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PaddleX
未验证 提交 fd42b2f6 编写于 作者: J Jason 提交者： GitHub
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Merge pull request #99 from FlyingQianMM/develop_qh 

add hrnet for classify, detect, segment
上级 c2548389 336a4fef
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docs/apis/models/classification.md
浏览文件 @ fd42b2f6
......@@ -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)
```
docs/apis/models/detection.md
浏览文件 @ fd42b2f6
......@@ -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]。
......
docs/apis/models/instance_segmentation.md
浏览文件 @ fd42b2f6
......@@ -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]。
......
docs/apis/models/semantic_segmentation.md
浏览文件 @ fd42b2f6
......@@ -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)。
docs/appendix/model_zoo.md
浏览文件 @ fd42b2f6
......@@ -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。
paddlex/cls.py
浏览文件 @ fd42b2f6
......@@ -36,5 +36,6 @@ DenseNet121 = cv.models.DenseNet121
DenseNet161 = cv.models.DenseNet161
DenseNet201 = cv.models.DenseNet201
ShuffleNetV2 = cv.models.ShuffleNetV2
HRNet_W18 = cv.models.HRNet_W18
transforms = cv.transforms.cls_transforms
paddlex/cv/models/__init__.py
浏览文件 @ fd42b2f6
......@@ -34,11 +34,13 @@ from .classifier import DenseNet121
from .classifier import DenseNet161
from .classifier import DenseNet201
from .classifier import ShuffleNetV2
from .classifier import HRNet_W18
from .base import BaseAPI
from .yolo_v3 import YOLOv3
from .faster_rcnn import FasterRCNN
from .mask_rcnn import MaskRCNN
from .unet import UNet
from .deeplabv3p import DeepLabv3p
from .hrnet import HRNet
from .load_model import load_model
from .slim import prune
paddlex/cv/models/base.py
浏览文件 @ fd42b2f6
......@@ -79,9 +79,9 @@ class BaseAPI:
return int(batch_size // len(self.places))
else:
raise Exception("Please support correct batch_size, \
which can be divided by available cards({}) in {}".
format(paddlex.env_info['num'],
paddlex.env_info['place']))
which can be divided by available cards({}) in {}"
.format(paddlex.env_info['num'], paddlex.env_info[
'place']))
def build_program(self):
# 构建训练网络
......@@ -198,6 +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:
......@@ -210,8 +212,8 @@ class BaseAPI:
paddlex.utils.utils.load_pretrain_weights(
self.exe, self.train_prog, resume_checkpoint, resume=True)
if not osp.exists(osp.join(resume_checkpoint, "model.yml")):
raise Exception(
"There's not model.yml in {}".format(resume_checkpoint))
raise Exception("There's not model.yml in {}".format(
resume_checkpoint))
with open(osp.join(resume_checkpoint, "model.yml")) as f:
info = yaml.load(f.read(), Loader=yaml.Loader)
self.completed_epochs = info['completed_epochs']
......@@ -269,13 +271,13 @@ class BaseAPI:
except:
pass
if hasattr(self.test_transforms, 'to_rgb'):
if self.test_transforms.to_rgb:
info['TransformsMode'] = 'RGB'
else:
info['TransformsMode'] = 'BGR'
if hasattr(self, 'test_transforms'):
if hasattr(self.test_transforms, 'to_rgb'):
if self.test_transforms.to_rgb:
info['TransformsMode'] = 'RGB'
else:
info['TransformsMode'] = 'BGR'
if self.test_transforms is not None:
info['Transforms'] = list()
for op in self.test_transforms.transforms:
......@@ -362,8 +364,8 @@ class BaseAPI:
# 模型保存成功的标志
open(osp.join(save_dir, '.success'), 'w').close()
logging.info(
"Model for inference deploy saved in {}.".format(save_dir))
logging.info("Model for inference deploy saved in {}.".format(
save_dir))
def train_loop(self,
num_epochs,
......@@ -377,7 +379,8 @@ class BaseAPI:
early_stop=False,
early_stop_patience=5):
if train_dataset.num_samples < train_batch_size:
raise Exception('The amount of training datset must be larger than batch size.')
raise Exception(
'The amount of training datset must be larger than batch size.')
