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# Copyright 2021 Huawei Technologies Co., Ltd |
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# |
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# Licensed under the Apache License, Version 2.0 (the "License"); |
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# you may not use this file except in compliance with the License. |
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# You may obtain a copy of the License at |
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# |
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# http://www.apache.org/licenses/LICENSE-2.0 |
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# |
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# Unless required by applicable law or agreed to in writing, software |
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# distributed under the License is distributed on an "AS IS" BASIS, |
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
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# See the License for the specific language governing permissions and |
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# limitations under the License. |
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# ============================================================================ |
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"""Hierarchical occlusion edit tree searcher.""" |
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from enum import Enum |
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import copy |
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import re |
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import math |
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import numpy as np |
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from scipy.ndimage import gaussian_filter |
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from mindspore import nn |
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from mindspore import Tensor |
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from mindspore.ops import Squeeze |
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from mindspore.train._utils import check_value_type |
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AUTO_LAYER_MAX = 3 # maximum number of layer by auto settings |
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AUTO_WIN_SIZE_MIN = 28 # minimum window size by auto settings |
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AUTO_WIN_SIZE_DIV = 2 # denominator of windows size calculations by auto settings |
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AUTO_STRIDE_DIV = 5 # denominator of stride calculations by auto settings |
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AUTO_MASK_GAUSSIAN_RADIUS_DIV = 25 # denominator of gaussian mask radius calculations by auto settings |
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DEFAULT_THRESHOLD = 0.5 # default target prediction threshold |
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DEFAULT_BATCH_SIZE = 64 # default batch size for batch inference search |
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MASK_GAUSSIAN_RE = r'^gaussian:(\d+)$' # gaussian mask string pattern |
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# minimum length of input images' short side with auto settings |
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AUTO_IMAGE_SHORT_SIDE_MIN = AUTO_WIN_SIZE_MIN * AUTO_WIN_SIZE_DIV |
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def is_valid_str_mask(mask): |
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"""Check if it is a valid string mask.""" |
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check_value_type('mask', mask, str) |
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match = re.match(MASK_GAUSSIAN_RE, mask) |
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return match and int(match.group(1)) > 0 |
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def compile_mask(mask, image): |
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"""Compile mask to a ready to use object.""" |
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if mask is None: |
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return compile_str_mask(auto_str_mask(image), image) |
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check_value_type('mask', mask, (str, tuple, float, np.ndarray)) |
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if isinstance(mask, str): |
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return compile_str_mask(mask, image) |
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if isinstance(mask, tuple): |
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_check_iterable_type('mask', mask, tuple, float) |
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elif isinstance(mask, np.ndarray): |
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if len(image.shape) == 4 and len(mask.shape) == 3: |
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mask = np.expand_dims(mask, axis=0) |
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elif len(image.shape) == 3 and len(mask.shape) == 4 and mask.shape[0] == 1: |
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mask = mask.squeeze(0) |
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if image.shape != mask.shape: |
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raise ValueError("Image and mask is not match in shape.") |
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return mask |
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def auto_str_mask(image): |
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"""Generate auto string mask for the image.""" |
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check_value_type('image', image, np.ndarray) |
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short_side = np.min(image.shape[-2:]) |
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radius = int(round(short_side/AUTO_MASK_GAUSSIAN_RADIUS_DIV)) |
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if radius == 0: |
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raise ValueError(f"Input image's short side:{short_side} is too small for auto mask, " |
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f"at least {AUTO_MASK_GAUSSIAN_RADIUS_DIV}pixels is required.") |
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return f'gaussian:{radius}' |
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def compile_str_mask(mask, image): |
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"""Concert string mask to numpy.ndarray.""" |
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check_value_type('mask', mask, str) |
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check_value_type('image', image, np.ndarray) |
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match = re.match(MASK_GAUSSIAN_RE, mask) |
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if match: |
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radius = int(match.group(1)) |
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if radius > 0: |
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sigma = [0] * len(image.shape) |
