目录
简介
导入
下载数据集
加载数据集
构建数据集
预处理
创建 TensorFlow 数据集
PointNet 模型
排列不变性
变换不变性
点之间的相互作用
实例化模型
训练
直观了解培训情况
推论
最后说明
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本文目标：实现基于点网的点云分割模型。
简介
点云 "是存储几何形状数据的一种重要数据结构类型。由于其格式不规则，在用于深度学习应用之前，通常要将其转换为规则的三维体素网格或图像集合，这一步骤会使数据变得不必要的庞大。PointNet 系列模型通过直接消耗点云解决了这一问题，同时尊重点数据的包络不变性属性。PointNet 系列模型提供了一个简单、统一的架构，适用于从物体分类、部件分割到场景语义解析等各种应用。
在本示例中，我们演示了用于形状分割的 PointNet 架构的实施。
导入
import os
import json
import random
import numpy as np
import pandas as pd
from tqdm import tqdm
from glob import glob
import tensorflow as tf # For tf.data
import keras
from keras import layers
import matplotlib.pyplot as plt下载数据集
ShapeNet 数据集是为建立一个注释丰富的大规模三维图形数据集而持续开展的一项工作。ShapeNetCore 是完整 ShapeNet 数据集的一个子集，其中包含干净的单个三维模型以及人工验证的类别和排列注释。它涵盖 55 个常见物体类别，拥有约 51,300 个独特的三维模型。
在这个例子中，我们使用了 PASCAL 3D+ 的 12 个对象类别之一，它是 ShapenetCore 数据集的一部分。
dataset_url = "https://git.io/JiY4i"
dataset_path = keras.utils.get_file(
fname="shapenet.zip",
origin=dataset_url,
cache_subdir="datasets",
hash_algorithm="auto",
extract=True,
archive_format="auto",
cache_dir="datasets",
)加载数据集
我们对数据集元数据进行解析，以便轻松地将模型类别映射到各自的目录中，并将分割类别映射到颜色中，从而实现可视化。
with open("/tmp/.keras/datasets/PartAnnotation/metadata.json") as json_file:
metadata = json.load(json_file)
print(metadata){'Airplane': {'directory': '02691156', 'lables': ['wing', 'body', 'tail', 'engine'], 'colors': ['blue', 'green', 'red', 'pink']}, 'Bag': {'directory': '02773838', 'lables': ['handle', 'body'], 'colors': ['blue', 'green']}, 'Cap': {'directory': '02954340', 'lables': ['panels', 'peak'], 'colors': ['blue', 'green']}, 'Car': {'directory': '02958343', 'lables': ['wheel', 'hood', 'roof'], 'colors': ['blue', 'green', 'red']}, 'Chair': {'directory': '03001627', 'lables': ['leg', 'arm', 'back', 'seat'], 'colors': ['blue', 'green', 'red', 'pink']}, 'Earphone': {'directory': '03261776', 'lables': ['earphone', 'headband'], 'colors': ['blue', 'green']}, 'Guitar': {'directory': '03467517', 'lables': ['head', 'body', 'neck'], 'colors': ['blue', 'green', 'red']}, 'Knife': {'directory': '03624134', 'lables': ['handle', 'blade'], 'colors': ['blue', 'green']}, 'Lamp': {'directory': '03636649', 'lables': ['canopy', 'lampshade', 'base'], 'colors': ['blue', 'green', 'red']}, 'Laptop': {'directory': '03642806', 'lables': ['keyboard'], 'colors': ['blue']}, 'Motorbike': {'directory': '03790512', 'lables': ['wheel', 'handle', 'gas_tank', 'light', 'seat'], 'colors': ['blue', 'green', 'red', 'pink', 'yellow']}, 'Mug': {'directory': '03797390', 'lables': ['handle'], 'colors': ['blue']}, 'Pistol': {'directory': '03948459', 'lables': ['trigger_and_guard', 'handle', 'barrel'], 'colors': ['blue', 'green', 'red']}, 'Rocket': {'directory': '04099429', 'lables': ['nose', 'body', 'fin'], 'colors': ['blue', 'green', 'red']}, 'Skateboard': {'directory': '04225987', 'lables': ['wheel', 'deck'], 'colors': ['blue', 'green']}, 'Table': {'directory': '04379243', 'lables': ['leg', 'top'], 'colors': ['blue', 'green']}}在本例中，我们训练 PointNet 对飞机模型的部件进行分割。
points_dir = "/tmp/.keras/datasets/PartAnnotation/{}/points".format(
metadata["Airplane"]["directory"]
)
labels_dir = "/tmp/.keras/datasets/PartAnnotation/{}/points_label".format(
metadata["Airplane"]["directory"]
)
LABELS = metadata["Airplane"]["lables"]
COLORS = metadata["Airplane"]["colors"]
VAL_SPLIT = 0.2
NUM_SAMPLE_POINTS = 1024
BATCH_SIZE = 32
EPOCHS = 60
INITIAL_LR = 1e-3构建数据集
我们根据飞机点云及其标签生成以下内存数据结构：
× point_clouds 是一个 np.array 对象列表，以 x、y 和 z 坐标的形式表示点云数据。轴 0 表示点云中的点数，轴 1 表示坐标。all_labels 是一个列表，以字符串形式表示每个坐标的标签（主要用于可视化目的）。
× test_point_clouds 与 point_clouds 格式相同，但没有相应的点云标签。
× all_labels 是一个 np.array 对象列表，表示每个坐标的点云标签，与 point_clouds 列表相对应。
× point_cloud_labels 是一个 np.array 对象列表，表示每个坐标的点云标签，以单击编码的形式表示，与 point_clouds 列表相对应。
point_clouds, test_point_clouds = [], []
point_cloud_labels, all_labels = [], []
points_files = glob(os.path.join(points_dir, "*.pts"))
for point_file in tqdm(points_files):
point_cloud = np.loadtxt(point_file)
if point_cloud.shape[0] < NUM_SAMPLE_POINTS:
