From 027c184dd77782983712b9cb87e6922be0adff14 Mon Sep 17 00:00:00 2001 From: LielinJiang Date: Tue, 7 Jan 2020 10:53:40 +0800 Subject: [PATCH] add LaneNet --- contrib/LaneNet/README.md | 127 + contrib/LaneNet/configs/lanenet.yaml | 55 + contrib/LaneNet/data_aug.py | 437 + contrib/LaneNet/dataset/download_tusimple.py | 33 + contrib/LaneNet/eval.py | 179 + contrib/LaneNet/imgs/0005_pred_binary.png | Bin 0 -> 2298 bytes contrib/LaneNet/imgs/0005_pred_instance.png | Bin 0 -> 3322 bytes contrib/LaneNet/imgs/0005_pred_lane.png | Bin 0 -> 1192907 bytes contrib/LaneNet/loss.py | 203 + contrib/LaneNet/models/__init__.py | 18 + contrib/LaneNet/models/backbone/__init__.py | 0 contrib/LaneNet/models/backbone/vgg.py | 110 + contrib/LaneNet/models/libs/__init__.py | 0 contrib/LaneNet/models/libs/model_libs.py | 166 + contrib/LaneNet/models/model_builder.py | 285 + contrib/LaneNet/models/modeling/__init__.py | 0 contrib/LaneNet/models/modeling/lanenet.py | 433 + contrib/LaneNet/reader.py | 603 + contrib/LaneNet/solver.py | 126 + contrib/LaneNet/train.py | 468 + contrib/LaneNet/utils/__init__.py | 0 contrib/LaneNet/utils/collect.py | 148 + contrib/LaneNet/utils/config.py | 222 + contrib/LaneNet/utils/dist_utils.py | 92 + contrib/LaneNet/utils/fp16_utils.py | 31 + .../utils/generate_tusimple_dataset.py | 180 + contrib/LaneNet/utils/lanenet_postprocess.py | 376 + contrib/LaneNet/utils/timer.py | 60 + contrib/LaneNet/utils/tusimple_ipm_remap.yml | 213154 +++++++++++++++ contrib/LaneNet/vis.py | 243 + pdseg/loss.py | 43 +- pdseg/models/model_builder.py | 5 +- pdseg/utils/config.py | 4 +- 33 files changed, 217789 insertions(+), 12 deletions(-) create mode 100644 contrib/LaneNet/README.md create mode 100644 contrib/LaneNet/configs/lanenet.yaml create mode 100644 contrib/LaneNet/data_aug.py create mode 100644 contrib/LaneNet/dataset/download_tusimple.py create mode 100644 contrib/LaneNet/eval.py create mode 100644 contrib/LaneNet/imgs/0005_pred_binary.png create mode 100644 contrib/LaneNet/imgs/0005_pred_instance.png create mode 100644 contrib/LaneNet/imgs/0005_pred_lane.png create mode 100644 contrib/LaneNet/loss.py create mode 100644 contrib/LaneNet/models/__init__.py create mode 100644 contrib/LaneNet/models/backbone/__init__.py create mode 100644 contrib/LaneNet/models/backbone/vgg.py create mode 100644 contrib/LaneNet/models/libs/__init__.py create mode 100644 contrib/LaneNet/models/libs/model_libs.py create mode 100644 contrib/LaneNet/models/model_builder.py create mode 100644 contrib/LaneNet/models/modeling/__init__.py create mode 100644 contrib/LaneNet/models/modeling/lanenet.py create mode 100644 contrib/LaneNet/reader.py create mode 100644 contrib/LaneNet/solver.py create mode 100644 contrib/LaneNet/train.py create mode 100644 contrib/LaneNet/utils/__init__.py create mode 100644 contrib/LaneNet/utils/collect.py create mode 100644 contrib/LaneNet/utils/config.py create mode 100755 contrib/LaneNet/utils/dist_utils.py create mode 100644 contrib/LaneNet/utils/fp16_utils.py create mode 100644 contrib/LaneNet/utils/generate_tusimple_dataset.py create mode 100644 contrib/LaneNet/utils/lanenet_postprocess.py create mode 100644 contrib/LaneNet/utils/timer.py create mode 100644 contrib/LaneNet/utils/tusimple_ipm_remap.yml create mode 100644 contrib/LaneNet/vis.py diff --git a/contrib/LaneNet/README.md b/contrib/LaneNet/README.md new file mode 100644 index 00000000..e949b435 --- /dev/null +++ b/contrib/LaneNet/README.md @@ -0,0 +1,127 @@ +# LaneNet 模型训练教程 + +* 本教程旨在介绍如何通过使用PaddleSeg进行车道线检测 + +* 在阅读本教程前，请确保您已经了解过PaddleSeg的[快速入门](../README.md#快速入门)和[基础功能](../README.md#基础功能)等章节，以便对PaddleSeg有一定的了解 + + +## 一. 准备待训练数据 + +我们提前准备好了一份处理好的数据集，通过以下代码进行下载，该数据集由图森车道线检测数据集转换而来，你也可以在这个[页面](https://github.com/TuSimple/tusimple-benchmark/issues/3)下载原始数据集。 + +```shell +python dataset/download_tusimple.py +``` + +数据目录结构 +LaneNet +|-- dataset + |-- tusimple_lane_detection + |-- training + |-- gt_binary_image + |-- gt_image + |-- gt_instance_image + |-- train_part.txt + |-- val_part.txt + +## 二. 下载预训练模型 + +下载[vgg预训练模型](https://paddle-imagenet-models-name.bj.bcebos.com/VGG16_pretrained.tar)，放在```pretrained_models```文件夹下。 + + +## 三. 准备配置 + +接着我们需要确定相关配置，从本教程的角度，配置分为三部分： + +* 数据集 + * 训练集主目录 + * 训练集文件列表 + * 测试集文件列表 + * 评估集文件列表 +* 预训练模型 + * 预训练模型名称 + * 预训练模型的backbone网络 + * 预训练模型路径 +* 其他 + * 学习率 + * Batch大小 + * ... + +在三者中，预训练模型的配置尤为重要，如果模型或者BACKBONE配置错误，会导致预训练的参数没有加载，进而影响收敛速度。预训练模型相关的配置如第二步所展示。 + +数据集的配置和数据路径有关，在本教程中，数据存放在`dataset/tusimple_lane_detection`中 + +其他配置则根据数据集和机器环境的情况进行调节，最终我们保存一个如下内容的yaml配置文件，存放路径为**configs/lanenet.yaml** + +```yaml +# 数据集配置 +DATASET: + DATA_DIR: "./dataset/tusimple_lane_detection" + IMAGE_TYPE: "rgb" # choice rgb or rgba + NUM_CLASSES: 2 + TEST_FILE_LIST: "./dataset/tusimple_lane_detection/training/val_part.txt" + TRAIN_FILE_LIST: "./dataset/tusimple_lane_detection/training/train_part.txt" + VAL_FILE_LIST: "./dataset/tusimple_lane_detection/training/val_part.txt" + SEPARATOR: " " + +# 预训练模型配置 +MODEL: + MODEL_NAME: "lanenet" + +# 其他配置 +EVAL_CROP_SIZE: (512, 256) +TRAIN_CROP_SIZE: (512, 256) +AUG: + AUG_METHOD: u"unpadding" # choice unpadding rangescaling and stepscaling + FIX_RESIZE_SIZE: (512, 256) # (width, height), for unpadding + MIRROR: False + RICH_CROP: + ENABLE: False +BATCH_SIZE: 4 +MEAN: [0.5, 0.5, 0.5] +STD: [0.5, 0.5, 0.5] +TEST: + TEST_MODEL: "./saved_model/lanenet/final/" +TRAIN: + MODEL_SAVE_DIR: "./saved_model/lanenet/" + PRETRAINED_MODEL_DIR: "./pretrained_models/VGG16_pretrained" + SNAPSHOT_EPOCH: 5 +SOLVER: + NUM_EPOCHS: 100 + LR: 0.0005 + LR_POLICY: "poly" + OPTIMIZER: "sgd" + WEIGHT_DECAY: 0.001 + CROSS_ENTROPY_WEIGHT: 'lanenet' +``` + + +## 五. 开始训练 + +使用下述命令启动训练 + +```shell +CUDA_VISIBLE_DEVICES=0 python -u train.py --cfg configs/lanenet.yaml --use_gpu --use_mpio --do_eval +``` + +## 六. 进行评估 + +模型训练完成，使用下述命令启动评估 + +```shell +CUDA_VISIBLE_DEVICES=0 python -u eval.py --use_gpu --cfg configs/lanenet.yaml +``` + +## 七. 可视化 +我们提供了一个训练好的模型，点击[链接](https://paddleseg.bj.bcebos.com/models/LaneNet_vgg_tusimple.tar)，下载后放在```./pretrained_model/```下，使用如下命令进行可视化 +```shell +CUDA_VISIBLE_DEVICES=0 python -u ./vis.py --cfg configs/lanenet.yaml --use_gpu --vis_dir vis_result +``` + +可视化结果示例： + + 预测结果：![](imgs/0005_pred_lane.png) + 分割结果：![](imgs/0005_pred_binary.png) + 车道线实预测结果：![](imgs/0005_pred_instance.png) + + diff --git a/contrib/LaneNet/configs/lanenet.yaml b/contrib/LaneNet/configs/lanenet.yaml new file mode 100644 index 00000000..18a98a05 --- /dev/null +++ b/contrib/LaneNet/configs/lanenet.yaml @@ -0,0 +1,55 @@ +EVAL_CROP_SIZE: (512, 256) # (width, height), for unpadding rangescaling and stepscaling +TRAIN_CROP_SIZE: (512, 256) # (width, height), for unpadding rangescaling and stepscaling +AUG: + AUG_METHOD: u"unpadding" # choice unpadding rangescaling and stepscaling + FIX_RESIZE_SIZE: (512, 256) # (width, height), for unpadding + INF_RESIZE_VALUE: 500 # for rangescaling + MAX_RESIZE_VALUE: 600 # for rangescaling + MIN_RESIZE_VALUE: 400 # for rangescaling + MAX_SCALE_FACTOR: 2.0 # for stepscaling + MIN_SCALE_FACTOR: 0.5 # for stepscaling + SCALE_STEP_SIZE: 0.25 # for stepscaling + MIRROR: False + RICH_CROP: + ENABLE: False + +BATCH_SIZE: 4 +MEAN: [0.5, 0.5, 0.5] +STD: [0.5, 0.5, 0.5] + +DATALOADER: + BUF_SIZE: 256 + NUM_WORKERS: 4 +DATASET: + DATA_DIR: "./dataset/tusimple_lane_detection" + IMAGE_TYPE: "rgb" # choice rgb or rgba + NUM_CLASSES: 2 + TEST_FILE_LIST: "./dataset/tusimple_lane_detection/training/val_part.txt" + TEST_TOTAL_IMAGES: 362 + TRAIN_FILE_LIST: "./dataset/tusimple_lane_detection/training/train_part.txt" + TRAIN_TOTAL_IMAGES: 3264 + VAL_FILE_LIST: "./dataset/tusimple_lane_detection/training/val_part.txt" + VAL_TOTAL_IMAGES: 362 + SEPARATOR: " " + IGNORE_INDEX: 255 + +FREEZE: + MODEL_FILENAME: "__model__" + PARAMS_FILENAME: "__params__" +MODEL: + MODEL_NAME: "lanenet" + DEFAULT_NORM_TYPE: "bn" +TEST: + TEST_MODEL: "./saved_model/lanenet/final/" +TRAIN: + MODEL_SAVE_DIR: "./saved_model/lanenet/" + PRETRAINED_MODEL_DIR: "./pretrained_models/VGG16_pretrained" + SNAPSHOT_EPOCH: 1 +SOLVER: + NUM_EPOCHS: 100 + LR: 0.0005 + LR_POLICY: "poly" + OPTIMIZER: "sgd" + WEIGHT_DECAY: 0.001 + CROSS_ENTROPY_WEIGHT: 'lanenet' + diff --git a/contrib/LaneNet/data_aug.py b/contrib/LaneNet/data_aug.py new file mode 100644 index 00000000..642f48a4 --- /dev/null +++ b/contrib/LaneNet/data_aug.py @@ -0,0 +1,437 @@ +# coding: utf8 +# copyright (c) 2019 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. + +from __future__ import print_function +import cv2 +import numpy as np +from utils.config import cfg +from models.model_builder import ModelPhase + + +def resize(img, grt=None, grt_instance=None, mode=ModelPhase.TRAIN): + """ + 改变图像及标签图像尺寸 + AUG.AUG_METHOD为unpadding，所有模式均直接resize到AUG.FIX_RESIZE_SIZE的尺寸 + AUG.AUG_METHOD为stepscaling, 按比例resize，训练时比例范围AUG.MIN_SCALE_FACTOR到AUG.MAX_SCALE_FACTOR,间隔为AUG.SCALE_STEP_SIZE，其他模式返回原图 + AUG.AUG_METHOD为rangescaling，长边对齐，短边按比例变化，训练时长边对齐范围AUG.MIN_RESIZE_VALUE到AUG.MAX_RESIZE_VALUE，其他模式长边对齐AUG.INF_RESIZE_VALUE + + Args： + img(numpy.ndarray): 输入图像 + grt(numpy.ndarray): 标签图像，默认为None + mode(string): 模式, 默认训练模式，即ModelPhase.TRAIN + + Returns： + resize后的图像和标签图 + + """ + + if cfg.AUG.AUG_METHOD == 'unpadding': + target_size = cfg.AUG.FIX_RESIZE_SIZE + img = cv2.resize(img, target_size, interpolation=cv2.INTER_LINEAR) + if grt is not None: + grt = cv2.resize(grt, target_size, interpolation=cv2.INTER_NEAREST) + if grt_instance is not None: + grt_instance = cv2.resize(grt_instance, target_size, interpolation=cv2.INTER_NEAREST) + elif cfg.AUG.AUG_METHOD == 'stepscaling': + if mode == ModelPhase.TRAIN: + min_scale_factor = cfg.AUG.MIN_SCALE_FACTOR + max_scale_factor = cfg.AUG.MAX_SCALE_FACTOR + step_size = cfg.AUG.SCALE_STEP_SIZE + scale_factor = get_random_scale(min_scale_factor, max_scale_factor, + step_size) + img, grt = randomly_scale_image_and_label( + img, grt, scale=scale_factor) + elif cfg.AUG.AUG_METHOD == 'rangescaling': + min_resize_value = cfg.AUG.MIN_RESIZE_VALUE + max_resize_value = cfg.AUG.MAX_RESIZE_VALUE + if mode == ModelPhase.TRAIN: + if min_resize_value == max_resize_value: + random_size = min_resize_value + else: + random_size = int( + np.random.uniform(min_resize_value, max_resize_value) + 0.5) + else: + random_size = cfg.AUG.INF_RESIZE_VALUE + + value = max(img.shape[0], img.shape[1]) + scale = float(random_size) / float(value) + img = cv2.resize( + img, (0, 0), fx=scale, fy=scale, interpolation=cv2.INTER_LINEAR) + if grt is not None: + grt = cv2.resize( + grt, (0, 0), + fx=scale, + fy=scale, + interpolation=cv2.INTER_NEAREST) + else: + raise Exception("Unexpect data augmention method: {}".format( + cfg.AUG.AUG_METHOD)) + + return img, grt, grt_instance + + +def get_random_scale(min_scale_factor, max_scale_factor, step_size): + """ + 在一定范围内得到随机值，范围为min_scale_factor到max_scale_factor，间隔为step_size + + Args： + min_scale_factor(float): 随机尺度下限，大于0 + max_scale_factor(float): 随机尺度上限，不小于下限值 + step_size(float): 尺度间隔，非负, 等于为0时直接返回min_scale_factor到max_scale_factor范围内任一值 + + Returns： + 随机尺度值 + + """ + + if min_scale_factor < 0 or min_scale_factor > max_scale_factor: + raise ValueError('Unexpected value of min_scale_factor.') + + if min_scale_factor == max_scale_factor: + return min_scale_factor + + if step_size == 0: + return np.random.uniform(min_scale_factor, max_scale_factor) + + num_steps = int((max_scale_factor - min_scale_factor) / step_size + 1) + scale_factors = np.linspace(min_scale_factor, max_scale_factor, + num_steps).tolist() + np.random.shuffle(scale_factors) + return scale_factors[0] + + +def randomly_scale_image_and_label(image, label=None, scale=1.0): + """ + 按比例resize图像和标签图, 如果scale为1，返回原图 + + Args： + image(numpy.ndarray): 输入图像 + label(numpy.ndarray): 标签图，默认None + sclae(float): 图片resize的比例，非负，默认1.0 + + Returns： + resize后的图像和标签图 + + """ + + if scale == 1.0: + return image, label + + height = image.shape[0] + width = image.shape[1] + new_height = int(height * scale + 0.5) + new_width = int(width * scale + 0.5) + + new_image = cv2.resize( + image, (new_width, new_height), interpolation=cv2.INTER_LINEAR) + if label is not None: + height = label.shape[0] + width = label.shape[1] + new_height = int(height * scale + 0.5) + new_width = int(width * scale + 0.5) + new_label = cv2.resize( + label, (new_width, new_height), interpolation=cv2.INTER_NEAREST) + return new_image, new_label + + +def random_rotation(crop_img, crop_seg, rich_crop_max_rotation, mean_value): + """ + 随机旋转图像和标签图 + + Args： + crop_img(numpy.ndarray): 输入图像 + crop_seg(numpy.ndarray): 标签图 + rich_crop_max_rotation(int)：旋转最大角度，0-90 + mean_value(list)：均值, 对图片旋转产生的多余区域使用均值填充 + + Returns： + 旋转后的图像和标签图 + + """ + ignore_index = cfg.DATASET.IGNORE_INDEX + if rich_crop_max_rotation > 0: + (h, w) = crop_img.shape[:2] + do_rotation = np.random.uniform(-rich_crop_max_rotation, + rich_crop_max_rotation) + pc = (w // 2, h // 2) + r = cv2.getRotationMatrix2D(pc, do_rotation, 1.0) + cos = np.abs(r[0, 0]) + sin = np.abs(r[0, 1]) + + nw = int((h * sin) + (w * cos)) + nh = int((h * cos) + (w * sin)) + + (cx, cy) = pc + r[0, 2] += (nw / 2) - cx + r[1, 2] += (nh / 2) - cy + dsize = (nw, nh) + crop_img = cv2.warpAffine( + crop_img, + r, + dsize=dsize, + flags=cv2.INTER_LINEAR, + borderMode=cv2.BORDER_CONSTANT, + borderValue=mean_value) + crop_seg = cv2.warpAffine( + crop_seg, + r, + dsize=dsize, + flags=cv2.INTER_NEAREST, + borderMode=cv2.BORDER_CONSTANT, + borderValue=(ignore_index, ignore_index, ignore_index)) + return crop_img, crop_seg + + +def rand_scale_aspect(crop_img, + crop_seg, + rich_crop_min_scale=0, + rich_crop_aspect_ratio=0): + """ + 从输入图像和标签图像中裁取随机宽高比的图像，并reszie回原始尺寸 + + Args: + crop_img(numpy.ndarray): 输入图像 + crop_seg(numpy.ndarray): 标签图像 + rich_crop_min_scale(float)：裁取图像占原始图像的面积比，0-1，默认0返回原图 + rich_crop_aspect_ratio(float): 裁取图像的宽高比范围，非负，默认0返回原图 + + Returns: + 裁剪并resize回原始尺寸的图像和标签图像 + + """ + if rich_crop_min_scale == 0 or rich_crop_aspect_ratio == 0: + return crop_img, crop_seg + else: + img_height = crop_img.shape[0] + img_width = crop_img.shape[1] + for i in range(0, 10): + area = img_height * img_width + target_area = area * np.random.uniform(rich_crop_min_scale, 1.0) + aspectRatio = np.random.uniform(rich_crop_aspect_ratio, + 1.0 / rich_crop_aspect_ratio) + + dw = int(np.sqrt(target_area * 1.0 * aspectRatio)) + dh = int(np.sqrt(target_area * 1.0 / aspectRatio)) + if (np.random.randint(10) < 5): + tmp = dw + dw = dh + dh = tmp + + if (dh < img_height and dw < img_width): + h1 = np.random.randint(0, img_height - dh) + w1 = np.random.randint(0, img_width - dw) + + crop_img = crop_img[h1:(h1 + dh), w1:(w1 + dw), :] + crop_seg = crop_seg[h1:(h1 + dh), w1:(w1 + dw)] + crop_img = cv2.resize( + crop_img, (img_width, img_height), + interpolation=cv2.INTER_LINEAR) + crop_seg = cv2.resize( + crop_seg, (img_width, img_height), + interpolation=cv2.INTER_NEAREST) + break + + return crop_img, crop_seg + + +def saturation_jitter(cv_img, jitter_range): + """ + 调节图像饱和度 + + Args: + cv_img(numpy.ndarray): 输入图像 + jitter_range(float): 调节程度，0-1 + + Returns: + 饱和度调整后的图像 + + """ + + greyMat = cv2.cvtColor(cv_img, cv2.COLOR_BGR2GRAY) + greyMat = greyMat[:, :, None] * np.ones(3, dtype=int)[None, None, :] + cv_img = cv_img.astype(np.float32) + cv_img = cv_img * (1 - jitter_range) + jitter_range * greyMat + cv_img = np.where(cv_img > 255, 255, cv_img) + cv_img = cv_img.astype(np.uint8) + return cv_img + + +def brightness_jitter(cv_img, jitter_range): + """ + 调节图像亮度 + + Args: + cv_img(numpy.ndarray): 输入图像 + jitter_range(float): 调节程度，0-1 + + Returns: + 亮度调整后的图像 + + """ + + cv_img = cv_img.astype(np.float32) + cv_img = cv_img * (1.0 - jitter_range) + cv_img = np.where(cv_img > 255, 255, cv_img) + cv_img = cv_img.astype(np.uint8) + return cv_img + + +def contrast_jitter(cv_img, jitter_range): + """ + 调节图像对比度 + + Args: + cv_img(numpy.ndarray): 输入图像 + jitter_range(float): 调节程度，0-1 + + Returns: + 对比度调整后的图像 + + """ + + greyMat = cv2.cvtColor(cv_img, cv2.COLOR_BGR2GRAY) + mean = np.mean(greyMat) + cv_img = cv_img.astype(np.float32) + cv_img = cv_img * (1 - jitter_range) + jitter_range * mean + cv_img = np.where(cv_img > 255, 255, cv_img) + cv_img = cv_img.astype(np.uint8) + return cv_img + + +def random_jitter(cv_img, saturation_range, brightness_range, contrast_range): + """ + 图像亮度、饱和度、对比度调节，在调整范围内随机获得调节比例，并随机顺序叠加三种效果 + + Args: + cv_img(numpy.ndarray): 输入图像 + saturation_range(float): 饱和对调节范围，0-1 + brightness_range(float): 亮度调节范围，0-1 + contrast_range(float): 对比度调节范围，0-1 + + Returns: + 亮度、饱和度、对比度调整后图像 + + """ + + saturation_ratio = np.random.uniform(-saturation_range, saturation_range) + brightness_ratio = np.random.uniform(-brightness_range, brightness_range) + contrast_ratio = np.random.uniform(-contrast_range, contrast_range) + + order = [1, 2, 3] + np.random.shuffle(order) + + for i in range(3): + if order[i] == 0: + cv_img = saturation_jitter(cv_img, saturation_ratio) + if order[i] == 1: + cv_img = brightness_jitter(cv_img, brightness_ratio) + if order[i] == 2: + cv_img = contrast_jitter(cv_img, contrast_ratio) + return cv_img + + +def hsv_color_jitter(crop_img, + brightness_jitter_ratio=0, + saturation_jitter_ratio=0, + contrast_jitter_ratio=0): + """ + 图像亮度、饱和度、对比度调节 + + Args: + crop_img(numpy.ndarray): 输入图像 + brightness_jitter_ratio(float): 亮度调节度最大值，1-0，默认0 + saturation_jitter_ratio(float): 饱和度调节度最大值，1-0，默认0 + contrast_jitter_ratio(float): 对比度调节度最大值，1-0，默认0 + + Returns： + 亮度、饱和度、对比度调节后图像 + + """ + + if brightness_jitter_ratio > 0 or \ + saturation_jitter_ratio > 0 or \ + contrast_jitter_ratio > 0: + crop_img = random_jitter(crop_img, saturation_jitter_ratio, + brightness_jitter_ratio, contrast_jitter_ratio) + return crop_img + + +def rand_crop(crop_img, crop_seg, mode=ModelPhase.TRAIN): + """ + 随机裁剪图片和标签图, 若crop尺寸大于原始尺寸，分别使用均值和ignore值填充再进行crop， + crop尺寸与原始尺寸一致，返回原图，crop尺寸小于原始尺寸直接crop + + Args: + crop_img(numpy.ndarray): 输入图像 + crop_seg(numpy.ndarray): 标签图 + mode(string): 模式, 默认训练模式，验证或预测、可视化模式时crop尺寸需大于原始图片尺寸 + + Returns： + 裁剪后的图片和标签图 + + """ + + img_height = crop_img.shape[0] + img_width = crop_img.shape[1] + + if ModelPhase.is_train(mode): + crop_width = cfg.TRAIN_CROP_SIZE[0] + crop_height = cfg.TRAIN_CROP_SIZE[1] + else: + crop_width = cfg.EVAL_CROP_SIZE[0] + crop_height = cfg.EVAL_CROP_SIZE[1] + + if not ModelPhase.is_train(mode): + if (crop_height < img_height or crop_width < img_width): + raise Exception( + "Crop size({},{}) must large than img size({},{}) when in EvalPhase." + .format(crop_width, crop_height, img_width, img_height)) + + if img_height == crop_height and img_width == crop_width: + return crop_img, crop_seg + else: + pad_height = max(crop_height - img_height, 0) + pad_width = max(crop_width - img_width, 0) + if (pad_height > 0 or pad_width > 0): + crop_img = cv2.copyMakeBorder( + crop_img, + 0, + pad_height, + 0, + pad_width, + cv2.BORDER_CONSTANT, + value=cfg.DATASET.PADDING_VALUE) + if crop_seg is not None: + crop_seg = cv2.copyMakeBorder( + crop_seg, + 0, + pad_height, + 0, + pad_width, + cv2.BORDER_CONSTANT, + value=cfg.DATASET.IGNORE_INDEX) + img_height = crop_img.shape[0] + img_width = crop_img.shape[1] + + if crop_height > 0 and crop_width > 0: + h_off = np.random.randint(img_height - crop_height + 1) + w_off = np.random.randint(img_width - crop_width + 1) + + crop_img = crop_img[h_off:(crop_height + h_off), w_off:( + w_off + crop_width), :] + if crop_seg is not None: + crop_seg = crop_seg[h_off:(crop_height + h_off), w_off:( + w_off + crop_width)] + return crop_img, crop_seg diff --git a/contrib/LaneNet/dataset/download_tusimple.py b/contrib/LaneNet/dataset/download_tusimple.py new file mode 100644 index 00000000..1549cafd --- /dev/null +++ b/contrib/LaneNet/dataset/download_tusimple.py @@ -0,0 +1,33 @@ +# 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 sys +import os + +LOCAL_PATH = os.path.dirname(os.path.abspath(__file__)) +TEST_PATH = os.path.join(LOCAL_PATH, "../../../", "test") +sys.path.append(TEST_PATH) + +from test_utils import download_file_and_uncompress + + +def download_tusimple_dataset(savepath, extrapath): + url = "https://paddleseg.bj.bcebos.com/dataset/tusimple_lane_detection.tar" + download_file_and_uncompress( + url=url, savepath=savepath, extrapath=extrapath) + + +if __name__ == "__main__": + download_tusimple_dataset(LOCAL_PATH, LOCAL_PATH) + print("Dataset download finish!") diff --git a/contrib/LaneNet/eval.py b/contrib/LaneNet/eval.py new file mode 100644 index 00000000..34c45290 --- /dev/null +++ b/contrib/LaneNet/eval.py @@ -0,0 +1,179 @@ +# coding: utf8 +# copyright (c) 2019 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. + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +# GPU memory garbage collection optimization flags +os.environ['FLAGS_eager_delete_tensor_gb'] = "0.0" + +import sys + +cur_path = os.path.abspath(os.path.dirname(__file__)) +root_path = os.path.split(os.path.split(cur_path)[0])[0] +sys.path.append(root_path) + +import time +import argparse +import functools +import pprint +import cv2 +import numpy as np +import paddle +import paddle.fluid as fluid + +from utils.config import cfg +from utils.timer import Timer, calculate_eta +from models.model_builder import build_model +from models.model_builder import ModelPhase +from reader import LaneNetDataset + + +def parse_args(): + parser = argparse.ArgumentParser(description='PaddleSeg model evalution') + parser.add_argument( + '--cfg', + dest='cfg_file', + help='Config file for training (and optionally testing)', + default=None, + type=str) + parser.add_argument( + '--use_gpu', + dest='use_gpu', + help='Use gpu or cpu', + action='store_true', + default=False) + parser.add_argument( + '--use_mpio', + dest='use_mpio', + help='Use multiprocess IO or not', + action='store_true', + default=False) + parser.add_argument( + 'opts', + help='See utils/config.py for all options', + default=None, + nargs=argparse.REMAINDER) + if len(sys.argv) == 1: + parser.print_help() + sys.exit(1) + return parser.parse_args() + + +def evaluate(cfg, ckpt_dir=None, use_gpu=False, use_mpio=False, **kwargs): + np.set_printoptions(precision=5, suppress=True) + + startup_prog = fluid.Program() + test_prog = fluid.Program() + + dataset = LaneNetDataset( + file_list=cfg.DATASET.VAL_FILE_LIST, + mode=ModelPhase.TRAIN, + shuffle=True, + data_dir=cfg.DATASET.DATA_DIR) + + def data_generator(): + #TODO: check is batch reader compatitable with Windows + if use_mpio: + data_gen = dataset.multiprocess_generator( + num_processes=cfg.DATALOADER.NUM_WORKERS, + max_queue_size=cfg.DATALOADER.BUF_SIZE) + else: + data_gen = dataset.generator() + + for b in data_gen: + yield b + + py_reader, pred, grts, masks, accuracy, fp, fn = build_model( + test_prog, startup_prog, phase=ModelPhase.EVAL) + + py_reader.decorate_sample_generator( + data_generator, drop_last=False, batch_size=cfg.BATCH_SIZE) + + # Get device environment + places = fluid.cuda_places() if use_gpu else fluid.cpu_places() + place = places[0] + dev_count = len(places) + print("#Device count: {}".format(dev_count)) + + exe = fluid.Executor(place) + exe.run(startup_prog) + + test_prog = test_prog.clone(for_test=True) + + ckpt_dir = cfg.TEST.TEST_MODEL if not ckpt_dir else ckpt_dir + + if ckpt_dir is not None: + print('load test model:', ckpt_dir) + fluid.io.load_params(exe, ckpt_dir, main_program=test_prog) + + # Use streaming confusion matrix to calculate mean_iou + np.set_printoptions( + precision=4, suppress=True, linewidth=160, floatmode="fixed") + fetch_list = [pred.name, grts.name, masks.name, accuracy.name, fp.name, fn.name] + num_images = 0 + step = 0 + avg_acc = 0.0 + avg_fp = 0.0 + avg_fn = 0.0 + # cur_images = 0 + all_step = cfg.DATASET.TEST_TOTAL_IMAGES // cfg.BATCH_SIZE + 1 + timer = Timer() + timer.start() + py_reader.start() + while True: + try: + step += 1 + pred, grts, masks, out_acc, out_fp, out_fn = exe.run( + test_prog, fetch_list=fetch_list, return_numpy=True) + + avg_acc += np.mean(out_acc) * pred.shape[0] + avg_fp += np.mean(out_fp) * pred.shape[0] + avg_fn += np.mean(out_fn) * pred.shape[0] + num_images += pred.shape[0] + + speed = 1.0 / timer.elapsed_time() + + print( + "[EVAL]step={} accuracy={:.4f} fp={:.4f} fn={:.4f} step/sec={:.2f} | ETA {}" + .format(step, avg_acc / num_images, avg_fp / num_images, avg_fn / num_images, speed, + calculate_eta(all_step - step, speed))) + + timer.restart() + sys.stdout.flush() + except fluid.core.EOFException: + break + + print("[EVAL]#image={} accuracy={:.4f} fp={:.4f} fn={:.4f}".format( + num_images, avg_acc / num_images, avg_fp / num_images, avg_fn / num_images)) + + return avg_acc / num_images, avg_fp / num_images, avg_fn / num_images + + +def main(): + args = parse_args() + if args.cfg_file is not None: + cfg.update_from_file(args.cfg_file) + if args.opts: + cfg.update_from_list(args.opts) + cfg.check_and_infer() + print(pprint.pformat(cfg)) + evaluate(cfg, **args.__dict__) + + +if __name__ == '__main__': + main() diff --git 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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. + +import paddle.fluid as fluid +import numpy as np +from utils.config import cfg + +def softmax_with_loss(logit, label, ignore_mask=None, num_classes=2, weight=None): + ignore_mask = fluid.layers.cast(ignore_mask, 'float32') + label = fluid.layers.elementwise_min( + label, fluid.layers.assign(np.array([num_classes - 1], dtype=np.int32))) + logit = fluid.layers.transpose(logit, [0, 2, 3, 1]) + logit = fluid.layers.reshape(logit, [-1, num_classes]) + label = fluid.layers.reshape(label, [-1, 1]) + label = fluid.layers.cast(label, 'int64') + + ignore_mask = fluid.layers.reshape(ignore_mask, [-1, 1]) + + if weight is None: + loss, probs = fluid.layers.softmax_with_cross_entropy( + logit, + label, + ignore_index=cfg.DATASET.IGNORE_INDEX, + return_softmax=True) + else: + label_one_hot = fluid.layers.one_hot(input=label, depth=num_classes) + if isinstance(weight, list): + assert len(weight) == num_classes, "weight length must equal num of classes" + weight = fluid.layers.assign(np.array([weight], dtype='float32')) + elif isinstance(weight, fluid.layers.Variable): + pass + else: + tmp = [] + total_num = fluid.layers.cast(fluid.layers.shape(label)[0], 'float32') + for i in range(num_classes): + cls_pixel_num = fluid.layers.reduce_sum(label_one_hot[:, i]) + ratio = total_num / (cls_pixel_num + 1) + tmp.append(ratio) + weight = fluid.layers.concat(tmp) + weight = weight / fluid.layers.reduce_sum(weight) * num_classes + weight = fluid.layers.reshape(weight, [1, num_classes]) + weighted_label_one_hot = fluid.layers.elementwise_mul(label_one_hot, weight) + probs = fluid.layers.softmax(logit) + loss = fluid.layers.cross_entropy( + probs, + weighted_label_one_hot, + soft_label=True, + ignore_index=cfg.DATASET.IGNORE_INDEX) + weighted_label_one_hot.stop_gradient = True + + loss = loss * ignore_mask + avg_loss = fluid.layers.mean(loss) / fluid.layers.mean(ignore_mask) + + label.stop_gradient = True + ignore_mask.stop_gradient = True + return avg_loss + + +def multi_softmax_with_loss(logits, label, ignore_mask=None, num_classes=2, weight=None): + if isinstance(logits, tuple): + avg_loss = 0 + for i, logit in enumerate(logits): + logit_label = fluid.layers.resize_nearest(label, logit.shape[2:]) + logit_mask = (logit_label.astype('int32') != + cfg.DATASET.IGNORE_INDEX).astype('int32') + loss = softmax_with_loss(logit, logit_label, logit_mask, + num_classes) + avg_loss += cfg.MODEL.MULTI_LOSS_WEIGHT[i] * loss + else: + avg_loss = softmax_with_loss(logits, label, ignore_mask, num_classes, weight) + return avg_loss + + +def unsorted_segment_sum(data, segment_ids, unique_labels, feature_dims): + zeros = fluid.layers.fill_constant_batch_size_like(unique_labels, shape=[1, feature_dims], + dtype='float32', value=0) + segment_ids = fluid.layers.unsqueeze(segment_ids, axes=[1]) + segment_ids.stop_gradient = True + segment_sum = fluid.layers.scatter_nd_add(zeros, segment_ids, data) + zeros.stop_gradient = True + + return segment_sum + + +def norm(x, axis=-1): + distance = fluid.layers.reduce_sum(fluid.layers.abs(x), dim=axis, keep_dim=True) + return distance + +def discriminative_loss_single( + prediction, + correct_label, + feature_dim, + label_shape, + delta_v, + delta_d, + param_var, + param_dist, + param_reg): + + correct_label = fluid.layers.reshape( + correct_label, [ + label_shape[1] * label_shape[0]]) + prediction = fluid.layers.transpose(prediction, [1, 2, 0]) + reshaped_pred = fluid.layers.reshape( + prediction, [ + label_shape[1] * label_shape[0], feature_dim]) + + unique_labels, unique_id, counts = fluid.layers.unique_with_counts(correct_label) + correct_label.stop_gradient = True + counts = fluid.layers.cast(counts, 'float32') + num_instances = fluid.layers.shape(unique_labels) + + segmented_sum = unsorted_segment_sum( + reshaped_pred, unique_id, unique_labels, feature_dims=feature_dim) + + counts_rsp = fluid.layers.reshape(counts, (-1, 1)) + mu = fluid.layers.elementwise_div(segmented_sum, counts_rsp) + counts_rsp.stop_gradient = True + mu_expand = fluid.layers.gather(mu, unique_id) + tmp = fluid.layers.elementwise_sub(mu_expand, reshaped_pred) + + distance = norm(tmp) + distance = distance - delta_v + + distance_pos = fluid.layers.greater_equal(distance, fluid.layers.zeros_like(distance)) + distance_pos = fluid.layers.cast(distance_pos, 'float32') + distance = distance * distance_pos + + distance = fluid.layers.square(distance) + + l_var = unsorted_segment_sum(distance, unique_id, unique_labels, feature_dims=1) + l_var = fluid.layers.elementwise_div(l_var, counts_rsp) + l_var = fluid.layers.reduce_sum(l_var) + l_var = l_var / fluid.layers.cast(num_instances * (num_instances - 1), 'float32') + + mu_interleaved_rep = fluid.layers.expand(mu, [num_instances, 1]) + mu_band_rep = fluid.layers.expand(mu, [1, num_instances]) + mu_band_rep = fluid.layers.reshape(mu_band_rep, (num_instances * num_instances, feature_dim)) + + mu_diff = fluid.layers.elementwise_sub(mu_band_rep, mu_interleaved_rep) + + intermediate_tensor = fluid.layers.reduce_sum(fluid.layers.abs(mu_diff), dim=1) + intermediate_tensor.stop_gradient = True + zero_vector = fluid.layers.zeros([1], 'float32') + bool_mask = fluid.layers.not_equal(intermediate_tensor, zero_vector) + temp = fluid.layers.where(bool_mask) + mu_diff_bool = fluid.layers.gather(mu_diff, temp) + + mu_norm = norm(mu_diff_bool) + mu_norm = 2. * delta_d - mu_norm + mu_norm_pos = fluid.layers.greater_equal(mu_norm, fluid.layers.zeros_like(mu_norm)) + mu_norm_pos = fluid.layers.cast(mu_norm_pos, 'float32') + mu_norm = mu_norm * mu_norm_pos + mu_norm_pos.stop_gradient = True + + mu_norm = fluid.layers.square(mu_norm) + + l_dist = fluid.layers.reduce_mean(mu_norm) + + l_reg = fluid.layers.reduce_mean(norm(mu, axis=1)) + + l_var = param_var * l_var + l_dist = param_dist * l_dist + l_reg = param_reg * l_reg + loss = l_var + l_dist + l_reg + return loss, l_var, l_dist, l_reg + + +def discriminative_loss(prediction, correct_label, feature_dim, image_shape, + delta_v, delta_d, param_var, param_dist, param_reg): + batch_size = int(cfg.BATCH_SIZE_PER_DEV) + output_ta_loss = 0. + output_ta_var = 0. + output_ta_dist = 0. + output_ta_reg = 0. + for i in range(batch_size): + disc_loss_single, l_var_single, l_dist_single, l_reg_single = discriminative_loss_single( + prediction[i], correct_label[i], feature_dim, image_shape, delta_v, delta_d, param_var, param_dist, + param_reg) + output_ta_loss += disc_loss_single + output_ta_var += l_var_single + output_ta_dist += l_dist_single + output_ta_reg += l_reg_single + + disc_loss = output_ta_loss / batch_size + l_var = output_ta_var / batch_size + l_dist = output_ta_dist / batch_size + l_reg = output_ta_reg / batch_size + return disc_loss, l_var, l_dist, l_reg + + diff --git a/contrib/LaneNet/models/__init__.py b/contrib/LaneNet/models/__init__.py new file mode 100644 index 00000000..f2a90934 --- /dev/null +++ b/contrib/LaneNet/models/__init__.py @@ -0,0 +1,18 @@ +# coding: utf8 +# copyright (c) 2019 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. + +import models.modeling +import models.libs +import models.backbone diff --git a/contrib/LaneNet/models/backbone/__init__.py b/contrib/LaneNet/models/backbone/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/contrib/LaneNet/models/backbone/vgg.py b/contrib/LaneNet/models/backbone/vgg.py new file mode 100644 index 00000000..f6173579 --- /dev/null +++ b/contrib/LaneNet/models/backbone/vgg.py @@ -0,0 +1,110 @@ +#copyright (c) 2019 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. + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import paddle +import paddle.fluid as fluid +from paddle.fluid import ParamAttr + +__all__ = ["VGGNet", "VGG11", "VGG13", "VGG16", "VGG19"] + + +class VGGNet(): + def __init__(self, layers=16): + self.layers = layers + + def net(self, input, class_dim=1000): + layers = self.layers + vgg_spec = { + 11: ([1, 1, 2, 2, 2]), + 13: ([2, 2, 2, 2, 2]), + 16: ([2, 2, 3, 3, 3]), + 19: ([2, 2, 4, 4, 4]) + } + assert layers in vgg_spec.keys(), \ + "supported layers are {} but input layer is {}".format(vgg_spec.keys(), layers) + + fetures_dict = {} + nums = vgg_spec[layers] + conv1 = self.conv_block(input, 64, nums[0], name="conv1_") + conv2 = self.conv_block(conv1, 128, nums[1], name="conv2_") + conv3 = self.conv_block(conv2, 256, nums[2], name="conv3_") + conv4 = self.conv_block(conv3, 512, nums[3], name="conv4_") + conv5 = self.conv_block(conv4, 512, nums[4], name="conv5_") + fetures_dict['pool3'] = conv3 + fetures_dict['pool4'] = conv4 + fetures_dict['pool5'] = conv5 + return fetures_dict + fc_dim = 4096 + fc_name = ["fc6", "fc7", "fc8"] + fc1 = fluid.layers.fc( + input=conv5, + size=fc_dim, + act='relu', + param_attr=fluid.param_attr.ParamAttr(name=fc_name[0] + "_weights"), + bias_attr=fluid.param_attr.ParamAttr(name=fc_name[0] + "_offset")) + fc1 = fluid.layers.dropout(x=fc1, dropout_prob=0.5) + fc2 = fluid.layers.fc( + input=fc1, + size=fc_dim, + act='relu', + param_attr=fluid.param_attr.ParamAttr(name=fc_name[1] + "_weights"), + bias_attr=fluid.param_attr.ParamAttr(name=fc_name[1] + "_offset")) + fc2 = fluid.layers.dropout(x=fc2, dropout_prob=0.5) + out = fluid.layers.fc( + input=fc2, + size=class_dim, + param_attr=fluid.param_attr.ParamAttr(name=fc_name[2] + "_weights"), + bias_attr=fluid.param_attr.ParamAttr(name=fc_name[2] + "_offset")) + + return out + + def conv_block(self, input, num_filter, groups, name=None): + conv = input + for i in range(groups): + conv = fluid.layers.conv2d( + input=conv, + num_filters=num_filter, + filter_size=3, + stride=1, + padding=1, + act='relu', + param_attr=fluid.param_attr.ParamAttr( + name=name + str(i + 1) + "_weights"), + bias_attr=False) + return fluid.layers.pool2d( + input=conv, pool_size=2, pool_type='max', pool_stride=2) + + +def VGG11(): + model = VGGNet(layers=11) + return model + + +def VGG13(): + model = VGGNet(layers=13) + return model + + +def VGG16(): + model = VGGNet(layers=16) + return model + + +def VGG19(): + model = VGGNet(layers=19) + return model diff --git a/contrib/LaneNet/models/libs/__init__.py b/contrib/LaneNet/models/libs/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/contrib/LaneNet/models/libs/model_libs.py b/contrib/LaneNet/models/libs/model_libs.py new file mode 100644 index 00000000..19afe542 --- /dev/null +++ b/contrib/LaneNet/models/libs/model_libs.py @@ -0,0 +1,166 @@ +# coding: utf8 +# copyright (c) 2019 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. + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function +import paddle +import paddle.fluid as fluid +from utils.config import cfg +import contextlib + +bn_regularizer = fluid.regularizer.L2DecayRegularizer(regularization_coeff=0.0) +name_scope = "" + + +@contextlib.contextmanager +def scope(name): + global name_scope + bk = name_scope + name_scope = name_scope + name + '/' + yield + name_scope = bk + + +def max_pool(input, kernel, stride, padding): + data = fluid.layers.pool2d( + input, + pool_size=kernel, + pool_type='max', + pool_stride=stride, + pool_padding=padding) + return data + + +def avg_pool(input, kernel, stride, padding=0): + data = fluid.layers.pool2d( + input, + pool_size=kernel, + pool_type='avg', + pool_stride=stride, + pool_padding=padding) + return data + + +def group_norm(input, G, eps=1e-5, param_attr=None, bias_attr=None): + N, C, H, W = input.shape + if C % G != 0: + # print "group can not divide channle:", C, G + for d in range(10): + for t in [d, -d]: + if G + t <= 0: continue + if C % (G + t) == 0: + G = G + t + break + if C % G == 0: + # print "use group size:", G + break + assert C % G == 0 + x = fluid.layers.group_norm( + input, + groups=G, + param_attr=param_attr, + bias_attr=bias_attr, + name=name_scope + 'group_norm') + return x + + +def bn(*args, **kargs): + if cfg.MODEL.DEFAULT_NORM_TYPE == 'bn': + with scope('BatchNorm'): + return fluid.layers.batch_norm( + *args, + epsilon=cfg.MODEL.DEFAULT_EPSILON, + momentum=cfg.MODEL.BN_MOMENTUM, + param_attr=fluid.ParamAttr( + name=name_scope + 'gamma', regularizer=bn_regularizer), + bias_attr=fluid.ParamAttr( + name=name_scope + 'beta', regularizer=bn_regularizer), + moving_mean_name=name_scope + 'moving_mean', + moving_variance_name=name_scope + 'moving_variance', + **kargs) + elif cfg.MODEL.DEFAULT_NORM_TYPE == 'gn': + with scope('GroupNorm'): + return group_norm( + args[0], + cfg.MODEL.DEFAULT_GROUP_NUMBER, + eps=cfg.MODEL.DEFAULT_EPSILON, + param_attr=fluid.ParamAttr( + name=name_scope + 'gamma', regularizer=bn_regularizer), + bias_attr=fluid.ParamAttr( + name=name_scope + 'beta', regularizer=bn_regularizer)) + else: + raise Exception("Unsupport norm type:" + cfg.MODEL.DEFAULT_NORM_TYPE) + + +def bn_relu(data): + return fluid.layers.relu(bn(data)) + + +def relu(data): + return fluid.layers.relu(data) + + +def conv(*args, **kargs): + kargs['param_attr'] = name_scope + 'weights' + if 'bias_attr' in kargs and kargs['bias_attr']: + kargs['bias_attr'] = fluid.ParamAttr( + name=name_scope + 'biases', + regularizer=None, + initializer=fluid.initializer.ConstantInitializer(value=0.0)) + else: + kargs['bias_attr'] = False + return fluid.layers.conv2d(*args, **kargs) + + +def deconv(*args, **kargs): + kargs['param_attr'] = name_scope + 'weights' + if 'bias_attr' in kargs and kargs['bias_attr']: + kargs['bias_attr'] = name_scope + 'biases' + else: + kargs['bias_attr'] = False + return fluid.layers.conv2d_transpose(*args, **kargs) + + +def separate_conv(input, channel, stride, filter, dilation=1, act=None): + param_attr = fluid.ParamAttr( + name=name_scope + 'weights', + regularizer=fluid.regularizer.L2DecayRegularizer( + regularization_coeff=0.0), + initializer=fluid.initializer.TruncatedNormal(loc=0.0, scale=0.33)) + with scope('depthwise'): + input = conv( + input, + input.shape[1], + filter, + stride, + groups=input.shape[1], + padding=(filter // 2) * dilation, + dilation=dilation, + use_cudnn=False, + param_attr=param_attr) + input = bn(input) + if act: input = act(input) + + param_attr = fluid.ParamAttr( + name=name_scope + 'weights', + regularizer=None, + initializer=fluid.initializer.TruncatedNormal(loc=0.0, scale=0.06)) + with scope('pointwise'): + input = conv( + input, channel, 1, 1, groups=1, padding=0, param_attr=param_attr) + input = bn(input) + if act: input = act(input) + return input diff --git a/contrib/LaneNet/models/model_builder.py b/contrib/LaneNet/models/model_builder.py new file mode 100644 index 00000000..20978e2c --- /dev/null +++ b/contrib/LaneNet/models/model_builder.py @@ -0,0 +1,285 @@ +# coding: utf8 +# copyright (c) 2019 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. +import sys +sys.path.append("..") +import struct +import importlib + +import paddle.fluid as fluid +from paddle.fluid.proto.framework_pb2 import VarType + +import solver +from utils.config import cfg +from loss import multi_softmax_with_loss +from loss import discriminative_loss + +class ModelPhase(object): + """ + Standard name for model phase in PaddleSeg + + The following standard keys are defined: + * `TRAIN`: training mode. + * `EVAL`: testing/evaluation mode. + * `PREDICT`: prediction/inference mode. + * `VISUAL` : visualization mode + """ + + TRAIN = 'train' + EVAL = 'eval' + PREDICT = 'predict' + VISUAL = 'visual' + + @staticmethod + def is_train(phase): + return phase == ModelPhase.TRAIN + + @staticmethod + def is_predict(phase): + return phase == ModelPhase.PREDICT + + @staticmethod + def is_eval(phase): + return phase == ModelPhase.EVAL + + @staticmethod + def is_visual(phase): + return phase == ModelPhase.VISUAL + + @staticmethod + def is_valid_phase(phase): + """ Check valid phase """ + if ModelPhase.is_train(phase) or ModelPhase.is_predict(phase) \ + or ModelPhase.is_eval(phase) or ModelPhase.is_visual(phase): + return True + + return False + + +def map_model_name(model_name): + name_dict = { + "lanenet": "lanenet.lanenet" + } + if model_name in name_dict.keys(): + return name_dict[model_name] + else: + raise Exception( + "unknow model name, only support unet, deeplabv3p, icnet") + + +def get_func(func_name): + """Helper to return a function object by name. func_name must identify a + function in this module or the path to a function relative to the base + 'modeling' module. + """ + if func_name == '': + return None + try: + parts = func_name.split('.') + # Refers to a function in this module + if len(parts) == 1: + return globals()[parts[0]] + # Otherwise, assume we're referencing a module under modeling + module_name = 'models.' + '.'.join(parts[:-1]) + module = importlib.import_module(module_name) + return getattr(module, parts[-1]) + except Exception: + print('Failed to find function: {}'.format(func_name)) + return module + + +def softmax(logit): + logit = fluid.layers.transpose(logit, [0, 2, 3, 1]) + logit = fluid.layers.softmax(logit) + logit = fluid.layers.transpose(logit, [0, 3, 1, 2]) + return logit + +def sigmoid_to_softmax(logit): + """ + one channel to two channel + """ + logit = fluid.layers.transpose(logit, [0, 2, 3, 1]) + logit = fluid.layers.sigmoid(logit) + logit_back = 1 - logit + logit = fluid.layers.concat([logit_back, logit], axis=-1) + logit = fluid.layers.transpose(logit, [0, 3, 1, 2]) + return logit + + +def build_model(main_prog, start_prog, phase=ModelPhase.TRAIN): + if not ModelPhase.is_valid_phase(phase): + raise ValueError("ModelPhase {} is not valid!".format(phase)) + if ModelPhase.is_train(phase): + width = cfg.TRAIN_CROP_SIZE[0] + height = cfg.TRAIN_CROP_SIZE[1] + else: + width = cfg.EVAL_CROP_SIZE[0] + height = cfg.EVAL_CROP_SIZE[1] + + image_shape = [cfg.DATASET.DATA_DIM, height, width] + grt_shape = [1, height, width] + class_num = cfg.DATASET.NUM_CLASSES + + with fluid.program_guard(main_prog, start_prog): + with fluid.unique_name.guard(): + image = fluid.layers.data( + name='image', shape=image_shape, dtype='float32') + label = fluid.layers.data( + name='label', shape=grt_shape, dtype='int32') + if cfg.MODEL.MODEL_NAME == 'lanenet': + label_instance = fluid.layers.data( + name='label_instance', shape=grt_shape, dtype='int32') + mask = fluid.layers.data( + name='mask', shape=grt_shape, dtype='int32') + + # use PyReader when doing traning and evaluation + if ModelPhase.is_train(phase) or ModelPhase.is_eval(phase): + py_reader = fluid.io.PyReader( + feed_list=[image, label, label_instance, mask], + capacity=cfg.DATALOADER.BUF_SIZE, + iterable=False, + use_double_buffer=True) + + model_name = map_model_name(cfg.MODEL.MODEL_NAME) + model_func = get_func("modeling." + model_name) + + loss_type = cfg.SOLVER.LOSS + if not isinstance(loss_type, list): + loss_type = list(loss_type) + + logits = model_func(image, class_num) + + if ModelPhase.is_train(phase): + loss_valid = False + valid_loss = [] + if cfg.MODEL.MODEL_NAME == 'lanenet': + embeding_logit = logits[1] + logits = logits[0] + disc_loss, _, _, l_reg = discriminative_loss(embeding_logit, label_instance, 4, + image_shape[1:], 0.5, 3.0, 1.0, 1.0, 0.001) + + if "softmax_loss" in loss_type: + if isinstance(cfg.SOLVER.CROSS_ENTROPY_WEIGHT, str) and \ + cfg.SOLVER.CROSS_ENTROPY_WEIGHT == 'lanenet': + weight = get_dynamic_weight(label) + else: + weight = cfg.SOLVER.CROSS_ENTROPY_WEIGHT + seg_loss = multi_softmax_with_loss(logits, label, mask, class_num, weight) + loss_valid = True + valid_loss.append("softmax_loss") + + if not loss_valid: + raise Exception("SOLVER.LOSS: {} is set wrong. it should " + "include one of (softmax_loss, bce_loss, dice_loss) at least" + " example: ['softmax_loss']".format(cfg.SOLVER.LOSS)) + + invalid_loss = [x for x in loss_type if x not in valid_loss] + if len(invalid_loss) > 0: + print("Warning: the loss {} you set is invalid. it will not be included in loss computed.".format(invalid_loss)) + + avg_loss = disc_loss + 0.00001 * l_reg + seg_loss + + #get pred result in original size + if isinstance(logits, tuple): + logit = logits[0] + else: + logit = logits + + if logit.shape[2:] != label.shape[2:]: + logit = fluid.layers.resize_bilinear(logit, label.shape[2:]) + + # return image input and logit output for inference graph prune + if ModelPhase.is_predict(phase): + if class_num == 1: + logit = sigmoid_to_softmax(logit) + else: + logit = softmax(logit) + return image, logit + + if class_num == 1: + out = sigmoid_to_softmax(logit) + out = fluid.layers.transpose(out, [0, 2, 3, 1]) + else: + out = fluid.layers.transpose(logit, [0, 2, 3, 1]) + + pred = fluid.layers.argmax(out, axis=3) + pred = fluid.layers.unsqueeze(pred, axes=[3]) + if ModelPhase.is_visual(phase): + if cfg.MODEL.MODEL_NAME == 'lanenet': + return pred, logits[1] + if class_num == 1: + logit = sigmoid_to_softmax(logit) + else: + logit = softmax(logit) + return pred, logit + + accuracy, fp, fn = compute_metric(pred, label) + if ModelPhase.is_eval(phase): + return py_reader, pred, label, mask, accuracy, fp, fn + + if ModelPhase.is_train(phase): + optimizer = solver.Solver(main_prog, start_prog) + decayed_lr = optimizer.optimise(avg_loss) + return py_reader, avg_loss, decayed_lr, pred, label, mask, disc_loss, seg_loss, accuracy, fp, fn + + +def compute_metric(pred, label): + label = fluid.layers.transpose(label, [0, 2, 3, 1]) + + idx = fluid.layers.where(pred == 1) + pix_cls_ret = fluid.layers.gather_nd(label, idx) + + correct_num = fluid.layers.reduce_sum(fluid.layers.cast(pix_cls_ret, 'float32')) + + gt_num = fluid.layers.cast(fluid.layers.shape(fluid.layers.gather_nd(label, + fluid.layers.where(label == 1)))[0], 'int64') + pred_num = fluid.layers.cast(fluid.layers.shape(fluid.layers.gather_nd(pred, idx))[0], 'int64') + accuracy = correct_num / gt_num + + false_pred = pred_num - correct_num + fp = fluid.layers.cast(false_pred, 'float32') / fluid.layers.cast(fluid.layers.shape(pix_cls_ret)[0], 'int64') + + label_cls_ret = fluid.layers.gather_nd(label, fluid.layers.where(label == 1)) + mis_pred = fluid.layers.cast(fluid.layers.shape(label_cls_ret)[0], 'int64') - correct_num + fn = fluid.layers.cast(mis_pred, 'float32') / fluid.layers.cast(fluid.layers.shape(label_cls_ret)[0], 'int64') + accuracy.stop_gradient = True + fp.stop_gradient = True + fn.stop_gradient = True + return accuracy, fp, fn + + +def get_dynamic_weight(label): + label = fluid.layers.reshape(label, [-1]) + unique_labels, unique_id, counts = fluid.layers.unique_with_counts(label) + counts = fluid.layers.cast(counts, 'float32') + weight = 1.0 / fluid.layers.log((counts / fluid.layers.reduce_sum(counts) + 1.02)) + return weight + + +def to_int(string, dest="I"): + return struct.unpack(dest, string)[0] + + +def parse_shape_from_file(filename): + with open(filename, "rb") as file: + version = file.read(4) + lod_level = to_int(file.read(8), dest="Q") + for i in range(lod_level): + _size = to_int(file.read(8), dest="Q") + _ = file.read(_size) + version = file.read(4) + tensor_desc_size = to_int(file.read(4)) + tensor_desc = VarType.TensorDesc() + tensor_desc.ParseFromString(file.read(tensor_desc_size)) + return tuple(tensor_desc.dims) diff --git a/contrib/LaneNet/models/modeling/__init__.py b/contrib/LaneNet/models/modeling/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/contrib/LaneNet/models/modeling/lanenet.py b/contrib/LaneNet/models/modeling/lanenet.py new file mode 100644 index 00000000..4a4cbc50 --- /dev/null +++ b/contrib/LaneNet/models/modeling/lanenet.py @@ -0,0 +1,433 @@ +# coding: utf8 +# copyright (c) 2019 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. + +from __future__ import division +from __future__ import print_function + +import paddle.fluid as fluid + + +from utils.config import cfg +from models.libs.model_libs import scope, name_scope +from models.libs.model_libs import bn, bn_relu, relu +from models.libs.model_libs import conv, max_pool, deconv +from models.backbone.vgg import VGGNet as vgg_backbone + +# Bottleneck type +REGULAR = 1 +DOWNSAMPLING = 2 +UPSAMPLING = 3 +DILATED = 4 +ASYMMETRIC = 5 + + +def prelu(x, decoder=False): + # If decoder, then perform relu else perform prelu + if decoder: + return fluid.layers.relu(x) + return fluid.layers.prelu(x, 'channel') + + +def iniatial_block(inputs, name_scope='iniatial_block'): + ''' + The initial block for Enet has 2 branches: The convolution branch and Maxpool branch. + The conv branch has 13 filters, while the maxpool branch gives 3 channels corresponding to the RGB channels. + Both output layers are then concatenated to give an output of 16 channels. + + :param inputs(Tensor): A 4D tensor of shape [batch_size, height, width, channels] + :return net_concatenated(Tensor): a 4D Tensor of new shape [batch_size, height, width, channels] + ''' + # Convolutional branch + with scope(name_scope): + net_conv = conv(inputs, 13, 3, stride=2, padding=1) + net_conv = bn(net_conv) + net_conv = fluid.layers.prelu(net_conv, 'channel') + + # Max pool branch + net_pool = max_pool(inputs, [2, 2], stride=2, padding='SAME') + + # Concatenated output - does it matter max pool comes first or conv comes first? probably not. + net_concatenated = fluid.layers.concat([net_conv, net_pool], axis=1) + return net_concatenated + + +def bottleneck(inputs, + output_depth, + filter_size, + regularizer_prob, + projection_ratio=4, + type=REGULAR, + seed=0, + output_shape=None, + dilation_rate=None, + decoder=False, + name_scope='bottleneck'): + + # Calculate the depth reduction based on the projection ratio used in 1x1 convolution. + reduced_depth = int(inputs.shape[1] / projection_ratio) + + # DOWNSAMPLING BOTTLENECK + if type == DOWNSAMPLING: + #=============MAIN BRANCH============= + #Just perform a max pooling + with scope('down_sample'): + inputs_shape = inputs.shape + with scope('main_max_pool'): + net_main = fluid.layers.conv2d(inputs, inputs_shape[1], filter_size=3, stride=2, padding='SAME') + + #First get the difference in depth to pad, then pad with zeros only on the last dimension. + depth_to_pad = abs(inputs_shape[1] - output_depth) + paddings = [0, 0, 0, depth_to_pad, 0, 0, 0, 0] + with scope('main_padding'): + net_main = fluid.layers.pad(net_main, paddings=paddings) + + with scope('block1'): + net = conv(inputs, reduced_depth, [2, 2], stride=2, padding='same') + net = bn(net) + net = prelu(net, decoder=decoder) + + with scope('block2'): + net = conv(net, reduced_depth, [filter_size, filter_size], padding='same') + net = bn(net) + net = prelu(net, decoder=decoder) + + with scope('block3'): + net = conv(net, output_depth, [1, 1], padding='same') + net = bn(net) + net = prelu(net, decoder=decoder) + + # Regularizer + net = fluid.layers.dropout(net, regularizer_prob, seed=seed) + + # Finally, combine the two branches together via an element-wise addition + net = fluid.layers.elementwise_add(net, net_main) + net = prelu(net, decoder=decoder) + + return net, inputs_shape + + # DILATION CONVOLUTION BOTTLENECK + # Everything is the same as a regular bottleneck except for the dilation rate argument + elif type == DILATED: + #Check if dilation rate is given + if not dilation_rate: + raise ValueError('Dilation rate is not given.') + + with scope('dilated'): + # Save the main branch for addition later + net_main = inputs + + # First projection with 1x1 kernel (dimensionality reduction) + with scope('block1'): + net = conv(inputs, reduced_depth, [1, 1]) + net = bn(net) + net = prelu(net, decoder=decoder) + + # Second conv block --- apply dilated convolution here + with scope('block2'): + net = conv(net, reduced_depth, filter_size, padding='SAME', dilation=dilation_rate) + net = bn(net) + net = prelu(net, decoder=decoder) + + # Final projection with 1x1 kernel (Expansion) + with scope('block3'): + net = conv(net, output_depth, [1,1]) + net = bn(net) + net = prelu(net, decoder=decoder) + + # Regularizer + net = fluid.layers.dropout(net, regularizer_prob, seed=seed) + net = prelu(net, decoder=decoder) + + # Add the main branch + net = fluid.layers.elementwise_add(net_main, net) + net = prelu(net, decoder=decoder) + + return net + + # ASYMMETRIC CONVOLUTION BOTTLENECK + # Everything is the same as a regular bottleneck except for a [5,5] kernel decomposed into two [5,1] then [1,5] + elif type == ASYMMETRIC: + # Save the main branch for addition later + with scope('asymmetric'): + net_main = inputs + # First projection with 1x1 kernel (dimensionality reduction) + with scope('block1'): + net = conv(inputs, reduced_depth, [1, 1]) + net = bn(net) + net = prelu(net, decoder=decoder) + + # Second conv block --- apply asymmetric conv here + with scope('block2'): + with scope('asymmetric_conv2a'): + net = conv(net, reduced_depth, [filter_size, 1], padding='same') + with scope('asymmetric_conv2b'): + net = conv(net, reduced_depth, [1, filter_size], padding='same') + net = bn(net) + net = prelu(net, decoder=decoder) + + # Final projection with 1x1 kernel + with scope('block3'): + net = conv(net, output_depth, [1, 1]) + net = bn(net) + net = prelu(net, decoder=decoder) + + # Regularizer + net = fluid.layers.dropout(net, regularizer_prob, seed=seed) + net = prelu(net, decoder=decoder) + + # Add the main branch + net = fluid.layers.elementwise_add(net_main, net) + net = prelu(net, decoder=decoder) + + return net + + # UPSAMPLING BOTTLENECK + # Everything is the same as a regular