diff --git a/README.md b/README.md
index 2622aec..d3605ea 100644
--- a/README.md
+++ b/README.md
@@ -1,14 +1,14 @@
# dourflow: Keras implementation of YOLO v2
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**dourflow** is a keras/tensorflow implementation of the state-of-the-art object detection system [You only look once](https://pjreddie.com/darknet/yolo/).
-- Original paper: [YOLO9000: Better, Faster, Stronger](https://arxiv.org/abs/1612.08242)
-- Github repo: [Darknet](https://github.com/pjreddie/darknet)
+Original paper and github: [YOLO9000: Better, Faster, Stronger](https://arxiv.org/abs/1612.08242) & [Darknet](https://github.com/pjreddie/darknet).
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### Dependancies
---
@@ -17,7 +17,7 @@
- [numpy](http://www.numpy.org/)
- [h5py](http://www.h5py.org/)
- [opencv](https://pypi.org/project/opencv-python/)
-- [python 3](https://www.python.org/)
+- [python3.5](https://www.python.org/)
### Usage
---
@@ -131,7 +131,7 @@ Example:
```bash
python3 dourflow.py validate -m voc_model.h5 -c voc_config.json
```
-Terminal output:
+Output:
```bash
Batch Processed: 100%|████████████████████████████████████████████| 4282/4282 [01:53<00:00, 37.84it/s]
AP( bus ): 0.806
diff --git a/net/netloss.py b/net/netloss.py
index 5f33079..93c145f 100644
--- a/net/netloss.py
+++ b/net/netloss.py
@@ -1,10 +1,10 @@
import tensorflow as tf
import numpy as np
-
+from keras import backend as K
from net.netparams import YoloParams
-EPSILON = 1e-6
+EPSILON = 1e-7
def calculate_ious(A1, A2, use_iou=True):
@@ -26,22 +26,22 @@ def calculate_ious(A1, A2, use_iou=True):
A2_mins = A2_xy - A2_wh_half
A2_maxes = A2_xy + A2_wh_half
- intersect_mins = tf.maximum(A2_mins, A1_mins)
- intersect_maxes = tf.minimum(A2_maxes, A1_maxes)
- intersect_wh = tf.maximum(intersect_maxes - intersect_mins, 0.)
+ intersect_mins = K.maximum(A2_mins, A1_mins)
+ intersect_maxes = K.minimum(A2_maxes, A1_maxes)
+ intersect_wh = K.maximum(intersect_maxes - intersect_mins, 0.)
intersect_areas = intersect_wh[..., 0] * intersect_wh[..., 1]
true_areas = A1_wh[..., 0] * A1_wh[..., 1]
pred_areas = A2_wh[..., 0] * A2_wh[..., 1]
union_areas = pred_areas + true_areas - intersect_areas
- iou_scores = tf.truediv(intersect_areas, union_areas)
+ iou_scores = intersect_areas / union_areas
return iou_scores
class YoloLoss(object):
- # ADD WARM UP CONDITIONS
+ # WARM UP
def __init__(self):
@@ -64,16 +64,16 @@ def coord_loss(self, y_true, y_pred):
b_xy = y_true[..., 0:2]
b_wh = y_true[..., 2:4]
- indicator_coord = tf.expand_dims(y_true[..., 4], axis=-1) * self.lambda_coord
+ indicator_coord = K.expand_dims(y_true[..., 4], axis=-1) * self.lambda_coord
norm_coord = 1
if self.norm:
- norm_coord = tf.reduce_sum(tf.to_float(indicator_coord > 0.0))
+ norm_coord = K.sum(K.cast(indicator_coord > 0.0, np.float32))
- loss_xy = tf.reduce_sum(tf.square(b_xy - b_xy_pred) * indicator_coord, axis=[1,2,3,4])
- #loss_wh = tf.reduce_sum(tf.square(b_wh - b_wh_pred) * indicator_coord, axis=[1,2,3,4])
- loss_wh = tf.reduce_sum(tf.square(tf.sqrt(b_wh) - tf.sqrt(b_wh_pred)) * indicator_coord, axis=[1,2,3,4])
+ loss_xy = K.sum(K.square(b_xy - b_xy_pred) * indicator_coord, axis=[1,2,3,4])
+ #loss_wh = K.sum(K.square(b_wh - b_wh_pred) * indicator_coord, axis=[1,2,3,4])
+ loss_wh = K.sum(K.square(K.sqrt(b_wh) - K.sqrt(b_wh_pred)) * indicator_coord, axis=[1,2,3,4])
