Comments (20)
weixiaolian21
commented on May 21, 2024
1
the same issue in python 3.
"需要修改convolution_param['pad'] = str(int(convolution_param['kernel_size'])/2) 为 convolution_param['pad'] = str(int(int(convolution_param['kernel_size'])/2))，否则产生的PAD可能为浮点"
can not fixed the issue
you can change the prototxt.py:
# print >> fp, 'name: \"%s\"' % props['name']
print('name: \"%s\"' % props['name'],file = fp)
change every "print" like this.
from caffe-yolov3.
ChenYingpeng
commented on May 21, 2024
Hi，doublegssc，
请把你运行代码的执行命令发上来。
from caffe-yolov3.
ChenYingpeng
commented on May 21, 2024
你的这个darknet2caffe.py代码是我这个工程里面的么，还是我给你的链接工程里面的？
from caffe-yolov3.
doublegssc
commented on May 21, 2024
是你这个工程里面的。。
from caffe-yolov3.
doublegssc
commented on May 21, 2024
我的命令是：
python3 darknet2caffe.py yolov3-tiny.cfg yolov3-tiny.weights yolov3-tiny.prototxt yolov3-tiny.caffemodel
from caffe-yolov3.
ChenYingpeng
commented on May 21, 2024
我需要看你的整个darknet2caffe.py的代码以及你运行错误的整个日志。
from caffe-yolov3.
doublegssc
commented on May 21, 2024
darknet2caffe.py的代码为：
－－－－－－－－－－－－－－－－－－－－－－－－－－－－－
The caffe module needs to be on the Python path;
we'll add it here explicitly.
caffe_root='/home/user/caffe/'
#os.chdir(caffe_root)
import sys
sys.path.insert(0,caffe_root+'python')
import caffe
import numpy as np
from collections import OrderedDict
from cfg import *
from prototxt import *
def darknet2caffe(cfgfile, weightfile, protofile, caffemodel):
net_info = cfg2prototxt(cfgfile)
save_prototxt(net_info , protofile, region=False)
net = caffe.Net(protofile, caffe.TEST)
params = net.params
blocks = parse_cfg(cfgfile)
#Open the weights file
fp = open(weightfile, "rb")
#The first 4 values are header information
# 1. Major version number
# 2. Minor Version Number
# 3. Subversion number
# 4. IMages seen
header = np.fromfile(fp, dtype = np.int32, count = 5)
#fp = open(weightfile, 'rb')
#header = np.fromfile(fp, count=5, dtype=np.int32)
#header = np.ndarray(shape=(5,),dtype='int32',buffer=fp.read(20))
#print(header)
buf = np.fromfile(fp, dtype = np.float32)
#print(buf)
fp.close()
layers = []
layer_id = 1
start = 0
for block in blocks:
if start >= buf.size:
break
if block['type'] == 'net':
continue
elif block['type'] == 'convolutional':
batch_normalize = int(block['batch_normalize'])
if 'name' in block:
conv_layer_name = block['name']
bn_layer_name = '%s-bn' % block['name']
scale_layer_name = '%s-scale' % block['name']
else:
conv_layer_name = 'layer%d-conv' % layer_id
bn_layer_name = 'layer%d-bn' % layer_id
scale_layer_name = 'layer%d-scale' % layer_id
if batch_normalize:
start = load_conv_bn2caffe(buf, start, params[conv_layer_name], params[bn_layer_name], params[scale_layer_name])
else:
start = load_conv2caffe(buf, start, params[conv_layer_name])
layer_id = layer_id+1
elif block['type'] == 'depthwise_convolutional':
batch_normalize = int(block['batch_normalize'])
if 'name' in block:
conv_layer_name = block['name']
bn_layer_name = '%s-bn' % block['name']
scale_layer_name = '%s-scale' % block['name']
else:
conv_layer_name = 'layer%d-dwconv' % layer_id
bn_layer_name = 'layer%d-bn' % layer_id
scale_layer_name = 'layer%d-scale' % layer_id
if batch_normalize:
start = load_conv_bn2caffe(buf, start, params[conv_layer_name], params[bn_layer_name], params[scale_layer_name])
else:
start = load_conv2caffe(buf, start, params[conv_layer_name])
layer_id = layer_id+1
elif block['type'] == 'connected':
if 'name' in block:
fc_layer_name = block['name']
else:
fc_layer_name = 'layer%d-fc' % layer_id
start = load_fc2caffe(buf, start, params[fc_layer_name])
layer_id = layer_id+1
elif block['type'] == 'maxpool':
layer_id = layer_id+1
elif block['type'] == 'avgpool':
layer_id = layer_id+1
elif block['type'] == 'region':
layer_id = layer_id + 1
elif block['type'] == 'route':
layer_id = layer_id + 1
elif block['type'] == 'shortcut':
layer_id = layer_id + 1
elif block['type'] == 'softmax':
layer_id = layer_id + 1
elif block['type'] == 'cost':
layer_id = layer_id + 1
elif block['type'] == 'upsample':
layer_id = layer_id + 1
else:
print('unknow layer type %s ' % block['type'])
layer_id = layer_id + 1
print('save prototxt to %s' % protofile)
save_prototxt(net_info , protofile, region=True)
print('save caffemodel to %s' % caffemodel)
net.save(caffemodel)
def load_conv2caffe(buf, start, conv_param):
weight = conv_param[0].data
bias = conv_param[1].data
conv_param[1].data[...] = np.reshape(buf[start:start+bias.size], bias.shape); start = start + bias.size
conv_param[0].data[...] = np.reshape(buf[start:start+weight.size], weight.shape); start = start + weight.size
return start
def load_fc2caffe(buf, start, fc_param):
weight = fc_param[0].data
bias = fc_param[1].data
fc_param[1].data[...] = np.reshape(buf[start:start+bias.size], bias.shape); start = start + bias.size
fc_param[0].data[...] = np.reshape(buf[start:start+weight.size], weight.shape); start = start + weight.size
return start
def load_conv_bn2caffe(buf, start, conv_param, bn_param, scale_param):
conv_weight = conv_param[0].data
running_mean = bn_param[0].data
running_var = bn_param[1].data
scale_weight = scale_param[0].data
scale_bias = scale_param[1].data
scale_param[1].data[...] = np.reshape(buf[start:start+scale_bias.size], scale_bias.shape); start = start + scale_bias.size
#print scale_bias.size
#print scale_bias
scale_param[0].data[...] = np.reshape(buf[start:start+scale_weight.size], scale_weight.shape); start = start + scale_weight.size
#print scale_weight.size
bn_param[0].data[...] = np.reshape(buf[start:start+running_mean.size], running_mean.shape); start = start + running_mean.size
#print running_mean.size
bn_param[1].data[...] = np.reshape(buf[start:start+running_var.size], running_var.shape); start = start + running_var.size
#print running_var.size
bn_param[2].data[...] = np.array([1.0])
conv_param[0].data[...] = np.reshape(buf[start:start+conv_weight.size], conv_weight.shape); start = start + conv_weight.size
#print conv_weight.size
return start
def cfg2prototxt(cfgfile):
blocks = parse_cfg(cfgfile)
prev_filters = 3
layers = []
props = OrderedDict()
bottom = 'data'
layer_id = 1
topnames = dict()
for block in blocks:
if block['type'] == 'net':
props['name'] = 'Darkent2Caffe'
props['input'] = 'data'
props['input_dim'] = ['1']
props['input_dim'].append(block['channels'])
props['input_dim'].append(block['height'])
props['input_dim'].append(block['width'])
continue
elif block['type'] == 'convolutional':
conv_layer = OrderedDict()
conv_layer['bottom'] = bottom
if 'name' in block:
conv_layer['top'] = block['name']
conv_layer['name'] = block['name']
else:
conv_layer['top'] = 'layer%d-conv' % layer_id
conv_layer['name'] = 'layer%d-conv' % layer_id
conv_layer['type'] = 'Convolution'
convolution_param = OrderedDict()
convolution_param['num_output'] = block['filters']
prev_filters = block['filters']
convolution_param['kernel_size'] = block['size']
if block['pad'] == '1':
convolution_param['pad'] = str(int(convolution_param['kernel_size'])/2)
convolution_param['stride'] = block['stride']
if block['batch_normalize'] == '1':
convolution_param['bias_term'] = 'false'
else:
convolution_param['bias_term'] = 'true'
conv_layer['convolution_param'] = convolution_param
layers.append(conv_layer)
bottom = conv_layer['top']
if block['batch_normalize'] == '1':
bn_layer = OrderedDict()
bn_layer['bottom'] = bottom
bn_layer['top'] = bottom
if 'name' in block:
bn_layer['name'] = '%s-bn' % block['name']
else:
bn_layer['name'] = 'layer%d-bn' % layer_id
bn_layer['type'] = 'BatchNorm'
batch_norm_param = OrderedDict()
batch_norm_param['use_global_stats'] = 'true'