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sigma[-2] = radius |
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sigma[-1] = radius |
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return gaussian_filter(image, sigma=sigma, mode='nearest') |
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raise ValueError(f"Invalid string mask: '{mask}'.") |
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class EditStep: |
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""" |
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Edit step that describes a box region, also represents an edit tree. |
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Args: |
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layer (int): Layer number, -1 is root layer, 0 or above is normal edit layer. |
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box (tuple[int, int, int, int]): Tuple of x, y, width, height. |
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""" |
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def __init__(self, layer, box): |
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self.layer = layer |
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self.box = box |
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self.network_output = 0 |
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self.step_change = 0 |
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self.children = None |
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@property |
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def x(self): |
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"""X-coordinate of the box.""" |
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return self.box[0] |
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@property |
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def y(self): |
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"""Y-coordinate of the box.""" |
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return self.box[1] |
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@property |
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def width(self): |
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"""Width of the box.""" |
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return self.box[2] |
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@property |
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def height(self): |
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"""Height of the box.""" |
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return self.box[3] |
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@property |
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def is_leaf(self): |
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"""Returns True if no child edit step.""" |
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return not self.children |
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@property |
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def leaf_steps(self): |
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"""Returns all leaf edit steps in the tree.""" |
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if self.is_leaf: |
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return [self] |
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steps = [] |
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for child in self.children: |
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steps.extend(child.leaf_steps) |
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return steps |
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@property |
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def max_layer(self): |
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"""Maximum layer number in the edit tree.""" |
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if self.is_leaf: |
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return self.layer |
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layer = self.layer |
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for child in self.children: |
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child_max_layer = child.max_layer |
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if child_max_layer > layer: |
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layer = child_max_layer |
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return layer |
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def add_child(self, child): |
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"""Add a child edit step.""" |
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if self.children is None: |
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self.children = [child] |
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else: |
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self.children.append(child) |
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def remove_all_children(self): |
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"""Remove all child steps.""" |
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self.children = None |
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def get_layer_or_leaf_steps(self, layer): |
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"""Get all edit steps of the layer and all leaf edit steps above the layer.""" |
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if self.layer == layer or (self.layer < layer and self.is_leaf): |
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return [self] |
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steps = [] |
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if self.layer < layer and self.children: |
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for child in self.children: |
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steps.extend(child.get_layer_or_leaf_steps(layer)) |
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return steps |
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def get_layer_steps(self, layer): |
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"""Get all edit steps of the layer.""" |
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if self.layer == layer: |
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return [self] |
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steps = [] |
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if self.layer < layer and self.children: |
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for child in self.children: |
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steps.extend(child.get_layer_steps(layer)) |
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return steps |
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@classmethod |
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def apply(cls, |
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image, |
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mask, |
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edit_steps, |
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by_masking=False, |
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inplace=False): |
