EVOLUTION-MANAGER

Edit File: _misc.py

""" Copyright 2017-2018 Fizyr (https://fizyr.com) Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ import tensorflow from tensorflow import keras from .. import backend from ..utils import anchors as utils_anchors import numpy as np class Anchors(keras.layers.Layer): """ Keras layer for generating achors for a given shape. """ def __init__(self, size, stride, ratios=None, scales=None, *args, **kwargs): """ Initializer for an Anchors layer. Args size: The base size of the anchors to generate. stride: The stride of the anchors to generate. ratios: The ratios of the anchors to generate (defaults to AnchorParameters.default.ratios). scales: The scales of the anchors to generate (defaults to AnchorParameters.default.scales). """ self.size = size self.stride = stride self.ratios = ratios self.scales = scales if ratios is None: self.ratios = utils_anchors.AnchorParameters.default.ratios elif isinstance(ratios, list): self.ratios = np.array(ratios) if scales is None: self.scales = utils_anchors.AnchorParameters.default.scales elif isinstance(scales, list): self.scales = np.array(scales) self.num_anchors = len(self.ratios) * len(self.scales) self.anchors = utils_anchors.generate_anchors( base_size=self.size, ratios=self.ratios, scales=self.scales, ).astype(np.float32) super(Anchors, self).__init__(*args, **kwargs) def call(self, inputs, **kwargs): features = inputs features_shape = keras.backend.shape(features) # generate proposals from bbox deltas and shifted anchors if keras.backend.image_data_format() == 'channels_first': anchors = backend.shift(features_shape[2:4], self.stride, self.anchors) else: anchors = backend.shift(features_shape[1:3], self.stride, self.anchors) anchors = keras.backend.tile(keras.backend.expand_dims(anchors, axis=0), (features_shape[0], 1, 1)) return anchors def compute_output_shape(self, input_shape): if None not in input_shape[1:]: if keras.backend.image_data_format() == 'channels_first': total = np.prod(input_shape[2:4]) * self.num_anchors else: total = np.prod(input_shape[1:3]) * self.num_anchors return (input_shape[0], total, 4) else: return (input_shape[0], None, 4) def get_config(self): config = super(Anchors, self).get_config() config.update({ 'size' : self.size, 'stride' : self.stride, 'ratios' : self.ratios.tolist(), 'scales' : self.scales.tolist(), }) return config class UpsampleLike(keras.layers.Layer): """ Keras layer for upsampling a Tensor to be the same shape as another Tensor. """ def call(self, inputs, **kwargs): source, target = inputs target_shape = keras.backend.shape(target) if keras.backend.image_data_format() == 'channels_first': source = tensorflow.transpose(source, (0, 2, 3, 1)) output = backend.resize_images(source, (target_shape[2], target_shape[3]), method='nearest') output = tensorflow.transpose(output, (0, 3, 1, 2)) return output else: return backend.resize_images(source, (target_shape[1], target_shape[2]), method='nearest') def compute_output_shape(self, input_shape): if keras.backend.image_data_format() == 'channels_first': return (input_shape[0][0], input_shape[0][1]) + input_shape[1][2:4] else: return (input_shape[0][0],) + input_shape[1][1:3] + (input_shape[0][-1],) class RegressBoxes(keras.layers.Layer): """ Keras layer for applying regression values to boxes. """ def __init__(self, mean=None, std=None, *args, **kwargs): """ Initializer for the RegressBoxes layer. Args mean: The mean value of the regression values which was used for normalization. std: The standard value of the regression values which was used for normalization. """ if mean is None: mean = np.array([0, 0, 0, 0]) if std is None: std = np.array([0.2, 0.2, 0.2, 0.2]) if isinstance(mean, (list, tuple)): mean = np.array(mean) elif not isinstance(mean, np.ndarray): raise ValueError('Expected mean to be a np.ndarray, list or tuple. Received: {}'.format(type(mean))) if isinstance(std, (list, tuple)): std = np.array(std) elif not isinstance(std, np.ndarray): raise ValueError('Expected std to be a np.ndarray, list or tuple. Received: {}'.format(type(std))) self.mean = mean self.std = std super(RegressBoxes, self).__init__(*args, **kwargs) def call(self, inputs, **kwargs): anchors, regression = inputs return backend.bbox_transform_inv(anchors, regression, mean=self.mean, std=self.std) def compute_output_shape(self, input_shape): return input_shape[0] def get_config(self): config = super(RegressBoxes, self).get_config() config.update({ 'mean': self.mean.tolist(), 'std' : self.std.tolist(), }) return config class ClipBoxes(keras.layers.Layer): """ Keras layer to clip box values to lie inside a given shape. """ def call(self, inputs, **kwargs): image, boxes = inputs shape = keras.backend.cast(keras.backend.shape(image), keras.backend.floatx()) if keras.backend.image_data_format() == 'channels_first': _, _, height, width = tensorflow.unstack(shape, axis=0) else: _, height, width, _ = tensorflow.unstack(shape, axis=0) x1, y1, x2, y2 = tensorflow.unstack(boxes, axis=-1) x1 = tensorflow.clip_by_value(x1, 0, width - 1) y1 = tensorflow.clip_by_value(y1, 0, height - 1) x2 = tensorflow.clip_by_value(x2, 0, width - 1) y2 = tensorflow.clip_by_value(y2, 0, height - 1) return keras.backend.stack([x1, y1, x2, y2], axis=2) def compute_output_shape(self, input_shape): return input_shape[1]