bn_layer['batch_norm_param'] = batch_norm_param
layers.append(bn_layer)
scale_layer = OrderedDict()
scale_layer['bottom'] = bottom
scale_layer['top'] = bottom
if 'name' in block:
scale_layer['name'] = '%s-scale' % block['name']
else:
scale_layer['name'] = 'layer%d-scale' % layer_id
scale_layer['type'] = 'Scale'
scale_param = OrderedDict()
scale_param['bias_term'] = 'true'
scale_layer['scale_param'] = scale_param
layers.append(scale_layer)
if block['activation'] != 'linear':
relu_layer = OrderedDict()
relu_layer['bottom'] = bottom
relu_layer['top'] = bottom
if 'name' in block:
relu_layer['name'] = '%s-act' % block['name']
else:
relu_layer['name'] = 'layer%d-act' % layer_id
relu_layer['type'] = 'ReLU'
if block['activation'] == 'leaky':
relu_param = OrderedDict()
relu_param['negative_slope'] = '0.1'
relu_layer['relu_param'] = relu_param
layers.append(relu_layer)
topnames[layer_id] = bottom
layer_id = layer_id+1
elif block['type'] == 'depthwise_convolutional':
conv_layer = OrderedDict()
conv_layer['bottom'] = bottom
if 'name' in block:
conv_layer['top'] = block['name']
conv_layer['name'] = block['name']
else:
conv_layer['top'] = 'layer%d-dwconv' % layer_id
conv_layer['name'] = 'layer%d-dwconv' % layer_id
conv_layer['type'] = 'ConvolutionDepthwise'
convolution_param = OrderedDict()
convolution_param['num_output'] = prev_filters
convolution_param['kernel_size'] = block['size']
if block['pad'] == '1':
convolution_param['pad'] = str(int(convolution_param['kernel_size'])/2)
convolution_param['stride'] = block['stride']
if block['batch_normalize'] == '1':
convolution_param['bias_term'] = 'false'
else:
convolution_param['bias_term'] = 'true'
conv_layer['convolution_param'] = convolution_param
layers.append(conv_layer)
bottom = conv_layer['top']
if block['batch_normalize'] == '1':
bn_layer = OrderedDict()
bn_layer['bottom'] = bottom
bn_layer['top'] = bottom
if 'name' in block:
bn_layer['name'] = '%s-bn' % block['name']
else:
bn_layer['name'] = 'layer%d-bn' % layer_id
bn_layer['type'] = 'BatchNorm'
batch_norm_param = OrderedDict()
batch_norm_param['use_global_stats'] = 'true'
bn_layer['batch_norm_param'] = batch_norm_param
layers.append(bn_layer)
scale_layer = OrderedDict()
scale_layer['bottom'] = bottom
scale_layer['top'] = bottom
if 'name' in block:
scale_layer['name'] = '%s-scale' % block['name']
else:
scale_layer['name'] = 'layer%d-scale' % layer_id
scale_layer['type'] = 'Scale'
scale_param = OrderedDict()
scale_param['bias_term'] = 'true'
scale_layer['scale_param'] = scale_param
layers.append(scale_layer)
if block['activation'] != 'linear':
relu_layer = OrderedDict()
relu_layer['bottom'] = bottom
relu_layer['top'] = bottom
if 'name' in block:
relu_layer['name'] = '%s-act' % block['name']
else:
relu_layer['name'] = 'layer%d-act' % layer_id
relu_layer['type'] = 'ReLU'
if block['activation'] == 'leaky':
relu_param = OrderedDict()
relu_param['negative_slope'] = '0.1'
relu_layer['relu_param'] = relu_param
layers.append(relu_layer)
topnames[layer_id] = bottom
layer_id = layer_id+1
elif block['type'] == 'maxpool':
max_layer = OrderedDict()
max_layer['bottom'] = bottom
if 'name' in block:
max_layer['top'] = block['name']
max_layer['name'] = block['name']
else:
max_layer['top'] = 'layer%d-maxpool' % layer_id
max_layer['name'] = 'layer%d-maxpool' % layer_id
max_layer['type'] = 'Pooling'
pooling_param = OrderedDict()
pooling_param['stride'] = block['stride']
pooling_param['pool'] = 'MAX'
if (int(block['size']) - int(block['stride'])) % 2 == 0:
pooling_param['kernel_size'] = block['size']
pooling_param['pad'] = str((int(block['size'])-1)/2)
if (int(block['size']) - int(block['stride'])) % 2 == 1:
pooling_param['kernel_size'] = str(int(block['size']) + 1)
pooling_param['pad'] = str((int(block['size']) + 1)/2)
max_layer['pooling_param'] = pooling_param
layers.append(max_layer)
bottom = max_layer['top']
topnames[layer_id] = bottom
layer_id = layer_id+1
elif block['type'] == 'avgpool':
avg_layer = OrderedDict()
avg_layer['bottom'] = bottom
if 'name' in block:
avg_layer['top'] = block['name']
avg_layer['name'] = block['name']
else:
avg_layer['top'] = 'layer%d-avgpool' % layer_id
avg_layer['name'] = 'layer%d-avgpool' % layer_id
avg_layer['type'] = 'Pooling'
pooling_param = OrderedDict()
pooling_param['kernel_size'] = 7
pooling_param['stride'] = 1
pooling_param['pool'] = 'AVE'
avg_layer['pooling_param'] = pooling_param
layers.append(avg_layer)
bottom = avg_layer['top']
topnames[layer_id] = bottom
layer_id = layer_id+1
elif block['type'] == 'region':
if True:
region_layer = OrderedDict()
region_layer['bottom'] = bottom
if 'name' in block:
region_layer['top'] = block['name']
region_layer['name'] = block['name']
else:
region_layer['top'] = 'layer%d-region' % layer_id
region_layer['name'] = 'layer%d-region' % layer_id
region_layer['type'] = 'Region'
region_param = OrderedDict()
region_param['anchors'] = block['anchors'].strip()
region_param['classes'] = block['classes']
region_param['num'] = block['num']
region_layer['region_param'] = region_param
layers.append(region_layer)
bottom = region_layer['top']
topnames[layer_id] = bottom
layer_id = layer_id + 1
elif block['type'] == 'route':
route_layer = OrderedDict()
layer_name = str(block['layers']).split(',')
#print(layer_name[0])
bottom_layer_size = len(str(block['layers']).split(','))
#print(bottom_layer_size)
if(1 == bottom_layer_size):
prev_layer_id = layer_id + int(block['layers'])
bottom = topnames[prev_layer_id]
#topnames[layer_id] = bottom
route_layer['bottom'] = bottom
if(2 == bottom_layer_size):
prev_layer_id1 = layer_id + int(layer_name[0])
#print(prev_layer_id1)
prev_layer_id2 = int(layer_name[1]) + 1
print(topnames)
bottom1 = topnames[prev_layer_id1]
bottom2 = topnames[prev_layer_id2]
route_layer['bottom'] = [bottom1, bottom2]
if(4 == bottom_layer_size):
prev_layer_id1 = layer_id + int(layer_name[0])
prev_layer_id2 = layer_id + int(layer_name[1])
prev_layer_id3 = layer_id + int(layer_name[2])
prev_layer_id4 = layer_id + int(layer_name[3])
bottom1 = topnames[prev_layer_id1]
bottom2 = topnames[prev_layer_id2]
bottom3 = topnames[prev_layer_id3]
bottom4 = topnames[prev_layer_id4]
route_layer['bottom'] = [bottom1, bottom2,bottom3,bottom4]
if 'name' in block:
route_layer['top'] = block['name']
route_layer['name'] = block['name']
else:
route_layer['top'] = 'layer%d-route' % layer_id
route_layer['name'] = 'layer%d-route' % layer_id
route_layer['type'] = 'Concat'
print(route_layer)
layers.append(route_layer)
bottom = route_layer['top']
print(layer_id)
topnames[layer_id] = bottom
layer_id = layer_id + 1
elif block['type'] == 'upsample':
upsample_layer = OrderedDict()
print(block['stride'])
upsample_layer['bottom'] = bottom
if 'name' in block:
upsample_layer['top'] = block['name']
upsample_layer['name'] = block['name']
else:
upsample_layer['top'] = 'layer%d-upsample' % layer_id
upsample_layer['name'] = 'layer%d-upsample' % layer_id
upsample_layer['type'] = 'Upsample'
upsample_param = OrderedDict()
upsample_param['scale'] = block['stride']
upsample_layer['upsample_param'] = upsample_param
print(upsample_layer)
layers.append(upsample_layer)
bottom = upsample_layer['top']
print('upsample:',layer_id)
topnames[layer_id] = bottom
layer_id = layer_id + 1
elif block['type'] == 'shortcut':
prev_layer_id1 = layer_id + int(block['from'])
prev_layer_id2 = layer_id - 1
bottom1 = topnames[prev_layer_id1]
bottom2= topnames[prev_layer_id2]
shortcut_layer = OrderedDict()
shortcut_layer['bottom'] = [bottom1, bottom2]
if 'name' in block:
shortcut_layer['top'] = block['name']
shortcut_layer['name'] = block['name']
else:
shortcut_layer['top'] = 'layer%d-shortcut' % layer_id
