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yolov4-tiny-3l issue resolved #325

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yolov4-tiny-3l issue resolved #325

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13e6adc
yolov4-tiny-3l issue resolved
aditya-5842 Mar 26, 2021
dbebe3c
updated the readme
aditya-5842 Mar 26, 2021
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14 changes: 14 additions & 0 deletions README.md
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Expand Up @@ -28,6 +28,11 @@ python detect.py --weights ./checkpoints/yolov4-416 --size 416 --model yolov4 --

python detect.py --weights ./checkpoints/yolov4-tiny-416 --size 416 --model yolov4 --image ./data/kite.jpg --tiny

# yolov4-tiny-3l
python save_model.py --weights ./data/yolov4-tiny-3l-608_5000.weights --output ./checkpoints/yolov4-tiny-3l-608 --input_size 608 --model yolov4-tiny-3l --tiny

# Run yolov4-tiny-3l tensorflow model
python detect.py --weights ./checkpoints/yolov4-tiny-3l-608 --size 608 --model yolov4 --images ./data/images/kite.jpg --tiny
```
If you want to run yolov3 or yolov3-tiny change ``--model yolov3`` in command

Expand All @@ -48,6 +53,15 @@ python save_model.py --weights ./data/yolov4.weights --output ./checkpoints/yolo
# yolov4
python convert_tflite.py --weights ./checkpoints/yolov4-416 --output ./checkpoints/yolov4-416.tflite

# yolov4-tiny
python save_model.py --weights ./data/yolov4-tiny-3l-608_5000.weights --output ./checkpoints/yolov4-tiny-3l-608 --input_size 608 --model yolov4-tiny-3l --tiny -framework tflite

# convert custom yolov4-tiny-3l tflite model
python convert_tflite.py --weights ./checkpoints/yolov4-tiny-3l-608 --output ./checkpoints/yolov4-tiny-3l-608.tflite

# yolov4-tiny-3l quantize float16
python convert_tflite.py --weights ./checkpoints/yolov4-tiny-3l-608 --output ./checkpoints/yolov4-tiny-3l-fp16.tflite --quantize_mode float16

# yolov4 quantize float16
python convert_tflite.py --weights ./checkpoints/yolov4-416 --output ./checkpoints/yolov4-416-fp16.tflite --quantize_mode float16

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1 change: 1 addition & 0 deletions convert_tflite.py
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Expand Up @@ -36,6 +36,7 @@ def save_tflite():
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS, tf.lite.OpsSet.SELECT_TF_OPS]
converter.allow_custom_ops = True
elif FLAGS.quantize_mode == 'int8':
converter.experimental_new_converter = False
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS, tf.lite.OpsSet.SELECT_TF_OPS]
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43 changes: 43 additions & 0 deletions core/backbone.py
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Expand Up @@ -146,6 +146,49 @@ def cspdarknet53_tiny(input_data):

return route_1, input_data

def cspdarknet53_tiny_3l(input_data):
input_data = common.convolutional(input_data, (3, 3, 3, 32), downsample=True)
input_data = common.convolutional(input_data, (3, 3, 32, 64), downsample=True)
input_data = common.convolutional(input_data, (3, 3, 64, 64))

route = input_data
input_data = common.route_group(input_data, 2, 1)
input_data = common.convolutional(input_data, (3, 3, 32, 32))
route_1 = input_data
input_data = common.convolutional(input_data, (3, 3, 32, 32))
input_data = tf.concat([input_data, route_1], axis=-1)
input_data = common.convolutional(input_data, (1, 1, 64, 64))
input_data = tf.concat([route, input_data], axis=-1)
input_data = tf.keras.layers.MaxPool2D(2, 2, 'same')(input_data)

input_data = common.convolutional(input_data, (3, 3, 128, 128))
route = input_data
input_data = common.route_group(input_data, 2, 1)
input_data = common.convolutional(input_data, (3, 3, 64, 64))
route_1 = input_data
input_data = common.convolutional(input_data, (3, 3, 64, 64))