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""" |
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Apply edit steps. |
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Args: |
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image (numpy.ndarray): Image tensor in CHW or NCHW(N=1) format. |
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mask (Union[str, tuple[float, float, float], float, numpy.ndarray]): The mask, type can be |
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str: String mask, e.g. 'gaussian:9' - Gaussian blur with radius of 9. |
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tuple[float, float, float]: RGB solid color mask, |
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float: Grey scale solid color mask. |
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numpy.ndarray: Image mask in CHW or NCHW(N=1) format. |
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edit_steps (list[EditStep], optional): Edit steps to be applied. |
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by_masking (bool): Whether it is masking mode. |
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inplace (bool): Whether the modification is going to take place in the input image tensor. False to |
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construct a new image tensor as result. |
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Returns: |
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numpy.ndarray, the result image tensor. |
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Raises: |
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TypeError: Be raised for any argument or data type problem. |
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ValueError: Be raised for any argument or data value problem. |
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""" |
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if by_masking: |
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return cls.apply_masking(image, mask, edit_steps, inplace) |
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return cls.apply_unmasking(image, mask, edit_steps, inplace) |
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@staticmethod |
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def apply_masking(image, |
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mask, |
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edit_steps, |
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inplace=False): |
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""" |
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Apply edit steps in masking mode. |
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Args: |
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image (numpy.ndarray): Image tensor in CHW or NCHW(N=1) format. |
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mask (Union[str, tuple[float, float, float], float, numpy.ndarray]): The mask, type can be |
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str: String mask, e.g. 'gaussian:9' - Gaussian blur with radius of 9. |
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tuple[float, float, float]: RGB solid color mask, |
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float: Grey scale solid color mask. |
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numpy.ndarray: Image mask in CHW or NCHW(N=1) format. |
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edit_steps (list[EditStep], optional): Edit steps to be applied. |
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inplace (bool): Whether the modification is going to take place in the input image tensor. False to |
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construct a new image tensor as result. |
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Returns: |
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numpy.ndarray, the result image tensor. |
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Raises: |
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TypeError: Be raised for any argument or data type problem. |
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ValueError: Be raised for any argument or data value problem. |
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""" |
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check_value_type('image', image, np.ndarray) |
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check_value_type('mask', mask, (str, tuple, float, np.ndarray)) |
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if isinstance(mask, tuple): |
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_check_iterable_type('mask', mask, tuple, float) |
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if edit_steps is not None: |
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_check_iterable_type('edit_steps', edit_steps, (tuple, list), EditStep) |
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mask = compile_mask(mask, image) |
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if inplace: |
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background = image |
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else: |
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background = np.copy(image) |
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if not edit_steps: |
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return background |
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for step in edit_steps: |
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x_max = step.x + step.width |
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y_max = step.y + step.height |
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if x_max > background.shape[-1]: |
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x_max = background.shape[-1] |
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if y_max > background.shape[-2]: |
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y_max = background.shape[-2] |
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if x_max <= step.x or y_max <= step.y: |
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continue |
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if isinstance(mask, np.ndarray): |
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background[..., step.y:y_max, step.x:x_max] = mask[..., step.y:y_max, step.x:x_max] |
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else: |
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if isinstance(mask, (int, float)): |
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mask = (mask, mask, mask) |
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for c in range(3): |
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background[..., c, step.y:y_max, step.x:x_max] = mask[c] |
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return background |
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@staticmethod |
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def apply_unmasking(image, |
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mask, |
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edit_steps, |
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inplace=False): |