shortcut_layer['name'] = 'layer%d-shortcut' % layer_id
shortcut_layer['type'] = 'Eltwise'
eltwise_param = OrderedDict()
eltwise_param['operation'] = 'SUM'
shortcut_layer['eltwise_param'] = eltwise_param
layers.append(shortcut_layer)
bottom = shortcut_layer['top']
if block['activation'] != 'linear':
relu_layer = OrderedDict()
relu_layer['bottom'] = bottom
relu_layer['top'] = bottom
if 'name' in block:
relu_layer['name'] = '%s-act' % block['name']
else:
relu_layer['name'] = 'layer%d-act' % layer_id
relu_layer['type'] = 'ReLU'
if block['activation'] == 'leaky':
relu_param = OrderedDict()
relu_param['negative_slope'] = '0.1'
relu_layer['relu_param'] = relu_param
layers.append(relu_layer)
topnames[layer_id] = bottom
layer_id = layer_id + 1
elif block['type'] == 'connected':
fc_layer = OrderedDict()
fc_layer['bottom'] = bottom
if 'name' in block:
fc_layer['top'] = block['name']
fc_layer['name'] = block['name']
else:
fc_layer['top'] = 'layer%d-fc' % layer_id
fc_layer['name'] = 'layer%d-fc' % layer_id
fc_layer['type'] = 'InnerProduct'
fc_param = OrderedDict()
fc_param['num_output'] = int(block['output'])
fc_layer['inner_product_param'] = fc_param
layers.append(fc_layer)
bottom = fc_layer['top']
if block['activation'] != 'linear':
relu_layer = OrderedDict()
relu_layer['bottom'] = bottom
relu_layer['top'] = bottom
if 'name' in block:
relu_layer['name'] = '%s-act' % block['name']
else:
relu_layer['name'] = 'layer%d-act' % layer_id
relu_layer['type'] = 'ReLU'
if block['activation'] == 'leaky':
relu_param = OrderedDict()
relu_param['negative_slope'] = '0.1'
relu_layer['relu_param'] = relu_param
layers.append(relu_layer)
topnames[layer_id] = bottom
layer_id = layer_id+1
else:
print('unknow layer type %s ' % block['type'])
topnames[layer_id] = bottom
layer_id = layer_id + 1
net_info = OrderedDict()
net_info['props'] = props
net_info['layers'] = layers
return net_info
if name == 'main':
import sys
if len(sys.argv) != 5:
print('try:')
print('python darknet2caffe.py tiny-yolo-voc.cfg tiny-yolo-voc.weights tiny-yolo-voc.prototxt tiny-yolo-voc.caffemodel')
print('')
print('please add name field for each block to avoid generated name')
exit()
cfgfile = sys.argv[1]
#net_info = cfg2prototxt(cfgfile)
#print_prototxt(net_info)
#save_prototxt(net_info, 'tmp.prototxt')
weightfile = sys.argv[2]
protofile = sys.argv[3]
caffemodel = sys.argv[4]
darknet2caffe(cfgfile, weightfile, protofile, caffemodel)
－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－
运行错误的整个日志为：
－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－
unknow layer type yolo
OrderedDict([('bottom', 'layer16-conv'), ('top', 'layer20-route'), ('name', 'layer20-route'), ('type', 'Concat')])
20
2
OrderedDict([('bottom', 'layer21-conv'), ('top', 'layer22-upsample'), ('name', 'layer22-upsample'), ('type', 'Upsample'), ('upsample_param', OrderedDict([('scale', '2')]))])
upsample: 22
{1: 'layer1-conv', 2: 'layer2-maxpool', 3: 'layer3-conv', 4: 'layer4-maxpool', 5: 'layer5-conv', 6: 'layer6-maxpool', 7: 'layer7-conv', 8: 'layer8-maxpool', 9: 'layer9-conv', 10: 'layer10-conv', 11: 'layer11-conv', 12: 'layer12-maxpool', 13: 'layer13-conv', 14: 'layer14-maxpool', 15: 'layer15-conv', 16: 'layer16-conv', 17: 'layer17-conv', 18: 'layer18-conv', 19: 'layer18-conv', 20: 'layer20-route', 21: 'layer21-conv', 22: 'layer22-upsample'}
OrderedDict([('bottom', ['layer22-upsample', 'layer9-conv']), ('top', 'layer23-route'), ('name', 'layer23-route'), ('type', 'Concat')])
23
unknow layer type yolo
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> name: "Darkent2Caffe"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> input: "data"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> input_dim: 1
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> input_dim: 3
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> input_dim: 416
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> input_dim: 416
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'>
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> layer {
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> bottom: "data"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> top: "layer1-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> name: "layer1-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> type: "Convolution"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> convolution_param {
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> num_output: 16
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> kernel_size: 3
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> stride: 1
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> bias_term: false
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> layer {
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> layer {
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> layer {
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> top: "layer1-conv"
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> layer {
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> layer {
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> layer {
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> layer {
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> layer {
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> top: "layer3-conv"
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> layer {
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> top: "layer4-maxpool"
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> pool: MAX
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> kernel_size: 2
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> layer {
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> layer {
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> top: "layer5-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> name: "layer5-bn"
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> batch_norm_param {
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> layer {
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> name: "layer5-scale"
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> scale_param {
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> layer {
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> top: "layer5-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> name: "layer5-act"
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> layer {
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> top: "layer6-maxpool"
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> pooling_param {
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> pool: MAX
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> kernel_size: 2
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> layer {
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> top: "layer7-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> name: "layer7-conv"
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> convolution_param {
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> stride: 1
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> bias_term: false
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> layer {
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> top: "layer7-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> name: "layer7-bn"
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> batch_norm_param {
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> layer {
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> top: "layer7-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> name: "layer7-scale"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> type: "Scale"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> scale_param {
