KeyError: 'layer1-conv' #28

doublegssc · 2019-02-25T08:19:56Z

hi, ChenYingPeng
我运行了你的darknet2caffe代码，然后遇到了这个错误：
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Traceback (most recent call last):
File "darknet2caffe.py", line 429, in
darknet2caffe(cfgfile, weightfile, protofile, caffemodel)
File "darknet2caffe.py", line 57, in darknet2caffe
start = load_conv_bn2caffe(buf, start, params[conv_layer_name], params[bn_layer_name], params[scale_layer_name])
KeyError: 'layer1-conv'

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我加载的是yolov3-tiny版模型。

请问是哪里出现问题了吗？

ChenYingpeng · 2019-02-26T01:52:43Z

Hi，doublegssc，
请把你运行代码的执行命令发上来。

ChenYingpeng · 2019-02-26T02:04:58Z

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？

doublegssc · 2019-02-26T02:06:26Z

是你这个工程里面的。。

doublegssc · 2019-02-26T02:11:04Z

我的命令是：
python3 darknet2caffe.py yolov3-tiny.cfg yolov3-tiny.weights yolov3-tiny.prototxt yolov3-tiny.caffemodel

ChenYingpeng · 2019-02-26T02:19:38Z

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。

doublegssc · 2019-02-26T02:40:23Z

darknet2caffe.py的代码为：
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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)

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运行错误的整个日志为：

－－－－－－－－－－－－－－－－－－－－－－－－－－－ unknow layer type yolo
OrderedDict([('bottom', 'layer16-conv'), ('top', 'layer20-route'), ('name', 20
2
OrderedDict([('bottom', 'layer21-conv'), ('top', 'layer22-upsample'), upsample: 22
{1: 'layer1-conv', 2: 'layer2-maxpool', 3: 'layer3-conv', 4: 'layer4-maxpool', OrderedDict([('bottom', ['layer22-upsample', 'layer9-conv']), ('top', 23
unknow layer type yolo
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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'> <_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'> <_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'> <_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'> <_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. 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], KeyError: 'layer1-conv' －－－－－－－－－
'layer20-route'), ('type', 'Concat')])
('name', 'layer22-upsample'), ('type', 'Upsample'), ('upsample_param', OrderedDict([('scale', '2')]))])
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'}
'layer23-route'), ('name', 'layer23-route'), ('type', 'Concat')])
name: "Darkent2Caffe"
input: "data"
input_dim: 1
input_dim: 3
input_dim: 416
input_dim: 416

