fix: file name

inference.py

@@ -8,7 +8,7 @@
 import cv2
 import sys
 from util.util import *
-from model.SSD300 import *
+from model.ssd300 import *
 
 def inference(image_name):
     if image_name is None:

model/ssd300.py

@@ -0,0 +1,100 @@
+"""
+SSD300 is SSD wrapper class.
+
+date: 10/18
+author: arabian9ts
+"""
+
+import tensorflow as tf
+import numpy as np
+
+from model.ssd import *
+from matcher import Matcher
+from model.computation import *
+from model.default_box import *
+
+
+class SSD300:
+    def __init__(self, sess):
+        """
+        initialize SSD model as SSD300 whose input size is  300x300
+        """
+        self.sess = sess
+
+        # define input placeholder and initialize ssd instance
+        self.input = tf.placeholder(shape=[None, 300, 300, 3], dtype=tf.float32)
+        self.ssd = SSD()
+
+        # build ssd network => feature-maps and confs and locs tensor is returned
+        fmaps, confs, locs = self.ssd.build(self.input, is_training=True)
+
+        # zip running set of tensor
+        self.pred_set = [fmaps, confs, locs]
+
+        # required param from default-box and loss function
+        fmap_shapes = [map.get_shape().as_list() for map in fmaps]
+        # print('fmap shapes is '+str(fmap_shapes))
+        self.dboxes = generate_boxes(fmap_shapes)
+        print(len(self.dboxes))
+
+        # required placeholder for loss
+        loss, loss_conf, loss_loc, self.pos, self.neg, self.gt_labels, self.gt_boxes = self.ssd.loss(len(self.dboxes))
+        self.train_set = [loss, loss_conf, loss_loc]
+        # optimizer = tf.train.AdamOptimizer(0.05)
+        optimizer = tf.train.AdamOptimizer(learning_rate=1e-3, beta1=0.9, beta2=0.999, epsilon=1e-08, use_locking=False, name='Adam')
+        self.train_step = optimizer.minimize(loss)
+
+        # provides matching method
+        self.matcher = Matcher(fmap_shapes, self.dboxes)
+
+    # inference process
+    def infer(self, images):
+        feature_maps, pred_confs, pred_locs = self.sess.run(self.pred_set, feed_dict={self.input: images})
+        return pred_confs, pred_locs
+
+    # training process
+    def train(self, images, actual_data):
+        # ================ RESET / EVAL ================ #
+        positives = []
+        negatives = []
+        ex_gt_labels = []
+        ex_gt_boxes = []
+        # ===================== END ===================== #
+
+        # call prepare_loss per image
+        # because matching method works with only one image
+        def prepare_loss(pred_confs, pred_locs, actual_labels, actual_locs):
+            pos_list, neg_list, t_gtl, t_gtb = self.matcher.matching(pred_confs, pred_locs, actual_labels, actual_locs)
+            positives.append(pos_list)
+            negatives.append(neg_list)
+            ex_gt_labels.append(t_gtl)
+            ex_gt_boxes.append(t_gtb)
+
+
+        feature_maps, pred_confs, pred_locs = self.sess.run(self.pred_set, feed_dict={self.input: images})
+
+        for i in range(len(images)):
+            actual_labels = []
+            actual_locs = []
+            # extract ground truth info
+            for obj in actual_data[i]:
+                loc = obj[:4]
+                label = np.argmax(obj[4:])
+
+                # transform location for voc2007
+                loc = convert2wh(loc)
+                loc = corner2center(loc)
+
+                actual_locs.append(loc)
+                actual_labels.append(label)
+
+            prepare_loss(pred_confs[i], pred_locs[i], actual_labels, actual_locs)
+
+        batch_loss, batch_conf, batch_loc = \
+        self.sess.run(self.train_set, \
+        feed_dict={self.input: images, self.pos: positives, self.neg: negatives, self.gt_labels: ex_gt_labels, self.gt_boxes: ex_gt_boxes})
+
+        self.sess.run(self.train_step, \
+        feed_dict={self.input: images, self.pos: positives, self.neg: negatives, self.gt_labels: ex_gt_labels, self.gt_boxes: ex_gt_boxes})
+
+        return pred_confs, pred_locs, batch_loc, batch_conf, batch_loss

train.py → trainer.py

@@ -22,7 +22,7 @@
 
 from util.util import *
 from tqdm import trange
-from model.SSD300 import *
+from model.ssd300 import *
 
 # ====================== Training Parameters ====================== #
 BATCH_SIZE = 10