From 4a923445bcd694aac97e2b76f0afbc7fe9cb8240 Mon Sep 17 00:00:00 2001
From: tomasz <tomek-1996-1996@o2.pl>
Date: Fri, 11 Sep 2020 18:52:00 +0200
Subject: [PATCH] Update

---
 cfg/yolov4-21cls.cfg                    | 1155 +++++++++++++++++++++++
 our_scripts/config_bayes.yml            |  128 +--
 our_scripts/run_yolov3_process_bayes.py |   39 +-
 our_scripts/utils.py                    |   22 +-
 train.py                                |    2 +-
 utils/datasets.py                       |   14 +-
 6 files changed, 1251 insertions(+), 109 deletions(-)
 create mode 100644 cfg/yolov4-21cls.cfg

diff --git a/cfg/yolov4-21cls.cfg b/cfg/yolov4-21cls.cfg
new file mode 100644
index 00000000..e9d4d711
--- /dev/null
+++ b/cfg/yolov4-21cls.cfg
@@ -0,0 +1,1155 @@
+[net]
+# Testing
+#batch=1
+#subdivisions=1
+# Training
+batch=64
+subdivisions=8
+width=608
+height=608
+channels=3
+momentum=0.949
+decay=0.0005
+angle=0
+saturation = 1.5
+exposure = 1.5
+hue=.1tea
+
+learning_rate=0.00261
+burn_in=1000
+max_batches = 500500
+policy=steps
+steps=400000,450000
+scales=.1,.1
+
+#cutmix=1
+mosaic=1
+
+#:104x104 54:52x52 85:26x26 104:13x13 for 416
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=1
+pad=1
+activation=mish
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=2
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -2
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -1,-7
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=2
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -2
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -1,-10
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=2
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -2
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -1,-28
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=2
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -2
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -1,-28
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=2
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -2
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -1,-16
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=1
+stride=1
+pad=1
+activation=mish
+
+##########################
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+### SPP ###
+[maxpool]
+stride=1
+size=5
+
+[route]
+layers=-2
+
+[maxpool]
+stride=1
+size=9
+
+[route]
+layers=-4
+
+[maxpool]
+stride=1
+size=13
+
+[route]
+layers=-1,-3,-5,-6
+### End SPP ###
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[upsample]
+stride=2
+
+[route]
+layers = 85
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers = -1, -3
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[upsample]
+stride=2
+
+[route]
+layers = 54
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers = -1, -3
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=256
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=256
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+##########################
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=256
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=78
+activation=linear
+
+
+[yolo]
+mask = 0,1,2
+anchors=26,16, 19,45, 36,27, 54,32, 34,80, 60,49, 74,71, 96,105, 135,145
+classes=21
+num=9
+jitter=.3
+ignore_thresh = .7
+truth_thresh = 1
+scale_x_y = 1.2
+iou_thresh=0.213
+cls_normalizer=1.0
+iou_normalizer=0.07
+iou_loss=ciou
+nms_kind=greedynms
+beta_nms=0.6
+
+
+[route]
+layers = -4
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=2
+pad=1
+filters=256
+activation=leaky
+
+[route]
+layers = -1, -16
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=78
+activation=linear
+
+
+[yolo]
+mask = 3,4,5
+anchors=26,16, 19,45, 36,27, 54,32, 34,80, 60,49, 74,71, 96,105, 135,145
+classes=21
+num=9
+jitter=.3
+ignore_thresh = .7
+truth_thresh = 1
+scale_x_y = 1.1
+iou_thresh=0.213
+cls_normalizer=1.0
+iou_normalizer=0.07
+iou_loss=ciou
+nms_kind=greedynms
+beta_nms=0.6
+
+
+[route]
+layers = -4
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=2
+pad=1
+filters=512
+activation=leaky
+
+[route]
+layers = -1, -37
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=78
+activation=linear
+
+
+[yolo]
+mask = 6,7,8
+anchors=26,16, 19,45, 36,27, 54,32, 34,80, 60,49, 74,71, 96,105, 135,145
+classes=21
+num=9
+jitter=.3
+ignore_thresh = .7
+truth_thresh = 1
+random=1
+scale_x_y = 1.05
+iou_thresh=0.213
+cls_normalizer=1.0
+iou_normalizer=0.07
+iou_loss=ciou
+nms_kind=greedynms
+beta_nms=0.6
diff --git a/our_scripts/config_bayes.yml b/our_scripts/config_bayes.yml
index 05ae609b..0a92711c 100644
--- a/our_scripts/config_bayes.yml
+++ b/our_scripts/config_bayes.yml