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""" |
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Apply edit steps in unmasking mode. |
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Args: |
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image (numpy.ndarray): Image tensor in CHW or NCHW(N=1) format. |
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mask (Union[str, tuple[float, float, float], float, numpy.ndarray]): The mask, type can be |
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str: String mask, e.g. 'gaussian:9' - Gaussian blur with radius of 9. |
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tuple[float, float, float]: RGB solid color mask, |
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float: Grey scale solid color mask. |
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numpy.ndarray: Image mask in CHW or NCHW(N=1) format. |
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edit_steps (list[EditStep]): Edit steps to be applied. |
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inplace (bool): Whether the modification is going to take place in the input mask tensor. False to |
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construct a new image tensor as result. |
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Returns: |
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numpy.ndarray, the result image tensor. |
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Raises: |
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TypeError: Be raised for any argument or data type problem. |
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ValueError: Be raised for any argument or data value problem. |
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""" |
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check_value_type('image', image, np.ndarray) |
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check_value_type('mask', mask, (str, tuple, float, np.ndarray)) |
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if isinstance(mask, tuple): |
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_check_iterable_type('mask', mask, tuple, float) |
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if edit_steps is not None: |
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_check_iterable_type('edit_steps', edit_steps, (tuple, list), EditStep) |
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mask = compile_mask(mask, image) |
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if isinstance(mask, np.ndarray): |
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if inplace: |
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background = mask |
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else: |
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background = np.copy(mask) |
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else: |
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if inplace: |
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raise ValueError('Inplace cannot be True when mask is not a numpy.ndarray') |
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background = np.zeros_like(image) |
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if isinstance(mask, (int, float)): |
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background.fill(mask) |
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else: |
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for c in range(3): |
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background[..., c, :, :] = mask[c] |
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if not edit_steps: |
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return background |
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for step in edit_steps: |
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x_max = step.x + step.width |
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y_max = step.y + step.height |
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if x_max > background.shape[-1]: |
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x_max = background.shape[-1] |
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if y_max > background.shape[-2]: |
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y_max = background.shape[-2] |
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if x_max <= step.x or y_max <= step.y: |
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continue |
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background[..., step.y:y_max, step.x:x_max] = image[..., step.y:y_max, step.x:x_max] |
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return background |
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class NoValidResultError(RuntimeError): |
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"""Error for no edit step layer's network output meet the threshold.""" |
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class OriginalOutputError(RuntimeError): |
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"""Error for network output of the original image is not strictly larger than the threshold.""" |
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class Searcher: |
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""" |
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Edit step searcher. |
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Args: |
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network (Cell): Image tensor in CHW or NCHW(N=1) format. |
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win_sizes (Union(list[int], optional): Moving square window size (length of side) of layers, |
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None means by auto calcuation. |
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strides (Union(list[int], optional): Stride of layers, None means by auto calcuation. |
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threshold (float): Threshold network output value of the target class. |
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by_masking (bool): Whether it is masking mode. |
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Examples: |
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>>> from mindspore import nn |
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>>> from mindspore.explainer.explanation._counterfactual.hierarchical_occlusion import Searcher, EditStep |
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>>> |
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>>> from user_defined import load_network, load_sample_image |
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>>> |
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>>> |
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>>> network = nn.SequentialCell([load_network(), nn.Sigmoid()]) |
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>>> |
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>>> # single image in CHW or NCHW(N=1) numpy.ndarray tensor, typical dimension is 224x224 |
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>>> image = load_sample_image() |
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>>> # target class index |
