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PaddleRS/paddlers/tasks/object_detector.py

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# Copyright (c) 2022 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
import collections
import copy
import os
import os.path as osp
import numpy as np
import paddle
from paddle.static import InputSpec
import paddlers.models.ppdet as ppdet
from paddlers.models.ppdet.modeling.proposal_generator.target_layer import BBoxAssigner, MaskAssigner
import paddlers
import paddlers.utils.logging as logging
from paddlers.transforms import decode_image
from paddlers.transforms.operators import _NormalizeBox, _PadBox, _BboxXYXY2XYWH, Resize, Pad
from paddlers.transforms.batch_operators import BatchCompose, BatchRandomResize, BatchRandomResizeByShort, \
_BatchPad, _Gt2YoloTarget
from .base import BaseModel
from .utils.det_metrics import VOCMetric, COCOMetric
from paddlers.models.ppdet.optimizer import ModelEMA
from paddlers.utils.checkpoint import det_pretrain_weights_dict
__all__ = [
"YOLOv3", "FasterRCNN", "PPYOLO", "PPYOLOTiny", "PPYOLOv2", "MaskRCNN"
]
class BaseDetector(BaseModel):
def __init__(self, model_name, num_classes=80, **params):
self.init_params.update(locals())
if 'with_net' in self.init_params:
del self.init_params['with_net']
super(BaseDetector, self).__init__('detector')
if not hasattr(ppdet.modeling, model_name):
raise ValueError("ERROR: There is no model named {}.".format(
model_name))
self.model_name = model_name
self.num_classes = num_classes
self.labels = None
if params.get('with_net', True):
params.pop('with_net', None)
self.net = self.build_net(**params)
def build_net(self, **params):
with paddle.utils.unique_name.guard():
net = ppdet.modeling.__dict__[self.model_name](**params)
return net
def _fix_transforms_shape(self, image_shape):
raise NotImplementedError("_fix_transforms_shape: not implemented!")
def _define_input_spec(self, image_shape):
input_spec = [{
"image": InputSpec(
shape=image_shape, name='image', dtype='float32'),
"im_shape": InputSpec(
shape=[image_shape[0], 2], name='im_shape', dtype='float32'),
"scale_factor": InputSpec(
shape=[image_shape[0], 2], name='scale_factor', dtype='float32')
}]
return input_spec
def _check_image_shape(self, image_shape):
if len(image_shape) == 2:
image_shape = [1, 3] + image_shape
if image_shape[-2] % 32 > 0 or image_shape[-1] % 32 > 0:
raise Exception(
"Height and width in fixed_input_shape must be a multiple of 32, but received {}.".
format(image_shape[-2:]))
return image_shape
def _get_test_inputs(self, image_shape):
if image_shape is not None:
image_shape = self._check_image_shape(image_shape)
self._fix_transforms_shape(image_shape[-2:])
else:
image_shape = [None, 3, -1, -1]
self.fixed_input_shape = image_shape
return self._define_input_spec(image_shape)
def _get_backbone(self, backbone_name, **params):
backbone = getattr(ppdet.modeling, backbone_name)(**params)
return backbone
def run(self, net, inputs, mode):
net_out = net(inputs)
if mode in ['train', 'eval']:
outputs = net_out
else:
outputs = dict()
for key in net_out:
outputs[key] = net_out[key].numpy()
return outputs
def default_optimizer(self,
parameters,
learning_rate,
warmup_steps,
warmup_start_lr,
lr_decay_epochs,
lr_decay_gamma,
num_steps_each_epoch,
reg_coeff=1e-04,
scheduler='Piecewise',
num_epochs=None):
if scheduler.lower() == 'piecewise':
if warmup_steps > 0 and warmup_steps > lr_decay_epochs[
0] * num_steps_each_epoch:
logging.error(
"In function train(), parameters must satisfy: "
"warmup_steps <= lr_decay_epochs[0] * num_samples_in_train_dataset. "
"See this doc for more information: "
"https://github.com/PaddlePaddle/PaddleRS/blob/develop/docs/parameters.md",
exit=False)
logging.error(
"Either `warmup_steps` be less than {} or lr_decay_epochs[0] be greater than {} "
"must be satisfied, please modify 'warmup_steps' or 'lr_decay_epochs' in train function".
format(lr_decay_epochs[0] * num_steps_each_epoch,
warmup_steps // num_steps_each_epoch),
exit=True)
boundaries = [b * num_steps_each_epoch for b in lr_decay_epochs]
values = [(lr_decay_gamma**i) * learning_rate
for i in range(len(lr_decay_epochs) + 1)]
scheduler = paddle.optimizer.lr.PiecewiseDecay(boundaries, values)
elif scheduler.lower() == 'cosine':
if num_epochs is None:
logging.error(
"`num_epochs` must be set while using cosine annealing decay scheduler, but received {}".
format(num_epochs),
exit=False)
if warmup_steps > 0 and warmup_steps > num_epochs * num_steps_each_epoch:
logging.error(
"In function train(), parameters must satisfy: "
"warmup_steps <= num_epochs * num_samples_in_train_dataset. "
"See this doc for more information: "
"https://github.com/PaddlePaddle/PaddleRS/blob/develop/docs/parameters.md",
exit=False)
logging.error(
"`warmup_steps` must be less than the total number of steps({}), "
"please modify 'num_epochs' or 'warmup_steps' in train function".
format(num_epochs * num_steps_each_epoch),
exit=True)
T_max = num_epochs * num_steps_each_epoch - warmup_steps
scheduler = paddle.optimizer.lr.CosineAnnealingDecay(
learning_rate=learning_rate,
T_max=T_max,
eta_min=0.0,
last_epoch=-1)
else:
logging.error(
"Invalid learning rate scheduler: {}!".format(scheduler),
exit=True)
if warmup_steps > 0:
scheduler = paddle.optimizer.lr.LinearWarmup(
learning_rate=scheduler,
warmup_steps=warmup_steps,
start_lr=warmup_start_lr,
end_lr=learning_rate)
optimizer = paddle.optimizer.Momentum(
scheduler,
momentum=.9,
weight_decay=paddle.regularizer.L2Decay(coeff=reg_coeff),
parameters=parameters)
return optimizer
def train(self,
num_epochs,
train_dataset,
train_batch_size=64,
eval_dataset=None,
optimizer=None,
save_interval_epochs=1,
log_interval_steps=10,
save_dir='output',
pretrain_weights='IMAGENET',
learning_rate=.001,
warmup_steps=0,
warmup_start_lr=0.0,
lr_decay_epochs=(216, 243),
lr_decay_gamma=0.1,
metric=None,
use_ema=False,
early_stop=False,
early_stop_patience=5,
use_vdl=True,
resume_checkpoint=None):
"""
Train the model.
Args:
num_epochs(int): The number of epochs.
train_dataset(paddlers.dataset): Training dataset.
train_batch_size(int, optional): Total batch size among all cards used in training. Defaults to 64.
eval_dataset(paddlers.dataset, optional):
Evaluation dataset. If None, the model will not be evaluated during training process. Defaults to None.
optimizer(paddle.optimizer.Optimizer or None, optional):
Optimizer used for training. If None, a default optimizer is used. Defaults to None.
save_interval_epochs(int, optional): Epoch interval for saving the model. Defaults to 1.
log_interval_steps(int, optional): Step interval for printing training information. Defaults to 10.
save_dir(str, optional): Directory to save the model. Defaults to 'output'.
pretrain_weights(str or None, optional):
None or name/path of pretrained weights. If None, no pretrained weights will be loaded. Defaults to 'IMAGENET'.
learning_rate(float, optional): Learning rate for training. Defaults to .001.
warmup_steps(int, optional): The number of steps of warm-up training. Defaults to 0.
warmup_start_lr(float, optional): Start learning rate of warm-up training. Defaults to 0..
lr_decay_epochs(list or tuple, optional): Epoch milestones for learning rate decay. Defaults to (216, 243).
lr_decay_gamma(float, optional): Gamma coefficient of learning rate decay. Defaults to .1.
metric({'VOC', 'COCO', None}, optional):
Evaluation metric. If None, determine the metric according to the dataset format. Defaults to None.
use_ema(bool, optional): Whether to use exponential moving average strategy. Defaults to False.
early_stop(bool, optional): Whether to adopt early stop strategy. Defaults to False.
early_stop_patience(int, optional): Early stop patience. Defaults to 5.
use_vdl(bool, optional): Whether to use VisualDL to monitor the training process. Defaults to True.
resume_checkpoint(str or None, optional): The path of the checkpoint to resume training from.
If None, no training checkpoint will be resumed. At most one of `resume_checkpoint` and
`pretrain_weights` can be set simultaneously. Defaults to None.
