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How to use pred method in hypothesis
Best Python code snippet using hypothesis
iou_loss.py
Source:iou_loss.py 
1import tensorflow as tf2import tensorflow.keras as keras3import math4def compute_iou(mode='fciou'):5 @tf.function(jit_compile=True)6 def iou_(y_true, y_pred):7 y_true = tf.maximum(y_true, 0)8 pred_left = y_pred[:, :, 0]9 pred_top = y_pred[:, :, 1]10 pred_right = y_pred[:, :, 2]11 pred_bottom = y_pred[:, :, 3]12 pred_width = pred_left + pred_right13 pred_height = pred_top + pred_bottom14 # (num_pos, )15 target_left = y_true[:, :, 0]16 target_top = y_true[:, :, 1]17 target_right = y_true[:, :, 2]18 target_bottom = y_true[:, :, 3]19 target_width = target_left + target_right20 target_height = target_top + target_bottom21 target_area = (target_left + target_right) * (target_top + target_bottom)22 pred_area = (pred_left + pred_right) * (pred_top + pred_bottom)23 w_intersect = tf.minimum(pred_left, target_left) + tf.minimum(pred_right, target_right)24 h_intersect = tf.minimum(pred_bottom, target_bottom) + tf.minimum(pred_top, target_top)25 area_intersect = w_intersect * h_intersect26 area_union = target_area + pred_area - area_intersect27 classic_iou = (area_intersect + 1e-7) / (area_union + 1e-7)28 if mode == 'iou':29 # (num_pos, )30 iou_loss = -tf.math.log(classic_iou)31 elif mode == 'giou':32 g_w_intersect = tf.maximum(pred_left, target_left) + tf.maximum(pred_right, target_right)33 g_h_intersect = tf.maximum(pred_bottom, target_bottom) + tf.maximum(pred_top, target_top)34 close_area = g_w_intersect * g_h_intersect35 g_iou = classic_iou - (close_area - area_union) / close_area36 iou_loss = 1 - g_iou37 elif mode == 'ciou':38 pred_atan = tf.atan(pred_width / (pred_height + 1e-9))39 target_atan = tf.atan(target_width / (target_height + 1e-9))40 v = 4.0 * keras.backend.pow((pred_atan - target_atan), 2) / (math.pi ** 2)41 a = v / (1 - classic_iou + v)42 c_iou = classic_iou - 1.0 * a * v43 iou_loss = 1 - c_iou44 elif mode == 'fciou':45 target_center_x = target_width * 0.546 target_center_y = target_height * 0.547 pred_center_x = target_left - pred_left + pred_width * 0.548 pred_center_y = target_top - pred_top + pred_height * 0.549 intersect_diagonal = (target_center_x - pred_center_x) ** 2 + (target_center_y - pred_center_y) ** 250 g_w_intersect = tf.maximum(pred_left, target_left) + tf.maximum(pred_right, target_right)51 g_h_intersect = tf.maximum(pred_bottom, target_bottom) + tf.maximum(pred_top, target_top)52 max_diagonal = g_h_intersect ** 2 + g_w_intersect ** 253 pred_atan = tf.atan(pred_width / (pred_height + 1e-9))54 target_atan = tf.atan(target_width / (target_height + 1e-9))55 v = 4.0 * keras.backend.pow((pred_atan - target_atan), 2) / (math.pi ** 2)56 a = v / (1 - classic_iou + v)57 c_iou = classic_iou - 1.0 * a * v - 1.0 * (intersect_diagonal / max_diagonal)58 iou_loss = 1 - c_iou59 else:60 iou_loss = -tf.math.log(classic_iou)61 # compute the normalizer: the number of positive anchors62 normalizer = tf.maximum(1, tf.reduce_prod(tf.shape(y_true)[0:2]))63 normalizer = tf.cast(normalizer, dtype=tf.float32)64 return tf.reduce_sum(iou_loss) / normalizer65 return iou_66def compute_iou_v2(mode='fciou'):67 @tf.function(jit_compile=True)68 def iou_(y_true, y_pred):69 y_true = tf.maximum(y_true, 0)70 pred_left = y_pred[:, :, 0]71 pred_top = y_pred[:, :, 1]72 pred_right = y_pred[:, :, 2]73 pred_bottom = y_pred[:, :, 3]74 pred_width = pred_left + pred_right75 pred_height = pred_top + pred_bottom76 # (num_pos, )77 target_left = y_true[:, :, 0]78 target_top = y_true[:, :, 1]79 target_right = y_true[:, :, 2]80 target_bottom = y_true[:, :, 3]81 target_width = target_left + target_right82 target_height = target_top + target_bottom83 target_area = (target_left + target_right) * (target_top + target_bottom)84 pred_area = (pred_left + pred_right) * (pred_top + pred_bottom)85 w_intersect = tf.minimum(pred_left, target_left) + tf.minimum(pred_right, target_right)86 h_intersect = tf.minimum(pred_bottom, target_bottom) + tf.minimum(pred_top, target_top)87 area_intersect = w_intersect * h_intersect88 area_union = target_area + pred_area - area_intersect89 classic_iou = (area_intersect + 1e-7) / (area_union + 1e-7)90 if mode == 'iou':91 # (num_pos, )92 iou_loss = -tf.math.log(classic_iou)93 elif mode == 'giou':94 g_w_intersect = tf.maximum(pred_left, target_left) + tf.maximum(pred_right, target_right)95 g_h_intersect = tf.maximum(pred_bottom, target_bottom) + tf.maximum(pred_top, target_top)96 close_area = g_w_intersect * g_h_intersect97 g_iou = classic_iou - (close_area - area_union) / close_area98 iou_loss = 1 - g_iou99 elif mode == 'ciou':100 pred_atan = tf.atan(pred_width / (pred_height + 1e-9))101 target_atan = tf.atan(target_width / (target_height + 1e-9))102 v = 4.0 * keras.backend.pow((pred_atan - target_atan), 2) / (math.pi ** 2)103 a = v / (1 - classic_iou + v)104 c_iou = classic_iou - 1.0 * a * v105 iou_loss = 1 - c_iou106 elif mode == 'fciou':107 target_center_x = target_width * 0.5108 target_center_y = target_height * 0.5109 pred_center_x = target_left - pred_left + pred_width * 0.5110 pred_center_y = target_top - pred_top + pred_height * 0.5111 intersect_diagonal = (target_center_x - pred_center_x) ** 2 + (target_center_y - pred_center_y) ** 2112 g_w_intersect = tf.maximum(pred_left, target_left) + tf.maximum(pred_right, target_right)113 g_h_intersect = tf.maximum(pred_bottom, target_bottom) + tf.maximum(pred_top, target_top)114 max_diagonal = g_h_intersect ** 2 + g_w_intersect ** 2115 pred_atan = tf.atan(pred_width / (pred_height + 1e-9))116 target_atan = tf.atan(target_width / (target_height + 1e-9))117 v = 4.0 * keras.backend.pow((pred_atan - target_atan), 2) / (math.pi ** 2)118 a = v / (1 - classic_iou + v)119 c_iou = classic_iou - 1.0 * a * v - 1.0 * (intersect_diagonal / max_diagonal)120 iou_loss = 1 - c_iou121 else:122 iou_loss = -tf.math.log(classic_iou)123 return iou_loss124 return iou_125def iou_mask(mode='fciou', factor=1.0):126 def iou_mask_(inputs):127 # y_ture: (Batch, Anchor-points, 4 + 2)128 y_true, y_pred, soft_weight, mask = inputs[0][..., :4], inputs[1], inputs[0][..., 4], inputs[0][..., 5]129 y_true = tf.maximum(y_true, 0)130 pred_left = y_pred[:, :, 0]131 pred_top = y_pred[:, :, 1]132 pred_right = y_pred[:, :, 2]133 pred_bottom = y_pred[:, :, 3]134 pred_width = pred_left + pred_right135 pred_height = pred_top + pred_bottom136 target_left = y_true[:, :, 0]137 target_top = y_true[:, :, 1]138 target_right = y_true[:, :, 2]139 target_bottom = y_true[:, :, 3]140 target_width = target_left + target_right141 target_height = target_top + target_bottom142 target_area = (target_left + target_right) * (target_top + target_bottom)143 masked_target_area = tf.boolean_mask(target_area, mask)144 pred_area = (pred_left + pred_right) * (pred_top + pred_bottom)145 masked_pred_area = tf.boolean_mask(pred_area, mask)146 w_intersect = tf.minimum(pred_left, target_left) + tf.minimum(pred_right, target_right)147 h_intersect = tf.minimum(pred_bottom, target_bottom) + tf.minimum(pred_top, target_top)148 g_w_intersect = tf.maximum(pred_left, target_left) + tf.maximum(pred_right, target_right)149 g_h_intersect = tf.maximum(pred_bottom, target_bottom) + tf.maximum(pred_top, target_top)150 close_area = g_w_intersect * g_h_intersect151 masked_close_area = tf.boolean_mask(close_area, mask)152 area_intersect = w_intersect * h_intersect153 masked_area_intersect = tf.boolean_mask(area_intersect, mask)154 masked_area_union = masked_target_area + masked_pred_area - masked_area_intersect155 masked_soft_weight = tf.boolean_mask(soft_weight, mask)156 # (B, N)157 masked_iou = (masked_area_intersect + 1e-7) / (masked_area_union + 1e-7)158 if mode == 'iou':159 masked_iou_loss = -tf.math.log(masked_iou) * masked_soft_weight160 elif mode == 