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> layer {
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> top: "layer7-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> name: "layer7-act"
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> layer {
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> top: "layer8-maxpool"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> name: "layer8-maxpool"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> type: "Pooling"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> pooling_param {
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> pool: MAX
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> kernel_size: 2
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> pad: 0.5
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> layer {
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> top: "layer9-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> name: "layer9-conv"
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> convolution_param {
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> stride: 1
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> bias_term: false
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> layer {
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> top: "layer9-conv"
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> type: "BatchNorm"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> batch_norm_param {
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> use_global_stats: true
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> layer {
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> bottom: "layer9-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> top: "layer9-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> name: "layer9-scale"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> type: "Scale"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> scale_param {
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> layer {
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> top: "layer9-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> name: "layer9-act"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> type: "ReLU"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> relu_param {
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> layer {
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> top: "layer10-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> name: "layer10-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> type: "Convolution"
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> kernel_size: 1
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> bias_term: false
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> layer {
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> top: "layer10-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> name: "layer10-bn"
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> batch_norm_param {
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> layer {
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> top: "layer10-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> name: "layer10-scale"
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> layer {
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> top: "layer10-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> name: "layer10-act"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> type: "ReLU"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> relu_param {
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> layer {
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> top: "layer11-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> name: "layer11-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> type: "Convolution"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> convolution_param {
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> kernel_size: 3
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> stride: 1
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> bias_term: false
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> layer {
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> top: "layer11-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> name: "layer11-bn"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> type: "BatchNorm"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> batch_norm_param {
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> layer {
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> top: "layer11-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> name: "layer11-scale"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> type: "Scale"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> scale_param {
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> layer {
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> top: "layer11-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> name: "layer11-act"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> type: "ReLU"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> relu_param {
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> layer {
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> top: "layer12-maxpool"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> name: "layer12-maxpool"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> type: "Pooling"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> pooling_param {
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> pool: MAX
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> kernel_size: 2
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> pad: 0.5
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> layer {
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> bottom: "layer12-maxpool"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> top: "layer13-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> name: "layer13-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> type: "Convolution"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> convolution_param {
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> kernel_size: 3
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> pad: 1.5
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> stride: 1
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> bias_term: false
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> layer {
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> top: "layer13-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> name: "layer13-bn"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> type: "BatchNorm"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> batch_norm_param {
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> use_global_stats: true
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> layer {
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> top: "layer13-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> name: "layer13-scale"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> type: "Scale"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> scale_param {
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> layer {
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> bottom: "layer13-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> top: "layer13-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> name: "layer13-act"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> type: "ReLU"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> relu_param {
