From d6d9409b3816b768b9a89aefaabe588f7d11cc6d Mon Sep 17 00:00:00 2001 From: Exception-star <53971568+Exception-star@users.noreply.github.com> Date: Tue, 10 Dec 2019 14:27:41 +0800 Subject: [PATCH] Revert "add danet to PaddleCV/Research (#3907)" (#4049) This reverts commit ab0920d37e382084304ae2bb2bac60e8d75b0936. --- PaddleCV/Research/danet/README.md | 143 ---- PaddleCV/Research/danet/danet.py | 638 ------------------ PaddleCV/Research/danet/eval.py | 344 ---------- PaddleCV/Research/danet/img/Network.png | Bin 300602 -> 0 bytes PaddleCV/Research/danet/img/channel.png | Bin 118889 -> 0 bytes PaddleCV/Research/danet/img/position.png | Bin 135361 -> 0 bytes PaddleCV/Research/danet/img/val_1.png | Bin 509036 -> 0 bytes PaddleCV/Research/danet/img/val_gt.png | Bin 20278 -> 0 bytes PaddleCV/Research/danet/img/val_output.png | Bin 43240 -> 0 bytes PaddleCV/Research/danet/iou.py | 71 -- PaddleCV/Research/danet/options.py | 174 ----- PaddleCV/Research/danet/train_dygraph.py | 315 --------- PaddleCV/Research/danet/train_executor.py | 390 ----------- PaddleCV/Research/danet/utils/__init__.py | 21 - PaddleCV/Research/danet/utils/base.py | 128 ---- PaddleCV/Research/danet/utils/cityscapes.py | 78 --- .../Research/danet/utils/cityscapes_data.py | 141 ---- PaddleCV/Research/danet/utils/lr_scheduler.py | 149 ---- PaddleCV/Research/danet/utils/voc.py | 98 --- PaddleCV/Research/danet/utils/voc_data.py | 141 ---- 20 files changed, 2831 deletions(-) delete mode 100644 PaddleCV/Research/danet/README.md delete mode 100644 PaddleCV/Research/danet/danet.py delete mode 100644 PaddleCV/Research/danet/eval.py delete mode 100644 PaddleCV/Research/danet/img/Network.png delete mode 100644 PaddleCV/Research/danet/img/channel.png delete mode 100644 PaddleCV/Research/danet/img/position.png delete mode 100644 PaddleCV/Research/danet/img/val_1.png delete mode 100644 PaddleCV/Research/danet/img/val_gt.png delete mode 100644 PaddleCV/Research/danet/img/val_output.png delete mode 100644 PaddleCV/Research/danet/iou.py delete mode 100644 PaddleCV/Research/danet/options.py delete mode 100644 PaddleCV/Research/danet/train_dygraph.py delete mode 100644 PaddleCV/Research/danet/train_executor.py delete mode 100644 PaddleCV/Research/danet/utils/__init__.py delete mode 100644 PaddleCV/Research/danet/utils/base.py delete mode 100644 PaddleCV/Research/danet/utils/cityscapes.py delete mode 100644 PaddleCV/Research/danet/utils/cityscapes_data.py delete mode 100644 PaddleCV/Research/danet/utils/lr_scheduler.py delete mode 100644 PaddleCV/Research/danet/utils/voc.py delete mode 100644 PaddleCV/Research/danet/utils/voc_data.py diff --git a/PaddleCV/Research/danet/README.md b/PaddleCV/Research/danet/README.md deleted file mode 100644 index 01a77a3d..00000000 --- a/PaddleCV/Research/danet/README.md +++ /dev/null @@ -1,143 +0,0 @@ -# [Dual Attention Network for Scene Segmentation (CVPR2019)](https://arxiv.org/pdf/1809.02983.pdf) - -本项目是[DANet](https://arxiv.org/pdf/1809.02983.pdf)的 PaddlePaddle 实现, 包含模型训练,验证等内容。 - -## 模型简介 -![net](img/Network.png) -骨干网络使用ResNet,为更好地进行语义分割任务,作者对ResNet做出以下改动: - - 1、将最后两个layer的downsampling取消,使得特征图是原图的1/8,保持较高空间分辨率。 - 2、最后两个layer采用空洞卷积扩大感受野。 -然后接上两个并行的注意力模块(位置注意力和通道注意力),最终将两个模块的结果进行elementwise操作,之后再接一层卷积输出分割图。 - -### 位置注意力 - -![position](img/position.png) - -A是骨干网络ResNet输出经过一层卷积生成的特征图,维度为CHW; -A经过3个卷积操作输出维度均为CHW的B、C、D。将B、C、D都reshape到CN(N = H*W); -然后将B reshape后的结果转置与C相乘,得到N * N的矩阵, 对于矩阵的每一个点进行softmax; -然后将D与softmax后的结果相乘并reshape到CHW,再与A进行elementwise。 - -### 通道注意力 -![channel](img/channel.png) - - -A是骨干网络ResNet输出经过一层卷积生成的特征图,维度为CHW; -A经过3个reshape操作输出维度均为CN(N = H*W)的B、C、D; -然后将B转置与C相乘,得到C * C的矩阵,对于矩阵的每一个点进行softmax; -然后将D与softmax后的结果相乘并reshape到CHW,再与A进行elementwise。 - - - -## 数据准备 - -公开数据集:Cityscapes - -训练集2975张,验证集500张,测试集1525张,图片分辨率都是1024*2048。 - -数据集来源:AIstudio数据集页面上[下载](https://aistudio.baidu.com/aistudio/datasetDetail/11503),放到dataset文件夹下,其目录结构如下: -```text -dataset - ├── cityscapes # Cityscapes数据集 - ├── gtFine # 精细化标注的label - ├── leftImg8bit # 训练,验证,测试图片 - ├── trainLabels.txt # 训练图片路径 - ├── valLabels.txt # 验证图片路径 - ... ... -``` -## 训练说明 - -#### 数据增强策略 - 1、随机尺度缩放:尺度范围0.75到2.0 - 2、随机左右翻转:发生概率0.5 - 3、同比例缩放:缩放的大小由选项1决定。 - 4、随机裁剪: - 5、高斯模糊:发生概率0.3(可选) - 6、颜色抖动,对比度,锐度,亮度; 发生概率0.3(可选) -###### 默认1、2、3、4都开启,5、6关闭 - -#### 学习率调节策略 - 1、使用热身策略,学习率由0递增到base_lr,热身轮数(epoch)是5 - 2、在热身策略之后使用学习率衰减策略(poly),学习率由base_lr递减到0 - -#### 优化器选择 - Momentum: 动量0.9,正则化系数1e-4 - -#### 加载预训练模型 - 设置 --load_pretrained_model True (默认为True) - 预训练文件: - checkpoint/DANet50_pretrained_model_paddle1.6.pdparams - checkpoint/DANet101_pretrained_model_paddle1.6.pdparams - -#### 不加载预训练模型 - 从0开始训练,需要设置 --load_pretrained_model False - -#### 加载训练好的模型 - 设置 --load_better_model True (默认为False) - 训练好的文件: - checkpoint/DANet101_better_model_paddle1.6.pdparams -##### 【注】 - 训练时paddle版本是1.5.2,代码已转为1.6版本(兼容1.6版本),预训练参数、训练好的参数来自1.5.2版本 - -#### 配置模型文件路径 -[预训练参数、最优模型参数下载](https://paddlemodels.bj.bcebos.com/DANet/DANet_models.tar) - -其目录结构如下: -```text -checkpoint - ├── DANet50_pretrained_model_paddle1.6.pdparams # DANet50预训练模型,适合1.6.0版本 - ├── DANet101_pretrained_model_paddle1.6.pdparams # DANet101预训练模型,适合1.6.0版本 - ├── DANet101_better_model_paddle1.6.pdparams # DANet101训练最优模型,适合1.6.0版本 - ├── DANet101_better_model_paddle1.5.2 # DANet101训练最优模型,只适合1.5.2版本 - -``` - -## 模型训练 -```sh -# open garbage collection to save memory -export FLAGS_eager_delete_tensor_gb=0.0 -# setting visible devices for train -export CUDA_VISIBLE_DEVICES=0,1,2,3 - - -python train_executor.py --backbone resnet101 --batch_size 2 --lr 0.003 --lr_scheduler poly --epoch_num 350 --crop_size 768 --base_size 1024 --warm_up True --cuda True --use_data_parallel True --dilated True --multi_grid True --multi_dilation [4, 8, 16] --scale True --load_pretrained_model True --load_better_model False -或者 -python train_dygraph.py --backbone resnet101 --batch_size 2 --lr 0.003 --lr_scheduler poly --epoch_num 350 --crop_size 768 --base_size 1024 --warm_up True --cuda True --use_data_parallel True --dilated True --multi_grid True --multi_dilation [4, 8, 16] --scale True --load_pretrained_model True --load_better_model False - -``` -#### 【注】 -##### train_executor.py使用executor方式训练(适合paddle1.5.2),train_dygraph.py使用动态图方式训练(适合paddle1.6.0),两种方式都可以 -##### 在训练阶段,验证的结果不是真实的,需要使用eval.py来获得验证的最终结果。 - - ## 模型验证 -```sh -# open garbage collection to save memory -export FLAGS_eager_delete_tensor_gb=0.0 -# setting visible devices for prediction -export CUDA_VISIBLE_DEVICES=0 - -python eval.py --backbone resnet101 --load_better_model True --batch_size 1 --crop_size 1024 --base_size 2048 --cuda True --multi_scales True --flip True --dilated True --multi_grid True --multi_dilation [4, 8, 16] -``` - -## 验证结果 -评测指标:mean IOU(平均交并比) - - -| 模型 | 单尺度 | 多尺度 | -| :---:|:---:| :---:| -|DANet101|0.8043836|0.8138021 - -##### 具体数值 -| 模型 | cls1 | cls2 | cls3 | cls4 | cls5 | cls6 | cls7 | cls8 | cls9 | cls10 | cls11 | cls12 | cls13 | cls14 | cls15 | cls16 |cls17 | cls18 | cls19 | -| :---:|:---: | :---:| :---:|:---: | :---:| :---:|:---: | :---:| :---:|:---: |:---: |:---: |:---: | :---: | :---: |:---: | :---:| :---: |:---: | -|DANet101-SS|0.98212|0.85372|0.92799|0.59976|0.63318|0.65819|0.72023|0.80000|0.92605|0.65788|0.94841|0.83377|0.65206|0.95566|0.87148|0.91233|0.84352|0.71948|0.78737| -|DANet101-MS|0.98047|0.84637|0.93084|0.62699|0.64839|0.67769|0.73650|0.81343|0.92942|0.67010|0.95127|0.84466|0.66635|0.95749|0.87755|0.92370|0.85344|0.73007|0.79742| - -## 输出结果可视化 -![val_1](img/val_1.png) -###### 输入图片 -![val_gt](img/val_gt.png) -###### 图片label -![val_output](img/val_output.png) -###### DANet101模型输出 diff --git a/PaddleCV/Research/danet/danet.py b/PaddleCV/Research/danet/danet.py deleted file mode 100644 index e535adba..00000000 --- a/PaddleCV/Research/danet/danet.py +++ /dev/null @@ -1,638 +0,0 @@ -# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. -# -# 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. - - -import shutil -import paddle.fluid as fluid -import os - - -__all__ = ['DANet'] - - -class ConvBN(fluid.dygraph.Layer): - - def __init__(self, - name_scope, - num_filters, - filter_size=3, - stride=1, - dilation=1, - act=None, - learning_rate=1.0, - dtype='float32', - bias_attr=False): - super(ConvBN, self).__init__(name_scope) - - if dilation != 1: - padding = dilation - else: - padding = (filter_size - 1) // 2 - - self._conv = fluid.dygraph.Conv2D(name_scope, - num_filters=num_filters, - filter_size=filter_size, - stride=stride, - padding=padding, - dilation=dilation, - act=None, - dtype=dtype, - bias_attr=bias_attr if bias_attr is False else fluid.ParamAttr( - learning_rate=learning_rate, - name='bias'), - param_attr=fluid.ParamAttr( - learning_rate=learning_rate, - name='weight') - ) - self._bn = fluid.dygraph.BatchNorm(name_scope, - num_channels=num_filters, - act=act, - dtype=dtype, - momentum=0.9, - epsilon=1e-5, - bias_attr=fluid.ParamAttr( - learning_rate=learning_rate, - name='bias'), - param_attr=fluid.ParamAttr( - learning_rate=learning_rate, - name='weight'), - moving_mean_name='running_mean', - moving_variance_name='running_var' - ) - - def forward(self, inputs): - x = self._conv(inputs) - x = self._bn(x) - return x - - -class BasicBlock(fluid.dygraph.Layer): - - def __init__(self, - name_scope, - num_filters, - stride=1, - dilation=1, - same=False): - super(BasicBlock, self).