layer {
bottom: "data"
top: "layer1-conv"
name: "layer1-conv"
type: "Convolution"
convolution_param {
num_output: 16
kernel_size: 3
pad: 1.5
stride: 1
bias_term: false
}
}
layer {
bottom: "layer1-conv"
top: "layer1-conv"
name: "layer1-bn"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
}
layer {
bottom: "layer1-conv"
top: "layer1-conv"
name: "layer1-scale"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
bottom: "layer1-conv"
top: "layer1-conv"
name: "layer1-act"
type: "ReLU"
relu_param {
negative_slope: 0.1
}
}
layer {
bottom: "layer1-conv"
top: "layer2-maxpool"
name: "layer2-maxpool"
type: "Pooling"
pooling_param {
stride: 2
pool: MAX
kernel_size: 2
pad: 0.5
}
}
layer {
bottom: "layer2-maxpool"
top: "layer3-conv"
name: "layer3-conv"
type: "Convolution"
convolution_param {
num_output: 32
kernel_size: 3
pad: 1.5
stride: 1
bias_term: false
}
}
layer {
bottom: "layer3-conv"
top: "layer3-conv"
name: "layer3-bn"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
}
layer {
bottom: "layer3-conv"
top: "layer3-conv"
name: "layer3-scale"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
bottom: "layer3-conv"
top: "layer3-conv"
name: "layer3-act"
type: "ReLU"
relu_param {
negative_slope: 0.1
}
}
layer {
bottom: "layer3-conv"
top: "layer4-maxpool"
name: "layer4-maxpool"
type: "Pooling"
pooling_param {
stride: 2
pool: MAX
kernel_size: 2
pad: 0.5
}
}
layer {
bottom: "layer4-maxpool"
top: "layer5-conv"
name: "layer5-conv"
type: "Convolution"
convolution_param {
num_output: 64
kernel_size: 3
pad: 1.5
stride: 1
bias_term: false
}
}
layer {
bottom: "layer5-conv"
top: "layer5-conv"
name: "layer5-bn"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
}
layer {
bottom: "layer5-conv"
top: "layer5-conv"
name: "layer5-scale"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
bottom: "layer5-conv"
top: "layer5-conv"
name: "layer5-act"
type: "ReLU"
relu_param {
negative_slope: 0.1
}
}
layer {
bottom: "layer5-conv"
top: "layer6-maxpool"
name: "layer6-maxpool"
type: "Pooling"
pooling_param {
stride: 2
pool: MAX
kernel_size: 2
pad: 0.5
}
}
layer {
bottom: "layer6-maxpool"
top: "layer7-conv"
name: "layer7-conv"
type: "Convolution"
convolution_param {
num_output: 128
kernel_size: 3
pad: 1.5
stride: 1
bias_term: false
}
}
layer {
bottom: "layer7-conv"
top: "layer7-conv"
name: "layer7-bn"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
}
layer {
bottom: "layer7-conv"
top: "layer7-conv"
name: "layer7-scale"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
bottom: "layer7-conv"
top: "layer7-conv"
name: "layer7-act"
type: "ReLU"
relu_param {
negative_slope: 0.1
}
}
layer {
bottom: "layer7-conv"
top: "layer8-maxpool"
name: "layer8-maxpool"
type: "Pooling"
pooling_param {
stride: 2
pool: MAX
kernel_size: 2
pad: 0.5
}
}
layer {
bottom: "layer8-maxpool"
top: "layer9-conv"
name: "layer9-conv"
type: "Convolution"
convolution_param {
num_output: 256
kernel_size: 3
pad: 1.5
stride: 1
bias_term: false
}
}
layer {
bottom: "layer9-conv"
top: "layer9-conv"
name: "layer9-bn"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
}
layer {
bottom: "layer9-conv"
top: "layer9-conv"
name: "layer9-scale"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
bottom: "layer9-conv"
top: "layer9-conv"
name: "layer9-act"
type: "ReLU"
relu_param {
negative_slope: 0.1
}
}
layer {
bottom: "layer9-conv"
top: "layer10-conv"
name: "layer10-conv"
type: "Convolution"
convolution_param {
num_output: 128
kernel_size: 1
pad: 0.5
stride: 1
bias_term: false
}
}
layer {
bottom: "layer10-conv"
top: "layer10-conv"
name: "layer10-bn"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
}
layer {
bottom: "layer10-conv"
top: "layer10-conv"
name: "layer10-scale"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
bottom: "layer10-conv"
top: "layer10-conv"
name: "layer10-act"
type: "ReLU"
relu_param {
negative_slope: 0.1
}
}
layer {
bottom: "layer10-conv"
top: "layer11-conv"
name: "layer11-conv"
type: "Convolution"
convolution_param {
num_output: 256
kernel_size: 3
pad: 1.5
stride: 1
bias_term: false
}
}
layer {
bottom: "layer11-conv"
top: "layer11-conv"
name: "layer11-bn"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
}
layer {
bottom: "layer11-conv"
top: "layer11-conv"
name: "layer11-scale"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
bottom: "layer11-conv"
top: "layer11-conv"
name: "layer11-act"
type: "ReLU"
relu_param {