"""
if self.status == 'Infer':
logging.error(
"Exported inference model does not support training.",
exit=True)
if pretrain_weights is not None and resume_checkpoint is not None:
logging.error(
"pretrain_weights and resume_checkpoint cannot be set simultaneously.",
exit=True)
if train_dataset.__class__.__name__ == 'VOCDetection':
train_dataset.data_fields = {
'im_id', 'image_shape', 'image', 'gt_bbox', 'gt_class',
'difficult'
}
elif train_dataset.__class__.__name__ == 'CocoDetection':
if self.__class__.__name__ == 'MaskRCNN':
train_dataset.data_fields = {
'im_id', 'image_shape', 'image', 'gt_bbox', 'gt_class',
'gt_poly', 'is_crowd'
}
else:
train_dataset.data_fields = {
'im_id', 'image_shape', 'image', 'gt_bbox', 'gt_class',
'is_crowd'
}
if metric is None:
if eval_dataset.__class__.__name__ == 'VOCDetection':
self.metric = 'voc'
elif eval_dataset.__class__.__name__ == 'CocoDetection':
self.metric = 'coco'
else:
assert metric.lower() in ['coco', 'voc'], \
"Evaluation metric {} is not supported, please choose form 'COCO' and 'VOC'"
self.metric = metric.lower()
self.labels = train_dataset.labels
self.num_max_boxes = train_dataset.num_max_boxes
train_dataset.batch_transforms = self._compose_batch_transform(
train_dataset.transforms, mode='train')
# build optimizer if not defined
if optimizer is None:
num_steps_each_epoch = len(train_dataset) // train_batch_size
self.optimizer = self.default_optimizer(
parameters=self.net.parameters(),
learning_rate=learning_rate,
warmup_steps=warmup_steps,
warmup_start_lr=warmup_start_lr,
lr_decay_epochs=lr_decay_epochs,
lr_decay_gamma=lr_decay_gamma,
num_steps_each_epoch=num_steps_each_epoch)
else:
self.optimizer = optimizer
# initiate weights
if pretrain_weights is not None and not osp.exists(pretrain_weights):
if pretrain_weights not in det_pretrain_weights_dict['_'.join(
[self.model_name, self.backbone_name])]:
logging.warning(
"Path of pretrain_weights('{}') does not exist!".format(
pretrain_weights))
pretrain_weights = det_pretrain_weights_dict['_'.join(
[self.model_name, self.backbone_name])][0]
logging.warning("Pretrain_weights is forcibly set to '{}'. "
"If you don't want to use pretrain weights, "
"set pretrain_weights to be None.".format(
pretrain_weights))
elif pretrain_weights is not None and osp.exists(pretrain_weights):
if osp.splitext(pretrain_weights)[-1] != '.pdparams':
logging.error(
"Invalid pretrain weights. Please specify a '.pdparams' file.",
exit=True)
pretrained_dir = osp.join(save_dir, 'pretrain')
self.net_initialize(
pretrain_weights=pretrain_weights,
save_dir=pretrained_dir,
resume_checkpoint=resume_checkpoint,
is_backbone_weights=(pretrain_weights == 'IMAGENET' and
'ESNet_' in self.backbone_name))
if use_ema:
ema = ModelEMA(model=self.net, decay=.9998, use_thres_step=True)
else:
ema = None
# start train loop
self.train_loop(
num_epochs=num_epochs,
train_dataset=train_dataset,
train_batch_size=train_batch_size,
eval_dataset=eval_dataset,
save_interval_epochs=save_interval_epochs,
log_interval_steps=log_interval_steps,
save_dir=save_dir,
ema=ema,
early_stop=early_stop,
early_stop_patience=early_stop_patience,
use_vdl=use_vdl)
def quant_aware_train(self,
num_epochs,
train_dataset,
train_batch_size=64,
eval_dataset=None,
optimizer=None,
save_interval_epochs=1,
log_interval_steps=10,
save_dir='output',
learning_rate=.00001,
warmup_steps=0,
warmup_start_lr=0.0,
lr_decay_epochs=(216, 243),
lr_decay_gamma=0.1,
metric=None,
use_ema=False,
early_stop=False,
early_stop_patience=5,
use_vdl=True,
resume_checkpoint=None,
quant_config=None):
"""
Quantization-aware training.
Args:
num_epochs(int): The number of epochs.
train_dataset(paddlers.dataset): Training dataset.
train_batch_size(int, optional): Total batch size among all cards used in training. Defaults to 64.
eval_dataset(paddlers.dataset, optional):
Evaluation dataset. If None, the model will not be evaluated during training process. Defaults to None.
optimizer(paddle.optimizer.Optimizer or None, optional):
Optimizer used for training. If None, a default optimizer is used. Defaults to None.
save_interval_epochs(int, optional): Epoch interval for saving the model. Defaults to 1.
log_interval_steps(int, optional): Step interval for printing training information. Defaults to 10.
save_dir(str, optional): Directory to save the model. Defaults to 'output'.
learning_rate(float, optional): Learning rate for training. Defaults to .001.
warmup_steps(int, optional): The number of steps of warm-up training. Defaults to 0.
warmup_start_lr(float, optional): Start learning rate of warm-up training. Defaults to 0..
lr_decay_epochs(list or tuple, optional): Epoch milestones for learning rate decay. Defaults to (216, 243).
lr_decay_gamma(float, optional): Gamma coefficient of learning rate decay. Defaults to .1.
metric({'VOC', 'COCO', None}, optional):
Evaluation metric. If None, determine the metric according to the dataset format. Defaults to None.
use_ema(bool, optional): Whether to use exponential moving average strategy. Defaults to False.
early_stop(bool, optional): Whether to adopt early stop strategy. Defaults to False.
early_stop_patience(int, optional): Early stop patience. Defaults to 5.
use_vdl(bool, optional): Whether to use VisualDL to monitor the training process. Defaults to True.
quant_config(dict or None, optional): Quantization configuration. If None, a default rule of thumb
configuration will be used. Defaults to None.
resume_checkpoint(str or None, optional): The path of the checkpoint to resume quantization-aware training
from. If None, no training checkpoint will be resumed. Defaults to None.
"""
self._prepare_qat(quant_config)
self.train(
num_epochs=num_epochs,
train_dataset=train_dataset,
train_batch_size=train_batch_size,
eval_dataset=eval_dataset,
optimizer=optimizer,
save_interval_epochs=save_interval_epochs,
log_interval_steps=log_interval_steps,
save_dir=save_dir,
pretrain_weights=None,
learning_rate=learning_rate,
warmup_steps=warmup_steps,
warmup_start_lr=warmup_start_lr,
lr_decay_epochs=lr_decay_epochs,
lr_decay_gamma=lr_decay_gamma,
metric=metric,
use_ema=use_ema,
early_stop=early_stop,
early_stop_patience=early_stop_patience,
use_vdl=use_vdl,
resume_checkpoint=resume_checkpoint)
def evaluate(self,
eval_dataset,
batch_size=1,
metric=None,
return_details=False):
"""
Evaluate the model.
Args:
eval_dataset(paddlers.dataset): Evaluation dataset.
batch_size(int, optional): Total batch size among all cards used for evaluation. Defaults to 1.
metric({'VOC', 'COCO', None}, optional):
Evaluation metric. If None, determine the metric according to the dataset format. Defaults to None.
return_details(bool, optional): Whether to return evaluation details. Defaults to False.
Returns:
collections.OrderedDict with key-value pairs: {"mAP(0.50, 11point)":`mean average precision`}.
"""
if metric is None:
if not hasattr(self, 'metric'):
if eval_dataset.__class__.__name__ == 'VOCDetection':
self.metric = 'voc'
elif eval_dataset.__class__.__name__ == 'CocoDetection':
self.metric = 'coco'
else:
assert metric.lower() in ['coco', 'voc'], \
"Evaluation metric {} is not supported, please choose form 'COCO' and 'VOC'"
self.metric = metric.lower()
if self.metric == 'voc':
eval_dataset.data_fields = {
'im_id', 'image_shape', 'image', 'gt_bbox', 'gt_class',
'difficult'
}
elif self.metric == 'coco':
if self.__class__.__name__ == 'MaskRCNN':
eval_dataset.data_fields = {
'im_id', 'image_shape', 'image', 'gt_bbox', 'gt_class',
'gt_poly', 'is_crowd'
}
else:
eval_dataset.data_fields = {
'im_id', 'image_shape', 'image', 'gt_bbox', 'gt_class',
'is_crowd'
}
eval_dataset.batch_transforms = self._compose_batch_transform(
eval_dataset.transforms, mode='eval')
self._check_transforms(eval_dataset.transforms, 'eval')
self.net.eval()
nranks = paddle.distributed.get_world_size()
local_rank = paddle.distributed.get_rank()
if nranks > 1:
# Initialize parallel environment if not done.
if not paddle.distributed.parallel.parallel_helper._is_parallel_ctx_initialized(
):
paddle.distributed.init_parallel_env()
if batch_size > 1:
logging.warning(
"Detector only supports single card evaluation with batch_size=1 "
"during evaluation, so batch_size is forcibly set to 1.")
batch_size = 1
if nranks < 2 or local_rank == 0:
self.eval_data_loader = self.build_data_loader(
eval_dataset, batch_size=batch_size, mode='eval')
is_bbox_normalized = False
if eval_dataset.batch_transforms is not None:
is_bbox_normalized = any(
isinstance(t, _NormalizeBox)
for t in eval_dataset.batch_transforms.batch_transforms)
if self.metric == 'voc':
eval_metric = VOCMetric(
labels=eval_dataset.labels,
coco_gt=copy.deepcopy(eval_dataset.coco_gt),
is_bbox_normalized=is_bbox_normalized,
classwise=False)
else:
eval_metric = COCOMetric(
coco_gt=copy.deepcopy(eval_dataset.coco_gt),
classwise=False)
scores = collections.OrderedDict()
logging.info(
"Start to evaluate(total_samples={}, total_steps={})...".format(
eval_dataset.num_samples, eval_dataset.num_samples))
with paddle.no_grad():
for step, data in enumerate(self.eval_data_loader):
outputs = self.run(self.net, data, 'eval')
eval_metric.update(data, outputs)
eval_metric.accumulate()
self.eval_details = eval_metric.details
scores.update(eval_metric.get())
eval_metric.reset()
if return_details:
return scores, self.eval_details
return scores
def predict(self, img_file, transforms=None):
"""
Do inference.
Args:
img_file(list[np.ndarray | str] | str | np.ndarray):
Image path or decoded image data, which also could constitute a list,meaning all images to be
predicted as a mini-batch.
transforms(paddlers.transforms.Compose or None, optional):
Transforms for inputs. If None, the transforms for evaluation process will be used. Defaults to None.
Returns:
If img_file is a string or np.array, the result is a list of dict with key-value pairs:
{"category_id": `category_id`, "category": `category`, "bbox": `[x, y, w, h]`, "score": `score`}.
