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Merge branch 'develop_reg' of https://github.com/weisy11/PaddleClas into develop_reg

pull/794/head
weishengyu 2021-06-06 18:22:06 +08:00
parent a22b8f6b04 3ded7ccf09
commit 2921934016
3 changed files with 44 additions and 57 deletions
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8
ppcls/configs/Vehicle/ResNet50.yaml
View File

@ -148,9 +148,9 @@ Infer:
Metric:
Train:
- Topk:
k: [1, 5]
- TopkAcc:
topk: [1, 5]
Eval:
- Topk:
k: [1, 5]
- TopkAcc:
topk: [1, 5]

29
ppcls/configs/Vehicle/ResNet50_ReID.yaml
View File

@ -1,6 +1,4 @@
# global configs
Trainer:
name: TrainerReID
Global:
checkpoints: null
pretrained_model: null
@ -16,8 +14,7 @@ Global:
# used for static mode and model export
image_shape: [3, 224, 224]
save_inference_dir: "./inference"
num_split: 1
feature_normalize: True
eval_mode: "retrieval"
# model architecture
Arch:
@ -99,10 +96,10 @@ DataLoader:
loader:
num_workers: 6
use_shared_memory: False
Query:
Eval:
Query:
# TOTO: modify to the latest trainer
dataset:
dataset:
name: "VeriWild"
image_root: "/work/dataset/VeRI-Wild/images"
cls_label_path: "/work/dataset/VeRI-Wild/train_test_split/test_3000_id_query.txt"
@ -114,18 +111,18 @@ DataLoader:
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
sampler:
sampler:
name: DistributedBatchSampler
batch_size: 64
drop_last: False
shuffle: False
loader:
loader:
num_workers: 6
use_shared_memory: False
Gallery:
Gallery:
# TOTO: modify to the latest trainer
dataset:
dataset:
name: "VeriWild"
image_root: "/work/dataset/VeRI-Wild/images"
cls_label_path: "/work/dataset/VeRI-Wild/train_test_split/test_3000_id.txt"
@ -137,15 +134,21 @@ DataLoader:
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
sampler:
sampler:
name: DistributedBatchSampler
batch_size: 64
drop_last: False
shuffle: False
loader:
loader:
num_workers: 6
use_shared_memory: False
Metric:
Eval:
- Recallk:
topk: [1, 5]
- mAP: {}
Infer:
infer_imgs: "docs/images/whl/demo.jpg"
batch_size: 10

64
ppcls/metric/metrics.py
View File

@ -15,6 +15,7 @@
import numpy as np
import paddle
import paddle.nn as nn
from functools import lru_cache
# TODO: fix the format
@ -38,23 +39,13 @@ class TopkAcc(nn.Layer):
class mAP(nn.Layer):
def __init__(self, max_rank=50):
def __init__(self):
super().__init__()
self.max_rank = max_rank
def forward(self, similarities_matrix, query_img_id, gallery_img_id):
metric_dict = dict()
num_q, num_g = similarities_matrix.shape
q_pids = query_img_id.numpy().reshape((query_img_id.shape[0]))
g_pids = gallery_img_id.numpy().reshape((gallery_img_id.shape[0]))
if num_g < self.max_rank:
self.max_rank = num_g
print('Note: number of gallery samples is quite small, got {}'.
format(num_g))
indices = paddle.argsort(
similarities_matrix, axis=1, descending=True).numpy()
_, all_AP, _ = get_metrics(indices, num_q, num_g, q_pids, g_pids,
self.max_rank)
_, all_AP, _ = get_metrics(similarities_matrix, query_img_id,
gallery_img_id)
mAP = np.mean(all_AP)
metric_dict["mAP"] = mAP
@ -62,23 +53,13 @@ class mAP(nn.Layer):
class mINP(nn.Layer):
def __init__(self, max_rank=50):
def __init__(self):
super().__init__()
self.max_rank = max_rank
def forward(self, similarities_matrix, query_img_id, gallery_img_id):
metric_dict = dict()
num_q, num_g = similarities_matrix.shape
q_pids = query_img_id.numpy().reshape((query_img_id.shape[0]))
g_pids = gallery_img_id.numpy().reshape((gallery_img_id.shape[0]))
if num_g < self.max_rank:
max_rank = num_g
print('Note: number of gallery samples is quite small, got {}'.
format(num_g))
indices = paddle.argsort(
similarities_matrix, axis=1, descending=True).numpy()
_, _, all_INP = get_metrics(indices, num_q, num_g, q_pids, g_pids,
self.max_rank)
_, _, all_INP = get_metrics(similarities_matrix, query_img_id,
gallery_img_id)
mINP = np.mean(all_INP)
metric_dict["mINP"] = mINP
@ -86,34 +67,37 @@ class mINP(nn.Layer):
class Recallk(nn.Layer):
def __init__(self, max_rank=50, topk=(1, 5)):
def __init__(self, topk=(1, 5)):
super().__init__()
self.max_rank = max_rank
assert isinstance(topk, (int, list))
if isinstance(topk, int):
topk = [topk]
self.topk = topk
self.max_rank = max(self.topk) if max(self.topk) > 50 else 50
def forward(self, similarities_matrix, query_img_id, gallery_img_id):
metric_dict = dict()
num_q, num_g = similarities_matrix.shape
q_pids = query_img_id.numpy().reshape((query_img_id.shape[0]))
g_pids = gallery_img_id.numpy().reshape((gallery_img_id.shape[0]))
if num_g < self.max_rank:
max_rank = num_g
print('Note: number of gallery samples is quite small, got {}'.
format(num_g))
indices = paddle.argsort(
similarities_matrix, axis=1, descending=True).numpy()
all_cmc, _, _ = get_metrics(indices, num_q, num_g, q_pids, g_pids,
self.max_rank)
all_cmc, _, _ = get_metrics(similarities_matrix, query_img_id,
gallery_img_id, self.max_rank)
for k in self.topk:
metric_dict["recall{}".format(k)] = all_cmc[k - 1]
return metric_dict
def get_metrics(indices, num_q, num_g, q_pids, g_pids, max_rank=50):
@lru_cache()
def get_metrics(similarities_matrix, query_img_id, gallery_img_id,
max_rank=50):
num_q, num_g = similarities_matrix.shape
q_pids = query_img_id.numpy().reshape((query_img_id.shape[0]))
g_pids = gallery_img_id.numpy().reshape((gallery_img_id.shape[0]))
if num_g < max_rank:
max_rank = num_g
print('Note: number of gallery samples is quite small, got {}'.format(
num_g))
indices = paddle.argsort(
similarities_matrix, axis=1, descending=True).numpy()
all_cmc = []
all_AP = []
all_INP = []