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> negative_slope: 0.1
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> layer {
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> bottom: "layer13-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> top: "layer14-maxpool"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> name: "layer14-maxpool"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> type: "Pooling"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> pooling_param {
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> pool: MAX
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> kernel_size: 3
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> pad: 1.5
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> layer {
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> bottom: "layer14-maxpool"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> top: "layer15-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> name: "layer15-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> type: "Convolution"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> convolution_param {
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<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> kernel_size: 3
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> pad: 1.5
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> stride: 1
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> bias_term: false
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> layer {
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> bottom: "layer15-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> top: "layer15-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> name: "layer15-bn"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> type: "BatchNorm"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> batch_norm_param {
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> use_global_stats: true
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> layer {
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> bottom: "layer15-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> top: "layer15-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> name: "layer15-scale"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> type: "Scale"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> scale_param {
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> bias_term: true
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> layer {
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> bottom: "layer15-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> top: "layer15-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> name: "layer15-act"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> type: "ReLU"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> relu_param {
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> negative_slope: 0.1
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> layer {
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> bottom: "layer15-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> top: "layer16-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> name: "layer16-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> type: "Convolution"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> convolution_param {
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> num_output: 256
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> kernel_size: 1
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> pad: 0.5
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> stride: 1
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> bias_term: false
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> layer {
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> bottom: "layer16-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> top: "layer16-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> name: "layer16-bn"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> type: "BatchNorm"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> batch_norm_param {
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> use_global_stats: true
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> layer {
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> bottom: "layer16-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> top: "layer16-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> name: "layer16-scale"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> type: "Scale"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> scale_param {
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> bias_term: true
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> layer {
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> bottom: "layer16-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> top: "layer16-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> name: "layer16-act"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> type: "ReLU"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> relu_param {
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> negative_slope: 0.1
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> layer {
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> bottom: "layer16-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> top: "layer17-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> name: "layer17-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> type: "Convolution"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> convolution_param {
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> num_output: 512
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> kernel_size: 3
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> pad: 1.5
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> stride: 1
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> bias_term: false
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> layer {
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> bottom: "layer17-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> top: "layer17-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> name: "layer17-bn"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> type: "BatchNorm"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> batch_norm_param {
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> use_global_stats: true
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> layer {
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> bottom: "layer17-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> top: "layer17-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> name: "layer17-scale"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> type: "Scale"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> scale_param {
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> bias_term: true
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> layer {
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> bottom: "layer17-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> top: "layer17-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> name: "layer17-act"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> type: "ReLU"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> relu_param {
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> negative_slope: 0.1
