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postprocessingdata.py
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postprocessingdata.py
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import tensorflow as tf
import matplotlib.pyplot as plt
import tensorflow.contrib.slim as slim
import numpy as np
import math
from preparedata import PrepareData
from nets.ssd512 import g_ssd_model
import tf_extended as tfe
import time
from tensorflow.python.ops import math_ops
class PostProcessingData(object):
def __init__(self):
return
def __compute_AP(self, c_scores, c_tp, c_fp, c_num_gbboxes):
aps_voc07 = {}
aps_voc12 = {}
for c in c_scores.keys():
num_gbboxes = c_num_gbboxes[c]
tp = c_tp[c]
fp = c_fp[c]
scores = c_scores[c]
# reshape data
num_gbboxes = math_ops.to_int64(num_gbboxes)
scores = math_ops.to_float(scores)
stype = tf.bool
tp = tf.cast(tp, stype)
fp = tf.cast(fp, stype)
# Reshape TP and FP tensors and clean away 0 class values.(difficult bboxes)
scores = tf.reshape(scores, [-1])
tp = tf.reshape(tp, [-1])
fp = tf.reshape(fp, [-1])
# Remove TP and FP both false.
mask = tf.logical_or(tp, fp)
rm_threshold = 1e-4
mask = tf.logical_and(mask, tf.greater(scores, rm_threshold))
scores = tf.boolean_mask(scores, mask)
tp = tf.boolean_mask(tp, mask)
fp = tf.boolean_mask(fp, mask)
num_gbboxes = tf.reduce_sum(num_gbboxes)
num_detections = tf.size(scores, out_type=tf.int32)
# Precison and recall values.
prec, rec = tfe.precision_recall(num_gbboxes, num_detections, tp, fp, scores)
v = tfe.average_precision_voc07(prec, rec)
aps_voc07[c] = v
# Average precision VOC12.
v = tfe.average_precision_voc12(prec, rec)
aps_voc12[c] = v
return aps_voc07, aps_voc12
def get_mAP_tf_current_batch(self, predictions, localisations, glabels, gbboxes, gdifficults):
# Performing post-processing on CPU: loop-intensive, usually more efficient.
with tf.device('/device:CPU:0'):
# Detected objects from SSD output.
localisations = g_ssd_model.decode_bboxes_all_layers_tf(localisations)
# Select via thresholding and also top_k bboxes from predictions
# Apply NMS algorithm.
rscores, rbboxes = g_ssd_model.detected_bboxes(predictions, localisations)
# Compute TP and FP statistics.
c_num_gbboxes, c_tp, c_fp, c_scores = \
tfe.bboxes_matching_batch(rscores.keys(), rscores, rbboxes,
glabels, gbboxes, gdifficults)
aps_voc07, aps_voc12 = self.__compute_AP(c_scores, c_tp, c_fp, c_num_gbboxes)
# Mean average precision VOC07.
# summary_name = 'AP_VOC07/mAP'
mAP_07_op = tf.add_n(list(aps_voc07.values())) / len(aps_voc07)
# op = tf.summary.scalar(summary_name, mAP, collections=[])
# print_mAP_07_op = tf.Print(mAP_07, [mAP_07], summary_name)
# tf.summary.scalar(summary_name, print_mAP_07_op)
# tf.add_to_collection(tf.GraphKeys.SUMMARIES, op)
# Mean average precision VOC12.
# summary_name = 'AP_VOC12/mAP'
mAP_12_op = tf.add_n(list(aps_voc12.values())) / len(aps_voc12)
# op = tf.summary.scalar(summary_name, mAP, collections=[])
# print_mAP_12_op = tf.Print(mAP, [mAP], summary_name)
# tf.summary.scalar(summary_name, print_mAP_12_op)
return mAP_07_op, mAP_12_op
def get_mAP_tf_accumulative(self, predictions, localisations, glabels, gbboxes, gdifficults):
# Performing post-processing on CPU: loop-intensive, usually more efficient.
with tf.device('/device:CPU:0'):
# Detected objects from SSD output.
localisations = g_ssd_model.decode_bboxes_all_layers_tf(localisations)
rscores, rbboxes = g_ssd_model.detected_bboxes(predictions, localisations)
# Compute TP and FP statistics.
num_gbboxes, tp, fp, rscores = \
tfe.bboxes_matching_batch(rscores.keys(), rscores, rbboxes,
glabels, gbboxes, gdifficults)
dict_metrics = {}
with tf.device('/device:CPU:0'):
# FP and TP metrics.
tp_fp_metric = tfe.streaming_tp_fp_arrays(num_gbboxes, tp, fp, rscores)
for c in tp_fp_metric[0].keys():
dict_metrics['tp_fp_%s' % c] = (tp_fp_metric[0][c],
tp_fp_metric[1][c])
# Add to summaries precision/recall values.
aps_voc07 = {}
aps_voc12 = {}
for c in tp_fp_metric[0].keys():
# Precison and recall values.
prec, rec = tfe.precision_recall(*tp_fp_metric[0][c])
# Average precision VOC07.
v = tfe.average_precision_voc07(prec, rec)
summary_name = 'AP_VOC07/%s' % c
op = tf.summary.scalar(summary_name, v, collections=[])
# op = tf.Print(op, [v], summary_name)
tf.add_to_collection(tf.GraphKeys.SUMMARIES, op)
aps_voc07[c] = v
# Average precision VOC12.
v = tfe.average_precision_voc12(prec, rec)
summary_name = 'AP_VOC12/%s' % c
op = tf.summary.scalar(summary_name, v, collections=[])
# op = tf.Print(op, [v], summary_name)
tf.add_to_collection(tf.GraphKeys.SUMMARIES, op)
aps_voc12[c] = v
# Mean average precision VOC07.
summary_name = 'AP_VOC07/mAP_accumulative'
mAP = tf.add_n(list(aps_voc07.values())) / len(aps_voc07)
op = tf.summary.scalar(summary_name, mAP, collections=[])
op = tf.Print(op, [mAP], summary_name)
tf.add_to_collection(tf.GraphKeys.SUMMARIES, op)
# Mean average precision VOC12.
summary_name = 'AP_VOC12/mAP_accumulative'
mAP = tf.add_n(list(aps_voc12.values())) / len(aps_voc12)
op = tf.summary.scalar(summary_name, mAP, collections=[])
op = tf.Print(op, [mAP], summary_name)
tf.add_to_collection(tf.GraphKeys.SUMMARIES, op)
# Split into values and updates ops.
names_to_values, names_to_updates = slim.metrics.aggregate_metric_map(dict_metrics)
return names_to_updates
def run(self):
return
g_post_processing_data = PostProcessingData()
if __name__ == "__main__":
obj = PostProcessingData()
obj.run()