def main():
parser = argparse.ArgumentParser(
description='Caffe prototxt to mxnet model parameter converter.\
Note that only basic functions are implemented. You are welcomed to contribute to this file.'
)
parser.add_argument('caffe_prototxt',
help='The prototxt file in Caffe format')
parser.add_argument('caffe_model',
help='The binary model parameter file in Caffe format')
parser.add_argument('save_model_name',
help='The name of the output model prefix')
args = parser.parse_args()
prob, input_dim = proto2symbol(args.caffe_prototxt)
layers = ''
layer_names = ''
if caffe_flag:
caffe.set_mode_cpu()
net_caffe = caffe.Net(args.caffe_prototxt, args.caffe_model,
caffe.TEST)
layer_names = net_caffe._layer_names
layers = net_caffe.layers
else:
layers = parse.parse_caffemodel(args.caffe_model)
arg_shapes, output_shapes, aux_shapes = prob.infer_shape(
data=tuple(input_dim))
arg_names = prob.list_arguments()
arg_shape_dic = dict(zip(arg_names, arg_shapes))
arg_params = {}
iter = ''
if caffe_flag:
iter = get_caffe_iter(layer_names, layers)
else:
iter = get_iter(layers)
first_conv = True
for layer_name, layer_type, layer_blobs in iter:
if layer_type == 'Convolution' or layer_type == 'InnerProduct' or layer_type == 4 or layer_type == 14 \
or layer_type == 'PReLU' or layer_type == 'Normalize':
if layer_type == 'PReLU':
assert (len(layer_blobs) == 1)
wmat = layer_blobs[0].data
weight_name = layer_name + '_gamma'
arg_params[weight_name] = mx.nd.zeros(wmat.shape)
arg_params[weight_name][:] = wmat
continue
if layer_type == 'Normalize':
assert (len(layer_blobs) == 1)
weight_name = layer_name + '_scale'
wmat = layer_blobs[0].data
arg_params[weight_name] = mx.nd.zeros((1, len(wmat), 1, 1))
arg_params[weight_name][:] = np.array(list(wmat)).reshape(
(1, len(wmat), 1, 1))
continue
assert (len(layer_blobs) == 2)
wmat_dim = []
if getattr(layer_blobs[0].shape, 'dim', None) is not None:
if len(layer_blobs[0].shape.dim) > 0:
wmat_dim = layer_blobs[0].shape.dim
else:
wmat_dim = [
layer_blobs[0].num, layer_blobs[0].channels,
layer_blobs[0].height, layer_blobs[0].width
]
else:
wmat_dim = list(layer_blobs[0].shape)
wmat = np.array(layer_blobs[0].data).reshape(wmat_dim)
bias = np.array(layer_blobs[1].data)
channels = wmat_dim[1]
if channels == 3 or channels == 4: # RGB or RGBA
if first_conv:
print('Swapping BGR of caffe into RGB in mxnet')
wmat[:, [0, 2], :, :] = wmat[:, [2, 0], :, :]
assert (wmat.flags['C_CONTIGUOUS'] is True)
assert (bias.flags['C_CONTIGUOUS'] is True)
print('converting layer {0}, wmat shape = {1}, bias shape = {2}'.
format(layer_name, wmat.shape, bias.shape))
wmat = wmat.reshape((wmat.shape[0], -1))
bias = bias.reshape((bias.shape[0], 1))
weight_name = layer_name + "_weight"
bias_name = layer_name + "_bias"
if weight_name not in arg_shape_dic:
print(weight_name + ' not found in arg_shape_dic.')
continue
wmat = wmat.reshape(arg_shape_dic[weight_name])
arg_params[weight_name] = mx.nd.zeros(wmat.shape)
arg_params[weight_name][:] = wmat
bias = bias.reshape(arg_shape_dic[bias_name])
arg_params[bias_name] = mx.nd.zeros(bias.shape)
arg_params[bias_name][:] = bias
if first_conv and (layer_type == 'Convolution' or layer_type == 4):
first_conv = False
model = mx.mod.Module(symbol=prob, label_names=None)
model.bind(data_shapes=[('data', tuple(input_dim))])
model.init_params(arg_params=arg_params, aux_params={})
model.save_checkpoint(args.save_model_name, 1)
def main():
parser = argparse.ArgumentParser(
description='Caffe prototxt to mxnet model parameter converter.\
Note that only basic functions are implemented. You are welcomed to contribute to this file.'