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> layer {
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> bottom: "layer17-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> top: "layer18-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> name: "layer18-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> type: "Convolution"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> convolution_param {
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> num_output: 36
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> kernel_size: 1
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> pad: 0.5
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> stride: 1
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> bias_term: true
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> layer {
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> bottom: "layer16-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> top: "layer20-route"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> name: "layer20-route"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> type: "Concat"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> layer {
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> bottom: "layer20-route"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> top: "layer21-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> name: "layer21-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> type: "Convolution"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> convolution_param {
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> num_output: 128
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> kernel_size: 1
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> pad: 0.5
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> stride: 1
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> bias_term: false
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> layer {
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> bottom: "layer21-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> top: "layer21-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> name: "layer21-bn"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> type: "BatchNorm"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> batch_norm_param {
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> use_global_stats: true
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> layer {
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> bottom: "layer21-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> top: "layer21-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> name: "layer21-scale"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> type: "Scale"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> scale_param {
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> bias_term: true
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> layer {
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> bottom: "layer21-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> top: "layer21-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> name: "layer21-act"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> type: "ReLU"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> relu_param {
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> negative_slope: 0.1
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> layer {
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> bottom: "layer21-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> top: "layer22-upsample"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> name: "layer22-upsample"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> type: "Upsample"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> upsample_param {
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> scale: 2
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> layer {
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> bottom: "layer22-upsample"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> bottom: "layer9-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> top: "layer23-route"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> name: "layer23-route"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> type: "Concat"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> layer {
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> bottom: "layer23-route"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> top: "layer24-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> name: "layer24-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> type: "Convolution"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> convolution_param {
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> num_output: 256
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> kernel_size: 3
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> pad: 1.5
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> stride: 1
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> bias_term: false
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> layer {
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> bottom: "layer24-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> top: "layer24-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> name: "layer24-bn"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> type: "BatchNorm"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> batch_norm_param {
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> use_global_stats: true
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> layer {
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> bottom: "layer24-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> top: "layer24-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> name: "layer24-scale"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> type: "Scale"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> scale_param {
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> bias_term: true
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> layer {
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> bottom: "layer24-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> top: "layer24-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> name: "layer24-act"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> type: "ReLU"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> relu_param {
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> negative_slope: 0.1
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> layer {
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> bottom: "layer24-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> top: "layer25-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> name: "layer25-conv"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> type: "Convolution"
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> convolution_param {
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> num_output: 36
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> kernel_size: 1
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> pad: 0.5
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> stride: 1
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> bias_term: true
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
<_io.TextIOWrapper name='yolov3-tiny.prototxt' mode='w' encoding='UTF-8'> }
WARNING: Logging before InitGoogleLogging() is written to STDERR
E0226 10:36:33.943483 7464 common.cpp:114] Cannot create Cublas handle. Cublas won't be available.
