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prototxt_basic.py
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prototxt_basic.py
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# prototxt_basic
import sys
import pprint
attrstr = "attrs"
#attrstr = "param"
def data(txt_file, info):
txt_file.write('name: "mxnet-mdoel"\n')
txt_file.write('layer {\n')
txt_file.write(' name: "data"\n')
txt_file.write(' type: "Input"\n')
txt_file.write(' top: "data"\n')
txt_file.write(' input_param {\n')
txt_file.write(' shape: { dim: 1 dim: 3 dim: 112 dim: 112 }\n') # TODO
txt_file.write(' }\n')
txt_file.write('}\n')
txt_file.write('\n')
def fuzzy_haskey(d, key):
for eachkey in d:
if key in eachkey:
return True
return False
def Convolution(txt_file, info):
#if info['attrs']['no_bias'] == 'True':
#bias_term = 'false'
#else:
#bias_term = 'true'
#if info['top'] == 'conv1_1':
#pprint.pprint(info)
if fuzzy_haskey(info['params'], 'bias'):
bias_term = 'true'
elif info[attrstr].has_key('no_bias') and info['attrs']['no_bias'] == 'True':
bias_term = 'false'
else:
bias_term = 'true'
txt_file.write('layer {\n')
txt_file.write(' bottom: "%s"\n' % info['bottom'][0])
txt_file.write(' top: "%s"\n' % info['top'])
txt_file.write(' name: "%s"\n' % info['top'])
txt_file.write(' type: "Convolution"\n')
txt_file.write(' convolution_param {\n')
txt_file.write(' num_output: %s\n' % info[attrstr]['num_filter'])
txt_file.write(' kernel_size: %s\n' % info[attrstr]['kernel'].split('(')[1].split(',')[0]) # TODO
if info[attrstr].has_key('pad'):
txt_file.write(' pad: %s\n' % info[attrstr]['pad'].split('(')[1].split(',')[0]) # TODO
if info[attrstr].has_key('num_group') and int(info[attrstr]['num_group']) >1:
txt_file.write(' group: %s\n' % info[attrstr]['num_group'])
txt_file.write(' engine: CAFFE\n')
if info[attrstr].has_key('stride'):
txt_file.write(' stride: %s\n' % info[attrstr]['stride'].split('(')[1].split(',')[0])
txt_file.write(' bias_term: %s\n' % bias_term)
txt_file.write(' }\n')
if 'share' in info.keys() and info['share']:
txt_file.write(' param {\n')
txt_file.write(' name: "%s"\n' % info['params'][0])
txt_file.write(' }\n')
txt_file.write('}\n')
txt_file.write('\n')
def ChannelwiseConvolution(txt_file, info):
Convolution(txt_file, info)
def BatchNorm(txt_file, info):
#pprint.pprint(info)
txt_file.write('layer {\n')
txt_file.write(' bottom: "%s"\n' % info['bottom'][0])
txt_file.write(' top: "%s"\n' % info['top'])
txt_file.write(' name: "%s"\n' % info['top'])
txt_file.write(' type: "BatchNorm"\n')
txt_file.write(' batch_norm_param {\n')
txt_file.write(' use_global_stats: true\n') # TODO
if info[attrstr].has_key('momentum'):
txt_file.write(' moving_average_fraction: %s\n' % info[attrstr]['momentum'])
else:
txt_file.write(' moving_average_fraction: 0.9\n')
if info[attrstr].has_key('eps'):
txt_file.write(' eps: %s\n' % info[attrstr]['eps'])
else:
txt_file.write(' eps: 2e-05\n')
txt_file.write(' }\n')
txt_file.write('}\n')
# if info['fix_gamma'] is "False": # TODO
txt_file.write('layer {\n')
txt_file.write(' bottom: "%s"\n' % info['top'])
txt_file.write(' top: "%s"\n' % info['top'])
txt_file.write(' name: "%s_scale"\n' % info['top'])
txt_file.write(' type: "Scale"\n')
txt_file.write(' scale_param { bias_term: true }\n')
txt_file.write('}\n')
txt_file.write('\n')
pass
def Activation(txt_file, info):
txt_file.write('layer {\n')
txt_file.write(' bottom: "%s"\n' % info['bottom'][0])
txt_file.write(' top: "%s"\n' % info['top'])
txt_file.write(' name: "%s"\n' % info['top'])
if info[attrstr]['act_type']=='sigmoid':
txt_file.write(' type: "Sigmoid"\n')
else:
txt_file.write(' type: "ReLU"\n') # TODO
txt_file.write('}\n')
txt_file.write('\n')
pass
def Concat(txt_file, info):
txt_file.write('layer {\n')
txt_file.write(' name: "%s"\n' % info['top'])
txt_file.write(' type: "Concat"\n')
for bottom_i in info['bottom']:
txt_file.write(' bottom: "%s"\n' % bottom_i)
txt_file.write(' top: "%s"\n' % info['top'])
txt_file.write('}\n')
txt_file.write('\n')
pass
def ElementWiseSum(txt_file, info):