If img_file is a list, the result is a list composed of dicts with the corresponding fields:
category_id(int): the predicted category ID. 0 represents the first category in the dataset, and so on.
category(str): category name
bbox(list): bounding box in [x, y, w, h] format
score(str): confidence
mask(dict): Only for instance segmentation task. Mask of the object in RLE format
"""
if transforms is None and not hasattr(self, 'test_transforms'):
raise Exception("transforms need to be defined, now is None.")
if transforms is None:
transforms = self.test_transforms
if isinstance(img_file, (str, np.ndarray)):
images = [img_file]
else:
images = img_file
batch_samples = self._preprocess(images, transforms)
self.net.eval()
outputs = self.run(self.net, batch_samples, 'test')
prediction = self._postprocess(outputs)
if isinstance(img_file, (str, np.ndarray)):
prediction = prediction[0]
return prediction
def _preprocess(self, images, transforms, to_tensor=True):
self._check_transforms(transforms, 'test')
batch_samples = list()
for im in images:
if isinstance(im, str):
im = decode_image(im, to_rgb=False)
sample = {'image': im}
sample = transforms(sample)
batch_samples.append(sample)
batch_transforms = self._compose_batch_transform(transforms, 'test')
batch_samples = batch_transforms(batch_samples)
if to_tensor:
for k in batch_samples:
batch_samples[k] = paddle.to_tensor(batch_samples[k])
return batch_samples
def _postprocess(self, batch_pred):
infer_result = {}
if 'bbox' in batch_pred:
bboxes = batch_pred['bbox']
bbox_nums = batch_pred['bbox_num']
det_res = []
k = 0
for i in range(len(bbox_nums)):
det_nums = bbox_nums[i]
for j in range(det_nums):
dt = bboxes[k]
k = k + 1
num_id, score, xmin, ymin, xmax, ymax = dt.tolist()
if int(num_id) < 0:
continue
category = self.labels[int(num_id)]
w = xmax - xmin
h = ymax - ymin
bbox = [xmin, ymin, w, h]
dt_res = {
'category_id': int(num_id),
'category': category,
'bbox': bbox,
'score': score
}
det_res.append(dt_res)
infer_result['bbox'] = det_res
if 'mask' in batch_pred:
masks = batch_pred['mask']
bboxes = batch_pred['bbox']
mask_nums = batch_pred['bbox_num']
seg_res = []
k = 0
for i in range(len(mask_nums)):
det_nums = mask_nums[i]
for j in range(det_nums):
mask = masks[k].astype(np.uint8)
score = float(bboxes[k][1])
label = int(bboxes[k][0])
k = k + 1
if label == -1:
continue
category = self.labels[int(label)]
sg_res = {
'category_id': int(label),
'category': category,
'mask': mask.astype('uint8'),
'score': score
}
seg_res.append(sg_res)
infer_result['mask'] = seg_res
bbox_num = batch_pred['bbox_num']
results = []
start = 0
for num in bbox_num:
end = start + num
curr_res = infer_result['bbox'][start:end]
if 'mask' in infer_result:
mask_res = infer_result['mask'][start:end]
for box, mask in zip(curr_res, mask_res):
box.update(mask)
results.append(curr_res)
start = end
return results
def _check_transforms(self, transforms, mode):
super()._check_transforms(transforms, mode)
if not isinstance(transforms.arrange,
paddlers.transforms.ArrangeDetector):
raise TypeError(
"`transforms.arrange` must be an ArrangeDetector object.")
class PicoDet(BaseDetector):
def __init__(self,
num_classes=80,
backbone='ESNet_m',
nms_score_threshold=.025,
nms_topk=1000,
nms_keep_topk=100,
nms_iou_threshold=.6,
**params):
self.init_params = locals()
if backbone not in {
'ESNet_s', 'ESNet_m', 'ESNet_l', 'LCNet', 'MobileNetV3',
'ResNet18_vd'
}:
raise ValueError(
"backbone: {} is not supported. Please choose one of "
"('ESNet_s', 'ESNet_m', 'ESNet_l', 'LCNet', 'MobileNetV3', 'ResNet18_vd')".
format(backbone))
self.backbone_name = backbone
if params.get('with_net', True):
if backbone == 'ESNet_s':
backbone = self._get_backbone(
'ESNet',
scale=.75,
feature_maps=[4, 11, 14],
act="hard_swish",
channel_ratio=[
0.875, 0.5, 0.5, 0.5, 0.625, 0.5, 0.625, 0.5, 0.5, 0.5,
0.5, 0.5, 0.5
])
neck_out_channels = 96
head_num_convs = 2
elif backbone == 'ESNet_m':
backbone = self._get_backbone(
'ESNet',
scale=1.0,
feature_maps=[4, 11, 14],
act="hard_swish",
channel_ratio=[
0.875, 0.5, 1.0, 0.625, 0.5, 0.75, 0.625, 0.625, 0.5,
0.625, 1.0, 0.625, 0.75
])
neck_out_channels = 128
head_num_convs = 4
elif backbone == 'ESNet_l':
backbone = self._get_backbone(
'ESNet',
scale=1.25,
feature_maps=[4, 11, 14],
act="hard_swish",
channel_ratio=[
0.875, 0.5, 1.0, 0.625, 0.5, 0.75, 0.625, 0.625, 0.5,
0.625, 1.0, 0.625, 0.75
])
neck_out_channels = 160
head_num_convs = 4
elif backbone == 'LCNet':
backbone = self._get_backbone(
'LCNet', scale=1.5, feature_maps=[3, 4, 5])
neck_out_channels = 128
head_num_convs = 4
elif backbone == 'MobileNetV3':
backbone = self._get_backbone(
'MobileNetV3',
scale=1.0,
with_extra_blocks=False,
extra_block_filters=[],
feature_maps=[7, 13, 16])
neck_out_channels = 128
head_num_convs = 4
else:
backbone = self._get_backbone(
'ResNet',
depth=18,
variant='d',
return_idx=[1, 2, 3],
freeze_at=-1,
freeze_norm=False,
norm_decay=0.)
neck_out_channels = 128
head_num_convs = 4
neck = ppdet.modeling.CSPPAN(
in_channels=[i.channels for i in backbone.out_shape],
out_channels=neck_out_channels,
num_features=4,
num_csp_blocks=1,
use_depthwise=True)
head_conv_feat = ppdet.modeling.PicoFeat(
feat_in=neck_out_channels,
feat_out=neck_out_channels,
num_fpn_stride=4,
num_convs=head_num_convs,
norm_type='bn',
share_cls_reg=True, )
loss_class = ppdet.modeling.VarifocalLoss(
use_sigmoid=True, iou_weighted=True, loss_weight=1.0)
loss_dfl = ppdet.modeling.DistributionFocalLoss(loss_weight=.25)
loss_bbox = ppdet.modeling.GIoULoss(loss_weight=2.0)
assigner = ppdet.modeling.SimOTAAssigner(
candidate_topk=10, iou_weight=6, num_classes=num_classes)
nms = ppdet.modeling.MultiClassNMS(
nms_top_k=nms_topk,
keep_top_k=nms_keep_topk,
score_threshold=nms_score_threshold,
nms_threshold=nms_iou_threshold)
head = ppdet.modeling.PicoHead(
conv_feat=head_conv_feat,
num_classes=num_classes,
fpn_stride=[8, 16, 32, 64],
prior_prob=0.01,
reg_max=7,
cell_offset=.5,
loss_class=loss_class,
loss_dfl=loss_dfl,
loss_bbox=loss_bbox,
assigner=assigner,
feat_in_chan=neck_out_channels,
nms=nms)
params.update({
'backbone': backbone,
'neck': neck,
'head': head,
})
super(PicoDet, self).__init__(
model_name='PicoDet', num_classes=num_classes, **params)
def _compose_batch_transform(self, transforms, mode='train'):
default_batch_transforms = [_BatchPad(pad_to_stride=32)]
if mode == 'eval':
collate_batch = True
else:
collate_batch = False
custom_batch_transforms = []
for i, op in enumerate(transforms.transforms):
if isinstance(op, (BatchRandomResize, BatchRandomResizeByShort)):
if mode != 'train':
raise Exception(
"{} cannot be present in the {} transforms. ".format(
op.__class__.__name__, mode) +
"Please check the {} transforms.".format(mode))
custom_batch_transforms.insert(0, copy.deepcopy(op))
batch_transforms = BatchCompose(
custom_batch_transforms + default_batch_transforms,
collate_batch=collate_batch)
return batch_transforms
def _fix_transforms_shape(self, image_shape):
if getattr(self, 'test_transforms', None):
has_resize_op = False
resize_op_idx = -1
normalize_op_idx = len(self.test_transforms.transforms)
for idx, op in enumerate(self.test_transforms.transforms):
name = op.__class__.__name__
if name == 'Resize':
has_resize_op = True
resize_op_idx = idx
if name == 'Normalize':
normalize_op_idx = idx
if not has_resize_op:
self.test_transforms.transforms.insert(
normalize_op_idx,
Resize(
target_size=image_shape, interp='CUBIC'))
else:
self.test_transforms.transforms[
resize_op_idx].target_size = image_shape
def _get_test_inputs(self, image_shape):
if image_shape is not None:
image_shape = self._check_image_shape(image_shape)
self._fix_transforms_shape(image_shape[-2:])
else:
image_shape = [None, 3, 320, 320]
if getattr(self, 'test_transforms', None):
for idx, op in enumerate(self.test_transforms.transforms):
name = op.__class__.__name__
if name == 'Resize':
image_shape = [None, 3] + list(
self.test_transforms.transforms[idx].target_size)
logging.warning(
'[Important!!!] When exporting inference model for {}, '
'if fixed_input_shape is not set, it will be forcibly set to {}. '
'Please ensure image shape after transforms is {}, if not, '
'fixed_input_shape should be specified manually.'
.format(self.__class__.__name__, image_shape, image_shape[1:]))
self.fixed_input_shape = image_shape
return self._define_input_spec(image_shape)
def train(self,
num_epochs,
train_dataset,
train_batch_size=64,
eval_dataset=None,
optimizer=None,
save_interval_epochs=1,
log_interval_steps=10,
save_dir='output',
pretrain_weights='IMAGENET',
learning_rate=.001,
warmup_steps=0,
warmup_start_lr=0.0,
lr_decay_epochs=(216, 243),
lr_decay_gamma=0.1,
metric=None,
use_ema=False,
early_stop=False,
early_stop_patience=5,
use_vdl=True,
resume_checkpoint=None):
"""
Train the model.
