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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import math
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import os.path as osp
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from collections import OrderedDict
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from operator import itemgetter
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import numpy as np
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import paddle
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import paddle.nn.functional as F
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from paddle.static import InputSpec
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import paddlers.models.ppcls as paddleclas
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import paddlers.rs_models.clas as cmcls
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import paddlers
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from paddlers.utils import get_single_card_bs, DisablePrint
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import paddlers.utils.logging as logging
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from .base import BaseModel
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from paddlers.models.ppcls.metric import build_metrics
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from paddlers.models.ppcls.loss import build_loss
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from paddlers.models.ppcls.data.postprocess import build_postprocess
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from paddlers.utils.checkpoint import cls_pretrain_weights_dict
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from paddlers.transforms import Resize, decode_image
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__all__ = [
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"ResNet50_vd", "MobileNetV3_small_x1_0", "HRNet_W18_C", "CondenseNetV2_b"
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]
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class BaseClassifier(BaseModel):
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def __init__(self,
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model_name,
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in_channels=3,
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num_classes=2,
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use_mixed_loss=False,
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**params):
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self.init_params = locals()
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if 'with_net' in self.init_params:
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del self.init_params['with_net']
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super(BaseClassifier, self).__init__('classifier')
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if not hasattr(paddleclas.arch.backbone, model_name) and \
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not hasattr(cmcls, model_name):
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raise Exception("ERROR: There's no model named {}.".format(
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model_name))
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self.model_name = model_name
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self.in_channels = in_channels
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self.num_classes = num_classes
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self.use_mixed_loss = use_mixed_loss
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self.metrics = None
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self.losses = None
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self.labels = None
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self._postprocess = None
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if params.get('with_net', True):
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params.pop('with_net', None)
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self.net = self.build_net(**params)
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self.find_unused_parameters = True
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def build_net(self, **params):
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with paddle.utils.unique_name.guard():
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model = dict(paddleclas.arch.backbone.__dict__,
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**cmcls.__dict__)[self.model_name]
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# TODO: Determine whether there is in_channels
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try:
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net = model(
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class_num=self.num_classes,
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in_channels=self.in_channels,
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**params)
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except:
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net = model(class_num=self.num_classes, **params)
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self.in_channels = 3
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return net
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def _fix_transforms_shape(self, image_shape):
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if hasattr(self, 'test_transforms'):
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if self.test_transforms is not None:
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has_resize_op = False
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resize_op_idx = -1
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normalize_op_idx = len(self.test_transforms.transforms)
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for idx, op in enumerate(self.test_transforms.transforms):
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name = op.__class__.__name__
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if name == 'Normalize':
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normalize_op_idx = idx
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if 'Resize' in name:
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has_resize_op = True
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resize_op_idx = idx
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if not has_resize_op:
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self.test_transforms.transforms.insert(
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normalize_op_idx, Resize(target_size=image_shape))
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else:
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self.test_transforms.transforms[resize_op_idx] = Resize(
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target_size=image_shape)
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def _get_test_inputs(self, image_shape):
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if image_shape is not None:
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if len(image_shape) == 2:
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image_shape = [1, 3] + image_shape
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self._fix_transforms_shape(image_shape[-2:])
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else:
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image_shape = [None, 3, -1, -1]
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self.fixed_input_shape = image_shape
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input_spec = [
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InputSpec(
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shape=image_shape, name='image', dtype='float32')
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]
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return input_spec
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def run(self, net, inputs, mode):
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net_out = net(inputs[0])
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if mode == 'test':
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return self._postprocess(net_out)
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outputs = OrderedDict()
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label = paddle.to_tensor(inputs[1], dtype="int64")
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if mode == 'eval':
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# print(self._postprocess(net_out)[0]) # for test
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label = paddle.unsqueeze(label, axis=-1)
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metric_dict = self.metrics(net_out, label)
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outputs['top1'] = metric_dict["top1"]
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outputs['top5'] = metric_dict["top5"]
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if mode == 'train':
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loss_list = self.losses(net_out, label)
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outputs['loss'] = loss_list['loss']
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return outputs
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def default_metric(self):
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default_config = [{"TopkAcc": {"topk": [1, 5]}}]
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return build_metrics(default_config)
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def default_loss(self):
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# TODO: use mixed loss and other loss
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default_config = [{"CELoss": {"weight": 1.0}}]
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return build_loss(default_config)
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def default_optimizer(self,
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parameters,
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learning_rate,
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num_epochs,
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num_steps_each_epoch,
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last_epoch=-1,
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L2_coeff=0.00007):
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decay_step = num_epochs * num_steps_each_epoch
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lr_scheduler = paddle.optimizer.lr.CosineAnnealingDecay(
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learning_rate, T_max=decay_step, eta_min=0, last_epoch=last_epoch)
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optimizer = paddle.optimizer.Momentum(
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learning_rate=lr_scheduler,
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parameters=parameters,
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momentum=0.9,
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weight_decay=paddle.regularizer.L2Decay(L2_coeff))
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return optimizer
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def default_postprocess(self, class_id_map_file):
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default_config = {
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"name": "Topk",
