# Copyright 2020-2021 Huawei Technologies Co., Ltd
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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# ============================================================================
"""image_ops"""
from ... import context
from ..._checkparam import Validator as validator
from ..._checkparam import Rel
from ...common import dtype as mstype
from ..primitive import PrimitiveWithInfer, prim_attr_register, Primitive
[docs]class CropAndResize(PrimitiveWithInfer):
"""
Extracts crops from the input image tensor and resizes them.
Note:
In case that the output shape depends on crop_size, the crop_size must be constant.
Args:
method (str): An optional string that specifies the sampling method for resizing.
It can be "bilinear", "nearest" or "bilinear_v2". The option "bilinear" stands for standard bilinear
interpolation algorithm, while "bilinear_v2" may result in better result in some cases. Default: "bilinear"
extrapolation_value (float): An optional float value used extrapolation, if applicable. Default: 0.0.
Inputs:
- **x** (Tensor) - The input image must be a 4-D tensor of shape [batch, image_height, image_width, depth].
Types allowed: int8, int16, int32, int64, float16, float32, float64, uint8, uint16.
- **boxes** (Tensor) - A 2-D tensor of shape [num_boxes, 4].
The i-th row of the tensor specifies the coordinates of a box in the box_ind[i] image
and is specified in normalized coordinates [y1, x1, y2, x2]. A normalized coordinate value of y is mapped to
the image coordinate at y * (image_height - 1), so as the [0, 1] interval of normalized image height is
mapped to [0, image_height - 1] in image height coordinates. We do allow y1 > y2, in which case the sampled
crop is an up-down flipped version of the original image. The width dimension is treated similarly.
Normalized coordinates outside the [0, 1] range are allowed, in which case we use extrapolation_value to
extrapolate the input image values. Types allowed: float32.
- **box_index** (Tensor) - A 1-D tensor of shape [num_boxes] with int32 values in [0, batch).
The value of box_ind[i] specifies the image that the i-th box refers to. Types allowed: int32.
- **crop_size** (Tuple[int]) - A tuple of two int32 elements: (crop_height, crop_width).
Only constant value is allowed. All cropped image patches are resized to this size.
The aspect ratio of the image content is not preserved. Both crop_height and crop_width need to be positive.
Outputs:
A 4-D tensor of shape [num_boxes, crop_height, crop_width, depth] with type: float32.
Raises:
TypeError: If `method` is not a str.
TypeError: If `extrapolation_value` is not a float.
ValueError: If `method` is not one of 'bilinear', 'nearest', 'bilinear_v2'.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> class CropAndResizeNet(nn.Cell):
... def __init__(self, crop_size):
... super(CropAndResizeNet, self).__init__()
... self.crop_and_resize = ops.CropAndResize()
... self.crop_size = crop_size
...
... def construct(self, x, boxes, box_index):
... return self.crop_and_resize(x, boxes, box_index, self.crop_size)
...
>>> BATCH_SIZE = 1
>>> NUM_BOXES = 5
>>> IMAGE_HEIGHT = 256
>>> IMAGE_WIDTH = 256
>>> CHANNELS = 3
>>> image = np.random.normal(size=[BATCH_SIZE, IMAGE_HEIGHT, IMAGE_WIDTH, CHANNELS]).astype(np.float32)
>>> boxes = np.random.uniform(size=[NUM_BOXES, 4]).astype(np.float32)
>>> box_index = np.random.uniform(size=[NUM_BOXES], low=0, high=BATCH_SIZE).astype(np.int32)
>>> crop_size = (24, 24)
>>> crop_and_resize = CropAndResizeNet(crop_size=crop_size)
>>> output = crop_and_resize(Tensor(image), Tensor(boxes), Tensor(box_index))
>>> print(output.shape)
(5, 24, 24, 3)
"""
@prim_attr_register
def __init__(self, method="bilinear", extrapolation_value=0.0):
"""Initialize CropAndResize"""
self.init_prim_io_names(inputs=['x', 'boxes', 'box_index', 'crop_size'], outputs=['y'])
validator.check_value_type("method", method, [str], self.name)
validator.check_string(method, ["bilinear", "nearest", "bilinear_v2"], "method", self.name)
self.method = method
validator.check_value_type("extrapolation_value", extrapolation_value, [float], self.name)
self.extrapolation_value = extrapolation_value
self.is_ge = context.get_context("enable_ge")
def __infer__(self, x, boxes, box_index, crop_size):
# get shape
x_shape = list(x['shape'])
boxes_shape = list(boxes['shape'])
box_index_shape = list(box_index['shape'])
# get value
if crop_size['value'] is None:
raise ValueError(f"For '{self.name}', the 'crop_size' cannot be None, but got {crop_size['value']}.")