Args:
num_epochs(int): The number of epochs.
train_dataset(paddlers.dataset): Training dataset.
train_batch_size(int, optional): Total batch size among all cards used in training. Defaults to 64.
eval_dataset(paddlers.dataset, optional):
Evaluation dataset. If None, the model will not be evaluated during training process. Defaults to None.
optimizer(paddle.optimizer.Optimizer or None, optional):
Optimizer used for training. If None, a default optimizer is used. Defaults to None.
save_interval_epochs(int, optional): Epoch interval for saving the model. Defaults to 1.
log_interval_steps(int, optional): Step interval for printing training information. Defaults to 10.
save_dir(str, optional): Directory to save the model. Defaults to 'output'.
pretrain_weights(str or None, optional):
None or name/path of pretrained weights. If None, no pretrained weights will be loaded. Defaults to 'IMAGENET'.
learning_rate(float, optional): Learning rate for training. Defaults to .001.
warmup_steps(int, optional): The number of steps of warm-up training. Defaults to 0.
warmup_start_lr(float, optional): Start learning rate of warm-up training. Defaults to 0..
lr_decay_epochs(list or tuple, optional): Epoch milestones for learning rate decay. Defaults to (216, 243).
lr_decay_gamma(float, optional): Gamma coefficient of learning rate decay. Defaults to .1.
metric({'VOC', 'COCO', None}, optional):
Evaluation metric. If None, determine the metric according to the dataset format. Defaults to None.
use_ema(bool, optional): Whether to use exponential moving average strategy. Defaults to False.
early_stop(bool, optional): Whether to adopt early stop strategy. Defaults to False.
early_stop_patience(int, optional): Early stop patience. Defaults to 5.
use_vdl(bool, optional): Whether to use VisualDL to monitor the training process. Defaults to True.
resume_checkpoint(str or None, optional): The path of the checkpoint to resume training from.
If None, no training checkpoint will be resumed. At most one of `resume_checkpoint` and
`pretrain_weights` can be set simultaneously. Defaults to None.
"""
if optimizer is None:
num_steps_each_epoch = len(train_dataset) // train_batch_size
optimizer = self.default_optimizer(
parameters=self.net.parameters(),
learning_rate=learning_rate,
warmup_steps=warmup_steps,
warmup_start_lr=warmup_start_lr,
lr_decay_epochs=lr_decay_epochs,
lr_decay_gamma=lr_decay_gamma,
num_steps_each_epoch=num_steps_each_epoch,
reg_coeff=4e-05,
scheduler='Cosine',
num_epochs=num_epochs)
super(PicoDet, self).train(
num_epochs=num_epochs,
train_dataset=train_dataset,
train_batch_size=train_batch_size,
eval_dataset=eval_dataset,
optimizer=optimizer,
save_interval_epochs=save_interval_epochs,
log_interval_steps=log_interval_steps,
save_dir=save_dir,
pretrain_weights=pretrain_weights,
learning_rate=learning_rate,
warmup_steps=warmup_steps,
warmup_start_lr=warmup_start_lr,
lr_decay_epochs=lr_decay_epochs,
lr_decay_gamma=lr_decay_gamma,
metric=metric,
use_ema=use_ema,
early_stop=early_stop,
early_stop_patience=early_stop_patience,
use_vdl=use_vdl,
resume_checkpoint=resume_checkpoint)
class YOLOv3(BaseDetector):
def __init__(self,
num_classes=80,
backbone='MobileNetV1',
anchors=[[10, 13], [16, 30], [33, 23], [30, 61], [62, 45],
[59, 119], [116, 90], [156, 198], [373, 326]],
anchor_masks=[[6, 7, 8], [3, 4, 5], [0, 1, 2]],
ignore_threshold=0.7,
nms_score_threshold=0.01,
nms_topk=1000,
nms_keep_topk=100,
nms_iou_threshold=0.45,
label_smooth=False,
**params):
self.init_params = locals()
if backbone not in {
'MobileNetV1', 'MobileNetV1_ssld', 'MobileNetV3',
'MobileNetV3_ssld', 'DarkNet53', 'ResNet50_vd_dcn', 'ResNet34'
}:
raise ValueError(
"backbone: {} is not supported. Please choose one of "
"('MobileNetV1', 'MobileNetV1_ssld', 'MobileNetV3', 'MobileNetV3_ssld', 'DarkNet53', "
"'ResNet50_vd_dcn', 'ResNet34')".format(backbone))
self.backbone_name = backbone
if params.get('with_net', True):
if paddlers.env_info['place'] == 'gpu' and paddlers.env_info[
'num'] > 1 and not os.environ.get('PADDLERS_EXPORT_STAGE'):
norm_type = 'sync_bn'
else:
norm_type = 'bn'
if 'MobileNetV1' in backbone:
norm_type = 'bn'
backbone = self._get_backbone('MobileNet', norm_type=norm_type)
elif 'MobileNetV3' in backbone:
backbone = self._get_backbone(
'MobileNetV3',
norm_type=norm_type,
feature_maps=[7, 13, 16])
elif backbone == 'ResNet50_vd_dcn':
backbone = self._get_backbone(
'ResNet',
norm_type=norm_type,
variant='d',
return_idx=[1, 2, 3],
dcn_v2_stages=[3],
freeze_at=-1,
freeze_norm=False)
elif backbone == 'ResNet34':
backbone = self._get_backbone(
'ResNet',
depth=34,
norm_type=norm_type,
return_idx=[1, 2, 3],
freeze_at=-1,
freeze_norm=False,
norm_decay=0.)
else:
backbone = self._get_backbone('DarkNet', norm_type=norm_type)
neck = ppdet.modeling.YOLOv3FPN(
norm_type=norm_type,
in_channels=[i.channels for i in backbone.out_shape])
loss = ppdet.modeling.YOLOv3Loss(
num_classes=num_classes,
ignore_thresh=ignore_threshold,
label_smooth=label_smooth)
yolo_head = ppdet.modeling.YOLOv3Head(
in_channels=[i.channels for i in neck.out_shape],
anchors=anchors,
anchor_masks=anchor_masks,
num_classes=num_classes,
loss=loss)
post_process = ppdet.modeling.BBoxPostProcess(
decode=ppdet.modeling.YOLOBox(num_classes=num_classes),
nms=ppdet.modeling.MultiClassNMS(
score_threshold=nms_score_threshold,
nms_top_k=nms_topk,
keep_top_k=nms_keep_topk,
nms_threshold=nms_iou_threshold))
params.update({
'backbone': backbone,
'neck': neck,
'yolo_head': yolo_head,
'post_process': post_process
})
super(YOLOv3, self).__init__(
model_name='YOLOv3', num_classes=num_classes, **params)
self.anchors = anchors
self.anchor_masks = anchor_masks
def _compose_batch_transform(self, transforms, mode='train'):
if mode == 'train':
default_batch_transforms = [
_BatchPad(pad_to_stride=-1), _NormalizeBox(),
_PadBox(getattr(self, 'num_max_boxes', 50)), _BboxXYXY2XYWH(),
_Gt2YoloTarget(
anchor_masks=self.anchor_masks,
anchors=self.anchors,
downsample_ratios=getattr(self, 'downsample_ratios',
[32, 16, 8]),
num_classes=self.num_classes)
]
else:
default_batch_transforms = [_BatchPad(pad_to_stride=-1)]
if mode == 'eval' and self.metric == 'voc':
collate_batch = False
else:
collate_batch = True
custom_batch_transforms = []
for i, op in enumerate(transforms.transforms):
if isinstance(op, (BatchRandomResize, BatchRandomResizeByShort)):
if mode != 'train':
raise Exception(
"{} cannot be present in the {} transforms. ".format(
op.__class__.__name__, mode) +
"Please check the {} transforms.".format(mode))
custom_batch_transforms.insert(0, copy.deepcopy(op))
batch_transforms = BatchCompose(
custom_batch_transforms + default_batch_transforms,
collate_batch=collate_batch)
return batch_transforms
def _fix_transforms_shape(self, image_shape):
if getattr(self, 'test_transforms', None):
has_resize_op = False
resize_op_idx = -1
normalize_op_idx = len(self.test_transforms.transforms)
for idx, op in enumerate(self.test_transforms.transforms):
name = op.__class__.__name__
if name == 'Resize':
has_resize_op = True
resize_op_idx = idx
if name == 'Normalize':
normalize_op_idx = idx
if not has_resize_op:
self.test_transforms.transforms.insert(
normalize_op_idx,
Resize(
target_size=image_shape, interp='CUBIC'))
else:
self.test_transforms.transforms[
resize_op_idx].target_size = image_shape
class FasterRCNN(BaseDetector):
def __init__(self,
num_classes=80,
backbone='ResNet50',
with_fpn=True,
with_dcn=False,
aspect_ratios=[0.5, 1.0, 2.0],
anchor_sizes=[[32], [64], [128], [256], [512]],
keep_top_k=100,
nms_threshold=0.5,
score_threshold=0.05,
fpn_num_channels=256,
rpn_batch_size_per_im=256,
rpn_fg_fraction=0.5,
test_pre_nms_top_n=None,
test_post_nms_top_n=1000,
**params):
self.init_params = locals()
if backbone not in {
'ResNet50', 'ResNet50_vd', 'ResNet50_vd_ssld', 'ResNet34',
'ResNet34_vd', 'ResNet101', 'ResNet101_vd', 'HRNet_W18'
}:
raise ValueError(
"backbone: {} is not supported. Please choose one of "
"('ResNet50', 'ResNet50_vd', 'ResNet50_vd_ssld', 'ResNet34', 'ResNet34_vd', "
"'ResNet101', 'ResNet101_vd', 'HRNet_W18')".format(backbone))
self.backbone_name = backbone
if params.get('with_net', True):
dcn_v2_stages = [1, 2, 3] if with_dcn else [-1]
if backbone == 'HRNet_W18':
if not with_fpn:
logging.warning(
"Backbone {} should be used along with fpn enabled, 'with_fpn' is forcibly set to True".
