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PaddleRS/paddlers/tasks/classifier.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.
import math
import os.path as osp
import numpy as np
from collections import OrderedDict
import paddle
import paddle.nn.functional as F
from paddle.static import InputSpec
import paddlers.models.ppcls as paddleclas
import paddlers.custom_models.cls as cmcls
import paddlers
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 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 cls_pretrain_weights_dict
from paddlers.transforms import ImgDecoder, Resize
__all__ = [
"ResNet50_vd", "MobileNetV3_small_x1_0", "HRNet_W18_C", "CondenseNetV2_b"
]
class BaseClassifier(BaseModel):
def __init__(self,
model_name,
in_channels=3,
num_classes=2,
use_mixed_loss=False,
**params):
self.init_params = locals()
if 'with_net' in self.init_params:
del self.init_params['with_net']
super(BaseClassifier, self).__init__('classifier')
if not hasattr(paddleclas.arch.backbone, model_name) and \
not hasattr(cmcls, model_name):
raise Exception("ERROR: There's no model named {}.".format(
model_name))
self.model_name = model_name
self.in_channels = in_channels
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)
self.find_unused_parameters = True
def build_net(self, **params):
with paddle.utils.unique_name.guard():
model = dict(paddleclas.arch.backbone.__dict__,
**cmcls.__dict__)[self.model_name]
# TODO: Determine whether there is in_channels
try:
net = model(
class_num=self.num_classes,
in_channels=self.in_channels,
**params)
except:
net = model(class_num=self.num_classes, **params)
self.in_channels = 3
return net
def _fix_transforms_shape(self, image_shape):
if hasattr(self, 'test_transforms'):
if self.test_transforms is not 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 == 'Normalize':
normalize_op_idx = idx
if 'Resize' in name:
has_resize_op = True
resize_op_idx = idx
if not has_resize_op:
self.test_transforms.transforms.insert(
normalize_op_idx, Resize(target_size=image_shape))
else:
self.test_transforms.transforms[resize_op_idx] = Resize(
target_size=image_shape)
def _get_test_inputs(self, image_shape):
if image_shape is not None:
if len(image_shape) == 2:
image_shape = [1, 3] + image_shape
self._fix_transforms_shape(image_shape[-2:])
else:
image_shape = [None, 3, -1, -1]
self.fixed_input_shape = image_shape
input_spec = [
InputSpec(
shape=image_shape, name='image', dtype='float32')
]
return input_spec
def run(self, net, inputs, mode):
net_out = net(inputs[0])
label = paddle.to_tensor(inputs[1], dtype="int64")
outputs = OrderedDict()
if mode == 'test':
result = self._postprocess(net_out)
outputs = result[0]
if mode == 'eval':
# 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 = self.losses(net_out, label)
outputs['loss'] = loss_list['loss']
return outputs
def default_metric(self):
default_config = [{"TopkAcc": {"topk": [1, 5]}}]
return build_metrics(default_config)
def default_loss(self):
# TODO: use mixed loss and other loss
default_config = [{"CELoss": {"weight": 1.0}}]
return build_loss(default_config)
def default_optimizer(self,
parameters,
learning_rate,
num_epochs,
num_steps_each_epoch,
last_epoch=-1,
L2_coeff=0.00007):
decay_step = num_epochs * num_steps_each_epoch
lr_scheduler = paddle.optimizer.lr.CosineAnnealingDecay(
learning_rate, T_max=decay_step, eta_min=0, last_epoch=last_epoch)
optimizer = paddle.optimizer.Momentum(
learning_rate=lr_scheduler,
parameters=parameters,
momentum=0.9,
weight_decay=paddle.regularizer.L2Decay(L2_coeff))
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,
train_batch_size=2,
eval_dataset=None,
optimizer=None,
save_interval_epochs=1,
log_interval_steps=2,
save_dir='output',
pretrain_weights='IMAGENET',
learning_rate=0.1,
lr_decay_power=0.9,
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 2.
eval_dataset(paddlers.dataset, optional):
Evaluation dataset. If None, the model will not be evaluated furing training process. Defaults to None.
optimizer(paddle.optimizer.Optimizer or None, optional):
Optimizer used in 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 'CITYSCAPES'.
learning_rate(float, optional): Learning rate for training. Defaults to .025.
lr_decay_power(float, optional): Learning decay power. Defaults to .9.
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)
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
self.optimizer = self.default_optimizer(
self.net.parameters(), learning_rate, num_epochs,
num_steps_each_epoch, lr_decay_power)
else:
self.optimizer = optimizer
if pretrain_weights is not None and not osp.exists(pretrain_weights):
if pretrain_weights not in cls_pretrain_weights_dict[
self.model_name]:
logging.warning(
"Path of pretrain_weights('{}') does not exist!".format(
pretrain_weights))
logging.warning("Pretrain_weights is forcibly set to '{}'. "
"If don't want to use pretrain weights, "
"set pretrain_weights to be None.".format(
cls_pretrain_weights_dict[self.model_name][
0]))
pretrain_weights = cls_pretrain_weights_dict[self.model_name][0]
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')
is_backbone_weights = False # pretrain_weights == 'IMAGENET' # TODO: this is backbone
self.net_initialize(
pretrain_weights=pretrain_weights,
save_dir=pretrained_dir,
resume_checkpoint=resume_checkpoint,
is_backbone_weights=is_backbone_weights)
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,
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=2,
eval_dataset=None,
optimizer=None,
save_interval_epochs=1,
log_interval_steps=2,
save_dir='output',
learning_rate=0.0001,
lr_decay_power=0.9,
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 2.
eval_dataset(paddlers.dataset, optional):
Evaluation dataset. If None, the model will not be evaluated furing training process. Defaults to None.
optimizer(paddle.optimizer.Optimizer or None, optional):
Optimizer used in 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 .025.
lr_decay_power(float, optional): Learning decay power. Defaults to .9.