@@ -1,29 +1,23 @@
 bayes:
-  iterations: 10
+  iterations: 1
 train:
   epochs:
     type: discrete
-    values: [30]
+    values: [400]
   batch-size:
     type: discrete
-    min: 1
-    max: 5
-    step: 1
+    values: [4]
   cfg: ./cfg/yolov3-spp-21cls.cfg
   data: ./data/widok_01_21.data
   multi-scale:
     type: discrete
-    values: [true, false]
+    values: [false]
   img-size-start:
     type: discrete
-    min: 512
-    max: 1088
-    step: 64
+    values: [896]
   img-size-end:
     type: discrete
-    min: 512
-    max: 1088
-    step: 64
+    values: [1344]
   rect:
     type: discrete
     values: [false]
@@ -33,104 +27,76 @@ train:
   evolve: false
   bucket:
   cache-images: false
-  weights: ./weights/yolov3-spp-ultralytics.pt
+  weights: ./experiments/model2/best.pt
   device: 1
   adam:
     type: discrete
     values: [true]
   single-cls: false
   snapshot-every:
-  freeze-layers: true
+  freeze-layers: false
   other-hyps:
     giou:
-      type: continuous
-      min: 0.0
-      max: 10.0
+      type: discrete
+      values: [3.54]
     cls:
-      type: continuous
-      min: 10.0
-      max: 100.0
+      type: discrete
+      values: [3.74]
     cls_pw:
-      type: continuous
-      min: 0.0
-      max: 10.0
+      type: discrete
+      values: [1.0]
     obj:
-      type: continuous
-      min: 10.0
-      max: 100.0
+      type: discrete
+      values: [64.3]
     obj_pw:
-      type: continuous
-      min: 0.0
-      max: 10.0
+      type: discrete
+      values: [1.0]
     iou_t:
-      type: continuous
-      min: 0.0
-      max: 1.0
+      type: discrete
+      values: [0.2]
     lr0:
-      type: continuous
-      min: 0.000001
-      max: 0.1
+      type: discrete
+      values: [0.01]
     lrf:
-      type: continuous
-      min: 0.000001
-      max: 0.1
+      type: discrete
+      values: [0.0005]
     momentum:
-      type: continuous
-      min: 0.0
-      max: 1.0
+      type: discrete
+      values: [0.937]
     weight_decay:
-      type: continuous
-      min: 0.0
-      max: 1.0
+      type: discrete
+      values: [0.0005]
     fl_gamma:
-      type: continuous
-      min: 0.0
-      max: 10.0
+      type: discrete
+      values: [0.0]
     hsv_h:
-      type: continuous
-      min: 0.0
-      max: 1.0
+      type: discrete
+      values: [0.0138]
     hsv_s:
-      type: continuous
-      min: 0.0
-      max: 1.0
+      type: discrete
+      values: [0.678]
     hsv_v:
-      type: continuous
-      min: 0.0
-      max: 1.0
+      type: discrete
+      values: [0.36]
     degrees:
-      type: continuous
-      min: 0.0
-      max: 30.0
+      type: discrete
+      values: [0.0]
     translate:
-      type: continuous
-      min: 0.0
-      max: 1.0
+      type: discrete
+      values: [0.0]
     scale:
-      type: continuous
-      min: 0.0
-      max: 1.0
+      type: discrete
+      values: [0.0]
     shear:
-      type: continuous
-      min: 0.0
-      max: 1.0
+      type: discrete
+      values: [0.0]
 experiments:
   dir: ./experiments
 detect:
   source: ./data/widok_01_21/widok_01_21_test_labels.txt
-  test-img-size:
-    type: discrete
-    min: 512
-    max: 1088
-    step: 64
-  conf-thres:
-    type: continuous
-    min: 0.3
-    max: 0.6
-  iou-thres:
-    type: continuous
-    min: 0.3
-    max: 0.6
+  test-img-size: 1024
+  conf-thres: 0.45
+  iou-thres: 0.6
   classes:
   agnostic-nms:
   augment:
diff --git a/our_scripts/run_yolov3_process_bayes.py b/our_scripts/run_yolov3_process_bayes.py
index 7d598ceb..c72a3d2c 100644
--- a/our_scripts/run_yolov3_process_bayes.py
+++ b/our_scripts/run_yolov3_process_bayes.py
@@ -84,9 +84,9 @@ def call_detection_script(gaussian_hyps, weights_path, names_path, dir):
           --output {detect_output_dir}
           --names {names_path}
           --weights {weights_path}
-          --test-img-size {gaussian_hyps['test-img-size']}
-          --conf-thres {gaussian_hyps['conf-thres']}
-          --iou-thres {gaussian_hyps['iou-thres']}
+          --test-img-size {getattr(config.detect, 'test-img-size')}
+          --conf-thres {getattr(config.detect, 'conf-thres')}
+          --iou-thres {getattr(config.detect, 'iou-thres')}
           --save-txt"""
     cmd += f" --device {config.train.device}" if config.train.device else ""
     cmd = " ".join(cmd.split())
@@ -134,37 +134,34 @@ def yolov3(x):
         'translate': float(x[:, 22]),
         'scale': float(x[:, 23]),
         'shear': float(x[:, 24]),  # train hyps end index
-        'test-img-size': int(x[:, 25]),
-        'conf-thres': float(x[:, 26]),
-        'iou-thres': float(x[:, 27])
     }
 