'giou':161 masked_g_iou = masked_iou - (masked_close_area - masked_area_union) / masked_close_area162 masked_iou_loss = (1 - masked_g_iou) * masked_soft_weight163 elif mode == 'ciou':164 pred_atan = tf.atan(pred_width / (pred_height + 1e-9))165 target_atan = tf.atan(target_width / (target_height + 1e-9))166 masked_pred_atan = tf.boolean_mask(pred_atan, mask)167 masked_target_atan = tf.boolean_mask(target_atan, mask)168 v = 4.0 * keras.backend.pow((masked_pred_atan - masked_target_atan), 2) / (math.pi ** 2)169 a = v / (1 - masked_iou + v)170 c_iou = masked_iou - 1.0 * a * v171 masked_iou_loss = (1 - c_iou) * masked_soft_weight172 elif mode == 'fciou':173 target_center_x = target_width * 0.5174 target_center_y = target_height * 0.5175 pred_center_x = target_left - pred_left + pred_width * 0.5176 pred_center_y = target_top - pred_top + pred_height * 0.5177 intersect_diagonal = (target_center_x - pred_center_x) ** 2 + (target_center_y - pred_center_y) ** 2178 max_diagonal = g_h_intersect ** 2 + g_w_intersect ** 2179 r_masked = tf.boolean_mask((intersect_diagonal / max_diagonal), mask)180 pred_atan = tf.atan(pred_width / (pred_height + 1e-9))181 target_atan = tf.atan(target_width / (target_height + 1e-9))182 masked_pred_atan = tf.boolean_mask(pred_atan, mask)183 masked_target_atan = tf.boolean_mask(target_atan, mask)184 v = 4.0 * keras.backend.pow((masked_pred_atan - masked_target_atan), 2) / (math.pi ** 2)185 a = v / (1 - masked_iou + v)186 c_iou = masked_iou - 1.0 * a * v - 1.0 * r_masked187 masked_iou_loss = (1 - c_iou) * masked_soft_weight188 # masked_iou_loss = (1 - c_iou)189 else:190 masked_iou_loss = -tf.math.log(masked_iou) * masked_soft_weight191 # compute the normalizer: the number of positive location192 num_pos = tf.reduce_sum(mask * soft_weight)193 normalizer = keras.backend.maximum(1., num_pos)194 return tf.reduce_sum(masked_iou_loss) * factor / normalizer195 return iou_mask_196def iou_mask_v2(mode='fciou', factor=1.0):197 @tf.function(jit_compile=True)198 def iou_mask_(inputs):199 y_true, y_pred, soft_weight, mask = inputs[0][..., :4], inputs[1], inputs[0][..., 4], inputs[0][..., 5]200 y_true = tf.maximum(y_true, 0)201 iou_loss = iou_method(soft_weight, y_pred, y_true)202 # mask203 # masked_iou_loss = tf.boolean_mask(iou_loss, mask)204 # compute the normalizer: the number of positive location205 # num_pos = tf.reduce_sum(mask * soft_weight)206 normalizer = keras.backend.maximum(1., tf.reduce_sum(mask * soft_weight))207 # return factor * tf.reduce_sum(masked_iou_loss) / normalizer208 return factor * tf.reduce_sum(mask * iou_loss) / normalizer209 @tf.function(jit_compile=True) # only for tensorflow 2.6, sometimes should use experiment_compile210 def iou_method(soft_weight, y_pred, y_true):211 # pred212 pred_left = y_pred[:, :, 0]213 pred_top = y_pred[:, :, 1]214 pred_right = y_pred[:, :, 2]215 pred_bottom = y_pred[:, :, 3]216 pred_width = pred_left + pred_right217 pred_height = pred_top + pred_bottom218 pred_area = (pred_left + pred_right) * (pred_top + pred_bottom)219 # target220 target_left = y_true[:, :, 0]221 target_top = y_true[:, :, 1]222 target_right = y_true[:, :, 2]223 target_bottom = y_true[:, :, 3]224 target_width = target_left + target_right225 target_height = target_top + target_bottom226 target_area = (target_left + target_right) * (target_top + target_bottom)227 # intersection between target and predict bboxes.228 w_intersect = tf.minimum(pred_left, target_left) + tf.minimum(pred_right, target_right)229 h_intersect = tf.minimum(pred_bottom, target_bottom) + tf.minimum(pred_top, target_top)230 area_intersect = w_intersect * h_intersect231 area_union = target_area + pred_area - area_intersect232 iou = (area_intersect + 1e-7) / (area_union + 1e-7)233 if mode == 'iou':234 iou_loss = -tf.math.log(iou) * soft_weight235 elif mode == 'giou':236 # max close area237 g_w_intersect = tf.maximum(pred_left, target_left) + tf.maximum(pred_right, target_right)238 