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- # Copyright (c) 2021 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
- from __future__ import division
- from __future__ import print_function
- import platform
- import paddle
- from paddlex.ppcls.utils import logger
- def retrieval_eval(engine, epoch_id=0):
- engine.model.eval()
- # step1. build gallery
- if engine.gallery_query_dataloader is not None:
- gallery_feas, gallery_img_id, gallery_unique_id = cal_feature(
- engine, name='gallery_query')
- query_feas, query_img_id, query_query_id = gallery_feas, gallery_img_id, gallery_unique_id
- else:
- gallery_feas, gallery_img_id, gallery_unique_id = cal_feature(
- engine, name='gallery')
- query_feas, query_img_id, query_query_id = cal_feature(
- engine, name='query')
- # step2. do evaluation
- sim_block_size = engine.config["Global"].get("sim_block_size", 64)
- sections = [sim_block_size] * (len(query_feas) // sim_block_size)
- if len(query_feas) % sim_block_size:
- sections.append(len(query_feas) % sim_block_size)
- fea_blocks = paddle.split(query_feas, num_or_sections=sections)
- if query_query_id is not None:
- query_id_blocks = paddle.split(
- query_query_id, num_or_sections=sections)
- image_id_blocks = paddle.split(query_img_id, num_or_sections=sections)
- metric_key = None
- if engine.eval_loss_func is None:
- metric_dict = {metric_key: 0.}
- else:
- metric_dict = dict()
- for block_idx, block_fea in enumerate(fea_blocks):
- similarity_matrix = paddle.matmul(
- block_fea, gallery_feas, transpose_y=True)
- if query_query_id is not None:
- query_id_block = query_id_blocks[block_idx]
- query_id_mask = (query_id_block != gallery_unique_id.t())
- image_id_block = image_id_blocks[block_idx]
- image_id_mask = (image_id_block != gallery_img_id.t())
- keep_mask = paddle.logical_or(query_id_mask, image_id_mask)
- similarity_matrix = similarity_matrix * keep_mask.astype(
- "float32")
- else:
- keep_mask = None
- metric_tmp = engine.eval_metric_func(similarity_matrix,
- image_id_blocks[block_idx],
- gallery_img_id, keep_mask)
- for key in metric_tmp:
- if key not in metric_dict:
- metric_dict[key] = metric_tmp[key] * block_fea.shape[
- 0] / len(query_feas)
- else:
- metric_dict[key] += metric_tmp[key] * block_fea.shape[
- 0] / len(query_feas)
- metric_info_list = []
- for key in metric_dict:
- if metric_key is None:
- metric_key = key
- metric_info_list.append("{}: {:.5f}".format(key, metric_dict[key]))
- metric_msg = ", ".join(metric_info_list)
- logger.info("[Eval][Epoch {}][Avg]{}".format(epoch_id, metric_msg))
- return metric_dict[metric_key]
- def cal_feature(engine, name='gallery'):
- all_feas = None
- all_image_id = None
- all_unique_id = None
- has_unique_id = False
- if name == 'gallery':
- dataloader = engine.gallery_dataloader
- elif name == 'query':
- dataloader = engine.query_dataloader
- elif name == 'gallery_query':
- dataloader = engine.gallery_query_dataloader
- else:
- raise RuntimeError("Only support gallery or query dataset")
- max_iter = len(dataloader) - 1 if platform.system() == "Windows" else len(
- dataloader)
- for idx, batch in enumerate(dataloader): # load is very time-consuming
- if idx >= max_iter:
- break
- if idx % engine.config["Global"]["print_batch_step"] == 0:
- logger.info(
- f"{name} feature calculation process: [{idx}/{len(dataloader)}]"
- )
- if engine.use_dali:
- batch = [
- paddle.to_tensor(batch[0]['data']),
- paddle.to_tensor(batch[0]['label'])
- ]
- batch = [paddle.to_tensor(x) for x in batch]
- batch[1] = batch[1].reshape([-1, 1]).astype("int64")
- if len(batch) == 3:
- has_unique_id = True
- batch[2] = batch[2].reshape([-1, 1]).astype("int64")
- out = engine.model(batch[0], batch[1])
- batch_feas = out["features"]
- # do norm
- if engine.config["Global"].get("feature_normalize", True):
- feas_norm = paddle.sqrt(
- paddle.sum(paddle.square(batch_feas), axis=1, keepdim=True))
- batch_feas = paddle.divide(batch_feas, feas_norm)
- # do binarize
- if engine.config["Global"].get("feature_binarize") == "round":
- batch_feas = paddle.round(batch_feas).astype("float32") * 2.0 - 1.0
- if engine.config["Global"].get("feature_binarize") == "sign":
- batch_feas = paddle.sign(batch_feas).astype("float32")
- if all_feas is None:
- all_feas = batch_feas
- if has_unique_id:
- all_unique_id = batch[2]
- all_image_id = batch[1]
- else:
- all_feas = paddle.concat([all_feas, batch_feas])
- all_image_id = paddle.concat([all_image_id, batch[1]])
- if has_unique_id:
- all_unique_id = paddle.concat([all_unique_id, batch[2]])
- if engine.use_dali:
- dataloader.reset()
- if paddle.distributed.get_world_size() > 1:
- feat_list = []
- img_id_list = []
- unique_id_list = []
- paddle.distributed.all_gather(feat_list, all_feas)
- paddle.distributed.all_gather(img_id_list, all_image_id)
- all_feas = paddle.concat(feat_list, axis=0)
- all_image_id = paddle.concat(img_id_list, axis=0)
- if has_unique_id:
- paddle.distributed.all_gather(unique_id_list, all_unique_id)
- all_unique_id = paddle.concat(unique_id_list, axis=0)
- logger.info("Build {} done, all feat shape: {}, begin to eval..".format(
- name, all_feas.shape))
- return all_feas, all_image_id, all_unique_id
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