123 lines
4.9 KiB
Python
123 lines
4.9 KiB
Python
# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
|
|
#
|
|
# 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 numpy as np
|
|
import paddle
|
|
import paddle.nn as nn
|
|
|
|
class TopkAcc(nn.Layer):
|
|
def __init__(self, topk=(1, 5)):
|
|
super().__init__()
|
|
assert isinstance(topk, (int, list, tuple))
|
|
if isinstance(topk, int):
|
|
topk = [topk]
|
|
self.topk = topk
|
|
|
|
def forward(self, x, label):
|
|
if isinstance(x, dict):
|
|
x = x["logits"]
|
|
|
|
metric_dict = dict()
|
|
for k in self.topk:
|
|
metric_dict["top{}".format(k)] = paddle.metric.accuracy(
|
|
x, label, k=k)
|
|
return metric_dict
|
|
|
|
class mAP(nn.Layer):
|
|
def __init__(self):
|
|
super().__init__()
|
|
|
|
def forward(self, similarities_matrix, query_img_id, gallery_img_id):
|
|
metric_dict = dict()
|
|
|
|
choosen_indices = paddle.argsort(similarities_matrix, axis=1, descending=True)
|
|
gallery_labels_transpose = paddle.transpose(gallery_img_id, [1,0])
|
|
gallery_labels_transpose = paddle.broadcast_to(gallery_labels_transpose, shape=[choosen_indices.shape[0], gallery_labels_transpose.shape[1]])
|
|
choosen_label = paddle.index_sample(gallery_labels_transpose, choosen_indices)
|
|
equal_flag = paddle.equal(choosen_label, query_img_id)
|
|
equal_flag = paddle.cast(equal_flag, 'float32')
|
|
|
|
acc_sum = paddle.cumsum(equal_flag, axis=1)
|
|
div = paddle.arange(acc_sum.shape[1]).astype("float32") + 1
|
|
precision = paddle.divide(acc_sum, div)
|
|
|
|
#calc map
|
|
precision_mask = paddle.multiply(equal_flag, precision)
|
|
ap = paddle.sum(precision_mask, axis=1) / paddle.sum(equal_flag, axis=1)
|
|
metric_dict["mAP"] = paddle.mean(ap).numpy()[0]
|
|
return metric_dict
|
|
|
|
class mINP(nn.Layer):
|
|
def __init__(self):
|
|
super().__init__()
|
|
|
|
def forward(self, similarities_matrix, query_img_id, gallery_img_id):
|
|
metric_dict = dict()
|
|
|
|
choosen_indices = paddle.argsort(similarities_matrix, axis=1, descending=True)
|
|
gallery_labels_transpose = paddle.transpose(gallery_img_id, [1,0])
|
|
gallery_labels_transpose = paddle.broadcast_to(gallery_labels_transpose, shape=[choosen_indices.shape[0], gallery_labels_transpose.shape[1]])
|
|
choosen_label = paddle.index_sample(gallery_labels_transpose, choosen_indices)
|
|
tmp = paddle.equal(choosen_label, query_img_id)
|
|
tmp = paddle.cast(tmp, 'float64')
|
|
|
|
#do accumulative sum
|
|
div = paddle.arange(tmp.shape[1]).astype("float64") + 2
|
|
minus = paddle.divide(tmp, div)
|
|
auxilary = paddle.subtract(tmp, minus)
|
|
hard_index = paddle.argmax(auxilary, axis=1).astype("float64")
|
|
all_INP = paddle.divide(paddle.sum(tmp, axis=1), hard_index)
|
|
mINP = paddle.mean(all_INP)
|
|
metric_dict["mINP"] = mINP.numpy()[0]
|
|
return metric_dict
|
|
|
|
class Recallk(nn.Layer):
|
|
def __init__(self, topk=(1, 5)):
|
|
super().__init__()
|
|
assert isinstance(topk, (int, list, tuple))
|
|
if isinstance(topk, int):
|
|
topk = [topk]
|
|
self.topk = topk
|
|
|
|
def forward(self, similarities_matrix, query_img_id, gallery_img_id):
|
|
metric_dict = dict()
|
|
|
|
#get cmc
|
|
choosen_indices = paddle.argsort(similarities_matrix, axis=1, descending=True)
|
|
gallery_labels_transpose = paddle.transpose(gallery_img_id, [1,0])
|
|
gallery_labels_transpose = paddle.broadcast_to(gallery_labels_transpose, shape=[choosen_indices.shape[0], gallery_labels_transpose.shape[1]])
|
|
choosen_label = paddle.index_sample(gallery_labels_transpose, choosen_indices)
|
|
equal_flag = paddle.equal(choosen_label, query_img_id)
|
|
equal_flag = paddle.cast(equal_flag, 'float32')
|
|
|
|
acc_sum = paddle.cumsum(equal_flag, axis=1)
|
|
mask = paddle.greater_than(acc_sum, paddle.to_tensor(0.)).astype("float32")
|
|
all_cmc = paddle.mean(mask, axis=0).numpy()
|
|
|
|
for k in self.topk:
|
|
metric_dict["recall{}".format(k)] = all_cmc[k - 1]
|
|
return metric_dict
|
|
|
|
class DistillationTopkAcc(TopkAcc):
|
|
def __init__(self, model_key, feature_key=None, topk=(1, 5)):
|
|
super().__init__(topk=topk)
|
|
self.model_key = model_key
|
|
self.feature_key = feature_key
|
|
|
|
def forward(self, x, label):
|
|
x = x[self.model_key]
|
|
if self.feature_key is not None:
|
|
x = x[self.feature_key]
|
|
return super().forward(x, label)
|