__init__(name_scope) - self._conv0 = ConvBN(self.full_name(), - num_filters=num_filters, - filter_size=3, - stride=stride, - dilation=dilation, - act='relu') - self._conv1 = ConvBN(self.full_name(), - num_filters=num_filters, - filter_size=3, - stride=1, - dilation=dilation, - act=None) - - self.same = same - - if not same: - self._skip = ConvBN(self.full_name(), - num_filters=num_filters, - filter_size=1, - stride=stride, - act=None) - - def forward(self, inputs): - x = self._conv0(inputs) - x = self._conv1(x) - if self.same: - skip = inputs - else: - skip = self._skip(inputs) - x = fluid.layers.elementwise_add(x, skip, act='relu') - return x - - -class BottleneckBlock(fluid.dygraph.Layer): - def __init__(self, name_scope, num_filters, stride, dilation=1, same=False): - super(BottleneckBlock, self).__init__(name_scope) - self.expansion = 4 - - self._conv0 = ConvBN(name_scope, - num_filters=num_filters, - filter_size=1, - stride=1, - act='relu') - self._conv1 = ConvBN(name_scope, - num_filters=num_filters, - filter_size=3, - stride=stride, - dilation=dilation, - act='relu') - self._conv2 = ConvBN(name_scope, - num_filters=num_filters * self.expansion, - filter_size=1, - stride=1, - act=None) - self.same = same - - if not same: - self._skip = ConvBN(name_scope, - num_filters=num_filters * self.expansion, - filter_size=1, - stride=stride, - act=None) - - def forward(self, inputs): - x = self._conv0(inputs) - x = self._conv1(x) - x = self._conv2(x) - if self.same: - skip = inputs - else: - skip = self._skip(inputs) - x = fluid.layers.elementwise_add(x, skip, act='relu') - return x - - -class ResNet(fluid.dygraph.Layer): - def __init__(self, - name_scope, - layer=152, - num_class=1000, - dilated=True, - multi_grid=True, - multi_dilation=[4, 8, 16], - need_fc=False): - super(ResNet, self).__init__(name_scope) - - support_layer = [18, 34, 50, 101, 152] - assert layer in support_layer, 'layer({}) not in {}'.format(layer, support_layer) - self.need_fc = need_fc - self.num_filters_list = [64, 128, 256, 512] - if layer == 18: - self.depth = [2, 2, 2, 2] - elif layer == 34: - self.depth = [3, 4, 6, 3] - elif layer == 50: - self.depth = [3, 4, 6, 3] - elif layer == 101: - self.depth = [3, 4, 23, 3] - elif layer == 152: - self.depth = [3, 8, 36, 3] - - if multi_grid: - assert multi_dilation is not None - self.multi_dilation = multi_dilation - - self._conv = ConvBN(name_scope, 64, 7, 2, act='relu') - self._pool = fluid.dygraph.Pool2D(name_scope, - pool_size=3, - pool_stride=2, - pool_padding=1, - pool_type='max') - if layer >= 50: - self.layer1 = self._make_layer(block=BottleneckBlock, - depth=self.depth[0], - num_filters=self.num_filters_list[0], - stride=1, - same=False, - name='layer1') - self.layer2 = self._make_layer(block=BottleneckBlock, - depth=self.depth[1], - num_filters=self.num_filters_list[1], - stride=2, - same=False, - name='layer2') - if dilated: - self.layer3 = self._make_layer(block=BottleneckBlock, - depth=self.depth[2], - num_filters=self.num_filters_list[2], - stride=2, - dilation=2, - same=False, - name='layer3') - if multi_grid: # layer4 采用不同的采样率 - self.layer4 = self._make_layer(block=BottleneckBlock, - depth=self.depth[3], - num_filters=self.num_filters_list[3], - stride=2, - dilation=4, - multi_grid=multi_grid, - multi_dilation=self.multi_dilation, - same=False, - name='layer4') - else: - self.layer4 = self._make_layer(block=BottleneckBlock, - depth=self.depth[3], - num_filters=self.num_filters_list[3], - stride=2, - dilation=4, - same=False, - name='layer4') - else: - self.layer3 = self._make_layer(block=BottleneckBlock, - depth=self.depth[2], - num_filters=self.num_filters_list[2], - stride=2, - dilation=1, - same=False, - name='layer3') - self.layer4 = self._make_layer(block=BottleneckBlock, - depth=self.depth[3], - num_filters=self.num_filters_list[3], - stride=2, - dilation=1, - same=False, - name='layer4') - - else: # layer=18 or layer=34 - self.layer1 = self._make_layer(block=BasicBlock, - depth=self.depth[0], - num_filters=self.num_filters_list[0], - stride=1, - same=True, - name=name_scope) - self.layer2 = self._make_layer(block=BasicBlock, - depth=self.depth[1], - num_filters=self.num_filters_list[1], - stride=2, - same=False, - name=name_scope) - self.layer3 = self._make_layer(block=BasicBlock, - depth=self.depth[2], - num_filters=self.num_filters_list[2], - stride=2, - dilation=1, - same=False, - name=name_scope) - self.layer4 = self._make_layer(block=BasicBlock, - depth=self.depth[3], - num_filters=self.num_filters_list[3], - stride=2, - dilation=1, - same=False, - name=name_scope) - - self._avgpool = fluid.dygraph.Pool2D(name_scope, - global_pooling=True, - pool_type='avg') - self.fc = fluid.dygraph.FC(name_scope, - size=num_class, - act='softmax') - - def _make_layer(self, block, depth, num_filters, stride=1, dilation=1, same=False, multi_grid=False, - multi_dilation=None, name=None): - layers = [] - if dilation != 1: - # stride(2x2) with a dilated convolution instead - stride = 1 - - if multi_grid: - assert len(multi_dilation) == 3 - for depth in range(depth): - temp = block(name + '.{}'.format(depth), - num_filters=num_filters, - stride=stride, - dilation=multi_dilation[depth], - same=same) - stride = 1 - same = True - layers.append(self.add_sublayer('_{}_{}'.format(name, depth + 1), temp)) - else: - for depth in range(depth): - temp = block(name + '.{}'.format(depth), - num_filters=num_filters, - stride=stride, - dilation=dilation if depth > 0 else 1, - same=same) - stride = 1 - same = True - layers.append(self.add_sublayer('_{}_{}'.format(name, depth + 1), temp)) - return layers - - def forward(self, inputs): - x = self._conv(inputs) - - x = self._pool(x) - for layer in self.layer1: - x = layer(x) - c1 = x - - for layer in self.layer2: - x = layer(x) - c2 = x - - for layer in self.layer3: - x = layer(x) - c3 = x - - for layer in self.layer4: - x = layer(x) - c4 = x - - if self.need_fc: - x = self._avgpool(x) - x = self.fc(x) - return x - else: - return c1, c2, c3, c4 - - -class CAM(fluid.dygraph.Layer): - def __init__(self, - name_scope, - in_channels=512, - default_value=0): - """ - channel_attention_module - """ - super(CAM, self).__init__(name_scope) - self.in_channels = in_channels - self.gamma = fluid.layers.create_parameter(shape=[1], - dtype='float32', - is_bias=True, - attr=fluid.ParamAttr( - learning_rate=10.0, - name='cam_gamma'), - default_initializer=fluid.initializer.ConstantInitializer( - value=default_value) - ) - - def forward(self, inputs): - batch_size, c, h, w = inputs.shape - out_b = fluid.layers.reshape(inputs, shape=[batch_size, self.in_channels, h * w]) - out_c = fluid.layers.reshape(inputs, shape=[batch_size, self.in_channels, h * w]) - out_c_t = fluid.layers.transpose(out_c, perm=[0, 2, 1]) - mul_bc = fluid.layers.matmul(out_b, out_c_t) - - mul_bc_max = fluid.layers.reduce_max(mul_bc, dim=-1, keep_dim=True) - mul_bc_max = fluid.layers.expand(mul_bc_max, expand_times=[1, 1, c]) - x = fluid.layers.elementwise_sub(mul_bc_max, mul_bc) - - attention = fluid.layers.softmax(x, use_cudnn=True, axis=-1) - - out_d = fluid.layers.reshape(inputs, shape=[batch_size, self.in_channels, h * w]) - attention_mul = fluid.layers.matmul(attention, out_d) - - attention_reshape = fluid.layers.reshape(attention_mul, shape=[batch_size, self.in_channels, h, w]) - gamma_attention = fluid.layers.elementwise_mul(attention_reshape, self.gamma) - out = fluid.layers.elementwise_add(gamma_attention, inputs) - return out - - -class PAM(fluid.dygraph.Layer): - def __init__(self, - name_scope, - in_channels=512, - default_value=0): - """ - position_attention_module - """ - super(PAM, self).__init__(name_scope) - - assert in_channels // 8, 'in_channel // 8 > 0 ' - self.channel_in = in_channels // 8 - self._convB = fluid.dygraph.Conv2D(name_scope, - num_filters=in_channels // 8, - filter_size=1, - bias_attr=fluid.ParamAttr( - learning_rate=10.0, - name='bias'), - param_attr=fluid.ParamAttr( - learning_rate=10.0, - name='weight') - ) - self._convC = fluid.dygraph.Conv2D(name_scope, - num_filters=in_channels // 8, - filter_size=1, - bias_attr=fluid.ParamAttr( - learning_rate=10.0, - name='bias'), - param_attr=fluid.ParamAttr( - learning_rate=10.0, - name='weight') - ) - self._convD = fluid.dygraph.Conv2D(name_scope, - num_filters=in_channels, - filter_size=1, - bias_attr=fluid.ParamAttr( - learning_rate=10.0, - name='bias'), - param_attr=fluid.ParamAttr( - learning_rate=10.0, - name='weight') - ) - self.gamma = fluid.layers.create_parameter(shape=[1], - dtype='float32', - is_bias=True, - attr=fluid.ParamAttr( - learning_rate=10.0, - name='pam_gamma'), - default_initializer=fluid.initializer.ConstantInitializer( - value=default_value)) - - def forward(self, inputs): - batch_size, c, h, w = inputs.shape - out_b = self._convB(inputs) - out_b_reshape = fluid.layers.reshape(out_b, shape=[batch_size, self.channel_in, h * w]) - out_b_reshape_t = fluid.layers.transpose(out_b_reshape, perm=[0, 2, 1]) - out_c = self._convC(inputs) - out_c_reshape = fluid.layers.reshape(out_c, shape=[batch_size, self.channel_in, h * w]) - - mul_bc = fluid.layers.matmul(out_b_reshape_t, out_c_reshape) - soft_max_bc = fluid.layers.softmax(mul_bc, use_cudnn=True, axis=-1) - - out_d = self._convD(inputs) - out_d_reshape = fluid.layers.reshape(out_d, shape=[batch_size, self.channel_in * 8, h * w]) - attention = fluid.layers.matmul(out_d_reshape, fluid.layers.transpose(soft_max_bc, perm=[0, 2, 1])) - attention = fluid.layers.reshape(attention, shape=[batch_size, self.channel_in * 8, h, w]) - - gamma_attention = fluid.layers.elementwise_mul(attention, self.gamma) - out = fluid.layers.elementwise_add(gamma_attention, inputs) - return out - - -class DAHead(fluid.dygraph.Layer): - def __init__(self, - name_scope, - in_channels, - out_channels, - batch_size): - super(DAHead, self).__init__(name_scope) - self.in_channel = in_channels // 4 - self.batch_size = batch_size - self._conv_bn_relu0 = ConvBN(name_scope, - num_filters=self.in_channel, - filter_size=3, - stride=1, - act='relu', - learning_rate=10.0, - bias_attr=False) - - self._conv_bn_relu1 = ConvBN(name_scope, - num_filters=self.in_channel, - filter_size=3, - stride=1, - act='relu', - learning_rate=10.0, - bias_attr=False) - - self._pam = PAM('pam', in_channels=self.in_channel, default_value=0.0) - self._cam = CAM('cam', in_channels=self.in_channel, default_value=0.0) - - self._conv_bn_relu2 = ConvBN(name_scope, - num_filters=self.in_channel, - filter_size=3, - stride=1, - act='relu', - learning_rate=10.0, - bias_attr=False) - - self._conv_bn_relu3 = ConvBN(name_scope, - num_filters=self.in_channel, - filter_size=3, - stride=1, - act='relu', - learning_rate=10.0, - bias_attr=False) - self._pam_last_conv = fluid.dygraph.Conv2D(name_scope, - num_filters=out_channels, - filter_size=1, - bias_attr=fluid.ParamAttr( - learning_rate=10.0, - name='bias'), - param_attr=fluid.ParamAttr( - learning_rate=10.0, - name='weight') - ) - self._cam_last_conv = fluid.dygraph.Conv2D(name_scope, - num_filters=out_channels, - filter_size=1, - bias_attr=fluid.ParamAttr( - learning_rate=10.0, - name='bias'), - param_attr=fluid.ParamAttr( - learning_rate=10.0, - name='weight') - ) - self._last_conv = fluid.dygraph.Conv2D(name_scope, - num_filters=out_channels, - filter_size=1, - bias_attr=fluid.ParamAttr( - learning_rate=10.0, - name='bias'), - param_attr=fluid.ParamAttr( - learning_rate=10.0, - name='weight') - ) - - def forward(self, inputs): - out = [] - inputs_pam = self._conv_bn_relu0(inputs) - pam = self._pam(inputs_pam) - position = self._conv_bn_relu2(pam) - - batch_size, num_channels = position.shape[:2] - - # dropout2d - ones = fluid.layers.ones(shape=[self.batch_size, num_channels], dtype='float32') - dropout1d_P = fluid.layers.dropout(ones, 0.1) - out_position_drop2d = fluid.layers.elementwise_mul(position, dropout1d_P, axis=0) - dropout1d_P.stop_gradient = True - - inputs_cam = self._conv_bn_relu1(inputs) - cam = self._cam(inputs_cam) - channel = self._conv_bn_relu3(cam) - - # dropout2d - ones2 = fluid.layers.ones(shape=[self.batch_size, num_channels], dtype='float32') - dropout1d_C = fluid.layers.dropout(ones2, 0.1) - out_channel_drop2d = fluid.layers.elementwise_mul(channel, dropout1d_C, axis=0) - dropout1d_C.stop_gradient = True - position_out = self._pam_last_conv(out_position_drop2d) - channel_out = self._cam_last_conv(out_channel_drop2d) - - feat_sum = fluid.layers.elementwise_add(position, channel, axis=1) - feat_sum_batch_size, feat_sum_num_channels = feat_sum.shape[:2] - - # dropout2d - feat_sum_ones = fluid.layers.ones(shape=[self.batch_size, feat_sum_num_channels], dtype='float32') - dropout1d_sum = fluid.layers.dropout(feat_sum_ones, 0.1) - dropout2d_feat_sum = fluid.layers.elementwise_mul(feat_sum, dropout1d_sum, axis=0) - dropout1d_sum.stop_gradient = True - feat_sum_out = self._last_conv(dropout2d_feat_sum) - - out.append(feat_sum_out) - out.append(position_out) - out.append(channel_out) - return tuple(out) - - -class DANet(fluid.dygraph.Layer): - def __init__(self, - name_scope, - backbone='resnet50', - num_classes=19, - batch_size=1, - dilated=True, - multi_grid=True, - multi_dilation=[4, 8, 16]): - super(DANet, self).