format(backbone))
with_fpn = True
if with_dcn:
logging.warning(
"Backbone {} should be used along with dcn disabled, 'with_dcn' is forcibly set to False".
format(backbone))
backbone = self._get_backbone(
'HRNet', width=18, freeze_at=0, return_idx=[0, 1, 2, 3])
elif backbone == 'ResNet50_vd_ssld':
if not with_fpn:
logging.warning(
"Backbone {} should be used along with fpn enabled, 'with_fpn' is forcibly set to True".
format(backbone))
with_fpn = True
backbone = self._get_backbone(
'ResNet',
variant='d',
norm_type='bn',
freeze_at=0,
return_idx=[0, 1, 2, 3],
num_stages=4,
lr_mult_list=[0.05, 0.05, 0.1, 0.15],
dcn_v2_stages=dcn_v2_stages)
elif 'ResNet50' in backbone:
if with_fpn:
backbone = self._get_backbone(
'ResNet',
variant='d' if '_vd' in backbone else 'b',
norm_type='bn',
freeze_at=0,
return_idx=[0, 1, 2, 3],
num_stages=4,
dcn_v2_stages=dcn_v2_stages)
else:
if with_dcn:
logging.warning(
"Backbone {} without fpn should be used along with dcn disabled, 'with_dcn' is forcibly set to False".
format(backbone))
backbone = self._get_backbone(
'ResNet',
variant='d' if '_vd' in backbone else 'b',
norm_type='bn',
freeze_at=0,
return_idx=[2],
num_stages=3)
elif 'ResNet34' in backbone:
if not with_fpn:
logging.warning(
"Backbone {} should be used along with fpn enabled, 'with_fpn' is forcibly set to True".
format(backbone))
with_fpn = True
backbone = self._get_backbone(
'ResNet',
depth=34,
variant='d' if 'vd' in backbone else 'b',
norm_type='bn',
freeze_at=0,
return_idx=[0, 1, 2, 3],
num_stages=4,
dcn_v2_stages=dcn_v2_stages)
else:
if not with_fpn:
logging.warning(
"Backbone {} should be used along with fpn enabled, 'with_fpn' is forcibly set to True".
format(backbone))
with_fpn = True
backbone = self._get_backbone(
'ResNet',
depth=101,
variant='d' if 'vd' in backbone else 'b',
norm_type='bn',
freeze_at=0,
return_idx=[0, 1, 2, 3],
num_stages=4,
dcn_v2_stages=dcn_v2_stages)
rpn_in_channel = backbone.out_shape[0].channels
if with_fpn:
self.backbone_name = self.backbone_name + '_fpn'
if 'HRNet' in self.backbone_name:
neck = ppdet.modeling.HRFPN(
in_channels=[i.channels for i in backbone.out_shape],
out_channel=fpn_num_channels,
spatial_scales=[
1.0 / i.stride for i in backbone.out_shape
],
share_conv=False)
else:
neck = ppdet.modeling.FPN(
in_channels=[i.channels for i in backbone.out_shape],
out_channel=fpn_num_channels,
spatial_scales=[
1.0 / i.stride for i in backbone.out_shape
])
rpn_in_channel = neck.out_shape[0].channels
anchor_generator_cfg = {
'aspect_ratios': aspect_ratios,
'anchor_sizes': anchor_sizes,
'strides': [4, 8, 16, 32, 64]
}
train_proposal_cfg = {
'min_size': 0.0,
'nms_thresh': .7,
'pre_nms_top_n': 2000,
'post_nms_top_n': 1000,
'topk_after_collect': True
}
test_proposal_cfg = {
'min_size': 0.0,
'nms_thresh': .7,
'pre_nms_top_n': 1000
if test_pre_nms_top_n is None else test_pre_nms_top_n,
'post_nms_top_n': test_post_nms_top_n
}
head = ppdet.modeling.TwoFCHead(
in_channel=neck.out_shape[0].channels, out_channel=1024)
roi_extractor_cfg = {
'resolution': 7,
'spatial_scale': [1. / i.stride for i in neck.out_shape],
'sampling_ratio': 0,
'aligned': True
}
with_pool = False
else:
neck = None
anchor_generator_cfg = {
'aspect_ratios': aspect_ratios,
'anchor_sizes': anchor_sizes,
'strides': [16]
}
train_proposal_cfg = {
'min_size': 0.0,
'nms_thresh': .7,
'pre_nms_top_n': 12000,
'post_nms_top_n': 2000,
'topk_after_collect': False
}
test_proposal_cfg = {
'min_size': 0.0,
'nms_thresh': .7,
'pre_nms_top_n': 6000
if test_pre_nms_top_n is None else test_pre_nms_top_n,
'post_nms_top_n': test_post_nms_top_n
}
head = ppdet.modeling.Res5Head()
roi_extractor_cfg = {
'resolution': 14,
'spatial_scale':
[1. / i.stride for i in backbone.out_shape],
'sampling_ratio': 0,
'aligned': True
}
with_pool = True
rpn_target_assign_cfg = {
'batch_size_per_im': rpn_batch_size_per_im,
'fg_fraction': rpn_fg_fraction,
'negative_overlap': .3,
'positive_overlap': .7,
'use_random': True
}
rpn_head = ppdet.modeling.RPNHead(
anchor_generator=anchor_generator_cfg,
rpn_target_assign=rpn_target_assign_cfg,
train_proposal=train_proposal_cfg,
test_proposal=test_proposal_cfg,
in_channel=rpn_in_channel)
bbox_assigner = BBoxAssigner(num_classes=num_classes)
bbox_head = ppdet.modeling.BBoxHead(
head=head,
in_channel=head.out_shape[0].channels,
roi_extractor=roi_extractor_cfg,
with_pool=with_pool,
bbox_assigner=bbox_assigner,
num_classes=num_classes)
bbox_post_process = ppdet.modeling.BBoxPostProcess(
num_classes=num_classes,
decode=ppdet.modeling.RCNNBox(num_classes=num_classes),
nms=ppdet.modeling.MultiClassNMS(
score_threshold=score_threshold,
keep_top_k=keep_top_k,
nms_threshold=nms_threshold))
params.update({
'backbone': backbone,
'neck': neck,
'rpn_head': rpn_head,
'bbox_head': bbox_head,
'bbox_post_process': bbox_post_process
})
else:
if backbone not in {'ResNet50', 'ResNet50_vd'}:
with_fpn = True
self.with_fpn = with_fpn
super(FasterRCNN, self).__init__(
model_name='FasterRCNN', num_classes=num_classes, **params)
def train(self,
num_epochs,
train_dataset,
train_batch_size=64,
eval_dataset=None,
optimizer=None,
save_interval_epochs=1,
log_interval_steps=10,
save_dir='output',
pretrain_weights='IMAGENET',
learning_rate=.001,
warmup_steps=0,
warmup_start_lr=0.0,
lr_decay_epochs=(216, 243),
lr_decay_gamma=0.1,
metric=None,
use_ema=False,
early_stop=False,
early_stop_patience=5,
use_vdl=True,
resume_checkpoint=None):
"""
Train the model.
Args:
num_epochs(int): The number of epochs.
train_dataset(paddlers.dataset): Training dataset.
train_batch_size(int, optional): Total batch size among all cards used in training. Defaults to 64.
eval_dataset(paddlers.dataset, optional):
Evaluation dataset. If None, the model will not be evaluated during training process. Defaults to None.
optimizer(paddle.optimizer.Optimizer or None, optional):
Optimizer used for training. If None, a default optimizer is used. Defaults to None.
save_interval_epochs(int, optional): Epoch interval for saving the model. Defaults to 1.
log_interval_steps(int, optional): Step interval for printing training information. Defaults to 10.
save_dir(str, optional): Directory to save the model. Defaults to 'output'.
pretrain_weights(str or None, optional):
None or name/path of pretrained weights. If None, no pretrained weights will be loaded. Defaults to 'IMAGENET'.
learning_rate(float, optional): Learning rate for training. Defaults to .001.
warmup_steps(int, optional): The number of steps of warm-up training. Defaults to 0.
warmup_start_lr(float, optional): Start learning rate of warm-up training. Defaults to 0..
lr_decay_epochs(list or tuple, optional): Epoch milestones for learning rate decay. Defaults to (216, 243).
lr_decay_gamma(float, optional): Gamma coefficient of learning rate decay. Defaults to .1.
metric({'VOC', 'COCO', None}, optional):
Evaluation metric. If None, determine the metric according to the dataset format. Defaults to None.
use_ema(bool, optional): Whether to use exponential moving average strategy. Defaults to False.
early_stop(bool, optional): Whether to adopt early stop strategy. Defaults to False.
early_stop_patience(int, optional): Early stop patience. Defaults to 5.
use_vdl(bool, optional): Whether to use VisualDL to monitor the training process. Defaults to True.
resume_checkpoint(str or None, optional): The path of the checkpoint to resume training from.
If None, no training checkpoint will be resumed. At most one of `resume_checkpoint` and
`pretrain_weights` can be set simultaneously. Defaults to None.