negative_slope: 0.1
}
}
layer {
bottom: "layer11-conv"
top: "layer12-maxpool"
name: "layer12-maxpool"
type: "Pooling"
pooling_param {
stride: 2
pool: MAX
kernel_size: 2
pad: 0.5
}
}
layer {
bottom: "layer12-maxpool"
top: "layer13-conv"
name: "layer13-conv"
type: "Convolution"
convolution_param {
num_output: 512
kernel_size: 3
pad: 1.5
stride: 1
bias_term: false
}
}
layer {
bottom: "layer13-conv"
top: "layer13-conv"
name: "layer13-bn"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
}
layer {
bottom: "layer13-conv"
top: "layer13-conv"
name: "layer13-scale"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
bottom: "layer13-conv"
top: "layer13-conv"
name: "layer13-act"
type: "ReLU"
relu_param {
negative_slope: 0.1
}
}
layer {
bottom: "layer13-conv"
top: "layer14-maxpool"
name: "layer14-maxpool"
type: "Pooling"
pooling_param {
stride: 1
pool: MAX
kernel_size: 3
pad: 1.5
}
}
layer {
bottom: "layer14-maxpool"
top: "layer15-conv"
name: "layer15-conv"
type: "Convolution"
convolution_param {
num_output: 1024
kernel_size: 3
pad: 1.5
stride: 1
bias_term: false
}
}
layer {
bottom: "layer15-conv"
top: "layer15-conv"
name: "layer15-bn"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
}
layer {
bottom: "layer15-conv"
top: "layer15-conv"
name: "layer15-scale"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
bottom: "layer15-conv"
top: "layer15-conv"
name: "layer15-act"
type: "ReLU"
relu_param {
negative_slope: 0.1
}
}
layer {
bottom: "layer15-conv"
top: "layer16-conv"
name: "layer16-conv"
type: "Convolution"
convolution_param {
num_output: 256
kernel_size: 1
pad: 0.5
stride: 1
bias_term: false
}
}
layer {
bottom: "layer16-conv"
top: "layer16-conv"
name: "layer16-bn"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
}
layer {
bottom: "layer16-conv"
top: "layer16-conv"
name: "layer16-scale"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
bottom: "layer16-conv"
top: "layer16-conv"
name: "layer16-act"
type: "ReLU"
relu_param {
negative_slope: 0.1
}
}
layer {
bottom: "layer16-conv"
top: "layer17-conv"
name: "layer17-conv"
type: "Convolution"
convolution_param {
num_output: 512
kernel_size: 3
pad: 1.5
stride: 1
bias_term: false
}
}
layer {
bottom: "layer17-conv"
top: "layer17-conv"
name: "layer17-bn"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
}
layer {
bottom: "layer17-conv"
top: "layer17-conv"
name: "layer17-scale"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
bottom: "layer17-conv"
top: "layer17-conv"
name: "layer17-act"
type: "ReLU"
relu_param {
negative_slope: 0.1
}
}
layer {
bottom: "layer17-conv"
top: "layer18-conv"
name: "layer18-conv"
type: "Convolution"
convolution_param {
num_output: 36
kernel_size: 1
pad: 0.5
stride: 1
bias_term: true
}
}
layer {
bottom: "layer16-conv"
top: "layer20-route"
name: "layer20-route"
type: "Concat"
}
layer {
bottom: "layer20-route"
top: "layer21-conv"
name: "layer21-conv"
type: "Convolution"
convolution_param {
num_output: 128
kernel_size: 1
pad: 0.5
stride: 1
bias_term: false
}
}
layer {
bottom: "layer21-conv"
top: "layer21-conv"
name: "layer21-bn"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
}
layer {
bottom: "layer21-conv"
top: "layer21-conv"
name: "layer21-scale"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
bottom: "layer21-conv"
top: "layer21-conv"
name: "layer21-act"
type: "ReLU"
relu_param {
negative_slope: 0.1
}
}
layer {
bottom: "layer21-conv"
top: "layer22-upsample"
name: "layer22-upsample"
type: "Upsample"
upsample_param {
scale: 2
}
}
layer {
bottom: "layer22-upsample"
bottom: "layer9-conv"
top: "layer23-route"
name: "layer23-route"
type: "Concat"
}
layer {
bottom: "layer23-route"
top: "layer24-conv"
name: "layer24-conv"
type: "Convolution"
convolution_param {
num_output: 256
kernel_size: 3
pad: 1.5
stride: 1
bias_term: false
}
}
layer {
bottom: "layer24-conv"
top: "layer24-conv"
name: "layer24-bn"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
}
layer {
bottom: "layer24-conv"
top: "layer24-conv"
name: "layer24-scale"
type: "Scale"
scale_param {
bias_term: true
}
}
layer {
bottom: "layer24-conv"
top: "layer24-conv"
name: "layer24-act"
type: "ReLU"
relu_param {
negative_slope: 0.1
}
}
layer {
bottom: "layer24-conv"
top: "layer25-conv"
name: "layer25-conv"
type: "Convolution"
convolution_param {
num_output: 36
kernel_size: 1
pad: 0.5
stride: 1
bias_term: true
}
}
Cublas won't be available.
params[scale_layer_name])