input_data = tf.concat([input_data, route_1], axis=-1)
input_data = common.convolutional(input_data, (1, 1, 128, 128))
route_2 = input_data
input_data = tf.concat([route, input_data], axis=-1)
input_data = tf.keras.layers.MaxPool2D(2, 2, 'same')(input_data)

input_data = common.convolutional(input_data, (3, 3, 256, 256))
route = input_data
input_data = common.route_group(input_data, 2, 1)
input_data = common.convolutional(input_data, (3, 3, 128, 128))
route_1 = input_data
input_data = common.convolutional(input_data, (3, 3, 128, 128))
input_data = tf.concat([input_data, route_1], axis=-1)
input_data = common.convolutional(input_data, (1, 1, 256, 256))
route_1 = input_data
input_data = tf.concat([route, input_data], axis=-1)
input_data = tf.keras.layers.MaxPool2D(2, 2, 'same')(input_data)

input_data = common.convolutional(input_data, (3, 3, 512, 512))

return route_1, route_2, input_data

def darknet53_tiny(input_data):
input_data = common.convolutional(input_data, (3, 3, 3, 16))
input_data = tf.keras.layers.MaxPool2D(2, 2, 'same')(input_data)
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8 changes: 6 additions & 2 deletions core/config.py
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Expand Up @@ -11,14 +11,18 @@
# YOLO options
__C.YOLO = edict()

__C.YOLO.CLASSES = "./data/classes/coco.names"
__C.YOLO.CLASSES = "./data/classes/obj.names"
__C.YOLO.ANCHORS = [12,16, 19,36, 40,28, 36,75, 76,55, 72,146, 142,110, 192,243, 459,401]
__C.YOLO.ANCHORS_V3 = [10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326]
__C.YOLO.ANCHORS_TINY = [23,27, 37,58, 81,82, 81,82, 135,169, 344,319]
# __C.YOLO.ANCHORS_TINY = [23,27, 37,58, 81,82, 81,82, 135,169, 344,319]
__C.YOLO.ANCHORS_TINY = [10,14, 23,27, 37,58, 81,82, 135,169, 344,319]
__C.YOLO.ANCHORS_TINY_3l = [12,16, 19,36, 40,28, 36,75, 76,55, 72,146, 142,110, 192,243, 459,401]
__C.YOLO.STRIDES = [8, 16, 32]
__C.YOLO.STRIDES_TINY = [16, 32]
__C.YOLO.STRIDES_TINY_3l = [8, 16, 32]
__C.YOLO.XYSCALE = [1.2, 1.1, 1.05]
__C.YOLO.XYSCALE_TINY = [1.05, 1.05]
__C.YOLO.XYSCALE_TINY_3l = [1.05, 1.05, 1.05]
__C.YOLO.ANCHOR_PER_SCALE = 3
__C.YOLO.IOU_LOSS_THRESH = 0.5

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25 changes: 18 additions & 7 deletions core/utils.py
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Expand Up @@ -23,6 +23,9 @@ def load_weights(model, weights_file, model_name='yolov4', is_tiny=False):
if model_name == 'yolov3':
layer_size = 13
output_pos = [9, 12]
elif model_name == 'yolov4-tiny-3l':
layer_size = 24
output_pos = [17, 20, 23]
else:
layer_size = 21
output_pos = [17, 20]
Expand Down Expand Up @@ -81,24 +84,32 @@ def read_class_names(class_file_name):

def load_config(FLAGS):
if FLAGS.tiny:
STRIDES = np.array(cfg.YOLO.STRIDES_TINY)
ANCHORS = get_anchors(cfg.YOLO.ANCHORS_TINY, FLAGS.tiny)
XYSCALE = cfg.YOLO.XYSCALE_TINY if FLAGS.model == 'yolov4' else [1, 1]
if FLAGS.model == 'yolov4-tiny-3l':
STRIDES = np.array(cfg.YOLO.STRIDES_TINY_3l)
ANCHORS = get_anchors(cfg.YOLO.ANCHORS_TINY_3l, FLAGS.tiny, FLAGS.model)
XYSCALE = cfg.YOLO.XYSCALE_TINY_3l
else:
STRIDES = np.array(cfg.YOLO.STRIDES_TINY)
ANCHORS = get_anchors(cfg.YOLO.ANCHORS_TINY, FLAGS.tiny, FLAGS.model)
XYSCALE = cfg.YOLO.XYSCALE_TINY if FLAGS.model == 'yolov4' else [1, 1]
else:
STRIDES = np.array(cfg.YOLO.STRIDES)
if FLAGS.model == 'yolov4':
ANCHORS = get_anchors(cfg.YOLO.ANCHORS, FLAGS.tiny)
ANCHORS = get_anchors(cfg.YOLO.ANCHORS, FLAGS.tiny, FLAGS.model)
elif FLAGS.model == 'yolov3':
ANCHORS = get_anchors(cfg.YOLO.ANCHORS_V3, FLAGS.tiny)
ANCHORS = get_anchors(cfg.YOLO.ANCHORS_V3, FLAGS.tiny, FLAGS.model)
XYSCALE = cfg.YOLO.XYSCALE if FLAGS.model == 'yolov4' else [1, 1, 1]
NUM_CLASS = len(read_class_names(cfg.YOLO.CLASSES))

return STRIDES, ANCHORS, NUM_CLASS, XYSCALE