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"topk": 1,
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"class_id_map_file": class_id_map_file
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}
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return build_postprocess(default_config)
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def build_postprocess_from_labels(self, topk=1):
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label_dict = dict()
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for i, label in enumerate(self.labels):
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label_dict[i] = label
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self._postprocess = build_postprocess({
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"name": "Topk",
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"topk": topk,
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"class_id_map_file": None
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})
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# Add class_id_map from model.yml
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self._postprocess.class_id_map = label_dict
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def train(self,
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num_epochs,
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train_dataset,
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train_batch_size=2,
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eval_dataset=None,
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optimizer=None,
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save_interval_epochs=1,
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log_interval_steps=2,
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save_dir='output',
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pretrain_weights='IMAGENET',
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learning_rate=0.1,
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lr_decay_power=0.9,
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early_stop=False,
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early_stop_patience=5,
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use_vdl=True,
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resume_checkpoint=None):
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"""
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Train the model.
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Args:
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num_epochs(int): The number of epochs.
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train_dataset(paddlers.dataset): Training dataset.
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train_batch_size(int, optional): Total batch size among all cards used in training. Defaults to 2.
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eval_dataset(paddlers.dataset, optional):
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Evaluation dataset. If None, the model will not be evaluated furing training process. Defaults to None.
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optimizer(paddle.optimizer.Optimizer or None, optional):
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Optimizer used in training. If None, a default optimizer is used. Defaults to None.
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save_interval_epochs(int, optional): Epoch interval for saving the model. Defaults to 1.
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log_interval_steps(int, optional): Step interval for printing training information. Defaults to 10.
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save_dir(str, optional): Directory to save the model. Defaults to 'output'.
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pretrain_weights(str or None, optional):
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None or name/path of pretrained weights. If None, no pretrained weights will be loaded. Defaults to 'CITYSCAPES'.
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learning_rate(float, optional): Learning rate for training. Defaults to .025.
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lr_decay_power(float, optional): Learning decay power. Defaults to .9.
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early_stop(bool, optional): Whether to adopt early stop strategy. Defaults to False.
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early_stop_patience(int, optional): Early stop patience. Defaults to 5.
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use_vdl(bool, optional): Whether to use VisualDL to monitor the training process. Defaults to True.
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resume_checkpoint(str or None, optional): The path of the checkpoint to resume training from.
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If None, no training checkpoint will be resumed. At most one of `resume_checkpoint` and
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`pretrain_weights` can be set simultaneously. Defaults to None.
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"""
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if self.status == 'Infer':
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logging.error(
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"Exported inference model does not support training.",
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exit=True)
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if pretrain_weights is not None and resume_checkpoint is not None:
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logging.error(
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"pretrain_weights and resume_checkpoint cannot be set simultaneously.",
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exit=True)
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self.labels = train_dataset.labels
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if self.losses is None:
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self.losses = self.default_loss()
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self.metrics = self.default_metric()
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self._postprocess = self.default_postprocess(train_dataset.label_list)
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# print(self._postprocess.class_id_map)
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if optimizer is None:
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num_steps_each_epoch = train_dataset.num_samples // train_batch_size
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self.optimizer = self.default_optimizer(
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self.net.parameters(), learning_rate, num_epochs,
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num_steps_each_epoch, lr_decay_power)
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else:
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self.optimizer = optimizer
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if pretrain_weights is not None and not osp.exists(pretrain_weights):
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if pretrain_weights not in cls_pretrain_weights_dict[
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self.model_name]:
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logging.warning(
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"Path of pretrain_weights('{}') does not exist!".format(
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pretrain_weights))
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logging.warning("Pretrain_weights is forcibly set to '{}'. "
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"If don't want to use pretrain weights, "
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"set pretrain_weights to be None.".format(
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cls_pretrain_weights_dict[self.model_name][
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0]))
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pretrain_weights = cls_pretrain_weights_dict[self.model_name][0]
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elif pretrain_weights is not None and osp.exists(pretrain_weights):
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if osp.splitext(pretrain_weights)[-1] != '.pdparams':
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logging.error(
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"Invalid pretrain weights. Please specify a '.pdparams' file.",
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exit=True)
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pretrained_dir = osp.join(save_dir, 'pretrain')
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is_backbone_weights = False # pretrain_weights == 'IMAGENET' # TODO: this is backbone
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self.net_initialize(
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pretrain_weights=pretrain_weights,
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save_dir=pretrained_dir,
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resume_checkpoint=resume_checkpoint,
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is_backbone_weights=is_backbone_weights)
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self.train_loop(
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num_epochs=num_epochs,
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train_dataset=train_dataset,
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train_batch_size=train_batch_size,
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eval_dataset=eval_dataset,
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save_interval_epochs=save_interval_epochs,
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log_interval_steps=log_interval_steps,
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save_dir=save_dir,
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early_stop=early_stop,
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early_stop_patience=early_stop_patience,
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use_vdl=use_vdl)
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def quant_aware_train(self,
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num_epochs,
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train_dataset,
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train_batch_size=2,
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eval_dataset=None,
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optimizer=None,
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save_interval_epochs=1,
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log_interval_steps=2,
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save_dir='output',
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learning_rate=0.0001,
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lr_decay_power=0.9,
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early_stop=False,
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early_stop_patience=5,
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use_vdl=True,
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resume_checkpoint=None,
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quant_config=None):
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"""
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Quantization-aware training.
