add feature extract

pull/1599/head
weishengyu 2021-12-15 10:39:26 +08:00
parent c941144568
commit b6a7c53182
1 changed files with 31 additions and 9 deletions
ppcls/utils

View File

@ -1,5 +1,6 @@
import os
import paddle
import cv2
from ppcls.arch import build_model
from ppcls.arch.gears.identity_head import IdentityHead
@ -9,8 +10,8 @@ from ppcls.utils.logger import init_logger
from ppcls.data import transform, create_operators
def build_gallery_feature(configs, feature_extractor):
transform_configs = configs["Infer"]["transforms"]
def build_gallery_layer(configs, feature_extractor):
transform_configs = configs["IndexProcess"]["transform_ops"]
preprocess_ops = create_operators(transform_configs)
embedding_size = configs["Arch"]["Head"]["embedding_size"]
@ -35,13 +36,34 @@ def build_gallery_feature(configs, feature_extractor):
gallery_images.append(image_file)
gallery_docs.append(ori_line.strip())
batch_index = 0
gallery_feature = paddle.zeros((len(gallery_images), embedding_size))
for i, image_path in enumerate(gallery_images):
image = cv2.imread(image_path)
for op in preprocess_ops:
image = op(image)
input_tensor[batch_index] = image
batch_index += 1
if batch_index == batch_size or i == len(gallery_images) - 1:
batch_feature = feature_extractor(input_tensor)
for j in range(batch_index):
feature = batch_feature[j]
norm_feature = paddle.nn.functional.normalize(feature)
gallery_feature[i + batch_index - j] = norm_feature
gallery_layer = paddle.nn.Linear(embedding_size, len(gallery_images), weight_attr=gallery_feature, bias_attr=False)
return gallery_layer
def save_fuse_model(fuse_model):
pass
def export_fuse_model(model, config):
model.eval()
model.quanter.save_quantized_model(
model.base_model,
save_path,
input_spec=[
paddle.static.InputSpec(
shape=[None] + config["Global"]["image_shape"],
dtype='float32')
])
class FuseModel(paddle.nn.Layer):
@ -50,11 +72,11 @@ class FuseModel(paddle.nn.Layer):
self.feature_extractor = build_model(configs)
load_dygraph_pretrain(self.feature_extractor, configs["Global"]["pretrained_model"])
self.feature_extractor.head = IdentityHead()
self.gallery_layer = build_gallery_feature(configs, self.feature_extractor)
self.gallery_layer = build_gallery_layer(configs, self.feature_extractor)
def forward(self, x):
x = self.feature_model(x)["features"]
x = paddle.norm(x)
x = paddle.nn.functional.normalize(x)
x = self.gallery_layer(x)
return x