More Keras additions

README.md

@@ -197,7 +197,7 @@ Then, in another terminal tab you can run `tensorboard --logdir=logs/run_X` and
 - [ ] cfg parser
 - [ ] Anchor generation for custom datasets
 - [ ] mAP write up
-
+- [ ] Add webcam support
 
 #### Inspired from
 

net/netdecode.py

@@ -61,7 +61,7 @@ def __call__(self, y_sing_pred):
 
         selected_boxes = K.gather(flatten_boxes, selected_indices)
         selected_scores = K.gather(flatten_scores, selected_indices)
-        selected_classes = tf.gather(flatten_classes, selected_indices)
+        selected_classes = K.gather(flatten_classes, selected_indices)
 
         # Repassem aixo vale
         score_mask = selected_scores>self.detection_threshold
@@ -72,69 +72,7 @@ def __call__(self, y_sing_pred):
 
         return process_outs(selected_boxes, selected_scores, K.cast(selected_classes, np.float32))
 
-    def proper_yolo_nms(self, y_sing_pred):
-        # NMS need to be applied per class, since two different boxes could predict with high confidence
-        # two objects that have high IOU
-        # At the same time, even though NMS has to be done per class, it can only be done with max values
-        # of P(O) * P(Class|O) since we want to avoid same box predicting 2 overlapping objects.
-        # Doing both these things turns out to be a fucking pain.
-
-        # CONSIDER USING tf.while_loop for the FOR
-
-        b_xy = tf.sigmoid(y_sing_pred[..., 0:2]) + YoloParams.c_grid[0]
-        b_wh = tf.exp(y_sing_pred[..., 2:4])*YoloParams.anchors[0]
-        b_xy1 = b_xy - b_wh / 2.
-        b_xy2 = b_xy + b_wh / 2.
-        boxes = tf.concat([b_xy1, b_xy2], axis=-1)
-
-
-        scores_all = tf.expand_dims(tf.sigmoid(y_sing_pred[..., 4]), axis=-1) * tf.nn.softmax(y_sing_pred[...,5:])
-        indicator_detection = scores_all > self.detection_threshold
-
-        scores_all = scores_all * tf.to_float(indicator_detection)
-
-        classes = tf.argmax(scores_all, axis=-1)
-
-        scores = tf.reduce_max(scores_all, axis=-1)
-
-        flatten_boxes = tf.reshape(boxes, 
-            shape=(YoloParams.GRID_SIZE*YoloParams.GRID_SIZE*YoloParams.NUM_BOUNDING_BOXES, 4))
-        flatten_scores = tf.reshape(scores, 
-            shape=(YoloParams.GRID_SIZE*YoloParams.GRID_SIZE*YoloParams.NUM_BOUNDING_BOXES, ))
-        flatten_classes = tf.reshape(classes, 
-            shape=(YoloParams.GRID_SIZE*YoloParams.GRID_SIZE*YoloParams.NUM_BOUNDING_BOXES, ))
-
-        output_boxes = []
-        output_scores = []
-        output_classes = []
-        for c in range(YoloParams.NUM_CLASSES):
-            if tf.reduce_sum(tf.to_float(tf.equal(flatten_classes, c))) > 0:
-                filtered_flatten_boxes = tf.boolean_mask(flatten_boxes, tf.equal(flatten_classes, c))
-                filtered_flatten_scores = tf.boolean_mask(flatten_scores, tf.equal(flatten_classes, c))
-                filtered_flatten_classes = tf.boolean_mask(flatten_classes, tf.equal(flatten_classes, c))
-
-                selected_indices = tf.image.non_max_suppression( 
-                    filtered_flatten_boxes, filtered_flatten_scores, self.max_boxes, self.iou_threshold)
-
-                selected_boxes = K.gather(filtered_flatten_boxes, selected_indices)
-                selected_scores = K.gather(filtered_flatten_scores, selected_indices)
-                selected_classes = K.gather(filtered_flatten_classes, selected_indices)
-
-
-                output_boxes.append( selected_boxes )
-                output_scores.append( selected_scores )
-                output_classes.append( selected_classes )
-
-
-        print(output_boxes)
-
-        print(tf.concat(output_boxes, axis=-1).eval()) 
-        print(tf.concat(output_scores, axis=-1).eval())
-        print(tf.concat(output_classes, axis=-1).eval())
-
-        return tf.concat(output_boxes, axis=-1), tf.concat(output_scores, axis=-1), tf.concat(output_classes, axis=-1)
-
-
+
 
 if __name__ == '__main__':
 

net/netloss.py

@@ -55,6 +55,7 @@ def __init__(self):
         self.lambda_class = YoloParams.CLASS_SCALE
 
         self.norm = False
+        self.paper_imp = False
 
     def coord_loss(self, y_true, y_pred):
 
@@ -116,6 +117,13 @@ def class_loss(self, y_true, y_pred):
         loss_class_arg = tf.nn.sparse_softmax_cross_entropy_with_logits(
             labels=b_class, logits=b_class_pred)
 
+        '''
+        if self.paper_imp:
+            loss_class_arg = 
+
+        '''
+
+
         loss_class = K.sum(loss_class_arg * indicator_class, axis=[1,2,3])
 
         return loss_class / (norm_class + EPSILON)

net/netparams.py

@@ -1,6 +1,6 @@
 
 import pickle, argparse, json, os, sys
-import tensorflow as tf
+from keras import backend as K
 import numpy as np
 