)
parser.add_argument('caffe_prototxt',
help='The prototxt file in Caffe format')
parser.add_argument('caffe_model',
help='The binary model parameter file in Caffe format')
parser.add_argument('save_model_name',
help='The name of the output model prefix')
args = parser.parse_args()
prob, input_dim = proto2symbol(args.caffe_prototxt)
layers = ''
layer_names = ''
if caffe_flag:
caffe.set_mode_cpu()
net_caffe = caffe.Net(args.caffe_prototxt, args.caffe_model,
caffe.TEST)
layer_names = net_caffe._layer_names
layers = net_caffe.layers
else:
layers = parse.parse_caffemodel(args.caffe_model)
arg_shapes, output_shapes, aux_shapes = prob.infer_shape(
data=tuple(input_dim))
arg_names = prob.list_arguments()
arg_shape_dic = dict(zip(arg_names, arg_shapes))
arg_params = {}
iter = ''
if caffe_flag:
iter = get_caffe_iter(layer_names, layers)
else:
iter = get_iter(layers)
first_conv = True
for layer_name, layer_type, layer_blobs in iter:
if layer_type == 'Convolution' or layer_type == 'InnerProduct' or layer_type == 4 or layer_type == 14:
assert (len(layer_blobs) == 2)
wmat_dim = []
if getattr(layer_blobs[0].shape, 'dim', None) is not None:
if len(layer_blobs[0].shape.dim) > 0:
wmat_dim = layer_blobs[0].shape.dim
else:
wmat_dim = [
layer_blobs[0].num, layer_blobs[0].channels,
layer_blobs[0].height, layer_blobs[0].width
]
else:
wmat_dim = list(layer_blobs[0].shape)
wmat = np.array(layer_blobs[0].data).reshape(wmat_dim)
bias = np.array(layer_blobs[1].data)
if first_conv:
print 'Swapping BGR of caffe into RGB in mxnet'
wmat[:, [0, 2], :, :] = wmat[:, [2, 0], :, :]
assert (wmat.flags['C_CONTIGUOUS'] is True)
assert (bias.flags['C_CONTIGUOUS'] is True)
print 'converting layer {0}, wmat shape = {1}, bias shape = {2}'.format(
layer_name, wmat.shape, bias.shape)
wmat = wmat.reshape((wmat.shape[0], -1))
bias = bias.reshape((bias.shape[0], 1))
weight_name = layer_name + "_weight"
bias_name = layer_name + "_bias"
if weight_name not in arg_shape_dic:
print weight_name + ' not found in arg_shape_dic.'
continue
wmat = wmat.reshape(arg_shape_dic[weight_name])
arg_params[weight_name] = mx.nd.zeros(wmat.shape)
arg_params[weight_name][:] = wmat
bias = bias.reshape(arg_shape_dic[bias_name])
arg_params[bias_name] = mx.nd.zeros(bias.shape)
arg_params[bias_name][:] = bias
if first_conv and (layer_type == 'Convolution' or layer_type == 4):
first_conv = False
model = mx.model.FeedForward(ctx=mx.cpu(),
symbol=prob,
arg_params=arg_params,
aux_params={},
num_epoch=1,
learning_rate=0.05,
momentum=0.9,
wd=0.0001)
model.save(args.save_model_name)
def main():
parser = argparse.ArgumentParser(description='Caffe prototxt to mxnet model parameter converter.\
Note that only basic functions are implemented. You are welcomed to contribute to this file.')