continue
# Get the file-id of the current point cloud for parsing its
# labels.
file_id = point_file.split("/")[-1].split(".")[0]
label_data, num_labels = {}, 0
for label in LABELS:
label_file = os.path.join(labels_dir, label, file_id + ".seg")
if os.path.exists(label_file):
label_data[label] = np.loadtxt(label_file).astype("float32")
num_labels = len(label_data[label])
# Point clouds having labels will be our training samples.
try:
label_map = ["none"] * num_labels
for label in LABELS:
for i, data in enumerate(label_data[label]):
label_map[i] = label if data == 1 else label_map[i]
label_data = [
LABELS.index(label) if label != "none" else len(LABELS)
for label in label_map
]
# Apply one-hot encoding to the dense label representation.
label_data = keras.utils.to_categorical(label_data, num_classes=len(LABELS) + 1)
point_clouds.append(point_cloud)
point_cloud_labels.append(label_data)
all_labels.append(label_map)
except KeyError:
test_point_clouds.append(point_cloud)100%|██████████████████████████████████████████████████████████████████████| 4045/4045 [01:30<00:00, 44.54it/s]接下来，我们看看刚刚生成的内存阵列中的一些样本：
for _ in range(5):
i = random.randint(0, len(point_clouds) - 1)
print(f"point_clouds[{i}].shape:", point_clouds[0].shape)
print(f"point_cloud_labels[{i}].shape:", point_cloud_labels[0].shape)
for j in range(5):
print(
f"all_labels[{i}][{j}]:",
all_labels[i][j],
f"\tpoint_cloud_labels[{i}][{j}]:",
point_cloud_labels[i][j],
"\n",
)演绎展示：
point_clouds[333].shape: (2571, 3) point_cloud_labels[333].shape: (2571, 5) all_labels[333][0]: tail point_cloud_labels[333][0]: [0. 0. 1. 0. 0.]all_labels[333][1]: wing point_cloud_labels[333][1]: [1. 0. 0. 0. 0.]all_labels[333][2]: tail point_cloud_labels[333][2]: [0. 0. 1. 0. 0.]all_labels[333][3]: engine point_cloud_labels[333][3]: [0. 0. 0. 1. 0.]all_labels[333][4]: wing point_cloud_labels[333][4]: [1. 0. 0. 0. 0.]point_clouds[3273].shape: (2571, 3) point_cloud_labels[3273].shape: (2571, 5) all_labels[3273][0]: body point_cloud_labels[3273][0]: [0. 1. 0. 0. 0.]all_labels[3273][1]: body point_cloud_labels[3273][1]: [0. 1. 0. 0. 0.]all_labels[3273][2]: tail point_cloud_labels[3273][2]: [0. 0. 1. 0. 0.]all_labels[3273][3]: wing point_cloud_labels[3273][3]: [1. 0. 0. 0. 0.]all_labels[3273][4]: wing point_cloud_labels[3273][4]: [1. 0. 0. 0. 0.]point_clouds[929].shape: (2571, 3) point_cloud_labels[929].shape: (2571, 5) all_labels[929][0]: body point_cloud_labels[929][0]: [0. 1. 0. 0. 0.]all_labels[929][1]: tail point_cloud_labels[929][1]: [0. 0. 1. 0. 0.]all_labels[929][2]: wing point_cloud_labels[929][2]: [1. 0. 0. 0. 0.]all_labels[929][3]: tail point_cloud_labels[929][3]: [0. 0. 1. 0. 0.]all_labels[929][4]: body point_cloud_labels[929][4]: [0. 1. 0. 0. 0.]point_clouds[496].shape: (2571, 3) point_cloud_labels[496].shape: (2571, 5) all_labels[496][0]: body point_cloud_labels[496][0]: [0. 1. 0. 0. 0.]all_labels[496][1]: body point_cloud_labels[496][1]: [0. 1. 0. 0. 0.]all_labels[496][2]: body point_cloud_labels[496][2]: [0. 1. 0. 0. 0.]all_labels[496][3]: wing point_cloud_labels[496][3]: [1. 0. 0. 0. 0.]all_labels[496][4]: body point_cloud_labels[496][4]: [0. 1. 0. 0. 0.]point_clouds[3508].shape: (2571, 3) point_cloud_labels[3508].shape: (2571, 5) all_labels[3508][0]: body point_cloud_labels[3508][0]: [0. 1. 0. 0. 0.]all_labels[3508][1]: body point_cloud_labels[3508][1]: [0. 1. 0. 0. 0.]all_labels[3508][2]: body point_cloud_labels[3508][2]: [0. 1. 0. 0. 0.]all_labels[3508][3]: body point_cloud_labels[3508][3]: [0. 1. 0. 0. 0.]all_labels[3508][4]: body point_cloud_labels[3508][4]: [0. 1. 0. 0. 0.]