-    line = ""
     try:
         call_training_script(bayes_hyps)
         weights_path, names_path, train_results_dir = move_training_results_to_experiments_dir()
         detect_output_dir = call_detection_script(bayes_hyps, weights_path, names_path, train_results_dir)
         conf_matrix_path = call_generate_confussion_matrix(detect_output_dir, names_path, train_results_dir)
 
-        y_dict = get_values_from_conff_matrix(conf_matrix_path)
+        y_dict = get_values_from_conff_matrix(conf_matrix_path) 
 
-        # tutaj wzór na wyliczanie funkcji 
-        y_val = 1 - (y_dict['match'] * 10 - y_dict['false positives'] * 3 - y_dict['mistakes']) / y_dict['all labels']
+        # tutaj wzór na wyliczanie funkcji
+        y_val = (1 - (y_dict['right'] * 10 - y_dict['false positives'] * 3 - y_dict['mistakes']) / y_dict['labeled']) / 30
 
         # zapisywanie do pliku zadeklarowanego globalnie
         line = "\t".join([bayes_hyps.__str__(), str(y_val)])
+        print('###### line ########')
+        print(line)
         bayes_params_file.writelines([line, '\n'])
         return y_val
     except:
         tb = traceback.format_exc()
+        y_max_val = 1
         print("An error occured during running training-detect-confussion process \n", tb)
-        print("Returning 1 from current bayessian iteration")
-        line = "\t".join([bayes_hyps.__str__(), str(1)])
-        return 1
-    finally:
+        print(f"Returning {y_max_val} from current bayessian iteration")
+        line = "\t".join([bayes_hyps.__str__(), str(y_max_val)])
         bayes_params_file.writelines([line, '\n'])
-
-
+        return y_max_val
+# na jakiej rozdzielczości jest puszczana detekcja
 if __name__ == '__main__':
     bounds = config.get_bayes_bounds()
 
@@ -172,7 +169,15 @@ if __name__ == '__main__':
     # wczytywanie z poprzednich eksperymentów plik bayes_params
     X, Y =  load_previous_bayes_experiments(config.experiments.dir)
 
+    constraints = [
+        {
+            'name':'img_size_constraint',
+            'constraint': '(x[:,3] - x[:,4])' # img-size-start - img-size-end <= 0
+        }
+    ]
+
+
     bayes_optimizer = GPyOpt.methods.BayesianOptimization(f=yolov3, domain=bounds, X=X, Y=Y, verbosity=True,
-                                                          initial_design_numdata=2)
+                                                          initial_design_numdata=5, constraints=constraints, )
     bayes_optimizer.run_optimization(config.bayes.iterations, verbosity=True)
     bayes_params_file.close()
diff --git a/our_scripts/utils.py b/our_scripts/utils.py
index 2732f806..fce51029 100644
--- a/our_scripts/utils.py
+++ b/our_scripts/utils.py
@@ -2,9 +2,10 @@ import ast
 import io
 import os
 import subprocess
-import numpy as np
 from glob import glob
 