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Args:
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num_epochs(int): The number of epochs.
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train_dataset(paddlers.dataset): Training dataset.
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train_batch_size(int, optional): Total batch size among all cards used in training. Defaults to 2.
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eval_dataset(paddlers.dataset, optional):
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Evaluation dataset. If None, the model will not be evaluated furing training process. Defaults to None.
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optimizer(paddle.optimizer.Optimizer or None, optional):
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Optimizer used in training. If None, a default optimizer is used. Defaults to None.
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save_interval_epochs(int, optional): Epoch interval for saving the model. Defaults to 1.
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log_interval_steps(int, optional): Step interval for printing training information. Defaults to 10.
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save_dir(str, optional): Directory to save the model. Defaults to 'output'.
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learning_rate(float, optional): Learning rate for training. Defaults to .025.
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lr_decay_power(float, optional): Learning decay power. Defaults to .9.
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early_stop(bool, optional): Whether to adopt early stop strategy. Defaults to False.
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early_stop_patience(int, optional): Early stop patience. Defaults to 5.
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use_vdl(bool, optional): Whether to use VisualDL to monitor the training process. Defaults to True.
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quant_config(dict or None, optional): Quantization configuration. If None, a default rule of thumb
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configuration will be used. Defaults to None.
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resume_checkpoint(str or None, optional): The path of the checkpoint to resume quantization-aware training
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from. If None, no training checkpoint will be resumed. Defaults to None.
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"""
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self._prepare_qat(quant_config)
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self.train(
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num_epochs=num_epochs,
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train_dataset=train_dataset,
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train_batch_size=train_batch_size,
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eval_dataset=eval_dataset,
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optimizer=optimizer,
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save_interval_epochs=save_interval_epochs,
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log_interval_steps=log_interval_steps,
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save_dir=save_dir,
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pretrain_weights=None,
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learning_rate=learning_rate,
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lr_decay_power=lr_decay_power,
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early_stop=early_stop,
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early_stop_patience=early_stop_patience,
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use_vdl=use_vdl,
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resume_checkpoint=resume_checkpoint)
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def evaluate(self, eval_dataset, batch_size=1, return_details=False):
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"""
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Evaluate the model.
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Args:
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|
eval_dataset(paddlers.dataset): Evaluation dataset.
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|
batch_size(int, optional): Total batch size among all cards used for evaluation. Defaults to 1.
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|
return_details(bool, optional): Whether to return evaluation details. Defaults to False.
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Returns:
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|
collections.OrderedDict with key-value pairs:
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|
{"top1": `acc of top1`,
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|
"top5": `acc of top5`}.
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"""
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self._check_transforms(eval_dataset.transforms, 'eval')
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self.net.eval()
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nranks = paddle.distributed.get_world_size()
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local_rank = paddle.distributed.get_rank()
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if nranks > 1:
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# Initialize parallel environment if not done.