crop_size_value = crop_size['value']
# get dtype
x_dtype = x['dtype']
boxes_dtype = boxes['dtype']
box_index_dtype = box_index['dtype']
crop_size_dtype = crop_size['dtype']
# check dytpe
validator.check_tensor_dtype_valid("x", x_dtype,
[mstype.int8, mstype.int16, mstype.int32, mstype.int64, mstype.float16,
mstype.float32, mstype.float64, mstype.uint8, mstype.uint16], self.name)
validator.check_tensor_dtype_valid("boxes", boxes_dtype, [mstype.float32], self.name)
validator.check_tensor_dtype_valid("box_index", box_index_dtype, [mstype.int32], self.name)
validator.check_value_type("crop_size", crop_size_value, [tuple], self.name)
# check input shape rank
validator.check("x rank", len(x_shape), "expected", 4, Rel.EQ, self.name)
validator.check("boxes rank", len(boxes_shape), "expected", 2, Rel.EQ, self.name)
validator.check("box_index rank", len(box_index_shape), "expected", 1, Rel.EQ, self.name)
validator.check("crop_size size", len(crop_size_value), "expected", 2, Rel.EQ, self.name)
validator.check("boxes dim_0", boxes_shape[0], "box_index dim_0", box_index_shape[0], Rel.EQ, self.name)
validator.check("boxes dim_1", boxes_shape[1], "expected", 4, Rel.EQ, self.name)
# check crop_size_value
validator.check("crop_height", crop_size_value[0], "minimum", 0, Rel.GT, self.name)
validator.check("crop_width", crop_size_value[1], "minimum", 0, Rel.GT, self.name)
# check crop_size element type
validator.check("crop_height dtype", crop_size_dtype[0], "expected", [mstype.int32, mstype.int64], Rel.IN,
self.name)
validator.check("crop_width dtype", crop_size_dtype[1], "expected", [mstype.int32, mstype.int64], Rel.IN,
self.name)
num_boxes = boxes_shape[0]
crop_height = crop_size_value[0]
crop_width = crop_size_value[1]
depth = x_shape[3]
out_shape = (num_boxes, crop_height, crop_width, depth)
if self.is_ge:
out_shape = (num_boxes, x_shape[1], crop_height, crop_width)
return {'shape': out_shape,
'dtype': mstype.float32,
'value': None}
class NonMaxSuppressionV3(Primitive):
r"""
Greedily selects a subset of bounding boxes in descending order of score.
.. warning::
When input "max_output_size" is negative, it will be treated as 0.
Note:
This algorithm is agnostic to where the origin is in the coordinate system.
This algorithm is invariant to orthogonal transformations and translations of the coordinate system;
thus translating or reflections of the coordinate system result in the same boxes being
selected by the algorithm.
Inputs:
- **boxes** (Tensor) - A 2-D Tensor of shape [num_boxes, 4].
- **scores** (Tensor) - A 1-D Tensor of shape [num_boxes] representing a single score
corresponding to each box (each row of boxes), the num_boxes of "scores" must be equal to
the num_boxes of "boxes".
- **max_output_size** (Union[Tensor, Number.Int]) - A scalar integer Tensor representing the maximum
number of boxes to be selected by non max suppression.
- **iou_threshold** (Union[Tensor, Number.Float]) - A 0-D float tensor representing the threshold for
deciding whether boxes overlap too much with respect to IOU, and iou_threshold must be equal or greater
than 0 and be equal or smaller than 1.
- **score_threshold** (Union[Tensor, Number.Float]) - A 0-D float tensor representing the threshold for
deciding when to remove boxes based on score.
Outputs:
A 1-D integer Tensor of shape [M] representing the selected indices from the boxes tensor,
where M <= max_output_size.
Raises:
TypeError: If the dtype of `boxes` and `scores` is different.
TypeError: If the dtype of `iou_threshold` and `score_threshold` is different.
TypeError: If `boxes` is not tensor or its dtype is not float16 or float32.
TypeEroor: If `scores` is not tensor or its dtype is not float16 or float32.
TypeError: If `max_output_size` is not tensor or scalar.If `max_output_size` is not int32 or int64.
TypeError: If `iou_threshold` is not tensor or scalar. If its type is not float16 or float32.
TypeError: If `score_threshold` is not tensor or scalar. If its type is not float16 or float32.
ValueError: If the size of shape of `boxes` is not 2 or the second value of its shape is not 4.
ValueError: If the size of shape of `scores` is not 1.
ValueError: If each of the size of shape of `max_output_size`, `iou_threshold`, `score_threshold` is not 0.
Supported Platforms:
``Ascend``
Examples:
>>> boxes = Tensor(np.array([[1, 2, 3, 4], [1, 3, 3, 4], [1, 3, 4, 4],
... [1, 1, 4, 4], [1, 1, 3, 4]]), mstype.float32)
>>> scores = Tensor(np.array([0.4, 0.5, 0.72, 0.9, 0.45]), mstype.float32)
>>> max_output_size = Tensor(5, mstype.int32)
>>> iou_threshold = Tensor(0.5, mstype.float32)
>>> score_threshold = Tensor(0, mstype.float32)
>>> nonmaxsuppression = ops.NonMaxSuppressionV3()
>>> output = nonmaxsuppression(boxes, scores, max_output_size, iou_threshold, score_threshold)
>>> print(output)
[3 2 0]
"""
@prim_attr_register
def __init__(self):
"""Initialize NonMaxSuppressionV3"""