def convert_to_caffe(self, name):
caffe_net = caffe.NetSpec()
layer = L.Input(shape=dict(dim=[1, 3, args.image_hw, args.image_hw]))
caffe_net.tops['data'] = layer
slim.generate_caffe_prototxt(self, caffe_net, layer)
print(caffe_net.to_proto())
with open(name + '.prototxt', 'wb') as f:
f.write(str(caffe_net.to_proto()).encode())
caffe_net = caffe.Net(name + '.prototxt', caffe.TEST)
slim.convert_pytorch_to_caffe(self, caffe_net)
caffe_net.save(name + '.caffemodel')
def generate_caffe_prototxt(self, caffe_net, layer):
if self.stride == 1:
layer_x1, layer_x2 = L.Slice(layer,
ntop=2,
axis=1,
slice_point=[self.in_channels // 2])
caffe_net[self.g_name + '/slice1'] = layer_x1
caffe_net[self.g_name + '/slice2'] = layer_x2
layer_x2 = slim.generate_caffe_prototxt(self.conv, caffe_net,
layer_x2)
else:
layer_x1 = slim.generate_caffe_prototxt(self.conv0, caffe_net,
layer)
layer_x2 = slim.generate_caffe_prototxt(self.conv, caffe_net,
layer)
layer = L.Concat(layer_x1, layer_x2, axis=1)
caffe_net[self.g_name + '/concat'] = layer
layer = slim.generate_caffe_prototxt(self.shuffle, caffe_net, layer)
return layer
def generate_caffe_prototxt(self, caffe_net, layer):
data_layer = layer
network = slim.generate_caffe_prototxt(self.network, caffe_net,
data_layer)
return network