return (loss_wh + loss_xy) / (norm_coord + EPSILON) / 2
@@ -84,50 +84,50 @@ def obj_loss(self, y_true, y_pred):
b_o_pred = y_pred[..., 4]
num_true_labels = YoloParams.GRID_SIZE*YoloParams.GRID_SIZE*YoloParams.NUM_BOUNDING_BOXES
- y_true_p = tf.reshape(y_true[..., :4], shape=(YoloParams.BATCH_SIZE, 1, 1, 1, num_true_labels, 4))
- iou_scores_buff = calculate_ious(y_true_p, tf.expand_dims(y_pred, axis=4))
- best_ious = tf.reduce_max(iou_scores_buff, axis=4)
+ y_true_p = K.reshape(y_true[..., :4], shape=(YoloParams.BATCH_SIZE, 1, 1, 1, num_true_labels, 4))
+ iou_scores_buff = calculate_ious(y_true_p, K.expand_dims(y_pred, axis=4))
+ best_ious = K.max(iou_scores_buff, axis=4)
- indicator_noobj = tf.to_float(best_ious < self.iou_threshold) * (1 - y_true[..., 4]) * self.lambda_noobj
+ indicator_noobj = K.cast(best_ious < self.iou_threshold, np.float32) * (1 - y_true[..., 4]) * self.lambda_noobj
indicator_obj = y_true[..., 4] * self.lambda_obj
norm_conf = 1
if self.norm:
- norm_conf = tf.reduce_sum(tf.to_float((indicator_obj + indicator_noobj) > 0.0))
+ norm_conf = K.sum(K.cast((indicator_obj + indicator_noobj) > 0.0), np.float32)
- loss_obj = tf.reduce_sum(tf.square(b_o-b_o_pred) * (indicator_obj + indicator_noobj), axis=[1,2,3])
+ loss_obj = K.sum(K.square(b_o-b_o_pred) * (indicator_obj + indicator_noobj), axis=[1,2,3])
return loss_obj / (norm_conf + EPSILON) / 2
def class_loss(self, y_true, y_pred):
- b_class = tf.argmax(y_true[..., 5:], axis=-1)
+ b_class = K.argmax(y_true[..., 5:], axis=-1)
b_class_pred = y_pred[..., 5:]
- indicator_class = y_true[..., 4] * tf.gather(
+ indicator_class = y_true[..., 4] * K.gather(
YoloParams.CLASS_WEIGHTS, b_class) * self.lambda_class
norm_class = 1
if self.norm:
- norm_class = tf.reduce_sum(tf.to_float(indicator_class > 0.0))
+ norm_class = K.sum(K.cast(indicator_class > 0.0, np.float32))
loss_class_arg = tf.nn.sparse_softmax_cross_entropy_with_logits(
labels=b_class, logits=b_class_pred)
- loss_class = tf.reduce_sum(loss_class_arg * indicator_class, axis=[1,2,3])
+ loss_class = K.sum(loss_class_arg * indicator_class, axis=[1,2,3])
return loss_class / (norm_class + EPSILON)
def _transform_netout(self, y_pred_raw):
- y_pred_xy = tf.sigmoid(y_pred_raw[..., :2]) + YoloParams.c_grid
- y_pred_wh = tf.exp(y_pred_raw[..., 2:4]) * YoloParams.anchors
- y_pred_conf = tf.sigmoid(y_pred_raw[..., 4:5])
+ y_pred_xy = K.sigmoid(y_pred_raw[..., :2]) + YoloParams.c_grid
+ y_pred_wh = K.exp(y_pred_raw[..., 2:4]) * YoloParams.anchors
+ y_pred_conf = K.sigmoid(y_pred_raw[..., 4:5])
y_pred_class = y_pred_raw[...,5:]
- return tf.concat([y_pred_xy, y_pred_wh, y_pred_conf, y_pred_class], axis=-1)
+ return K.concatenate([y_pred_xy, y_pred_wh, y_pred_conf, y_pred_class], axis=-1)
@@ -149,8 +149,6 @@ def __call__(self, y_true, y_pred_raw):
return loss
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if __name__ == '__main__':
sess = tf.InteractiveSession()
@@ -160,26 +158,4 @@ def __call__(self, y_true, y_pred_raw):
var = YoloLoss()
- print( var(y_true, y_pred).eval() )
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+ print( var(y_true, y_pred).eval() )
\ No newline at end of file
diff --git a/result_plots/the_office.png b/result_plots/the_office.png
deleted file mode 100644
index e6204ee..0000000
Binary files a/result_plots/the_office.png and /dev/null differ