基于ICNet模型实现航拍遥感图像图像分割python源码(附数据集+模型+项目使用说明).zip

# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve. # #Licensed 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. ################################################################# # # Based on #--------------------------------------------------------------- # https://github.com/hszhao/ICNet # Written by hszhao@cse.cuhk.edu.hk #--------------------------------------------------------------- from __future__ import absolute_import from __future__ import division from __future__ import print_function import paddle.fluid as fluid import numpy as np import sys def conv(input, k_h, k_w, c_o, s_h, s_w, relu=False, padding="VALID", biased=False, name=None): act = None tmp = input if relu: act = "relu" if padding == "SAME": padding_h = max(k_h - s_h, 0) padding_w = max(k_w - s_w, 0) padding_top = padding_h // 2 padding_left = padding_w // 2 padding_bottom = padding_h - padding_top padding_right = padding_w - padding_left padding = [ 0, 0, 0, 0, padding_top, padding_bottom, padding_left, padding_right ] tmp = fluid.layers.pad(tmp, padding) tmp = fluid.layers.conv2d( tmp, num_filters=c_o, filter_size=[k_h, k_w], stride=[s_h, s_w], groups=1, act=act, bias_attr=biased, use_cudnn=False, name=name) return tmp def atrous_conv(input, k_h, k_w, c_o, dilation, relu=False, padding="VALID", biased=False, name=None): act = None if relu: act = "relu" tmp = input if padding == "SAME": padding_h = max(k_h - s_h, 0) padding_w = max(k_w - s_w, 0) padding_top = padding_h // 2 padding_left = padding_w // 2 padding_bottom = padding_h - padding_top padding_right = padding_w - padding_left padding = [ 0, 0, 0, 0, padding_top, padding_bottom, padding_left, padding_right ] tmp = fluid.layers.pad(tmp, padding) tmp = fluid.layers.conv2d( input, num_filters=c_o, filter_size=[k_h, k_w], dilation=dilation, groups=1, act=act, bias_attr=biased, use_cudnn=False, name=name) return tmp def zero_padding(input, padding): return fluid.layers.pad(input, [0, 0, 0, 0, padding, padding, padding, padding]) def bn(input, relu=False, name=None, is_test=False): act = None if relu: act = 'relu' name = input.name.split(".")[0] + "_bn" tmp = fluid.layers.batch_norm( input, act=act, momentum=0.95, epsilon=1e-5, name=name) return tmp def avg_pool(input, k_h, k_w, s_h, s_w, name=None, padding=0): temp = fluid.layers.pool2d( input, pool_size=[k_h, k_w], pool_type="avg", pool_stride=[s_h, s_w], pool_padding=padding, name=name) return temp def max_pool(input, k_h, k_w, s_h, s_w, name=None, padding=0): temp = fluid.layers.pool2d( input, pool_size=[k_h, k_w], pool_type="max", pool_stride=[s_h, s_w], pool_padding=padding, name=name) return temp def interp(input, out_shape): out_shape = list(out_shape.astype("int32")) return fluid.layers.resize_bilinear(input, out_shape=out_shape) def dilation_convs(input): tmp = res_block(input, filter_num=256, padding=1, name="conv3_2") tmp = res_block(tmp, filter_num=256, padding=1, name="conv3_3") tmp = res_block(tmp, filter_num=256, padding=1, name="conv3_4") tmp = proj_block(tmp, filter_num=512, padding=2, dilation=2, name="conv4_1") tmp = res_block(tmp, filter_num=512, padding=2, dilation=2, name="conv4_2") tmp = res_block(tmp, filter_num=512, padding=2, dilation=2, name="conv4_3") tmp = res_block(tmp, filter_num=512, padding=2, dilation=2, name="conv4_4") tmp = res_block(tmp, filter_num=512, padding=2, dilation=2, name="conv4_5") tmp = res_block(tmp, filter_num=512, padding=2, dilation=2, name="conv4_6") tmp = proj_block( tmp, filter_num=1024, padding=4, dilation=4, name="conv5_1") tmp = res_block(tmp, filter_num=1024, padding=4, dilation=4, name="conv5_2") tmp = res_block(tmp, filter_num=1024, padding=4, dilation=4, name="conv5_3") return tmp def pyramis_pooling(input, input_shape): shape = np.ceil(input_shape // 32).astype("int32") h, w = shape pool1 = avg_pool(input, h, w, h, w) pool1_interp = interp(pool1, shape) pool2 = avg_pool(input, h // 2, w // 2, h // 2, w // 2) pool2_interp = interp(pool2, shape) pool3 = avg_pool(input, h // 3, w // 3, h // 3, w // 3) pool3_interp = interp(pool3, shape) pool4 = avg_pool(input, h // 4, w // 4, h // 4, w // 4) pool4_interp = interp(pool4, shape) conv5_3_sum = input + pool4_interp + pool3_interp + pool2_interp + pool1_interp return conv5_3_sum def shared_convs(image): tmp = conv(image, 3, 3, 32, 2, 2, padding='SAME', name="conv1_1_3_3_s2") tmp = bn(tmp, relu=True) tmp = conv(tmp, 3, 3, 32, 1, 1, padding='SAME', name="conv1_2_3_3") tmp = bn(tmp, relu=True) tmp = conv(tmp, 3, 3, 64, 1, 1, padding='SAME', name="conv1_3_3_3") tmp = bn(tmp, relu=True) tmp = max_pool(tmp, 3, 3, 2, 2, padding=[1, 1]) tmp = proj_block(tmp, filter_num=128, padding=0, name="conv2_1") tmp = res_block(tmp, filter_num=128, padding=1, name="conv2_2") tmp = res_block(tmp, filter_num=128, padding=1, name="conv2_3") tmp = proj_block(tmp, filter_num=256, padding=1, stride=2, name="conv3_1") return tmp def res_block(input, filter_num, padding=0, dilation=None, name=None): tmp = conv(input, 1, 1, filter_num // 4, 1, 1, name=name + "_1_1_reduce") tmp = bn(tmp, relu=True) tmp = zero_padding(tmp, padding=padding) if dilation is None: tmp = conv(tmp, 3, 3, filter_num // 4, 1, 1, name=name + "_3_3") else: tmp = atrous_conv( tmp, 3, 3, filter_num // 4, dilation, name=name + "_3_3") tmp = bn(tmp, relu=True) tmp = conv(tmp, 1, 1, filter_num, 1, 1, name=name + "_1_1_increase") tmp = bn(tmp, relu=False) tmp = input + tmp tmp = fluid.layers.relu(tmp) return tmp def proj_block(input, filter_num, padding=0, dilation=None, stride=1, name=None): proj = conv( input, 1, 1, filter_num, stride, stride, name=name + "_1_1_proj") proj_bn = bn(proj, relu=False) tmp = conv( input, 1, 1, filter_num // 4, stride, stride, name=name + "_1_1_reduce") tmp = bn(tmp, relu=True) tmp = zero_padding(tmp, padding=padding) if padding == 0: padding = 'SAME' else: padding = 'VALID' if dilation is None: tmp = conv( tmp, 3, 3, filter_num // 4, 1, 1, padding=padding, name=name + "_3_3") else: tmp = atrous_conv( tmp, 3, 3, filter_num // 4, dilation, padding=padding, name=name + "_3_3") tmp = bn(tmp, relu=True) tmp = conv(tmp, 1, 1, filter_num, 1, 1, name=name + "_1_1_increase") tmp = bn(tmp, relu=False) tmp = proj_bn + tmp tmp =