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>>> class_idx = 5 |
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>>> |
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>>> # by default, maximum 3 search layers, auto calculate window sizes and strides |
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>>> searcher = Searcher(network) |
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>>> |
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>>> edit_tree, layer_outputs = searcher.search(image, class_idx) |
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>>> # get the outcome image of the deepest layer in CHW(or NCHW(N=1) if input image is NCHW) format |
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>>> outcome = EditStep.apply(image, searcher.compiled_mask, edit_tree.leaf_steps) |
|
|
|
""" |
|
|
|
|
|
|
|
def __init__(self, |
|
|
|
network, |
|
|
|
win_sizes=None, |
|
|
|
strides=None, |
|
|
|
threshold=DEFAULT_THRESHOLD, |
|
|
|
by_masking=False): |
|
|
|
|
|
|
|
check_value_type('network', network, nn.Cell) |
|
|
|
|
|
|
|
if win_sizes is not None: |
|
|
|
_check_iterable_type('win_sizes', win_sizes, list, int) |
|
|
|
if not win_sizes: |
|
|
|
raise ValueError('Argument win_sizes is empty.') |
|
|
|
|
|
|
|
for i in range(1, len(win_sizes)): |
|
|
|
if win_sizes[i] >= win_sizes[i-1]: |
|
|
|
raise ValueError('Argument win_sizes is not strictly descending.') |
|
|
|
|
|
|
|
if win_sizes[-1] <= 0: |
|
|
|
raise ValueError('Argument win_sizes has non-positive number.') |
|
|
|
elif strides is not None: |
|
|
|
raise ValueError('Argument win_sizes cannot be None if strides is not None.') |
|
|
|
|
|
|
|
if strides is not None: |
|
|
|
_check_iterable_type('strides', strides, list, int) |
|
|
|
for i in range(1, len(strides)): |
|
|
|
if strides[i] >= strides[i-1]: |
|
|
|
raise ValueError('Argument win_sizes is not strictly descending.') |
|
|
|
|
|
|
|
if strides[-1] <= 0: |
|
|
|
raise ValueError('Argument strides has non-positive number.') |
|
|
|
|
|
|
|
if len(strides) != len(win_sizes): |
|
|
|
raise ValueError('Length of strides and win_sizes is not equal.') |
|
|
|
elif win_sizes is not None: |
|
|
|
raise ValueError('Argument strides cannot be None if win_sizes is not None.') |
|
|
|
|
|
|
|
self._network = copy.deepcopy(network) |
|
|
|
self._compiled_mask = None |
|
|
|
self._threshold = threshold |
|
|
|
self._win_sizes = copy.copy(win_sizes) if win_sizes else None |
|
|
|
self._strides = copy.copy(strides) if strides else None |
|
|
|
self._by_masking = by_masking |
|
|
|
|
|
|
|
@property |
|
|
|
def network(self): |
|
|
|
"""Get the network.""" |
|
|
|
return self._network |
|
|
|
|
|
|
|
@property |
|
|
|
def by_masking(self): |
|
|
|
"""Check if it is masking mode.""" |
|
|
|
return self._by_masking |
|
|
|
|
|
|
|
@property |
|
|
|
def threshold(self): |
|
|
|
"""The network output threshold to stop the search.""" |
|
|
|
return self._threshold |
|
|
|
|
|
|
|
@property |
|
|
|
def win_sizes(self): |
|
|
|
"""Windows sizes in pixels.""" |
|
|
|
return self._win_sizes |
|
|
|
|
|
|
|
@property |
|
|
|
def strides(self): |
|
|
|
"""Strides in pixels.""" |
|
|
|
return self._strides |
|
|
|
|
|
|
|
@property |
|
|
|
def compiled_mask(self): |
|
|
|
"""The compiled mask after a successful search() call.""" |
|
|
|
return self._compiled_mask |
|
|
|
|
|
|
|
def search(self, image, class_idx, mask=None): |
|
|
|
""" |
|
|
|
Search smallest sufficient/destruction region on an image. |
|
|
|
|
|
|
|
Args: |
|
|
|
image (numpy.ndarray): Image tensor in CHW or NCHW(N=1) format. |
|
|
|
class_idx (int): Target class index. |
|
|
|
mask (Union[str, tuple[float, float, float], float], optional): The mask, type can be |
|
|
|
str: String mask, e.g. 'gaussian:9' - Gaussian blur with radius of 9. |
|
|
|
tuple[float, float, float]: RGB solid color mask, |
|
|
|
float: Grey scale solid color mask. |
|
|
|
None: By auto calculation. |
|
|
|
|
|
|
|
Returns: |
|
|
|
tuple[EditStep, list[float]], the root edit step and network output of each layer after applied the |
|
|
|
layer steps. |
|
|
|
|
|
|
|
Raise: |
|
|
|
TypeError: Be raised for any argument or data type problem. |
|
|
|
ValueError: Be raised for any argument or data value problem. |
|
|
|
NoValidResultError: Be raised if no valid result was found. |
|
|
|
OriginalOutputError: Be raised if network output of the original image is not strictly larger than |
|
|
|
the threshold. |
|
|
|
""" |
|
|
|
check_value_type('image', image, (Tensor, np.ndarray)) |
|
|
|
|
|
|
|
if isinstance(image, Tensor): |
|
|
|
image = image.asnumpy() |
|
|
|
|
|
|
|
if len(image.shape) == 4: |
|
|
|
if image.shape[0] != 1: |
|
|
|
raise ValueError("Argument image's batch size is not 1.") |
|
|
|
elif len(image.shape) == 3: |
|
|
|
image = np.expand_dims(image, axis=0) |
|
|
|
else: |
|
|
|
raise ValueError("Argument image is not in CHW or NCHW(N=1) format.") |
|
|
|
|
|
|
|
check_value_type('class_idx', class_idx, int) |
|
|
|
|
|
|
|
if class_idx < 0: |
|
|
|
raise ValueError("Argument class_idx is less then zero.") |
|
|
|
|
|
|
|
self._compiled_mask = compile_mask(mask, image) |
|
|
|
|
|
|
|
short_side = np.min(image.shape[-2:]) |
|
|
|
if self._win_sizes is None: |
|
|
|
win_sizes, strides = self._auto_win_sizes_strides(short_side) |
|
|
|
else: |
|
|
|
win_sizes, strides = self._win_sizes, self._strides |
|
|
|
|
|
|
|
if short_side <= win_sizes[0]: |
|
|
|
raise ValueError(f"Input image's short side is shorter then or " |
|
|
|
f"equals to the first window size:{win_sizes[0]}.") |
|
|
|
|
|
|
|
self._network.set_train(False) |
|
|
|
|
|
|
|
# the search result will be store as a edit tree that attached to the root step. |
|
|
|
root_step = EditStep(-1, (0, 0, image.shape[-1], image.shape[-2])) |
|
|
|
root_job = _SearchJob(by_masking=self._by_masking, |
|
|
|
class_idx=class_idx, |
|
|
|
win_sizes=win_sizes, |
|
|
|
strides=strides, |
|
|
|
layer=0, |
|
|
|
search_field=root_step.box, |
|
|
|
pre_edit_steps=None, |
|
|
|
parent_step=root_step) |
|
|
|
self._process_root_job(image, root_job) |
|
|
|
|
|
|
|
# the leaf layer's network output may not meet the threshold, |
|
|
|
# we have to cutoff the unqualified layers |
|
|
|
layer_count = root_step.max_layer + 1 |
|
|
|
if layer_count == 0: |
|
|
|
raise NoValidResultError("No edit step layer was found.") |
|
|
|
|
|
|
|
# gather the network output of each layer |
|
|
|
layer_outputs = [None] * layer_count |
|
|
|
for layer in range(layer_count): |
|
|
|
steps = root_step.get_layer_or_leaf_steps(layer) |
|
|
|
if not steps: |
|
|
|
continue |
|
|
|
masked_image = EditStep.apply(image, self._compiled_mask, steps, by_masking=self._by_masking) |
|
|
|
output = self._network(Tensor(masked_image)) |
|
|
|
output = output[0, class_idx].asnumpy().item() |
|
|
|
layer_outputs[layer] = output |
|
|
|
|
|
|
|
# determine which layer we have to cutoff |
|
|
|
cutoff_layer = None |
|
|
|
for layer in reversed(range(layer_count)): |
|
|
|
if layer_outputs[layer] is not None and self._is_threshold_met(layer_outputs[layer]): |
|
|
|
cutoff_layer = layer |
|
|
|
break |
|
|
|
|
|
|
|
if cutoff_layer is None or root_step.is_leaf: |
|
|
|
raise NoValidResultError(f"No edit step layer's network output meet the threshold: {self._threshold}.") |
