in tools/caffe_converter/convert_symbol.py [0:0]
def _parse_proto(prototxt_fname):
"""Parse Caffe prototxt into symbol string
"""
proto = caffe_parser.read_prototxt(prototxt_fname)
# process data layer
input_name, input_dim, layers = _get_input(proto)
# only support single input, so always use `data` as the input data
mapping = {input_name: 'data'}
need_flatten = {input_name: False}
symbol_string = "import mxnet as mx\ndata = mx.symbol.Variable(name='data')\n"
flatten_count = 0
output_name = ""
prev_name = None
# convert reset layers one by one
for i, layer in enumerate(layers):
type_string = ''
param_string = ''
skip_layer = False
name = re.sub('[-/]', '_', layer.name)
if layer.type == 'Convolution' or layer.type == 4:
type_string = 'mx.symbol.Convolution'
param_string = _convert_conv_param(layer.convolution_param)
need_flatten[name] = True
if layer.type == 'Deconvolution' or layer.type == 39:
type_string = 'mx.symbol.Deconvolution'
param_string = _convert_conv_param(layer.convolution_param)
need_flatten[name] = True
if layer.type == 'Pooling' or layer.type == 17:
type_string = 'mx.symbol.Pooling'
param_string = _convert_pooling_param(layer.pooling_param)
need_flatten[name] = True
if layer.type == 'ReLU' or layer.type == 18:
type_string = 'mx.symbol.Activation'
param_string = "act_type='relu'"
need_flatten[name] = need_flatten[mapping[layer.bottom[0]]]
if layer.type == 'TanH' or layer.type == 23:
type_string = 'mx.symbol.Activation'
param_string = "act_type='tanh'"
need_flatten[name] = need_flatten[mapping[layer.bottom[0]]]
if layer.type == 'Sigmoid' or layer.type == 19:
type_string = 'mx.symbol.Activation'
param_string = "act_type='sigmoid'"
need_flatten[name] = need_flatten[mapping[layer.bottom[0]]]
if layer.type == 'LRN' or layer.type == 15:
type_string = 'mx.symbol.LRN'
param = layer.lrn_param
param_string = "alpha=%f, beta=%f, knorm=%f, nsize=%d" % (
param.alpha, param.beta, param.k, param.local_size)
need_flatten[name] = True
if layer.type == 'InnerProduct' or layer.type == 14:
type_string = 'mx.symbol.FullyConnected'
param = layer.inner_product_param
param_string = "num_hidden=%d, no_bias=%s" % (
param.num_output, not param.bias_term)
need_flatten[name] = False
if layer.type == 'Dropout' or layer.type == 6:
type_string = 'mx.symbol.Dropout'
param = layer.dropout_param
param_string = "p=%f" % param.dropout_ratio
need_flatten[name] = need_flatten[mapping[layer.bottom[0]]]
if layer.type == 'Softmax' or layer.type == 20:
type_string = 'mx.symbol.SoftmaxOutput'
if layer.type == 'Flatten' or layer.type == 8:
type_string = 'mx.symbol.Flatten'
need_flatten[name] = False
if layer.type == 'Split' or layer.type == 22:
type_string = 'split' # will process later
if layer.type == 'Concat' or layer.type == 3:
type_string = 'mx.symbol.Concat'
need_flatten[name] = True
if layer.type == 'Crop':
type_string = 'mx.symbol.Crop'
need_flatten[name] = True
param_string = 'center_crop=True'
if layer.type == 'BatchNorm':
type_string = 'mx.symbol.BatchNorm'
param = layer.batch_norm_param
# CuDNN requires eps to be greater than 1e-05
# We compensate for this change in convert_model
epsilon = param.eps
if (epsilon <= 1e-05):
epsilon = 1e-04
# if next layer is scale, don't fix gamma
fix_gamma = layers[i+1].type != 'Scale'
param_string = 'use_global_stats=%s, fix_gamma=%s, eps=%f' % (
param.use_global_stats, fix_gamma, epsilon)
need_flatten[name] = need_flatten[mapping[layer.bottom[0]]]
if layer.type == 'Scale':
assert layers[i-1].type == 'BatchNorm'
need_flatten[name] = need_flatten[mapping[layer.bottom[0]]]
skip_layer = True
prev_name = re.sub('[-/]', '_', layers[i-1].name)
if layer.type == 'PReLU':
type_string = 'mx.symbol.LeakyReLU'
param = layer.prelu_param
param_string = "act_type='prelu', slope=%f" % param.filler.value
need_flatten[name] = need_flatten[mapping[layer.bottom[0]]]
if layer.type == 'Eltwise':
type_string = 'mx.symbol.broadcast_add'
param_string = ""
need_flatten[name] = False
if layer.type == 'Reshape':
type_string = 'mx.symbol.Reshape'
need_flatten[name] = False
param = layer.reshape_param
param_string = "shape=(%s)" % (','.join(param.shape.dim),)
if layer.type == 'AbsVal':
type_string = 'mx.symbol.abs'
need_flatten[name] = need_flatten[mapping[layer.bottom[0]]]
if skip_layer:
assert len(layer.bottom) == 1
symbol_string += "%s = %s\n" % (name, prev_name)
elif type_string == '':
raise ValueError('Unknown layer %s!' % layer.type)
elif type_string != 'split':
bottom = layer.bottom
if param_string != "":
param_string = ", " + param_string
if len(bottom) == 1:
if need_flatten[mapping[bottom[0]]] and type_string == 'mx.symbol.FullyConnected':
flatten_name = "flatten_%d" % flatten_count
symbol_string += "%s=mx.symbol.Flatten(name='%s', data=%s)\n" % (
flatten_name, flatten_name, mapping[bottom[0]])
flatten_count += 1
need_flatten[flatten_name] = False
bottom[0] = flatten_name
mapping[bottom[0]] = bottom[0]
symbol_string += "%s = %s(name='%s', data=%s %s)\n" % (
name, type_string, name, mapping[bottom[0]], param_string)
else:
symbol_string += "%s = %s(name='%s', *[%s] %s)\n" % (
name, type_string, name, ','.join([mapping[x] for x in bottom]), param_string)
for j in range(len(layer.top)):
mapping[layer.top[j]] = name
output_name = name
return symbol_string, output_name, input_dim