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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import numpy as np
import time
import paddle
import paddle.nn.functional as F
from paddleseg.utils import metrics, TimeAverager, calculate_eta, logger, progbar
from paddleseg.core import infer
np.set_printoptions(suppress=True)
def evaluate(model,
eval_dataset,
aug_eval=False,
scales=1.0,
flip_horizontal=False,
flip_vertical=False,
is_slide=False,
stride=None,
crop_size=None,
precision='fp32',
amp_level='O1',
num_workers=0,
print_detail=True,
auc_roc=False):
"""
Launch evalution.
Args:
model（nn.Layer): A semantic segmentation model.
eval_dataset (paddle.io.Dataset): Used to read and process validation datasets.
aug_eval (bool, optional): Whether to use mulit-scales and flip augment for evaluation. Default: False.
scales (list|float, optional): Scales for augment. It is valid when `aug_eval` is True. Default: 1.0.
flip_horizontal (bool, optional): Whether to use flip horizontally augment. It is valid when `aug_eval` is True. Default: True.
flip_vertical (bool, optional): Whether to use flip vertically augment. It is valid when `aug_eval` is True. Default: False.
is_slide (bool, optional): Whether to evaluate by sliding window. Default: False.
stride (tuple|list, optional): The stride of sliding window, the first is width and the second is height.
It should be provided when `is_slide` is True.
crop_size (tuple|list, optional): The crop size of sliding window, the first is width and the second is height.
It should be provided when `is_slide` is True.
precision (str, optional): Use AMP if precision='fp16'. If precision='fp32', the evaluation is normal.
amp_level (str, optional): Auto mixed precision level. Accepted values are “O1” and “O2”: O1 represent mixed precision, the input data type of each operator will be casted by white_list and black_list; O2 represent Pure fp16, all operators parameters and input data will be casted to fp16, except operators in black_list, don’t support fp16 kernel and batchnorm. Default is O1(amp)
num_workers (int, optional): Num workers for data loader. Default: 0.
print_detail (bool, optional): Whether to print detailed information about the evaluation process. Default: True.
auc_roc(bool, optional): whether add auc_roc metric
Returns:
float: The mIoU of validation datasets.
float: The accuracy of validation datasets.
"""
model.eval()
nranks = paddle.distributed.ParallelEnv().nranks
local_rank = paddle.distributed.ParallelEnv().local_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_sampler = paddle.io.DistributedBatchSampler(
eval_dataset, batch_size=1, shuffle=False, drop_last=False)
loader = paddle.io.DataLoader(
eval_dataset,
batch_sampler=batch_sampler,
num_workers=num_workers,
return_list=True, )
total_iters = len(loader)
intersect_area_all = paddle.zeros([1], dtype='int64')
pred_area_all = paddle.zeros([1], dtype='int64')
label_area_all = paddle.zeros([1], dtype='int64')
logits_all = None
label_all = None
if print_detail:
logger.info("Start evaluating (total_samples: {}, total_iters: {})...".