I0226 10:36:33.947749 7464 net.cpp:51] Initializing net from parameters:
state {
phase: TEST
level: 0
}
I0226 10:36:33.947772 7464 net.cpp:255] Network initialization done.
Traceback (most recent call last):
File "yolov3_darknet2caffe.py", line 534, in
darknet2caffe(cfgfile, weightfile, protofile, caffemodel)
File "yolov3_darknet2caffe.py", line 61, in darknet2caffe
start = load_conv_bn2caffe(buf, start, params[conv_layer_name], params[bn_layer_name], params[scale_layer_name])
KeyError: 'layer1-conv'
－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－
from caffe-yolov3.
ChenYingpeng
commented on May 21, 2024
看上去是你的文件格式有问题，转换上是没问题的，你检查一下你的python。看一下为什么你生成prototxt里面pad的值还有0.5这种参数，在我的理解是这个参数都是int的。多多baidu和google一下吧！
from caffe-yolov3.
doublegssc
commented on May 21, 2024
好的。。我用的python3.5，Ubuntu16.04。。python只能使用python2吗？还是python3也可以？
from caffe-yolov3.
ChenYingpeng
commented on May 21, 2024
应该是可以的，我没用python3。我这个代码用的是python2.7 + caffe + pytorch0.4的环境。
from caffe-yolov3.
doublegssc
commented on May 21, 2024
好的。。谢谢您，我再看一下。。
from caffe-yolov3.
ddkkevin
commented on May 21, 2024
在python3环境下，需要修改convolution_param['pad'] = str(int(convolution_param['kernel_size'])/2) 为 convolution_param['pad'] = str(int(int(convolution_param['kernel_size'])/2))，否则产生的PAD可能为浮点。我也觉得很奇怪，难道python2.7没有个问题吗？
from caffe-yolov3.
engineer1109
commented on May 21, 2024
lz应该用了python3 我Python3也是这样 雪坑
from caffe-yolov3.
AntoineGerardeaux
commented on May 21, 2024
Get the same error ! With python3..
from caffe-yolov3.
AntoineGerardeaux
commented on May 21, 2024
Solved by setup my config with Ubuntu 16.04, cuda 8.0, Python 2.7 and pytorch 0.4.
from caffe-yolov3.
weixiaolian21
commented on May 21, 2024
请问解决了吗
from caffe-yolov3.
Ashing00
commented on May 21, 2024
the same issue in python 3.
"需要修改convolution_param['pad'] = str(int(convolution_param['kernel_size'])/2) 为 convolution_param['pad'] = str(int(int(convolution_param['kernel_size'])/2))，否则产生的PAD可能为浮点"
can not fixed the issue
from caffe-yolov3.
Ashing00
commented on May 21, 2024
hi fanhongyuan
"you can change the prototxt.py:
print >> fp, 'name: "%s"' % props['name']
print('name: "%s"' % props['name'],file = fp)
change every "print" like this."
=========>it works, thanks.
but i meet another issue :
"[libprotobuf ERROR google/protobuf/text_format.cc:298] Error parsing text-format caffe.NetParameter: 3:12: Expected integer, got: "1"
WARNING: Logging before InitGoogleLogging() is written to STDERR
F0807 09:11:34.854478 17666 upgrade_proto.cpp:88] Check failed: ReadProtoFromTextFile(param_file, param) Failed to parse NetParameter file: yolov3_hand.prototxt
*** Check failure stack trace: ***"
yolov3_hand.prototxt==>
name: "Darkent2Caffe"
input: "data"
input_dim: "1"
input_dim: "3"
input_dim: "416"
input_dim: "416"
I have no idea.
from caffe-yolov3.
weixiaolian21
commented on May 21, 2024
hi fanhongyuan
"you can change the prototxt.py:
print >> fp, 'name: "%s"' % props['name']
print('name: "%s"' % props['name'],file = fp)
change every "print" like this."=========>it works, thanks.
but i meet another issue :
"[libprotobuf ERROR google/protobuf/text_format.cc:298] Error parsing text-format caffe.NetParameter: 3:12: Expected integer, got: "1"
WARNING: Logging before InitGoogleLogging() is written to STDERR
F0807 09:11:34.854478 17666 upgrade_proto.cpp:88] Check failed: ReadProtoFromTextFile(param_file, param) Failed to parse NetParameter file: yolov3_hand.prototxt
*** Check failure stack trace: ***"yolov3_hand.prototxt==>
name: "Darkent2Caffe"
input: "data"
input_dim: "1"
input_dim: "3"
input_dim: "416"
input_dim: "416"
I have no idea.
there is something wrong with your prototxt， you should check it.
from caffe-yolov3.