parser.add_argument('caffe_prototxt', help='The prototxt file in Caffe format')
parser.add_argument('caffe_model', help='The binary model parameter file in Caffe format')
parser.add_argument('save_model_name', help='The name of the output model prefix')
args = parser.parse_args()
prob, input_dim = proto2symbol(args.caffe_prototxt)
layers = ''
layer_names = ''
if caffe_flag:
caffe.set_mode_cpu()
net_caffe = caffe.Net(args.caffe_prototxt, args.caffe_model, caffe.TEST)
layer_names = net_caffe._layer_names
layers = net_caffe.layers
else:
layers = parse.parse_caffemodel(args.caffe_model)
arg_shapes, output_shapes, aux_shapes = prob.infer_shape(data=tuple(input_dim))
arg_names = prob.list_arguments()
arg_shape_dic = dict(zip(arg_names, arg_shapes))
arg_params = {}
iter = ''
if caffe_flag:
iter = get_caffe_iter(layer_names, layers)
else:
iter = get_iter(layers)
first_conv = True
for layer_name, layer_type, layer_blobs in iter:
if layer_type == 'Convolution' or layer_type == 'InnerProduct' or layer_type == 4 or layer_type == 14:
assert(len(layer_blobs) == 2)
wmat_dim = []
if len(layer_blobs[0].shape.dim) > 0:
wmat_dim = layer_blobs[0].shape.dim
else:
wmat_dim = [layer_blobs[0].num, layer_blobs[0].channels, layer_blobs[0].height, layer_blobs[0].width]
wmat = np.array(layer_blobs[0].data).reshape(wmat_dim)
bias = np.array(layer_blobs[1].data)
if first_conv:
print 'Swapping BGR of caffe into RGB in mxnet'
wmat[:, [0, 2], :, :] = wmat[:, [2, 0], :, :]
assert(wmat.flags['C_CONTIGUOUS'] is True)
assert(bias.flags['C_CONTIGUOUS'] is True)
print 'converting layer {0}, wmat shape = {1}, bias shape = {2}'.format(layer_name, wmat.shape, bias.shape)
wmat = wmat.reshape((wmat.shape[0], -1))
bias = bias.reshape((bias.shape[0], 1))
weight_name = layer_name + "_weight"
bias_name = layer_name + "_bias"
if weight_name not in arg_shape_dic:
print weight_name + ' not found in arg_shape_dic.'
continue
wmat = wmat.reshape(arg_shape_dic[weight_name])
arg_params[weight_name] = mx.nd.zeros(wmat.shape)
arg_params[weight_name][:] = wmat
bias = bias.reshape(arg_shape_dic[bias_name])
arg_params[bias_name] = mx.nd.zeros(bias.shape)
arg_params[bias_name][:] = bias
if first_conv and (layer_type == 'Convolution' or layer_type == 4):
first_conv = False
model = mx.model.FeedForward(ctx=mx.cpu(), symbol=prob,
arg_params=arg_params, aux_params={}, num_epoch=1,
learning_rate=0.05, momentum=0.9, wd=0.0001)
model.save(args.save_model_name)
def main():
parser = argparse.ArgumentParser(description='Caffe prototxt to mxnet model parameter converter.\
Note that only basic functions are implemented. You are welcomed to contribute to this file.')
parser.add_argument('caffe_prototxt', help='The prototxt file in Caffe format')
parser.add_argument('caffe_model', help='The binary model parameter file in Caffe format')
parser.add_argument('save_model_name', help='The name of the output model prefix')
args = parser.parse_args()
prob, input_dim = proto2symbol(args.caffe_prototxt)
layers = ''
layer_names = ''
if caffe_flag:
caffe.set_mode_cpu()
net_caffe = caffe.Net(args.caffe_prototxt, args.caffe_model, caffe.TEST)
layer_names = net_caffe._layer_names
layers = net_caffe.layers
else:
layers = parse.parse_caffemodel(args.caffe_model)
arg_shapes, output_shapes, aux_shapes = prob.infer_shape(data=tuple(input_dim))
arg_names = prob.list_arguments()
arg_shape_dic = dict(zip(arg_names, arg_shapes))