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ChenYingpeng · 2019-02-26T02:58:23Z

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！

doublegssc · 2019-02-26T03:01:08Z

好的。。我用的python3.5，Ubuntu16.04。。python只能使用python2吗？还是python3也可以？

ChenYingpeng · 2019-02-26T03:04:41Z

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。

doublegssc · 2019-02-26T03:07:03Z

好的。。谢谢您，我再看一下。。

ddkkevin · 2019-02-28T07:54:05Z

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？

engineer1109 · 2019-04-14T09:44:21Z

lz应该用了python3 我Python3也是这样雪坑

AntoineGerardeaux · 2019-06-05T10:04:45Z

Get the same error ! With python3..

AntoineGerardeaux · 2019-06-14T09:01:56Z

Solved by setup my config with Ubuntu 16.04, cuda 8.0, Python 2.7 and pytorch 0.4.

weixiaolian21 · 2019-08-05T10:27:13Z

请问解决了吗

Ashing00 · 2019-08-05T10:41:11Z

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

weixiaolian21 · 2019-08-05T12:18:11Z

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.

Ashing00 · 2019-08-07T01:21:52Z

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.

weixiaolian21 · 2019-08-07T01:28:44Z

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.

Iamlaoli · 2023-08-18T04:03:40Z

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'

ChenYingpeng closed this as completed Jan 8, 2020

ChenYingpeng mentioned this issue Sep 9, 2020

YOLOv3 conversion failed ChenYingpeng/darknet2caffe#19

Closed

ChenYingpeng mentioned this issue Nov 7, 2020

Have difficulty in fixing KeyError: 'layer1-conv' #81

Open

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KeyError: 'layer1-conv' #28

KeyError: 'layer1-conv' #28

doublegssc commented Feb 25, 2019

ChenYingpeng commented Feb 26, 2019

ChenYingpeng commented Feb 26, 2019

doublegssc commented Feb 26, 2019

doublegssc commented Feb 26, 2019

ChenYingpeng commented Feb 26, 2019

doublegssc commented Feb 26, 2019

ChenYingpeng commented Feb 26, 2019

doublegssc commented Feb 26, 2019

ChenYingpeng commented Feb 26, 2019

doublegssc commented Feb 26, 2019

ddkkevin commented Feb 28, 2019

engineer1109 commented Apr 14, 2019

AntoineGerardeaux commented Jun 5, 2019

AntoineGerardeaux commented Jun 14, 2019

weixiaolian21 commented Aug 5, 2019

Ashing00 commented Aug 5, 2019

weixiaolian21 commented Aug 5, 2019 •

edited

Ashing00 commented Aug 7, 2019 •

edited

weixiaolian21 commented Aug 7, 2019

=========>it works, thanks.

but i meet another issue :

yolov3_hand.prototxt==>

name: "Darkent2Caffe"

Iamlaoli commented Aug 18, 2023

KeyError: 'layer1-conv' #28

KeyError: 'layer1-conv' #28

Comments

doublegssc commented Feb 25, 2019

ChenYingpeng commented Feb 26, 2019

ChenYingpeng commented Feb 26, 2019

doublegssc commented Feb 26, 2019

doublegssc commented Feb 26, 2019

ChenYingpeng commented Feb 26, 2019

doublegssc commented Feb 26, 2019

The caffe module needs to be on the Python path;

we'll add it here explicitly.

ChenYingpeng commented Feb 26, 2019

doublegssc commented Feb 26, 2019

ChenYingpeng commented Feb 26, 2019

doublegssc commented Feb 26, 2019

ddkkevin commented Feb 28, 2019

engineer1109 commented Apr 14, 2019

AntoineGerardeaux commented Jun 5, 2019

AntoineGerardeaux commented Jun 14, 2019

weixiaolian21 commented Aug 5, 2019

Ashing00 commented Aug 5, 2019

weixiaolian21 commented Aug 5, 2019 • edited

Ashing00 commented Aug 7, 2019 • edited

=========>it works, thanks.

yolov3_hand.prototxt==>

name: "Darkent2Caffe" input: "data" input_dim: "1" input_dim: "3" input_dim: "416" input_dim: "416"

weixiaolian21 commented Aug 7, 2019

=========>it works, thanks.

but i meet another issue :

yolov3_hand.prototxt==>

name: "Darkent2Caffe"

Iamlaoli commented Aug 18, 2023

weixiaolian21 commented Aug 5, 2019 •

edited

Ashing00 commented Aug 7, 2019 •

edited

name: "Darkent2Caffe"
input: "data"
input_dim: "1"
input_dim: "3"
input_dim: "416"
input_dim: "416"