Merge pull request #81 from ar90n/add-yolov3-spp-support

Add yolov3-spp support

README.md

@@ -19,6 +19,7 @@ To run demo type this in the command line:
     1. Download binary file with desired weights: 
         1. Full weights: `wget https://pjreddie.com/media/files/yolov3.weights`
         1. Tiny weights: `wget https://pjreddie.com/media/files/yolov3-tiny.weights` 
+        1. SPP weights: `wget https://pjreddie.com/media/files/yolov3-spp.weights` 
     2. Run `python ./convert_weights.py` and `python ./convert_weights_pb.py`        
 3. Run `python ./demo.py --input_img <path-to-image> --output_img <name-of-output-image> --frozen_model <path-to-frozen-model>`
 
@@ -33,7 +34,9 @@ To run demo type this in the command line:
         1.  `NCHW` (gpu only) or `NHWC`
     4. `--tiny`
         1. Use yolov3-tiny
-    5. `--ckpt_file`
+    5. `--spp`
+        1. Use yolov3-spp
+    6. `--ckpt_file`
         1. Output checkpoint file
 2. convert_weights_pb.py:
     1. `--class_names`
@@ -44,7 +47,9 @@ To run demo type this in the command line:
         1.  `NCHW` (gpu only) or `NHWC`
     4. `--tiny`
         1. Use yolov3-tiny
-    5. `--output_graph`
+    5. `--spp`
+        1. Use yolov3-spp
+    6. `--output_graph`
         1. Location to write the output .pb graph to
 3. demo.py
     1. `--class_names`
@@ -62,4 +67,4 @@ To run demo type this in the command line:
     7. `--iou_threshold`
         1. Desired iou threshold
     8. `--gpu_memory_fraction`
-        1. Fraction of gpu memory to work with
+        1. Fraction of gpu memory to work with

convert_weights.py

@@ -17,13 +17,17 @@
     'data_format', 'NCHW', 'Data format: NCHW (gpu only) / NHWC')
 tf.app.flags.DEFINE_bool(
     'tiny', False, 'Use tiny version of YOLOv3')
+tf.app.flags.DEFINE_bool(
+    'spp', False, 'Use SPP version of YOLOv3')
 tf.app.flags.DEFINE_string(
     'ckpt_file', './saved_model/model.ckpt', 'Chceckpoint file')
 
 
 def main(argv=None):
     if FLAGS.tiny:
         model = yolo_v3_tiny.yolo_v3_tiny
+    elif FLAGS.spp:
+        model = yolo_v3.yolo_v3_spp
     else:
         model = yolo_v3.yolo_v3
 

convert_weights_pb.py

@@ -21,6 +21,8 @@
 
 tf.app.flags.DEFINE_bool(
     'tiny', False, 'Use tiny version of YOLOv3')
+tf.app.flags.DEFINE_bool(
+    'spp', False, 'Use SPP version of YOLOv3')
 tf.app.flags.DEFINE_integer(
     'size', 416, 'Image size')
 
@@ -29,6 +31,8 @@
 def main(argv=None):
     if FLAGS.tiny:
         model = yolo_v3_tiny.yolo_v3_tiny
+    elif FLAGS.spp:
+        model = yolo_v3.yolo_v3_spp
     else:
         model = yolo_v3.yolo_v3
 

demo.py

@@ -29,6 +29,8 @@
     'frozen_model', '', 'Frozen tensorflow protobuf model')
 tf.app.flags.DEFINE_bool(
     'tiny', False, 'Use tiny version of YOLOv3')
+tf.app.flags.DEFINE_bool(
+    'spp', False, 'Use SPP version of YOLOv3')
 
 tf.app.flags.DEFINE_integer(
     'size', 416, 'Image size')
@@ -71,6 +73,8 @@ def main(argv=None):
     else:
         if FLAGS.tiny:
             model = yolo_v3_tiny.yolo_v3_tiny
+        elif FLAGS.spp:
+            model = yolo_v3.yolo_v3_spp
         else:
             model = yolo_v3.yolo_v3
 