arg_params = {}
iter = ''
if caffe_flag:
iter = get_caffe_iter(layer_names, layers)
else:
iter = get_iter(layers)
first_conv = True
for layer_name, layer_type, layer_blobs in iter:
if layer_type == 'Convolution' or layer_type == 'InnerProduct' or layer_type == 4 or layer_type == 14 \
or layer_type == 'PReLU' or layer_type == 'Normalize':
if layer_type == 'PReLU':
assert (len(layer_blobs) == 1)
wmat = layer_blobs[0].data
weight_name = layer_name + '_gamma'
arg_params[weight_name] = mx.nd.zeros(wmat.shape)
arg_params[weight_name][:] = wmat
continue
if layer_type == 'Normalize':
assert (len(layer_blobs) == 1)
weight_name = layer_name + '_scale'
wmat = layer_blobs[0].data
arg_params[weight_name] = mx.nd.zeros((1, len(wmat), 1, 1))
arg_params[weight_name][:] = np.array(list(wmat)).reshape((1, len(wmat), 1, 1))
continue
assert (len(layer_blobs) == 2)
wmat_dim = []
if getattr(layer_blobs[0].shape, 'dim', None) is not None:
if len(layer_blobs[0].shape.dim) > 0:
wmat_dim = layer_blobs[0].shape.dim
else:
wmat_dim = [layer_blobs[0].num, layer_blobs[0].channels, layer_blobs[0].height,
layer_blobs[0].width]
else:
wmat_dim = list(layer_blobs[0].shape)
wmat = np.array(layer_blobs[0].data).reshape(wmat_dim)
bias = np.array(layer_blobs[1].data)
channels = wmat_dim[1]
if channels == 3 or channels == 4: # RGB or RGBA
if first_conv:
print('Swapping BGR of caffe into RGB in mxnet')
wmat[:, [0, 2], :, :] = wmat[:, [2, 0], :, :]
assert (wmat.flags['C_CONTIGUOUS'] is True)
assert (bias.flags['C_CONTIGUOUS'] is True)
print('converting layer {0}, wmat shape = {1}, bias shape = {2}'.format(layer_name, wmat.shape, bias.shape))
wmat = wmat.reshape((wmat.shape[0], -1))
bias = bias.reshape((bias.shape[0], 1))
weight_name = layer_name + "_weight"
bias_name = layer_name + "_bias"
if weight_name not in arg_shape_dic:
print(weight_name + ' not found in arg_shape_dic.')
continue
wmat = wmat.reshape(arg_shape_dic[weight_name])
arg_params[weight_name] = mx.nd.zeros(wmat.shape)
arg_params[weight_name][:] = wmat
bias = bias.reshape(arg_shape_dic[bias_name])
arg_params[bias_name] = mx.nd.zeros(bias.shape)
arg_params[bias_name][:] = bias
if first_conv and (layer_type == 'Convolution' or layer_type == 4):
first_conv = False
model = mx.mod.Module(symbol=prob, label_names=None)
model.bind(data_shapes=[('data', tuple(input_dim))])
model.init_params(arg_params=arg_params, aux_params={})
model.save_checkpoint(args.save_model_name, 1)
def process_caffe_model(caffe_prototxt,
caffe_model,
output_file=None,
data=None,
data_shapes=None):
prob, input_dim = proto2symbol(caffe_prototxt)
layers = ''
layer_names = ''
if caffe_flag:
caffe.set_mode_cpu()
net_caffe = caffe.Net(caffe_prototxt, caffe_model, caffe.TEST)
layer_names = net_caffe._layer_names
layers = net_caffe.layers
else:
layers = parse.parse_caffemodel(caffe_model)
arg_shapes, output_shapes, aux_shapes = prob.infer_shape(
data=tuple(input_dim))
arg_names = prob.list_arguments()
aux_names = prob.list_auxiliary_states()
arg_shape_dic = dict(zip(arg_names, arg_shapes))
aux_shape_dic = dict(zip(aux_names, aux_shapes))
arg_params = {}
aux_params = {}
iter = ''
if caffe_flag:
iter = get_caffe_iter(layer_names, layers)
else:
iter = get_iter(layers)
first_conv = True
for layer_name, layer_type, layer_blobs in iter:
if layer_type == 'Convolution' or layer_type == 'InnerProduct' or layer_type == 4 or layer_type == 14 \