现在，让我们将一些点云及其标签可视化。
def visualize_data(point_cloud, labels):
df = pd.DataFrame(
data={
"x": point_cloud[:, 0],
"y": point_cloud[:, 1],
"z": point_cloud[:, 2],
"label": labels,
}
)
fig = plt.figure(figsize=(15, 10))
ax = plt.axes(projection="3d")
for index, label in enumerate(LABELS):
c_df = df[df["label"] == label]
try:
ax.scatter(
c_df["x"], c_df["y"], c_df["z"], label=label, alpha=0.5, c=COLORS[index]
)
except IndexError:
pass
ax.legend()
plt.show()
visualize_data(point_clouds[0], all_labels[0])
visualize_data(point_clouds[300], all_labels[300])
预处理
需要注意的是，我们加载的所有点云都由数量不等的点组成，这使得我们很难将它们集中在一起。为了解决这个问题，我们从每个点云中随机抽取固定数量的点。我们还对点云进行了归一化处理，以使数据与比例尺无关。
for index in tqdm(range(len(point_clouds))):
current_point_cloud = point_clouds[index]
current_label_cloud = point_cloud_labels[index]
current_labels = all_labels[index]
num_points = len(current_point_cloud)
# Randomly sampling respective indices.
sampled_indices = random.sample(list(range(num_points)), NUM_SAMPLE_POINTS)
# Sampling points corresponding to sampled indices.
sampled_point_cloud = np.array([current_point_cloud[i] for i in sampled_indices])
# Sampling corresponding one-hot encoded labels.
sampled_label_cloud = np.array([current_label_cloud[i] for i in sampled_indices])
# Sampling corresponding labels for visualization.
sampled_labels = np.array([current_labels[i] for i in sampled_indices])
# Normalizing sampled point cloud.
norm_point_cloud = sampled_point_cloud - np.mean(sampled_point_cloud, axis=0)
norm_point_cloud /= np.max(np.linalg.norm(norm_point_cloud, axis=1))
point_clouds[index] = norm_point_cloud
point_cloud_labels[index] = sampled_label_cloud
all_labels[index] = sampled_labels100%|█████████████████████████████████████████████████████████████████████| 3694/3694 [00:08<00:00, 446.45it/s]让我们将采样和归一化的点云及其相应的标签可视化。
visualize_data(point_clouds[0], all_labels[0])
visualize_data(point_clouds[300], all_labels[300])
创建 TensorFlow 数据集
我们为训练数据和验证数据创建 tf.data.Dataset 对象。我们还通过随机抖动来增强训练点云。
def load_data(point_cloud_batch, label_cloud_batch):
point_cloud_batch.set_shape([NUM_SAMPLE_POINTS, 3])
label_cloud_batch.set_shape([NUM_SAMPLE_POINTS, len(LABELS) + 1])
return point_cloud_batch, label_cloud_batch
def augment(point_cloud_batch, label_cloud_batch):
noise = tf.random.uniform(
tf.shape(label_cloud_batch), -0.001, 0.001, dtype=tf.float64
)
point_cloud_batch += noise[:, :, :3]
return point_cloud_batch, label_cloud_batch
def generate_dataset(point_clouds, label_clouds, is_training=True):
dataset = tf.data.Dataset.from_tensor_slices((point_clouds, label_clouds))
dataset = dataset.shuffle(BATCH_SIZE * 100) if is_training else dataset
dataset = dataset.map(load_data, num_parallel_calls=tf.data.AUTOTUNE)
dataset = dataset.batch(batch_size=BATCH_SIZE)
dataset = (
dataset.map(augment, num_parallel_calls=tf.data.AUTOTUNE)
if is_training
else dataset
)
return dataset
split_index = int(len(point_clouds) * (1 - VAL_SPLIT))
train_point_clouds = point_clouds[:split_index]
train_label_cloud = point_cloud_labels[:split_index]
total_training_examples = len(train_point_clouds)
val_point_clouds = point_clouds[split_index:]
val_label_cloud = point_cloud_labels[split_index:]
print("Num train point clouds:", len(train_point_clouds))
print("Num train point cloud labels:", len(train_label_cloud))
print("Num val point clouds:", len(val_point_clouds))
print("Num val point cloud labels:", len(val_label_cloud))
train_dataset = generate_dataset(train_point_clouds, train_label_cloud)
val_dataset = generate_dataset(val_point_clouds, val_label_cloud, is_training=False)
print("Train Dataset:", train_dataset)
print("Validation Dataset:", val_dataset)Num train point clouds: 2955
Num train point cloud labels: 2955
Num val point clouds: 739
Num val point cloud labels: 739
Train Dataset: <_ParallelMapDataset element_spec=(TensorSpec(shape=(None, 1024, 3), dtype=tf.float64, name=None), TensorSpec(shape=(None, 1024, 5), dtype=tf.float64, name=None))>
Validation Dataset: <_BatchDataset element_spec=(TensorSpec(shape=(None, 1024, 3), dtype=tf.float64, name=None), TensorSpec(shape=(None, 1024, 5), dtype=tf.float64, name=None))>PointNet 模型
下图描述了 PointNet 型号系列的内部结构：
鉴于 PointNet 将无序的坐标集作为输入数据，因此其架构需要与点云数据的以下特性相匹配：
排列不变性
鉴于点云数据的非结构化性质，由 n 个点组成的扫描有 n 种排列组合。随后的数据处理必须对不同的表示方法保持不变。为了使 PointNet 不受输入排列的影响，我们在将 n 个输入点映射到高维空间后使用了对称函数（如最大池化）。这样就得到了一个全局特征向量，旨在捕捉 n 个输入点的总体特征。
全局特征向量与局部点特征一起用于分割。
变换不变性
如果物体发生某些变换，如平移或缩放，分割输出结果应保持不变。对于给定的输入点云，我们会应用适当的刚性或仿射变换来实现姿态归一化。由于 n 个输入点中的每个点都表示为一个向量，并独立映射到嵌入空间，因此应用几何变换只需将每个点与一个变换矩阵相乘即可。这就是空间变换器网络概念的由来。
构成 T-Net 的操作是受 PointNet 高级架构的启发。MLP（或全连接层）用于将输入点独立且相同地映射到高维空间；最大池化用于编码全局特征向量，然后通过全连接层降低其维度。最后全连接层的输入相关特征与全局可训练权重和偏置相结合，形成一个 3 乘 3 的变换矩阵。
点之间的相互作用
相邻点之间的相互作用往往蕴含着有用的信息（即不应孤立地处理单个点）。分类只需利用全局特征，而分割则必须能够利用局部点特征和全局点特征。
既然知道了 PointNet 模型的组成要素，我们就可以实现该模型了。我们首先要实现基本模块，即卷积模块和多层感知器模块。
def conv_block(x, filters, name):
x = layers.Conv1D(filters, kernel_size=1, padding="valid", name=f"{name}_conv")(x)
x = layers.BatchNormalization(name=f"{name}_batch_norm")(x)
return layers.Activation("relu", name=f"{name}_relu")(x)
def mlp_block(x, filters, name):
x = layers.Dense(filters, name=f"{name}_dense")(x)
x = layers.BatchNormalization(name=f"{name}_batch_norm")(x)
return layers.Activation("relu", name=f"{name}_relu")(x)我们实施了一个正则化器（取自本例），以加强特征空间的正交性。这是确保变换后的特征幅度不会有太大变化所必需的。
class OrthogonalRegularizer(keras.regularizers.Regularizer):
"""Reference: https://keras.io/examples/vision/pointnet/#build-a-model"""
def __init__(self, num_features, l2reg=0.001):
self.num_features = num_features
self.l2reg = l2reg
self.identity = keras.ops.eye(num_features)
def __call__(self, x):
x = keras.ops.reshape(x, (-1, self.num_features, self.num_features))
xxt = keras.ops.tensordot(x, x, axes=(2, 2))
xxt = keras.ops.reshape(xxt, (-1, self.num_features, self.num_features))
return keras.ops.sum(self.l2reg * keras.ops.square(xxt - self.identity))
def get_config(self):
config = super().get_config()
config.update({"num_features": self.num_features, "l2reg_strength": self.l2reg})
return config下一个部分是我们之前介绍过的转换网络。
def transformation_net(inputs, num_features, name):
"""
Reference: https://keras.io/examples/vision/pointnet/#build-a-model.