|
|
|
|
|
|
|
# cutoff the layer by removing all children of the layer's steps. |
|
|
|
steps = root_step.get_layer_steps(cutoff_layer) |
|
|
|
for step in steps: |
|
|
|
step.remove_all_children() |
|
|
|
layer_outputs = layer_outputs[:cutoff_layer + 1] |
|
|
|
|
|
|
|
return root_step, layer_outputs |
|
|
|
|
|
|
|
def _process_root_job(self, sample_input, root_job): |
|
|
|
""" |
|
|
|
Process job queue. |
|
|
|
|
|
|
|
Args: |
|
|
|
sample_input (numpy.ndarray): Image tensor in NCHW(N=1) format. |
|
|
|
root_job (_SearchJob): Root search job. |
|
|
|
""" |
|
|
|
job_queue = [root_job] |
|
|
|
while job_queue: |
|
|
|
job = job_queue.pop(0) |
|
|
|
sub_job_queue = [] |
|
|
|
job_edit_steps, stop_reason = self._process_job(job, sample_input, sub_job_queue) |
|
|
|
|
|
|
|
if stop_reason in (self._StopReason.THRESHOLD_MET, self._StopReason.STEP_CHANGE_MET): |
|
|
|
for step in job_edit_steps: |
|
|
|
job.parent_step.add_child(step) |
|
|
|
job_queue.extend(sub_job_queue) |
|
|
|
|
|
|
|
def _process_job(self, job, sample_input, job_queue): |
|
|
|
""" |
|
|
|
Process a job. |
|
|
|
|
|
|
|
Args: |
|
|
|
job (_SearchJob): Search job to be processed. |
|
|
|
sample_input (numpy.ndarray): Image tensor in NCHW(N=1) format. |
|
|
|
job_queue (list[_SearchJob]): Job queue. |
|
|
|
|
|
|
|
Returns: |
|
|
|
tuple[list[EditStep], _StopReason], result edit stop and the stop reason. |
|
|
|
""" |
|
|
|
edit_steps = [] |
|
|
|
|
|
|
|
# make the network output with the original image is strictly larger than the threshold |
|
|
|
if job.layer == 0: |
|
|
|
original_output = self._network(Tensor(sample_input))[0, job.class_idx].asnumpy().item() |
|
|
|
if original_output <= self._threshold: |
|
|
|
raise OriginalOutputError(f'The original output is not strictly larger the threshold: ' |
|
|
|
f'{self._threshold}') |
|
|
|
|
|
|
|
# applying the pre-edit steps from the parent steps |
|
|
|
if job.pre_edit_steps: |
|
|
|
# use the latest leaf steps to increase the accuracy |
|
|
|
leaf_steps = [] |
|
|
|
for step in job.pre_edit_steps: |
|
|
|
leaf_steps.extend(step.leaf_steps) |
|
|
|
pre_edit_steps = leaf_steps |
|
|
|
else: |
|
|
|
pre_edit_steps = None |
|
|
|
workpiece = EditStep.apply(sample_input, |
|
|
|
self._compiled_mask, |
|
|
|
pre_edit_steps, |
|
|
|
self._by_masking) |
|
|
|
|
|
|
|
job.on_start(sample_input, workpiece, self._compiled_mask, self._network) |
|
|
|
start_output = self._network(Tensor(workpiece))[0, job.class_idx].asnumpy().item() |
|
|
|
last_output = start_output |
|
|
|
|
|
|
|
# greedy search loop |
|
|
|
while True: |
|
|
|
|
|
|
|
if self._is_threshold_met(last_output): |
|
|
|
return edit_steps, self._StopReason.THRESHOLD_MET |
|
|
|
|
|
|
|
try: |
|
|
|
best_edit = job.find_best_edit() |
|
|
|
except _NoNewStepError: |
|
|
|
return edit_steps, self._StopReason.NO_NEW_STEP |
|
|
|
except _RepeatedStepError: |
|
|
|
return edit_steps, self._StopReason.REPEATED_STEP |
|
|
|
|
|
|
|
best_edit.step_change = best_edit.network_output - last_output |
|
|
|
|
|
|
|
if job.layer < job.layer_count - 1 and self._is_greedy(best_edit.step_change): |
|
|
|
# create net layer search job if new edit step is valid and not yet reaching |
|
|
|
# the final layer |
|
|
|
if job.pre_edit_steps: |
|
|
|
pre_edit_steps = list(job.pre_edit_steps) |
|
|
|
pre_edit_steps.extend(edit_steps) |
|
|
|
else: |
|
|
|
pre_edit_steps = list(edit_steps) |
|
|
|
|
|
|
|
sub_job = job.create_sub_job(best_edit, pre_edit_steps) |
|
|
|
job_queue.append(sub_job) |
|
|
|
|
|
|
|
edit_steps.append(best_edit) |
|
|
|
|
|
|
|
if job.layer > 0: |
|
|
|
# stop if the step change meet the parent step change only after layer 0 |
|
|
|
change = best_edit.network_output - start_output |
|
|
|
if self._is_step_change_met(job.parent_step.step_change, change): |
|
|
|
return edit_steps, self._StopReason.STEP_CHANGE_MET |
|
|
|
|
|
|
|
last_output = best_edit.network_output |
|
|
|
|
|
|
|
def _is_threshold_met(self, network_output): |
|
|
|
"""Check if the threshold was met.""" |
|
|
|
if self._by_masking: |
|
|
|
return network_output <= self._threshold |
|
|
|
return network_output >= self._threshold |
|
|
|
|
|
|
|
def _is_step_change_met(self, target, step_change): |
|
|
|
"""Check if the change target was met.""" |
|
|
|
if self._by_masking: |
|
|
|
return step_change <= target |
|
|
|
return step_change >= target |
|
|
|
|
|
|
|
def _is_greedy(self, step_change): |
|
|
|
"""Check if it is a greedy step.""" |
|
|
|
if self._by_masking: |
|
|
|
return step_change < 0 |
|
|
|
return step_change > 0 |
|
|
|
|
|
|
|
@classmethod |
|
|
|
def _auto_win_sizes_strides(cls, short_side): |
|
|
|
""" |
|
|
|
Calculate auto window sizes and strides. |
|
|
|
|
|
|
|
Args: |
|
|
|
short_side (int): Length of search space. |
|
|
|
|
|
|
|
Returns: |
|
|
|
tuple[list[int], list[int]], window sizes and strides. |
|
|
|
""" |
|
|
|
win_sizes = [] |
|
|
|
strides = [] |
|
|
|
cur_len = int(short_side/AUTO_WIN_SIZE_DIV) |
|
|
|
while len(win_sizes) < AUTO_LAYER_MAX and cur_len >= AUTO_WIN_SIZE_MIN: |
|
|
|
stride = int(cur_len/AUTO_STRIDE_DIV) |
|
|
|
if stride <= 0: |
|
|
|
break |
|
|
|
win_sizes.append(cur_len) |
|
|
|
strides.append(stride) |
|
|
|
cur_len = int(cur_len/AUTO_WIN_SIZE_DIV) |
|
|
|
if not win_sizes: |
|
|
|
raise ValueError(f"Image's short side is less then {AUTO_IMAGE_SHORT_SIDE_MIN}, " |
|
|
|
f"unable to calculates auto settings.") |
|
|
|
return win_sizes, strides |
|
|
|
|
|
|
|
class _StopReason(Enum): |
|
|
|
"""Stop reason of search job.""" |
|
|
|
THRESHOLD_MET = 0 # threshold was met. |
|
|
|
STEP_CHANGE_MET = 1 # parent step change was met. |
|
|
|
NO_NEW_STEP = 2 # no new step was found. |
|
|
|
REPEATED_STEP = 3 # repeated step was found. |
|
|
|
|
|
|
|
|
|
|
|
def _check_iterable_type(arg_name, arg_value, container_type, elem_types): |
|
|
|
"""Concert iterable argument data type.""" |
|
|
|
check_value_type(arg_name, arg_value, container_type) |
|
|
|
for elem in arg_value: |
|
|
|
check_value_type(arg_name + ' element', elem, elem_types) |
|
|
|
|
|
|
|
|
|
|
|
class _NoNewStepError(Exception): |
|
|
|
"""Error for no new step was found.""" |
|
|
|
|
|
|
|
|
|
|
|
class _RepeatedStepError(Exception): |
|
|
|
"""Error for repeated step was found.""" |
|
|
|
|
|
|
|
|
|
|
|
class _SearchJob: |
|
|
|
""" |
|
|
|
Search job. |
|
|
|
|
|
|
|
Args: |
|
|
|
by_masking (bool): Whether it is masking mode. |
|
|
|
class_idx (int): Target class index. |
|
|
|
win_sizes (list[int]): Moving square window size (length of side) of layers. |
|
|
|
strides (list[int]): Strides of layers. |
|
|
|
layer (int): Layer number. |
|
|
|
search_field (tuple[int, int, int, int]): Search field in x, y, width, height format. |
|
|
|
pre_edit_steps (list[EditStep], optional): Edit steps to be applied before searching. |
|
|
|
parent_step (EditStep): Parent edit step. |
|
|
|
batch_size (int): Batch size of batched inferences. |
|
|
|
""" |
|
|
|
|
|
|
|
def __init__(self, |
|
|
|
by_masking, |
|
|
|
class_idx, |
|
|
|
win_sizes, |
|
|
|
strides, |
|
|
|
layer, |
|
|
|
search_field, |
|
|
|
pre_edit_steps, |
|
|
|
parent_step, |
|
|
|
batch_size=DEFAULT_BATCH_SIZE): |
|
|
|
|
|
|
|
if layer >= len(win_sizes): |
|
|
|
