From cb477ebd12c348749284dc14061cfb3571dffb8c Mon Sep 17 00:00:00 2001 From: FlyingQianMM <245467267@qq.com> Date: Sun, 24 May 2020 19:56:09 +0800 Subject: [PATCH] add upgrade_version.md --- docs/apis/models/classification.md | 6 +- docs/apis/models/detection.md | 6 +- docs/apis/models/instance_segmentation.md | 2 +- docs/apis/models/semantic_segmentation.md | 89 ++++++- docs/appendix/model_zoo.md | 3 +- paddlex/cv/models/base.py | 4 +- paddlex/cv/models/mask_rcnn.py | 4 +- paddlex/cv/models/utils/pretrain_weights.py | 14 +- paddlex/cv/nets/darknet.py | 13 +- paddlex/cv/nets/densenet.py | 2 +- paddlex/cv/nets/detection/mask_rcnn.py | 3 +- paddlex/cv/nets/mobilenet_v1.py | 22 +- paddlex/cv/nets/mobilenet_v3.py | 242 +++++++++++--------- paddlex/cv/nets/resnet.py | 25 +- paddlex/cv/nets/segmentation/__init__.py | 1 + paddlex/seg.py | 1 + tutorials/train/segmentation/hrnet.py | 50 ++++ 17 files changed, 335 insertions(+), 152 deletions(-) create mode 100644 tutorials/train/segmentation/hrnet.py diff --git a/docs/apis/models/classification.md b/docs/apis/models/classification.md index 4fa083d..14bbcb1 100755 --- a/docs/apis/models/classification.md +++ b/docs/apis/models/classification.md @@ -35,7 +35,7 @@ train(self, num_epochs, train_dataset, train_batch_size=64, eval_dataset=None, s > > - **use_vdl** (bool): 是否使用VisualDL进行可视化。默认值为False。 > > - **sensitivities_file** (str): 若指定为路径时，则加载路径下敏感度信息进行裁剪；若为字符串'DEFAULT'，则自动下载在ImageNet图片数据上获得的敏感度信息进行裁剪；若为None，则不进行裁剪。默认为None。 > > - **eval_metric_loss** (float): 可容忍的精度损失。默认为0.05。 -> > - **early_stop** (float): 是否使用提前终止训练策略。默认值为False。 +> > - **early_stop** (bool): 是否使用提前终止训练策略。默认值为False。 > > - **early_stop_patience** (int): 当使用提前终止训练策略时，如果验证集精度在`early_stop_patience`个epoch内连续下降或持平，则终止训练。默认值为5。 > > - **resume_checkpoint** (str): 恢复训练时指定上次训练保存的模型路径。若为None，则不会恢复训练。默认值为None。 @@ -186,3 +186,7 @@ paddlex.cls.DenseNet161(num_classes=1000) paddlex.cls.DenseNet201(num_classes=1000) ``` +### HRNet_W18 +```python +paddlex.cls.HRNet_W18(num_classes=1000) +``` diff --git a/docs/apis/models/detection.md b/docs/apis/models/detection.md index 2039be6..dbd3130 100755 --- a/docs/apis/models/detection.md +++ b/docs/apis/models/detection.md @@ -9,7 +9,7 @@ paddlex.det.YOLOv3(num_classes=80, backbone='MobileNetV1', anchors=None, anchor_ > 构建YOLOv3检测器。**注意在YOLOv3，num_classes不需要包含背景类，如目标包括human、dog两种，则num_classes设为2即可，这里与FasterRCNN/MaskRCNN有差别** > **参数** -> +> > > - **num_classes** (int): 类别数。默认为80。 > > - **backbone** (str): YOLOv3的backbone网络，取值范围为['DarkNet53', 'ResNet34', 'MobileNetV1', 'MobileNetV3_large']。默认为'MobileNetV1'。 > > - **anchors** (list|tuple): anchor框的宽度和高度，为None时表示使用默认值 @@ -53,7 +53,7 @@ train(self, num_epochs, train_dataset, train_batch_size=8, eval_dataset=None, sa > > - **use_vdl** (bool): 是否使用VisualDL进行可视化。默认值为False。 > > - **sensitivities_file** (str): 若指定为路径时，则加载路径下敏感度信息进行裁剪；若为字符串'DEFAULT'，则自动下载在PascalVOC数据上获得的敏感度信息进行裁剪；若为None，则不进行裁剪。默认为None。 > > - **eval_metric_loss** (float): 可容忍的精度损失。默认为0.05。 -> > - **early_stop** (float): 是否使用提前终止训练策略。默认值为False。 +> > - **early_stop** (bool): 是否使用提前终止训练策略。默认值为False。 > > - **early_stop_patience** (int): 当使用提前终止训练策略时，如果验证集精度在`early_stop_patience`个epoch内连续下降或持平，则终止训练。默认值为5。 > > - **resume_checkpoint** (str): 恢复训练时指定上次训练保存的模型路径。若为None，则不会恢复训练。默认值为None。 @@ -107,7 +107,7 @@ paddlex.det.FasterRCNN(num_classes=81, backbone='ResNet50', with_fpn=True, aspec > **参数** > > - **num_classes** (int): 包含了背景类的类别数。默认为81。 -> > - **backbone** (str): FasterRCNN的backbone网络，取值范围为['ResNet18', 'ResNet50', 'ResNet50_vd', 'ResNet101', 'ResNet101_vd']。默认为'ResNet50'。 +> > - **backbone** (str): FasterRCNN的backbone网络，取值范围为['ResNet18', 'ResNet50', 'ResNet50_vd', 'ResNet101', 'ResNet101_vd', 'HRNet_W18']。默认为'ResNet50'。 > > - **with_fpn** (bool): 是否使用FPN结构。默认为True。 > > - **aspect_ratios** (list): 生成anchor高宽比的可选值。默认为[0.5, 1.0, 2.0]。 > > - **anchor_sizes** (list): 生成anchor大小的可选值。默认为[32, 64, 128, 256, 512]。 diff --git a/docs/apis/models/instance_segmentation.md b/docs/apis/models/instance_segmentation.md index f086454..4461554 100755 --- a/docs/apis/models/instance_segmentation.md +++ b/docs/apis/models/instance_segmentation.md @@ -12,7 +12,7 @@ paddlex.det.MaskRCNN(num_classes=81, backbone='ResNet50', with_fpn=True, aspect_ > **参数** > > - **num_classes** (int): 包含了背景类的类别数。默认为81。 -> > - **backbone** (str): MaskRCNN的backbone网络，取值范围为['ResNet18', 'ResNet50', 'ResNet50_vd', 'ResNet101', 'ResNet101_vd']。默认为'ResNet50'。 +> > - **backbone** (str): MaskRCNN的backbone网络，取值范围为['ResNet18', 'ResNet50', 'ResNet50_vd', 'ResNet101', 'ResNet101_vd', 'HRNet_W18']。默认为'ResNet50'。 > > - **with_fpn** (bool): 是否使用FPN结构。默认为True。 > > - **aspect_ratios** (list): 生成anchor高宽比的可选值。默认为[0.5, 1.0, 2.0]。 > > - **anchor_sizes** (list): 生成anchor大小的可选值。默认为[32, 64, 128, 256, 512]。 diff --git a/docs/apis/models/semantic_segmentation.md b/docs/apis/models/semantic_segmentation.md index 12b54d8..2321b45 100755 --- a/docs/apis/models/semantic_segmentation.md +++ b/docs/apis/models/semantic_segmentation.md @@ -47,7 +47,7 @@ train(self, num_epochs, train_dataset, train_batch_size=2, eval_dataset=None, ev > > - **use_vdl** (bool): 是否使用VisualDL进行可视化。默认False。 > > - **sensitivities_file** (str): 若指定为路径时，则加载路径下敏感度信息进行裁剪；若为字符串'DEFAULT'，则自动下载在ImageNet图片数据上获得的敏感度信息进行裁剪；若为None，则不进行裁剪。默认为None。 > > - **eval_metric_loss** (float): 可容忍的精度损失。默认为0.05。 -> > - **early_stop** (float): 是否使用提前终止训练策略。默认值为False。 +> > - **early_stop** (bool): 是否使用提前终止训练策略。默认值为False。 > > - **early_stop_patience** (int): 当使用提前终止训练策略时，如果验证集精度在`early_stop_patience`个epoch内连续下降或持平，则终止训练。默认值为5。 > > - **resume_checkpoint** (str): 恢复训练时指定上次训练保存的模型路径。若为None，则不会恢复训练。默认值为None。 @@ -124,7 +124,7 @@ train(self, num_epochs, train_dataset, train_batch_size=2, eval_dataset=None, ev > > - **save_interval_epochs** (int): 模型保存间隔（单位：迭代轮数）。默认为1。 > > - **log_interval_steps** (int): 训练日志输出间隔（单位：迭代次数）。默认为2。 > > - **save_dir** (str): 模型保存路径。默认'output' -> > - **pretrain_weights** (str): 若指定为路径时，则加载路径下预训练模型；若为字符串'IMAGENET'，则自动下载在COCO图片数据上预训练的模型权重；若为None，则不使用预训练模型。默认'COCO'。 +> > - **pretrain_weights** (str): 若指定为路径时，则加载路径下预训练模型；若为字符串'COCO'，则自动下载在COCO图片数据上预训练的模型权重；若为None，则不使用预训练模型。默认'COCO'。 > > - **optimizer** (paddle.fluid.optimizer): 优化器。当该参数为None时，使用默认的优化器：使用fluid.optimizer.Momentum优化方法，polynomial的学习率衰减策略。 > > - **learning_rate** (float): 默认优化器的初始学习率。默认0.01。 > > - **lr_decay_power** (float): 默认优化器学习率衰减指数。默认0.9。 @@ -173,3 +173,88 @@ predict(self, im_file, transforms=None): > **返回值** > > > > - **dict**: 包含关键字'label_map'和'score_map', 'label_map'存储预测结果灰度图，像素值表示对应的类别，'score_map'存储各类别的概率，shape=(h, w, num_classes)。 + + +## HRNet类 + +```python +paddlex.seg.HRNet(num_classes=2, width=18, use_bce_loss=False, use_dice_loss=False, class_weight=None, ignore_index=255) +``` + +> 构建HRNet分割器。 + +> **参数** + +> > - **num_classes** (int): 类别数。 +> > - **width** (int): 高分辨率分支中特征层的通道数量。默认值为18。可选择取值为[18, 30, 32, 40, 44, 48, 60, 64]。 +> > - **use_bce_loss** (bool): 是否使用bce loss作为网络的损失函数，只能用于两类分割。可与dice loss同时使用。默认False。 +> > - **use_dice_loss** (bool): 是否使用dice loss作为网络的损失函数，只能用于两类分割，可与bce loss同时使用。当use_bce_loss和use_dice_loss都为False时，使用交叉熵损失函数。默认False。 +> > - **class_weight** (list/str): 交叉熵损失函数各类损失的权重。当`class_weight`为list的时候，长度应为`num_classes`。当`class_weight`为str时， weight.lower()应为'dynamic'，这时会根据每一轮各类像素的比重自行计算相应的权重，每一类的权重为：每类的比例 * num_classes。class_weight取默认值None是，各类的权重1，即平时使用的交叉熵损失函数。 +> > - **ignore_index** (int): label上忽略的值，label为`ignore_index`的像素不参与损失函数的计算。默认255。 + +### train 训练接口 + +```python +train(self, num_epochs, train_dataset, train_batch_size=2, eval_dataset=None, eval_batch_size=1, save_interval_epochs=1, log_interval_steps=2, save_dir='output', pretrain_weights='IMAGENET', optimizer=None, learning_rate=0.01, lr_decay_power=0.9, use_vdl=False, sensitivities_file=None, eval_metric_loss=0.05, early_stop=False, early_stop_patience=5, resume_checkpoint=None): +``` + +> HRNet模型训练接口。 + +> **参数** +> > +> > - **num_epochs** (int): 训练迭代轮数。 +> > - **train_dataset** (paddlex.datasets): 训练数据读取器。 +> > - **train_batch_size** (int): 训练数据batch大小。同时作为验证数据batch大小。默认2。 +> > - **eval_dataset** (paddlex.datasets): 评估数据读取器。 +> > - **save_interval_epochs** (int): 模型保存间隔（单位：迭代轮数）。默认为1。 +> > - **log_interval_steps** (int): 训练日志输出间隔（单位：迭代次数）。默认为2。 +> > - **save_dir** (str): 模型保存路径。默认'output' +> > - **pretrain_weights** (str): 若指定为路径时，则加载路径下预训练模型；若为字符串'IMAGENET'，则自动下载在ImageNet数据集上预训练的模型权重；若为None，则不使用预训练模型。默认'IMAGENET'。 +> > - **optimizer** (paddle.fluid.optimizer): 优化器。当该参数为None时，使用默认的优化器：使用fluid.optimizer.Momentum优化方法，polynomial的学习率衰减策略。 +> > - **learning_rate** (float): 默认优化器的初始学习率。默认0.01。 +> > - **lr_decay_power** (float): 默认优化器学习率衰减指数。默认0.9。 +> > - **use_vdl** (bool): 是否使用VisualDL进行可视化。默认False。 +> > - **sensitivities_file** (str): 若指定为路径时，则加载路径下敏感度信息进行裁剪；若为字符串'DEFAULT'，则自动下载在ImageNet图片数据上获得的敏感度信息进行裁剪；若为None，则不进行裁剪。默认为None。 +> > - **eval_metric_loss** (float): 可容忍的精度损失。默认为0.05。 +> > - **early_stop** (float): 是否使用提前终止训练策略。默认值为False。 +> > - **early_stop_patience** (int): 当使用提前终止训练策略时，如果验证集精度在`early_stop_patience`个epoch内连续下降或持平，则终止训练。默认值为5。 +> > - **resume_checkpoint** (str): 恢复训练时指定上次训练保存的模型路径。若为None，则不会恢复训练。默认值为None。 + +#### evaluate 评估接口 + +``` +evaluate(self, eval_dataset, batch_size=1, epoch_id=None, return_details=False): +``` + +> HRNet模型评估接口。 + +> **参数** +> > +> > - **eval_dataset** (paddlex.datasets): 评估数据读取器。 +> > - **batch_size** (int): 评估时的batch大小。默认1。 +> > - **epoch_id** (int): 当前评估模型所在的训练轮数。 +> > - **return_details** (bool): 是否返回详细信息。默认False。 + +> **返回值** +> > +> > - **dict**: 当return_details为False时，返回dict。包含关键字：'miou'、'category_iou'、'macc'、 +> > 'category_acc'和'kappa'，分别表示平均iou、各类别iou、平均准确率、各类别准确率和kappa系数。 +> > - **tuple** (metrics, eval_details)：当return_details为True时，增加返回dict (eval_details)， +> > 包含关键字：'confusion_matrix'，表示评估的混淆矩阵。 + +#### predict 预测接口 + +``` +predict(self, im_file, transforms=None): +``` + +> HRNet模型预测接口。需要注意的是，只有在训练过程中定义了eval_dataset，模型在保存时才会将预测时的图像处理流程保存在`UNet.test_transforms`和`UNet.eval_transforms`中。如未在训练时定义eval_dataset，那在调用预测`predict`接口时，用户需要再重新定义test_transforms传入给`predict`接口。 + +> **参数** +> > +> > - **img_file** (str): 预测图像路径。 +> > - **transforms** (paddlex.seg.transforms): 数据预处理操作。 + +> **返回值** +> > +> > - **dict**: 包含关键字'label_map'和'score_map', 'label_map'存储预测结果灰度图，像素值表示对应的类别，'score_map'存储各类别的概率，shape=(h, w, num_classes)。 diff --git a/docs/appendix/model_zoo.md b/docs/appendix/model_zoo.md index f48ea51..c75f417 100644 --- a/docs/appendix/model_zoo.md +++ b/docs/appendix/model_zoo.md @@ -27,6 +27,7 @@ | DenseNet161|116.3MB | 8.863 | 78.6 | 94.1 | | DenseNet201| 84.6MB | 8.173 | 77.6 | 93.7 | | ShuffleNetV2 | 9.0MB | 10.941 | 68.8 | 88.5 | +| HRNet_W18 | 21.29MB | 7.368 (V100 GPU) | 76.9 | 93.4 | ## 目标检测模型 @@ -41,6 +42,7 @@ |FasterRCNN-ResNet50_vd-FPN|168.7MB | 45.773 | 38.9 | |FasterRCNN-ResNet101-FPN| 251.7MB | 55.782 | 38.7 | |FasterRCNN-ResNet101_vd-FPN |252MB | 58.785 | 40.5 | +|FasterRCNN-HRNet_W18-FPN |115.5MB | 57.11 | 36 | |YOLOv3-DarkNet53|252.4MB | 21.944 | 38.9 | |YOLOv3-MobileNetv1 |101.2MB | 12.771 | 29.3 | |YOLOv3-MobileNetv3|94.6MB | - | 31.6 | @@ -49,4 +51,3 @@ ## 实例分割模型 > 表中模型相关指标均为在MSCOCO数据集上测试得到。 - diff --git a/paddlex/cv/models/base.py b/paddlex/cv/models/base.py index ccf00b5..ac8989f 100644 --- a/paddlex/cv/models/base.py +++ b/paddlex/cv/models/base.py @@ -31,8 +31,6 @@ from collections import OrderedDict from os import path as osp from paddle.fluid.framework import Program from .utils.pretrain_weights import get_pretrain_weights -fluid.default_startup_program().random_seed = 1000 -fluid.default_main_program().random_seed = 1000 def dict2str(dict_input): @@ -200,6 +198,8 @@ class BaseAPI: backbone = self.backbone else: backbone = self.__class__.__name__ + if backbone == "HRNet": + backbone = backbone + "_W{}".format(self.width) pretrain_weights = get_pretrain_weights( pretrain_weights, self.model_type, backbone, pretrain_dir) if startup_prog is None: diff --git a/paddlex/cv/models/mask_rcnn.py b/paddlex/cv/models/mask_rcnn.py index 51fadc9..61894fe 100644 --- a/paddlex/cv/models/mask_rcnn.py +++ b/paddlex/cv/models/mask_rcnn.py @@ -195,7 +195,9 @@ class MaskRCNN(FasterRCNN): # 构建训练、验证、测试网络 self.build_program() fuse_bn = True - if self.with_fpn and self.backbone in ['ResNet18', 'ResNet50']: + if self.with_fpn and self.backbone in [ + 'ResNet18', 'ResNet50', 'HRNet_W18' + ]: fuse_bn = False self.net_initialize( startup_prog=fluid.default_startup_program(), diff --git a/paddlex/cv/models/utils/pretrain_weights.py b/paddlex/cv/models/utils/pretrain_weights.py index ae36be3..3684def 100644 --- a/paddlex/cv/models/utils/pretrain_weights.py +++ b/paddlex/cv/models/utils/pretrain_weights.py @@ -58,6 +58,18 @@ image_pretrain = { 'https://paddle-imagenet-models-name.bj.bcebos.com/ShuffleNetV2_pretrained.tar', 'HRNet_W18': 'https://paddle-imagenet-models-name.bj.bcebos.com/HRNet_W18_C_pretrained.tar', + 'HRNet_W30': + 'https://paddle-imagenet-models-name.bj.bcebos.com/HRNet_W30_C_pretrained.tar', + 'HRNet_W32': + 'https://paddle-imagenet-models-name.bj.bcebos.com/HRNet_W32_C_pretrained.tar', + 'HRNet_W40': + 'https://paddle-imagenet-models-name.bj.bcebos.com/HRNet_W40_C_pretrained.tar', + 'HRNet_W48': + 'https://paddle-imagenet-models-name.bj.bcebos.com/HRNet_W48_C_pretrained.tar', + 'HRNet_W60': + 'https://paddle-imagenet-models-name.bj.bcebos.com/HRNet_W60_C_pretrained.tar', + 'HRNet_W64': + 'https://paddle-imagenet-models-name.bj.bcebos.com/HRNet_W64_C_pretrained.tar', } coco_pretrain = { @@ -87,7 +99,7 @@ def get_pretrain_weights(flag, model_type, backbone, save_dir): backbone = 'DetResNet50' assert backbone in image_pretrain, "There is not ImageNet pretrain weights for {}, you may try COCO.".format( backbone) - if backbone == "HRNet_W18": + if backbone.startswith("HRNet"): url = image_pretrain[backbone] fname = osp.split(url)[-1].split('.')[0] paddlex.utils.download_and_decompress(url, path=new_save_dir) diff --git a/paddlex/cv/nets/darknet.py b/paddlex/cv/nets/darknet.py index 345f1c1..71c296c 100644 --- a/paddlex/cv/nets/darknet.py +++ b/paddlex/cv/nets/darknet.py @@ -68,13 +68,14 @@ class DarkNet(object): bias_attr=False) bn_name = name + ".bn" - + if self.num_classes: + regularizer = None + else: + regularizer = L2Decay(float(self.norm_decay)) bn_param_attr = ParamAttr( - regularizer=L2Decay(float(self.norm_decay)), - name=bn_name + '.scale') + regularizer=regularizer, name=bn_name + '.scale') bn_bias_attr = ParamAttr( - regularizer=L2Decay(float(self.norm_decay)), - name=bn_name + '.offset') + regularizer=regularizer, name=bn_name + '.offset') out = fluid.layers.batch_norm( input=conv, @@ -182,4 +183,4 @@ class DarkNet(object): bias_attr=ParamAttr(name='fc_offset')) return out - return blocks \ No newline at end of file + return blocks diff --git a/paddlex/cv/nets/densenet.py b/paddlex/cv/nets/densenet.py index 5ec49a2..a7238b2 100644 --- a/paddlex/cv/nets/densenet.py +++ b/paddlex/cv/nets/densenet.py @@ -173,4 +173,4 @@ class DenseNet(object): bn_ac_conv = fluid.layers.dropout( x=bn_ac_conv, dropout_prob=dropout) bn_ac_conv = fluid.layers.concat([input, bn_ac_conv], axis=1) - return bn_ac_conv \ No newline at end of file + return bn_ac_conv diff --git a/paddlex/cv/nets/detection/mask_rcnn.py b/paddlex/cv/nets/detection/mask_rcnn.py index b67f44a..22fb4d1 100644 --- a/paddlex/cv/nets/detection/mask_rcnn.py +++ b/paddlex/cv/nets/detection/mask_rcnn.py @@ -204,8 +204,7 @@ class MaskRCNN(object): bg_thresh_hi=self.bg_thresh_hi, bg_thresh_lo=self.bg_thresh_lo, bbox_reg_weights=self.bbox_reg_weights, - class_nums=self.num_classes, - use_random=self.rpn_head.use_random) + class_nums=self.num_classes) rois = outputs[0] labels_int32 = outputs[1] diff --git a/paddlex/cv/nets/mobilenet_v1.py b/paddlex/cv/nets/mobilenet_v1.py index 3ee1a8c..c9b9925 100755 --- a/paddlex/cv/nets/mobilenet_v1.py +++ b/paddlex/cv/nets/mobilenet_v1.py @@ -79,10 +79,14 @@ class MobileNetV1(object): bn_name = name + "_bn" norm_decay = self.norm_decay + if self.num_classes: + regularizer = None + else: + regularizer = L2Decay(norm_decay) bn_param_attr = ParamAttr( - regularizer=L2Decay(norm_decay), name=bn_name + '_scale') + regularizer=regularizer, name=bn_name + '_scale') bn_bias_attr = ParamAttr( - regularizer=L2Decay(norm_decay), name=bn_name + '_offset') + regularizer=regularizer, name=bn_name + '_offset') return fluid.layers.batch_norm( input=conv, act=act, @@ -189,12 +193,12 @@ class MobileNetV1(object): if self.num_classes: out = fluid.layers.pool2d( input=out, pool_type='avg', global_pooling=True) - output = fluid.layers.fc( - input=out, - size=self.num_classes, - param_attr=ParamAttr( - initializer=fluid.initializer.MSRA(), name="fc7_weights"), - bias_attr=ParamAttr(name="fc7_offset")) + output = fluid.layers.fc(input=out, + size=self.num_classes, + param_attr=ParamAttr( + initializer=fluid.initializer.MSRA(), + name="fc7_weights"), + bias_attr=ParamAttr(name="fc7_offset")) return output if not self.with_extra_blocks: @@ -213,4 +217,4 @@ class MobileNetV1(object): module17 = self._extra_block(module16, num_filters[3][0], num_filters[3][1], 1, 2, self.prefix_name + "conv7_4") - return module11, module13, module14, module15, module16, module17 \ No newline at end of file + return module11, module13, module14, module15, module16, module17 diff --git a/paddlex/cv/nets/mobilenet_v3.py b/paddlex/cv/nets/mobilenet_v3.py index 5f85ccd..6adcee0 100644 --- a/paddlex/cv/nets/mobilenet_v3.py +++ b/paddlex/cv/nets/mobilenet_v3.py @@ -31,6 +31,7 @@ class MobileNetV3(): with_extra_blocks (bool): if extra blocks should be added. extra_block_filters (list): number of filter for each extra block. """ + def __init__(self, scale=1.0, model_name='small', @@ -113,29 +114,36 @@ class MobileNetV3(): lr_idx = self.curr_stage // self.lr_interval lr_idx = min(lr_idx, len(self.lr_mult_list) - 1) lr_mult = self.lr_mult_list[lr_idx] - conv_param_attr = ParamAttr(name=name + '_weights', - learning_rate=lr_mult, - regularizer=L2Decay(self.conv_decay)) - conv = fluid.layers.conv2d(input=input, - num_filters=num_filters, - filter_size=filter_size, - stride=stride, - padding=padding, - groups=num_groups, - act=None, - use_cudnn=use_cudnn, - param_attr=conv_param_attr, - bias_attr=False) + if self.num_classes: + regularizer = None + else: + regularizer = L2Decay(self.conv_decay) + conv_param_attr = ParamAttr( + name=name + '_weights', + learning_rate=lr_mult, + regularizer=regularizer) + conv = fluid.layers.conv2d( + input=input, + num_filters=num_filters, + filter_size=filter_size, + stride=stride, + padding=padding, + groups=num_groups, + act=None, + use_cudnn=use_cudnn, + param_attr=conv_param_attr, + bias_attr=False) bn_name = name + '_bn' - bn_param_attr = ParamAttr(name=bn_name + "_scale", - regularizer=L2Decay(self.norm_decay)) - bn_bias_attr = ParamAttr(name=bn_name + "_offset", - regularizer=L2Decay(self.norm_decay)) - bn = fluid.layers.batch_norm(input=conv, - param_attr=bn_param_attr, - bias_attr=bn_bias_attr, - moving_mean_name=bn_name + '_mean', - moving_variance_name=bn_name + '_variance') + bn_param_attr = ParamAttr( + name=bn_name + "_scale", regularizer=L2Decay(self.norm_decay)) + bn_bias_attr = ParamAttr( + name=bn_name + "_offset", regularizer=L2Decay(self.norm_decay)) + bn = fluid.layers.batch_norm( + input=conv, + param_attr=bn_param_attr, + bias_attr=bn_bias_attr, + moving_mean_name=bn_name + '_mean', + moving_variance_name=bn_name + '_variance') if if_act: if act == 'relu': bn = fluid.layers.relu(bn) @@ -152,12 +160,10 @@ class MobileNetV3(): lr_idx = self.curr_stage // self.lr_interval lr_idx = min(lr_idx, len(self.lr_mult_list) - 1) lr_mult = self.lr_mult_list[lr_idx] - + num_mid_filter = int(num_out_filter // ratio) - pool = fluid.layers.pool2d(input=input, - pool_type='avg', - global_pooling=True, - use_cudnn=False) + pool = fluid.layers.pool2d( + input=input, pool_type='avg', global_pooling=True, use_cudnn=False) conv1 = fluid.layers.conv2d( input=pool, filter_size=1, @@ -191,43 +197,46 @@ class MobileNetV3(): use_se=False, name=None): input_data = input - conv0 = self._conv_bn_layer(input=input, - filter_size=1, - num_filters=num_mid_filter, - stride=1, - padding=0, - if_act=True, - act=act, - name=name + '_expand') + conv0 = self._conv_bn_layer( + input=input, + filter_size=1, + num_filters=num_mid_filter, + stride=1, + padding=0, + if_act=True, + act=act, + name=name + '_expand') if self.block_stride == 16 and stride == 2: self.end_points.append(conv0) - conv1 = self._conv_bn_layer(input=conv0, - filter_size=filter_size, - num_filters=num_mid_filter, - stride=stride, - padding=int((filter_size - 1) // 2), - if_act=True, - act=act, - num_groups=num_mid_filter, - use_cudnn=False, - name=name + '_depthwise') + conv1 = self._conv_bn_layer( + input=conv0, + filter_size=filter_size, + num_filters=num_mid_filter, + stride=stride, + padding=int((filter_size - 1) // 2), + if_act=True, + act=act, + num_groups=num_mid_filter, + use_cudnn=False, + name=name + '_depthwise') if use_se: - conv1 = self._se_block(input=conv1, - num_out_filter=num_mid_filter, - name=name + '_se') + conv1 = self._se_block( + input=conv1, num_out_filter=num_mid_filter, name=name + '_se') - conv2 = self._conv_bn_layer(input=conv1, - filter_size=1, - num_filters=num_out_filter, - stride=1, - padding=0, - if_act=False, - name=name + '_linear') + conv2 = self._conv_bn_layer( + input=conv1, + filter_size=1, + num_filters=num_out_filter, + stride=1, + padding=0, + if_act=False, + name=name + '_linear') if num_in_filter != num_out_filter or stride != 1: return conv2 else: - return fluid.layers.elementwise_add(x=input_data, y=conv2, act=None) + return fluid.layers.elementwise_add( + x=input_data, y=conv2, act=None) def _extra_block_dw(self, input, @@ -235,29 +244,32 @@ class MobileNetV3(): num_filters2, stride, name=None): - pointwise_conv = self._conv_bn_layer(input=input, - filter_size=1, - num_filters=int(num_filters1), - stride=1, - padding="SAME", - act='relu6', - name=name + "_extra1") - depthwise_conv = self._conv_bn_layer(input=pointwise_conv, - filter_size=3, - num_filters=int(num_filters2), - stride=stride, - padding="SAME", - num_groups=int(num_filters1), - act='relu6', - use_cudnn=False, - name=name + "_extra2_dw") - normal_conv = self._conv_bn_layer(input=depthwise_conv, - filter_size=1, - num_filters=int(num_filters2), - stride=1, - padding="SAME", - act='relu6', - name=name + "_extra2_sep") + pointwise_conv = self._conv_bn_layer( + input=input, + filter_size=1, + num_filters=int(num_filters1), + stride=1, + padding="SAME", + act='relu6', + name=name + "_extra1") + depthwise_conv = self._conv_bn_layer( + input=pointwise_conv, + filter_size=3, + num_filters=int(num_filters2), + stride=stride, + padding="SAME", + num_groups=int(num_filters1), + act='relu6', + use_cudnn=False, + name=name + "_extra2_dw") + normal_conv = self._conv_bn_layer( + input=depthwise_conv, + filter_size=1, + num_filters=int(num_filters2), + stride=1, + padding="SAME", + act='relu6', + name=name + "_extra2_sep") return normal_conv def __call__(self, input): @@ -282,36 +294,39 @@ class MobileNetV3(): self.block_stride *= layer_cfg[5] if layer_cfg[5] == 2: blocks.append(conv) - conv = self._residual_unit(input=conv, - num_in_filter=inplanes, - num_mid_filter=int(scale * layer_cfg[1]), - num_out_filter=int(scale * layer_cfg[2]), - act=layer_cfg[4], - stride=layer_cfg[5], - filter_size=layer_cfg[0], - use_se=layer_cfg[3], - name='conv' + str(i + 2)) - + conv = self._residual_unit( + input=conv, + num_in_filter=inplanes, + num_mid_filter=int(scale * layer_cfg[1]), + num_out_filter=int(scale * layer_cfg[2]), + act=layer_cfg[4], + stride=layer_cfg[5], + filter_size=layer_cfg[0], + use_se=layer_cfg[3], + name='conv' + str(i + 2)) + inplanes = int(scale * layer_cfg[2]) i += 1 self.curr_stage = i blocks.append(conv) if self.num_classes: - conv = self._conv_bn_layer(input=conv, - filter_size=1, - num_filters=int(scale * self.cls_ch_squeeze), - stride=1, - padding=0, - num_groups=1, - if_act=True, - act='hard_swish', - name='conv_last') - - conv = fluid.layers.pool2d(input=conv, - pool_type='avg', - global_pooling=True, - use_cudnn=False) + conv = self._conv_bn_layer( + input=conv, + filter_size=1, + num_filters=int(scale * self.cls_ch_squeeze), + stride=1, + padding=0, + num_groups=1, + if_act=True, + act='hard_swish', + name='conv_last') + + conv = fluid.layers.pool2d( + input=conv, + pool_type='avg', + global_pooling=True, + use_cudnn=False) conv = fluid.layers.conv2d( input=conv, num_filters=self.cls_ch_expand, @@ -326,22 +341,23 @@ class MobileNetV3(): out = fluid.layers.fc(input=drop, size=self.num_classes, param_attr=ParamAttr(name='fc_weights'), - bias_attr=ParamAttr(name='fc_offset')) + bias_attr=ParamAttr(name='fc_offset')) return out if not self.with_extra_blocks: return blocks # extra block - conv_extra = self._conv_bn_layer(conv, - filter_size=1, - num_filters=int(scale * cfg[-1][1]), - stride=1, - padding="SAME", - num_groups=1, - if_act=True, - act='hard_swish', - name='conv' + str(i + 2)) + conv_extra = self._conv_bn_layer( + conv, + filter_size=1, + num_filters=int(scale * cfg[-1][1]), + stride=1, + padding="SAME", + num_groups=1, + if_act=True, + act='hard_swish', + name='conv' + str(i + 2)) self.end_points.append(conv_extra) i += 1 for block_filter in self.extra_block_filters: diff --git a/paddlex/cv/nets/resnet.py b/paddlex/cv/nets/resnet.py index 40c6965..ff7a8d1 100644 --- a/paddlex/cv/nets/resnet.py +++ b/paddlex/cv/nets/resnet.py @@ -135,8 +135,10 @@ class ResNet(object): filter_size=filter_size, stride=stride, padding=padding, - param_attr=ParamAttr(initializer=Constant(0.0), name=name + ".w_0"), - bias_attr=ParamAttr(initializer=Constant(0.0), name=name + ".b_0"), + param_attr=ParamAttr( + initializer=Constant(0.0), name=name + ".w_0"), + bias_attr=ParamAttr( + initializer=Constant(0.0), name=name + ".b_0"), act=act, name=name) return out @@ -151,7 +153,8 @@ class ResNet(object): name=None, dcn_v2=False, use_lr_mult_list=False): - lr_mult = self.lr_mult_list[self.curr_stage] if use_lr_mult_list else 1.0 + lr_mult = self.lr_mult_list[ + self.curr_stage] if use_lr_mult_list else 1.0 _name = self.prefix_name + name if self.prefix_name != '' else name if not dcn_v2: conv = fluid.layers.conv2d( @@ -162,8 +165,8 @@ class ResNet(object): padding=(filter_size - 1) // 2, groups=groups, act=None, - param_attr=ParamAttr(name=_name + "_weights", - learning_rate=lr_mult), + param_attr=ParamAttr( + name=_name + "_weights", learning_rate=lr_mult), bias_attr=False, name=_name + '.conv2d.output.1') else: @@ -202,14 +205,18 @@ class ResNet(object): norm_lr = 0. if self.freeze_norm else lr_mult norm_decay = self.norm_decay + if self.num_classes: + regularizer = None + else: + regularizer = L2Decay(norm_decay) pattr = ParamAttr( name=bn_name + '_scale', learning_rate=norm_lr, - regularizer=L2Decay(norm_decay)) + regularizer=regularizer) battr = ParamAttr( name=bn_name + '_offset', learning_rate=norm_lr, - regularizer=L2Decay(norm_decay)) + regularizer=regularizer) if self.norm_type in ['bn', 'sync_bn']: global_stats = True if self.freeze_norm else False @@ -262,8 +269,8 @@ class ResNet(object): pool_padding=0, ceil_mode=True, pool_type='avg') - return self._conv_norm(input, ch_out, 1, 1, name=name, - use_lr_mult_list=True) + return self._conv_norm( + input, ch_out, 1, 1, name=name, use_lr_mult_list=True) return self._conv_norm(input, ch_out, 1, stride, name=name) else: return input diff --git a/paddlex/cv/nets/segmentation/__init__.py b/paddlex/cv/nets/segmentation/__init__.py index f5af8c9..9b8f439 100644 --- a/paddlex/cv/nets/segmentation/__init__.py +++ b/paddlex/cv/nets/segmentation/__init__.py @@ -14,5 +14,6 @@ from .unet import UNet from .deeplabv3p import DeepLabv3p +from .hrnet import HRNet from .model_utils import libs from .model_utils import loss diff --git a/paddlex/seg.py b/paddlex/seg.py index 0f92813..c19ec30 100644 --- a/paddlex/seg.py +++ b/paddlex/seg.py @@ -17,5 +17,6 @@ from . import cv UNet = cv.models.UNet DeepLabv3p = cv.models.DeepLabv3p +HRNet = cv.models.HRNet transforms = cv.transforms.seg_transforms visualize = cv.models.utils.visualize.visualize_segmentation diff --git a/tutorials/train/segmentation/hrnet.py b/tutorials/train/segmentation/hrnet.py new file mode 100644 index 0000000..060f515 --- /dev/null +++ b/tutorials/train/segmentation/hrnet.py @@ -0,0 +1,50 @@ +import os +# 选择使用0号卡 +os.environ['CUDA_VISIBLE_DEVICES'] = '0' + +import paddlex as pdx +from paddlex.seg import transforms + +# 下载和解压视盘分割数据集 +optic_dataset = 'https://bj.bcebos.com/paddlex/datasets/optic_disc_seg.tar.gz' +pdx.utils.download_and_decompress(optic_dataset, path='./') + +# 定义训练和验证时的transforms +train_transforms = transforms.Compose([ + transforms.RandomHorizontalFlip(), transforms.ResizeRangeScaling(), + transforms.RandomPaddingCrop(crop_size=512), transforms.Normalize() +]) + +eval_transforms = transforms.Compose([ + transforms.ResizeByLong(long_size=512), + transforms.Padding(target_size=512), transforms.Normalize() +]) + +# 定义训练和验证所用的数据集 +train_dataset = pdx.datasets.SegDataset( + data_dir='optic_disc_seg', + file_list='optic_disc_seg/train_list.txt', + label_list='optic_disc_seg/labels.txt', + transforms=train_transforms, + shuffle=True) +eval_dataset = pdx.datasets.SegDataset( + data_dir='optic_disc_seg', + file_list='optic_disc_seg/val_list.txt', + label_list='optic_disc_seg/labels.txt', + transforms=eval_transforms) + +# 初始化模型，并进行训练 +# 可使用VisualDL查看训练指标 +# VisualDL启动方式: visualdl --logdir output/unet/vdl_log --port 8001 +# 浏览器打开 https://0.0.0.0:8001即可 +# 其中0.0.0.0为本机访问，如为远程服务, 改成相应机器IP +num_classes = len(train_dataset.labels) +model = pdx.seg.HRNet(num_classes=num_classes, width=64) +model.train( + num_epochs=20, + train_dataset=train_dataset, + train_batch_size=4, + eval_dataset=eval_dataset, + learning_rate=0.01, + save_dir='output/hrnet', + use_vdl=True) -- GitLab