format(len(eval_dataset), total_iters))
#TODO(chenguowei): fix log print error with multi-gpus
progbar_val = progbar.Progbar(
target=total_iters, verbose=1 if nranks < 2 else 2)
reader_cost_averager = TimeAverager()
batch_cost_averager = TimeAverager()
batch_start = time.time()
with paddle.no_grad():
for iter, data in enumerate(loader):
reader_cost_averager.record(time.time() - batch_start)
label = data['label'].astype('int64')
if aug_eval:
if precision == 'fp16':
with paddle.amp.auto_cast(
level=amp_level,
enable=True,
custom_white_list={
"elementwise_add", "batch_norm",
"sync_batch_norm"
},
custom_black_list={'bilinear_interp_v2'}):
pred, logits = infer.aug_inference(
model,
data['img'],
trans_info=data['trans_info'],
scales=scales,
flip_horizontal=flip_horizontal,
flip_vertical=flip_vertical,
is_slide=is_slide,
stride=stride,
crop_size=crop_size)
else:
pred, logits = infer.aug_inference(
model,
data['img'],
trans_info=data['trans_info'],
scales=scales,
flip_horizontal=flip_horizontal,
flip_vertical=flip_vertical,
is_slide=is_slide,
stride=stride,
crop_size=crop_size)
else:
if precision == 'fp16':
with paddle.amp.auto_cast(
level=amp_level,
enable=True,
custom_white_list={
"elementwise_add", "batch_norm",
"sync_batch_norm"
},
custom_black_list={'bilinear_interp_v2'}):
pred, logits = infer.inference(
model,
data['img'],
trans_info=data['trans_info'],
is_slide=is_slide,
stride=stride,
crop_size=crop_size)
else:
pred, logits = infer.inference(
model,
data['img'],
trans_info=data['trans_info'],
is_slide=is_slide,
stride=stride,
crop_size=crop_size)
intersect_area, pred_area, label_area = metrics.calculate_area(
pred,
label,
eval_dataset.num_classes,
ignore_index=eval_dataset.ignore_index)
# Gather from all ranks
if nranks > 1:
intersect_area_list = []
pred_area_list = []
label_area_list = []
paddle.distributed.all_gather(intersect_area_list,
intersect_area)
paddle.distributed.all_gather(pred_area_list, pred_area)
paddle.distributed.all_gather(label_area_list, label_area)
# Some image has been evaluated and should be eliminated in last iter
if (iter + 1) * nranks > len(eval_dataset):
valid = len(eval_dataset) - iter * nranks
intersect_area_list = intersect_area_list[:valid]
pred_area_list = pred_area_list[:valid]
label_area_list = label_area_list[:valid]
for i in range(len(intersect_area_list)):
intersect_area_all = intersect_area_all + intersect_area_list[
i]
pred_area_all = pred_area_all + pred_area_list[i]
label_area_all = label_area_all + label_area_list[i]
else:
intersect_area_all = intersect_area_all + intersect_area
pred_area_all = pred_area_all + pred_area
label_area_all = label_area_all + label_area
if auc_roc:
logits = F.softmax(logits, axis=1)
if logits_all is None:
logits_all = logits.numpy()
label_all = label.numpy()
else:
logits_all = np.concatenate(
[logits_all, logits.numpy()]) # (KN, C, H, W)
label_all = np.concatenate([label_all, label.numpy()])
batch_cost_averager.record(
time.time() - batch_start, num_samples=len(label))
batch_cost = batch_cost_averager.get_average()
reader_cost = reader_cost_averager.get_average()
if local_rank == 0 and print_detail:
progbar_val.update(iter + 1, [('batch_cost', batch_cost),
('reader cost', reader_cost)])
reader_cost_averager.reset()
batch_cost_averager.reset()
batch_start = time.time()
metrics_input = (intersect_area_all, pred_area_all, label_area_all)
class_iou, miou = metrics.mean_iou(*metrics_input)
acc, class_precision, class_recall = metrics.class_measurement(
*metrics_input)
kappa = metrics.kappa(*metrics_input)
class_dice, mdice = metrics.dice(*metrics_input)
if auc_roc:
auc_roc = metrics.auc_roc(
logits_all, label_all, num_classes=eval_dataset.num_classes)
auc_infor = ' Auc_roc: {:.4f}'.format(auc_roc)
if print_detail:
infor = "[EVAL] #Images: {} mIoU: {:.4f} Acc: {:.4f} Kappa: {:.4f} Dice: {:.4f}".format(
len(eval_dataset), miou, acc, kappa, mdice)
infor = infor + auc_infor if auc_roc else infor
logger.info(infor)
logger.info("[EVAL] Class IoU: \n" + str(np.round(class_iou, 4)))
logger.info("[EVAL] Class Precision: \n" + str(
np.round(class_precision, 4)))
logger.info("[EVAL] Class Recall: \n" + str(np.round(class_recall, 4)))
return miou, acc, class_iou, class_precision, kappa
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