or layer_type == 'PReLU':
if layer_type == 'PReLU':
assert (len(layer_blobs) == 1)
wmat = layer_blobs[0].data
weight_name = layer_name + '_gamma'
arg_params[weight_name] = mx.nd.zeros(wmat.shape)
arg_params[weight_name][:] = wmat
continue
wmat_dim = []
if getattr(layer_blobs[0].shape, 'dim', None) is not None:
if len(layer_blobs[0].shape.dim) > 0:
wmat_dim = layer_blobs[0].shape.dim
else:
wmat_dim = [
layer_blobs[0].num, layer_blobs[0].channels,
layer_blobs[0].height, layer_blobs[0].width
]
else:
wmat_dim = list(layer_blobs[0].shape)
wmat = np.array(layer_blobs[0].data).reshape(wmat_dim)
channels = wmat_dim[1]
if channels == 3 or channels == 4: # RGB or RGBA
if first_conv:
print('Swapping BGR of caffe into RGB in mxnet')
wmat[:, [0, 2], :, :] = wmat[:, [2, 0], :, :]
assert (wmat.flags['C_CONTIGUOUS'] is True)
sys.stdout.write('converting layer {0}, wmat shape = {1}'.format(
layer_name, wmat.shape))
if len(layer_blobs) == 2:
bias = np.array(layer_blobs[1].data)
bias = bias.reshape((bias.shape[0], 1))
assert (bias.flags['C_CONTIGUOUS'] is True)
bias_name = layer_name + "_bias"
bias = bias.reshape(arg_shape_dic[bias_name])
arg_params[bias_name] = mx.nd.zeros(bias.shape)
arg_params[bias_name][:] = bias
sys.stdout.write(', bias shape = {}'.format(bias.shape))
sys.stdout.write('\n')
sys.stdout.flush()
wmat = wmat.reshape((wmat.shape[0], -1))
weight_name = layer_name + "_weight"
if weight_name not in arg_shape_dic:
print(weight_name + ' not found in arg_shape_dic.')
continue
wmat = wmat.reshape(arg_shape_dic[weight_name])
arg_params[weight_name] = mx.nd.zeros(wmat.shape)
arg_params[weight_name][:] = wmat
if first_conv and (layer_type == 'Convolution' or layer_type == 4):
first_conv = False
elif layer_type == 'Scale':
bn_name = layer_name.replace('scale', 'bn')
gamma = layer_blobs[0].data
beta = layer_blobs[1].data
# beta = np.expand_dims(beta, 1)
beta_name = '{}_beta'.format(bn_name)
gamma_name = '{}_gamma'.format(bn_name)
beta = beta.reshape(arg_shape_dic[beta_name])
gamma = gamma.reshape(arg_shape_dic[gamma_name])
arg_params[beta_name] = mx.nd.zeros(beta.shape)
arg_params[gamma_name] = mx.nd.zeros(gamma.shape)
arg_params[beta_name][:] = beta
arg_params[gamma_name][:] = gamma
assert gamma.flags['C_CONTIGUOUS'] is True
assert beta.flags['C_CONTIGUOUS'] is True
print('converting scale layer, beta shape = {}, gamma shape = {}'.
format(beta.shape, gamma.shape))
elif layer_type == 'BatchNorm':
bn_name = layer_name
mean = layer_blobs[0].data
var = layer_blobs[1].data
moving_average_factor = layer_blobs[2].data
mean_name = '{}_moving_mean'.format(bn_name)
var_name = '{}_moving_var'.format(bn_name)
maf_name = '{}_momentum'.format(bn_name)
mean = mean.reshape(aux_shape_dic[mean_name])
var = var.reshape(aux_shape_dic[var_name])
aux_params[mean_name] = mx.nd.zeros(mean.shape)
aux_params[var_name] = mx.nd.zeros(var.shape)
arg_params[maf_name] = mx.nd.zeros(moving_average_factor.shape)
aux_params[mean_name][:] = mean
aux_params[var_name][:] = var
arg_params[maf_name][:] = moving_average_factor
assert var.flags['C_CONTIGUOUS'] is True
assert mean.flags['C_CONTIGUOUS'] is True
print(
'converting batchnorm layer, mean shape = {}, var shape = {}'.
format(mean.shape, var.shape))
else:
assert len(layer_blobs) == 0
print('\tskipping layer {} of type {}'.format(
layer_name, layer_type))
return prob, arg_params, aux_params, input_dim