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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 cv2
import numpy as np
import time
import paddle
import paddle.nn.functional as F
from paddleseg.utils import TimeAverager, calculate_eta, logger, progbar
from ppmatting.metrics import metrics_class_dict
np.set_printoptions(suppress=True)
def save_alpha_pred(alpha, path):
"""
The value of alpha is range [0, 1], shape should be [h,w]
"""
dirname = os.path.dirname(path)
if not os.path.exists(dirname):
os.makedirs(dirname)
alpha = (alpha).astype('uint8')
cv2.imwrite(path, alpha)
def reverse_transform(alpha, trans_info):
"""recover pred to origin shape"""
for item in trans_info[::-1]:
if item[0][0] == 'resize':
h, w = item[1][0], item[1][1]
alpha = F.interpolate(alpha, [h, w], mode='bilinear')
elif item[0][0] == 'padding':
h, w = item[1][0], item[1][1]
alpha = alpha[:, :, 0:h, 0:w]
else:
raise Exception("Unexpected info '{}' in im_info".format(item[0]))
return alpha
def evaluate(model,
eval_dataset,
num_workers=0,
print_detail=True,
save_dir='output/results',
save_results=True,
metrics='sad'):
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()
loader = paddle.io.DataLoader(
eval_dataset,
batch_size=1,
drop_last=False,
num_workers=num_workers,
return_list=True, )
total_iters = len(loader)
# Get metric instances and data saving
metrics_ins = {}
metrics_data = {}
if isinstance(metrics, str):
metrics = [metrics]
elif not isinstance(metrics, list):
metrics = ['sad']
for key in metrics:
key = key.lower()
metrics_ins[key] = metrics_class_dict[key]()
metrics_data[key] = None
if print_detail:
logger.info("Start evaluating (total_samples: {}, total_iters: {})...".
format(len(eval_dataset), total_iters))
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()
img_name = ''
i = 0
with paddle.no_grad():
for iter, data in enumerate(loader):
reader_cost_averager.record(time.time() - batch_start)
alpha_pred = model(data)
alpha_pred = reverse_transform(alpha_pred, data['trans_info'])
alpha_pred = alpha_pred.numpy()
alpha_gt = data['alpha'].numpy() * 255
trimap = data.get('ori_trimap')
if trimap is not None:
trimap = trimap.numpy().astype('uint8')
alpha_pred = np.round(alpha_pred * 255)
for key in metrics_ins.keys():
metrics_data[key] = metrics_ins[key].update(alpha_pred,
alpha_gt, trimap)
if save_results:
alpha_pred_one = alpha_pred[0].squeeze()
if trimap is not None:
trimap = trimap.squeeze().astype('uint8')
alpha_pred_one[trimap == 255] = 255
alpha_pred_one[trimap == 0] = 0
save_name = data['img_name'][0]
name, ext = os.path.splitext(save_name)
if save_name == img_name:
save_name = name + '_' + str(i) + ext
i += 1
else:
img_name = save_name
save_name = name + '_' + str(i) + ext
i = 1
save_alpha_pred(alpha_pred_one,
os.path.join(save_dir, save_name))
batch_cost_averager.record(
time.time() - batch_start, num_samples=len(alpha_gt))
batch_cost = batch_cost_averager.get_average()
reader_cost = reader_cost_averager.get_average()
if local_rank == 0 and print_detail:
show_list = [(k, v) for k, v in metrics_data.items()]
show_list = show_list + [('batch_cost', batch_cost),
('reader cost', reader_cost)]
progbar_val.update(iter + 1, show_list)
reader_cost_averager.reset()
batch_cost_averager.reset()
batch_start = time.time()
for key in metrics_ins.keys():
metrics_data[key] = metrics_ins[key].evaluate()
log_str = '[EVAL] '
for key, value in metrics_data.items():
log_str = log_str + key + ': {:.4f}, '.format(value)
log_str = log_str[:-2]
logger.info(log_str)
return metrics_data
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