yolo_v3.py

@@ -63,6 +63,14 @@ def _darknet53_block(inputs, filters):
     return inputs
 
 
+def _spp_block(inputs, data_format='NCHW'):
+    return tf.concat([slim.max_pool2d(inputs, 13, 1, 'SAME'),
+                      slim.max_pool2d(inputs, 9, 1, 'SAME'),
+                      slim.max_pool2d(inputs, 5, 1, 'SAME'),
+                      inputs],
+                     axis=1 if data_format == 'NCHW' else 3)
+
+
 @tf.contrib.framework.add_arg_scope
 def _fixed_padding(inputs, kernel_size, *args, mode='CONSTANT', **kwargs):
     """
@@ -95,10 +103,15 @@ def _fixed_padding(inputs, kernel_size, *args, mode='CONSTANT', **kwargs):
     return padded_inputs
 
 
-def _yolo_block(inputs, filters):
+def _yolo_block(inputs, filters, data_format='NCHW', with_spp=False):
     inputs = _conv2d_fixed_padding(inputs, filters, 1)
     inputs = _conv2d_fixed_padding(inputs, filters * 2, 3)
     inputs = _conv2d_fixed_padding(inputs, filters, 1)
+
+    if with_spp:
+        inputs = _spp_block(inputs, data_format)
+        inputs = _conv2d_fixed_padding(inputs, filters, 1)
+
     inputs = _conv2d_fixed_padding(inputs, filters * 2, 3)
     inputs = _conv2d_fixed_padding(inputs, filters, 1)
     route = inputs
@@ -187,7 +200,7 @@ def _upsample(inputs, out_shape, data_format='NCHW'):
     return inputs
 
 
-def yolo_v3(inputs, num_classes, is_training=False, data_format='NCHW', reuse=False):
+def yolo_v3(inputs, num_classes, is_training=False, data_format='NCHW', reuse=False, with_spp=False):
     """
     Creates YOLO v3 model.
 
@@ -197,6 +210,7 @@ def yolo_v3(inputs, num_classes, is_training=False, data_format='NCHW', reuse=Fa
     :param is_training: whether is training or not.
     :param data_format: data format NCHW or NHWC.
     :param reuse: whether or not the network and its variables should be reused.
+    :param with_spp: whether or not is using spp layer.
     :return:
     """
     # it will be needed later on
@@ -228,7 +242,8 @@ def yolo_v3(inputs, num_classes, is_training=False, data_format='NCHW', reuse=Fa
                 route_1, route_2, inputs = darknet53(inputs)
 
             with tf.variable_scope('yolo-v3'):
-                route, inputs = _yolo_block(inputs, 512)
+                route, inputs = _yolo_block(inputs, 512, data_format, with_spp)
+
                 detect_1 = _detection_layer(
                     inputs, num_classes, _ANCHORS[6:9], img_size, data_format)
                 detect_1 = tf.identity(detect_1, name='detect_1')
@@ -260,3 +275,18 @@ def yolo_v3(inputs, num_classes, is_training=False, data_format='NCHW', reuse=Fa
                 detections = tf.concat([detect_1, detect_2, detect_3], axis=1)
                 detections = tf.identity(detections, name='detections')
                 return detections
+
+
+def yolo_v3_spp(inputs, num_classes, is_training=False, data_format='NCHW', reuse=False):
+    """
+    Creates YOLO v3 with SPP  model.
+
+    :param inputs: a 4-D tensor of size [batch_size, height, width, channels].
+        Dimension batch_size may be undefined. The channel order is RGB.
+    :param num_classes: number of predicted classes.
+    :param is_training: whether is training or not.
+    :param data_format: data format NCHW or NHWC.
+    :param reuse: whether or not the network and its variables should be reused.
+    :return:
+    """
+    return yolo_v3(inputs, num_classes, is_training=is_training, data_format=data_format, reuse=reuse, with_spp=True)