if not osp.isdir(save_dir):
if osp.exists(save_dir):
os.remove(save_dir)
......@@ -415,8 +418,8 @@ class BaseAPI:
build_strategy=build_strategy,
exec_strategy=exec_strategy)
total_num_steps = math.floor(
train_dataset.num_samples / train_batch_size)
total_num_steps = math.floor(train_dataset.num_samples /
train_batch_size)
num_steps = 0
time_stat = list()
time_train_one_epoch = None
......@@ -430,8 +433,8 @@ class BaseAPI:
if self.model_type == 'detector':
eval_batch_size = self._get_single_card_bs(train_batch_size)
if eval_dataset is not None:
total_num_steps_eval = math.ceil(
eval_dataset.num_samples / eval_batch_size)
total_num_steps_eval = math.ceil(eval_dataset.num_samples /
eval_batch_size)
if use_vdl:
# VisualDL component
......@@ -473,7 +476,9 @@ class BaseAPI:
if use_vdl:
for k, v in step_metrics.items():
log_writer.add_scalar('Metrics/Training(Step): {}'.format(k), v, num_steps)
log_writer.add_scalar(
'Metrics/Training(Step): {}'.format(k), v,
num_steps)
# 估算剩余时间
avg_step_time = np.mean(time_stat)
......@@ -481,11 +486,12 @@ class BaseAPI:
eta = (num_epochs - i - 1) * time_train_one_epoch + (
total_num_steps - step - 1) * avg_step_time
else:
eta = ((num_epochs - i) * total_num_steps - step -
1) * avg_step_time
eta = ((num_epochs - i) * total_num_steps - step - 1
) * avg_step_time
if time_eval_one_epoch is not None:
eval_eta = (total_eval_times - i //
save_interval_epochs) * time_eval_one_epoch
eval_eta = (
total_eval_times - i // save_interval_epochs
) * time_eval_one_epoch
else:
eval_eta = (
total_eval_times - i // save_interval_epochs
......@@ -495,10 +501,11 @@ class BaseAPI:
logging.info(
"[TRAIN] Epoch={}/{}, Step={}/{}, {}, time_each_step={}s, eta={}"
.format(i + 1, num_epochs, step + 1, total_num_steps,
dict2str(step_metrics), round(
avg_step_time, 2), eta_str))
dict2str(step_metrics),
round(avg_step_time, 2), eta_str))
train_metrics = OrderedDict(
zip(list(self.train_outputs.keys()), np.mean(records, axis=0)))
zip(list(self.train_outputs.keys()), np.mean(
records, axis=0)))
logging.info('[TRAIN] Epoch {} finished, {} .'.format(
i + 1, dict2str(train_metrics)))
time_train_one_epoch = time.time() - epoch_start_time
......@@ -534,7 +541,8 @@ class BaseAPI:
if isinstance(v, np.ndarray):
if v.size > 1:
continue
log_writer.add_scalar("Metrics/Eval(Epoch): {}".format(k), v, i+1)
log_writer.add_scalar(
"Metrics/Eval(Epoch): {}".format(k), v, i + 1)
self.save_model(save_dir=current_save_dir)
time_eval_one_epoch = time.time() - eval_epoch_start_time
eval_epoch_start_time = time.time()
......@@ -545,4 +553,4 @@ class BaseAPI:
best_accuracy))
if eval_dataset is not None and early_stop:
if earlystop(current_accuracy):
break
\ No newline at end of file
break
paddlex/cv/models/classifier.py
浏览文件 @ fd42b2f6
......@@ -40,8 +40,8 @@ class BaseClassifier(BaseAPI):
self.init_params = locals()
super(BaseClassifier, self).__init__('classifier')
if not hasattr(paddlex.cv.nets, str.lower(model_name)):
raise Exception(
"ERROR: There's no model named {}.".format(model_name))
raise Exception("ERROR: There's no model named {}.".format(
model_name))
self.model_name = model_name
self.labels = None
self.num_classes = num_classes
......@@ -218,15 +218,14 @@ class BaseClassifier(BaseAPI):
num_pad_samples = batch_size - num_samples
pad_images = np.tile(images[0:1], (num_pad_samples, 1, 1, 1))
images = np.concatenate([images, pad_images])
outputs = self.exe.run(
self.parallel_test_prog,
feed={'image': images},
fetch_list=list(self.test_outputs.values()))
outputs = self.exe.run(self.parallel_test_prog,
feed={'image': images},
fetch_list=list(self.test_outputs.values()))
outputs = [outputs[0][:num_samples]]
true_labels.extend(labels)
pred_scores.extend(outputs[0].tolist())
logging.debug("[EVAL] Epoch={}, Step={}/{}".format(
epoch_id, step + 1, total_steps))
logging.debug("[EVAL] Epoch={}, Step={}/{}".format(epoch_id, step +
1, total_steps))
pred_top1_label = np.argsort(pred_scores)[:, -1]
pred_topk_label = np.argsort(pred_scores)[:, -k:]
......@@ -263,10 +262,9 @@ class BaseClassifier(BaseAPI):
self.arrange_transforms(
transforms=self.test_transforms, mode='test')
im = self.test_transforms(img_file)
result = self.exe.run(
self.test_prog,
feed={'image': im},
fetch_list=list(self.test_outputs.values()))
result = self.exe.run(self.test_prog,
feed={'image': im},
fetch_list=list(self.test_outputs.values()))
pred_label = np.argsort(result[0][0])[::-1][:true_topk]
res = [{
'category_id': l,
......@@ -400,3 +398,9 @@ class ShuffleNetV2(BaseClassifier):
def __init__(self, num_classes=1000):
super(ShuffleNetV2, self).__init__(
model_name='ShuffleNetV2', num_classes=num_classes)
class HRNet_W18(BaseClassifier):
def __init__(self, num_classes=1000):
super(HRNet_W18, self).__init__(
model_name='HRNet_W18', num_classes=num_classes)
paddlex/cv/models/faster_rcnn.py
浏览文件 @ fd42b2f6
......@@ -32,7 +32,7 @@ class FasterRCNN(BaseAPI):
Args:
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'。
'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]。
......@@ -47,7 +47,8 @@ class FasterRCNN(BaseAPI):
self.init_params = locals()
super(FasterRCNN, self).__init__('detector')
backbones = [
'ResNet18', 'ResNet50', 'ResNet50_vd', 'ResNet101', 'ResNet101_vd'
'ResNet18', 'ResNet50', 'ResNet50_vd', 'ResNet101', 'ResNet101_vd',
'HRNet_W18'
]
assert backbone in backbones, "backbone should be one of {}".format(
backbones)
......@@ -79,6 +80,12 @@ class FasterRCNN(BaseAPI):
layers = 101
variant = 'd'
norm_type = 'affine_channel'
elif backbone_name == 'HRNet_W18':
backbone = paddlex.cv.nets.hrnet.HRNet(
width=18, freeze_norm=True, norm_decay=0., freeze_at=0)
if self.with_fpn is False:
self.with_fpn = True
return backbone
if self.with_fpn:
backbone = paddlex.cv.nets.resnet.ResNet(
norm_type='bn' if norm_type is None else norm_type,
......@@ -227,7 +234,9 @@ class FasterRCNN(BaseAPI):
# 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(),
......