 
@@ -53,14 +53,11 @@
         config = json.loads(config_buffer.read())
 
 
-
 def generate_yolo_grid(batch, g, num_bb):
-    c_x = tf.to_float(tf.reshape(tf.tile(tf.range(g), [g]), (1, g, g, 1, 1)))
-    c_y = tf.transpose(c_x, (0,2,1,3,4))
-    return tf.tile(tf.concat([c_x, c_y], -1), [batch, 1, 1, num_bb, 1])
-
-
-
+    c_x = K.cast(K.reshape(K.tile(K.arange(g), [g]), (1, g, g, 1, 1)), K.floatx())
+    c_y = K.permute_dimensions(c_x, (0,2,1,3,4))
+    return K.tile(K.concatenate([c_x, c_y], -1), [batch, 1, 1, num_bb, 1])
+
 
 def get_threshold(value):
     if value > 1. or value < 0:

net/proper_nms.py

@@ -0,0 +1,62 @@
+   def proper_yolo_nms(self, y_sing_pred):
+        # NMS need to be applied per class, since two different boxes could predict with high confidence
+        # two objects that have high IOU
+        # At the same time, even though NMS has to be done per class, it can only be done with max values
+        # of P(O) * P(Class|O) since we want to avoid same box predicting 2 overlapping objects.
+        # Doing both these things turns out to be a fucking pain.
+
+        # CONSIDER USING tf.while_loop for the FOR
+
+        b_xy = tf.sigmoid(y_sing_pred[..., 0:2]) + YoloParams.c_grid[0]
+        b_wh = tf.exp(y_sing_pred[..., 2:4])*YoloParams.anchors[0]
+        b_xy1 = b_xy - b_wh / 2.
+        b_xy2 = b_xy + b_wh / 2.
+        boxes = tf.concat([b_xy1, b_xy2], axis=-1)
+
+
+        scores_all = tf.expand_dims(tf.sigmoid(y_sing_pred[..., 4]), axis=-1) * tf.nn.softmax(y_sing_pred[...,5:])
+        indicator_detection = scores_all > self.detection_threshold
+
+        scores_all = scores_all * tf.to_float(indicator_detection)
+
+        classes = tf.argmax(scores_all, axis=-1)
+
+        scores = tf.reduce_max(scores_all, axis=-1)
+
+        flatten_boxes = tf.reshape(boxes, 
+            shape=(YoloParams.GRID_SIZE*YoloParams.GRID_SIZE*YoloParams.NUM_BOUNDING_BOXES, 4))
+        flatten_scores = tf.reshape(scores, 
+            shape=(YoloParams.GRID_SIZE*YoloParams.GRID_SIZE*YoloParams.NUM_BOUNDING_BOXES, ))
+        flatten_classes = tf.reshape(classes, 
+            shape=(YoloParams.GRID_SIZE*YoloParams.GRID_SIZE*YoloParams.NUM_BOUNDING_BOXES, ))
+
+        output_boxes = []
+        output_scores = []
+        output_classes = []
+        for c in range(YoloParams.NUM_CLASSES):
+            if tf.reduce_sum(tf.to_float(tf.equal(flatten_classes, c))) > 0:
+                filtered_flatten_boxes = tf.boolean_mask(flatten_boxes, tf.equal(flatten_classes, c))
+                filtered_flatten_scores = tf.boolean_mask(flatten_scores, tf.equal(flatten_classes, c))
+                filtered_flatten_classes = tf.boolean_mask(flatten_classes, tf.equal(flatten_classes, c))
+
+                selected_indices = tf.image.non_max_suppression( 
+                    filtered_flatten_boxes, filtered_flatten_scores, self.max_boxes, self.iou_threshold)
+
+                selected_boxes = K.gather(filtered_flatten_boxes, selected_indices)
+                selected_scores = K.gather(filtered_flatten_scores, selected_indices)
+                selected_classes = K.gather(filtered_flatten_classes, selected_indices)
+
+
+                output_boxes.append( selected_boxes )
+                output_scores.append( selected_scores )
+                output_classes.append( selected_classes )
+
+
+        print(output_boxes)
+
+        print(tf.concat(output_boxes, axis=-1).eval()) 
+        print(tf.concat(output_scores, axis=-1).eval())
+        print(tf.concat(output_classes, axis=-1).eval())
+
+        return tf.concat(output_boxes, axis=-1), tf.concat(output_scores, axis=-1), tf.concat(output_classes, axis=-1)
+

net/utils.py

@@ -252,8 +252,9 @@ def setup_logging(logging_path='logs'):
     log_path = os.path.join(os.getcwd(),logging_path)
     mkdir_p(log_path)
 
-    get_ind = lambda x: int(x.split('_')[1])
-    #run_counter = max([get_ind(r) for r in os.listdir(log_path)]) + 1
+    check_names = lambda y: y if isinstance(y, int) else -1
+    get_ind = lambda x: int(check_names(x.split('_')[1]))
+
     run_counter = max(map(get_ind, os.listdir(log_path)), default=-1) + 1
 
     run_path = os.path.join(log_path, 'run_%s'%run_counter)

yolov2.py

@@ -1,6 +1,5 @@
 
 import pickle, argparse, json, cv2, os
-import tensorflow as tf
 import numpy as np
 from tqdm import tqdm
 import matplotlib.pyplot as plt