raise ValueError('Layer is larger then number of window sizes.') |
|
|
|
|
|
|
|
self.by_masking = by_masking |
|
|
|
self.class_idx = class_idx |
|
|
|
self.win_sizes = win_sizes |
|
|
|
self.strides = strides |
|
|
|
self.layer = layer |
|
|
|
self.search_field = search_field |
|
|
|
self.pre_edit_steps = pre_edit_steps |
|
|
|
self.parent_step = parent_step |
|
|
|
self.batch_size = batch_size |
|
|
|
self.network = None |
|
|
|
self.mask = None |
|
|
|
self.original_input = None |
|
|
|
|
|
|
|
self._workpiece = None |
|
|
|
self._found_best_edits = None |
|
|
|
self._found_uvs = None |
|
|
|
self._u_pixels = None |
|
|
|
self._v_pixels = None |
|
|
|
|
|
|
|
@property |
|
|
|
def layer_count(self): |
|
|
|
"""Number of layers.""" |
|
|
|
return len(self.win_sizes) |
|
|
|
|
|
|
|
def on_start(self, original_input, workpiece, mask, network): |
|
|
|
""" |
|
|
|
Notification of the start of the search job. |
|
|
|
|
|
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Args: |
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original_input (numpy.ndarray): The original image tensor in CHW or NCHW(N=1) format. |
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workpiece (numpy.ndarray): The intermediate image tensor in CHW or NCHW(N=1) format. |
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mask (Union[tuple[float, float, float], float, numpy.ndarray]): The mask, type can be |
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tuple[float, float, float]: RGB solid color mask, |
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float: Grey scale solid color mask. |
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numpy.ndarray: Image mask, has same format of original_input. |
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network (nn.Cell): Classification network. |
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""" |
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self.original_input = original_input |
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self.mask = mask |
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self.network = network |
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self._workpiece = workpiece |
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self._found_best_edits = [] |
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self._found_uvs = [] |
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self._u_pixels = self._calc_uv_pixels(self.search_field[0], self.search_field[2]) |
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self._v_pixels = self._calc_uv_pixels(self.search_field[1], self.search_field[3]) |
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def create_sub_job(self, parent_step, pre_edit_steps): |
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"""Create next layer search job.""" |
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return self.__class__(by_masking=self.by_masking, |
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class_idx=self.class_idx, |
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win_sizes=self.win_sizes, |
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strides=self.strides, |
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layer=self.layer + 1, |
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search_field=copy.copy(parent_step.box), |
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pre_edit_steps=pre_edit_steps, |
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parent_step=parent_step, |
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batch_size=self.batch_size) |
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def find_best_edit(self): |
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""" |
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Find the next best edit step. |
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Returns: |
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EditStep, the next best edit step. |
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""" |
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workpiece = self._workpiece |
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if len(workpiece.shape) == 3: |
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workpiece = np.expand_dims(workpiece, axis=0) |
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# generate input tensors with shifted masked/unmasked region and pack into a batch |
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squeeze = Squeeze() |
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best_new_workpiece = None |
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best_output = None |
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best_edit = None |
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best_uv = None |
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batch = np.repeat(workpiece, repeats=self.batch_size, axis=0) |
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batch_uvs = [] |
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batch_steps = [] |
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batch_i = 0 |
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win_size = self.win_sizes[self.layer] |
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for u, x in enumerate(self._u_pixels): |
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for v, y in enumerate(self._v_pixels): |
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if (u, v) in self._found_uvs: |
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continue |
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edit_step = EditStep(self.layer, (x, y, win_size, win_size)) |
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if self.by_masking: |
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EditStep.apply(batch[batch_i], |
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self.mask, |
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[edit_step], |
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self.by_masking, |
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inplace=True) |
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else: |
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EditStep.apply(self.original_input, |
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batch[batch_i], |
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[edit_step], |
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self.by_masking, |
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inplace=True) |
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batch_i += 1 |
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batch_uvs.append((u, v)) |
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batch_steps.append(edit_step) |
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if batch_i == self.batch_size: |
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# the batch is full, inference and empty it |
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batch_output = self.network(Tensor(batch)) |
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batch_output = batch_output[:, self.class_idx] |
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if len(batch_output.shape) > 1: |