"""
if train_dataset.pos_num < len(train_dataset.file_list):
train_dataset.num_workers = 0
super(FasterRCNN, self).train(
num_epochs, train_dataset, train_batch_size, eval_dataset,
optimizer, save_interval_epochs, log_interval_steps, save_dir,
pretrain_weights, learning_rate, warmup_steps, warmup_start_lr,
lr_decay_epochs, lr_decay_gamma, metric, use_ema, early_stop,
early_stop_patience, use_vdl, resume_checkpoint)
def _compose_batch_transform(self, transforms, mode='train'):
if mode == 'train':
default_batch_transforms = [
_BatchPad(pad_to_stride=32 if self.with_fpn else -1)
]
else:
default_batch_transforms = [
_BatchPad(pad_to_stride=32 if self.with_fpn else -1)
]
custom_batch_transforms = []
for i, op in enumerate(transforms.transforms):
if isinstance(op, (BatchRandomResize, BatchRandomResizeByShort)):
if mode != 'train':
raise Exception(
"{} cannot be present in the {} transforms. ".format(
op.__class__.__name__, mode) +
"Please check the {} transforms.".format(mode))
custom_batch_transforms.insert(0, copy.deepcopy(op))
batch_transforms = BatchCompose(
custom_batch_transforms + default_batch_transforms,
collate_batch=False)
return batch_transforms
def _fix_transforms_shape(self, image_shape):
if getattr(self, 'test_transforms', None):
has_resize_op = False
resize_op_idx = -1
normalize_op_idx = len(self.test_transforms.transforms)
for idx, op in enumerate(self.test_transforms.transforms):
name = op.__class__.__name__
if name == 'ResizeByShort':
has_resize_op = True
resize_op_idx = idx
if name == 'Normalize':
normalize_op_idx = idx
if not has_resize_op:
self.test_transforms.transforms.insert(
normalize_op_idx,
Resize(
target_size=image_shape,
keep_ratio=True,
interp='CUBIC'))
else:
self.test_transforms.transforms[resize_op_idx] = Resize(
target_size=image_shape, keep_ratio=True, interp='CUBIC')
self.test_transforms.transforms.append(
Pad(im_padding_value=[0., 0., 0.]))
def _get_test_inputs(self, image_shape):
if image_shape is not None:
image_shape = self._check_image_shape(image_shape)
self._fix_transforms_shape(image_shape[-2:])
else:
image_shape = [None, 3, -1, -1]
if self.with_fpn:
self.test_transforms.transforms.append(
Pad(im_padding_value=[0., 0., 0.]))
self.fixed_input_shape = image_shape
return self._define_input_spec(image_shape)
class PPYOLO(YOLOv3):
def __init__(self,
num_classes=80,
backbone='ResNet50_vd_dcn',
anchors=None,
anchor_masks=None,
use_coord_conv=True,
use_iou_aware=True,
use_spp=True,
use_drop_block=True,
scale_x_y=1.05,
ignore_threshold=0.7,
label_smooth=False,
use_iou_loss=True,
use_matrix_nms=True,
nms_score_threshold=0.01,
nms_topk=-1,
nms_keep_topk=100,
nms_iou_threshold=0.45,
**params):
self.init_params = locals()
if backbone not in {
'ResNet50_vd_dcn', 'ResNet18_vd', 'MobileNetV3_large',
'MobileNetV3_small'
}:
raise ValueError(
"backbone: {} is not supported. Please choose one of "
"('ResNet50_vd_dcn', 'ResNet18_vd', 'MobileNetV3_large', 'MobileNetV3_small')".
format(backbone))
self.backbone_name = backbone
self.downsample_ratios = [
32, 16, 8
] if backbone == 'ResNet50_vd_dcn' else [32, 16]
if params.get('with_net', True):
if paddlers.env_info['place'] == 'gpu' and paddlers.env_info[
'num'] > 1 and not os.environ.get('PADDLERS_EXPORT_STAGE'):
norm_type = 'sync_bn'
else:
norm_type = 'bn'
if anchors is None and anchor_masks is None:
if 'MobileNetV3' in backbone:
anchors = [[11, 18], [34, 47], [51, 126], [115, 71],
[120, 195], [254, 235]]
anchor_masks = [[3, 4, 5], [0, 1, 2]]
elif backbone == 'ResNet50_vd_dcn':
anchors = [[10, 13], [16, 30], [33, 23], [30, 61],
[62, 45], [59, 119], [116, 90], [156, 198],
[373, 326]]
anchor_masks = [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
else:
anchors = [[10, 14], [23, 27], [37, 58], [81, 82],
[135, 169], [344, 319]]
anchor_masks = [[3, 4, 5], [0, 1, 2]]
elif anchors is None or anchor_masks is None:
raise ValueError("Please define both anchors and anchor_masks.")
if backbone == 'ResNet50_vd_dcn':
backbone = self._get_backbone(
'ResNet',
variant='d',
norm_type=norm_type,
return_idx=[1, 2, 3],
dcn_v2_stages=[3],
freeze_at=-1,
freeze_norm=False,
norm_decay=0.)
elif backbone == 'ResNet18_vd':
backbone = self._get_backbone(
'ResNet',
depth=18,
variant='d',
norm_type=norm_type,
return_idx=[2, 3],
freeze_at=-1,
freeze_norm=False,
norm_decay=0.)
elif backbone == 'MobileNetV3_large':
backbone = self._get_backbone(
'MobileNetV3',
model_name='large',
norm_type=norm_type,
scale=1,
with_extra_blocks=False,
extra_block_filters=[],
feature_maps=[13, 16])
elif backbone == 'MobileNetV3_small':
backbone = self._get_backbone(
'MobileNetV3',
model_name='small',
norm_type=norm_type,
scale=1,
with_extra_blocks=False,
extra_block_filters=[],
feature_maps=[9, 12])
neck = ppdet.modeling.PPYOLOFPN(
norm_type=norm_type,
in_channels=[i.channels for i in backbone.out_shape],
coord_conv=use_coord_conv,
drop_block=use_drop_block,
spp=use_spp,
conv_block_num=0
if ('MobileNetV3' in self.backbone_name or
self.backbone_name == 'ResNet18_vd') else 2)
loss = ppdet.modeling.YOLOv3Loss(
num_classes=num_classes,
ignore_thresh=ignore_threshold,
downsample=self.downsample_ratios,
label_smooth=label_smooth,
scale_x_y=scale_x_y,
iou_loss=ppdet.modeling.IouLoss(
loss_weight=2.5, loss_square=True)
if use_iou_loss else None,
iou_aware_loss=ppdet.modeling.IouAwareLoss(loss_weight=1.0)
if use_iou_aware else None)
yolo_head = ppdet.modeling.YOLOv3Head(
in_channels=[i.channels for i in neck.out_shape],
anchors=anchors,
anchor_masks=anchor_masks,
num_classes=num_classes,
loss=loss,
iou_aware=use_iou_aware)
if use_matrix_nms:
nms = ppdet.modeling.MatrixNMS(
keep_top_k=nms_keep_topk,
score_threshold=nms_score_threshold,
post_threshold=.05
if 'MobileNetV3' in self.backbone_name else .01,
nms_top_k=nms_topk,
background_label=-1)
else:
nms = ppdet.modeling.MultiClassNMS(
score_threshold=nms_score_threshold,
nms_top_k=nms_topk,
keep_top_k=nms_keep_topk,
nms_threshold=nms_iou_threshold)
post_process = ppdet.modeling.BBoxPostProcess(
decode=ppdet.modeling.YOLOBox(
num_classes=num_classes,
conf_thresh=.005
if 'MobileNetV3' in self.backbone_name else .01,
scale_x_y=scale_x_y),
nms=nms)
params.update({
'backbone': backbone,
'neck': neck,
'yolo_head': yolo_head,
'post_process': post_process
})
super(YOLOv3, self).__init__(
model_name='YOLOv3', num_classes=num_classes, **params)
self.anchors = anchors
self.anchor_masks = anchor_masks
self.model_name = 'PPYOLO'
def _get_test_inputs(self, image_shape):
if image_shape is not None:
image_shape = self._check_image_shape(image_shape)
self._fix_transforms_shape(image_shape[-2:])
else:
image_shape = [None, 3, 608, 608]
if getattr(self, 'test_transforms', None):
for idx, op in enumerate(self.test_transforms.transforms):
name = op.__class__.__name__
if name == 'Resize':
image_shape = [None, 3] + list(
self.test_transforms.transforms[idx].target_size)
logging.warning(
'[Important!!!] When exporting inference model for {}, '
'if fixed_input_shape is not set, it will be forcibly set to {}. '
'Please ensure image shape after transforms is {}, if not, '
'fixed_input_shape should be specified manually.'
.format(self.__class__.__name__, image_shape, image_shape[1:]))
self.fixed_input_shape = image_shape
return self._define_input_spec(image_shape)
class PPYOLOTiny(YOLOv3):
def __init__(self,
num_classes=80,
backbone='MobileNetV3',
anchors=[[10, 15], [24, 36], [72, 42], [35, 87], [102, 96],
[60, 170], [220, 125], [128, 222], [264, 266]],
anchor_masks=[[6, 7, 8], [3, 4, 5], [0, 1, 2]],
use_iou_aware=False,
use_spp=True,
use_drop_block=True,
scale_x_y=1.05,
ignore_threshold=0.5,
label_smooth=False,
use_iou_loss=True,
use_matrix_nms=False,
nms_score_threshold=0.005,
nms_topk=1000,
nms_keep_topk=100,
nms_iou_threshold=0.45,
**params):
self.init_params = locals()
if backbone != 'MobileNetV3':
logging.warning("PPYOLOTiny only supports MobileNetV3 as backbone. "
"Backbone is forcibly set to MobileNetV3.")