paddlex/cv/models/hrnet.py 0 → 100644
浏览文件 @ fd42b2f6
#copyright (c) 2020 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
import paddle.fluid as fluid
import paddlex
from collections import OrderedDict
from .deeplabv3p import DeepLabv3p
class HRNet(DeepLabv3p):
"""实现HRNet网络的构建并进行训练、评估、预测和模型导出。
Args:
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。
Raises:
ValueError: use_bce_loss或use_dice_loss为真且num_calsses > 2。
ValueError: class_weight为list, 但长度不等于num_class。
class_weight为str, 但class_weight.low()不等于dynamic。
TypeError: class_weight不为None时，其类型不是list或str。
"""
def __init__(self,
num_classes=2,
width=18,
use_bce_loss=False,
use_dice_loss=False,
class_weight=None,
ignore_index=255):
self.init_params = locals()
super(DeepLabv3p, self).__init__('segmenter')
# dice_loss或bce_loss只适用两类分割中
if num_classes > 2 and (use_bce_loss or use_dice_loss):
raise ValueError(
"dice loss and bce loss is only applicable to binary classfication"
)
if class_weight is not None:
if isinstance(class_weight, list):
if len(class_weight) != num_classes:
raise ValueError(
"Length of class_weight should be equal to number of classes"
)
elif isinstance(class_weight, str):
if class_weight.lower() != 'dynamic':
raise ValueError(
"if class_weight is string, must be dynamic!")
else:
raise TypeError(
'Expect class_weight is a list or string but receive {}'.
format(type(class_weight)))
self.num_classes = num_classes
self.width = width
self.use_bce_loss = use_bce_loss
self.use_dice_loss = use_dice_loss
self.class_weight = class_weight
self.ignore_index = ignore_index
self.labels = None
def build_net(self, mode='train'):
model = paddlex.cv.nets.segmentation.HRNet(
self.num_classes,
width=self.width,
mode=mode,
use_bce_loss=self.use_bce_loss,
use_dice_loss=self.use_dice_loss,
class_weight=self.class_weight,
ignore_index=self.ignore_index)
inputs = model.generate_inputs()
model_out = model.build_net(inputs)
outputs = OrderedDict()
if mode == 'train':
self.optimizer.minimize(model_out)
outputs['loss'] = model_out
elif mode == 'eval':
outputs['loss'] = model_out[0]
outputs['pred'] = model_out[1]
outputs['label'] = model_out[2]
outputs['mask'] = model_out[3]
else:
outputs['pred'] = model_out[0]
outputs['logit'] = model_out[1]
return inputs, outputs
def default_optimizer(self,
learning_rate,
num_epochs,
num_steps_each_epoch,
lr_decay_power=0.9):
decay_step = num_epochs * num_steps_each_epoch
lr_decay = fluid.layers.polynomial_decay(
learning_rate,
decay_step,
end_learning_rate=0,
power=lr_decay_power)
optimizer = fluid.optimizer.Momentum(
lr_decay,
momentum=0.9,
regularization=fluid.regularizer.L2Decay(
regularization_coeff=5e-04))
return optimizer
def train(self,
num_epochs,
train_dataset,
train_batch_size=2,
eval_dataset=None,
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):
"""训练。
Args:
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。默认为'IMAGENET'。
optimizer (paddle.fluid.optimizer): 优化器。当改参数为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): 默认优化器学习率多项式衰减系数。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 (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。
Raises:
ValueError: 模型从inference model进行加载。
"""
return super(HRNet, self).train(
num_epochs, train_dataset, train_batch_size, eval_dataset,
save_interval_epochs, log_interval_steps, save_dir,
pretrain_weights, optimizer, learning_rate, lr_decay_power,
use_vdl, sensitivities_file, eval_metric_loss, early_stop,
early_stop_patience, resume_checkpoint)
paddlex/cv/models/mask_rcnn.py
浏览文件 @ fd42b2f6
......@@ -32,7 +32,7 @@ class MaskRCNN(FasterRCNN):
Args:
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'。
'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]。
......@@ -46,7 +46,8 @@ class MaskRCNN(FasterRCNN):
anchor_sizes=[32, 64, 128, 256, 512]):
self.init_params = locals()
backbones = [
'ResNet18', 'ResNet50', 'ResNet50_vd', 'ResNet101', 'ResNet101_vd'
'ResNet18', 'ResNet50', 'ResNet50_vd', 'ResNet101', 'ResNet101_vd',
'HRNet_W18'
]
assert backbone in backbones, "backbone should be one of {}".format(
backbones)
......@@ -194,7 +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(),
......
paddlex/cv/models/utils/pretrain_weights.py
浏览文件 @ fd42b2f6
......@@ -56,6 +56,20 @@ image_pretrain = {
'https://paddle-imagenet-models-name.bj.bcebos.com/Xception65_deeplab_pretrained.tar',
'ShuffleNetV2':
'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 = {
......
paddlex/cv/nets/__init__.py
浏览文件 @ fd42b2f6
......@@ -23,6 +23,7 @@ from .segmentation import DeepLabv3p
from .xception import Xception
from .densenet import DenseNet
from .shufflenet_v2 import ShuffleNetV2
from .hrnet import HRNet
def resnet18(input, num_classes=1000):
......@@ -51,14 +52,20 @@ def resnet50_vd(input, num_classes=1000):
def resnet50_vd_ssld(input, num_classes=1000):
model = ResNet(layers=50, num_classes=num_classes,
variant='d', lr_mult_list=[1.0, 0.1, 0.2, 0.2, 0.3])
model = ResNet(
layers=50,
num_classes=num_classes,
variant='d',
lr_mult_list=[1.0, 0.1, 0.2, 0.2, 0.3])
return model(input)
def resnet101_vd_ssld(input, num_classes=1000):
model = ResNet(layers=101, num_classes=num_classes,
variant='d', lr_mult_list=[1.0, 0.1, 0.2, 0.2, 0.3])
model = ResNet(
layers=101,
num_classes=num_classes,
variant='d',
lr_mult_list=[1.0, 0.1, 0.2, 0.2, 0.3])
return model(input)
......@@ -93,14 +100,18 @@ def mobilenetv3_large(input, num_classes=1000):
def mobilenetv3_small_ssld(input, num_classes=1000):
model = MobileNetV3(num_classes=num_classes, model_name='small',
lr_mult_list=[0.25, 0.25, 0.5, 0.5, 0.75])
model = MobileNetV3(
num_classes=num_classes,
model_name='small',
lr_mult_list=[0.25, 0.25, 0.5, 0.5, 0.75])
return model(input)
def mobilenetv3_large_ssld(input, num_classes=1000):
model = MobileNetV3(num_classes=num_classes, model_name='large',
lr_mult_list=[0.25, 0.25, 0.5, 0.5, 0.75])
model = MobileNetV3(
num_classes=num_classes,
model_name='large',
lr_mult_list=[0.25, 0.25, 0.5, 0.5, 0.75])
return model(input)
......@@ -133,6 +144,12 @@ def densenet201(input, num_classes=1000):
model = DenseNet(layers=201, num_classes=num_classes)
return model(input)
def shufflenetv2(input, num_classes=1000):
model = ShuffleNetV2(num_classes=num_classes)
return model(input)
def hrnet_w18(input, num_classes=1000):
model = HRNet(width=18, num_classes=num_classes)
return model(input)
paddlex/cv/nets/darknet.py
浏览文件 @ fd42b2f6
......@@ -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
paddlex/cv/nets/densenet.py
浏览文件 @ fd42b2f6
......@@ -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
paddlex/cv/nets/detection/faster_rcnn.py
浏览文件 @ fd42b2f6
......@@ -21,7 +21,7 @@ import copy
from paddle import fluid
from .fpn import FPN
from .fpn import (FPN, HRFPN)
from .rpn_head import (RPNHead, FPNRPNHead)
from .roi_extractor import (RoIAlign, FPNRoIAlign)
from .bbox_head import (BBoxHead, TwoFCHead)
......@@ -82,7 +82,12 @@ class FasterRCNN(object):
self.backbone = backbone
self.mode = mode
if with_fpn and fpn is None:
fpn = FPN()
if self.backbone.__class__.__name__.startswith('HRNet'):
fpn = HRFPN()
fpn.min_level = 2
fpn.max_level = 6
else:
fpn = FPN()
self.fpn = fpn
self.num_classes = num_classes
if rpn_head is None:
......
paddlex/cv/nets/detection/fpn.py
浏览文件 @ fd42b2f6
......@@ -23,7 +23,7 @@ from paddle.fluid.param_attr import ParamAttr
from paddle.fluid.initializer import Xavier
from paddle.fluid.regularizer import L2Decay
__all__ = ['FPN']
__all__ = ['FPN', 'HRFPN']
def ConvNorm(input,
......@@ -219,8 +219,8 @@ class FPN(object):
body_name = body_name_list[i]
body_input = body_dict[body_name]
top_output = self.fpn_inner_output[i - 1]
fpn_inner_single = self._add_topdown_lateral(
body_name, body_input, top_output)
fpn_inner_single = self._add_topdown_lateral(body_name, body_input,
top_output)
self.fpn_inner_output[i] = fpn_inner_single
fpn_dict = {}
fpn_name_list = []
......@@ -293,3 +293,107 @@ class FPN(object):
spatial_scale.insert(0, spatial_scale[0] * 0.5)
res_dict = OrderedDict([(k, fpn_dict[k]) for k in fpn_name_list])
return res_dict, spatial_scale
class HRFPN(object):
"""
HRNet, see https://arxiv.org/abs/1908.07919
Args:
num_chan (int): number of feature channels
pooling_type (str): pooling type of downsampling
share_conv (bool): whethet to share conv for different layers' reduction
spatial_scale (list): feature map scaling factor
"""
def __init__(
self,
num_chan=256,
pooling_type="avg",
share_conv=False,
spatial_scale=[1. / 64, 1. / 32, 1. / 16, 1. / 8, 1. / 4], ):
self.num_chan = num_chan
self.pooling_type = pooling_type
self.share_conv = share_conv
self.spatial_scale = spatial_scale
def get_output(self, body_dict):
num_out = len(self.spatial_scale)
body_name_list = list(body_dict.keys())
num_backbone_stages = len(body_name_list)
outs = []
outs.append(body_dict[body_name_list[0]])
# resize
for i in range(1, len(body_dict)):
resized = self.resize_input_tensor(body_dict[body_name_list[i]],
outs[0], 2**i)
outs.append(resized)
# concat
out = fluid.layers.concat(outs, axis=1)
# reduction
out = fluid.layers.conv2d(
input=out,
num_filters=self.num_chan,
filter_size=1,
stride=1,
padding=0,
param_attr=ParamAttr(name='hrfpn_reduction_weights'),
bias_attr=False)
# conv
outs = [out]
for i in range(1, num_out):
outs.append(
self.pooling(
out,
size=2**i,
stride=2**i,
pooling_type=self.pooling_type))
outputs = []
for i in range(num_out):
conv_name = "shared_fpn_conv" if self.share_conv else "shared_fpn_conv_" + str(
i)
conv = fluid.layers.conv2d(
input=outs[i],
num_filters=self.num_chan,
filter_size=3,
stride=1,
padding=1,
param_attr=ParamAttr(name=conv_name + "_weights"),
bias_attr=False)
outputs.append(conv)
for idx in range(0, num_out - len(body_name_list)):
body_name_list.append("fpn_res5_sum_subsampled_{}x".format(2**(
idx + 1)))
outputs = outputs[::-1]
body_name_list = body_name_list[::-1]
res_dict = OrderedDict([(body_name_list[k], outputs[k])
for k in range(len(body_name_list))])
return res_dict, self.spatial_scale
def resize_input_tensor(self, body_input, ref_output, scale):
shape = fluid.layers.shape(ref_output)
shape_hw = fluid.layers.slice(shape, axes=[0], starts=[2], ends=[4])
out_shape_ = shape_hw
out_shape = fluid.layers.cast(out_shape_, dtype='int32')
out_shape.stop_gradient = True
body_output = fluid.layers.resize_bilinear(
body_input, scale=scale, out_shape=out_shape)
return body_output
def pooling(self, input, size, stride, pooling_type):
pool = fluid.layers.pool2d(
input=input,
pool_size=size,
pool_stride=stride,
pool_type=pooling_type)
return pool
paddlex/cv/nets/detection/mask_rcnn.py
浏览文件 @ fd42b2f6
......@@ -21,7 +21,7 @@ import copy
import paddle.fluid as fluid
from .fpn import FPN
from .fpn import (FPN, HRFPN)
from .rpn_head import (RPNHead, FPNRPNHead)
from .roi_extractor import (RoIAlign, FPNRoIAlign)
from .bbox_head import (BBoxHead, TwoFCHead)
......@@ -92,11 +92,15 @@ class MaskRCNN(object):
self.backbone = backbone
self.mode = mode
if with_fpn and fpn is None:
fpn = FPN(
num_chan=num_chan,
min_level=min_level,
max_level=max_level,
spatial_scale=spatial_scale)
if self.backbone.__class__.__name__.startswith('HRNet'):
fpn = HRFPN()
fpn.min_level = 2
fpn.max_level = 6
else:
fpn = FPN(num_chan=num_chan,
min_level=min_level,
max_level=max_level,
spatial_scale=spatial_scale)
self.fpn = fpn
self.num_classes = num_classes
if rpn_head is None:
......@@ -200,7 +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,
calass_nums=self.num_classes,
use_random=self.rpn_head.use_random)
rois = outputs[0]
......
paddlex/cv/nets/hrnet.py 0 → 100644
浏览文件 @ fd42b2f6
# Copyright (c) 2020 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 absolute_import
from __future__ import division
from __future__ import print_function
from collections import OrderedDict
from paddle import fluid
from paddle.fluid.param_attr import ParamAttr
from paddle.fluid.framework import Variable
from paddle.fluid.regularizer import L2Decay
from numbers import Integral
from paddle.fluid.initializer import MSRA
import math
__all__ = ['HRNet']
class HRNet(object):
def __init__(self,
width=40,
has_se=False,
freeze_at=0,
norm_type='bn',
freeze_norm=False,
norm_decay=0.,
feature_maps=[2, 3, 4, 5],
num_classes=None):
super(HRNet, self).__init__()
if isinstance(feature_maps, Integral):
feature_maps = [feature_maps]
assert 0 <= freeze_at <= 4, "freeze_at should be 0, 1, 2, 3 or 4"
assert len(feature_maps) > 0, "need one or more feature maps"
assert norm_type in ['bn', 'sync_bn']
self.width = width
self.has_se = has_se
self.channels = {
18: [[18, 36], [18, 36, 72], [18, 36, 72, 144]],
30: [[30, 60], [30, 60, 120], [30, 60, 120, 240]],
32: [[32, 64], [32, 64, 128], [32, 64, 128, 256]],
40: [[40, 80], [40, 80, 160], [40, 80, 160, 320]],
44: [[44, 88], [44, 88, 176], [44, 88, 176, 352]],
48: [[48, 96], [48, 96, 192], [48, 96, 192, 384]],
60: [[60, 120], [60, 120, 240], [60, 120, 240, 480]],
64: [[64, 128], [64, 128, 256], [64, 128, 256, 512]],
}
self.freeze_at = freeze_at
self.norm_type = norm_type
self.norm_decay = norm_decay
self.freeze_norm = freeze_norm
self.feature_maps = feature_maps
self.num_classes = num_classes
self.end_points = []
return
def net(self, input, class_dim=1000):
width = self.width
channels_2, channels_3, channels_4 = self.channels[width]
num_modules_2, num_modules_3, num_modules_4 = 1, 4, 3
x = self.conv_bn_layer(
input=input,
filter_size=3,
num_filters=64,
stride=2,
if_act=True,
name='layer1_1')
x = self.conv_bn_layer(
input=x,
filter_size=3,
num_filters=64,
stride=2,
if_act=True,
name='layer1_2')
la1 = self.layer1(x, name='layer2')
tr1 = self.transition_layer([la1], [256], channels_2, name='tr1')
st2 = self.stage(tr1, num_modules_2, channels_2, name='st2')
tr2 = self.transition_layer(st2, channels_2, channels_3, name='tr2')
st3 = self.stage(tr2, num_modules_3, channels_3, name='st3')
tr3 = self.transition_layer(st3, channels_3, channels_4, name='tr3')
st4 = self.stage(tr3, num_modules_4, channels_4, name='st4')
# classification
if self.num_classes:
last_cls = self.last_cls_out(x=st4, name='cls_head')
y = last_cls[0]
last_num_filters = [256, 512, 1024]
for i in range(3):
y = fluid.layers.elementwise_add(
last_cls[i + 1],
self.conv_bn_layer(
input=y,
filter_size=3,
num_filters=last_num_filters[i],
stride=2,
name='cls_head_add' + str(i + 1)))
y = self.conv_bn_layer(
input=y,
filter_size=1,
num_filters=2048,
stride=1,
name='cls_head_last_conv')
pool = fluid.layers.pool2d(
input=y, pool_type='avg', global_pooling=True)
stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
out = fluid.layers.fc(
input=pool,
size=class_dim,
param_attr=ParamAttr(
name='fc_weights',
initializer=fluid.initializer.Uniform(-stdv, stdv)),
bias_attr=ParamAttr(name='fc_offset'))
return out
# segmentation
if self.feature_maps == "stage4":
return st4
self.end_points = st4
return st4[-1]
def layer1(self, input, name=None):
conv = input
for i in range(4):
conv = self.bottleneck_block(
conv,
num_filters=64,
downsample=True if i == 0 else False,
name=name + '_' + str(i + 1))
return conv
def transition_layer(self, x, in_channels, out_channels, name=None):
num_in = len(in_channels)
num_out = len(out_channels)
out = []
for i in range(num_out):
if i < num_in:
if in_channels[i] != out_channels[i]:
residual = self.conv_bn_layer(
x[i],
filter_size=3,
num_filters=out_channels[i],
name=name + '_layer_' + str(i + 1))
out.append(residual)
else:
out.append(x[i])
else:
residual = self.conv_bn_layer(
x[-1],
filter_size=3,
num_filters=out_channels[i],
stride=2,
name=name + '_layer_' + str(i + 1))
out.append(residual)
return out
def branches(self, x, block_num, channels, name=None):
out = []
for i in range(len(channels)):
residual = x[i]
for j in range(block_num):
residual = self.basic_block(
residual,
channels[i],
name=name + '_branch_layer_' + str(i + 1) + '_' +
str(j + 1))
out.append(residual)
return out
def fuse_layers(self, x, channels, multi_scale_output=True, name=None):
out = []
for i in range(len(channels) if multi_scale_output else 1):
residual = x[i]
if self.feature_maps == "stage4":
shape = fluid.layers.shape(residual)
width = shape[-1]
height = shape[-2]
for j in range(len(channels)):
if j > i:
y = self.conv_bn_layer(
x[j],
filter_size=1,
num_filters=channels[i],
if_act=False,
name=name + '_layer_' + str(i + 1) + '_' + str(j + 1))
if self.feature_maps == "stage4":
y = fluid.layers.resize_bilinear(
input=y, out_shape=[height, width])
else:
y = fluid.layers.resize_nearest(
input=y, scale=2**(j - i))
residual = fluid.layers.elementwise_add(
x=residual, y=y, act=None)
elif j < i:
y = x[j]
for k in range(i - j):
if k == i - j - 1:
y = self.conv_bn_layer(
y,
filter_size=3,
num_filters=channels[i],
stride=2,
if_act=False,
name=name + '_layer_' + str(i + 1) + '_' +
str(j + 1) + '_' + str(k + 1))
else:
y = self.conv_bn_layer(
y,
filter_size=3,
num_filters=channels[j],
stride=2,
name=name + '_layer_' + str(i + 1) + '_' +
str(j + 1) + '_' + str(k + 1))
residual = fluid.layers.elementwise_add(
x=residual, y=y, act=None)
residual = fluid.layers.relu(residual)
out.append(residual)
return out
def high_resolution_module(self,
x,
channels,
multi_scale_output=True,
name=None):
residual = self.branches(x, 4, channels, name=name)
out = self.fuse_layers(
residual,
channels,
multi_scale_output=multi_scale_output,
name=name)
return out
def stage(self,
x,
num_modules,
channels,
multi_scale_output=True,
name=None):
out = x
for i in range(num_modules):
if i == num_modules - 1 and multi_scale_output == False:
out = self.high_resolution_module(
out,
channels,
multi_scale_output=False,
name=name + '_' + str(i + 1))
else:
out = self.high_resolution_module(
out, channels, name=name + '_' + str(i + 1))
return out
def last_cls_out(self, x, name=None):
out = []
num_filters_list = [32, 64, 128, 256]
for i in range(len(x)):
out.append(
self.bottleneck_block(
input=x[i],
num_filters=num_filters_list[i],
name=name + 'conv_' + str(i + 1),
downsample=True))
return out
def basic_block(self,
input,
num_filters,
stride=1,
downsample=False,
name=None):
residual = input
conv = self.conv_bn_layer(
input=input,
filter_size=3,
num_filters=num_filters,
stride=stride,
name=name + '_conv1')
conv = self.conv_bn_layer(
input=conv,
filter_size=3,
num_filters=num_filters,
if_act=False,
name=name + '_conv2')
if downsample:
residual = self.conv_bn_layer(
input=input,
filter_size=1,
num_filters=num_filters,
if_act=False,
name=name + '_downsample')
if self.has_se:
conv = self.squeeze_excitation(
input=conv,
num_channels=num_filters,
reduction_ratio=16,
name=name + '_fc')
return fluid.layers.elementwise_add(x=residual, y=conv, act='relu')
def bottleneck_block(self,
input,
num_filters,
stride=1,
downsample=False,
name=None):
residual = input
conv = self.conv_bn_layer(
input=input,
filter_size=1,
num_filters=num_filters,
name=name + '_conv1')
conv = self.conv_bn_layer(
input=conv,
filter_size=3,
num_filters=num_filters,
stride=stride,
name=name + '_conv2')
conv = self.conv_bn_layer(
input=conv,
filter_size=1,
num_filters=num_filters * 4,
if_act=False,
name=name + '_conv3')
if downsample:
residual = self.conv_bn_layer(
input=input,
filter_size=1,
num_filters=num_filters * 4,
if_act=False,
name=name + '_downsample')
if self.has_se:
conv = self.squeeze_excitation(
input=conv,
num_channels=num_filters * 4,
reduction_ratio=16,
name=name + '_fc')
return fluid.layers.elementwise_add(x=residual, y=conv, act='relu')
def squeeze_excitation(self,
input,
num_channels,
reduction_ratio,
name=None):
pool = fluid.layers.pool2d(
input=input, pool_size=0, pool_type='avg', global_pooling=True)
stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
squeeze = fluid.layers.fc(
input=pool,
size=num_channels / reduction_ratio,
act='relu',
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=name + '_sqz_weights'),
bias_attr=ParamAttr(name=name + '_sqz_offset'))
stdv = 1.0 / math.sqrt(squeeze.shape[1] * 1.0)
excitation = fluid.layers.fc(
input=squeeze,
size=num_channels,
act='sigmoid',
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=name + '_exc_weights'),
bias_attr=ParamAttr(name=name + '_exc_offset'))
scale = fluid.layers.elementwise_mul(x=input, y=excitation, axis=0)
return scale
def conv_bn_layer(self,
input,
filter_size,
num_filters,
stride=1,
padding=1,
num_groups=1,
if_act=True,
name=None):
conv = fluid.layers.conv2d(
input=input,
num_filters=num_filters,
filter_size=filter_size,
stride=stride,
padding=(filter_size - 1) // 2,
groups=num_groups,
act=None,
param_attr=ParamAttr(
initializer=MSRA(), name=name + '_weights'),
bias_attr=False)
bn_name = name + '_bn'
bn = self._bn(input=conv, bn_name=bn_name)
if if_act:
bn = fluid.layers.relu(bn)
return bn
def _bn(self, input, act=None, bn_name=None):
norm_lr = 0. if self.freeze_norm else 1.
norm_decay = self.norm_decay
if self.num_classes or self.feature_maps == "stage4":
regularizer = None
pattr_initializer = fluid.initializer.Constant(1.0)
battr_initializer = fluid.initializer.Constant(0.0)
else:
regularizer = L2Decay(norm_decay)
pattr_initializer = None
battr_initializer = None
pattr = ParamAttr(
name=bn_name + '_scale',
learning_rate=norm_lr,
regularizer=regularizer,
initializer=pattr_initializer)
battr = ParamAttr(
name=bn_name + '_offset',
learning_rate=norm_lr,
regularizer=regularizer,
initializer=battr_initializer)
global_stats = True if self.freeze_norm else False
out = fluid.layers.batch_norm(
input=input,
act=act,
name=bn_name + '.output.1',
param_attr=pattr,
bias_attr=battr,
moving_mean_name=bn_name + '_mean',
moving_variance_name=bn_name + '_variance',
use_global_stats=global_stats)
scale = fluid.framework._get_var(pattr.name)
bias = fluid.framework._get_var(battr.name)
if self.freeze_norm:
scale.stop_gradient = True
bias.stop_gradient = True
return out
def __call__(self, input):
assert isinstance(input, Variable)
if isinstance(self.feature_maps, (list, tuple)):
assert not (set(self.feature_maps) - set([2, 3, 4, 5])), \
"feature maps {} not in [2, 3, 4, 5]".format(self.feature_maps)
res_endpoints = []
res = input
feature_maps = self.feature_maps
out = self.net(input)
if self.num_classes or self.feature_maps == "stage4":
return out
for i in feature_maps:
res = self.end_points[i - 2]
if i in self.feature_maps:
res_endpoints.append(res)
if self.freeze_at >= i:
res.stop_gradient = True
return OrderedDict([('res{}_sum'.format(self.feature_maps[idx]), feat)
for idx, feat in enumerate(res_endpoints)])
paddlex/cv/nets/mobilenet_v1.py
浏览文件 @ fd42b2f6
......@@ -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
paddlex/cv/nets/mobilenet_v3.py
浏览文件 @ fd42b2f6
......@@ -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:
......
paddlex/cv/nets/resnet.py
浏览文件 @ fd42b2f6
......@@ -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
......