g_h_intersect = tf.maximum(pred_bottom, target_bottom) + tf.maximum(pred_top, target_top)239 close_area = g_w_intersect * g_h_intersect240 g_iou = iou - (close_area - area_union) / close_area241 iou_loss = (1 - g_iou) * soft_weight242 elif mode == 'ciou':243 # without center fixing244 pred_atan = tf.atan(pred_width / (pred_height + 1e-9))245 target_atan = tf.atan(target_width / (target_height + 1e-9))246 v = 4.0 * keras.backend.pow((pred_atan - target_atan), 2) / (math.pi ** 2)247 a = v / (1 - iou + v)248 c_iou = iou - 1.0 * a * v249 iou_loss = (1 - c_iou) * soft_weight250 elif mode == 'fciou':251 # full version of CIoU252 g_w_intersect = tf.maximum(pred_left, target_left) + tf.maximum(pred_right, target_right)253 g_h_intersect = tf.maximum(pred_bottom, target_bottom) + tf.maximum(pred_top, target_top)254 max_diagonal = g_h_intersect ** 2 + g_w_intersect ** 2255 target_center_x = target_width * 0.5256 target_center_y = target_height * 0.5257 pred_center_x = target_left - pred_left + pred_width * 0.5258 pred_center_y = target_top - pred_top + pred_height * 0.5259 intersect_diagonal = (target_center_x - pred_center_x) ** 2 + (target_center_y - pred_center_y) ** 2260 pred_atan = tf.atan(pred_width / (pred_height + 1e-9))261 target_atan = tf.atan(target_width / (target_height + 1e-9))262 v = 4.0 * keras.backend.pow((pred_atan - target_atan), 2) / (math.pi ** 2)263 a = v / (1 - iou + v)264 c_iou = iou - 1.0 * a * v - 1.0 * (intersect_diagonal / max_diagonal)265 iou_loss = (1 - c_iou) * soft_weight266 else:267 iou_loss = -tf.math.log(iou) * soft_weight268 return iou_loss269 return iou_mask_270def iou_mask_v3(mode='fciou', factor=1.0):271 def iou_mask_(inputs):272 y_true, y_pred, soft_weight, mask = inputs[0][..., :4], inputs[1], inputs[0][..., 4], inputs[0][..., 5]273 y_true = tf.maximum(y_true, 0)274 fmn_out = inputs[2]275 soft_weight *= fmn_out[..., 0]276 iou_loss = iou_method(soft_weight, y_pred, y_true)277 # mask278 masked_iou_loss = tf.boolean_mask(iou_loss, mask)279 # compute the normalizer: the number of positive location280 normalizer = keras.backend.maximum(1., tf.reduce_sum(mask * soft_weight))281 return factor * tf.reduce_sum(masked_iou_loss) / normalizer282 @tf.function(jit_compile=True) # only for tensorflow 2.6, sometimes should use experiment_compile283 def iou_method(soft_weight, y_pred, y_true):284 # pred285 pred_left = y_pred[:, :, 0]286 pred_top = y_pred[:, :, 1]287 pred_right = y_pred[:, :, 2]288 pred_bottom = y_pred[:, :, 3]289 pred_width = pred_left + pred_right290 pred_height = pred_top + pred_bottom291 pred_area = (pred_left + pred_right) * (pred_top + pred_bottom)292 # target293 target_left = y_true[:, :, 0]294 target_top = y_true[:, :, 1]295 target_right = y_true[:, :, 2]296 target_bottom = y_true[:, :, 3]297 target_width = target_left + target_right298 target_height = target_top + target_bottom299 target_area = (target_left + target_right) * (target_top + target_bottom)300 # intersection between target and predict bboxes.301 w_intersect = tf.minimum(pred_left, target_left) + tf.minimum(pred_right, target_right)302 h_intersect = tf.minimum(pred_bottom, target_bottom) + tf.minimum(pred_top, target_top)303 area_intersect = w_intersect * h_intersect304 area_union = target_area + pred_area - area_intersect305 iou = (area_intersect + 1e-7) / (area_union + 1e-7)306 if mode == 'iou':307 iou_loss = -tf.math.log(iou) * soft_weight308 elif mode == 'giou':309 # max close area310 g_w_intersect = tf.maximum(pred_left, target_left) + tf.maximum(pred_right, target_right)311 g_h_intersect = tf.maximum(pred_bottom, target_bottom) + tf.maximum(pred_top, target_top)312 close_area = g_w_intersect * g_h_intersect313 g_iou = iou - (close_area - area_union) / close_area314 iou_loss = (1 - g_iou) * soft_weight315 elif mode == 'ciou':316 # without center fixing317 pred_atan = tf.atan(pred_width / (pred_height + 1e-9))318 target_atan = tf.atan(target_width / (target_height + 1e-9))319 v = 4.0 * keras.backend.pow((pred_atan - target_atan), 2) / (math.pi ** 2)320 a = v / (1 - iou + v)321 c_iou = iou - 1.0 * a * v322 iou_loss = (1 - c_iou) * soft_weight323 elif mode == 'fciou':324 # full version of CIoU325 g_w_intersect = tf.maximum(pred_left, target_left) + tf.maximum(pred_right, target_right)326 g_h_intersect = tf.maximum(pred_bottom, target_bottom) + tf.maximum(pred_top, target_top)327 max_diagonal = g_h_intersect ** 2 + g_w_intersect ** 2328 target_center_x = target_width * 0.5329 target_center_y = target_height * 0.5330 pred_center_x = target_left - pred_left + pred_width * 0.5331 pred_center_y = target_top - pred_top + pred_height * 0.5332 intersect_diagonal = (target_center_x - pred_center_x) ** 2 + (target_center_y - pred_center_y) ** 2333 pred_atan = tf.atan(pred_width / (pred_height + 1e-9))334 target_atan = tf.atan(target_width / (target_height + 1e-9))335 v = 4.0 * keras.backend.pow((pred_atan - target_atan), 2) / (math.pi ** 2)336 a = v / (1 - iou + v)337 c_iou = iou - 1.0 * a * v - 1.0 * (intersect_diagonal / max_diagonal)338 iou_loss = (1 - c_iou) * soft_weight339 else:340 iou_loss = -tf.math.log(iou) * soft_weight341 return iou_loss342 return iou_mask_343class IoULoss(keras.layers.Layer):344 def __init__(self, mode='fciou', factor=1.0, name='loc_loss', test=False, **kwargs):345 super(IoULoss, self).__init__(dtype='float32', name=name, **kwargs)346 self.mode = mode347 self.factor = factor348 if not test:349 self.fnc = iou_mask_v2(mode, factor)350 else:351 self.fnc = iou_mask_v3(mode, factor)352 def call(self, inputs, **kwargs):353 loss = self.fnc(inputs)354 self.add_loss(loss)355 self.add_metric(loss, name=self.name)356 return loss357 def get_config(self):358 cfg = super(IoULoss, self).get_config()359 cfg.update(360 {'mode': self.mode, 'factor': self.factor}361 )362 return cfg363if __name__ == '__main__':...
metric.py
Source:metric.py
1# ---------------------------------------------------------------2# SNIPER: Efficient Multi-scale Training3# Licensed under The Apache-2.0 License [see LICENSE for details]4# Modified from https://github.com/msracver/Deformable-ConvNets5# Modified by Mahyar Najibi6# ---------------------------------------------------------------7import mxnet as mx8import numpy as np9import os10import pickle11def get_rpn_names():12 pred = ['rpn_cls_prob', 'rpn_bbox_loss']13 label = ['rpn_label', 'rpn_bbox_target', 'rpn_bbox_weight']14 return pred, label15def get_mask_names():16 pred = ['mask_prob', 'mask_targets']17 label = []18 return pred, label19def get_rcnn_names(cfg):20 pred = ['rcnn_cls_prob', 'rcnn_bbox_loss']21 label = ['rcnn_label', 'rcnn_bbox_target', 'rcnn_bbox_weight']22 if cfg.TRAIN.ENABLE_OHEM or cfg.TRAIN.END2END:23 pred.append('rcnn_label')24 if cfg.TRAIN.END2END:25 rpn_pred, rpn_label = get_rpn_names()26 pred = rpn_pred + pred27 label = rpn_label28 if cfg.TRAIN.WITH_MASK:29 mask_pred, mask_label = get_mask_names()30 pred += mask_pred31 label += mask_label32 return pred, label33def get_rcnn_names_4vis(cfg):34 pred,label = get_rcnn_names(cfg)35 pred += ['rcnn_bbox_pred', 'rois', 'rcnn_label']36 return pred, label37class RPNAccMetric(mx.metric.EvalMetric):38 def __init__(self):39 super(RPNAccMetric, self).__init__('RPNAcc')40 self.pred, self.label = get_rpn_names()41 def update(self, labels, preds):42 pred = preds[self.pred.index('rpn_cls_prob')]43 label = labels[self.label.index('rpn_label')]44 # pred (b, c, p) or (b, c, h, w)45 pred_label = mx.ndarray.argmax_channel(pred).asnumpy().astype('int32')46 pred_label = pred_label.reshape((pred_label.shape[0], -1))47 # label (b, p)48 label = label.asnumpy().astype('int32')49 # filter with keep_inds50 keep_inds = np.where(label != -1)51 pred_label = pred_label[keep_inds]52 label = label[keep_inds]53 self.sum_metric += np.sum(pred_label.flat == label.flat)54 self.num_inst += len(pred_label.flat)55class RCNNAccMetric(mx.metric.EvalMetric):56 def __init__(self, cfg):57 super(RCNNAccMetric, self).