__init__(name_scope) - if backbone == 'resnet50': - print('backbone resnet50, dilated={}, multi_grid={}, ' - 'multi_dilation={}'.format(dilated, multi_grid, multi_dilation)) - self._backone = ResNet('resnet50', layer=50, dilated=dilated, - multi_grid=multi_grid, multi_dilation=multi_dilation) - elif backbone == 'resnet101': - print('backbone resnet101, dilated={}, multi_grid={}, ' - 'multi_dilation={}'.format(dilated, multi_grid, multi_dilation)) - self._backone = ResNet('resnet101', layer=101, dilated=dilated, - multi_grid=multi_grid, multi_dilation=multi_dilation) - elif backbone == 'resnet152': - print('backbone resnet152, dilated={}, multi_grid={}, ' - 'multi_dilation={}'.format(dilated, multi_grid, multi_dilation)) - self._backone = ResNet('resnet152', layer=152, dilated=dilated, - multi_grid=multi_grid, multi_dilation=multi_dilation) - else: - raise ValueError('unknown backbone: {}'.format(backbone)) - - self._head = DAHead('DA_head', in_channels=2048, out_channels=num_classes, batch_size=batch_size) - - def forward(self, inputs): - h, w = inputs.shape[2:] - _, _, c3, c4 = self._backone(inputs) - x1, x2, x3 = self._head(c4) - out = [] - out1 = fluid.layers.resize_bilinear(x1, out_shape=[h, w]) - out2 = fluid.layers.resize_bilinear(x2, out_shape=[h, w]) - out3 = fluid.layers.resize_bilinear(x3, out_shape=[h, w]) - out.append(out1) - out.append(out2) - out.append(out3) - return out - - -def copy_model(path, new_path): - shutil.rmtree(new_path, ignore_errors=True) - shutil.copytree(path, new_path) - model_path = os.path.join(new_path, '__model__') - if os.path.exists(model_path): - os.remove(model_path) - - -if __name__ == '__main__': - import numpy as np - - with fluid.dygraph.guard(fluid.CPUPlace()): - x = np.random.randn(2, 3, 224, 224).astype('float32') - x = fluid.dygraph.to_variable(x) - model = DANet('test', backbone='resnet101', num_classes=19, batch_size=2) - y = model(x) - print(y[0].shape) diff --git a/PaddleCV/Research/danet/eval.py b/PaddleCV/Research/danet/eval.py deleted file mode 100644 index e2c0d6db..00000000 --- a/PaddleCV/Research/danet/eval.py +++ /dev/null @@ -1,344 +0,0 @@ -# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. -# -# 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. - - -import os - -os.environ['FLAGS_eager_delete_tensor_gb'] = "0.0" -os.environ['FLAGS_fraction_of_gpu_memory_to_use'] = "0.99" - - -import paddle.fluid as fluid -import paddle -import logging -import math -import numpy as np -import shutil -import os - -from PIL import ImageOps, Image, ImageEnhance, ImageFilter -from datetime import datetime - -from danet import DANet -from options import Options -from utils.cityscapes_data import cityscapes_train -from utils.cityscapes_data import cityscapes_val -from utils.cityscapes_data import cityscapes_test -from utils.lr_scheduler import Lr -from iou import IOUMetric - - -# globals -data_mean = np.array([0.485, 0.456, 0.406]).reshape(3, 1, 1) -data_std = np.array([0.229, 0.224, 0.225]).reshape(3, 1, 1) - - -def pad_single_image(image, crop_size): - w, h = image.size - pad_h = crop_size - h if h < crop_size else 0 - pad_w = crop_size - w if w < crop_size else 0 - image = ImageOps.expand(image, border=(0, 0, pad_w, pad_h), fill=0) - assert (image.size[0] >= crop_size and image.size[1] >= crop_size) - return image - - -def crop_image(image, h0, w0, h1, w1): - return image.crop((w0, h0, w1, h1)) - - -def flip_left_right_image(image): - return image.transpose(Image.FLIP_LEFT_RIGHT) - - -def resize_image(image, out_h, out_w, mode=Image.BILINEAR): - return image.resize((out_w, out_h), mode) - - -def mapper_image(image): - image_array = np.array(image) # HWC - image_array = image_array.transpose((2, 0, 1)) # CHW - image_array = image_array / 255.0 - image_array = (image_array - data_mean) / data_std - image_array = image_array.astype('float32') - image_array = image_array[np.newaxis, :] - return image_array - - -def get_model(args): - model = DANet('DANet', - backbone=args.backbone, - num_classes=args.num_classes, - batch_size=1, - dilated=args.dilated, - multi_grid=args.multi_grid, - multi_dilation=args.multi_dilation) - return model - - -def copy_model(path, new_path): - shutil.rmtree(new_path, ignore_errors=True) - shutil.copytree(path, new_path) - model_path = os.path.join(new_path, '__model__') - if os.path.exists(model_path): - os.remove(model_path) - - -def mean_iou(pred, label, num_classes=19): - label = fluid.layers.elementwise_min(fluid.layers.cast(label, np.int32), - fluid.layers.assign(np.array([num_classes], dtype=np.int32))) - label_ig = (label == num_classes).astype('int32') - label_ng = (label != num_classes).astype('int32') - pred = fluid.layers.cast(fluid.layers.argmax(pred, axis=1), 'int32') - pred = pred * label_ng + label_ig * num_classes - miou, wrong, correct = fluid.layers.mean_iou(pred, label, num_classes + 1) - label.stop_gradient = True - return miou, wrong, correct - - -def eval(args, model_path): - - with fluid.dygraph.guard(): - num_classes = args.num_classes - base_size = args.base_size # 图片最长边 2048 - crop_size = args.crop_size # 输入网络大小 1024 - multi_scales = args.multi_scales # 多尺度测试 - flip = args.flip # 左右翻转测试 - - if not multi_scales: - scales = [1.0] - else: - # scales = [0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0, 2.2] - scales = [0.5, 0.75, 1.0, 1.25, 1.35, 1.5, 1.75, 2.0, 2.2] # 可能效果会更好 - - if len(scales) == 1: # single scale - # stride_rate = 2.0 / 3.0 - stride_rate = 1.0 / 2.0 # 可能效果会更好 - else: - stride_rate = 1.0 / 2.0 - stride = int(crop_size * stride_rate) # 滑动stride - - model = get_model(args) - x = np.random.randn(1, 3, 224, 224).astype('float32') - x = fluid.dygraph.to_variable(x) - y = model(x) - iou = IOUMetric(num_classes) - # 加载最优模型 - if args.load_better_model and paddle.__version__ == '1.5.2': - assert os.path.exists(model_path), "请核对模型文件地址是否存在" - print('better model exist!') - new_model_path = 'dygraph/' + model_path - copy_model(model_path, new_model_path) - model_param, _ = fluid.dygraph.load_persistables(new_model_path) - model.load_dict(model_param) - elif args.load_better_model and paddle.__version__ == '1.6.0': - assert os.path.exists(model_path + '.pdparams'), "请核对模型文件地址是否存在, 1.6版本只能加载一个独立文件" - print('better model exist!') - model_param, _ = fluid.dygraph.load_dygraph(model_path) - model.load_dict(model_param) - else: - raise ValueError('请设置load_better_model = True!') - assert len(model_param) == len(model.state_dict()), "参数量不一致,加载参数失败," \ - "请核对模型是否初始化/模型是否一致" - model.eval() - - prev_time = datetime.now() - # reader = cityscapes_test(split='test', base_size=2048, crop_size=1024, scale=True, xmap=True) - reader = cityscapes_test(split='val', base_size=2048, crop_size=1024, scale=True, xmap=True) - - print('MultiEvalModule: base_size {}, crop_size {}'. - format(base_size, crop_size)) - print('scales: {}'.format(scales)) - print('val ing...') - palette = pat() - for data in reader(): - # print(data) - image = data[0] - label_path = data[1] # val_label is a picture, test_label is a path - label = Image.open(label_path, mode='r') # val_label is a picture, test_label is a path - save_png_path = label_path.replace('val', '{}_val'.format(args.backbone)).replace('test', '{}_test'.format(args.backbone)) - label_np = np.array(label) - w, h = image.size # h 1024, w 2048 - scores = np.zeros(shape=[num_classes, h, w], dtype='float32') # 得分矩阵 - for scale in scales: - long_size = int(math.ceil(base_size * scale)) # long_size - if h > w: - height = long_size - width = int(1.0 * w * long_size / h + 0.5) - short_size = width - else: - width = long_size - height = int(1.0 * h * long_size / w + 0.5) - short_size = height - - cur_img = resize_image(image, height, width) - # 右下角pad - if long_size <= crop_size: - pad_img = pad_single_image(cur_img, crop_size) - pad_img = mapper_image(pad_img) - pad_img = fluid.dygraph.to_variable(pad_img) - pred1, pred2, pred3 = model(pad_img) - pred1 = pred1.numpy() - outputs = pred1[:, :, :height, :width] - if flip: - pad_img_filp = flip_left_right_image(cur_img) - pad_img_filp = pad_single_image(pad_img_filp, crop_size) # pad - pad_img_filp = mapper_image(pad_img_filp) - pad_img_filp = fluid.dygraph.to_variable(pad_img_filp) - pred1, pred2, pred3 = model(pad_img_filp) - pred1 = fluid.layers.reverse(pred1, axis=3) - pred1 = pred1.numpy() - outputs += pred1[:, :, :height, :width] - else: - if short_size < crop_size: - # pad if needed - pad_img = pad_single_image(cur_img, crop_size) - else: - pad_img = cur_img - pw, ph = pad_img.size - assert (ph >= height and pw >= width) - - # 滑动网格 - h_grids = int(math.ceil(1.0 * (ph - crop_size) / stride)) + 1 - w_grids = int(math.ceil(1.0 * (pw - crop_size) / stride)) + 1 - outputs = np.zeros(shape=[1, num_classes, ph, pw], dtype='float32') - count_norm = np.zeros(shape=[1, 1, ph, pw], dtype='int32') - for idh in range(h_grids): - for idw in range(w_grids): - h0 = idh * stride - w0 = idw * stride - h1 = min(h0 + crop_size, ph) - w1 = min(w0 + crop_size, pw) - crop_img = crop_image(pad_img, h0, w0, h1, w1) - pad_crop_img = pad_single_image(crop_img, crop_size) - pad_crop_img = mapper_image(pad_crop_img) - pad_crop_img = fluid.dygraph.to_variable(pad_crop_img) - pred1, pred2, pred3 = model(pad_crop_img) # shape [1, num_class, h, w] - pred = pred1.numpy() # channel, h, w - outputs[:, :, h0:h1, w0:w1] += pred[:, :, 0:h1 - h0, 0:w1 - w0] - count_norm[:, :, h0:h1, w0:w1] += 1 - if flip: - pad_img_filp = flip_left_right_image(crop_img) - pad_img_filp = pad_single_image(pad_img_filp, crop_size) # pad - pad_img_array = mapper_image(pad_img_filp) - pad_img_array = fluid.dygraph.to_variable(pad_img_array) - pred1, pred2, pred3 = model(pad_img_array) - pred1 = fluid.layers.reverse(pred1, axis=3) - pred = pred1.numpy() - outputs[:, :, h0:h1, w0:w1] += pred[:, :, 0:h1 - h0, 0:w1 - w0] - count_norm[:, :, h0:h1, w0:w1] += 1 - assert ((count_norm == 0).sum() == 0) - outputs = outputs / count_norm - outputs = outputs[:, :, :height, :width] - outputs = fluid.dygraph.to_variable(outputs) - outputs = fluid.layers.resize_bilinear(outputs, out_shape=[h, w]) - score = outputs.numpy()[0] - scores += score # scopes 是所有尺度的和, shape: [channel, h, w] - pred = np.argmax(score, axis=0).astype('uint8') - picture_path = '{}'.format(save_png_path).replace('.png', '_scale_{}'.format(scale)) - save_png(pred, palette, picture_path) - pred = np.argmax(scores, axis=0).astype('uint8') - picture_path = '{}'.format(save_png_path).replace('.png', '_scores') - save_png(pred, palette, picture_path) - iou.add_batch(pred, label_np) # 计算iou - print('eval done!') - acc, acc_cls, iu, mean_iu, fwavacc, kappa = iou.evaluate() - print('acc = {}'.format(acc)) - print('acc_cls = {}'.format(acc_cls)) - print('iu = {}'.format(iu)) - print('mean_iou(含有255) = {}'.format(mean_iu)) - print('mean_iou = {}'.format(np.nanmean(iu[:-1]))) # 真正的iou - print('fwavacc = {}'.format(fwavacc)) - print('kappa = {}'.format(kappa)) - cur_time = datetime.now() - h, remainder = divmod((cur_time - prev_time).seconds, 3600) - m, s = divmod(remainder, 60) - time_str = "Time %02d:%02d:%02d" % (h, m, s) - print('val ' + time_str) - - -def save_png(pred_value, palette, name): - if isinstance(pred_value, np.ndarray): - if pred_value.ndim == 3: - batch_size = pred_value.shape[0] - if batch_size == 1: - pred_value = pred_value.squeeze(axis=0) - image = Image.fromarray(pred_value).convert('P') - image.putpalette(palette) - save_path = '{}.png'.format(name) - save_dir = os.path.dirname(save_path) - if not os.path.exists(save_dir): - os.makedirs(save_dir) - image.save(save_path) - else: - for batch_id in range(batch_size): - value = pred_value[batch_id] - image = Image.fromarray(value).convert('P') - image.putpalette(palette) - save_path = '{}.png'.format(name[batch_id]) - save_dir = os.path.dirname(save_path) - if not os.path.exists(save_dir): - os.makedirs(save_dir) - image.save(save_path) - elif pred_value.ndim == 2: - image = Image.fromarray(pred_value).convert('P') - image.putpalette(palette) - save_path = '{}.png'.format(name) - save_dir = os.path.dirname(save_path) - if not os.path.exists(save_dir): - os.makedirs(save_dir) - image.save(save_path) - else: - raise ValueError('暂只支持nd array') - - -def save_png_test(path): - im = Image.open(path) - im_array = np.array(im).astype('uint8') - save_png(im_array, pat(), 'save_png_test') - - -def pat(): - palette = [] - for i in range(256): - palette.extend((i, i, i)) - palette[:3 * 19] = np.array([[128, 64, 128], - [244, 35, 232], - [70, 70, 70], - [102, 102, 156], - [190, 153, 153], - [153, 153, 153], - [250, 170, 30], - [220, 220, 0], - [107, 142, 35], - [152, 251, 152], - [70, 130, 180], - [220, 20, 60], - [255, 0, 0], - [0, 0, 142], - [0, 0, 70], - [0, 60, 100], - [0, 80, 100], - [0, 0, 230], - [119, 11, 32]], dtype='uint8').flatten() - return palette - - -if __name__ == '__main__': - options = Options() - args = options.parse() - options.print_args() - # model_path = 'checkpoint/DANet101_better_model_paddle1.5.2' - model_path = 'checkpoint/DANet101_better_model_paddle1.6' - eval(args, model_path) - diff --git a/PaddleCV/Research/danet/img/Network.png b/PaddleCV/Research/danet/img/Network.png deleted file mode 100644 index ac109b403a122a0241cb391c2d17b45ca43cb41b..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 300602 zcma&NRa{hI_dN~>f|RJVG$P$0jdaJ5(nvFOH%KGhg7i>BOAH;--2>>*4Fb|#|Kt09 zKkwD=;(x&fGjpDE&J%mDz1G@a)l_70FiA0ykdScX<)qY+kkEONke*#)pdcY3r?FVU zfG?0;)nz4+s>aFofPYY}#FfR7kZR(w?mwde|G#jO({)8cBBcBGhb*uD?idN_sYqT* zT+_?wAREmKUxz5%*>fHhcIXcGIyYJ7toI$DkhiKD9?jON%ZSJ#21n&Iu2uDFLt0eFIYDzPXq;F2z%W_YVxX 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