txt_file.write('layer {\n')
txt_file.write(' name: "%s"\n' % info['top'])
txt_file.write(' type: "Eltwise"\n')
for bottom_i in info['bottom']:
txt_file.write(' bottom: "%s"\n' % bottom_i)
txt_file.write(' top: "%s"\n' % info['top'])
txt_file.write(' eltwise_param { operation: SUM }\n')
txt_file.write('}\n')
txt_file.write('\n')
pass
def Pooling(txt_file, info):
pool_type = 'AVE' if info[attrstr]['pool_type'] == 'avg' else 'MAX'
txt_file.write('layer {\n')
txt_file.write(' bottom: "%s"\n' % info['bottom'][0])
txt_file.write(' top: "%s"\n' % info['top'])
txt_file.write(' name: "%s"\n' % info['top'])
txt_file.write(' type: "Pooling"\n')
txt_file.write(' pooling_param {\n')
txt_file.write(' pool: %s\n' % pool_type) # TODO
if info[attrstr].has_key('global_pool') and info[attrstr]['global_pool'] == 'True':
txt_file.write(' global_pooling: true\n')
else:
txt_file.write(' kernel_size: %s\n' % info[attrstr]['kernel'].split('(')[1].split(',')[0])
txt_file.write(' stride: %s\n' % info[attrstr]['stride'].split('(')[1].split(',')[0])
if info[attrstr].has_key('pad'):
txt_file.write(' pad: %s\n' % info[attrstr]['pad'].split('(')[1].split(',')[0])
txt_file.write(' }\n')
txt_file.write('}\n')
txt_file.write('\n')
pass
def FullyConnected(txt_file, info):
txt_file.write('layer {\n')
txt_file.write(' bottom: "%s"\n' % info['bottom'][0])
txt_file.write(' top: "%s"\n' % info['top'])
txt_file.write(' name: "%s"\n' % info['top'])
txt_file.write(' type: "InnerProduct"\n')
txt_file.write(' inner_product_param {\n')
txt_file.write(' num_output: %s\n' % info[attrstr]['num_hidden'])
txt_file.write(' }\n')
txt_file.write('}\n')
txt_file.write('\n')
print("fullconnect: ",info[attrstr]['num_hidden'],info['params'])
pass
def Flatten(txt_file, info):
pass
def SoftmaxOutput(txt_file, info):
pass
def LeakyReLU(txt_file, info):
if info[attrstr]['act_type'] == 'elu':
txt_file.write('layer {\n')
txt_file.write(' bottom: "%s"\n' % info['bottom'][0])
txt_file.write(' top: "%s"\n' % info['top'])
txt_file.write(' name: "%s"\n' % info['top'])
txt_file.write(' type: "ELU"\n')
txt_file.write(' elu_param { alpha: 0.25 }\n')
txt_file.write('}\n')
txt_file.write('\n')
elif info[attrstr]['act_type'] == 'prelu':
txt_file.write('layer {\n')
txt_file.write(' bottom: "%s"\n' % info['bottom'][0])
txt_file.write(' top: "%s"\n' % info['top'])
txt_file.write(' name: "%s"\n' % info['top'])
txt_file.write(' type: "PReLU"\n')
txt_file.write('}\n')
txt_file.write('\n')
else:
raise Exception("unsupported Activation")
def Eltwise(txt_file, info, op):
txt_file.write('layer {\n')
txt_file.write(' type: "Eltwise"\n')
txt_file.write(' top: "%s"\n' % info['top'])
txt_file.write(' name: "%s"\n' % info['top'])
for btom in info['bottom']:
txt_file.write(' bottom: "%s"\n' % btom)
txt_file.write(' eltwise_param { operation: %s }\n' % op)
txt_file.write('}\n')
txt_file.write('\n')
# ----------------------------------------------------------------
def write_node(txt_file, info):
if 'label' in info['name']:
return
if info['op'] == 'null' and info['name'] == 'data':
data(txt_file, info)
elif info['op'] == 'Convolution':
Convolution(txt_file, info)
elif info['op'] == 'ChannelwiseConvolution':
ChannelwiseConvolution(txt_file, info)
elif info['op'] == 'BatchNorm':
BatchNorm(txt_file, info)
elif info['op'] == 'Activation':
Activation(txt_file, info)
elif info['op'] == 'ElementWiseSum': #elemwise_add
ElementWiseSum(txt_file, info)
elif info['op'] == '_Plus':
ElementWiseSum(txt_file, info)
elif info['op'] == 'Concat':
Concat(txt_file, info)
elif info['op'] == 'Pooling':
Pooling(txt_file, info)
elif info['op'] == 'Flatten':
Flatten(txt_file, info)
elif info['op'] == 'FullyConnected':
FullyConnected(txt_file, info)
elif info['op'] == 'SoftmaxOutput':
SoftmaxOutput(txt_file, info)
elif info['op'] == 'LeakyReLU':
LeakyReLU(txt_file, info)
elif info['op'] == 'elemwise_add':
ElementWiseSum(txt_file, info)
else:
#pprint.pprint(info)
#sys.exit("Warning! Unknown mxnet op:{}".format(info['op']))
print "Warning! Skip Unknown mxnet op:{}".format(info['op'])