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batch_output = squeeze(batch_output) |
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if self.by_masking: |
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batch_best_i = np.argmin(batch_output.asnumpy()) |
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else: |
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batch_best_i = np.argmax(batch_output.asnumpy()) |
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batch_best_output = batch_output[int(batch_best_i)].asnumpy().item() |
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if best_output is None or self._is_output0_better(batch_best_output, best_output): |
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best_output = batch_best_output |
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best_uv = batch_uvs[batch_best_i] |
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best_edit = batch_steps[batch_best_i] |
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best_new_workpiece = batch[batch_best_i] |
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batch = np.repeat(workpiece, repeats=self.batch_size, axis=0) |
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batch_uvs = [] |
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batch_i = 0 |
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if batch_i > 0: |
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# don't forget the last half full batch |
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batch_output = self.network(Tensor(batch)) |
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batch_output = batch_output[:, self.class_idx] |
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if len(batch_output.shape) > 1: |
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batch_output = squeeze(batch_output) |
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if self.by_masking: |
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batch_best_i = np.argmin(batch_output.asnumpy()[:batch_i, ...]) |
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else: |
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batch_best_i = np.argmax(batch_output.asnumpy()[:batch_i, ...]) |
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batch_best_output = batch_output[int(batch_best_i)].asnumpy().item() |
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if best_output is None or self._is_output0_better(batch_best_output, best_output): |
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best_output = batch_best_output |
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best_uv = batch_uvs[batch_best_i] |
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best_edit = batch_steps[batch_best_i] |
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best_new_workpiece = batch[batch_best_i] |
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if best_edit is None: |
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raise _NoNewStepError |
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if best_uv in self._found_uvs: |
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raise _RepeatedStepError |
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self._found_uvs.append(best_uv) |
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self._found_best_edits.append(best_edit) |
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best_edit.network_output = best_output |
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# continue on the best workpiece in the next function call |
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self._workpiece = best_new_workpiece |
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return best_edit |
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def _is_output0_better(self, output0, output1): |
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"""Check if the network output0 is better.""" |
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if self.by_masking: |
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return output0 < output1 |
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return output0 > output1 |
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def _calc_uv_pixels(self, begin, length): |
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""" |
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Calculate the pixel coordinate of shifts. |
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Args: |
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begin (int): The beginning pixel coordinate of search field. |
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length (int): The length of search field. |
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Returns: |
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list[int], pixel coordinate of shifts. |
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""" |
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win_size = self.win_sizes[self.layer] |
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stride = self.strides[self.layer] |
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shift_count = self._calc_shift_count(length, win_size, stride) |
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pixels = [0] * shift_count |
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for i in range(shift_count): |
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if i == shift_count - 1: |
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pixels[i] = begin + length - win_size |
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else: |
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pixels[i] = begin + i*stride |
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return pixels |
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@staticmethod |
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def _calc_shift_count(length, win_size, stride): |
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""" |
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Calculate the number of shifts in search field. |
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Args: |
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length (int): The length of search field. |
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win_size (int): The length of sides of moving window. |
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stride (int): The stride. |
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Returns: |
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int, number of shifts. |
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""" |
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if length <= win_size or win_size < stride or stride <= 0: |
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raise ValueError("Invalid length, win_size or stride.") |
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count = int(math.ceil((length - win_size)/stride)) |
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if (count - 1)*stride + win_size < length: |
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return count + 1 |
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return count |
mindspore-ci-bot
Gitee
5 years ago