| import find_mxnet |
| import mxnet as mx |
| import os, sys |
| from collections import namedtuple |
| |
| ConvExecutor = namedtuple('ConvExecutor', ['executor', 'data', 'data_grad', 'style', 'content', 'arg_dict']) |
| |
| def get_symbol(): |
| # declare symbol |
| data = mx.sym.Variable("data") |
| conv1_1 = mx.symbol.Convolution(name='conv1_1', data=data , num_filter=64, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False, workspace=1024) |
| relu1_1 = mx.symbol.Activation(name='relu1_1', data=conv1_1 , act_type='relu') |
| conv1_2 = mx.symbol.Convolution(name='conv1_2', data=relu1_1 , num_filter=64, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False, workspace=1024) |
| relu1_2 = mx.symbol.Activation(name='relu1_2', data=conv1_2 , act_type='relu') |
| pool1 = mx.symbol.Pooling(name='pool1', data=relu1_2 , pad=(0,0), kernel=(2,2), stride=(2,2), pool_type='avg') |
| conv2_1 = mx.symbol.Convolution(name='conv2_1', data=pool1 , num_filter=128, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False, workspace=1024) |
| relu2_1 = mx.symbol.Activation(name='relu2_1', data=conv2_1 , act_type='relu') |
| conv2_2 = mx.symbol.Convolution(name='conv2_2', data=relu2_1 , num_filter=128, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False, workspace=1024) |
| relu2_2 = mx.symbol.Activation(name='relu2_2', data=conv2_2 , act_type='relu') |
| pool2 = mx.symbol.Pooling(name='pool2', data=relu2_2 , pad=(0,0), kernel=(2,2), stride=(2,2), pool_type='avg') |
| conv3_1 = mx.symbol.Convolution(name='conv3_1', data=pool2 , num_filter=256, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False, workspace=1024) |
| relu3_1 = mx.symbol.Activation(name='relu3_1', data=conv3_1 , act_type='relu') |
| conv3_2 = mx.symbol.Convolution(name='conv3_2', data=relu3_1 , num_filter=256, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False, workspace=1024) |
| relu3_2 = mx.symbol.Activation(name='relu3_2', data=conv3_2 , act_type='relu') |
| conv3_3 = mx.symbol.Convolution(name='conv3_3', data=relu3_2 , num_filter=256, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False, workspace=1024) |
| relu3_3 = mx.symbol.Activation(name='relu3_3', data=conv3_3 , act_type='relu') |
| conv3_4 = mx.symbol.Convolution(name='conv3_4', data=relu3_3 , num_filter=256, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False, workspace=1024) |
| relu3_4 = mx.symbol.Activation(name='relu3_4', data=conv3_4 , act_type='relu') |
| pool3 = mx.symbol.Pooling(name='pool3', data=relu3_4 , pad=(0,0), kernel=(2,2), stride=(2,2), pool_type='avg') |
| conv4_1 = mx.symbol.Convolution(name='conv4_1', data=pool3 , num_filter=512, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False, workspace=1024) |
| relu4_1 = mx.symbol.Activation(name='relu4_1', data=conv4_1 , act_type='relu') |
| conv4_2 = mx.symbol.Convolution(name='conv4_2', data=relu4_1 , num_filter=512, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False, workspace=1024) |
| relu4_2 = mx.symbol.Activation(name='relu4_2', data=conv4_2 , act_type='relu') |
| conv4_3 = mx.symbol.Convolution(name='conv4_3', data=relu4_2 , num_filter=512, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False, workspace=1024) |
| relu4_3 = mx.symbol.Activation(name='relu4_3', data=conv4_3 , act_type='relu') |
| conv4_4 = mx.symbol.Convolution(name='conv4_4', data=relu4_3 , num_filter=512, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False, workspace=1024) |
| relu4_4 = mx.symbol.Activation(name='relu4_4', data=conv4_4 , act_type='relu') |
| pool4 = mx.symbol.Pooling(name='pool4', data=relu4_4 , pad=(0,0), kernel=(2,2), stride=(2,2), pool_type='avg') |
| conv5_1 = mx.symbol.Convolution(name='conv5_1', data=pool4 , num_filter=512, pad=(1,1), kernel=(3,3), stride=(1,1), no_bias=False, workspace=1024) |
| relu5_1 = mx.symbol.Activation(name='relu5_1', data=conv5_1 , act_type='relu') |
| |
| # style and content layers |
| style = mx.sym.Group([relu1_1, relu2_1, relu3_1, relu4_1, relu5_1]) |
| content = mx.sym.Group([relu4_2]) |
| return style, content |
| |
| |
| def get_executor(style, content, input_size, ctx): |
| out = mx.sym.Group([style, content]) |
| # make executor |
| arg_shapes, output_shapes, aux_shapes = out.infer_shape(data=(1, 3, input_size[0], input_size[1])) |
| arg_names = out.list_arguments() |
| arg_dict = dict(zip(arg_names, [mx.nd.zeros(shape, ctx=ctx) for shape in arg_shapes])) |
| grad_dict = {"data": arg_dict["data"].copyto(ctx)} |
| # init with pretrained weight |
| pretrained = mx.nd.load("./model/vgg19.params") |
| for name in arg_names: |
| if name == "data": |
| continue |
| key = "arg:" + name |
| if key in pretrained: |
| pretrained[key].copyto(arg_dict[name]) |
| else: |
| print("Skip argument %s" % name) |
| executor = out.bind(ctx=ctx, args=arg_dict, args_grad=grad_dict, grad_req="write") |
| return ConvExecutor(executor=executor, |
| data=arg_dict["data"], |
| data_grad=grad_dict["data"], |
| style=executor.outputs[:-1], |
| content=executor.outputs[-1], |
| arg_dict=arg_dict) |
| |
| |
| def get_model(input_size, ctx): |
| style, content = get_symbol() |
| return get_executor(style, content, input_size, ctx) |