Iamlaoli
commented on May 21, 2024
楼主我也是这样的问题问下您该如何解决呢
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> bias_term: true
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> }
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> }
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> layer {
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> bottom: "layer158-conv"
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> top: "layer158-conv"
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> name: "layer158-act"
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> type: "ReLU"
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> relu_param {
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> negative_slope: 0.1
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> }
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> }
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> layer {
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> bottom: "layer158-conv"
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> top: "layer159-conv"
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> name: "layer159-conv"
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> type: "Convolution"
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> convolution_param {
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> num_output: 512
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> kernel_size: 1
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> pad: 0
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> stride: 1
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> bias_term: false
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> }
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> }
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> layer {
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> bottom: "layer159-conv"
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> top: "layer159-conv"
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> name: "layer159-bn"
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> type: "BatchNorm"
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> batch_norm_param {
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> use_global_stats: true
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> }
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> }
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> layer {
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> bottom: "layer159-conv"
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> top: "layer159-conv"
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> name: "layer159-scale"
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> type: "Scale"
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> scale_param {
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> bias_term: true
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> }
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> }
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> layer {
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> bottom: "layer159-conv"
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> top: "layer159-conv"
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> name: "layer159-act"
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> type: "ReLU"
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> relu_param {
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> negative_slope: 0.1
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> }
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> }
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> layer {
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> bottom: "layer159-conv"
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> top: "layer160-conv"
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> name: "layer160-conv"
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> type: "Convolution"
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> convolution_param {
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> num_output: 1024
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> kernel_size: 3
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> pad: 1
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> stride: 1
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> bias_term: false
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> }
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> }
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> layer {
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> bottom: "layer160-conv"
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> top: "layer160-conv"
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> name: "layer160-bn"
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> type: "BatchNorm"
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> batch_norm_param {
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> use_global_stats: true
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> }
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> }
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> layer {
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> bottom: "layer160-conv"
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> top: "layer160-conv"
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> name: "layer160-scale"
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> type: "Scale"
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> scale_param {
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> bias_term: true
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> }
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> }
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> layer {
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> bottom: "layer160-conv"
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> top: "layer160-conv"
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> name: "layer160-act"
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> type: "ReLU"
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> relu_param {
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> negative_slope: 0.1
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> }
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> }
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> layer {
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> bottom: "layer160-conv"
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> top: "layer161-conv"
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> name: "layer161-conv"
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> type: "Convolution"
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> convolution_param {
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> num_output: 255
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> kernel_size: 1
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> pad: 0
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> stride: 1
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> bias_term: true
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> }
<_io.TextIOWrapper name='prototxt/yolov4.prototxt' mode='w' encoding='ANSI_X3.4-1968'> }
WARNING: Logging before InitGoogleLogging() is written to STDERR
I0817 20:51:46.393153 20524 net.cpp:53] Initializing net from parameters:
state {
phase: TEST
level: 0
}
I0817 20:51:46.393208 20524 net.cpp:257] Network initialization done.
Traceback (most recent call last):
File "darknet2caffe.py", line 519, in
darknet2caffe(cfgfile, weightfile, protofile, caffemodel)
File "darknet2caffe.py", line 61, in darknet2caffe
start = load_conv_bn2caffe(buf, start, params[conv_layer_name], params[bn_layer_name], params[scale_layer_name])
KeyError: 'layer1-conv'
from caffe-yolov3.
Related Issues (20)
- 请问如何进行批量测试
- Check failed: error == cudaSuccess (35 vs. 0) CUDA driver version is insufficient for CUDA runtime version HOT 1
- Neural Network Accelerator support HOT 1
- 在运行demo时出现"段错误 (核心已转储)" HOT 7
- yolov4转换后识别有问题。 HOT 2
- image im = mat_to_image(mat);
- 运行后出现void check_error(cudaError_t): Assertion `0' failed. CUDA Error: no kernel image is available for execution on the device HOT 1
- A question about how to run other caffemodel?
- make caffe-yolov3时报错（libyolov3-plugin.so）
- 大佬，我的电脑上没有cuda如何将cmake里面的cuda去掉啊 HOT 5
- Please publish models for download form other service
- Segment fault HOT 3
- Have difficulty in fixing KeyError: 'layer1-conv' HOT 3
- How to convert Yolov4-tiny darknet to caffe?
- Segmentation fault (core dumped) HOT 1
- 转换后的yolov4模型，检测框偏大，不准确
- 似乎没有提供训练的代码？是用AB版训然后转caffe模型？
- model link失效
- make[1]: *** [CMakeFiles/yolov3-plugin.dir/all] Error 2
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from caffe-yolov3.