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if not paddle.distributed.parallel.parallel_helper._is_parallel_ctx_initialized(
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):
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paddle.distributed.init_parallel_env()
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batch_size_each_card = get_single_card_bs(batch_size)
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if batch_size_each_card > 1:
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batch_size_each_card = 1
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batch_size = batch_size_each_card * paddlers.env_info['num']
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logging.warning(
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|
"Classifier only supports batch_size=1 for each gpu/cpu card " \
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"during evaluation, so batch_size " \
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"is forcibly set to {}.".format(batch_size))
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self.eval_data_loader = self.build_data_loader(
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eval_dataset, batch_size=batch_size, mode='eval')
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logging.info(
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"Start to evaluate(total_samples={}, total_steps={})...".format(
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eval_dataset.num_samples,
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math.ceil(eval_dataset.num_samples * 1.0 / batch_size)))
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top1s = []
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top5s = []
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with paddle.no_grad():
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for step, data in enumerate(self.eval_data_loader):
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data.append(eval_dataset.transforms.transforms)
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outputs = self.run(self.net, data, 'eval')
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top1s.append(outputs["top1"])
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top5s.append(outputs["top5"])
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top1 = np.mean(top1s)
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top5 = np.mean(top5s)
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eval_metrics = OrderedDict(zip(['top1', 'top5'], [top1, top5]))
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if return_details:
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|
# TODO: add details
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return eval_metrics, None
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|
return eval_metrics
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|
def predict(self, img_file, transforms=None):
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|
"""
|
|
|
|
Do inference.
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|
|
Args:
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|
|
Args:
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|
|
img_file(list[np.ndarray | str] | str | np.ndarray):
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|
Image path or decoded image data, which also could constitute a list, meaning all images to be
|
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|
predicted as a mini-batch.
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|
|
transforms(paddlers.transforms.Compose or None, optional):
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|
Transforms for inputs. If None, the transforms for evaluation process will be used. Defaults to None.
|
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|
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|
|
Returns:
|
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|
If img_file is a string or np.array, the result is a dict with key-value pairs:
|
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|
{"label map": `class_ids_map`, "scores_map": `label_names_map`}.
|
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|
|
If img_file is a list, the result is a list composed of dicts with the corresponding fields:
|
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|
class_ids_map(np.ndarray): class_ids
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|
scores_map(np.ndarray): scores
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|
|
label_names_map(np.ndarray): label_names
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|
|
|
|
|
|
"""
|
|
|
|
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
|
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|
|
if isinstance(img_file, (str, np.ndarray)):
|
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|
|
images = [img_file]
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|
else:
|
|
|
|
images = img_file
|
|
|
|
batch_im, batch_origin_shape = self._preprocess(images, transforms,
|
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|
self.model_type)
|
|
|
|
self.net.eval()
|
|
|
|
data = (batch_im, batch_origin_shape, transforms.transforms)
|
|
|
|
|
|
|
|
if self._postprocess is None:
|
|
|
|
self.build_postprocess_from_labels()
|
|
|
|
|
|
|
|
outputs = self.run(self.net, data, 'test')
|
|
|
|
class_ids = map(itemgetter('class_ids'), outputs)
|
|
|
|
scores = map(itemgetter('scores'), outputs)
|
|
|
|
label_names = map(itemgetter('label_names'), outputs)
|
|
|
|
if isinstance(img_file, list):
|
|
|
|
prediction = [{
|
|
|
|
'class_ids_map': l,
|
|
|
|
'scores_map': s,
|
|
|
|
'label_names_map': n,
|
|
|
|
} for l, s, n in zip(class_ids, scores, label_names)]
|
|
|
|
else:
|
|
|
|
prediction = {
|
|
|
|
'class_ids_map': next(class_ids),
|
|
|
|
'scores_map': next(scores),
|
|
|
|
'label_names_map': next(label_names)
|
|
|
|
}
|
|
|
|
return prediction
|
|
|
|
|
|
|
|
def _preprocess(self, images, transforms, to_tensor=True):
|
|
|
|
self._check_transforms(transforms, 'test')
|
|
|
|
batch_im = list()
|
|
|
|
batch_ori_shape = list()
|
|
|
|
for im in images:
|
|
|
|
if isinstance(im, str):
|
|
|
|
im = decode_image(im, to_rgb=False)
|
|
|
|
ori_shape = im.shape[:2]
|
|
|
|
sample = {'image': im}
|
|
|
|
im = transforms(sample)
|
|
|
|
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__ == 'Pad':
|
|
|
|
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
|
|
|
|
|
|
|
|
def _check_transforms(self, transforms, mode):
|
|
|
|
super()._check_transforms(transforms, mode)
|
|
|
|
if not isinstance(transforms.arrange,
|
|
|
|
paddlers.transforms.ArrangeClassifier):
|
|
|
|
raise TypeError(
|
|
|
|
"`transforms.arrange` must be an ArrangeClassifier object.")
|
|
|
|
|
|
|
|
|
|
|
|
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)
|