The `filters` values come from the original paper:
https://arxiv.org/abs/1612.00593.
"""
x = conv_block(inputs, filters=64, name=f"{name}_1")
x = conv_block(x, filters=128, name=f"{name}_2")
x = conv_block(x, filters=1024, name=f"{name}_3")
x = layers.GlobalMaxPooling1D()(x)
x = mlp_block(x, filters=512, name=f"{name}_1_1")
x = mlp_block(x, filters=256, name=f"{name}_2_1")
return layers.Dense(
num_features * num_features,
kernel_initializer="zeros",
bias_initializer=keras.initializers.Constant(np.eye(num_features).flatten()),
activity_regularizer=OrthogonalRegularizer(num_features),
name=f"{name}_final",
)(x)
def transformation_block(inputs, num_features, name):
transformed_features = transformation_net(inputs, num_features, name=name)
transformed_features = layers.Reshape((num_features, num_features))(
transformed_features
)
return layers.Dot(axes=(2, 1), name=f"{name}_mm")([inputs, transformed_features])最后，我们将上述模块拼接在一起，实现分割模型。
def get_shape_segmentation_model(num_points, num_classes):
input_points = keras.Input(shape=(None, 3))
# PointNet Classification Network.
transformed_inputs = transformation_block(
input_points, num_features=3, name="input_transformation_block"
)
features_64 = conv_block(transformed_inputs, filters=64, name="features_64")
features_128_1 = conv_block(features_64, filters=128, name="features_128_1")
features_128_2 = conv_block(features_128_1, filters=128, name="features_128_2")
transformed_features = transformation_block(
features_128_2, num_features=128, name="transformed_features"
)
features_512 = conv_block(transformed_features, filters=512, name="features_512")
features_2048 = conv_block(features_512, filters=2048, name="pre_maxpool_block")
global_features = layers.MaxPool1D(pool_size=num_points, name="global_features")(
features_2048
)
global_features = keras.ops.tile(global_features, [1, num_points, 1])
# Segmentation head.
segmentation_input = layers.Concatenate(name="segmentation_input")(
[
features_64,
features_128_1,
features_128_2,
transformed_features,
features_512,
global_features,
]
)
segmentation_features = conv_block(
segmentation_input, filters=128, name="segmentation_features"
)
outputs = layers.Conv1D(
num_classes, kernel_size=1, activation="softmax", name="segmentation_head"
)(segmentation_features)
return keras.Model(input_points, outputs)实例化模型
x, y = next(iter(train_dataset))
num_points = x.shape[1]
num_classes = y.shape[-1]
segmentation_model = get_shape_segmentation_model(num_points, num_classes)
segmentation_model.summary()演绎展示：
Model: "functional_1"┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ Connected to ┃ ┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━┩ │ input_layer │ (None, None, 3) │ 0 │ - │ │ (InputLayer) │ │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ input_transformati… │ (None, None, 64) │ 256 │ input_layer[0][0] │ │ (Conv1D) │ │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ input_transformati… │ (None, None, 64) │ 256 │ input_transformatio… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ input_transformati… │ (None, None, 64) │ 0 │ input_transformatio… │ │ (Activation) │ │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ input_transformati… │ (None, None, 128) │ 8,320 │ input_transformatio… │ │ (Conv1D) │ │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ input_transformati… │ (None, None, 128) │ 512 │ input_transformatio… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ input_transformati… │ (None, None, 128) │ 0 │ input_transformatio… │ │ (Activation) │ │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ input_transformati… │ (None, None, │ 132,096 │ input_transformatio… │ │ (Conv1D) │ 1024) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ input_transformati… │ (None, None, │ 4,096 │ input_transformatio… │ │ (BatchNormalizatio… │ 1024) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ input_transformati… │ (None, None, │ 0 │ input_transformatio… │ │ (Activation) │ 1024) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ global_max_pooling… │ (None, 1024) │ 0 │ input_transformatio… │ │ (GlobalMaxPooling1… │ │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ input_transformati… │ (None, 512) │ 524,800 │ global_max_pooling1… │ │ (Dense) │ │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ input_transformati… │ (None, 512) │ 2,048 │ input_transformatio… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ input_transformati… │ (None, 512) │ 0 │ input_transformatio… │ │ (Activation) │ │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ input_transformati… │ (None, 256) │ 131,328 │ input_transformatio… │ │ (Dense) │ │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ input_transformati… │ (None, 256) │ 1,024 │ input_transformatio… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ input_transformati… │ (None, 256) │ 0 │ input_transformatio… │ │ (Activation) │ │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ input_transformati… │ (None, 9) │ 2,313 │ input_transformatio… │ │ (Dense) │ │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ reshape (Reshape) │ (None, 3, 3) │ 0 │ input_transformatio… │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ input_transformati… │ (None, None, 3) │ 0 │ input_layer[0][0], │ │ (Dot) │ │ │ reshape[0][0] │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ features_64_conv │ (None, None, 64) │ 256 │ input_transformatio… │ │ (Conv1D) │ │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ features_64_batch_… │ (None, None, 64) │ 256 │ features_64_conv[0]… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ features_64_relu │ (None, None, 64) │ 0 │ features_64_batch_n… │ │ (Activation) │ │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ features_128_1_conv │ (None, None, 128) │ 8,320 │ features_64_relu[0]… │ │ (Conv1D) │ │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ features_128_1_bat… │ (None, None, 128) │ 512 │ features_128_1_conv… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ features_128_1_relu │ (None, None, 128) │ 0 │ features_128_1_batc… │ │ (Activation) │ │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ features_128_2_conv │ (None, None, 128) │ 16,512 │ features_128_1_relu… │ │ (Conv1D) │ │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ features_128_2_bat… │ (None, None, 128) │ 512 │ features_128_2_conv… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ features_128_2_relu │ (None, None, 128) │ 0 │ features_128_2_batc… │ │ (Activation) │ │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ transformed_featur… │ (None, None, 64) │ 8,256 │ features_128_2_relu… │ │ (Conv1D) │ │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ transformed_featur… │ (None, None, 64) │ 256 │ transformed_feature… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ transformed_featur… │ (None, None, 64) │ 0 │ transformed_feature… │ │ (Activation) │ │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ transformed_featur… │ (None, None, 128) │ 8,320 │ transformed_feature… │ │ (Conv1D) │ │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ transformed_featur… │ (None, None, 128) │ 512 │ transformed_feature… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ transformed_featur… │ (None, None, 128) │ 0 │ transformed_feature… │ │ (Activation) │ │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ transformed_featur… │ (None, None, │ 132,096 │ transformed_feature… │ │ (Conv1D) │ 1024) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ transformed_featur… │ (None, None, │ 4,096 │ transformed_feature… │ │ (BatchNormalizatio… │ 1024) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ transformed_featur… │ (None, None, │ 0 │ transformed_feature… │ │ (Activation) │ 1024) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ global_max_pooling… │ (None, 1024) │ 0 │ transformed_feature… │ │ (GlobalMaxPooling1… │ │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ transformed_featur… │ (None, 512) │ 524,800 │ global_max_pooling1… │ │ (Dense) │ │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ transformed_featur… │ (None, 512) │ 2,048 │ transformed_feature… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ transformed_featur… │ (None, 512) │ 0 │ transformed_feature… │ │ (Activation) │ │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ transformed_featur… │ (None, 256) │ 131,328 │ transformed_feature… │ │ (Dense) │ │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ transformed_featur… │ (None, 256) │ 1,024 │ transformed_feature… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ transformed_featur… │ (None, 256) │ 0 │ transformed_feature… │ │ (Activation) │ │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ transformed_featur… │ (None, 16384) │ 4,210,… │ transformed_feature… │ │ (Dense) │ │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ reshape_1 (Reshape) │ (None, 128, 128) │ 0 │ transformed_feature… │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ transformed_featur… │ (None, None, 128) │ 0 │ features_128_2_relu… │ │ (Dot) │ │ │ reshape_1[0][0] │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ features_512_conv │ (None, None, 512) │ 66,048 │ transformed_feature… │ │ (Conv1D) │ │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ features_512_batch… │ (None, None, 512) │ 2,048 │ features_512_conv[0… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ features_512_relu │ (None, None, 512) │ 0 │ features_512_batch_… │ │ (Activation) │ │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ pre_maxpool_block_… │ (None, None, │ 1,050,… │ features_512_relu[0… │ │ (Conv1D) │ 2048) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ pre_maxpool_block_… │ (None, None, │ 8,192 │ pre_maxpool_block_c… │ │ (BatchNormalizatio… │ 2048) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ pre_maxpool_block_… │ (None, None, │ 0 │ pre_maxpool_block_b… │ │ (Activation) │ 2048) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ global_features │ (None, None, │ 0 │ pre_maxpool_block_r… │ │ (MaxPooling1D) │ 2048) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ tile (Tile) │ (None, None, │ 0 │ global_features[0][… │ │ │ 2048) │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ segmentation_input │ (None, None, │ 0 │ features_64_relu[0]… │ │ (Concatenate) │ 3008) │ │ features_128_1_relu… │ │ │ │ │ features_128_2_relu… │ │ │ │ │ transformed_feature… │ │ │ │ │ features_512_relu[0… │ │ │ │ │ tile[0][0] │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ segmentation_featu… │ (None, None, 128) │ 385,152 │ segmentation_input[… │ │ (Conv1D) │ │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ segmentation_featu… │ (None, None, 128) │ 512 │ segmentation_featur… │ │ (BatchNormalizatio… │ │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ segmentation_featu… │ (None, None, 128) │ 0 │ segmentation_featur… │ │ (Activation) │ │ │ │ ├─────────────────────┼───────────────────┼─────────┼──────────────────────┤ │ segmentation_head │ (None, None, 5) │ 645 │ segmentation_featur… │ │ (Conv1D) │ │ │ │ └─────────────────────┴───────────────────┴─────────┴──────────────────────┘Total params: 7,370,062 (28.11 MB)Trainable params: 7,356,110 (28.06 MB)Non-trainable params: 13,952 (54.50 KB)
训练
对于训练，作者建议使用学习率计划，即每 20 个历元将初始学习率减半。在本例中，我们使用的是 5 个历元。
steps_per_epoch = total_training_examples // BATCH_SIZE
total_training_steps = steps_per_epoch * EPOCHS
print(f"Steps per epoch: {steps_per_epoch}.")