def get_anchors(anchors_path, tiny=False):
def get_anchors(anchors_path, tiny=False, model='yolov4'):
anchors = np.array(anchors_path)
if tiny:
return anchors.reshape(2, 3, 2)
if model == 'yolov4-tiny-3l':
return anchors.reshape(3, 3, 2)
else:
return anchors.reshape(2, 3, 2)
else:
return anchors.reshape(3, 3, 2)

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30 changes: 30 additions & 0 deletions core/yolov4.py
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Expand Up @@ -18,6 +18,13 @@ def YOLO(input_layer, NUM_CLASS, model='yolov4', is_tiny=False):
if is_tiny:
if model == 'yolov4':
return YOLOv4_tiny(input_layer, NUM_CLASS)
elif model == 'yolov4-tiny-3l':
print("#"*100)
print("\n"*5)
print("loading tiny-3l")
print("\n"*5)
print("#"*100)
return YOLOv4_tiny_3l(input_layer, NUM_CLASS)
elif model == 'yolov3':
return YOLOv3_tiny(input_layer, NUM_CLASS)
else:
Expand Down Expand Up @@ -143,6 +150,29 @@ def YOLOv4_tiny(input_layer, NUM_CLASS):

return [conv_mbbox, conv_lbbox]

def YOLOv4_tiny_3l(input_layer, NUM_CLASS):
route_1, route_2, conv = backbone.cspdarknet53_tiny_3l(input_layer)

conv = common.convolutional(conv, (1, 1, 512, 256))

conv_lobj_branch = common.convolutional(conv, (3, 3, 256, 512))
conv_lbbox = common.convolutional(conv_lobj_branch, (1, 1, 512, 3 * (NUM_CLASS + 5)), activate=False, bn=False)

conv = common.convolutional(conv, (1, 1, 256, 128))
conv = common.upsample(conv)
conv = tf.concat([conv, route_1], axis=-1)

conv = common.convolutional(conv, (3, 3, 384, 256))
conv_mbbox = common.convolutional(conv, (1, 1, 256, 3 * (NUM_CLASS + 5)), activate=False, bn=False)

conv = common.convolutional(conv, (1, 1, 256, 64))
conv = common.upsample(conv)
conv = tf.concat([conv, route_2], axis=-1)

conv_sobj_branch = common.convolutional(conv, (3, 3, 192, 128))
conv_sbbox = common.convolutional(conv_sobj_branch, (1, 1, 128, 3 * (NUM_CLASS + 5)), activate=False, bn=False)
return [conv_sbbox, conv_mbbox, conv_lbbox]

def YOLOv3_tiny(input_layer, NUM_CLASS):
route_1, conv = backbone.darknet53_tiny(input_layer)

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1 change: 1 addition & 0 deletions data/classes/obj.names
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@@ -0,0 +1 @@
license_plate
26 changes: 19 additions & 7 deletions save_model.py
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Expand Up @@ -21,13 +21,25 @@ def save_tf():
bbox_tensors = []
prob_tensors = []
if FLAGS.tiny:
for i, fm in enumerate(feature_maps):
if i == 0:
output_tensors = decode(fm, FLAGS.input_size // 16, NUM_CLASS, STRIDES, ANCHORS, i, XYSCALE, FLAGS.framework)
else:
output_tensors = decode(fm, FLAGS.input_size // 32, NUM_CLASS, STRIDES, ANCHORS, i, XYSCALE, FLAGS.framework)
bbox_tensors.append(output_tensors[0])
prob_tensors.append(output_tensors[1])
if FLAGS.model == 'yolov4-tiny-3l':
for i, fm in enumerate(feature_maps):
# import pdb; pdb.set_trace()
if i == 0:
output_tensors = decode(fm, FLAGS.input_size // 8, NUM_CLASS, STRIDES, ANCHORS, i, XYSCALE, FLAGS.framework)
elif i == 1:
output_tensors = decode(fm, FLAGS.input_size // 16, NUM_CLASS, STRIDES, ANCHORS, i, XYSCALE, FLAGS.framework)
else:
output_tensors = decode(fm, FLAGS.input_size // 32, NUM_CLASS, STRIDES, ANCHORS, i, XYSCALE, FLAGS.framework)
bbox_tensors.append(output_tensors[0])
prob_tensors.append(output_tensors[1])
else:
for i, fm in enumerate(feature_maps):
if i == 0:
output_tensors = decode(fm, FLAGS.input_size // 16, NUM_CLASS, STRIDES, ANCHORS, i, XYSCALE, FLAGS.framework)
else:
output_tensors = decode(fm, FLAGS.input_size // 32, NUM_CLASS, STRIDES, ANCHORS, i, XYSCALE, FLAGS.framework)
bbox_tensors.append(output_tensors[0])
prob_tensors.append(output_tensors[1])
else:
for i, fm in enumerate(feature_maps):
if i == 0:
Expand Down