self.backbone_name = 'MobileNetV3'
self.downsample_ratios = [32, 16, 8]
if params.get('with_net', True):
if paddlers.env_info['place'] == 'gpu' and paddlers.env_info[
'num'] > 1 and not os.environ.get('PADDLERS_EXPORT_STAGE'):
norm_type = 'sync_bn'
else:
norm_type = 'bn'
backbone = self._get_backbone(
'MobileNetV3',
model_name='large',
norm_type=norm_type,
scale=.5,
with_extra_blocks=False,
extra_block_filters=[],
feature_maps=[7, 13, 16])
neck = ppdet.modeling.PPYOLOTinyFPN(
detection_block_channels=[160, 128, 96],
in_channels=[i.channels for i in backbone.out_shape],
spp=use_spp,
drop_block=use_drop_block)
loss = ppdet.modeling.YOLOv3Loss(
num_classes=num_classes,
ignore_thresh=ignore_threshold,
downsample=self.downsample_ratios,
label_smooth=label_smooth,
scale_x_y=scale_x_y,
iou_loss=ppdet.modeling.IouLoss(
loss_weight=2.5, loss_square=True)
if use_iou_loss else None,
iou_aware_loss=ppdet.modeling.IouAwareLoss(loss_weight=1.0)
if use_iou_aware else None)
yolo_head = ppdet.modeling.YOLOv3Head(
in_channels=[i.channels for i in neck.out_shape],
anchors=anchors,
anchor_masks=anchor_masks,
num_classes=num_classes,
loss=loss,
iou_aware=use_iou_aware)
if use_matrix_nms:
nms = ppdet.modeling.MatrixNMS(
keep_top_k=nms_keep_topk,
score_threshold=nms_score_threshold,
post_threshold=.05,
nms_top_k=nms_topk,
background_label=-1)
else:
nms = ppdet.modeling.MultiClassNMS(
score_threshold=nms_score_threshold,
nms_top_k=nms_topk,
keep_top_k=nms_keep_topk,
nms_threshold=nms_iou_threshold)
post_process = ppdet.modeling.BBoxPostProcess(
decode=ppdet.modeling.YOLOBox(
num_classes=num_classes,
conf_thresh=.005,
downsample_ratio=32,
clip_bbox=True,
scale_x_y=scale_x_y),
nms=nms)
params.update({
'backbone': backbone,
'neck': neck,
'yolo_head': yolo_head,
'post_process': post_process
})
super(YOLOv3, self).__init__(
model_name='YOLOv3', num_classes=num_classes, **params)
self.anchors = anchors
self.anchor_masks = anchor_masks
self.model_name = 'PPYOLOTiny'
def _get_test_inputs(self, image_shape):
if image_shape is not None:
image_shape = self._check_image_shape(image_shape)
self._fix_transforms_shape(image_shape[-2:])
else:
image_shape = [None, 3, 320, 320]
if getattr(self, 'test_transforms', None):
for idx, op in enumerate(self.test_transforms.transforms):
name = op.__class__.__name__
if name == 'Resize':
image_shape = [None, 3] + list(
self.test_transforms.transforms[idx].target_size)
logging.warning(
'[Important!!!] When exporting inference model for {},'.format(
self.__class__.__name__) +
' if fixed_input_shape is not set, it will be forcibly set to {}. '.
format(image_shape) +
'Please check image shape after transforms is {}, if not, fixed_input_shape '.
format(image_shape[1:]) + 'should be specified manually.')
self.fixed_input_shape = image_shape
return self._define_input_spec(image_shape)
class PPYOLOv2(YOLOv3):
def __init__(self,
num_classes=80,
backbone='ResNet50_vd_dcn',
anchors=[[10, 13], [16, 30], [33, 23], [30, 61], [62, 45],
[59, 119], [116, 90], [156, 198], [373, 326]],
anchor_masks=[[6, 7, 8], [3, 4, 5], [0, 1, 2]],
use_iou_aware=True,
use_spp=True,
use_drop_block=True,
scale_x_y=1.05,
ignore_threshold=0.7,
label_smooth=False,
use_iou_loss=True,
use_matrix_nms=True,
nms_score_threshold=0.01,
nms_topk=-1,
nms_keep_topk=100,
nms_iou_threshold=0.45,
**params):
self.init_params = locals()
if backbone not in {'ResNet50_vd_dcn', 'ResNet101_vd_dcn'}:
raise ValueError(
"backbone: {} is not supported. Please choose one of "
"('ResNet50_vd_dcn', 'ResNet101_vd_dcn')".format(backbone))
self.backbone_name = backbone
self.downsample_ratios = [32, 16, 8]
if params.get('with_net', True):
if paddlers.env_info['place'] == 'gpu' and paddlers.env_info[
'num'] > 1 and not os.environ.get('PADDLERS_EXPORT_STAGE'):
norm_type = 'sync_bn'
else:
norm_type = 'bn'
if backbone == 'ResNet50_vd_dcn':
backbone = self._get_backbone(
'ResNet',
variant='d',
norm_type=norm_type,
return_idx=[1, 2, 3],
dcn_v2_stages=[3],
freeze_at=-1,
freeze_norm=False,
norm_decay=0.)
elif backbone == 'ResNet101_vd_dcn':
backbone = self._get_backbone(
'ResNet',
depth=101,
variant='d',
norm_type=norm_type,
return_idx=[1, 2, 3],
dcn_v2_stages=[3],
freeze_at=-1,
freeze_norm=False,
norm_decay=0.)
neck = ppdet.modeling.PPYOLOPAN(
norm_type=norm_type,
in_channels=[i.channels for i in backbone.out_shape],
drop_block=use_drop_block,
block_size=3,
keep_prob=.9,
spp=use_spp)
loss = ppdet.modeling.YOLOv3Loss(
num_classes=num_classes,
ignore_thresh=ignore_threshold,
downsample=self.downsample_ratios,
label_smooth=label_smooth,
scale_x_y=scale_x_y,
iou_loss=ppdet.modeling.IouLoss(
loss_weight=2.5, loss_square=True)
if use_iou_loss else None,
iou_aware_loss=ppdet.modeling.IouAwareLoss(loss_weight=1.0)
if use_iou_aware else None)
yolo_head = ppdet.modeling.YOLOv3Head(
in_channels=[i.channels for i in neck.out_shape],
anchors=anchors,
anchor_masks=anchor_masks,
num_classes=num_classes,
loss=loss,
iou_aware=use_iou_aware,
iou_aware_factor=.5)
if use_matrix_nms:
nms = ppdet.modeling.MatrixNMS(
keep_top_k=nms_keep_topk,
score_threshold=nms_score_threshold,
post_threshold=.01,
nms_top_k=nms_topk,
background_label=-1)
else:
nms = ppdet.modeling.MultiClassNMS(
score_threshold=nms_score_threshold,
nms_top_k=nms_topk,
keep_top_k=nms_keep_topk,
nms_threshold=nms_iou_threshold)
post_process = ppdet.modeling.BBoxPostProcess(
decode=ppdet.modeling.YOLOBox(
num_classes=num_classes,
conf_thresh=.01,
downsample_ratio=32,
clip_bbox=True,
scale_x_y=scale_x_y),
nms=nms)
params.update({
'backbone': backbone,
'neck': neck,
'yolo_head': yolo_head,
'post_process': post_process
})
super(YOLOv3, self).__init__(
model_name='YOLOv3', num_classes=num_classes, **params)
self.anchors = anchors
self.anchor_masks = anchor_masks
self.model_name = 'PPYOLOv2'
def _get_test_inputs(self, image_shape):
if image_shape is not None:
image_shape = self._check_image_shape(image_shape)
self._fix_transforms_shape(image_shape[-2:])
else:
image_shape = [None, 3, 640, 640]
if getattr(self, 'test_transforms', None):
for idx, op in enumerate(self.test_transforms.transforms):
name = op.__class__.__name__
if name == 'Resize':
image_shape = [None, 3] + list(
self.test_transforms.transforms[idx].target_size)
logging.warning(
'[Important!!!] When exporting inference model for {},'.format(
self.__class__.__name__) +
' if fixed_input_shape is not set, it will be forcibly set to {}. '.
format(image_shape) +
'Please check image shape after transforms is {}, if not, fixed_input_shape '.
format(image_shape[1:]) + 'should be specified manually.')
self.fixed_input_shape = image_shape
return self._define_input_spec(image_shape)
class MaskRCNN(BaseDetector):
def __init__(self,
num_classes=80,
backbone='ResNet50_vd',
with_fpn=True,
with_dcn=False,
aspect_ratios=[0.5, 1.0, 2.0],
anchor_sizes=[[32], [64], [128], [256], [512]],
keep_top_k=100,
nms_threshold=0.5,
score_threshold=0.05,
fpn_num_channels=256,
rpn_batch_size_per_im=256,
rpn_fg_fraction=0.5,
test_pre_nms_top_n=None,
test_post_nms_top_n=1000,
**params):
self.init_params = locals()
if backbone not in {
'ResNet50', 'ResNet50_vd', 'ResNet50_vd_ssld', 'ResNet101',
'ResNet101_vd'
}:
raise ValueError(
"backbone: {} is not supported. Please choose one of "
"('ResNet50', 'ResNet50_vd', 'ResNet50_vd_ssld', 'ResNet101', 'ResNet101_vd')".
format(backbone))
self.backbone_name = backbone + '_fpn' if with_fpn else backbone
dcn_v2_stages = [1, 2, 3] if with_dcn else [-1]
if params.get('with_net', True):
if backbone == 'ResNet50':
if with_fpn:
backbone = self._get_backbone(
'ResNet',
norm_type='bn',
freeze_at=0,
return_idx=[0, 1, 2, 3],
num_stages=4,
dcn_v2_stages=dcn_v2_stages)
else:
if with_dcn:
logging.warning(
"Backbone {} should be used along with dcn disabled, 'with_dcn' is forcibly set to False".
format(backbone))
backbone = self._get_backbone(
'ResNet',
norm_type='bn',
freeze_at=0,
return_idx=[2],
num_stages=3)
elif 'ResNet50_vd' in backbone:
if not with_fpn:
logging.warning(
"Backbone {} should be used along with fpn enabled, 'with_fpn' is forcibly set to True".
format(backbone))
with_fpn = True
backbone = self._get_backbone(
'ResNet',
variant='d',
norm_type='bn',
freeze_at=0,
return_idx=[0, 1, 2, 3],
num_stages=4,
lr_mult_list=[0.05, 0.05, 0.1, 0.15]
if '_ssld' in backbone else [1.0, 1.0, 1.0, 1.0],
dcn_v2_stages=dcn_v2_stages)
else:
if not with_fpn:
logging.warning(
"Backbone {} should be used along with fpn enabled, 'with_fpn' is forcibly set to True".
