removed exmple apps code

[ealt-edge.git] / example-apps / PDD / pcb-defect-detection / libs / export_pbs / exportPb.py

diff --git a/example-apps/PDD/pcb-defect-detection/libs/export_pbs/exportPb.py b/example-apps/PDD/pcb-defect-detection/libs/export_pbs/exportPb.py
deleted file mode 100755 (executable)
index 8f4b217..0000000
--- a/example-apps/PDD/pcb-defect-detection/libs/export_pbs/exportPb.py
+++ /dev/null
@@ -1,87 +0,0 @@
-# -*- coding: utf-8 -*-
-
-from __future__ import absolute_import, print_function, division
-
-import os, sys
-import tensorflow as tf
-import tensorflow.contrib.slim as slim
-from tensorflow.python.tools import freeze_graph
-
-sys.path.append('../../')
-from data.io.image_preprocess import short_side_resize_for_inference_data
-from libs.configs import cfgs
-from libs.networks import build_whole_network
-
-CKPT_PATH = '/home/yjr/PycharmProjects/Faster-RCNN_Tensorflow/output/trained_weights/FasterRCNN_20180517/voc_200000model.ckpt'
-OUT_DIR = '../../output/Pbs'
-PB_NAME = 'FasterRCNN_Res101_Pascal.pb'
-
-
-def build_detection_graph():
-    # 1. preprocess img
-    img_plac = tf.placeholder(dtype=tf.uint8, shape=[None, None, 3],
-                              name='input_img')  # is RGB. not GBR
-    raw_shape = tf.shape(img_plac)
-    raw_h, raw_w = tf.to_float(raw_shape[0]), tf.to_float(raw_shape[1])
-
-    img_batch = tf.cast(img_plac, tf.float32)
-    img_batch = short_side_resize_for_inference_data(img_tensor=img_batch,
-                                                     target_shortside_len=cfgs.IMG_SHORT_SIDE_LEN,
-                                                     length_limitation=cfgs.IMG_MAX_LENGTH)
-    img_batch = img_batch - tf.constant(cfgs.PIXEL_MEAN)
-    img_batch = tf.expand_dims(img_batch, axis=0)  # [1, None, None, 3]
-
-    det_net = build_whole_network.DetectionNetwork(base_network_name=cfgs.NET_NAME,
-                                                   is_training=False)
-
-    detected_boxes, detection_scores, detection_category = det_net.build_whole_detection_network(
-        input_img_batch=img_batch,
-        gtboxes_batch=None)
-
-    xmin, ymin, xmax, ymax = detected_boxes[:, 0], detected_boxes[:, 1], \
-                             detected_boxes[:, 2], detected_boxes[:, 3]
-
-    resized_shape = tf.shape(img_batch)
-    resized_h, resized_w = tf.to_float(resized_shape[1]), tf.to_float(resized_shape[2])
-
-    xmin = xmin * raw_w / resized_w
-    xmax = xmax * raw_w / resized_w
-
-    ymin = ymin * raw_h / resized_h
-    ymax = ymax * raw_h / resized_h
-
-    boxes = tf.transpose(tf.stack([xmin, ymin, xmax, ymax]))
-    dets = tf.concat([tf.reshape(detection_category, [-1, 1]),
-                     tf.reshape(detection_scores, [-1, 1]),
-                     boxes], axis=1, name='DetResults')
-
-    return dets
-
-
-def export_frozenPB():
-
-    tf.reset_default_graph()
-
-    dets = build_detection_graph()
-
-    saver = tf.train.Saver()
-
-    with tf.Session() as sess:
-        print("we have restred the weights from =====>>\n", CKPT_PATH)
-        saver.restore(sess, CKPT_PATH)
-
-        tf.train.write_graph(sess.graph_def, OUT_DIR, PB_NAME)
-        freeze_graph.freeze_graph(input_graph=os.path.join(OUT_DIR, PB_NAME),
-                                  input_saver='',
-                                  input_binary=False,
-                                  input_checkpoint=CKPT_PATH,
-                                  output_node_names="DetResults",
-                                  restore_op_name="save/restore_all",
-                                  filename_tensor_name='save/Const:0',
-                                  output_graph=os.path.join(OUT_DIR, PB_NAME.replace('.pb', '_Frozen.pb')),
-                                  clear_devices=False,
-                                  initializer_nodes='')
-
-if __name__ == '__main__':
-    os.environ["CUDA_VISIBLE_DEVICES"] = ''
-    export_frozenPB()