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fix bugs at examples/yolov3.py (#11614)

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* Update load_weight. Give valid model url

* Fix bug in iou function
This commit is contained in:
Sardor
2025-08-12 10:14:47 +09:00
committed by GitHub
parent 0c97d6de1b
commit ca7a641442
1 changed files with 9 additions and 9 deletions
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18
examples/yolov3.py
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@@ -71,8 +71,8 @@ def bbox_iou(box1, box2):
# get the coordinates of the intersection rectangle # get the coordinates of the intersection rectangle
inter_rect_x1 = np.maximum(b1_x1, b2_x1) inter_rect_x1 = np.maximum(b1_x1, b2_x1)
inter_rect_y1 = np.maximum(b1_y1, b2_y1) inter_rect_y1 = np.maximum(b1_y1, b2_y1)
inter_rect_x2 = np.maximum(b1_x2, b2_x2) inter_rect_x2 = np.minimum(b1_x2, b2_x2)
inter_rect_y2 = np.maximum(b1_y2, b2_y2) inter_rect_y2 = np.minimum(b1_y2, b2_y2)
#Intersection area #Intersection area
inter_area = np.clip(inter_rect_x2 - inter_rect_x1 + 1, 0, 99999) * np.clip(inter_rect_y2 - inter_rect_y1 + 1, 0, 99999) inter_area = np.clip(inter_rect_x2 - inter_rect_x1 + 1, 0, 99999) * np.clip(inter_rect_y2 - inter_rect_y1 + 1, 0, 99999)
#Union Area #Union Area
@@ -297,13 +297,13 @@ class Darknet:
# Get the number of weights of batchnorm # Get the number of weights of batchnorm
num_bn_biases = math.prod(bn.bias.shape) num_bn_biases = math.prod(bn.bias.shape)
# Load weights # Load weights
bn_biases = Tensor(weights[ptr:ptr + num_bn_biases]) bn_biases = Tensor(weights[ptr:ptr + num_bn_biases].astype(np.float32))
ptr += num_bn_biases ptr += num_bn_biases
bn_weights = Tensor(weights[ptr:ptr+num_bn_biases]) bn_weights = Tensor(weights[ptr:ptr+num_bn_biases].astype(np.float32))
ptr += num_bn_biases ptr += num_bn_biases
bn_running_mean = Tensor(weights[ptr:ptr+num_bn_biases]) bn_running_mean = Tensor(weights[ptr:ptr+num_bn_biases].astype(np.float32))
ptr += num_bn_biases ptr += num_bn_biases
bn_running_var = Tensor(weights[ptr:ptr+num_bn_biases]) bn_running_var = Tensor(weights[ptr:ptr+num_bn_biases].astype(np.float32))
ptr += num_bn_biases ptr += num_bn_biases
# Cast the loaded weights into dims of model weights # Cast the loaded weights into dims of model weights
bn_biases = bn_biases.reshape(shape=tuple(bn.bias.shape)) bn_biases = bn_biases.reshape(shape=tuple(bn.bias.shape))
@@ -319,7 +319,7 @@ class Darknet:
# load biases of the conv layer # load biases of the conv layer
num_biases = math.prod(conv.bias.shape) num_biases = math.prod(conv.bias.shape)
# Load weights # Load weights
conv_biases = Tensor(weights[ptr: ptr+num_biases]) conv_biases = Tensor(weights[ptr: ptr+num_biases].astype(np.float32))
ptr += num_biases ptr += num_biases
# Reshape # Reshape
conv_biases = conv_biases.reshape(shape=tuple(conv.bias.shape)) conv_biases = conv_biases.reshape(shape=tuple(conv.bias.shape))
@@ -327,7 +327,7 @@ class Darknet:
conv.bias = conv_biases conv.bias = conv_biases
# Load weighys for conv layers # Load weighys for conv layers
num_weights = math.prod(conv.weight.shape) num_weights = math.prod(conv.weight.shape)
conv_weights = Tensor(weights[ptr:ptr+num_weights]) conv_weights = Tensor(weights[ptr:ptr+num_weights].astype(np.float32))
ptr += num_weights ptr += num_weights
conv_weights = conv_weights.reshape(shape=tuple(conv.weight.shape)) conv_weights = conv_weights.reshape(shape=tuple(conv.weight.shape))
conv.weight = conv_weights conv.weight = conv_weights
@@ -371,7 +371,7 @@ class Darknet:
if __name__ == "__main__": if __name__ == "__main__":
model = Darknet(fetch('https://raw.githubusercontent.com/pjreddie/darknet/master/cfg/yolov3.cfg').read_bytes()) model = Darknet(fetch('https://raw.githubusercontent.com/pjreddie/darknet/master/cfg/yolov3.cfg').read_bytes())
print("Loading weights file (237MB). This might take a while…") print("Loading weights file (237MB). This might take a while…")
model.load_weights('https://pjreddie.com/media/files/yolov3.weights') model.load_weights('https://github.com/shadiakiki1986/yolov3.weights/releases/download/3.0.1/yolov3.weights')
if len(sys.argv) > 1: if len(sys.argv) > 1:
url = sys.argv[1] url = sys.argv[1]
else: else: