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[Feature] Fix ppcls and test pass

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geoyee 3 years ago
parent 33fb4abc1f
commit 7f88bd8e69
5 changed files with 128 additions and 240 deletions
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  1. 1
      .gitignore
  2. 3
      paddlers/datasets/clas_dataset.py
  3. 2
      paddlers/models/ppcls/loss/__init__.py
  4. 304
      paddlers/tasks/classifier.py
  5. 58
      tutorials/train/classification/resnet50_vd_rs.py

1
.gitignore vendored
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@ -131,6 +131,7 @@ dmypy.json
# testdata
tutorials/train/change_detection/DataSet/
tutorials/train/classification/DataSet/
optic_disc_seg.tar
optic_disc_seg/
output/

3
paddlers/datasets/clas_dataset.py
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@ -25,7 +25,7 @@ class ClasDataset(Dataset):
Args:
data_dir (str): 数据集所在的目录路径。
file_list (str): 描述数据集图片文件和对应标注序号（文本内每行路径为相对data_dir的相对路）。
label_list (str): 描述数据集包含的类别信息文件路径。默认值为None。
label_list (str): 描述数据集包含的类别信息文件路径，文件格式为（类别 说明）。默认值为None。
transforms (paddlers.transforms): 数据集中每个样本的预处理/增强算子。
num_workers (int|str): 数据集中样本在预处理过程中的线程或进程数。默认为'auto'。
shuffle (bool): 是否需要对数据集中样本打乱顺序。默认为False。
@ -45,6 +45,7 @@ class ClasDataset(Dataset):
self.num_workers = get_num_workers(num_workers)
self.shuffle = shuffle
self.file_list = list()
self.label_list = label_list
self.labels = list()
# TODO：非None时，让用户跳转数据集分析生成label_list

2
paddlers/models/ppcls/loss/__init__.py
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@ -63,5 +63,5 @@ class CombinedLoss(nn.Layer):
def build_loss(config):
module_class = CombinedLoss(copy.deepcopy(config))
logger.debug("build loss {} success.".format(module_class))
# logger.debug("build loss {} success.".format(module_class))
return module_class

304
paddlers/tasks/classifier.py
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@ -15,7 +15,6 @@
import math
import os.path as osp
import numpy as np
import cv2
from collections import OrderedDict
import paddle
import paddle.nn.functional as F
@ -26,8 +25,10 @@ from paddlers.transforms import arrange_transforms
from paddlers.utils import get_single_card_bs, DisablePrint
import paddlers.utils.logging as logging
from .base import BaseModel
from .utils import seg_metrics as metrics
from paddlers.utils.checkpoint import seg_pretrain_weights_dict
from paddlers.models.ppcls.metric import build_metrics
from paddlers.models.ppcls.loss import build_loss
from paddlers.models.ppcls.data.postprocess import build_postprocess
from paddlers.utils.checkpoint import imagenet_weights
from paddlers.transforms import Decode, Resize
__all__ = ["ResNet50_vd", "MobileNetV3_small_x1_0", "HRNet_W18_C"]
@ -49,8 +50,10 @@ class BaseClassifier(BaseModel):
self.model_name = model_name
self.num_classes = num_classes
self.use_mixed_loss = use_mixed_loss
self.metrics = None
self.losses = None
self.labels = None
self._postprocess = None
if params.get('with_net', True):
params.pop('with_net', None)
self.net = self.build_net(**params)
@ -97,95 +100,35 @@ class BaseClassifier(BaseModel):
]
return input_spec
# FIXME: use ppcls instead of ppseg, in infet / metrics and etc.
def run(self, net, inputs, mode):
net_out = net(inputs[0])
logit = net_out[0]
label = paddle.to_tensor(inputs[1], dtype="int64")
outputs = OrderedDict()
if mode == 'test':
origin_shape = inputs[1]
if self.status == 'Infer':
label_map_list, score_map_list = self._postprocess(
net_out, origin_shape, transforms=inputs[2])
else:
logit_list = self._postprocess(
logit, origin_shape, transforms=inputs[2])
label_map_list = []
score_map_list = []
for logit in logit_list:
logit = paddle.transpose(logit, perm=[0, 2, 3, 1]) # NHWC
label_map_list.append(
paddle.argmax(
logit, axis=-1, keepdim=False, dtype='int32')
.squeeze().numpy())
score_map_list.append(
F.softmax(
logit, axis=-1).squeeze().numpy().astype(
'float32'))
outputs['label_map'] = label_map_list
outputs['score_map'] = score_map_list
result = self._postprocess(net_out)
outputs = result[0]
if mode == 'eval':
if self.status == 'Infer':
pred = paddle.unsqueeze(net_out[0], axis=1) # NCHW
else:
pred = paddle.argmax(
logit, axis=1, keepdim=True, dtype='int32')
label = inputs[1]
origin_shape = [label.shape[-2:]]
pred = self._postprocess(
pred, origin_shape, transforms=inputs[2])[0] # NCHW
intersect_area, pred_area, label_area = paddleseg.utils.metrics.calculate_area(
pred, label, self.num_classes)
outputs['intersect_area'] = intersect_area
outputs['pred_area'] = pred_area
outputs['label_area'] = label_area
outputs['conf_mat'] = metrics.confusion_matrix(pred, label,
self.num_classes)
# print(self._postprocess(net_out)[0]) # for test
label = paddle.unsqueeze(label, axis=-1)
metric_dict = self.metrics(net_out, label)
outputs['top1'] = metric_dict["top1"]
outputs['top5'] = metric_dict["top5"]
if mode == 'train':
loss_list = metrics.loss_computation(
logits_list=net_out, labels=inputs[1], losses=self.losses)
loss = sum(loss_list)
outputs['loss'] = loss
loss_list = self.losses(net_out, label)
outputs['loss'] = loss_list['loss']
return outputs
# FIXME: use ppcls instead of ppseg, in loss.
def default_metric(self):
# TODO: other metrics
default_config = [{"TopkAcc":{"topk": [1, 5]}}]
return build_metrics(default_config)
def default_loss(self):
if isinstance(self.use_mixed_loss, bool):
if self.use_mixed_loss:
losses = [
paddleseg.models.CrossEntropyLoss(),
paddleseg.models.LovaszSoftmaxLoss()
]
coef = [.8, .2]
loss_type = [
paddleseg.models.MixedLoss(
losses=losses, coef=coef),
]
else:
loss_type = [paddleseg.models.CrossEntropyLoss()]
else:
losses, coef = list(zip(*self.use_mixed_loss))
if not set(losses).issubset(
['CrossEntropyLoss', 'DiceLoss', 'LovaszSoftmaxLoss']):
raise ValueError(
"Only 'CrossEntropyLoss', 'DiceLoss', 'LovaszSoftmaxLoss' are supported."
)
losses = [getattr(paddleseg.models, loss)() for loss in losses]
loss_type = [
paddleseg.models.MixedLoss(
losses=losses, coef=list(coef))
]
if self.model_name == 'FastSCNN':
loss_type *= 2
loss_coef = [1.0, 0.4]
elif self.model_name == 'BiSeNetV2':
loss_type *= 5
loss_coef = [1.0] * 5
else:
loss_coef = [1.0]
losses = {'types': loss_type, 'coef': loss_coef}
return losses
# TODO: mixed_loss
default_config = [{"CELoss":{"weight": 1.0}}]
return build_loss(default_config)
def default_optimizer(self,
parameters,
@ -203,6 +146,14 @@ class BaseClassifier(BaseModel):
weight_decay=4e-5)
return optimizer
def default_postprocess(self, class_id_map_file):
default_config = {
"name": "Topk",
"topk": 1,
"class_id_map_file": class_id_map_file
}
return build_postprocess(default_config)
def train(self,
num_epochs,
train_dataset,
@ -212,7 +163,7 @@ class BaseClassifier(BaseModel):
save_interval_epochs=1,
log_interval_steps=2,
save_dir='output',
pretrain_weights='CITYSCAPES', # FIXME: fix clas's pretrain weights
pretrain_weights='IMAGENET',
learning_rate=0.01,
lr_decay_power=0.9,
early_stop=False,
@ -255,6 +206,9 @@ class BaseClassifier(BaseModel):
self.labels = train_dataset.labels
if self.losses is None:
self.losses = self.default_loss()
self.metrics = self.default_metric()
self._postprocess = self.default_postprocess(train_dataset.label_list)
# print(self._postprocess.class_id_map)
if optimizer is None:
num_steps_each_epoch = train_dataset.num_samples // train_batch_size
@ -265,7 +219,7 @@ class BaseClassifier(BaseModel):
self.optimizer = optimizer
if pretrain_weights is not None and not osp.exists(pretrain_weights):
if pretrain_weights not in seg_pretrain_weights_dict[
if pretrain_weights not in imagenet_weights[
self.model_name]:
logging.warning(
"Path of pretrain_weights('{}') does not exist!".format(
@ -273,9 +227,9 @@ class BaseClassifier(BaseModel):
logging.warning("Pretrain_weights is forcibly set to '{}'. "
"If don't want to use pretrain weights, "
"set pretrain_weights to be None.".format(
seg_pretrain_weights_dict[self.model_name][
imagenet_weights[self.model_name][
0]))
pretrain_weights = seg_pretrain_weights_dict[self.model_name][
pretrain_weights = imagenet_weights[self.model_name][
0]
elif pretrain_weights is not None and osp.exists(pretrain_weights):
if osp.splitext(pretrain_weights)[-1] != '.pdparams':
@ -370,12 +324,8 @@ class BaseClassifier(BaseModel):
Returns:
collections.OrderedDict with key-value pairs:
{"miou": `mean intersection over union`,
"category_iou": `category-wise mean intersection over union`,
"oacc": `overall accuracy`,
"category_acc": `category-wise accuracy`,
"kappa": ` kappa coefficient`,
"category_F1-score": `F1 score`}.
{"top1": `acc of top1`,
"top5": `acc of top5`}.
"""
arrange_transforms(
@ -403,73 +353,26 @@ class BaseClassifier(BaseModel):
self.eval_data_loader = self.build_data_loader(
eval_dataset, batch_size=batch_size, mode='eval')
intersect_area_all = 0
pred_area_all = 0
label_area_all = 0
conf_mat_all = []
logging.info(
"Start to evaluate(total_samples={}, total_steps={})...".format(
eval_dataset.num_samples,
math.ceil(eval_dataset.num_samples * 1.0 / batch_size)))
top1s = []
top5s = []
with paddle.no_grad():
for step, data in enumerate(self.eval_data_loader):
data.append(eval_dataset.transforms.transforms)
outputs = self.run(self.net, data, 'eval')
pred_area = outputs['pred_area']
label_area = outputs['label_area']
intersect_area = outputs['intersect_area']
conf_mat = outputs['conf_mat']
# Gather from all ranks
if nranks > 1:
intersect_area_list = []
pred_area_list = []
label_area_list = []
conf_mat_list = []
paddle.distributed.all_gather(intersect_area_list,
intersect_area)
paddle.distributed.all_gather(pred_area_list, pred_area)
paddle.distributed.all_gather(label_area_list, label_area)
paddle.distributed.all_gather(conf_mat_list, conf_mat)
# Some image has been evaluated and should be eliminated in last iter
if (step + 1) * nranks > len(eval_dataset):
valid = len(eval_dataset) - step * nranks
intersect_area_list = intersect_area_list[:valid]
pred_area_list = pred_area_list[:valid]
label_area_list = label_area_list[:valid]
conf_mat_list = conf_mat_list[:valid]
intersect_area_all += sum(intersect_area_list)
pred_area_all += sum(pred_area_list)
label_area_all += sum(label_area_list)
conf_mat_all.extend(conf_mat_list)
else:
intersect_area_all = intersect_area_all + intersect_area
pred_area_all = pred_area_all + pred_area
label_area_all = label_area_all + label_area
conf_mat_all.append(conf_mat)
# FIXME: fix metrics
class_iou, miou = paddleseg.utils.metrics.mean_iou(
intersect_area_all, pred_area_all, label_area_all)
# TODO 确认是按oacc还是macc
class_acc, oacc = paddleseg.utils.metrics.accuracy(intersect_area_all,
pred_area_all)
kappa = paddleseg.utils.metrics.kappa(intersect_area_all,
pred_area_all, label_area_all)
category_f1score = metrics.f1_score(intersect_area_all, pred_area_all,
label_area_all)
eval_metrics = OrderedDict(
zip([
'miou', 'category_iou', 'oacc', 'category_acc', 'kappa',
'category_F1-score'
], [miou, class_iou, oacc, class_acc, kappa, category_f1score]))
top1s.append(outputs["top1"])
top5s.append(outputs["top5"])
top1 = np.mean(top1s)
top5 = np.mean(top5s)
eval_metrics = OrderedDict(zip(['top1', 'top5'], [top1, top5]))
if return_details:
conf_mat = sum(conf_mat_all)
eval_details = {'confusion_matrix': conf_mat.tolist()}
return eval_metrics, eval_details
# TODO: add details
return eval_metrics, None
return eval_metrics
def predict(self, img_file, transforms=None):
@ -485,10 +388,11 @@ class BaseClassifier(BaseModel):
Returns:
If img_file is a string or np.array, the result is a dict with key-value pairs:
{"label map": `label map`, "score_map": `score map`}.
{"label map": `class_ids_map`, "scores_map": `label_names_map`}.
If img_file is a list, the result is a list composed of dicts with the corresponding fields:
label_map(np.ndarray): the predicted label map (HW)
score_map(np.ndarray): the prediction score map (HWC)
class_ids_map(np.ndarray): class_ids
scores_map(np.ndarray): scores
label_names_map(np.ndarray): label_names
"""
if transforms is None and not hasattr(self, 'test_transforms'):
@ -504,21 +408,23 @@ class BaseClassifier(BaseModel):
self.net.eval()
data = (batch_im, batch_origin_shape, transforms.transforms)
outputs = self.run(self.net, data, 'test')
label_map_list = outputs['label_map']
score_map_list = outputs['score_map']
label_list = outputs['class_ids']
score_list = outputs['scores']
name_list = outputs['label_names']
if isinstance(img_file, list):
prediction = [{
'label_map': l,
'score_map': s
} for l, s in zip(label_map_list, score_map_list)]
'class_ids_map': l,
'scores_map': s,
'label_names_map': n,
} for l, s, n in zip(label_list, score_list, name_list)]
else:
prediction = {
'label_map': label_map_list[0],
'score_map': score_map_list[0]
'class_ids': label_list[0],
'scores': score_list[0],
'label_names': name_list[0]
}
return prediction
# FIXME: adaptive clas
def _preprocess(self, images, transforms, to_tensor=True):
arrange_transforms(
model_type=self.model_type, transforms=transforms, mode='test')
@ -587,84 +493,6 @@ class BaseClassifier(BaseModel):
batch_restore_list.append(restore_list)
return batch_restore_list
# FIXME: adaptive clas
def _postprocess(self, batch_pred, batch_origin_shape, transforms):
batch_restore_list = BaseClassifier.get_transforms_shape_info(
batch_origin_shape, transforms)
if isinstance(batch_pred, (tuple, list)) and self.status == 'Infer':
return self._infer_postprocess(
batch_label_map=batch_pred[0],
batch_score_map=batch_pred[1],
batch_restore_list=batch_restore_list)
results = []
if batch_pred.dtype == paddle.float32:
mode = 'bilinear'
else:
mode = 'nearest'
for pred, restore_list in zip(batch_pred, batch_restore_list):
pred = paddle.unsqueeze(pred, axis=0)
for item in restore_list[::-1]:
h, w = item[1][0], item[1][1]
if item[0] == 'resize':
pred = F.interpolate(
pred, (h, w), mode=mode, data_format='NCHW')
elif item[0] == 'padding':
x, y = item[2]
pred = pred[:, :, y:y + h, x:x + w]
else:
pass
results.append(pred)
return results
# FIXME: adaptive clas
def _infer_postprocess(self, batch_label_map, batch_score_map,
batch_restore_list):
label_maps = []
score_maps = []
for label_map, score_map, restore_list in zip(
batch_label_map, batch_score_map, batch_restore_list):
if not isinstance(label_map, np.ndarray):
label_map = paddle.unsqueeze(label_map, axis=[0, 3])
score_map = paddle.unsqueeze(score_map, axis=0)
for item in restore_list[::-1]:
h, w = item[1][0], item[1][1]
if item[0] == 'resize':
if isinstance(label_map, np.ndarray):
label_map = cv2.resize(
label_map, (w, h), interpolation=cv2.INTER_NEAREST)
score_map = cv2.resize(
score_map, (w, h), interpolation=cv2.INTER_LINEAR)
else:
label_map = F.interpolate(
label_map, (h, w),
mode='nearest',
data_format='NHWC')
score_map = F.interpolate(
score_map, (h, w),
mode='bilinear',
data_format='NHWC')
elif item[0] == 'padding':
x, y = item[2]
if isinstance(label_map, np.ndarray):
label_map = label_map[..., y:y + h, x:x + w]
score_map = score_map[..., y:y + h, x:x + w]
else:
label_map = label_map[:, :, y:y + h, x:x + w]
score_map = score_map[:, :, y:y + h, x:x + w]
else:
pass
label_map = label_map.squeeze()
score_map = score_map.squeeze()
if not isinstance(label_map, np.ndarray):
label_map = label_map.numpy()
score_map = score_map.numpy()
label_maps.append(label_map.squeeze())
score_maps.append(score_map.squeeze())
return label_maps, score_maps
__all__ = ["ResNet50_vd", "MobileNetV3_small_x1_0", "HRNet_W18_C"]
class ResNet50_vd(BaseClassifier):
def __init__(self,
num_classes=2,

58
tutorials/train/classification/resnet50_vd_rs.py
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@ -0,0 +1,58 @@
import sys
sys.path.append("E:/dataFiles/github/PaddleRS")
import paddlers as pdrs
from paddlers import transforms as T
# 下载aistudio的数据到当前文件夹并解压、整理
# https://aistudio.baidu.com/aistudio/datasetdetail/63189
# 定义训练和验证时的transforms
# API说明：https://github.com/PaddlePaddle/paddlers/blob/develop/docs/apis/transforms/transforms.md
train_transforms = T.Compose([
T.Resize(target_size=512),
T.RandomHorizontalFlip(),
T.Normalize(
mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
])
eval_transforms = T.Compose([
T.Resize(target_size=512),
T.Normalize(
mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
])
# 定义训练和验证所用的数据集
# API说明：https://github.com/PaddlePaddle/paddlers/blob/develop/docs/apis/datasets.md
train_dataset = pdrs.datasets.ClasDataset(
data_dir='E:/dataFiles/github/PaddleRS/tutorials/train/classification/DataSet',
file_list='tutorials/train/classification/DataSet/train_list.txt',
label_list='tutorials/train/classification/DataSet/label_list.txt',
transforms=train_transforms,
num_workers=0,
shuffle=True)
eval_dataset = pdrs.datasets.ClasDataset(
data_dir='E:/dataFiles/github/PaddleRS/tutorials/train/classification/DataSet',
file_list='tutorials/train/classification/DataSet/test_list.txt',
label_list='tutorials/train/classification/DataSet/label_list.txt',
transforms=eval_transforms,
num_workers=0,
shuffle=False)
# 初始化模型，并进行训练
# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/paddlers/blob/develop/docs/visualdl.md
num_classes = len(train_dataset.labels)
model = pdrs.tasks.ResNet50_vd(num_classes=num_classes)
# API说明：https://github.com/PaddlePaddle/paddlers/blob/develop/docs/apis/models/semantic_segmentation.md
# 各参数介绍与调整说明：https://github.com/PaddlePaddle/paddlers/blob/develop/docs/parameters.md
model.train(
num_epochs=10,
train_dataset=train_dataset,
train_batch_size=4,
eval_dataset=eval_dataset,
learning_rate=0.01,
pretrain_weights=None,
save_dir='output/resnet_vd')
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