one, except convolution becomes transposed. + elif type == UPSAMPLING: + #Check if pooling indices is given + + #Check output_shape given or not + if output_shape is None: + raise ValueError('Output depth is not given') + + #=======MAIN BRANCH======= + #Main branch to upsample. output shape must match with the shape of the layer that was pooled initially, in order + #for the pooling indices to work correctly. However, the initial pooled layer was padded, so need to reduce dimension + #before unpooling. In the paper, padding is replaced with convolution for this purpose of reducing the depth! + with scope('upsampling'): + with scope('unpool'): + net_unpool = conv(inputs, output_depth, [1, 1]) + net_unpool = bn(net_unpool) + net_unpool = fluid.layers.resize_bilinear(net_unpool, out_shape=output_shape[2:]) + + # First 1x1 projection to reduce depth + with scope('block1'): + net = conv(inputs, reduced_depth, [1, 1]) + net = bn(net) + net = prelu(net, decoder=decoder) + + with scope('block2'): + net = deconv(net, reduced_depth, filter_size=filter_size, stride=2, padding='same') + net = bn(net) + net = prelu(net, decoder=decoder) + + # Final projection with 1x1 kernel + with scope('block3'): + net = conv(net, output_depth, [1, 1]) + net = bn(net) + net = prelu(net, decoder=decoder) + + # Regularizer + net = fluid.layers.dropout(net, regularizer_prob, seed=seed) + net = prelu(net, decoder=decoder) + + # Finally, add the unpooling layer and the sub branch together + net = fluid.layers.elementwise_add(net, net_unpool) + net = prelu(net, decoder=decoder) + + return net + + # REGULAR BOTTLENECK + else: + with scope('regular'): + net_main = inputs + + # First projection with 1x1 kernel + with scope('block1'): + net = conv(inputs, reduced_depth, [1, 1]) + net = bn(net) + net = prelu(net, decoder=decoder) + + # Second conv block + with scope('block2'): + net = conv(net, reduced_depth, [filter_size, filter_size], padding='same') + net = bn(net) + net = prelu(net, decoder=decoder) + + # Final projection with 1x1 kernel + with scope('block3'): + net = conv(net, output_depth, [1, 1]) + net = bn(net) + net = prelu(net, decoder=decoder) + + # Regularizer + net = fluid.layers.dropout(net, regularizer_prob, seed=seed) + net = prelu(net, decoder=decoder) + + # Add the main branch + net = fluid.layers.elementwise_add(net_main, net) + net = prelu(net, decoder=decoder) + + return net + + +def ENet_stage1(inputs, name_scope='stage1_block'): + with scope(name_scope): + with scope('bottleneck1_0'): + net, inputs_shape_1 \ + = bottleneck(inputs, output_depth=64, filter_size=3, regularizer_prob=0.01, type=DOWNSAMPLING, + name_scope='bottleneck1_0') + with scope('bottleneck1_1'): + net = bottleneck(net, output_depth=64, filter_size=3, regularizer_prob=0.01, + name_scope='bottleneck1_1') + with scope('bottleneck1_2'): + net = bottleneck(net, output_depth=64, filter_size=3, regularizer_prob=0.01, + name_scope='bottleneck1_2') + with scope('bottleneck1_3'): + net = bottleneck(net, output_depth=64, filter_size=3, regularizer_prob=0.01, + name_scope='bottleneck1_3') + with scope('bottleneck1_4'): + net = bottleneck(net, output_depth=64, filter_size=3, regularizer_prob=0.01, + name_scope='bottleneck1_4') + return net, inputs_shape_1 + + +def ENet_stage2(inputs, name_scope='stage2_block'): + with scope(name_scope): + net, inputs_shape_2 \ + = bottleneck(inputs, output_depth=128, filter_size=3, regularizer_prob=0.1, type=DOWNSAMPLING, + name_scope='bottleneck2_0') + for i in range(2): + with scope('bottleneck2_{}'.format(str(4 * i + 1))): + net = bottleneck(net, output_depth=128, filter_size=3, regularizer_prob=0.1, + name_scope='bottleneck2_{}'.format(str(4 * i + 1))) + with scope('bottleneck2_{}'.format(str(4 * i + 2))): + net = bottleneck(net, output_depth=128, filter_size=3, regularizer_prob=0.1, type=DILATED, dilation_rate=(2 ** (2*i+1)), + name_scope='bottleneck2_{}'.format(str(4 * i + 2))) + with scope('bottleneck2_{}'.format(str(4 * i + 3))): + net = bottleneck(net, output_depth=128, filter_size=5, regularizer_prob=0.1, type=ASYMMETRIC, + name_scope='bottleneck2_{}'.format(str(4 * i + 3))) + with scope('bottleneck2_{}'.format(str(4 * i + 4))): + net = bottleneck(net, output_depth=128, filter_size=3, regularizer_prob=0.1, type=DILATED, dilation_rate=(2 ** (2*i+2)), + name_scope='bottleneck2_{}'.format(str(4 * i + 4))) + return net, inputs_shape_2 + + +def ENet_stage3(inputs, name_scope='stage3_block'): + with scope(name_scope): + for i in range(2): + with scope('bottleneck3_{}'.format(str(4 * i + 0))): + net = bottleneck(inputs, output_depth=128, filter_size=3, regularizer_prob=0.1, + name_scope='bottleneck3_{}'.format(str(4 * i + 0))) + with scope('bottleneck3_{}'.format(str(4 * i + 1))): + net = bottleneck(net, output_depth=128, filter_size=3, regularizer_prob=0.1, type=DILATED, dilation_rate=(2 ** (2*i+1)), + name_scope='bottleneck3_{}'.format(str(4 * i + 1))) + with scope('bottleneck3_{}'.format(str(4 * i + 2))): + net = bottleneck(net, output_depth=128, filter_size=5, regularizer_prob=0.1, type=ASYMMETRIC, + name_scope='bottleneck3_{}'.format(str(4 * i + 2))) + with scope('bottleneck3_{}'.format(str(4 * i + 3))): + net = bottleneck(net, output_depth=128, filter_size=3, regularizer_prob=0.1, type=DILATED, dilation_rate=(2 ** (2*i+2)), + name_scope='bottleneck3_{}'.format(str(4 * i + 3))) + return net + + +def ENet_stage4(inputs, inputs_shape, connect_tensor, + skip_connections=True, name_scope='stage4_block'): + with scope(name_scope): + with scope('bottleneck4_0'): + net = bottleneck(inputs, output_depth=64, filter_size=3, regularizer_prob=0.1, + type=UPSAMPLING, decoder=True, output_shape=inputs_shape, + name_scope='bottleneck4_0') + + if skip_connections: + net = fluid.layers.elementwise_add(net, connect_tensor) + with scope('bottleneck4_1'): + net = bottleneck(net, output_depth=64, filter_size=3, regularizer_prob=0.1, decoder=True, + name_scope='bottleneck4_1') + with scope('bottleneck4_2'): + net = bottleneck(net, output_depth=64, filter_size=3, regularizer_prob=0.1, decoder=True, + name_scope='bottleneck4_2') + + return net + + +def ENet_stage5(inputs, inputs_shape, connect_tensor, skip_connections=True, + name_scope='stage5_block'): + with scope(name_scope): + net = bottleneck(inputs, output_depth=16, filter_size=3, regularizer_prob=0.1, type=UPSAMPLING, + decoder=True, output_shape=inputs_shape, + name_scope='bottleneck5_0') + + if skip_connections: + net = fluid.layers.elementwise_add(net, connect_tensor) + with scope('bottleneck5_1'): + net = bottleneck(net, output_depth=16, filter_size=3, regularizer_prob=0.1, decoder=True, + name_scope='bottleneck5_1') + return net + + +def decoder(input, num_classes): + + if 'enet' in cfg.MODEL.LANENET.BACKBONE: + # Segmentation branch + with scope('LaneNetSeg'): + initial, stage1, stage2, inputs_shape_1, inputs_shape_2 = input + segStage3 = ENet_stage3(stage2) + segStage4 = ENet_stage4(segStage3, inputs_shape_2, stage1) + segStage5 = ENet_stage5(segStage4, inputs_shape_1, initial) + segLogits = deconv(segStage5, num_classes, filter_size=2, stride=2, padding='SAME') + + # Embedding branch + with scope('LaneNetEm'): + emStage3 = ENet_stage3(stage2) + emStage4 = ENet_stage4(emStage3, inputs_shape_2, stage1) + emStage5 = ENet_stage5(emStage4, inputs_shape_1, initial) + emLogits = deconv(emStage5, 4, filter_size=2, stride=2, padding='SAME') + + elif 'vgg' in cfg.MODEL.LANENET.BACKBONE: + encoder_list = ['pool5', 'pool4', 'pool3'] + # score stage + input_tensor = input[encoder_list[0]] + with scope('score_origin'): + score = conv(input_tensor, 64, 1) + encoder_list = encoder_list[1:] + for i in range(len(encoder_list)): + with scope('deconv_{:d}'.format(i + 1)): + deconv_out = deconv(score, 64, filter_size=4, stride=2, padding='SAME') + input_tensor = input[encoder_list[i]] + with scope('score_{:d}'.format(i + 1)): + score = conv(input_tensor, 64, 1) + score = fluid.layers.elementwise_add(deconv_out, score) + + with scope('deconv_final'): + emLogits = deconv(score, 64, filter_size=16, stride=8, padding='SAME') + with scope('score_final'): + segLogits = conv(emLogits, num_classes, 1) + emLogits = relu(conv(emLogits, 4, 1)) + return segLogits, emLogits + + +def encoder(input): + if 'vgg' in cfg.MODEL.LANENET.BACKBONE: + model = vgg_backbone(layers=16) + output = model.net(input) + elif 'enet' in cfg.MODEL.LANET.BACKBONE: + with scope('LaneNetBase'): + initial = iniatial_block(input) + stage1, inputs_shape_1 = ENet_stage1(initial) + stage2, inputs_shape_2 = ENet_stage2(stage1) + output = (initial, stage1, stage2, inputs_shape_1, inputs_shape_2) + else: + raise Exception("LaneNet expect enet and vgg backbone, but received {}". + format(cfg.MODEL.LANENET.BACKBONE)) + return output + + +def lanenet(img, num_classes): + + output = encoder(img) + segLogits, emLogits = decoder(output, num_classes) + + return segLogits, emLogits diff --git a/contrib/LaneNet/reader.py b/contrib/LaneNet/reader.py new file mode 100644 index 00000000..8bb23745 --- /dev/null +++ b/contrib/LaneNet/reader.py @@ -0,0 +1,603 @@ +# coding: utf8 +# copyright (c) 2019 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. + +from __future__ import print_function +import sys +import os +import time +import codecs + +import numpy as np +import cv2 + +import data_aug as aug +from utils.config import cfg +from pdseg.data_utils import GeneratorEnqueuer +from models.model_builder import ModelPhase +import copy + +def cv2_imread(file_path, flag=cv2.IMREAD_COLOR): + # resolve cv2.imread open Chinese file path issues on Windows Platform. + return cv2.imdecode(np.fromfile(file_path, dtype=np.uint8), flag) + + +class SegDataset(object): + def __init__(self, + file_list, + data_dir, + shuffle=False, + mode=ModelPhase.TRAIN): + self.mode = mode + self.shuffle = shuffle + self.data_dir = data_dir + + self.shuffle_seed = 0 + # NOTE: Please ensure file list was save in UTF-8 coding format + with codecs.open(file_list, 'r', 'utf-8') as flist: + self.lines = [line.strip() for line in flist] + self.all_lines = copy.deepcopy(self.lines) + if shuffle and cfg.NUM_TRAINERS > 1: + np.random.RandomState(self.shuffle_seed).shuffle(self.all_lines) + elif shuffle: + np.random.shuffle(self.lines) + + def generator(self): + if self.shuffle and cfg.NUM_TRAINERS > 1: + np.random.RandomState(self.shuffle_seed).shuffle(self.all_lines) + num_lines = len(self.all_lines) // cfg.NUM_TRAINERS + self.lines = self.all_lines[num_lines * cfg.TRAINER_ID: num_lines * (cfg.TRAINER_ID + 1)] + self.shuffle_seed += 1 + elif self.shuffle: + np.random.shuffle(self.lines) + + for line in self.lines: + yield self.process_image(line, self.data_dir, self.mode) + + def sharding_generator(self, pid=0, num_processes=1): + """ + Use line id as shard key for multiprocess io + It's a normal generator if pid=0, num_processes=1 + """ + for index, line in enumerate(self.lines): + # Use index and pid to shard file list + if index % num_processes == pid: + yield self.process_image(line, self.data_dir, self.mode) + + def batch_reader(self, batch_size): + br = self.batch(self.reader, batch_size) + for batch in br: + yield batch[0], batch[1], batch[2] + + def multiprocess_generator(self, max_queue_size=32, num_processes=8): + # Re-shuffle file list + if self.shuffle and cfg.NUM_TRAINERS > 1: + np.random.RandomState(self.shuffle_seed).shuffle(self.all_lines) + num_lines = len(self.all_lines) // self.num_trainers + self.lines = self.all_lines[num_lines * self.trainer_id: num_lines * (self.trainer_id + 1)] + self.shuffle_seed += 1 + elif self.shuffle: + np.random.shuffle(self.lines) + + # Create multiple sharding generators according to num_processes for multiple processes + generators = [] + for pid in range(num_processes): + generators.append(self.sharding_generator(pid, num_processes)) + + try: + enqueuer = GeneratorEnqueuer(generators) + enqueuer.start(max_queue_size=max_queue_size, workers=num_processes) + while True: + generator_out = None + while enqueuer.is_running(): + if not enqueuer.queue.empty(): + generator_out = enqueuer.queue.get(timeout=5) + break + else: + time.sleep(0.01) + if generator_out is None: + break + yield generator_out + finally: + if enqueuer is not None: + enqueuer.stop() + + def batch(self, reader, batch_size, is_test=False, drop_last=False): + def batch_reader(is_test=False, drop_last=drop_last): + if is_test: + imgs, grts, img_names, valid_shapes, org_shapes = [], [], [], [], [] + for img, grt, img_name, valid_shape, org_shape in reader(): + imgs.append(img) + grts.append(grt) + img_names.append(img_name) + valid_shapes.append(valid_shape) + org_shapes.append(org_shape) + if len(imgs) == batch_size: + yield np.array(imgs), np.array( + grts), img_names, np.array(valid_shapes), np.array( + org_shapes) + imgs, grts, img_names, valid_shapes, org_shapes = [], [], [], [], [] + + if not drop_last and len(imgs) > 0: + yield np.array(imgs), np.array(grts), img_names, np.array( + valid_shapes), np.array(org_shapes) + else: + imgs, labs, ignore = [], [], [] + bs = 0 + for img, lab, ig in reader(): + imgs.append(img) + labs.append(lab) + ignore.append(ig) + bs += 1 + if bs == batch_size: + yield np.array(imgs), np.array(labs), np.array(ignore) + bs = 0 + imgs, labs, ignore = [], [], [] + + if not drop_last and bs > 0: + yield np.array(imgs), np.array(labs), np.array(ignore) + + return batch_reader(is_test, drop_last) + + def load_image(self, line, src_dir, mode=ModelPhase.TRAIN): + # original image cv2.imread flag setting + cv2_imread_flag = cv2.IMREAD_COLOR + if cfg.DATASET.IMAGE_TYPE == "rgba": + # If use RBGA 4 channel ImageType, use IMREAD_UNCHANGED flags to + # reserver alpha channel + cv2_imread_flag = cv2.IMREAD_UNCHANGED + + parts = line.strip().split(cfg.DATASET.SEPARATOR) + if len(parts) != 2: + if mode == ModelPhase.TRAIN or mode == ModelPhase.EVAL: + raise Exception("File list format incorrect! It should be" + " image_name{}label_name\\n".format( + cfg.DATASET.SEPARATOR)) + img_name, grt_name = parts[0], None + else: + img_name, grt_name = parts[0], parts[1] + + img_path = os.path.join(src_dir, img_name) + img = cv2_imread(img_path, cv2_imread_flag) + + if grt_name is not None: + grt_path = os.path.join(src_dir, grt_name) + grt = cv2_imread(grt_path, cv2.IMREAD_GRAYSCALE) + else: + grt = None + + if img is None: + raise Exception( + "Empty image, src_dir: {}, img: {} & lab: {}".format( + src_dir, img_path, grt_path)) + + img_height = img.shape[0] + img_width = img.shape[1] + + if grt is not None: + grt_height = grt.shape[0] + grt_width = grt.shape[1] + + if img_height != grt_height or img_width != grt_width: + raise Exception( + "source img and label img must has the same size") + else: + if mode == ModelPhase.TRAIN or mode == ModelPhase.EVAL: + raise Exception( + "Empty image, src_dir: {}, img: {} & lab: {}".format( + src_dir, img_path, grt_path)) + + if len(img.shape) < 3: + img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) + + img_channels = img.shape[2] + if img_channels < 3: + raise Exception("PaddleSeg only supports gray, rgb or rgba image") + if img_channels != cfg.DATASET.DATA_DIM: + raise Exception( + "Input image channel({}) is not match cfg.DATASET.DATA_DIM({}), img_name={}" + .format(img_channels, cfg.DATASET.DATADIM, img_name)) + if img_channels != len(cfg.MEAN): + raise Exception( + "img name {}, img chns {} mean size {}, size unequal".format( + img_name, img_channels, len(cfg.MEAN))) + if img_channels != len(cfg.STD): + raise Exception( + "img name {}, img chns {} std size {}, size unequal".format( + img_name, img_channels, len(cfg.STD))) + + return img, grt, img_name, grt_name + + def normalize_image(self, img): + """ 像素归一化后减均值除方差 """ + img = img.transpose((2, 0, 1)).astype('float32') / 255.0 + img_mean = np.array(cfg.MEAN).reshape((len(cfg.MEAN), 1, 1)) + img_std = np.array(cfg.STD).reshape((len(cfg.STD), 1, 1)) + img -= img_mean + img /= img_std + + return img + + def process_image(self, line, data_dir, mode): + """ process_image """ + img, grt, grt_instance, img_name, grt_name = self.load_image( + line, data_dir, mode=mode) + if mode == ModelPhase.TRAIN: + img, grt = aug.resize(img, grt, mode) + if cfg.AUG.RICH_CROP.ENABLE: + if cfg.AUG.RICH_CROP.BLUR: + if cfg.AUG.RICH_CROP.BLUR_RATIO <= 0: + n = 0 + elif cfg.AUG.RICH_CROP.BLUR_RATIO >= 1: + n = 1 + else: + n = int(1.0 / cfg.AUG.RICH_CROP.BLUR_RATIO) + if n > 0: + if np.random.randint(0, n) == 0: + radius = np.random.randint(3, 10) + if radius % 2 != 1: + radius = radius + 1 + if radius > 9: + radius = 9 + img = cv2.GaussianBlur(img, (radius, radius), 0, 0) + + img, grt = aug.random_rotation( + img, + grt, + rich_crop_max_rotation=cfg.AUG.RICH_CROP.MAX_ROTATION, + mean_value=cfg.DATASET.PADDING_VALUE) + + img, grt = aug.rand_scale_aspect( + img, + grt, + rich_crop_min_scale=cfg.AUG.RICH_CROP.MIN_AREA_RATIO, + rich_crop_aspect_ratio=cfg.AUG.RICH_CROP.ASPECT_RATIO) + img = aug.hsv_color_jitter( + img, + brightness_jitter_ratio=cfg.AUG.RICH_CROP. + BRIGHTNESS_JITTER_RATIO, + saturation_jitter_ratio=cfg.AUG.RICH_CROP. + SATURATION_JITTER_RATIO, + contrast_jitter_ratio=cfg.AUG.RICH_CROP. + CONTRAST_JITTER_RATIO) + + if cfg.AUG.FLIP: + if cfg.AUG.FLIP_RATIO <= 0: + n = 0 + elif cfg.AUG.FLIP_RATIO >= 1: + n = 1 + else: + n = int(1.0 / cfg.AUG.FLIP_RATIO) + if n > 0: + if np.random.randint(0, n) == 0: + img = img[::-1, :, :] + grt = grt[::-1, :] + + if cfg.AUG.MIRROR: + if np.random.randint(0, 2) == 1: + img = img[:, ::-1, :] + grt = grt[:, ::-1] + + img, grt = aug.rand_crop(img, grt, mode=mode) + elif ModelPhase.is_eval(mode): + img, grt = aug.resize(img, grt, mode=mode) + img, grt = aug.rand_crop(img, grt, mode=mode) + elif ModelPhase.is_visual(mode): + org_shape = [img.shape[0], img.shape[1]] + img, grt = aug.resize(img, grt, mode=mode) + valid_shape = [img.shape[0], img.shape[1]] + img, grt = aug.rand_crop(img, grt, mode=mode) + else: + raise ValueError("Dataset mode={} Error!".format(mode)) + + # Normalize image + img = self.normalize_image(img) + + if ModelPhase.is_train(mode) or