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,
lr_decay_power=lr_decay_power,
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, 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.
return_details(bool, optional): Whether to return evaluation details. Defaults to False.
Returns:
collections.OrderedDict with key-value pairs:
{"top1": `acc of top1`,
"top5": `acc of top5`}.
"""
arrange_transforms(
model_type=self.model_type,
transforms=eval_dataset.transforms,
mode='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()
batch_size_each_card = get_single_card_bs(batch_size)
if batch_size_each_card > 1:
batch_size_each_card = 1
batch_size = batch_size_each_card * paddlers.env_info['num']
logging.warning(
"Classifier only supports batch_size=1 for each gpu/cpu card " \
"during evaluation, so batch_size " \
"is forcibly set to {}.".format(batch_size))
self.eval_data_loader = self.build_data_loader(
eval_dataset, batch_size=batch_size, mode='eval')
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')
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:
# TODO: add details
return eval_metrics, None
return eval_metrics
def predict(self, img_file, transforms=None):
"""
Do inference.
Args:
Args:
img_file(List[np.ndarray or str], str or np.ndarray):
Image path or decoded image data in a BGR format, 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 dict with key-value pairs:
{"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:
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'):
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_im, batch_origin_shape = self._preprocess(images, transforms,
self.model_type)
self.net.eval()
data = (batch_im, batch_origin_shape, transforms.transforms)
outputs = self.run(self.net, data, 'test')
label_list = outputs['class_ids']
score_list = outputs['scores']
name_list = outputs['label_names']
if isinstance(img_file, list):
prediction = [{
'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 = {
'class_ids': label_list[0],
'scores': score_list[0],
'label_names': name_list[0]
}
return prediction
def _preprocess(self, images, transforms, to_tensor=True):
arrange_transforms(
model_type=self.model_type, transforms=transforms, mode='test')
batch_im = list()
batch_ori_shape = list()
for im in images:
sample = {'image': im}
if isinstance(sample['image'], str):
sample = ImgDecoder(to_rgb=False)(sample)
ori_shape = sample['image'].shape[:2]
im = transforms(sample)[0]
batch_im.append(im)
batch_ori_shape.append(ori_shape)
if to_tensor:
batch_im = paddle.to_tensor(batch_im)
else:
batch_im = np.asarray(batch_im)
return batch_im, batch_ori_shape
@staticmethod
def get_transforms_shape_info(batch_ori_shape, transforms):
batch_restore_list = list()
for ori_shape in batch_ori_shape:
restore_list = list()
h, w = ori_shape[0], ori_shape[1]
for op in transforms:
if op.__class__.__name__ == 'Resize':
restore_list.append(('resize', (h, w)))
h, w = op.target_size
elif op.__class__.__name__ == 'ResizeByShort':
restore_list.append(('resize', (h, w)))
im_short_size = min(h, w)
im_long_size = max(h, w)
scale = float(op.short_size) / float(im_short_size)
if 0 < op.max_size < np.round(scale * im_long_size):
scale = float(op.max_size) / float(im_long_size)
h = int(round(h * scale))
w = int(round(w * scale))
elif op.__class__.__name__ == 'ResizeByLong':
restore_list.append(('resize', (h, w)))
im_long_size = max(h, w)
scale = float(op.long_size) / float(im_long_size)
h = int(round(h * scale))
w = int(round(w * scale))
elif op.__class__.__name__ == 'Padding':
if op.target_size:
target_h, target_w = op.target_size
else:
target_h = int(
(np.ceil(h / op.size_divisor) * op.size_divisor))
target_w = int(
(np.ceil(w / op.size_divisor) * op.size_divisor))
if op.pad_mode == -1:
offsets = op.offsets
elif op.pad_mode == 0:
offsets = [0, 0]
elif op.pad_mode == 1:
offsets = [(target_h - h) // 2, (target_w - w) // 2]
else:
offsets = [target_h - h, target_w - w]
restore_list.append(('padding', (h, w), offsets))
h, w = target_h, target_w
batch_restore_list.append(restore_list)
return batch_restore_list
class ResNet50_vd(BaseClassifier):
def __init__(self, num_classes=2, use_mixed_loss=False, **params):
super(ResNet50_vd, self).__init__(
model_name='ResNet50_vd',
num_classes=num_classes,
use_mixed_loss=use_mixed_loss,
**params)
class MobileNetV3_small_x1_0(BaseClassifier):
def __init__(self, num_classes=2, use_mixed_loss=False, **params):
super(MobileNetV3_small_x1_0, self).__init__(
model_name='MobileNetV3_small_x1_0',
num_classes=num_classes,
use_mixed_loss=use_mixed_loss,
**params)
class HRNet_W18_C(BaseClassifier):
def __init__(self, num_classes=2, use_mixed_loss=False, **params):
super(HRNet_W18_C, self).__init__(
model_name='HRNet_W18_C',
num_classes=num_classes,
use_mixed_loss=use_mixed_loss,
**params)
class CondenseNetV2_b(BaseClassifier):
def __init__(self, num_classes=2, use_mixed_loss=False, **params):
super(CondenseNetV2_b, self).__init__(
model_name='CondenseNetV2_b',
num_classes=num_classes,
use_mixed_loss=use_mixed_loss,
**params)