print(f"Total training steps: {total_training_steps}.")
lr_schedule = keras.optimizers.schedules.ExponentialDecay(
initial_learning_rate=0.003,
decay_steps=steps_per_epoch * 5,
decay_rate=0.5,
staircase=True,
)
steps = range(total_training_steps)
lrs = [lr_schedule(step) for step in steps]
plt.plot(lrs)
plt.xlabel("Steps")
plt.ylabel("Learning Rate")
plt.show()演绎展示：
Steps per epoch: 92.
Total training steps: 5520.
最后，我们实现了一个用于运行实验和启动模型训练的实用程序。
def run_experiment(epochs):
segmentation_model = get_shape_segmentation_model(num_points, num_classes)
segmentation_model.compile(
optimizer=keras.optimizers.Adam(learning_rate=lr_schedule),
loss=keras.losses.CategoricalCrossentropy(),
metrics=["accuracy"],
)
checkpoint_filepath = "checkpoint.weights.h5"
checkpoint_callback = keras.callbacks.ModelCheckpoint(
checkpoint_filepath,
monitor="val_loss",
save_best_only=True,
save_weights_only=True,
)
history = segmentation_model.fit(
train_dataset,
validation_data=val_dataset,
epochs=epochs,
callbacks=[checkpoint_callback],
)
segmentation_model.load_weights(checkpoint_filepath)
return segmentation_model, history
segmentation_model, history = run_experiment(epochs=EPOCHS)Epoch 1/60
2/93 [37m━━━━━━━━━━━━━━━━━━━━ 7s 86ms/step - accuracy: 0.1427 - loss: 48748.8203
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1699916678.434176 90326 device_compiler.h:187] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.
93/93 ━━━━━━━━━━━━━━━━━━━━ 53s 259ms/step - accuracy: 0.3739 - loss: 27980.7305 - val_accuracy: 0.4340 - val_loss: 10361231.0000
Epoch 2/60
93/93 ━━━━━━━━━━━━━━━━━━━━ 48s 82ms/step - accuracy: 0.6355 - loss: 339.9151 - val_accuracy: 0.3820 - val_loss: 19069320.0000
Epoch 3/60
93/93 ━━━━━━━━━━━━━━━━━━━━ 8s 82ms/step - accuracy: 0.6695 - loss: 281.5728 - val_accuracy: 0.2859 - val_loss: 15993839.0000
Epoch 4/60
93/93 ━━━━━━━━━━━━━━━━━━━━ 8s 89ms/step - accuracy: 0.6812 - loss: 253.0939 - val_accuracy: 0.2287 - val_loss: 9633191.0000
Epoch 5/60
93/93 ━━━━━━━━━━━━━━━━━━━━ 8s 89ms/step - accuracy: 0.6873 - loss: 231.1317 - val_accuracy: 0.3030 - val_loss: 6001454.0000
Epoch 6/60
93/93 ━━━━━━━━━━━━━━━━━━━━ 8s 88ms/step - accuracy: 0.6860 - loss: 216.6793 - val_accuracy: 0.0620 - val_loss: 1945100.8750
Epoch 7/60
93/93 ━━━━━━━━━━━━━━━━━━━━ 8s 82ms/step - accuracy: 0.6947 - loss: 210.2683 - val_accuracy: 0.4539 - val_loss: 7908162.5000
Epoch 8/60
93/93 ━━━━━━━━━━━━━━━━━━━━ 8s 82ms/step - accuracy: 0.7014 - loss: 203.2560 - val_accuracy: 0.4035 - val_loss: 17741164.0000
Epoch 9/60
93/93 ━━━━━━━━━━━━━━━━━━━━ 8s 82ms/step - accuracy: 0.7006 - loss: 197.3710 - val_accuracy: 0.1900 - val_loss: 34120616.0000
Epoch 10/60
93/93 ━━━━━━━━━━━━━━━━━━━━ 8s 82ms/step - accuracy: 0.7047 - loss: 192.0777 - val_accuracy: 0.3391 - val_loss: 33157422.0000
Epoch 11/60
93/93 ━━━━━━━━━━━━━━━━━━━━ 8s 82ms/step - accuracy: 0.7102 - loss: 188.4875 - val_accuracy: 0.3394 - val_loss: 4630613.5000
Epoch 12/60
93/93 ━━━━━━━━━━━━━━━━━━━━ 8s 89ms/step - accuracy: 0.7186 - loss: 184.9940 - val_accuracy: 0.1662 - val_loss: 487790.1250
Epoch 13/60
93/93 ━━━━━━━━━━━━━━━━━━━━ 8s 89ms/step - accuracy: 0.7175 - loss: 182.7206 - val_accuracy: 0.1602 - val_loss: 70590.3203
Epoch 14/60
93/93 ━━━━━━━━━━━━━━━━━━━━ 8s 88ms/step - accuracy: 0.7159 - loss: 180.5028 - val_accuracy: 0.1631 - val_loss: 16990.2324
Epoch 15/60
93/93 ━━━━━━━━━━━━━━━━━━━━ 8s 88ms/step - accuracy: 0.7201 - loss: 180.1674 - val_accuracy: 0.2318 - val_loss: 4992.7783
Epoch 16/60
93/93 ━━━━━━━━━━━━━━━━━━━━ 8s 88ms/step - accuracy: 0.7222 - loss: 176.5523 - val_accuracy: 0.6246 - val_loss: 647.5634