ModelPhase.is_eval(mode): + grt = np.expand_dims(np.array(grt).astype('int32'), axis=0) + ignore = (grt != cfg.DATASET.IGNORE_INDEX).astype('int32') + + if ModelPhase.is_train(mode): + return (img, grt, ignore) + elif ModelPhase.is_eval(mode): + return (img, grt, ignore) + elif ModelPhase.is_visual(mode): + return (img, grt, img_name, valid_shape, org_shape) + + +class LaneNetDataset(SegDataset): + def __init__(self, + file_list, + data_dir, + shuffle=False, + mode=ModelPhase.TRAIN): + self.mode = mode + self.shuffle = shuffle + self.data_dir = data_dir + + self.shuffle_seed = 0 + # NOTE: Please ensure file list was save in UTF-8 coding format + with codecs.open(file_list, 'r', 'utf-8') as flist: + self.lines = [line.strip() for line in flist] + self.all_lines = copy.deepcopy(self.lines) + if shuffle and cfg.NUM_TRAINERS > 1: + np.random.RandomState(self.shuffle_seed).shuffle(self.all_lines) + elif shuffle: + np.random.shuffle(self.lines) + + def generator(self): + if self.shuffle and cfg.NUM_TRAINERS > 1: + np.random.RandomState(self.shuffle_seed).shuffle(self.all_lines) + num_lines = len(self.all_lines) // cfg.NUM_TRAINERS + self.lines = self.all_lines[num_lines * cfg.TRAINER_ID: num_lines * (cfg.TRAINER_ID + 1)] + self.shuffle_seed += 1 + elif self.shuffle: + np.random.shuffle(self.lines) + + for line in self.lines: + yield self.process_image(line, self.data_dir, self.mode) + + def sharding_generator(self, pid=0, num_processes=1): + """ + Use line id as shard key for multiprocess io + It's a normal generator if pid=0, num_processes=1 + """ + for index, line in enumerate(self.lines): + # Use index and pid to shard file list + if index % num_processes == pid: + yield self.process_image(line, self.data_dir, self.mode) + + def batch_reader(self, batch_size): + br = self.batch(self.reader, batch_size) + for batch in br: + yield batch[0], batch[1], batch[2] + + def multiprocess_generator(self, max_queue_size=32, num_processes=8): + # Re-shuffle file list + if self.shuffle and cfg.NUM_TRAINERS > 1: + np.random.RandomState(self.shuffle_seed).shuffle(self.all_lines) + num_lines = len(self.all_lines) // self.num_trainers + self.lines = self.all_lines[num_lines * self.trainer_id: num_lines * (self.trainer_id + 1)] + self.shuffle_seed += 1 + elif self.shuffle: + np.random.shuffle(self.lines) + + # Create multiple sharding generators according to num_processes for multiple processes + generators = [] + for pid in range(num_processes): + generators.append(self.sharding_generator(pid, num_processes)) + + try: + enqueuer = GeneratorEnqueuer(generators) + enqueuer.start(max_queue_size=max_queue_size, workers=num_processes) + while True: + generator_out = None + while enqueuer.is_running(): + if not enqueuer.queue.empty(): + generator_out = enqueuer.queue.get(timeout=5) + break + else: + time.sleep(0.01) + if generator_out is None: + break + yield generator_out + finally: + if enqueuer is not None: + enqueuer.stop() + + def batch(self, reader, batch_size, is_test=False, drop_last=False): + def batch_reader(is_test=False, drop_last=drop_last): + if is_test: + imgs, grts, grts_instance, img_names, valid_shapes, org_shapes = [], [], [], [], [], [] + for img, grt, grt_instance, img_name, valid_shape, org_shape in reader(): + imgs.append(img) + grts.append(grt) + grts_instance.append(grt_instance) + img_names.append(img_name) + valid_shapes.append(valid_shape) + org_shapes.append(org_shape) + if len(imgs) == batch_size: + yield np.array(imgs), np.array( + grts), np.array(grts_instance), img_names, np.array(valid_shapes), np.array( + org_shapes) + imgs, grts, grts_instance, img_names, valid_shapes, org_shapes = [], [], [], [], [], [] + + if not drop_last and len(imgs) > 0: + yield np.array(imgs), np.array(grts), np.array(grts_instance), img_names, np.array( + valid_shapes), np.array(org_shapes) + else: + imgs, labs, labs_instance, ignore = [], [], [], [] + bs = 0 + for img, lab, lab_instance, ig in reader(): + imgs.append(img) + labs.append(lab) + labs_instance.append(lab_instance) + ignore.append(ig) + bs += 1 + if bs == batch_size: + yield np.array(imgs), np.array(labs), np.array(labs_instance), np.array(ignore) + bs = 0 + imgs, labs, labs_instance, ignore = [], [], [], [] + + if not drop_last and bs > 0: + yield np.array(imgs), np.array(labs), np.array(labs_instance), np.array(ignore) + + return batch_reader(is_test, drop_last) + + def load_image(self, line, src_dir, mode=ModelPhase.TRAIN): + # original image cv2.imread flag setting + cv2_imread_flag = cv2.IMREAD_COLOR + if cfg.DATASET.IMAGE_TYPE == "rgba": + # If use RBGA 4 channel ImageType, use IMREAD_UNCHANGED flags to + # reserver alpha channel + cv2_imread_flag = cv2.IMREAD_UNCHANGED + + parts = line.strip().split(cfg.DATASET.SEPARATOR) + if len(parts) != 3: + if mode == ModelPhase.TRAIN or mode == ModelPhase.EVAL: + raise Exception("File list format incorrect! It should be" + " image_name{}label_name\\n".format( + cfg.DATASET.SEPARATOR)) + img_name, grt_name, grt_instance_name = parts[0], None, None + else: + img_name, grt_name, grt_instance_name = parts[0], parts[1], parts[2] + + img_path = os.path.join(src_dir, img_name) + img = cv2_imread(img_path, cv2_imread_flag) + + if grt_name is not None: + grt_path = os.path.join(src_dir, grt_name) + grt_instance_path = os.path.join(src_dir, grt_instance_name) + grt = cv2_imread(grt_path, cv2.IMREAD_GRAYSCALE) + grt[grt == 255] = 1 + grt[grt != 1] = 0 + grt_instance = cv2_imread(grt_instance_path, cv2.IMREAD_GRAYSCALE) + else: + grt = None + grt_instance = None + + if img is None: + raise Exception( + "Empty image, src_dir: {}, img: {} & lab: {}".format( + src_dir, img_path, grt_path)) + + img_height = img.shape[0] + img_width = img.shape[1] + + if grt is not None: + grt_height = grt.shape[0] + grt_width = grt.shape[1] + + if img_height != grt_height or img_width != grt_width: + raise Exception( + "source img and label img must has the same size") + else: + if mode == ModelPhase.TRAIN or mode == ModelPhase.EVAL: + raise Exception( + "Empty image, src_dir: {}, img: {} & lab: {}".format( + src_dir, img_path, grt_path)) + + if len(img.shape) < 3: + img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) + + img_channels = img.shape[2] + if img_channels < 3: + raise Exception("PaddleSeg only supports gray, rgb or rgba image") + if img_channels != cfg.DATASET.DATA_DIM: + raise Exception( + "Input image channel({}) is not match cfg.DATASET.DATA_DIM({}), img_name={}" + .format(img_channels, cfg.DATASET.DATADIM, img_name)) + if img_channels != len(cfg.MEAN): + raise Exception( + "img name {}, img chns {} mean size {}, size unequal".format( + img_name, img_channels, len(cfg.MEAN))) + if img_channels != len(cfg.STD): + raise Exception( + "img name {}, img chns {} std size {}, size unequal".format( + img_name, img_channels, len(cfg.STD))) + + return img, grt, grt_instance, img_name, grt_name + + def normalize_image(self, img): + """ 像素归一化后减均值除方差 """ + img = img.transpose((2, 0, 1)).astype('float32') / 255.0 + img_mean = np.array(cfg.MEAN).reshape((len(cfg.MEAN), 1, 1)) + img_std = np.array(cfg.STD).reshape((len(cfg.STD), 1, 1)) + img -= img_mean + img /= img_std + + return img + + def process_image(self, line, data_dir, mode): + """ process_image """ + img, grt, grt_instance, img_name, grt_name = self.load_image( + line, data_dir, mode=mode) + if mode == ModelPhase.TRAIN: + img, grt, grt_instance = aug.resize(img, grt, grt_instance, mode) + if cfg.AUG.RICH_CROP.ENABLE: + if cfg.AUG.RICH_CROP.BLUR: + if cfg.AUG.RICH_CROP.BLUR_RATIO <= 0: + n = 0 + elif cfg.AUG.RICH_CROP.BLUR_RATIO >= 1: + n = 1 + else: + n = int(1.0 / cfg.AUG.RICH_CROP.BLUR_RATIO) + if n > 0: + if np.random.randint(0, n) == 0: + radius = np.random.randint(3, 10) + if radius % 2 != 1: + radius = radius + 1 + if radius > 9: + radius = 9 + img = cv2.GaussianBlur(img, (radius, radius), 0, 0) + + img, grt = aug.random_rotation( + img, + grt, + rich_crop_max_rotation=cfg.AUG.RICH_CROP.MAX_ROTATION, + mean_value=cfg.DATASET.PADDING_VALUE) + + img, grt = aug.rand_scale_aspect( + img, + grt, + rich_crop_min_scale=cfg.AUG.RICH_CROP.MIN_AREA_RATIO, + rich_crop_aspect_ratio=cfg.AUG.RICH_CROP.ASPECT_RATIO) + img = aug.hsv_color_jitter( + img, + brightness_jitter_ratio=cfg.AUG.RICH_CROP. + BRIGHTNESS_JITTER_RATIO, + saturation_jitter_ratio=cfg.AUG.RICH_CROP. + SATURATION_JITTER_RATIO, + contrast_jitter_ratio=cfg.AUG.RICH_CROP. + CONTRAST_JITTER_RATIO) + + if cfg.AUG.FLIP: + if cfg.AUG.FLIP_RATIO <= 0: + n = 0 + elif cfg.AUG.FLIP_RATIO >= 1: + n = 1 + else: + n = int(1.0 / cfg.AUG.FLIP_RATIO) + if n > 0: + if np.random.randint(0, n) == 0: + img = img[::-1, :, :] + grt = grt[::-1, :] + + if cfg.AUG.MIRROR: + if np.random.randint(0, 2) == 1: + img = img[:, ::-1, :] + grt = grt[:, ::-1] + + img, grt = aug.rand_crop(img, grt, mode=mode) + elif ModelPhase.is_eval(mode): + img, grt, grt_instance = aug.resize(img, grt, grt_instance, mode=mode) + elif ModelPhase.is_visual(mode): + ori_img = img.copy() + img, grt, grt_instance = aug.resize(img, grt, grt_instance, mode=mode) + valid_shape = [img.shape[0], img.shape[1]] + else: + raise ValueError("Dataset mode={} Error!".format(mode)) + + # Normalize image + img = self.normalize_image(img) + + if ModelPhase.is_train(mode) or ModelPhase.is_eval(mode): + grt = np.expand_dims(np.array(grt).astype('int32'), axis=0) + ignore = (grt != cfg.DATASET.IGNORE_INDEX).astype('int32') + if ModelPhase.is_train(mode): + return (img, grt, grt_instance, ignore) + elif ModelPhase.is_eval(mode): + return (img, grt, grt_instance, ignore) + elif ModelPhase.is_visual(mode): + return (img, grt, grt_instance, img_name, valid_shape, ori_img) diff --git a/contrib/LaneNet/solver.py b/contrib/LaneNet/solver.py new file mode 100644 index 00000000..7d6cd86e --- /dev/null +++ b/contrib/LaneNet/solver.py @@ -0,0 +1,126 @@ +# coding: utf8 +# copyright (c) 2019 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. + +import sys +import paddle.fluid as fluid +import numpy as np +import importlib +from utils.config import cfg + + +class Solver(object): + def __init__(self, main_prog, start_prog): + total_images = cfg.DATASET.TRAIN_TOTAL_IMAGES + self.weight_decay = cfg.SOLVER.WEIGHT_DECAY + self.momentum = cfg.SOLVER.MOMENTUM + self.momentum2 = cfg.SOLVER.MOMENTUM2 + self.step_per_epoch = total_images // cfg.BATCH_SIZE + if total_images % cfg.BATCH_SIZE != 0: + self.step_per_epoch += 1 + self.total_step = cfg.SOLVER.NUM_EPOCHS * self.step_per_epoch + self.main_prog = main_prog + self.start_prog = start_prog + + def lr_warmup(self, learning_rate, warmup_steps, start_lr, end_lr): + linear_step = end_lr - start_lr + lr = fluid.layers.tensor.create_global_var( + shape=[1], + value=0.0, + dtype='float32', + persistable=True, + name="learning_rate_warmup") + + global_step = fluid.layers.learning_rate_scheduler._decay_step_counter() + + with fluid.layers.control_flow.Switch() as switch: + with switch.case(global_step < warmup_steps): + decayed_lr = start_lr + linear_step * (global_step / warmup_steps) + fluid.layers.tensor.assign(decayed_lr, lr) + with switch.default(): + fluid.layers.tensor.assign(learning_rate, lr) + return lr + + def piecewise_decay(self): + gamma = cfg.SOLVER.GAMMA + bd = [self.step_per_epoch * e for e in cfg.SOLVER.DECAY_EPOCH] + lr = [cfg.SOLVER.LR * (gamma**i) for i in range(len(bd) + 1)] + decayed_lr = fluid.layers.piecewise_decay(boundaries=bd, values=lr) + return decayed_lr + + def poly_decay(self): + power = cfg.SOLVER.POWER + decayed_lr = fluid.layers.polynomial_decay( + cfg.SOLVER.LR, self.total_step, end_learning_rate=0, power=power) + return decayed_lr + + def cosine_decay(self): + decayed_lr = fluid.layers.cosine_decay( + cfg.SOLVER.LR, self.step_per_epoch, cfg.SOLVER.NUM_EPOCHS) + return decayed_lr + + def get_lr(self, lr_policy): + if lr_policy.lower() == 'poly': + decayed_lr = self.poly_decay() + elif lr_policy.lower() == 'piecewise': + decayed_lr = self.piecewise_decay() + elif lr_policy.lower() == 'cosine': + decayed_lr = self.cosine_decay() + else: + raise Exception( + "unsupport learning decay policy! only support poly,piecewise,cosine" + ) + + if cfg.SOLVER.LR_WARMUP: + start_lr = 0 + end_lr = cfg.SOLVER.LR + warmup_steps = cfg.SOLVER.LR_WARMUP_STEPS + decayed_lr = self.lr_warmup(decayed_lr, warmup_steps, start_lr, end_lr) + return decayed_lr + + def sgd_optimizer(self, lr_policy, loss): + decayed_lr = self.get_lr(lr_policy) + optimizer = fluid.optimizer.Momentum( + learning_rate=decayed_lr, + momentum=self.momentum, + regularization=fluid.regularizer.L2Decay( + regularization_coeff=self.weight_decay), + ) + + optimizer.minimize(loss) + return decayed_lr + + def adam_optimizer(self, lr_policy, loss): + decayed_lr = self.get_lr(lr_policy) + optimizer = fluid.optimizer.Adam( + learning_rate=decayed_lr, + beta1=self.momentum, + beta2=self.momentum2, + regularization=fluid.regularizer.L2Decay( + regularization_coeff=self.weight_decay), + ) + optimizer.minimize(loss) + return decayed_lr + + def optimise(self, loss): + lr_policy = cfg.SOLVER.LR_POLICY + opt = cfg.SOLVER.OPTIMIZER + + if opt.lower() == 'adam': + return self.adam_optimizer(lr_policy, loss) + elif opt.lower() == 'sgd': + return self.sgd_optimizer(lr_policy, loss) + else: + raise Exception( + "unsupport optimizer solver, only support adam and sgd") diff --git a/contrib/LaneNet/train.py b/contrib/LaneNet/train.py new file mode 100644 index 00000000..15be8a2e --- /dev/null +++ b/contrib/LaneNet/train.py @@ -0,0 +1,468 @@ +# coding: utf8 +# copyright (c) 2019 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. + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +# GPU memory garbage collection optimization flags +os.environ['FLAGS_eager_delete_tensor_gb'] = "0.0" + +import sys + +cur_path = os.path.abspath(os.path.dirname(__file__)) +root_path = os.path.split(os.path.split(cur_path)[0])[0] +sys.path.append(root_path) + +import argparse +import pprint + +import numpy as np +import paddle.fluid as fluid + +from utils.config import cfg +from utils.timer import Timer, calculate_eta +from reader import LaneNetDataset +from models.model_builder import build_model +from models.model_builder import ModelPhase +from models.model_builder import parse_shape_from_file +from eval import evaluate +from vis import visualize +from utils import dist_utils + + +def parse_args(): + parser = argparse.ArgumentParser(description='PaddleSeg training') + parser.add_argument( + '--cfg', + dest='cfg_file', + help='Config file for training (and optionally testing)', + default=None, + type=str) + parser.add_argument( + '--use_gpu', + dest='use_gpu', + help='Use gpu or cpu', + action='store_true', + default=False) + parser.add_argument( + '--use_mpio', + dest='use_mpio', + help='Use multiprocess I/O or not', + action='store_true', + default=False) + parser.add_argument( + '--log_steps', + dest='log_steps', + help='Display logging information at every log_steps', + default=10, + type=int) + parser.add_argument( + '--debug', + dest='debug', + help='debug mode, display detail information of training', + action='store_true') + parser.add_argument( + '--use_tb', + dest='use_tb', + help='whether to record the data during training to Tensorboard', + action='store_true') + parser.add_argument( + '--tb_log_dir', + dest='tb_log_dir', + help='Tensorboard logging directory', + default=None, + type=str) + parser.add_argument( + '--do_eval', + dest='do_eval', + help='Evaluation models result on every new checkpoint', + action='store_true') + parser.add_argument( + 'opts', + help='See utils/config.py for all options', + default=None, + nargs=argparse.REMAINDER) + return parser.parse_args() + + +def save_vars(executor, dirname, program=None, vars=None): + """ + Temporary resolution for Win save variables compatability. + Will fix in PaddlePaddle v1.5.2 + """ + + save_program = fluid.Program() + save_block = save_program.global_block() + + for each_var in vars: + # NOTE: don't save the variable which type is RAW + if each_var.type == fluid.core.VarDesc.VarType.RAW: + continue + new_var = save_block.create_var( + name=each_var.name, + shape=each_var.shape, + dtype=each_var.dtype, + type=each_var.type, + lod_level=each_var.lod_level, + persistable=True) + file_path = os.path.join(dirname, new_var.name) + file_path = os.path.normpath(file_path) + save_block.append_op( + type='save', + inputs={'X': [new_var]}, + outputs={}, + attrs={'file_path': file_path}) + + executor.run(save_program) + + +def save_checkpoint(exe, program, ckpt_name): + """ + Save checkpoint for evaluation or resume training + """ + ckpt_dir = os.path.join(cfg.TRAIN.MODEL_SAVE_DIR, str(ckpt_name)) + print("Save model checkpoint to {}".format(ckpt_dir)) + if not os.path.isdir(ckpt_dir): + os.makedirs(ckpt_dir) + + save_vars( + exe, + ckpt_dir, + program, + vars=list(filter(fluid.io.is_persistable, program.list_vars()))) + + return ckpt_dir + + +def load_checkpoint(exe, program): + """ + Load checkpoiont from pretrained model directory for resume training + """ + + print('Resume model training from:', cfg.TRAIN.RESUME_MODEL_DIR) + if not os.path.exists(cfg.TRAIN.RESUME_MODEL_DIR): + raise ValueError("TRAIN.PRETRAIN_MODEL {} not exist!".format( + cfg.TRAIN.RESUME_MODEL_DIR)) + + fluid.io.load_persistables( + exe, cfg.TRAIN.RESUME_MODEL_DIR, main_program=program) + + model_path = cfg.TRAIN.RESUME_MODEL_DIR + # Check is path ended by path spearator + if model_path[-1] == os.sep: + model_path = model_path[0:-1] + epoch_name = os.path.basename(model_path) + # If resume model is final model + if epoch_name == 'final': + begin_epoch = cfg.SOLVER.NUM_EPOCHS + # If resume model path is end of digit, restore epoch status + elif epoch_name.isdigit(): + epoch = int(epoch_name) + begin_epoch = epoch + 1 + else: + raise ValueError("Resume model path is not valid!") + print("Model checkpoint loaded successfully!") + + return begin_epoch + + +def print_info(*msg): + if cfg.TRAINER_ID == 0: + print(*msg) + + +def train(cfg): + startup_prog = fluid.Program() + train_prog = fluid.Program() + drop_last = True + + dataset = LaneNetDataset( + file_list=cfg.DATASET.TRAIN_FILE_LIST, + mode=ModelPhase.TRAIN, + shuffle=True, + data_dir=cfg.DATASET.DATA_DIR) + + def data_generator(): + if args.use_mpio: + data_gen = dataset.multiprocess_generator( + num_processes=cfg.DATALOADER.NUM_WORKERS, + max_queue_size=cfg.DATALOADER.BUF_SIZE) + else: + data_gen = dataset.generator() + + batch_data = [] + for b in data_gen: + batch_data.append(b) + if len(batch_data) == (cfg.BATCH_SIZE // cfg.NUM_TRAINERS): + for item in batch_data: + yield item + batch_data = [] + + # Get device environment + gpu_id = int(os.environ.get('FLAGS_selected_gpus', 0)) + place = fluid.CUDAPlace(gpu_id) if args.use_gpu else fluid.CPUPlace() + places = fluid.cuda_places() if args.use_gpu else fluid.cpu_places() + + # Get number of GPU + dev_count = cfg.NUM_TRAINERS if cfg.NUM_TRAINERS > 1 else len(places) + print_info("#Device count: {}".format(dev_count)) + + # Make sure BATCH_SIZE can divided by GPU cards + assert cfg.BATCH_SIZE % dev_count == 0, ( + 'BATCH_SIZE:{} not divisble by number of GPUs:{}'.format( + cfg.BATCH_SIZE, dev_count)) + # If use multi-gpu training mode, batch data will allocated to each GPU evenly + batch_size_per_dev = cfg.BATCH_SIZE // dev_count + cfg.BATCH_SIZE_PER_DEV = batch_size_per_dev + print_info("batch_size_per_dev: {}".format(batch_size_per_dev)) + + py_reader, avg_loss, lr, pred, grts, masks, emb_loss, seg_loss, accuracy, fp, fn = build_model( + train_prog, startup_prog, phase=ModelPhase.TRAIN) + py_reader.decorate_sample_generator( + data_generator, batch_size=batch_size_per_dev, drop_last=drop_last) + + exe = fluid.Executor(place) + exe.run(startup_prog) + + exec_strategy = fluid.ExecutionStrategy() + # Clear temporary variables every 100 iteration + if args.use_gpu: + exec_strategy.num_threads = fluid.core.get_cuda_device_count() + exec_strategy.num_iteration_per_drop_scope = 100 + build_strategy = fluid.BuildStrategy() + + if cfg.NUM_TRAINERS > 1 and args.use_gpu: + dist_utils.prepare_for_multi_process(exe, build_strategy, train_prog) + exec_strategy.num_threads = 1 + + if cfg.TRAIN.SYNC_BATCH_NORM and args.use_gpu: + if dev_count > 1: + # Apply sync batch norm strategy + print_info("Sync BatchNorm strategy is effective.") + build_strategy.sync_batch_norm = True + else: + print_info( + "Sync BatchNorm strategy will not be effective if GPU device" + " count <= 1") + compiled_train_prog = fluid.CompiledProgram(train_prog).with_data_parallel( + loss_name=avg_loss.name, + exec_strategy=exec_strategy, + build_strategy=build_strategy) + + # Resume training + begin_epoch = cfg.SOLVER.BEGIN_EPOCH + if cfg.TRAIN.RESUME_MODEL_DIR: + begin_epoch = load_checkpoint(exe, train_prog) + # Load pretrained model + elif os.path.exists(cfg.TRAIN.PRETRAINED_MODEL_DIR): + print_info('Pretrained model dir: ', cfg.TRAIN.PRETRAINED_MODEL_DIR) + load_vars = [] + load_fail_vars = [] + + def var_shape_matched(var, shape): + """ + Check whehter persitable variable shape is match with current network + """ + var_exist = os.path.exists( + os.path.join(cfg.TRAIN.PRETRAINED_MODEL_DIR, var.name)) + if var_exist: + var_shape = parse_shape_from_file( + os.path.join(cfg.TRAIN.PRETRAINED_MODEL_DIR, var.name)) + if var_shape != shape: + print(var.name, var_shape, shape) + return var_shape == shape + return False + + for x in train_prog.list_vars(): + if isinstance(x, fluid.framework.Parameter): + shape = tuple(fluid.global_scope().find_var( + x.name).get_tensor().shape()) + if var_shape_matched(x, shape): + load_vars.append(x) + else: + load_fail_vars.append(x) + + fluid.io.load_vars( + exe, dirname=cfg.TRAIN.PRETRAINED_MODEL_DIR, vars=load_vars) + for var in load_vars: + print_info("Parameter[{}] loaded sucessfully!".format(var.name)) + for var in load_fail_vars: + print_info( + "Parameter[{}] don't exist or shape does not match current network, skip" + " to load it.".format(var.name)) + print_info("{}/{} pretrained parameters loaded successfully!".format( + len(load_vars), + len(load_vars) + len(load_fail_vars))) + else: + print_info( + 'Pretrained model dir {} not exists, training from scratch...'. + format(cfg.TRAIN.PRETRAINED_MODEL_DIR)) + + # fetch_list = [avg_loss.name, lr.name, accuracy.name, precision.name, recall.name] + fetch_list = [avg_loss.name, lr.name, seg_loss.name, emb_loss.name, accuracy.name, fp.name, fn.name] + if args.debug: + # Fetch more variable info and use streaming confusion matrix to + # calculate IoU results if in debug mode + np.set_printoptions( + precision=4, suppress=True, linewidth=160, floatmode="fixed") + fetch_list.extend([pred.name, grts.name, masks.name]) + # cm = ConfusionMatrix(cfg.DATASET.NUM_CLASSES, streaming=True) + + if args.use_tb: + if not args.tb_log_dir: + print_info("Please specify the log directory by --tb_log_dir.") + exit(1) + + from tb_paddle import SummaryWriter + log_writer = SummaryWriter(args.tb_log_dir) + + # trainer_id = int(os.getenv("PADDLE_TRAINER_ID", 0)) + # num_trainers = int(os.environ.get('PADDLE_TRAINERS_NUM', 1)) + global_step = 0 + all_step = cfg.DATASET.TRAIN_TOTAL_IMAGES // cfg.BATCH_SIZE + if cfg.DATASET.TRAIN_TOTAL_IMAGES % cfg.BATCH_SIZE and drop_last != True: + all_step += 1 + all_step *= (cfg.SOLVER.NUM_EPOCHS - begin_epoch + 1) + + avg_loss = 0.0 + avg_seg_loss = 0.0 + avg_emb_loss = 0.0 + avg_acc = 0.0 + avg_fp = 0.0 + avg_fn = 0.0 + timer = Timer() + timer.start() + if begin_epoch > cfg.SOLVER.NUM_EPOCHS: + raise ValueError( + ("begin epoch[{}] is larger than cfg.SOLVER.NUM_EPOCHS[{}]").format( + begin_epoch, cfg.SOLVER.NUM_EPOCHS)) + + if args.use_mpio: + print_info("Use multiprocess reader") + else: + print_info("Use multi-thread reader") + + for epoch in range(begin_epoch, cfg.SOLVER.NUM_EPOCHS + 1): + py_reader.start() + while True: + try: + # If not in debug mode, avoid unnessary log and calculate + loss, lr, out_seg_loss, out_emb_loss, out_acc, out_fp, out_fn = exe.run( + program=compiled_train_prog, + fetch_list=fetch_list, + return_numpy=True) + + avg_loss += np.mean(np.array(loss)) + avg_seg_loss += np.mean(np.array(out_seg_loss)) + avg_emb_loss += np.mean(np.array(out_emb_loss)) + avg_acc += np.mean(out_acc) + avg_fp += np.mean(out_fp) + avg_fn += np.mean(out_fn) + global_step += 1 + + if global_step % args.log_steps == 0 and cfg.TRAINER_ID == 0: + avg_loss /= args.log_steps + avg_seg_loss /= args.log_steps + avg_emb_loss /= args.log_steps + avg_acc /= args.log_steps + avg_fp /= args.log_steps + avg_fn /= args.log_steps + speed = args.log_steps / timer.elapsed_time() + print(( + "epoch={} step={} lr={:.5f} loss={:.4f} seg_loss={:.4f} emb_loss={:.4f} accuracy={:.4} fp={:.4} fn={:.4} step/sec={:.3f} | ETA {}" + ).format(epoch, global_step, lr[0], avg_loss, avg_seg_loss, avg_emb_loss, avg_acc, avg_fp, avg_fn, speed, + calculate_eta(all_step - global_step, speed))) + if args.use_tb: + log_writer.add_scalar('Train/loss', avg_loss, + global_step) + log_writer.add_scalar('Train/lr', lr[0], + global_step) + log_writer.add_scalar('Train/speed', speed, + global_step) + sys.stdout.flush() + avg_loss = 0.0 + avg_seg_loss = 0.0 + avg_emb_loss = 0.0 + avg_acc = 0.0 + avg_fp = 0.0 + avg_fn = 0.0 + timer.restart() + + except fluid.core.EOFException: + py_reader.reset() + break + except Exception as e: + print(e) + + if epoch % cfg.TRAIN.SNAPSHOT_EPOCH == 0 and cfg.TRAINER_ID == 0: + ckpt_dir = save_checkpoint(exe, train_prog, epoch) + + if args.do_eval: + print("Evaluation start") + accuracy, fp, fn = evaluate( + cfg=cfg, + ckpt_dir=ckpt_dir, + use_gpu=args.use_gpu, + use_mpio=args.use_mpio) + if args.use_tb: + log_writer.add_scalar('Evaluate/accuracy', accuracy, + global_step) + log_writer.add_scalar('Evaluate/fp', fp, + global_step) + log_writer.add_scalar('Evaluate/fn', fn, + global_step) + + # Use Tensorboard to visualize results + if args.use_tb and cfg.DATASET.VIS_FILE_LIST is not None: + visualize( + cfg=cfg, + use_gpu=args.use_gpu, + vis_file_list=cfg.DATASET.VIS_FILE_LIST, + vis_dir="visual", + ckpt_dir=ckpt_dir, + log_writer=log_writer) + + # save final model + if cfg.TRAINER_ID == 0: + save_checkpoint(exe, train_prog, 'final') + + +def main(args): + if args.cfg_file is not None: + cfg.update_from_file(args.cfg_file) + if args.opts: + cfg.update_from_list(args.opts) + + cfg.TRAINER_ID = int(os.getenv("PADDLE_TRAINER_ID", 0)) + cfg.NUM_TRAINERS = int(os.environ.get('PADDLE_TRAINERS_NUM', 1)) + + cfg.check_and_infer() + print_info(pprint.pformat(cfg)) + train(cfg) + + +if __name__ == '__main__': + args = parse_args() + if fluid.core.is_compiled_with_cuda() != True and args.use_gpu == True: + print( + "You can not set use_gpu = True in the model because you are using paddlepaddle-cpu." + ) + print( + "Please: 1. Install paddlepaddle-gpu to run your models on GPU or 2. Set use_gpu=False to run models on CPU." + ) + sys.exit(1) + main(args) diff --git a/contrib/LaneNet/utils/__init__.py b/contrib/LaneNet/utils/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/contrib/LaneNet/utils/collect.py b/contrib/LaneNet/utils/collect.py new file mode 100644 index 00000000..78baf63f --- /dev/null +++ b/contrib/LaneNet/utils/collect.py @@ -0,0 +1,148 @@ +# 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. +"""A simple attribute dictionary used for representing configuration options.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function +from __future__ import unicode_literals + +import copy +import codecs +from ast import literal_eval + +import yaml +import six + + +class SegConfig(dict): + def __init__(self, *args, **kwargs): + super(SegConfig, self).__init__(*args, **kwargs) + self.immutable = False + + def __setattr__(self, key, value, create_if_not_exist=True): + if key in ["immutable"]: + self.__dict__[key] = value + return + + t = self + keylist = key.split(".") + for k in keylist[:-1]: + t = t.__getattr__(k, create_if_not_exist) + + t.__getattr__(keylist[-1], create_if_not_exist) + t[keylist[-1]] = value + + def __getattr__(self, key, create_if_not_exist=True): + if key in ["immutable"]: + return self.__dict__[key] + + if not key in self: + if not create_if_not_exist: + raise KeyError + self[key] = SegConfig() + return self[key] + + def __setitem__(self, key, value): + # + if self.immutable: + raise AttributeError( + 'Attempted to set "{}" to "{}", but SegConfig is immutable'. + format(key, value)) + # + if isinstance(value, six.string_types): + try: + value = literal_eval(value) + except ValueError: + pass + except SyntaxError: + pass + super(SegConfig, self).__setitem__(key, value) + + def update_from_segconfig(self, other): + if isinstance(other, dict): + other = SegConfig(other) + assert isinstance(other, SegConfig) + diclist = [("", other)] + while len(diclist): + prefix, tdic = diclist[0] + diclist = diclist[1:] + for key, value in tdic.items(): + key = "{}.{}".format(prefix, key) if prefix else key + if isinstance(value, dict): + diclist.append((key, value)) + continue + try: + self.__setattr__(key, value, create_if_not_exist=False) + except KeyError: + raise KeyError('Non-existent config key: {}'.format(key)) + + def check_and_infer(self): + if self.DATASET.IMAGE_TYPE in ['rgb', 'gray']: + self.DATASET.DATA_DIM = 3 + elif self.DATASET.IMAGE_TYPE in ['rgba']: + self.DATASET.DATA_DIM = 4 + else: + raise KeyError( + 'DATASET.IMAGE_TYPE config error, only support `rgb`, `gray` and `rgba`' + ) + if self.MEAN is not None: + self.DATASET.PADDING_VALUE = [x*255.0 for x in self.MEAN] + + if not self.TRAIN_CROP_SIZE: + raise ValueError( + 'TRAIN_CROP_SIZE is empty! Please set a pair of values in format (width, height)' + ) + + if not self.EVAL_CROP_SIZE: + raise ValueError( + 'EVAL_CROP_SIZE is empty! Please set a pair of values in format (width, height)' + ) + + # Ensure file list is use UTF-8 encoding + train_sets = codecs.open(self.DATASET.TRAIN_FILE_LIST, 'r', 'utf-8').readlines() + val_sets = codecs.open(self.DATASET.VAL_FILE_LIST, 'r', 'utf-8').readlines() + test_sets = codecs.open(self.DATASET.TEST_FILE_LIST, 'r', 'utf-8').readlines() + self.DATASET.TRAIN_TOTAL_IMAGES = len(train_sets) + self.DATASET.VAL_TOTAL_IMAGES = len(val_sets) + self.DATASET.TEST_TOTAL_IMAGES = len(test_sets) + + if self.MODEL.MODEL_NAME == 'icnet' and \ + len(self.MODEL.MULTI_LOSS_WEIGHT) != 3: + self.MODEL.MULTI_LOSS_WEIGHT = [1.0, 0.4, 0.16] + + def update_from_list(self, config_list): + if len(config_list) % 2 != 0: + raise ValueError( + "Command line options config format error! Please check it: {}". + format(config_list)) + for key, value in zip(config_list[0::2], config_list[1::2]): + try: + self.__setattr__(key, value, create_if_not_exist=False) + except KeyError: + raise KeyError('Non-existent config key: {}'.format(key)) + + def update_from_file(self, config_file): + with codecs.open(config_file, 'r', 'utf-8') as file: + dic = yaml.load(file, Loader=yaml.FullLoader) + self.update_from_segconfig(dic) + + def set_immutable(self, immutable): + self.immutable = immutable + for value in self.values(): + if isinstance(value, SegConfig): + value.set_immutable(immutable) + + def is_immutable(self): + return self.immutable diff --git a/contrib/LaneNet/utils/config.py b/contrib/LaneNet/utils/config.py new file mode 100644 index 00000000..3e179a53 --- /dev/null +++ b/contrib/LaneNet/utils/config.py @@ -0,0 +1,222 @@ +# -*- coding: utf-8 -*- +# 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. + +from __future__ import print_function +from __future__ import unicode_literals +from utils.collect import SegConfig +import numpy as np + +cfg = SegConfig() + +########################## 基本配置 ########################################### +# 均值，图像预处理减去的均值 +cfg.MEAN = [0.5, 0.5, 0.5] +# 标准差，图像预处理除以标准差· +cfg.STD = [0.5, 0.5, 0.5] +# 批处理大小 +cfg.BATCH_SIZE = 1 +# 验证时图像裁剪尺寸（宽，高） +cfg.EVAL_CROP_SIZE = tuple() +# 训练时图像裁剪尺寸（宽，高） +cfg.TRAIN_CROP_SIZE = tuple() +# 多进程训练总进程数 +cfg.NUM_TRAINERS = 1 +# 多进程训练进程ID +cfg.TRAINER_ID = 0 +# 每张gpu上的批大小，无需设置，程序会自动根据batch调整 +cfg.BATCH_SIZE_PER_DEV = 1 +########################## 数据载入配置 ####################################### +# 数据载入时的并发数, 建议值8 +cfg.DATALOADER.NUM_WORKERS = 8 +# 数据载入时缓存队列大小, 建议值256 +cfg.DATALOADER.BUF_SIZE = 256 + +########################## 数据集配置 ######################################### +# 数据主目录目录 +cfg.DATASET.DATA_DIR = './dataset/cityscapes/' +# 训练集列表 +cfg.DATASET.TRAIN_FILE_LIST = './dataset/cityscapes/train.list' +# 训练集数量 +cfg.DATASET.TRAIN_TOTAL_IMAGES = 2975 +# 验证集列表 +cfg.DATASET.VAL_FILE_LIST = './dataset/cityscapes/val.list' +# 验证数据数量 +cfg.DATASET.VAL_TOTAL_IMAGES = 500 +# 测试数据列表 +cfg.DATASET.TEST_FILE_LIST = './dataset/cityscapes/test.list' +# 测试数据数量 +cfg.DATASET.TEST_TOTAL_IMAGES = 500 +# Tensorboard 可视化的数据集 +cfg.DATASET.VIS_FILE_LIST = None +# 类别数(需包括背景类) +cfg.DATASET.NUM_CLASSES = 19 +# 输入图像类型, 支持三通道'rgb',四通道'rgba',单通道灰度图'gray' +cfg.DATASET.IMAGE_TYPE = 'rgb' +# 输入图片的通道数 +cfg.DATASET.DATA_DIM = 3 +# 数据列表分割符, 默认为空格 +cfg.DATASET.SEPARATOR = ' ' +# 忽略的像素标签值, 默认为255，一般无需改动 +cfg.DATASET.IGNORE_INDEX = 255 +# 数据增强是图像的padding值 +cfg.DATASET.PADDING_VALUE = [127.5,127.5,127.5] + +########################### 数据增强配置 ###################################### +# 图像镜像左右翻转 +cfg.AUG.MIRROR = True +# 图像上下翻转开关，True/False +cfg.AUG.FLIP = False +# 图像启动上下翻转的概率，0-1 +cfg.AUG.FLIP_RATIO = 0.5 +# 图像resize的固定尺寸（宽，高），非负 +cfg.AUG.FIX_RESIZE_SIZE = tuple() +# 图像resize的方式有三种： +# unpadding（固定尺寸），stepscaling（按比例resize），rangescaling（长边对齐） +cfg.AUG.AUG_METHOD = 'rangescaling' +# 图像resize方式为stepscaling，resize最小尺度，非负 +cfg.AUG.MIN_SCALE_FACTOR = 0.5 +# 图像resize方式为stepscaling，resize最大尺度，不小于MIN_SCALE_FACTOR +cfg.AUG.MAX_SCALE_FACTOR = 2.0 +# 图像resize方式为stepscaling，resize尺度范围间隔，非负 +cfg.AUG.SCALE_STEP_SIZE = 0.25 +# 图像resize方式为rangescaling，训练时长边resize的范围最小值，非负 +cfg.AUG.MIN_RESIZE_VALUE = 400 +# 图像resize方式为rangescaling，训练时长边resize的范围最大值， +# 不小于MIN_RESIZE_VALUE +cfg.AUG.MAX_RESIZE_VALUE = 600 +# 图像resize方式为rangescaling, 测试验证可视化模式下长边resize的长度， +# 在MIN_RESIZE_VALUE到MAX_RESIZE_VALUE范围内 +cfg.AUG.INF_RESIZE_VALUE = 500 + +# RichCrop数据增广开关，用于提升模型鲁棒性 +cfg.AUG.RICH_CROP.ENABLE = False +# 图像旋转最大角度，0-90 +cfg.AUG.RICH_CROP.MAX_ROTATION = 15 +# 裁取图像与原始图像面积比，0-1 +cfg.AUG.RICH_CROP.MIN_AREA_RATIO = 0.5 +# 裁取图像宽高比范围，非负 +cfg.AUG.RICH_CROP.ASPECT_RATIO = 0.33 +# 亮度调节范围，0-1 +cfg.AUG.RICH_CROP.BRIGHTNESS_JITTER_RATIO = 0.5 +# 饱和度调节范围，0-1 +cfg.AUG.RICH_CROP.SATURATION_JITTER_RATIO = 0.5 +# 对比度调节范围，0-1 +cfg.AUG.RICH_CROP.CONTRAST_JITTER_RATIO = 0.5 +# 图像模糊开关，True/False +cfg.AUG.RICH_CROP.BLUR = False +# 图像启动模糊百分比，0-1 +cfg.AUG.RICH_CROP.BLUR_RATIO = 0.1 + +########################### 训练配置 ########################################## +# 模型保存路径 +cfg.TRAIN.MODEL_SAVE_DIR = '' +# 预训练模型路径 +cfg.TRAIN.PRETRAINED_MODEL_DIR = '' +# 是否resume，继续训练 +cfg.TRAIN.RESUME_MODEL_DIR = '' +# 是否使用多卡间同步BatchNorm均值和方差 +cfg.TRAIN.SYNC_BATCH_NORM = False +# 模型参数保存的epoch间隔数，可用来继续训练中断的模型 +cfg.TRAIN.SNAPSHOT_EPOCH = 10 + +########################### 模型优化相关配置 ################################## +# 初始学习率 +cfg.SOLVER.LR = 0.1 +# 学习率下降方法, 支持poly piecewise cosine 三种 +cfg.SOLVER.LR_POLICY = "poly" +# 优化算法, 支持SGD和Adam两种算法 +cfg.SOLVER.OPTIMIZER = "sgd" +# 动量参数 +cfg.SOLVER.MOMENTUM = 0.9 +# 二阶矩估计的指数衰减率 +cfg.SOLVER.MOMENTUM2 = 0.999 +# 学习率Poly下降指数 +cfg.SOLVER.POWER = 0.9 +# step下降指数 +cfg.SOLVER.GAMMA = 0.1 +# step下降间隔 +cfg.SOLVER.DECAY_EPOCH = [10, 20] +# 学习率权重衰减，0-1 +cfg.SOLVER.WEIGHT_DECAY = 0.00004 +# 训练开始epoch数，默认为1 +cfg.SOLVER.BEGIN_EPOCH = 1 +# 训练epoch数，正整数 +cfg.SOLVER.NUM_EPOCHS = 30 +# loss的选择，支持softmax_loss, bce_loss, dice_loss +cfg.SOLVER.LOSS = ["softmax_loss"] +# cross entropy weight, 如果不为None，会根据每个batch中各个类别的数目，动态调整类别权重。 +# 也可以设置一个静态权重，比如有3类，每个类别权重可以设置为[0.1, 2.0, 0.9] +cfg.SOLVER.CROSS_ENTROPY_WEIGHT = None +########################## 测试配置 ########################################### +# 测试模型路径 +cfg.TEST.TEST_MODEL = '' + +########################## 模型通用配置 ####################################### +# 模型名称, 支持deeplab, unet, icnet三种 +cfg.MODEL.MODEL_NAME = '' +# BatchNorm类型: bn、gn(group_norm) +cfg.MODEL.DEFAULT_NORM_TYPE = 'bn' +# 多路损失加权值 +cfg.MODEL.MULTI_LOSS_WEIGHT = [1.0] +# DEFAULT_NORM_TYPE为gn时group数 +cfg.MODEL.DEFAULT_GROUP_NUMBER = 32 +# 极小值, 防止分母除0溢出，一般无需改动 +cfg.MODEL.DEFAULT_EPSILON = 1e-5 +# BatchNorm动量, 一般无需改动 +cfg.MODEL.BN_MOMENTUM = 0.99 +# 是否使用FP16训练 +cfg.MODEL.FP16 = False +# 混合精度训练需对LOSS进行scale, 默认为动态scale，静态scale可以设置为512.0 +cfg.MODEL.SCALE_LOSS = "DYNAMIC" + +########################## DeepLab模型配置 #################################### +# DeepLab backbone 配置, 可选项xception_65, mobilenetv2 +cfg.MODEL.DEEPLAB.BACKBONE = "xception_65" +# DeepLab output stride +cfg.MODEL.DEEPLAB.OUTPUT_STRIDE = 16 +# MobileNet backbone scale 设置 +cfg.MODEL.DEEPLAB.DEPTH_MULTIPLIER = 1.0 +# MobileNet backbone scale 设置 +cfg.MODEL.DEEPLAB.ENCODER_WITH_ASPP = True +# MobileNet backbone scale 设置 +cfg.MODEL.DEEPLAB.ENABLE_DECODER = True +# ASPP是否使用可分离卷积 +cfg.MODEL.DEEPLAB.ASPP_WITH_SEP_CONV = True +# 解码器是否使用可分离卷积 +cfg.MODEL.DEEPLAB.DECODER_USE_SEP_CONV = True + +########################## UNET模型配置 ####################################### +# 上采样方式, 默认为双线性插值 +cfg.MODEL.UNET.UPSAMPLE_MODE = 'bilinear' + +########################## ICNET模型配置 ###################################### +# RESNET backbone scale 设置 +cfg.MODEL.ICNET.DEPTH_MULTIPLIER = 0.5 +# RESNET 层数 设置 +cfg.MODEL.ICNET.LAYERS = 50 + +########################## PSPNET模型配置 ###################################### +# Lannet backbone name +cfg.MODEL.LANENET.BACKBONE = "vgg" + +########################## LaneNet模型配置 ###################################### + +########################## 预测部署模型配置 ################################### +# 预测保存的模型名称 +cfg.FREEZE.MODEL_FILENAME = '__model__' +# 预测保存的参数名称 +cfg.FREEZE.PARAMS_FILENAME = '__params__' +# 预测模型参数保存的路径 +cfg.FREEZE.SAVE_DIR = 'freeze_model' diff --git a/contrib/LaneNet/utils/dist_utils.py b/contrib/LaneNet/utils/dist_utils.py new file mode 100755 index 00000000..64c8800f --- /dev/null +++ b/contrib/LaneNet/utils/dist_utils.py @@ -0,0 +1,92 @@ +#copyright (c) 2019 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. + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function +import os +import paddle.fluid as fluid + + +def nccl2_prepare(args, startup_prog, main_prog): + config = fluid.DistributeTranspilerConfig() + config.mode = "nccl2" + t = fluid.DistributeTranspiler(config=config) + + envs = args.dist_env + + t.transpile( + envs["trainer_id"], + trainers=','.join(envs["trainer_endpoints"]), + current_endpoint=envs["current_endpoint"], + startup_program=startup_prog, + program=main_prog) + + +def pserver_prepare(args, train_prog, startup_prog): + config = fluid.DistributeTranspilerConfig() + config.slice_var_up = args.split_var + t = fluid.DistributeTranspiler(config=config) + envs = args.dist_env + training_role = envs["training_role"] + + t.transpile( + envs["trainer_id"], + program=train_prog, + pservers=envs["pserver_endpoints"], + trainers=envs["num_trainers"], + sync_mode=not args.async_mode, + startup_program=startup_prog) + if training_role == "PSERVER": + pserver_program = t.get_pserver_program(envs["current_endpoint"]) + pserver_startup_program = t.get_startup_program( + envs["current_endpoint"], + pserver_program, + startup_program=startup_prog) + return pserver_program, pserver_startup_program + elif training_role == "TRAINER": + train_program = t.get_trainer_program() + return train_program, startup_prog + else: + raise ValueError( + 'PADDLE_TRAINING_ROLE environment variable must be either TRAINER or PSERVER' + ) + + +def nccl2_prepare_paddle(trainer_id, startup_prog, main_prog): + config = fluid.DistributeTranspilerConfig() + config.mode = "nccl2" + t = fluid.DistributeTranspiler(config=config) + t.transpile( + trainer_id, + trainers=os.environ.get('PADDLE_TRAINER_ENDPOINTS'), + current_endpoint=os.environ.get('PADDLE_CURRENT_ENDPOINT'), + startup_program=startup_prog, + program=main_prog) + + +def prepare_for_multi_process(exe, build_strategy, train_prog): + # prepare for multi-process + trainer_id = int(os.environ.get('PADDLE_TRAINER_ID', 0)) + num_trainers = int(os.environ.get('PADDLE_TRAINERS_NUM', 1)) + if num_trainers < 2: return + + build_strategy.num_trainers = num_trainers + build_strategy.trainer_id = trainer_id + # NOTE(zcd): use multi processes to train the model, + # and each process use one GPU card. + startup_prog = fluid.Program() + nccl2_prepare_paddle(trainer_id, startup_prog, train_prog) + # the startup_prog are run two times, but it doesn't matter. + exe.run(startup_prog) diff --git a/contrib/LaneNet/utils/fp16_utils.py b/contrib/LaneNet/utils/fp16_utils.py new file mode 100644 index 00000000..38edda50 --- /dev/null +++ b/contrib/LaneNet/utils/fp16_utils.py @@ -0,0 +1,31 @@ +import os +from paddle import fluid + +def load_fp16_vars(executor, dirname, program): + load_dirname = os.path.normpath(dirname) + + def _if_exist(var): + name = var.name[:-7] if var.name.endswith('.master') else var.name + b = os.path.exists(os.path.join(load_dirname, name)) + if not b and isinstance(var, fluid.framework.Parameter): + print("===== {} not found ====".format(var.name)) + return b + + load_prog = fluid.Program() + load_block = load_prog.global_block() + vars = list(filter(_if_exist, program.list_vars())) + + for var in vars: + new_var = fluid.io._clone_var_in_block_(load_block, var) + name = var.name[:-7] if var.name.endswith('.master') else var.name + file_path = os.path.join(load_dirname, name) + load_block.append_op( + type='load', + inputs={}, + outputs={'Out': [new_var]}, + attrs={ + 'file_path': file_path, + 'load_as_fp16': var.dtype == fluid.core.VarDesc.VarType.FP16 + }) + + executor.run(load_prog) \ No newline at end of file diff --git a/contrib/LaneNet/utils/generate_tusimple_dataset.py b/contrib/LaneNet/utils/generate_tusimple_dataset.py new file mode 100644 index 00000000..91b9cb7f --- /dev/null +++ b/contrib/LaneNet/utils/generate_tusimple_dataset.py @@ -0,0 +1,180 @@ +""" +generate tusimple training dataset +""" +import argparse +import glob +import json +import os +import os.path as ops +import shutil + +import cv2 +import numpy as np + + +def init_args(): + parser = argparse.ArgumentParser() + parser.add_argument('--src_dir', type=str, help='The origin path of unzipped tusimple dataset') + + return parser.parse_args() + + +def process_json_file(json_file_path, src_dir, ori_dst_dir, binary_dst_dir, instance_dst_dir): + + assert ops.exists(json_file_path), '{:s} not exist'.format(json_file_path) + + image_nums = len(os.listdir(os.path.join(src_dir, ori_dst_dir))) + + with open(json_file_path, 'r') as file: + for line_index, line in enumerate(file): + info_dict = json.loads(line) + + image_dir = ops.split(info_dict['raw_file'])[0] + image_dir_split = image_dir.split('/')[1:] + image_dir_split.append(ops.split(info_dict['raw_file'])[1]) + image_name = '_'.join(image_dir_split) + image_path = ops.join(src_dir, info_dict['raw_file']) + assert ops.exists(image_path), '{:s} not exist'.format(image_path) + + h_samples = info_dict['h_samples'] + lanes = info_dict['lanes'] + + image_name_new = '{:s}.png'.format('{:d}'.format(line_index + image_nums).zfill(4)) + + src_image = cv2.imread(image_path, cv2.IMREAD_COLOR) + dst_binary_image = np.zeros([src_image.shape[0], src_image.shape[1]], np.uint8) + dst_instance_image = np.zeros([src_image.shape[0], src_image.shape[1]], np.uint8) + + for lane_index, lane in enumerate(lanes): + assert len(h_samples) == len(lane) + lane_x = [] + lane_y = [] + for index in range(len(lane)): + if lane[index] == -2: + continue + else: + ptx = lane[index] + pty = h_samples[index] + lane_x.append(ptx) + lane_y.append(pty) + if not lane_x: + continue + lane_pts = np.vstack((lane_x, lane_y)).transpose() + lane_pts = np.array([lane_pts], np.int64) + + cv2.polylines(dst_binary_image, lane_pts, isClosed=False, + color=255, thickness=5) + cv2.polylines(dst_instance_image, lane_pts, isClosed=False, + color=lane_index * 50 + 20, thickness=5) + + dst_binary_image_path = ops.join(src_dir, binary_dst_dir, image_name_new) + dst_instance_image_path = ops.join(src_dir, instance_dst_dir, image_name_new) + dst_rgb_image_path = ops.join(src_dir, ori_dst_dir, image_name_new) + + cv2.imwrite(dst_binary_image_path, dst_binary_image) + cv2.imwrite(dst_instance_image_path, dst_instance_image) + cv2.imwrite(dst_rgb_image_path, src_image) + + print('Process {:s} success'.format(image_name)) + + +def gen_sample(src_dir, b_gt_image_dir, i_gt_image_dir, image_dir, phase='train', split=False): + + label_list = [] + with open('{:s}/{}ing/{}.txt'.format(src_dir, phase, phase), 'w') as file: + + for image_name in os.listdir(b_gt_image_dir): + if not image_name.endswith('.png'): + continue + + binary_gt_image_path = ops.join(b_gt_image_dir, image_name) + instance_gt_image_path = ops.join(i_gt_image_dir, image_name) + image_path = ops.join(image_dir, image_name) + + assert ops.exists(image_path), '{:s} not exist'.format(image_path) + assert ops.exists(instance_gt_image_path), '{:s} not exist'.format(instance_gt_image_path) + + b_gt_image = cv2.imread(binary_gt_image_path, cv2.IMREAD_COLOR) + i_gt_image = cv2.imread(instance_gt_image_path, cv2.IMREAD_COLOR) + image = cv2.imread(image_path, cv2.IMREAD_COLOR) + + if b_gt_image is None or image is None or i_gt_image is None: + print('image: {:s} corrupt'.format(image_name)) + continue + else: + info = '{:s} {:s} {:s}'.format(image_path, binary_gt_image_path, instance_gt_image_path) + file.write(info + '\n') + label_list.append(info) + if phase == 'train' and split: + np.random.RandomState(0).shuffle(label_list) + val_list_len = len(label_list) // 10 + val_label_list = label_list[:val_list_len] + train_label_list = label_list[val_list_len:] + with open('{:s}/{}ing/train_part.txt'.format(src_dir, phase, phase), 'w') as file: + for info in train_label_list: + file.write(info + '\n') + with open('{:s}/{}ing/val_part.txt'.format(src_dir, phase, phase), 'w') as file: + for info in val_label_list: + file.write(info + '\n') + return + + +def process_tusimple_dataset(src_dir): + + traing_folder_path = ops.join(src_dir, 'training') + testing_folder_path = ops.join(src_dir, 'testing') + + os.makedirs(traing_folder_path, exist_ok=True) + os.makedirs(testing_folder_path, exist_ok=True) + + for json_label_path in glob.glob('{:s}/label*.json'.format(src_dir)): + json_label_name = ops.split(json_label_path)[1] + + shutil.copyfile(json_label_path, ops.join(traing_folder_path, json_label_name)) + + for json_label_path in glob.glob('{:s}/test_label.json'.format(src_dir)): + json_label_name = ops.split(json_label_path)[1] + + shutil.copyfile(json_label_path, ops.join(testing_folder_path, json_label_name)) + + # train_gt_image_dir = ops.join(traing_folder_path, 'gt_image') + # train_gt_binary_dir = ops.join(traing_folder_path, 'gt_binary_image') + # train_gt_instance_dir = ops.join(traing_folder_path, 'gt_instance_image') + # + # test_gt_image_dir = ops.join(testing_folder_path, 'gt_image') + # test_gt_binary_dir = ops.join(testing_folder_path, 'gt_binary_image') + # test_gt_instance_dir = ops.join(testing_folder_path, 'gt_instance_image') + + train_gt_image_dir = ops.join('training', 'gt_image') + train_gt_binary_dir = ops.join('training', 'gt_binary_image') + train_gt_instance_dir = ops.join('training', 'gt_instance_image') + + test_gt_image_dir = ops.join('testing', 'gt_image') + test_gt_binary_dir = ops.join('testing', 'gt_binary_image') + test_gt_instance_dir = ops.join('testing', 'gt_instance_image') + + os.makedirs(os.path.join(src_dir, train_gt_image_dir), exist_ok=True) + os.makedirs(os.path.join(src_dir, train_gt_binary_dir), exist_ok=True) + os.makedirs(os.path.join(src_dir, train_gt_instance_dir), exist_ok=True) + + os.makedirs(os.path.join(src_dir, test_gt_image_dir), exist_ok=True) + os.makedirs(os.path.join(src_dir, test_gt_binary_dir), exist_ok=True) + os.makedirs(os.path.join(src_dir, test_gt_instance_dir), exist_ok=True) + + for json_label_path in glob.glob('{:s}/*.json'.format(traing_folder_path)): + process_json_file(json_label_path, src_dir, train_gt_image_dir, train_gt_binary_dir, train_gt_instance_dir) + + gen_sample(src_dir, train_gt_binary_dir, train_gt_instance_dir, train_gt_image_dir, 'train', True) + + for json_label_path in glob.glob('{:s}/*.json'.format(testing_folder_path)): + process_json_file(json_label_path, src_dir, test_gt_image_dir, test_gt_binary_dir, test_gt_instance_dir) + + gen_sample(src_dir, test_gt_binary_dir, test_gt_instance_dir, test_gt_image_dir, 'test') + + return + + +if __name__ == '__main__': + args = init_args() + + process_tusimple_dataset(args.src_dir) diff --git a/contrib/LaneNet/utils/lanenet_postprocess.py b/contrib/LaneNet/utils/lanenet_postprocess.py new file mode 100644 index 00000000..21230279 --- /dev/null +++ b/contrib/LaneNet/utils/lanenet_postprocess.py @@ -0,0 +1,376 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +# this code heavily base on https://github.com/MaybeShewill-CV/lanenet-lane-detection/blob/master/lanenet_model/lanenet_postprocess.py +""" +LaneNet model post process +""" +import os.path as ops +import math + +import cv2 +import time +import numpy as np +from sklearn.cluster import DBSCAN +from sklearn.preprocessing import StandardScaler + + +def _morphological_process(image, kernel_size=5): + """ + morphological process to fill the hole in the binary segmentation result + :param image: + :param kernel_size: + :return: + """ + if len(image.shape) == 3: + raise ValueError('Binary segmentation result image should be a single channel image') + + if image.dtype is not np.uint8: + image = np.array(image, np.uint8) + + kernel = cv2.getStructuringElement(shape=cv2.MORPH_ELLIPSE, ksize=(kernel_size, kernel_size)) + + # close operation fille hole + closing = cv2.morphologyEx(image, cv2.MORPH_CLOSE, kernel, iterations=1) + + return closing + + +def _connect_components_analysis(image): + """ + connect components analysis to remove the small components + :param image: + :return: + """ + if len(image.shape) == 3: + gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) + else: + gray_image = image + + return cv2.connectedComponentsWithStats(gray_image, connectivity=8, ltype=cv2.CV_32S) + + +class _LaneFeat(object): + """ + + """ + def __init__(self, feat, coord, class_id=-1): + """ + lane feat object + :param feat: lane embeddng feats [feature_1, feature_2, ...] + :param coord: lane coordinates [x, y] + :param class_id: lane class id + """ + self._feat = feat + self._coord = coord + self._class_id = class_id + + @property + def feat(self): + return self._feat + + @feat.setter + def feat(self, value): + if not isinstance(value, np.ndarray): + value = np.array(value, dtype=np.float64) + + if value.dtype != np.float32: + value = np.array(value, dtype=np.float64) + + self._feat = value + + @property + def coord(self): + return self._coord + + @coord.setter + def coord(self, value): + if not isinstance(value, np.ndarray): + value = np.array(value) + + if value.dtype != np.int32: + value = np.array(value, dtype=np.int32) + + self._coord = value + + @property + def class_id(self): + return self._class_id + + @class_id.setter + def class_id(self, value): + if not isinstance(value, np.int64): + raise ValueError('Class id must be integer') + + self._class_id = value + + +class _LaneNetCluster(object): + """ + Instance segmentation result cluster + """ + def __init__(self): + """ + + """ + self._color_map = [np.array([255, 0, 0]), + np.array([0, 255, 0]), + np.array([0, 0, 255]), + np.array([125, 125, 0]), + np.array([0, 125, 125]), + np.array([125, 0, 125]), + np.array([50, 100, 50]), + np.array([100, 50, 100])] + + @staticmethod + def _embedding_feats_dbscan_cluster(embedding_image_feats): + """ + dbscan cluster + """ + db = DBSCAN(eps=0.4, min_samples=500) + + try: + features = StandardScaler().fit_transform(embedding_image_feats) + db.fit(features) + except Exception as err: + print(err) + ret = { + 'origin_features': None, + 'cluster_nums': 0, + 'db_labels': None, + 'unique_labels': None, + 'cluster_center': None + } + return ret + db_labels = db.labels_ + unique_labels = np.unique(db_labels) + num_clusters = len(unique_labels) + cluster_centers = db.components_ + + ret = { + 'origin_features': features, + 'cluster_nums': num_clusters, + 'db_labels': db_labels, + 'unique_labels': unique_labels, + 'cluster_center': cluster_centers + } + + return ret + + @staticmethod + def _get_lane_embedding_feats(binary_seg_ret, instance_seg_ret): + """ + get lane embedding features according the binary seg result + """ + + idx = np.where(binary_seg_ret == 255) + lane_embedding_feats = instance_seg_ret[idx] + + lane_coordinate = np.vstack((idx[1], idx[0])).transpose() + + assert lane_embedding_feats.shape[0] == lane_coordinate.shape[0] + + ret = { + 'lane_embedding_feats': lane_embedding_feats, + 'lane_coordinates': lane_coordinate + } + + return ret + + def apply_lane_feats_cluster(self, binary_seg_result, instance_seg_result): + """ + + :param binary_seg_result: + :param instance_seg_result: + :return: + """ + # get embedding feats and coords + get_lane_embedding_feats_result = self._get_lane_embedding_feats( + binary_seg_ret=binary_seg_result, + instance_seg_ret=instance_seg_result + ) + + # dbscan cluster + dbscan_cluster_result = self._embedding_feats_dbscan_cluster( + embedding_image_feats=get_lane_embedding_feats_result['lane_embedding_feats'] + ) + + mask = np.zeros(shape=[binary_seg_result.shape[0], binary_seg_result.shape[1], 3], dtype=np.uint8) + db_labels = dbscan_cluster_result['db_labels'] + unique_labels = dbscan_cluster_result['unique_labels'] + coord = get_lane_embedding_feats_result['lane_coordinates'] + + if db_labels is None: + return None, None + + lane_coords = [] + + for index, label in enumerate(unique_labels.tolist()): + if label == -1: + continue + idx = np.where(db_labels == label) + pix_coord_idx = tuple((coord[idx][:, 1], coord[idx][:, 0])) + mask[pix_coord_idx] = self._color_map[index] + lane_coords.append(coord[idx]) + + return mask, lane_coords + + +class LaneNetPostProcessor(object): + """ + lanenet post process for lane generation + """ + def __init__(self, ipm_remap_file_path='./utils/tusimple_ipm_remap.yml'): + """ + convert front car view to bird view + """ + assert ops.exists(ipm_remap_file_path), '{:s} not exist'.format(ipm_remap_file_path) + + self._cluster = _LaneNetCluster() + self._ipm_remap_file_path = ipm_remap_file_path + + remap_file_load_ret = self._load_remap_matrix() + self._remap_to_ipm_x = remap_file_load_ret['remap_to_ipm_x'] + self._remap_to_ipm_y = remap_file_load_ret['remap_to_ipm_y'] + + self._color_map = [np.array([255, 0, 0]), + np.array([0, 255, 0]), + np.array([0, 0, 255]), + np.array([125, 125, 0]), + np.array([0, 125, 125]), + np.array([125, 0, 125]), + np.array([50, 100, 50]), + np.array([100, 50, 100])] + + def _load_remap_matrix(self): + fs = cv2.FileStorage(self._ipm_remap_file_path, cv2.FILE_STORAGE_READ) + + remap_to_ipm_x = fs.getNode('remap_ipm_x').mat() + remap_to_ipm_y = fs.getNode('remap_ipm_y').mat() + + ret = { + 'remap_to_ipm_x': remap_to_ipm_x, + 'remap_to_ipm_y': remap_to_ipm_y, + } + + fs.release() + + return ret + + def postprocess(self, binary_seg_result, instance_seg_result=None, + min_area_threshold=100, source_image=None, + data_source='tusimple'): + + # convert binary_seg_result + binary_seg_result = np.array(binary_seg_result * 255, dtype=np.uint8) + # apply image morphology operation to fill in the hold and reduce the small area + morphological_ret = _morphological_process(binary_seg_result, kernel_size=5) + connect_components_analysis_ret = _connect_components_analysis(image=morphological_ret) + + labels = connect_components_analysis_ret[1] + stats = connect_components_analysis_ret[2] + for index, stat in enumerate(stats): + if stat[4] <= min_area_threshold: + idx = np.where(labels == index) + morphological_ret[idx] = 0 + + # apply embedding features cluster + mask_image, lane_coords = self._cluster.apply_lane_feats_cluster( + binary_seg_result=morphological_ret, + instance_seg_result=instance_seg_result + ) + + if mask_image is None: + return { + 'mask_image': None, + 'fit_params': None, + 'source_image': None, + } + + # lane line fit + fit_params = [] + src_lane_pts = [] + for lane_index, coords in enumerate(lane_coords): + if data_source == 'tusimple': + tmp_mask = np.zeros(shape=(720, 1280), dtype=np.uint8) + tmp_mask[tuple((np.int_(coords[:, 1] * 720 / 256), np.int_(coords[:, 0] * 1280 / 512)))] = 255 + else: + raise ValueError('Wrong data source now only support tusimple') + tmp_ipm_mask = cv2.remap( + tmp_mask, + self._remap_to_ipm_x, + self._remap_to_ipm_y, + interpolation=cv2.INTER_NEAREST + ) + nonzero_y = np.array(tmp_ipm_mask.nonzero()[0]) + nonzero_x = np.array(tmp_ipm_mask.nonzero()[1]) + + fit_param = np.polyfit(nonzero_y, nonzero_x, 2) + fit_params.append(fit_param) + + [ipm_image_height, ipm_image_width] = tmp_ipm_mask.shape + plot_y = np.linspace(10, ipm_image_height, ipm_image_height - 10) + fit_x = fit_param[0] * plot_y ** 2 + fit_param[1] * plot_y + fit_param[2] + + lane_pts = [] + for index in range(0, plot_y.shape[0], 5): + src_x = self._remap_to_ipm_x[ + int(plot_y[index]), int(np.clip(fit_x[index], 0, ipm_image_width - 1))] + if src_x <= 0: + continue + src_y = self._remap_to_ipm_y[ + int(plot_y[index]), int(np.clip(fit_x[index], 0, ipm_image_width - 1))] + src_y = src_y if src_y > 0 else 0 + + lane_pts.append([src_x, src_y]) + + src_lane_pts.append(lane_pts) + + # tusimple test data sample point along y axis every 10 pixels + source_image_width = source_image.shape[1] + for index, single_lane_pts in enumerate(src_lane_pts): + single_lane_pt_x = np.array(single_lane_pts, dtype=np.float32)[:, 0] + single_lane_pt_y = np.array(single_lane_pts, dtype=np.float32)[:, 1] + if data_source == 'tusimple': + start_plot_y = 240 + end_plot_y = 720 + else: + raise ValueError('Wrong data source now only support tusimple') + step = int(math.floor((end_plot_y - start_plot_y) / 10)) + for plot_y in np.linspace(start_plot_y, end_plot_y, step): + diff = single_lane_pt_y - plot_y + fake_diff_bigger_than_zero = diff.copy() + fake_diff_smaller_than_zero = diff.copy() + fake_diff_bigger_than_zero[np.where(diff <= 0)] = float('inf') + fake_diff_smaller_than_zero[np.where(diff > 0)] = float('-inf') + idx_low = np.argmax(fake_diff_smaller_than_zero) + idx_high = np.argmin(fake_diff_bigger_than_zero) + + previous_src_pt_x = single_lane_pt_x[idx_low] + previous_src_pt_y = single_lane_pt_y[idx_low] + last_src_pt_x = single_lane_pt_x[idx_high] + last_src_pt_y = single_lane_pt_y[idx_high] + + if previous_src_pt_y < start_plot_y or last_src_pt_y < start_plot_y or \ + fake_diff_smaller_than_zero[idx_low] == float('-inf') or \ + fake_diff_bigger_than_zero[idx_high] == float('inf'): + continue + + interpolation_src_pt_x = (abs(previous_src_pt_y - plot_y) * previous_src_pt_x + + abs(last_src_pt_y - plot_y) * last_src_pt_x) / \ + (abs(previous_src_pt_y - plot_y) + abs(last_src_pt_y - plot_y)) + interpolation_src_pt_y = (abs(previous_src_pt_y - plot_y) * previous_src_pt_y + + abs(last_src_pt_y - plot_y) * last_src_pt_y) / \ + (abs(previous_src_pt_y - plot_y) + abs(last_src_pt_y - plot_y)) + + if interpolation_src_pt_x > source_image_width or interpolation_src_pt_x < 10: + continue + + lane_color = self._color_map[index].tolist() + cv2.circle(source_image, (int(interpolation_src_pt_x), + int(interpolation_src_pt_y)), 5, lane_color, -1) + ret = { + 'mask_image': mask_image, + 'fit_params': fit_params, + 'source_image': source_image, + } + return ret diff --git a/contrib/LaneNet/utils/timer.py b/contrib/LaneNet/utils/timer.py new file mode 100644 index 00000000..8e32c343 --- /dev/null +++ b/contrib/LaneNet/utils/timer.py @@ -0,0 +1,60 @@ +# 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 time + + +def calculate_eta(remaining_step, speed): + if remaining_step < 0: + remaining_step = 0 + remaining_time = int(remaining_step / speed) + result = "{:0>2}:{:0>2}:{:0>2}" + arr = [] + for i in range(2, -1, -1): + arr.append(int(remaining_time / 60**i)) + remaining_time %= 60**i + return result.format(*arr) + + +class Timer(object): + """ Simple timer class for measuring time consuming """ + + def __init__(self): + self._start_time = 0.0 + self._end_time = 0.0 + self._elapsed_time = 0.0 + self._is_running = False + + def start(self): + self._is_running = True + self._start_time = time.time() + + def restart(self): + self.start() + + def stop(self): + self._is_running = False + self._end_time = time.time() + + def elapsed_time(self): + self._end_time = time.time() + self._elapsed_time = self._end_time - self._start_time + if not self.is_running: + return 0.0 + + return self._elapsed_time + + @property + def is_running(self): + return self._is_running diff --git a/contrib/LaneNet/utils/tusimple_ipm_remap.yml b/contrib/LaneNet/utils/tusimple_ipm_remap.yml new file mode 100644 index 00000000..0cc1ac83 --- /dev/null +++ b/contrib/LaneNet/utils/tusimple_ipm_remap.yml @@ -0,0 +1,213154 @@ +%YAML:1.0 +--- +remap_ipm_x: !!opencv-matrix + rows: 640 + cols: 640 + dt: f + data: [ 5.62353492e-01, 2.81194115e+00, 5.05978918e+00, + 7.30625963e+00, 9.55108261e+00, 1.17944412e+01, 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4.71193390e+02, 4.70644806e+02, + 4.70097992e+02, 4.69552856e+02, 4.69009430e+02, 4.68467743e+02, + 4.67927673e+02, 4.67389404e+02, 4.66852753e+02, 4.66317841e+02, + 4.65784546e+02, 4.65252869e+02, 4.64722931e+02, 4.64194580e+02, + 4.63667847e+02, 4.63142761e+02, 4.62619354e+02, 4.62097473e+02, + 4.61577240e+02, 4.61058594e+02, 4.60541565e+02, 4.60026123e+02, + 4.59512238e+02 ] diff --git a/contrib/LaneNet/vis.py b/contrib/LaneNet/vis.py new file mode 100644 index 00000000..e42502fa --- /dev/null +++ b/contrib/LaneNet/vis.py @@ -0,0 +1,243 @@ +# coding: utf8 +# copyright (c) 2019 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. + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +# GPU memory garbage collection optimization flags +os.environ['FLAGS_eager_delete_tensor_gb'] = "0.0" + +import sys + +cur_path = os.path.abspath(os.path.dirname(__file__)) +root_path = os.path.split(os.path.split(cur_path)[0])[0] +sys.path.append(root_path) +import matplotlib +matplotlib.use('Agg') +import time +import argparse +import pprint +import cv2 +import numpy as np +import paddle.fluid as fluid + +from utils.config import cfg +from reader import LaneNetDataset +from models.model_builder import build_model +from models.model_builder import ModelPhase +from utils import lanenet_postprocess +import matplotlib.pyplot as plt + +def parse_args(): + parser = argparse.ArgumentParser(description='PaddeSeg visualization tools') + parser.add_argument( + '--cfg', + dest='cfg_file', + help='Config file for training (and optionally testing)', + default=None, + type=str) + parser.add_argument( + '--use_gpu', dest='use_gpu', help='Use gpu or cpu', action='store_true') + parser.add_argument( + '--vis_dir', + dest='vis_dir', + help='visual save dir', + type=str, + default='visual') + parser.add_argument( + '--also_save_raw_results', + dest='also_save_raw_results', + help='whether to save raw result', + action='store_true') + parser.add_argument( + '--local_test', + dest='local_test', + help='if in local test mode, only visualize 5 images for testing', + action='store_true') + parser.add_argument( + 'opts', + help='See config.py for all options', + default=None, + nargs=argparse.REMAINDER) + if len(sys.argv) == 1: + parser.print_help() + sys.exit(1) + return parser.parse_args() + + +def makedirs(directory): + if not os.path.exists(directory): + os.makedirs(directory) + + +def get_color_map(num_classes): + """ Returns the color map for visualizing the segmentation mask, + which can support arbitrary number of classes. + Args: + num_classes: Number of classes + Returns: + The color map + """ + #color_map = num_classes * 3 * [0] + color_map = num_classes * [[0, 0, 0]] + for i in range(0, num_classes): + j = 0 + color_map[i] = [0, 0, 0] + lab = i + while lab: + color_map[i][0] |= (((lab >> 0) & 1) << (7 - j)) + color_map[i][1] |= (((lab >> 1) & 1) << (7 - j)) + color_map[i][2] |= (((lab >> 2) & 1) << (7 - j)) + j += 1 + lab >>= 3 + + return color_map + + +def colorize(image, shape, color_map): + """ + Convert segment result to color image. + """ + color_map = np.array(color_map).astype("uint8") + # Use OpenCV LUT for color mapping + c1 = cv2.LUT(image, color_map[:, 0]) + c2 = cv2.LUT(image, color_map[:, 1]) + c3 = cv2.LUT(image, color_map[:, 2]) + color_res = np.dstack((c1, c2, c3)) + + return color_res + + +def to_png_fn(fn, name=""): + """ + Append png as filename postfix + """ + directory, filename = os.path.split(fn) + basename, ext = os.path.splitext(filename) + + return basename + name + ".png" + + +def minmax_scale(input_arr): + min_val = np.min(input_arr) + max_val = np.max(input_arr) + + output_arr = (input_arr - min_val) * 255.0 / (max_val - min_val) + + return output_arr + + + +def visualize(cfg, + vis_file_list=None, + use_gpu=False, + vis_dir="visual", + also_save_raw_results=False, + ckpt_dir=None, + log_writer=None, + local_test=False, + **kwargs): + if vis_file_list is None: + vis_file_list = cfg.DATASET.TEST_FILE_LIST + + + dataset = LaneNetDataset( + file_list=vis_file_list, + mode=ModelPhase.VISUAL, + shuffle=True, + data_dir=cfg.DATASET.DATA_DIR) + + startup_prog = fluid.Program() + test_prog = fluid.Program() + pred, logit = build_model(test_prog, startup_prog, phase=ModelPhase.VISUAL) + # Clone forward graph + test_prog = test_prog.clone(for_test=True) + + # Get device environment + place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace() + exe = fluid.Executor(place) + exe.run(startup_prog) + + ckpt_dir = cfg.TEST.TEST_MODEL if not ckpt_dir else ckpt_dir + + fluid.io.load_params(exe, ckpt_dir, main_program=test_prog) + + save_dir = os.path.join(vis_dir, 'visual_results') + makedirs(save_dir) + if also_save_raw_results: + raw_save_dir = os.path.join(vis_dir, 'raw_results') + makedirs(raw_save_dir) + + fetch_list = [pred.name, logit.name] + test_reader = dataset.batch(dataset.generator, batch_size=1, is_test=True) + + postprocessor = lanenet_postprocess.LaneNetPostProcessor() + for imgs, grts, grts_instance, img_names, valid_shapes, org_imgs in test_reader: + segLogits, emLogits = exe.run( + program=test_prog, + feed={'image': imgs}, + fetch_list=fetch_list, + return_numpy=True) + num_imgs = segLogits.shape[0] + + for i in range(num_imgs): + gt_image = org_imgs[i] + binary_seg_image, instance_seg_image = segLogits[i].squeeze(-1), emLogits[i].transpose((1,2,0)) + + postprocess_result = postprocessor.postprocess( + binary_seg_result=binary_seg_image, + instance_seg_result=instance_seg_image, + source_image=gt_image + ) + pred_binary_fn = os.path.join(save_dir, to_png_fn(img_names[i], name='_pred_binary')) + pred_lane_fn = os.path.join(save_dir, to_png_fn(img_names[i], name='_pred_lane')) + pred_instance_fn = os.path.join(save_dir, to_png_fn(img_names[i], name='_pred_instance')) + dirname = os.path.dirname(pred_binary_fn) + + makedirs(dirname) + mask_image = postprocess_result['mask_image'] + for i in range(4): + instance_seg_image[:, :, i] = minmax_scale(instance_seg_image[:, :, i]) + embedding_image = np.array(instance_seg_image).astype(np.uint8) + + plt.figure('mask_image') + plt.imshow(mask_image[:, :, (2, 1, 0)]) + plt.figure('src_image') + plt.imshow(gt_image[:, :, (2, 1, 0)]) + plt.figure('instance_image') + plt.imshow(embedding_image[:, :, (2, 1, 0)]) + plt.figure('binary_image') + plt.imshow(binary_seg_image * 255, cmap='gray') + plt.show() + + # from collections import + cv2.imwrite(pred_binary_fn, np.array(binary_seg_image * 255).astype(np.uint8)) + cv2.imwrite(pred_lane_fn, postprocess_result['source_image']) + cv2.imwrite(pred_instance_fn, mask_image) + print(pred_lane_fn, 'saved!') + + + +if __name__ == '__main__': + args = parse_args() + if args.cfg_file is not None: + cfg.update_from_file(args.cfg_file) + if args.opts: + cfg.update_from_list(args.opts) + cfg.check_and_infer() + print(pprint.pformat(cfg)) + visualize(cfg, **args.__dict__) diff --git a/pdseg/loss.py b/pdseg/loss.py index 36ba43b2..3bbc5b5f 100644 --- a/pdseg/loss.py +++ b/pdseg/loss.py @@ -20,7 +20,7 @@ import importlib from utils.config import cfg -def softmax_with_loss(logit, label, ignore_mask=None, num_classes=2): +def softmax_with_loss(logit, label, ignore_mask=None, num_classes=2, weight=None): ignore_mask = fluid.layers.cast(ignore_mask, 'float32') label = fluid.layers.elementwise_min( label, fluid.layers.assign(np.array([num_classes - 1], dtype=np.int32))) @@ -29,12 +29,37 @@ def softmax_with_loss(logit, label, ignore_mask=None, num_classes=2): label = fluid.layers.reshape(label, [-1, 1]) label = fluid.layers.cast(label, 'int64') ignore_mask = fluid.layers.reshape(ignore_mask, [-1, 1]) - - loss, probs = fluid.layers.softmax_with_cross_entropy( - logit, - label, - ignore_index=cfg.DATASET.IGNORE_INDEX, - return_softmax=True) + if weight is None: + loss, probs = fluid.layers.softmax_with_cross_entropy( + logit, + label, + ignore_index=cfg.DATASET.IGNORE_INDEX, + return_softmax=True) + else: + label_one_hot = fluid.layers.one_hot(input=label, depth=num_classes) + if isinstance(weight, list): + assert len(weight) == num_classes, "weight length must equal num of classes" + weight = fluid.layers.assign(np.array([weight], dtype='float32')) + elif isinstance(weight, fluid.layers.Variable): + pass + else: + tmp = [] + total_num = fluid.layers.cast(fluid.layers.shape(label)[0], 'float32') + for i in range(num_classes): + cls_pixel_num = fluid.layers.reduce_sum(label_one_hot[:, i]) + ratio = total_num / (cls_pixel_num + 1) + tmp.append(ratio) + weight = fluid.layers.concat(tmp) + weight = weight / fluid.layers.reduce_sum(weight) * num_classes + weight = fluid.layers.reshape(weight, [1, num_classes]) + weighted_label_one_hot = fluid.layers.elementwise_mul(label_one_hot, weight) + probs = fluid.layers.softmax(logit) + loss = fluid.layers.cross_entropy( + probs, + weighted_label_one_hot, + soft_label=True, + ignore_index=cfg.DATASET.IGNORE_INDEX) + weighted_label_one_hot.stop_gradient = True loss = loss * ignore_mask avg_loss = fluid.layers.mean(loss) / fluid.layers.mean(ignore_mask) @@ -80,7 +105,7 @@ def bce_loss(logit, label, ignore_mask=None): return loss -def multi_softmax_with_loss(logits, label, ignore_mask=None, num_classes=2): +def multi_softmax_with_loss(logits, label, ignore_mask=None, num_classes=2, weight=None): if isinstance(logits, tuple): avg_loss = 0 for i, logit in enumerate(logits): @@ -91,7 +116,7 @@ def multi_softmax_with_loss(logits, label, ignore_mask=None, num_classes=2): num_classes) avg_loss += cfg.MODEL.MULTI_LOSS_WEIGHT[i] * loss else: - avg_loss = softmax_with_loss(logits, label, ignore_mask, num_classes) + avg_loss = softmax_with_loss(logits, label, ignore_mask, num_classes, weight=weight) return avg_loss def multi_dice_loss(logits, label, ignore_mask=None): diff --git a/pdseg/models/model_builder.py b/pdseg/models/model_builder.py index 2b26c662..e1e407aa 100644 --- a/pdseg/models/model_builder.py +++ b/pdseg/models/model_builder.py @@ -176,9 +176,10 @@ def build_model(main_prog, start_prog, phase=ModelPhase.TRAIN): loss_valid = False avg_loss_list = [] valid_loss = [] - if "softmax_loss" in loss_type: + if "softmax_loss" in loss_type: + weight = cfg.SOLVER.CROSS_ENTROPY_WEIGHT avg_loss_list.append(multi_softmax_with_loss(logits, - label, mask,class_num)) + label, mask, class_num, weight)) loss_valid = True valid_loss.append("softmax_loss") if "dice_loss" in loss_type: diff --git a/pdseg/utils/config.py b/pdseg/utils/config.py index 5d66c2f0..9deb824e 100644 --- a/pdseg/utils/config.py +++ b/pdseg/utils/config.py @@ -158,7 +158,9 @@ cfg.SOLVER.LOSS = ["softmax_loss"] cfg.SOLVER.LR_WARMUP = False # warmup的迭代次数 cfg.SOLVER.LR_WARMUP_STEPS = 2000 - +# cross entropy weight, 如果不为None，会根据每个batch中各个类别的数目，动态调整类别权重。 +# 也可以设置一个静态权重，比如有3类，每个类别权重可以设置为[0.1, 2.0, 0.9] +cfg.SOLVER.CROSS_ENTROPY_WEIGHT = None ########################## 测试配置 ########################################### # 测试模型路径 cfg.TEST.TEST_MODEL = '' -- GitLab