paddlex/cv/nets/segmentation/__init__.py
浏览文件 @ fd42b2f6
......@@ -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
paddlex/cv/nets/segmentation/hrnet.py 0 → 100644
浏览文件 @ fd42b2f6
# coding: utf8
# copyright (c) 2020 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
from collections import OrderedDict
import paddle.fluid as fluid
from paddle.fluid.initializer import MSRA
from paddle.fluid.param_attr import ParamAttr
from .model_utils.libs import sigmoid_to_softmax
from .model_utils.loss import softmax_with_loss
from .model_utils.loss import dice_loss
from .model_utils.loss import bce_loss
import paddlex
import paddlex.utils.logging as logging
class HRNet(object):
def __init__(self,
num_classes,
mode='train',
width=18,
use_bce_loss=False,
use_dice_loss=False,
class_weight=None,
ignore_index=255):
# dice_loss或bce_loss只适用两类分割中
if num_classes > 2 and (use_bce_loss or use_dice_loss):
raise ValueError(
"dice loss and bce loss is only applicable to binary classfication"
)
if class_weight is not None:
if isinstance(class_weight, list):
if len(class_weight) != num_classes:
raise ValueError(
"Length of class_weight should be equal to number of classes"
)
elif isinstance(class_weight, str):
if class_weight.lower() != 'dynamic':
raise ValueError(
"if class_weight is string, must be dynamic!")
else:
raise TypeError(
'Expect class_weight is a list or string but receive {}'.
format(type(class_weight)))
self.num_classes = num_classes
self.mode = mode
self.use_bce_loss = use_bce_loss
self.use_dice_loss = use_dice_loss
self.class_weight = class_weight
self.ignore_index = ignore_index
self.backbone = paddlex.cv.nets.hrnet.HRNet(
width=width, feature_maps="stage4")
def build_net(self, inputs):
if self.use_dice_loss or self.use_bce_loss:
self.num_classes = 1
image = inputs['image']
st4 = self.backbone(image)
# upsample
shape = fluid.layers.shape(st4[0])[-2:]
st4[1] = fluid.layers.resize_bilinear(st4[1], out_shape=shape)
st4[2] = fluid.layers.resize_bilinear(st4[2], out_shape=shape)
st4[3] = fluid.layers.resize_bilinear(st4[3], out_shape=shape)
out = fluid.layers.concat(st4, axis=1)
last_channels = sum(self.backbone.channels[self.backbone.width][-1])
out = self._conv_bn_layer(
input=out,
filter_size=1,
num_filters=last_channels,
stride=1,
if_act=True,
name='conv-2')
out = fluid.layers.conv2d(
input=out,
num_filters=self.num_classes,
filter_size=1,
stride=1,
padding=0,
act=None,
param_attr=ParamAttr(
initializer=MSRA(), name='conv-1_weights'),
bias_attr=False)
input_shape = fluid.layers.shape(image)[-2:]
logit = fluid.layers.resize_bilinear(out, input_shape)
if self.num_classes == 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 self.mode == 'train':
label = inputs['label']
mask = label != self.ignore_index
return self._get_loss(logit, label, mask)
elif self.mode == 'eval':
label = inputs['label']
mask = label != self.ignore_index
loss = self._get_loss(logit, label, mask)
return loss, pred, label, mask
else:
if self.num_classes == 1:
logit = sigmoid_to_softmax(logit)
else:
logit = fluid.layers.softmax(logit, axis=1)
return pred, logit
def generate_inputs(self):
inputs = OrderedDict()
inputs['image'] = fluid.data(
dtype='float32', shape=[None, 3, None, None], name='image')
if self.mode == 'train':
inputs['label'] = fluid.data(
dtype='int32', shape=[None, 1, None, None], name='label')
elif self.mode == 'eval':
inputs['label'] = fluid.data(
dtype='int32', shape=[None, 1, None, None], name='label')
return inputs
def _get_loss(self, logit, label, mask):
avg_loss = 0
if not (self.use_dice_loss or self.use_bce_loss):
avg_loss += softmax_with_loss(
logit,
label,
mask,
num_classes=self.num_classes,
weight=self.class_weight,
ignore_index=self.ignore_index)
else:
if self.use_dice_loss:
avg_loss += dice_loss(logit, label, mask)
if self.use_bce_loss:
avg_loss += bce_loss(
logit, label, mask, ignore_index=self.ignore_index)
return avg_loss
def _conv_bn_layer(self,
input,
filter_size,
num_filters,
stride=1,
padding=1,
num_groups=1,
if_act=True,
name=None):
conv = fluid.layers.conv2d(
input=input,
num_filters=num_filters,
filter_size=filter_size,
stride=stride,
padding=(filter_size - 1) // 2,
groups=num_groups,
act=None,
param_attr=ParamAttr(
initializer=MSRA(), name=name + '_weights'),
bias_attr=False)
bn_name = name + '_bn'
bn = fluid.layers.batch_norm(
input=conv,
param_attr=ParamAttr(
name=bn_name + "_scale",
initializer=fluid.initializer.Constant(1.0)),
bias_attr=ParamAttr(
name=bn_name + "_offset",
initializer=fluid.initializer.Constant(0.0)),
moving_mean_name=bn_name + '_mean',
moving_variance_name=bn_name + '_variance')
if if_act:
bn = fluid.layers.relu(bn)
return bn
paddlex/seg.py
浏览文件 @ fd42b2f6
......@@ -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
tutorials/train/segmentation/hrnet.py 0 → 100644
浏览文件 @ fd42b2f6
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)
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)
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