Epoch 17/60
93/93 ━━━━━━━━━━━━━━━━━━━━ 8s 88ms/step - accuracy: 0.7291 - loss: 175.6139 - val_accuracy: 0.6551 - val_loss: 324.0956
Epoch 18/60
93/93 ━━━━━━━━━━━━━━━━━━━━ 8s 88ms/step - accuracy: 0.7285 - loss: 175.0228 - val_accuracy: 0.6430 - val_loss: 257.9340
Epoch 19/60
93/93 ━━━━━━━━━━━━━━━━━━━━ 8s 88ms/step - accuracy: 0.7300 - loss: 172.7668 - val_accuracy: 0.6399 - val_loss: 253.2745
Epoch 20/60
93/93 ━━━━━━━━━━━━━━━━━━━━ 8s 89ms/step - accuracy: 0.7316 - loss: 172.9001 - val_accuracy: 0.6084 - val_loss: 232.9293
Epoch 21/60
93/93 ━━━━━━━━━━━━━━━━━━━━ 8s 89ms/step - accuracy: 0.7364 - loss: 170.8767 - val_accuracy: 0.6451 - val_loss: 191.7183
Epoch 22/60
93/93 ━━━━━━━━━━━━━━━━━━━━ 8s 88ms/step - accuracy: 0.7395 - loss: 171.4525 - val_accuracy: 0.6825 - val_loss: 180.2473
Epoch 23/60
93/93 ━━━━━━━━━━━━━━━━━━━━ 8s 82ms/step - accuracy: 0.7392 - loss: 170.1975 - val_accuracy: 0.6095 - val_loss: 180.3243
Epoch 24/60
93/93 ━━━━━━━━━━━━━━━━━━━━ 8s 88ms/step - accuracy: 0.7362 - loss: 169.2144 - val_accuracy: 0.6017 - val_loss: 178.3013
Epoch 25/60
93/93 ━━━━━━━━━━━━━━━━━━━━ 8s 82ms/step - accuracy: 0.7409 - loss: 169.2571 - val_accuracy: 0.6582 - val_loss: 178.3481
Epoch 26/60
93/93 ━━━━━━━━━━━━━━━━━━━━ 8s 89ms/step - accuracy: 0.7415 - loss: 167.7480 - val_accuracy: 0.6808 - val_loss: 177.8774
Epoch 27/60
93/93 ━━━━━━━━━━━━━━━━━━━━ 8s 89ms/step - accuracy: 0.7440 - loss: 167.7844 - val_accuracy: 0.7131 - val_loss: 176.5841
Epoch 28/60
93/93 ━━━━━━━━━━━━━━━━━━━━ 8s 88ms/step - accuracy: 0.7423 - loss: 167.5307 - val_accuracy: 0.6891 - val_loss: 176.1687
Epoch 29/60
93/93 ━━━━━━━━━━━━━━━━━━━━ 8s 88ms/step - accuracy: 0.7409 - loss: 166.4581 - val_accuracy: 0.7136 - val_loss: 174.9417
Epoch 30/60
93/93 ━━━━━━━━━━━━━━━━━━━━ 8s 88ms/step - accuracy: 0.7419 - loss: 165.9243 - val_accuracy: 0.7407 - val_loss: 173.0663
Epoch 31/60
93/93 ━━━━━━━━━━━━━━━━━━━━ 8s 88ms/step - accuracy: 0.7471 - loss: 166.9746 - val_accuracy: 0.7454 - val_loss: 172.9663
Epoch 32/60
93/93 ━━━━━━━━━━━━━━━━━━━━ 8s 82ms/step - accuracy: 0.7472 - loss: 165.9707 - val_accuracy: 0.7480 - val_loss: 173.9868
Epoch 33/60
93/93 ━━━━━━━━━━━━━━━━━━━━ 8s 82ms/step - accuracy: 0.7443 - loss: 165.9368 - val_accuracy: 0.7076 - val_loss: 174.4526
Epoch 34/60
93/93 ━━━━━━━━━━━━━━━━━━━━ 8s 82ms/step - accuracy: 0.7496 - loss: 165.5322 - val_accuracy: 0.7441 - val_loss: 174.6099
Epoch 35/60
93/93 ━━━━━━━━━━━━━━━━━━━━ 8s 82ms/step - accuracy: 0.7453 - loss: 164.2007 - val_accuracy: 0.7469 - val_loss: 174.2793
Epoch 36/60
93/93 ━━━━━━━━━━━━━━━━━━━━ 8s 82ms/step - accuracy: 0.7503 - loss: 165.3418 - val_accuracy: 0.7469 - val_loss: 174.0812
Epoch 37/60
93/93 ━━━━━━━━━━━━━━━━━━━━ 8s 82ms/step - accuracy: 0.7491 - loss: 164.4796 - val_accuracy: 0.7524 - val_loss: 173.9656
Epoch 38/60
93/93 ━━━━━━━━━━━━━━━━━━━━ 10s 82ms/step - accuracy: 0.7489 - loss: 164.4573 - val_accuracy: 0.7516 - val_loss: 175.3401
Epoch 39/60
93/93 ━━━━━━━━━━━━━━━━━━━━ 8s 82ms/step - accuracy: 0.7437 - loss: 163.4484 - val_accuracy: 0.7532 - val_loss: 173.8172
Epoch 40/60
93/93 ━━━━━━━━━━━━━━━━━━━━ 8s 82ms/step - accuracy: 0.7507 - loss: 163.6720 - val_accuracy: 0.7537 - val_loss: 173.9127
Epoch 41/60
93/93 ━━━━━━━━━━━━━━━━━━━━ 8s 82ms/step - accuracy: 0.7506 - loss: 164.0555 - val_accuracy: 0.7556 - val_loss: 173.0979
Epoch 42/60
93/93 ━━━━━━━━━━━━━━━━━━━━ 8s 89ms/step - accuracy: 0.7517 - loss: 164.1554 - val_accuracy: 0.7562 - val_loss: 172.8895
Epoch 43/60
