object_detection.models.ssd_feature_extractor_test的特征提取器功能验证的Python代码示例
发布时间:2024-01-03 23:48:19
下面是一个使用 object_detection.models.ssd_feature_extractor_test 中特征提取器功能验证的 Python 代码示例:
首先,我们需要导入必要的依赖项:
import tensorflow as tf import numpy as np import os from object_detection.models import ssd_feature_extractor_test from object_detection.utils import config_util from object_detection.builders import model_builder
接下来,我们需要设置一些参数:
pipeline_config_path = 'path/to/your/pipeline_config' # 配置文件路径 checkpoint_dir = 'path/to/your/checkpoint_dir' # 检查点文件夹路径 output_dir = 'path/to/your/output_dir' # 输出文件夹路径 num_classes = 90 # 目标类别数量 image_height = 300 # 图像高度 image_width = 300 # 图像宽度
然后,我们加载配置文件并创建模型:
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path) model_config = configs['model'] model_fn = model_builder.build(model_config=model_config, is_training=False)
接下来,我们创建特征提取器的实例:
feature_extractor = ssd_feature_extractor_test.SsdFeatureExtractorTest(
is_training=False,
depth_multiplier=1,
min_depth=16,
pad_to_multiple=1,
conv_hyperparams_fn=model_builder.building_utils.get_conv_hyperparams(
model_config.feature_extractor),
freeze_bn=False
)
然后,我们加载模型的检查点:
ckpt = tf.compat.v2.train.Checkpoint(model=model_fn) ckpt.restore(os.path.join(checkpoint_dir, 'ckpt-0')).expect_partial()
接下来,我们准备图像数据进行推理:
input_shape = tf.TensorShape([image_height, image_width, 3]) input_image = tf.random.uniform(input_shape, minval=0, maxval=255) input_image = tf.cast(input_image, dtype=tf.float32) input_images = tf.expand_dims(input_image, 0)
然后,我们进行特征提取:
imported_layers, _ = feature_extractor.extract_features(input_images)
最后,我们保存提取的特征:
feature_map_directory = os.path.join(output_dir, 'feature_maps')
os.makedirs(feature_map_directory, exist_ok=True)
for i, feat_map in enumerate(imported_layers.values()):
feat_map_path = os.path.join(feature_map_directory, f'feature_map_{i}.npy')
np.save(feat_map_path, feat_map.numpy())
print(f'Saved feature map {i+1} to {feat_map_path}')
以上就是一个完整的示例,它演示了如何使用 object_detection.models.ssd_feature_extractor_test 中的特征提取器功能进行目标检测模型的推理。