+import numpy as np
+
 
 def call_subprocess(cmd):
     process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, shell=True)
@@ -19,7 +20,7 @@ def call_subprocess(cmd):
 
 
 def get_values_from_conff_matrix(path):
-    lines = open(path, 'r').readlines()[:7]
+    lines = open(path, 'r').readlines()[:6]
     d = {}
     for l in lines:
         key, value, *_ = l.split("\t")
@@ -53,14 +54,15 @@ def get_bayes_params_as_dict(x):
         'translate': float(x[:, 22]),
         'scale': float(x[:, 23]),
         'shear': float(x[:, 24]),  # train hyps end index
-        'test-img-size': int(x[:, 25]),
-        'conf-thres': float(x[:, 26]),
-        'iou-thres': float(x[:, 27])
     }
 
 
 def load_previous_bayes_experiments(experiments_dir):
     paths = list(glob(os.path.join(experiments_dir, '*bayes_params.txt')))
+    if len(paths) == 0:
+        print("No bayes files found")
+        return None, None
+
     y_values = []
     x_values = []
 
@@ -74,8 +76,14 @@ def load_previous_bayes_experiments(experiments_dir):
                 bayes_values = dict_to_numpy(bayes_dict)
                 x_values.append(bayes_values)
                 y_values.append(float(y_val))
+
+                print("Loaded values from prevous experiments ", dict_str, y_val)
             except:
                 raise Exception(f"Cannot parse line {line} from file {p}")
+    if not y_values or not x_values:
+        print("No bayes files found")
+        return None, None
+
     return np.array(x_values), np.array(y_values).reshape((len(y_values), 1))
 
 
@@ -91,3 +99,7 @@ def dict_to_numpy(d):
         else:
             x.append(float(value))
     return x
+
+
+if __name__ == '__main__':
+    get_values_from_conff_matrix('/home/tomekb/yolov3/experiments/2020-08-17_02-05-43/confussion-matrix.tsv')
\ No newline at end of file
diff --git a/train.py b/train.py
index 2d616d6c..60bbd24e 100644
--- a/train.py
+++ b/train.py
@@ -234,7 +234,7 @@ def train(hyp):
     nb = len(dataloader)  # number of batches
     n_burn = max(3 * nb, 500)  # burn-in iterations, max(3 epochs, 500 iterations)
     maps = np.zeros(nc)  # mAP per class
-    # torch.autograd.set_detect_anomaly(True)
+    # torch.autograd.set_baddetect_anomaly(True)
     results = (0, 0, 0, 0, 0, 0, 0)  # 'P', 'R', 'mAP', 'F1', 'val GIoU', 'val Objectness', 'val Classification'
     t0 = time.time()
     print('Image sizes %g - %g train, %g test' % (imgsz_min, imgsz_max, imgsz_test))
diff --git a/utils/datasets.py b/utils/datasets.py
index 3be391a6..f3962962 100755
--- a/utils/datasets.py
+++ b/utils/datasets.py
@@ -44,10 +44,14 @@ def exif_size(img):
 class LoadImages:  # for inference
     def __init__(self, path, img_size=416):
         path = str(Path(path))  # os-agnostic
-        files = [f.strip() for f in open(path, 'r').readlines()]
-
-
-
+        files = []
+        if path.endswith("txt"):
+            files = [f.strip() for f in open(path, 'r').readlines()]
+        else:
+            if os.path.isdir(path):
+                files = sorted(glob.glob(os.path.join(path, '*.*')))
+            elif os.path.isfile(path):
+                files = [path]
 
         images = [x for x in files if os.path.splitext(x)[-1].lower() in img_formats]
         videos = [x for x in files if os.path.splitext(x)[-1].lower() in vid_formats]
@@ -198,7 +202,7 @@ class LoadStreams:  # multiple IP or RTSP cameras
         self.sources = sources
         for i, s in enumerate(sources):
             # Start the thread to read frames from the video stream
-            print('%g/%g: %s... ' % (i + 1, n, s), end='')
+            print('%g/%g: %s... ' % (i + 1, n, s))
             cap = cv2.VideoCapture(0 if s == '0' else s)
             assert cap.isOpened(), 'Failed to open %s' % s
             w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))