation(avgpool_node, data_node)
maxpool_node = ng.max_pool(
data_node,
strides,
dilations,
pads_begin,
pads_end,
kernel_shape,
rounding_type,
auto_pad,
index_et,
)
comp = rt.computation(maxpool_node, data_node)
result = comp(data)
assert np.allclose(result, data)
assert np.allclose(result[0], data)
assert np.allclose(result[1], np.arange(0, 16, dtype=np.int32).reshape((1, 1, 4, 4)))
def test_max_pool_kernel_shape3d():
def test_max_pool_kernel_shape3x3():
rt = get_runtime()
# array([[[[ 0.5, 1.5, 2.5, 3.5],
@@ -150,17 +199,31 @@ def test_max_pool_kernel_shape3d():
# [12.5, 13.5, 14.5, 15.5]]]], dtype=float32)
data = np.arange(0.5, 16, dtype=np.float32).reshape((1, 1, 4, 4))
strides = [1, 1]
dilations = [1, 1]
pads_begin = [0, 0]
pads_end = [0, 0]
kernel_shape = [3, 3]
rounding_type = "floor"
auto_pad = None
index_et = "i32"
data_node = ng.parameter(data.shape, name="A", dtype=np.float32)
avgpool_node = ng.max_pool(data_node, strides, pads_begin, pads_end, kernel_shape)
comp = rt.computation(avgpool_node, data_node)
maxpool_node = ng.max_pool(
data_node,
strides,
dilations,
pads_begin,
pads_end,
kernel_shape,
rounding_type,
auto_pad,
index_et,
)
comp = rt.computation(maxpool_node, data_node)
result = comp(data)
expected = np.array([[[[10.5, 11.5], [14.5, 15.5]]]], dtype=np.float32)
assert np.allclose(result, expected)
assert np.allclose(result[0], expected)
def test_max_pool_non_zero_pads():
@@ -172,6 +235,7 @@ def test_max_pool_non_zero_pads():
# [12.5, 13.5, 14.5, 15.5]]]], dtype=float32)
data = np.arange(0.5, 16, dtype=np.float32).reshape((1, 1, 4, 4))
strides = [1, 1]
dilations = [1, 1]
pads_begin = [1, 1]
pads_end = [1, 1]
# 0 0 , 0 , 0 , 0, 0
@@ -181,10 +245,23 @@ def test_max_pool_non_zero_pads():
# 0 [12.5, 13.5, 14.5, 15.5], 0
# 0 0 , 0 , 0 , 0, 0
kernel_shape = [2, 2]
rounding_type = "floor"
auto_pad = None
index_et = "i32"
data_node = ng.parameter(data.shape, name="A", dtype=np.float32)
avgpool_node = ng.max_pool(data_node, strides, pads_begin, pads_end, kernel_shape)
comp = rt.computation(avgpool_node, data_node)
maxpool_node = ng.max_pool(
data_node,
strides,
dilations,
pads_begin,
pads_end,
kernel_shape,
rounding_type,
auto_pad,
index_et,
)
comp = rt.computation(maxpool_node, data_node)
result = comp(data)
expected = np.array(
@@ -201,7 +278,22 @@ def test_max_pool_non_zero_pads():
],
dtype=np.float32,
)
assert np.allclose(result, expected)
expected_idx = np.array(
[
[
[
[0, 1, 2, 3, 3],
[4, 5, 6, 7, 7],
[8, 9, 10, 11, 11],
[12, 13, 14, 15, 15],
[12, 13, 14, 15, 15],
]
]
],
dtype=np.int32,
)
assert np.allclose(result[0], expected)
assert np.allclose(result[1], expected_idx)
def test_max_pool_same_upper_auto_pads():
@@ -213,6 +305,7 @@ def test_max_pool_same_upper_auto_pads():
# [12.5, 13.5, 14.5, 15.5]]]], dtype=float32)
data = np.arange(0.5, 16, dtype=np.float32).reshape((1, 1, 4, 4))
strides = [1, 1]
dilations = [1, 1]
pads_begin = [0, 0]
pads_end = [0, 0]
# [ 0.5, 1.5, 2.5, 3.5], 0,
@@ -222,10 +315,22 @@ def test_max_pool_same_upper_auto_pads():
# 0 , 0 , 0 , 0, 0
kernel_shape = [2, 2]
auto_pad = "same_upper"
rounding_type = "floor"
index_et = "i32"
data_node = ng.parameter(data.shape, name="A", dtype=np.float32)
avgpool_node = ng.max_pool(data_node, strides, pads_begin, pads_end, kernel_shape, auto_pad=auto_pad)
comp = rt.computation(avgpool_node, data_node)
maxpool_node = ng.max_pool(
data_node,
strides,
dilations,
pads_begin,
pads_end,
kernel_shape,
rounding_type,
auto_pad,
index_et,
)
comp = rt.computation(maxpool_node, data_node)
result = comp(data)
expected = np.array(
@@ -241,7 +346,21 @@ def test_max_pool_same_upper_auto_pads():
],
dtype=np.float32,
)
assert np.allclose(result, expected)
expected_idx = np.array(
[
[
[
[5, 6, 7, 7],
[9, 10, 11, 11],
[13, 14, 15, 15],
[13, 14, 15, 15],
]
]
],
dtype=np.int32,
)
assert np.allclose(result[0], expected)
assert np.allclose(result[1], expected_idx)
def test_max_pool_same_lower_auto_pads():
@@ -253,6 +372,7 @@ def test_max_pool_same_lower_auto_pads():
# [12.5, 13.5, 14.5, 15.5]]]], dtype=float32)
data = np.arange(0.5, 16, dtype=np.float32).reshape((1, 1, 4, 4))
strides = [1, 1]
dilations = [1, 1]
pads_begin = [0, 0]
pads_end = [0, 0]
# 0 0 , 0 , 0 , 0,
@@ -262,14 +382,49 @@ def test_max_pool_same_lower_auto_pads():
# 0 [12.5, 13.5, 14.5, 15.5],
kernel_shape = [2, 2]
auto_pad = "same_lower"
rounding_type = "floor"
index_et = "i32"
data_node = ng.parameter(data.shape, name="A", dtype=np.float32)
avgpool_node = ng.max_pool(data_node, strides, pads_begin, pads_end, kernel_shape, auto_pad=auto_pad)
comp = rt.computation(avgpool_node, data_node)
maxpool_node = ng.max_pool(
data_node,
strides,
dilations,
pads_begin,
pads_end,
kernel_shape,
rounding_type,
auto_pad,
index_et,
)
comp = rt.computation(maxpool_node, data_node)
result = comp(data)
expected = np.array(
[[[[0.5, 1.5, 2.5, 3.5], [4.5, 5.5, 6.5, 7.5], [8.5, 9.5, 10.5, 11.5], [12.5, 13.5, 14.5, 15.5]]]],
[
[
[
[0.5, 1.5, 2.5, 3.5],
[4.5, 5.5, 6.5, 7.5],
[8.5, 9.5, 10.5, 11.5],
[12.5, 13.5, 14.5, 15.5],
]
]
],
dtype=np.float32,
)
assert np.allclose(result, expected)
expected_idx = np.array(
[
[
[
[0, 1, 2, 3],
[4, 5, 6, 7],
[8, 9, 10, 11],
[12, 13, 14, 15],
]
]
],
dtype=np.int32,
)
assert np.allclose(result[0], expected)
assert np.allclose(result[1], expected_idx)