__init__('RCNNAcc')58 self.e2e = cfg.TRAIN.END2END59 self.ohem = cfg.TRAIN.ENABLE_OHEM60 self.pred, self.label = get_rcnn_names(cfg)61 def update(self, labels, preds):62 pred = preds[self.pred.index('rcnn_cls_prob')]63 if self.ohem or self.e2e:64 label = preds[self.pred.index('rcnn_label')]65 else:66 label = labels[self.label.index('rcnn_label')]67 last_dim = pred.shape[-1]68 pred_label = pred.asnumpy().reshape(-1, last_dim).argmax(axis=1).astype('int32')69 label = label.asnumpy().reshape(-1,).astype('int32')70 # filter with keep_inds71 keep_inds = np.where(label != -1)72 pred_label = pred_label[keep_inds]73 label = label[keep_inds]74 self.sum_metric += np.sum(pred_label.flat == label.flat)75 self.num_inst += len(pred_label.flat)76class RCNNAccFgMetric(mx.metric.EvalMetric):77 def __init__(self, cfg):78 super(RCNNAccFgMetric, self).__init__('RCNNFgAcc')79 self.e2e = cfg.TRAIN.END2END80 self.ohem = cfg.TRAIN.ENABLE_OHEM81 self.pred, self.label = get_rcnn_names(cfg)82 def update(self, labels, preds):83 pred = preds[3]84 label = preds[4]85 last_dim = pred.shape[-1]86 pred_label = pred.asnumpy().reshape(-1, last_dim).argmax(axis=1).astype('int32')87 label = label.asnumpy().reshape(-1,).astype('int32')88 # filter with keep_inds89 keep_inds = np.where(label != -1)90 pred_label = pred_label[keep_inds]91 label = label[keep_inds]92 self.sum_metric += np.sum(pred_label.flat == label.flat)93 self.num_inst += len(pred_label.flat)94class MaskLogLossMetric(mx.metric.EvalMetric):95 def __init__(self, config):96 super(MaskLogLossMetric, self).__init__('MaskLogLoss')97 self.pred, self.label = get_rcnn_names(config)98 def update(self, labels, preds):99 pred = preds[self.pred.index('mask_prob')]100 label = preds[self.pred.index('mask_targets')]101 # label (b, p)102 label = label.asnumpy().astype('int32').reshape((-1))103 # pred (b, c, p) or (b, c, h, w) --> (b, p, c) --> (b*p, c)104 pred = pred.asnumpy().reshape((pred.shape[0], pred.shape[1], -1)).transpose((0, 2, 1))105 pred = pred.reshape((label.shape[0], -1))106 # filter with keep_inds107 keep_inds = np.where(label != -1)[0]108 label = label[keep_inds]109 cls = pred[keep_inds, label]110 cls += 1e-14111 cls_loss = -1 * np.log(cls)112 cls_loss = np.sum(cls_loss)113 self.sum_metric += cls_loss114 self.num_inst += label.shape[0]115class RPNLogLossMetric(mx.metric.EvalMetric):116 def __init__(self):117 super(RPNLogLossMetric, self).__init__('RPNLogLoss')118 self.pred, self.label = get_rpn_names()119 def update(self, labels, preds):120 pred = preds[self.pred.index('rpn_cls_prob')]121 label = labels[self.label.index('rpn_label')]122 # label (b, p)123 label = label.asnumpy().astype('int32').reshape((-1))124 # pred (b, c, p) or (b, c, h, w) --> (b, p, c) --> (b*p, c)125 pred = pred.asnumpy().reshape((pred.shape[0], pred.shape[1], -1)).transpose((0, 2, 1))126 pred = pred.reshape((label.shape[0], -1))127 # filter with keep_inds128 keep_inds = np.where(label != -1)[0]129 label = label[keep_inds]130 cls = pred[keep_inds, label]131 cls += 1e-14132 cls_loss = -1 * np.log(cls)133 cls_loss = np.sum(cls_loss)134 self.sum_metric += cls_loss135 self.num_inst += label.shape[0]136class RCNNLogLossMetric(mx.metric.EvalMetric):137 def __init__(self, cfg):138 super(RCNNLogLossMetric, self).__init__('RCNNLogLoss')139 self.e2e = cfg.TRAIN.END2END140 self.ohem = cfg.TRAIN.ENABLE_OHEM141 self.pred, self.label = get_rcnn_names(cfg)142 def update(self, labels, preds):143 pred = preds[self.pred.index('rcnn_cls_prob')]144 if self.ohem or self.e2e:145 label = preds[self.pred.index('rcnn_label')]146 else:147 label = labels[self.label.index('rcnn_label')]148 last_dim = pred.shape[-1]149 pred = pred.asnumpy().reshape(-1, last_dim)150 label = label.asnumpy().reshape(-1,).astype('int32')151 # filter with keep_inds152 keep_inds = np.where(label != -1)[0]153 label = label[keep_inds]154 cls = pred[keep_inds, label]155 cls += 1e-14156 cls_loss = -1 * np.log(cls)157 cls_loss = np.sum(cls_loss)158 self.sum_metric += cls_loss159 self.num_inst += label.shape[0]160class RCNNFgLogLossMetric(mx.metric.EvalMetric):161 def __init__(self, cfg):162 super(RCNNFgLogLossMetric, self).