format(backbone))
with_fpn = True
backbone = self._get_backbone(
'ResNet',
variant='d' if '_vd' in backbone else 'b',
depth=101,
norm_type='bn',
freeze_at=0,
return_idx=[0, 1, 2, 3],
num_stages=4,
dcn_v2_stages=dcn_v2_stages)
rpn_in_channel = backbone.out_shape[0].channels
if with_fpn:
neck = ppdet.modeling.FPN(
in_channels=[i.channels for i in backbone.out_shape],
out_channel=fpn_num_channels,
spatial_scales=[
1.0 / i.stride for i in backbone.out_shape
])
rpn_in_channel = neck.out_shape[0].channels
anchor_generator_cfg = {
'aspect_ratios': aspect_ratios,
'anchor_sizes': anchor_sizes,
'strides': [4, 8, 16, 32, 64]
}
train_proposal_cfg = {
'min_size': 0.0,
'nms_thresh': .7,
'pre_nms_top_n': 2000,
'post_nms_top_n': 1000,
'topk_after_collect': True
}
test_proposal_cfg = {
'min_size': 0.0,
'nms_thresh': .7,
'pre_nms_top_n': 1000
if test_pre_nms_top_n is None else test_pre_nms_top_n,
'post_nms_top_n': test_post_nms_top_n
}
bb_head = ppdet.modeling.TwoFCHead(
in_channel=neck.out_shape[0].channels, out_channel=1024)
bb_roi_extractor_cfg = {
'resolution': 7,
'spatial_scale': [1. / i.stride for i in neck.out_shape],
'sampling_ratio': 0,
'aligned': True
}
with_pool = False
m_head = ppdet.modeling.MaskFeat(
in_channel=neck.out_shape[0].channels,
out_channel=256,
num_convs=4)
m_roi_extractor_cfg = {
'resolution': 14,
'spatial_scale': [1. / i.stride for i in neck.out_shape],
'sampling_ratio': 0,
'aligned': True
}
mask_assigner = MaskAssigner(
num_classes=num_classes, mask_resolution=28)
share_bbox_feat = False
else:
neck = None
anchor_generator_cfg = {
'aspect_ratios': aspect_ratios,
'anchor_sizes': anchor_sizes,
'strides': [16]
}
train_proposal_cfg = {
'min_size': 0.0,
'nms_thresh': .7,
'pre_nms_top_n': 12000,
'post_nms_top_n': 2000,
'topk_after_collect': False
}
test_proposal_cfg = {
'min_size': 0.0,
'nms_thresh': .7,
'pre_nms_top_n': 6000
if test_pre_nms_top_n is None else test_pre_nms_top_n,
'post_nms_top_n': test_post_nms_top_n
}
bb_head = ppdet.modeling.Res5Head()
bb_roi_extractor_cfg = {
'resolution': 14,
'spatial_scale':
[1. / i.stride for i in backbone.out_shape],
'sampling_ratio': 0,
'aligned': True
}
with_pool = True
m_head = ppdet.modeling.MaskFeat(
in_channel=bb_head.out_shape[0].channels,
out_channel=256,
num_convs=0)
m_roi_extractor_cfg = {
'resolution': 14,
'spatial_scale':
[1. / i.stride for i in backbone.out_shape],
'sampling_ratio': 0,
'aligned': True
}
mask_assigner = MaskAssigner(
num_classes=num_classes, mask_resolution=14)
share_bbox_feat = True
rpn_target_assign_cfg = {
'batch_size_per_im': rpn_batch_size_per_im,
'fg_fraction': rpn_fg_fraction,
'negative_overlap': .3,
'positive_overlap': .7,
'use_random': True
}
rpn_head = ppdet.modeling.RPNHead(
anchor_generator=anchor_generator_cfg,
rpn_target_assign=rpn_target_assign_cfg,
train_proposal=train_proposal_cfg,
test_proposal=test_proposal_cfg,
in_channel=rpn_in_channel)
bbox_assigner = BBoxAssigner(num_classes=num_classes)
bbox_head = ppdet.modeling.BBoxHead(
head=bb_head,
in_channel=bb_head.out_shape[0].channels,
roi_extractor=bb_roi_extractor_cfg,
with_pool=with_pool,
bbox_assigner=bbox_assigner,
num_classes=num_classes)
mask_head = ppdet.modeling.MaskHead(
head=m_head,
roi_extractor=m_roi_extractor_cfg,
mask_assigner=mask_assigner,
share_bbox_feat=share_bbox_feat,
num_classes=num_classes)
bbox_post_process = ppdet.modeling.BBoxPostProcess(
num_classes=num_classes,
decode=ppdet.modeling.RCNNBox(num_classes=num_classes),
nms=ppdet.modeling.MultiClassNMS(
score_threshold=score_threshold,
keep_top_k=keep_top_k,
nms_threshold=nms_threshold))
mask_post_process = ppdet.modeling.MaskPostProcess(binary_thresh=.5)
params.update({
'backbone': backbone,
'neck': neck,
'rpn_head': rpn_head,
'bbox_head': bbox_head,
'mask_head': mask_head,
'bbox_post_process': bbox_post_process,
'mask_post_process': mask_post_process
})
self.with_fpn = with_fpn
super(MaskRCNN, self).__init__(
model_name='MaskRCNN', num_classes=num_classes, **params)
def train(self,
num_epochs,
train_dataset,
train_batch_size=64,
eval_dataset=None,
optimizer=None,
save_interval_epochs=1,
log_interval_steps=10,
save_dir='output',
pretrain_weights='IMAGENET',
learning_rate=.001,
warmup_steps=0,
warmup_start_lr=0.0,
lr_decay_epochs=(216, 243),
lr_decay_gamma=0.1,
metric=None,
use_ema=False,
early_stop=False,
early_stop_patience=5,
use_vdl=True,
resume_checkpoint=None):
"""
Train the model.
Args:
num_epochs(int): The number of epochs.
train_dataset(paddlers.dataset): Training dataset.
train_batch_size(int, optional): Total batch size among all cards used in training. Defaults to 64.
eval_dataset(paddlers.dataset, optional):
Evaluation dataset. If None, the model will not be evaluated during training process. Defaults to None.
optimizer(paddle.optimizer.Optimizer or None, optional):
Optimizer used for training. If None, a default optimizer is used. Defaults to None.
save_interval_epochs(int, optional): Epoch interval for saving the model. Defaults to 1.
log_interval_steps(int, optional): Step interval for printing training information. Defaults to 10.
save_dir(str, optional): Directory to save the model. Defaults to 'output'.
pretrain_weights(str or None, optional):
None or name/path of pretrained weights. If None, no pretrained weights will be loaded. Defaults to 'IMAGENET'.
learning_rate(float, optional): Learning rate for training. Defaults to .001.
warmup_steps(int, optional): The number of steps of warm-up training. Defaults to 0.
warmup_start_lr(float, optional): Start learning rate of warm-up training. Defaults to 0..
lr_decay_epochs(list or tuple, optional): Epoch milestones for learning rate decay. Defaults to (216, 243).
lr_decay_gamma(float, optional): Gamma coefficient of learning rate decay. Defaults to .1.
metric({'VOC', 'COCO', None}, optional):
Evaluation metric. If None, determine the metric according to the dataset format. Defaults to None.
use_ema(bool, optional): Whether to use exponential moving average strategy. Defaults to False.
early_stop(bool, optional): Whether to adopt early stop strategy. Defaults to False.
early_stop_patience(int, optional): Early stop patience. Defaults to 5.
use_vdl(bool, optional): Whether to use VisualDL to monitor the training process. Defaults to True.
resume_checkpoint(str or None, optional): The path of the checkpoint to resume training from.
If None, no training checkpoint will be resumed. At most one of `resume_checkpoint` and
`pretrain_weights` can be set simultaneously. Defaults to None.
"""
if train_dataset.pos_num < len(train_dataset.file_list):
train_dataset.num_workers = 0
super(MaskRCNN, self).train(
num_epochs, train_dataset, train_batch_size, eval_dataset,
optimizer, save_interval_epochs, log_interval_steps, save_dir,
pretrain_weights, learning_rate, warmup_steps, warmup_start_lr,
lr_decay_epochs, lr_decay_gamma, metric, use_ema, early_stop,
early_stop_patience, use_vdl, resume_checkpoint)
def _compose_batch_transform(self, transforms, mode='train'):
if mode == 'train':
default_batch_transforms = [
_BatchPad(pad_to_stride=32 if self.with_fpn else -1)
]
else:
default_batch_transforms = [
_BatchPad(pad_to_stride=32 if self.with_fpn else -1)
]
custom_batch_transforms = []
for i, op in enumerate(transforms.transforms):
if isinstance(op, (BatchRandomResize, BatchRandomResizeByShort)):
if mode != 'train':
raise Exception(
"{} cannot be present in the {} transforms. ".format(
op.__class__.__name__, mode) +
"Please check the {} transforms.".format(mode))
custom_batch_transforms.insert(0, copy.deepcopy(op))
batch_transforms = BatchCompose(
custom_batch_transforms + default_batch_transforms,
collate_batch=False)
return batch_transforms
def _fix_transforms_shape(self, image_shape):
if getattr(self, 'test_transforms', None):
has_resize_op = False
resize_op_idx = -1
normalize_op_idx = len(self.test_transforms.transforms)
for idx, op in enumerate(self.test_transforms.transforms):
name = op.__class__.__name__
if name == 'ResizeByShort':
has_resize_op = True
resize_op_idx = idx
if name == 'Normalize':
normalize_op_idx = idx
if not has_resize_op:
self.test_transforms.transforms.insert(
normalize_op_idx,
Resize(
target_size=image_shape,
keep_ratio=True,
interp='CUBIC'))
else:
self.test_transforms.transforms[resize_op_idx] = Resize(
target_size=image_shape, keep_ratio=True, interp='CUBIC')
self.test_transforms.transforms.append(
Pad(im_padding_value=[0., 0., 0.]))
def _get_test_inputs(self, image_shape):
if image_shape is not None:
image_shape = self._check_image_shape(image_shape)
self._fix_transforms_shape(image_shape[-2:])
else:
image_shape = [None, 3, -1, -1]
if self.with_fpn:
self.test_transforms.transforms.append(
Pad(im_padding_value=[0., 0., 0.]))
self.fixed_input_shape = image_shape
return self._define_input_spec(image_shape)