| # Licensed to the Apache Software Foundation (ASF) under one |
| # or more contributor license agreements. See the NOTICE file |
| # distributed with this work for additional information |
| # regarding copyright ownership. The ASF licenses this file |
| # to you under the Apache License, Version 2.0 (the |
| # "License"); you may not use this file except in compliance |
| # with the License. You may obtain a copy of the License at |
| # |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, |
| # software distributed under the License is distributed on an |
| # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY |
| # KIND, either express or implied. See the License for the |
| # specific language governing permissions and limitations |
| # under the License. |
| |
| # pylint: skip-file |
| import mxnet as mx |
| import numpy as np |
| import sys |
| import logging |
| |
| logger = logging.getLogger() |
| logger.setLevel(logging.INFO) |
| |
| # make a bilinear interpolation kernel, return a numpy.ndarray |
| def upsample_filt(size): |
| factor = (size + 1) // 2 |
| if size % 2 == 1: |
| center = factor - 1.0 |
| else: |
| center = factor - 0.5 |
| og = np.ogrid[:size, :size] |
| return (1 - abs(og[0] - center) / factor) * \ |
| (1 - abs(og[1] - center) / factor) |
| |
| def init_from_vgg16(ctx, fcnxs_symbol, vgg16fc_args, vgg16fc_auxs): |
| fcnxs_args = vgg16fc_args.copy() |
| fcnxs_auxs = vgg16fc_auxs.copy() |
| for k,v in fcnxs_args.items(): |
| if(v.context != ctx): |
| fcnxs_args[k] = mx.nd.zeros(v.shape, ctx) |
| v.copyto(fcnxs_args[k]) |
| for k,v in fcnxs_auxs.items(): |
| if(v.context != ctx): |
| fcnxs_auxs[k] = mx.nd.zeros(v.shape, ctx) |
| v.copyto(fcnxs_auxs[k]) |
| data_shape=(1,3,500,500) |
| arg_names = fcnxs_symbol.list_arguments() |
| arg_shapes, _, _ = fcnxs_symbol.infer_shape(data=data_shape) |
| rest_params = dict([(x[0], mx.nd.zeros(x[1], ctx)) for x in zip(arg_names, arg_shapes) |
| if x[0] in ['score_weight', 'score_bias', 'score_pool4_weight', 'score_pool4_bias', \ |
| 'score_pool3_weight', 'score_pool3_bias']]) |
| fcnxs_args.update(rest_params) |
| deconv_params = dict([(x[0], x[1]) for x in zip(arg_names, arg_shapes) |
| if x[0] in ["bigscore_weight", 'score2_weight', 'score4_weight']]) |
| for k, v in deconv_params.items(): |
| filt = upsample_filt(v[3]) |
| initw = np.zeros(v) |
| initw[range(v[0]), range(v[1]), :, :] = filt # becareful here is the slice assing |
| fcnxs_args[k] = mx.nd.array(initw, ctx) |
| return fcnxs_args, fcnxs_auxs |
| |
| def init_from_fcnxs(ctx, fcnxs_symbol, fcnxs_args_from, fcnxs_auxs_from): |
| """ use zero initialization for better convergence, because it tends to oputut 0, |
| and the label 0 stands for background, which may occupy most size of one image. |
| """ |
| fcnxs_args = fcnxs_args_from.copy() |
| fcnxs_auxs = fcnxs_auxs_from.copy() |
| for k,v in fcnxs_args.items(): |
| if(v.context != ctx): |
| fcnxs_args[k] = mx.nd.zeros(v.shape, ctx) |
| v.copyto(fcnxs_args[k]) |
| for k,v in fcnxs_auxs.items(): |
| if(v.context != ctx): |
| fcnxs_auxs[k] = mx.nd.zeros(v.shape, ctx) |
| v.copyto(fcnxs_auxs[k]) |
| data_shape=(1,3,500,500) |
| arg_names = fcnxs_symbol.list_arguments() |
| arg_shapes, _, _ = fcnxs_symbol.infer_shape(data=data_shape) |
| rest_params = {} |
| deconv_params = {} |
| # this is fcn8s init from fcn16s |
| if 'score_pool3_weight' in arg_names: |
| rest_params = dict([(x[0], mx.nd.zeros(x[1], ctx)) for x in zip(arg_names, arg_shapes) |
| if x[0] in ['score_pool3_bias', 'score_pool3_weight']]) |
| deconv_params = dict([(x[0], x[1]) for x in zip(arg_names, arg_shapes) if x[0] \ |
| in ["bigscore_weight", 'score4_weight']]) |
| # this is fcn16s init from fcn32s |
| elif 'score_pool4_weight' in arg_names: |
| rest_params = dict([(x[0], mx.nd.zeros(x[1], ctx)) for x in zip(arg_names, arg_shapes) |
| if x[0] in ['score_pool4_weight', 'score_pool4_bias']]) |
| deconv_params = dict([(x[0], x[1]) for x in zip(arg_names, arg_shapes) if x[0] \ |
| in ["bigscore_weight", 'score2_weight']]) |
| # this is fcn32s init |
| else: |
| logging.error("you are init the fcn32s model, so you should use init_from_vgg16()") |
| sys.exit() |
| fcnxs_args.update(rest_params) |
| for k, v in deconv_params.items(): |
| filt = upsample_filt(v[3]) |
| initw = np.zeros(v) |
| initw[range(v[0]), range(v[1]), :, :] = filt # becareful here is the slice assing |
| fcnxs_args[k] = mx.nd.array(initw, ctx) |
| return fcnxs_args, fcnxs_auxs |