__init__('RCNNFgLogLoss')163 self.e2e = cfg.TRAIN.END2END164 self.ohem = cfg.TRAIN.ENABLE_OHEM165 self.pred, self.label = get_rcnn_names(cfg)166 def update(self, labels, preds):167 pred = preds[3]168 label = preds[4]169 last_dim = pred.shape[-1]170 pred = pred.asnumpy().reshape(-1, last_dim)171 label = label.asnumpy().reshape(-1,).astype('int32')172 # filter with keep_inds173 keep_inds = np.where(label != -1)[0]174 label = label[keep_inds]175 cls = pred[keep_inds, label]176 cls += 1e-14177 cls_loss = -1 * np.log(cls)178 cls_loss = np.sum(cls_loss)179 self.sum_metric += cls_loss180 self.num_inst += label.shape[0]181class RPNL1LossMetric(mx.metric.EvalMetric):182 def __init__(self):183 super(RPNL1LossMetric, self).__init__('RPNL1Loss')184 self.pred, self.label = get_rpn_names()185 def update(self, labels, preds):186 bbox_loss = preds[self.pred.index('rpn_bbox_loss')].asnumpy()187 # calculate num_inst (average on those kept anchors)188 label = labels[self.label.index('rpn_label')].asnumpy()189 num_inst = np.sum(label != -1)190 self.sum_metric += np.sum(bbox_loss)191 self.num_inst += num_inst192class RCNNL1LossMetric(mx.metric.EvalMetric):193 def __init__(self, cfg):194 super(RCNNL1LossMetric, self).__init__('RCNNL1Loss')195 self.e2e = cfg.TRAIN.END2END196 self.ohem = cfg.TRAIN.ENABLE_OHEM197 self.pred, self.label = get_rcnn_names(cfg)198 def update(self, labels, preds):199 bbox_loss = preds[self.pred.index('rcnn_bbox_loss')].asnumpy()200 if self.ohem:201 label = preds[self.pred.index('rcnn_label')].asnumpy()202 else:203 if self.e2e:204 label = preds[self.pred.index('rcnn_label')].asnumpy()205 else:206 label = labels[self.label.index('rcnn_label')].asnumpy()207 # calculate num_inst (average on those kept anchors)208 num_inst = np.sum(label != -1)209 self.sum_metric += np.sum(bbox_loss)210 self.num_inst += num_inst211class RCNNL1LossCRCNNMetric(mx.metric.EvalMetric):212 def __init__(self, cfg):213 super(RCNNL1LossCRCNNMetric, self).__init__('RCNNL1LossCRCNN')214 self.e2e = cfg.TRAIN.END2END215 self.ohem = cfg.TRAIN.ENABLE_OHEM216 self.pred, self.label = get_rcnn_names(cfg)217 self.cfg = cfg218 def update(self, labels, preds):219 bbox_loss = preds[self.pred.index('rcnn_bbox_loss')].asnumpy()220 if self.ohem:221 label = preds[self.pred.index('rcnn_label')].asnumpy()222 else:223 if self.e2e:224 label = preds[self.pred.index('rcnn_label')].asnumpy()225 else:226 label = labels[self.label.index('rcnn_label')].asnumpy()227 num_inst = np.sum(label != -1)228 self.sum_metric += np.sum(bbox_loss)229 self.num_inst += num_inst230class VisMetric(mx.metric.EvalMetric):231 def __init__(self, cfg):232 super(VisMetric, self).__init__('Vis')233 self.freq = cfg.TRAIN.visualization_freq234 self.root_path = cfg.TRAIN.visualization_path235 self.pred, self.label = get_rcnn_names_4vis(cfg)236 self.nGPU = len(cfg.gpus.split(','))237 self.file_name = os.path.basename(os.path.normpath(cfg.output_path))238 def update(self, labels, preds):239 if self.num_inst % self.freq == 0 and self.num_inst%self.nGPU==0:240 pred = preds[self.pred.index('rcnn_cls_prob')].asnumpy()241 bbox_pred = preds[self.pred.index('rcnn_bbox_pred')].asnumpy()242 labels = preds[self.pred.index('rcnn_label')].asnumpy()243 rois = preds[self.pred.index('rois')].asnumpy()244 inds = np.where(rois[:, 0] == 0)[0]245 path = os.path.join(self.root_path, 'dump_preds_{}.pkl'.format(self.file_name))246 with open(path,'wb') as f:247 pickle.dump((pred[0], bbox_pred[inds], rois[inds], labels[inds]),f)248 249 self.num_inst += 1250 self.sum_metric = 0...
