X-Git-Url: https://gerrit.akraino.org/r/gitweb?a=blobdiff_plain;f=example-apps%2FPDD%2Fpcb-defect-detection%2Flibs%2Flosses%2Flosses.py;fp=example-apps%2FPDD%2Fpcb-defect-detection%2Flibs%2Flosses%2Flosses.py;h=0000000000000000000000000000000000000000;hb=3ed2c61d9d7e7916481650c41bfe5604f7db22e9;hp=32c9429465ac67533e6899286601c499e70edff2;hpb=e6d40ddb2640f434a9d7d7ed99566e5e8fa60cc1;p=ealt-edge.git

diff --git a/example-apps/PDD/pcb-defect-detection/libs/losses/losses.py b/example-apps/PDD/pcb-defect-detection/libs/losses/losses.py
deleted file mode 100755
index 32c9429..0000000
--- a/example-apps/PDD/pcb-defect-detection/libs/losses/losses.py
+++ /dev/null
@@ -1,135 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-@author: jemmy li
-@contact: zengarden2009@gmail.com
-"""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import tensorflow as tf
-
-
-def _smooth_l1_loss_base(bbox_pred, bbox_targets, sigma=1.0):
-    '''
-
-    :param bbox_pred: [-1, 4] in RPN. [-1, cls_num+1, 4] in Fast-rcnn
-    :param bbox_targets: shape is same as bbox_pred
-    :param sigma:
-    :return:
-    '''
-    sigma_2 = sigma**2
-
-    box_diff = bbox_pred - bbox_targets
-
-    abs_box_diff = tf.abs(box_diff)
-
-    smoothL1_sign = tf.stop_gradient(
-        tf.to_float(tf.less(abs_box_diff, 1. / sigma_2)))
-    loss_box = tf.pow(box_diff, 2) * (sigma_2 / 2.0) * smoothL1_sign \
-               + (abs_box_diff - (0.5 / sigma_2)) * (1.0 - smoothL1_sign)
-    return loss_box
-
-def smooth_l1_loss_rpn(bbox_pred, bbox_targets, label, sigma=1.0):
-    '''
-
-    :param bbox_pred: [-1, 4]
-    :param bbox_targets: [-1, 4]
-    :param label: [-1]
-    :param sigma:
-    :return:
-    '''
-    value = _smooth_l1_loss_base(bbox_pred, bbox_targets, sigma=sigma)
-    value = tf.reduce_sum(value, axis=1)  # to sum in axis 1
-    rpn_positive = tf.where(tf.greater(label, 0))
-
-    # rpn_select = tf.stop_gradient(rpn_select) # to avoid
-    selected_value = tf.gather(value, rpn_positive)
-    non_ignored_mask = tf.stop_gradient(
-        1.0 - tf.to_float(tf.equal(label, -1)))  # positve is 1.0 others is 0.0
-
-    bbox_loss = tf.reduce_sum(selected_value) / tf.maximum(1.0, tf.reduce_sum(non_ignored_mask))
-
-    return bbox_loss
-
-def smooth_l1_loss_rcnn(bbox_pred, bbox_targets, label, num_classes, sigma=1.0):
-    '''
-
-    :param bbox_pred: [-1, (cfgs.CLS_NUM +1) * 4]
-    :param bbox_targets:[-1, (cfgs.CLS_NUM +1) * 4]
-    :param label:[-1]
-    :param num_classes:
-    :param sigma:
-    :return:
-    '''
-
-    outside_mask = tf.stop_gradient(tf.to_float(tf.greater(label, 0)))
-
-    bbox_pred = tf.reshape(bbox_pred, [-1, num_classes, 4])
-    bbox_targets = tf.reshape(bbox_targets, [-1, num_classes, 4])
-
-    value = _smooth_l1_loss_base(bbox_pred,
-                                 bbox_targets,
-                                 sigma=sigma)
-    value = tf.reduce_sum(value, 2)
-    value = tf.reshape(value, [-1, num_classes])
-
-    inside_mask = tf.one_hot(tf.reshape(label, [-1, 1]),
-                             depth=num_classes, axis=1)
-
-    inside_mask = tf.stop_gradient(
-        tf.to_float(tf.reshape(inside_mask, [-1, num_classes])))
-
-    normalizer = tf.to_float(tf.shape(bbox_pred)[0])
-    bbox_loss = tf.reduce_sum(
-        tf.reduce_sum(value * inside_mask, 1)*outside_mask) / normalizer
-
-    return bbox_loss
-
-def sum_ohem_loss(cls_score, label, bbox_pred, bbox_targets,
-                  num_classes, num_ohem_samples=256, sigma=1.0):
-
-    '''
-    :param cls_score: [-1, cls_num+1]
-    :param label: [-1]
-    :param bbox_pred: [-1, 4*(cls_num+1)]
-    :param bbox_targets: [-1, 4*(cls_num+1)]
-    :param num_ohem_samples: 256 by default
-    :param num_classes: cls_num+1
-    :param sigma:
-    :return:
-    '''
-    cls_loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=cls_score, labels=label)  # [-1, ]
-    # cls_loss = tf.Print(cls_loss, [tf.shape(cls_loss)], summarize=10, message='CLS losss shape ****')
-
-    outside_mask = tf.stop_gradient(tf.to_float(tf.greater(label, 0)))
-    bbox_pred = tf.reshape(bbox_pred, [-1, num_classes, 4])
-    bbox_targets = tf.reshape(bbox_targets, [-1, num_classes, 4])
-
-    value = _smooth_l1_loss_base(bbox_pred,
-                                 bbox_targets,
-                                 sigma=sigma)
-    value = tf.reduce_sum(value, 2)
-    value = tf.reshape(value, [-1, num_classes])
-
-    inside_mask = tf.one_hot(tf.reshape(label, [-1, 1]),
-                             depth=num_classes, axis=1)
-
-    inside_mask = tf.stop_gradient(
-        tf.to_float(tf.reshape(inside_mask, [-1, num_classes])))
-    loc_loss = tf.reduce_sum(value * inside_mask, 1)*outside_mask
-    # loc_loss = tf.Print(loc_loss, [tf.shape(loc_loss)], summarize=10, message='loc_loss shape***')
-
-    sum_loss = cls_loss + loc_loss
-
-    num_ohem_samples = tf.stop_gradient(tf.minimum(num_ohem_samples, tf.shape(sum_loss)[0]))
-    _, top_k_indices = tf.nn.top_k(sum_loss, k=num_ohem_samples)
-
-    cls_loss_ohem = tf.gather(cls_loss, top_k_indices)
-    cls_loss_ohem = tf.reduce_mean(cls_loss_ohem)
-
-    loc_loss_ohem = tf.gather(loc_loss, top_k_indices)
-    normalizer = tf.to_float(num_ohem_samples)
-    loc_loss_ohem = tf.reduce_sum(loc_loss_ohem) / normalizer
-
-    return cls_loss_ohem, loc_loss_ohem
\ No newline at end of file