93/93 ━━━━━━━━━━━━━━━━━━━━ 10s 82ms/step - accuracy: 0.7527 - loss: 164.6351 - val_accuracy: 0.7567 - val_loss: 173.0476
Epoch 44/60
93/93 ━━━━━━━━━━━━━━━━━━━━ 8s 88ms/step - accuracy: 0.7505 - loss: 164.1568 - val_accuracy: 0.7571 - val_loss: 172.2751
Epoch 45/60
93/93 ━━━━━━━━━━━━━━━━━━━━ 8s 88ms/step - accuracy: 0.7500 - loss: 163.8129 - val_accuracy: 0.7579 - val_loss: 171.8897
Epoch 46/60
93/93 ━━━━━━━━━━━━━━━━━━━━ 8s 82ms/step - accuracy: 0.7534 - loss: 163.6473 - val_accuracy: 0.7577 - val_loss: 172.5457
Epoch 47/60
93/93 ━━━━━━━━━━━━━━━━━━━━ 8s 82ms/step - accuracy: 0.7510 - loss: 163.7318 - val_accuracy: 0.7580 - val_loss: 172.2256
Epoch 48/60
93/93 ━━━━━━━━━━━━━━━━━━━━ 8s 82ms/step - accuracy: 0.7517 - loss: 163.3274 - val_accuracy: 0.7575 - val_loss: 172.3276
Epoch 49/60
93/93 ━━━━━━━━━━━━━━━━━━━━ 8s 89ms/step - accuracy: 0.7511 - loss: 163.5069 - val_accuracy: 0.7581 - val_loss: 171.2155
Epoch 50/60
93/93 ━━━━━━━━━━━━━━━━━━━━ 8s 89ms/step - accuracy: 0.7507 - loss: 163.7366 - val_accuracy: 0.7578 - val_loss: 171.1100
Epoch 51/60
93/93 ━━━━━━━━━━━━━━━━━━━━ 8s 82ms/step - accuracy: 0.7519 - loss: 163.1190 - val_accuracy: 0.7580 - val_loss: 171.7971
Epoch 52/60
93/93 ━━━━━━━━━━━━━━━━━━━━ 8s 81ms/step - accuracy: 0.7510 - loss: 162.7351 - val_accuracy: 0.7579 - val_loss: 171.9780
Epoch 53/60
93/93 ━━━━━━━━━━━━━━━━━━━━ 8s 82ms/step - accuracy: 0.7510 - loss: 162.9639 - val_accuracy: 0.7577 - val_loss: 171.6770
Epoch 54/60
93/93 ━━━━━━━━━━━━━━━━━━━━ 8s 88ms/step - accuracy: 0.7530 - loss: 162.7419 - val_accuracy: 0.7578 - val_loss: 170.5556
Epoch 55/60
93/93 ━━━━━━━━━━━━━━━━━━━━ 8s 82ms/step - accuracy: 0.7515 - loss: 163.2893 - val_accuracy: 0.7582 - val_loss: 171.9172
Epoch 56/60
93/93 ━━━━━━━━━━━━━━━━━━━━ 8s 82ms/step - accuracy: 0.7505 - loss: 164.2843 - val_accuracy: 0.7584 - val_loss: 171.9182
Epoch 57/60
93/93 ━━━━━━━━━━━━━━━━━━━━ 8s 82ms/step - accuracy: 0.7498 - loss: 162.6679 - val_accuracy: 0.7587 - val_loss: 173.7610
Epoch 58/60
93/93 ━━━━━━━━━━━━━━━━━━━━ 8s 82ms/step - accuracy: 0.7523 - loss: 163.3332 - val_accuracy: 0.7585 - val_loss: 172.5207
Epoch 59/60
93/93 ━━━━━━━━━━━━━━━━━━━━ 8s 82ms/step - accuracy: 0.7529 - loss: 162.4575 - val_accuracy: 0.7586 - val_loss: 171.6861
Epoch 60/60
93/93 ━━━━━━━━━━━━━━━━━━━━ 8s 82ms/step - accuracy: 0.7498 - loss: 162.9523 - val_accuracy: 0.7586 - val_loss: 172.3012直观了解培训情况
def plot_result(item):
plt.plot(history.history[item], label=item)
plt.plot(history.history["val_" + item], label="val_" + item)
plt.xlabel("Epochs")
plt.ylabel(item)
plt.title("Train and Validation {} Over Epochs".format(item), fontsize=14)
plt.legend()
plt.grid()
plt.show()
plot_result("loss")
plot_result("accuracy")
推论
validation_batch = next(iter(val_dataset))
val_predictions = segmentation_model.predict(validation_batch[0])
print(f"Validation prediction shape: {val_predictions.shape}")
def visualize_single_point_cloud(point_clouds, label_clouds, idx):
label_map = LABELS + ["none"]
point_cloud = point_clouds[idx]
label_cloud = label_clouds[idx]
visualize_data(point_cloud, [label_map[np.argmax(label)] for label in label_cloud])
idx = np.random.choice(len(validation_batch[0]))
print(f"Index selected: {idx}")
# Plotting with ground-truth.
visualize_single_point_cloud(validation_batch[0], validation_batch[1], idx)
# Plotting with predicted labels.
visualize_single_point_cloud(validation_batch[0], val_predictions, idx)演绎展示：
1/1 ━━━━━━━━━━━━━━━━━━━━ 1s 1s/step
Validation prediction shape: (32, 1024, 5)
Index selected: 26
最后说明
如果您有兴趣了解有关此主题的更多信息，您可能会发现本资料库非常有用。