metrics.py
Source:metrics.py
1"""2å®ä¹åç±»æ§è½ææ 3"""4from sklearn.metrics import roc_auc_score5def mean(item: list) -> float:6 """7 计ç®å表ä¸å
ç´ çå¹³åå¼8 :param item: å表对象9 :return:10 """11 res = sum(item) / len(item) if len(item) > 0 else 012 return res13def accuracy(pred_y, true_y):14 """15 计ç®äºç±»åå¤ç±»çåç¡®ç16 :param pred_y: é¢æµç»æ17 :param true_y: çå®ç»æ18 :return:19 """20 if isinstance(pred_y[0], list):21 pred_y = [item[0] for item in pred_y]22 corr = 023 for i in range(len(pred_y)):24 if pred_y[i] == true_y[i]:25 corr += 126 acc = corr / len(pred_y) if len(pred_y) > 0 else 027 return acc28def binary_auc(pred_y, true_y):29 """30 äºç±»å«çaucå¼31 :param pred_y: é¢æµç»æ32 :param true_y: çå®ç»æ33 :return:34 """35 auc = roc_auc_score(true_y, pred_y)36 return auc37def binary_precision(pred_y, true_y, positive=1):38 """39 äºç±»ç精确ç计ç®40 :param pred_y: é¢æµç»æ41 :param true_y: çå®ç»æ42 :param positive: æ£ä¾çç´¢å¼è¡¨ç¤º43 :return:44 """45 corr = 046 pred_corr = 047 for i in range(len(pred_y)):48 if pred_y[i] == positive:49 pred_corr += 150 if pred_y[i] == true_y[i]:51 corr += 152 prec = corr / pred_corr if pred_corr > 0 else 053 return prec54def binary_recall(pred_y, true_y, positive=1):55 """56 äºç±»çå¬åç57 :param pred_y: é¢æµç»æ58 :param true_y: çå®ç»æ59 :param positive: æ£ä¾çç´¢å¼è¡¨ç¤º60 :return:61 """62 corr = 063 true_corr = 064 for i in range(len(pred_y)):65 if true_y[i] == positive:66 true_corr += 167 if pred_y[i] == true_y[i]:68 corr += 169 rec = corr / true_corr if true_corr > 0 else 070 return rec71def binary_f_beta(pred_y, true_y, beta=1.0, positive=1):72 """73 äºç±»çf betaå¼74 :param pred_y: é¢æµç»æ75 :param true_y: çå®ç»æ76 :param beta: betaå¼77 :param positive: æ£ä¾çç´¢å¼è¡¨ç¤º78 :return:79 """80 precision = binary_precision(pred_y, true_y, positive)81 recall = binary_recall(pred_y, true_y, positive)82 try:83 f_b = (1 + beta * beta) * precision * recall / (beta * beta * precision + recall)84 except:85 f_b = 086 return f_b87def multi_precision(pred_y, true_y, labels):88 """89 å¤ç±»ç精确ç90 :param pred_y: é¢æµç»æ91 :param true_y: çå®ç»æ92 :param labels: æ ç¾å表93 :return:94 """95 if isinstance(pred_y[0], list):96 pred_y = [item[0] for item in pred_y]97 precisions = [binary_precision(pred_y, true_y, label) for label in labels]98 prec = mean(precisions)99 return prec100def multi_recall(pred_y, true_y, labels):101 """102 å¤ç±»çå¬åç103 :param pred_y: é¢æµç»æ104 :param true_y: çå®ç»æ105 :param labels: æ ç¾å表106 :return:107 """108 if isinstance(pred_y[0], list):109 pred_y = [item[0] for item in pred_y]110 recalls = [binary_recall(pred_y, true_y, label) for label in labels]111 rec = mean(recalls)112 return rec113def multi_f_beta(pred_y, true_y, labels, beta=1.0):114 """115 å¤ç±»çf betaå¼116 :param pred_y: é¢æµç»æ117 :param true_y: çå®ç»æ118 :param labels: æ ç¾å表119 :param beta: betaå¼120 :return:121 """122 if isinstance(pred_y[0], list):123 pred_y = [item[0] for item in pred_y]124 f_betas = [binary_f_beta(pred_y, true_y, beta, label) for label in labels]125 f_beta = mean(f_betas)126 return f_beta127def get_binary_metrics(pred_y, true_y, f_beta=1.0):128 """129 å¾å°äºåç±»çæ§è½ææ 130 :param pred_y:131 :param true_y:132 :param f_beta:133 :return:134 """135 acc = accuracy(pred_y, true_y)136 auc = binary_auc(pred_y, true_y)137 recall = binary_recall(pred_y, true_y)138 precision = binary_precision(pred_y, true_y)139 f_beta = binary_f_beta(pred_y, true_y, f_beta)140 return acc, auc, recall, precision, f_beta141def get_multi_metrics(pred_y, true_y, labels, f_beta=1.0):142 """143 å¾å°å¤åç±»çæ§è½ææ 144 :param pred_y:145 :param true_y:146 :param labels:147 :param f_beta:148 :return:149 """150 acc = accuracy(pred_y, true_y)151 recall = multi_recall(pred_y, true_y, labels)152 precision = multi_precision(pred_y, true_y, labels)153 f_beta_ = multi_f_beta(pred_y, true_y, labels, f_beta)...
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