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prodiff-model / ProDiff /test_trained_model.sh

Wuhuwill

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#!/bin/bash

# =====================================
# ProDiff 训练完成模型测试脚本
# =====================================

# 配置参数
EXP_NAME="trajectory_a40_turbo_optimized"
DEVICE_ID=0
SAMPLING_TYPE="ddim"  # 使用DDIM快速采样
TEST_EPOCH=50  # 测试第50个epoch的模型

# 实验目录
EXP_DIR="./Experiments/trajectory_a40_turbo_optimized_TKY_temporal_len32_ddim_20250730-093850"
MODEL_PATH="${EXP_DIR}"
TIMESTAMP="20250730-093850"

# 日志设置
LOG_DIR="./test_logs"
mkdir -p ${LOG_DIR}
TIMESTAMP_NOW=$(date +%Y%m%d_%H%M%S)
LOG_FILE="${LOG_DIR}/test_${EXP_NAME}_epoch${TEST_EPOCH}_${TIMESTAMP_NOW}.log"

echo "=====================================" | tee ${LOG_FILE}
echo "ProDiff 训练模型测试启动" | tee -a ${LOG_FILE}
echo "=====================================" | tee -a ${LOG_FILE}
echo "实验名称: ${EXP_NAME}" | tee -a ${LOG_FILE}
echo "GPU设备: ${DEVICE_ID}" | tee -a ${LOG_FILE}
echo "采样类型: ${SAMPLING_TYPE}" | tee -a ${LOG_FILE}
echo "测试Epoch: ${TEST_EPOCH}" | tee -a ${LOG_FILE}
echo "模型路径: ${MODEL_PATH}" | tee -a ${LOG_FILE}
echo "日志文件: ${LOG_FILE}" | tee -a ${LOG_FILE}
echo "启动时间: $(date)" | tee -a ${LOG_FILE}
echo "=====================================" | tee -a ${LOG_FILE}

# 检查GPU状态
echo "检查GPU状态..." | tee -a ${LOG_FILE}
nvidia-smi | tee -a ${LOG_FILE}

# 设置环境变量优化
export PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:512
export OMP_NUM_THREADS=8

echo "开始模型测试..." | tee -a ${LOG_FILE}

# 执行测试
python3 -u main.py \
    --mode test \
    --model_path ${MODEL_PATH} \
    --model_epoch ${TEST_EPOCH} \
    --sampling_type ${SAMPLING_TYPE} \
    --ddim_steps 50 \
    --ddim_eta 0.0 \
    --device_id ${DEVICE_ID} \
    --exp_name ${EXP_NAME}_test \
    --debug \
    2>&1 | tee -a ${LOG_FILE}

EXIT_CODE=${PIPESTATUS[0]}

echo "=====================================" | tee -a ${LOG_FILE}
if [ ${EXIT_CODE} -eq 0 ]; then
    echo "✅ 模型测试成功完成！" | tee -a ${LOG_FILE}
else
    echo "❌ 模型测试失败，退出码: ${EXIT_CODE}" | tee -a ${LOG_FILE}
fi
echo "完成时间: $(date)" | tee -a ${LOG_FILE}
echo "=====================================" | tee -a ${LOG_FILE}

# 显示测试结果位置
if [ ${EXIT_CODE} -eq 0 ]; then
    echo "" | tee -a ${LOG_FILE}
    echo "📊 测试结果已保存到:" | tee -a ${LOG_FILE}
    echo "   日志文件: ${LOG_FILE}" | tee -a ${LOG_FILE}
    echo "   结果目录: ${EXP_DIR}/results/" | tee -a ${LOG_FILE}
    echo "" | tee -a ${LOG_FILE}
    echo "📈 查看测试指标:" | tee -a ${LOG_FILE}
    echo "   MTD (平均轨迹距离): 衡量生成轨迹的空间精度" | tee -a ${LOG_FILE}
    echo "   MPPE (平均位置预测误差): 衡量位置预测准确性" | tee -a ${LOG_FILE}
    echo "   TC@X (轨迹覆盖率): 在X米阈值内的预测准确率" | tee -a ${LOG_FILE}
    echo "" | tee -a ${LOG_FILE}
fi

exit ${EXIT_CODE}

	#!/bin/bash

	# =====================================
	# ProDiff 训练完成模型测试脚本
	# =====================================

	# 配置参数
	EXP_NAME="trajectory_a40_turbo_optimized"
	DEVICE_ID=0
	SAMPLING_TYPE="ddim" # 使用DDIM快速采样
	TEST_EPOCH=50 # 测试第50个epoch的模型

	# 实验目录
	EXP_DIR="./Experiments/trajectory_a40_turbo_optimized_TKY_temporal_len32_ddim_20250730-093850"
	MODEL_PATH="${EXP_DIR}"
	TIMESTAMP="20250730-093850"

	# 日志设置
	LOG_DIR="./test_logs"
	mkdir -p ${LOG_DIR}
	TIMESTAMP_NOW=$(date +%Y%m%d_%H%M%S)
	LOG_FILE="${LOG_DIR}/test_${EXP_NAME}_epoch${TEST_EPOCH}_${TIMESTAMP_NOW}.log"

	echo "=====================================" \| tee ${LOG_FILE}
	echo "ProDiff 训练模型测试启动" \| tee -a ${LOG_FILE}
	echo "=====================================" \| tee -a ${LOG_FILE}
	echo "实验名称: ${EXP_NAME}" \| tee -a ${LOG_FILE}
	echo "GPU设备: ${DEVICE_ID}" \| tee -a ${LOG_FILE}
	echo "采样类型: ${SAMPLING_TYPE}" \| tee -a ${LOG_FILE}
	echo "测试Epoch: ${TEST_EPOCH}" \| tee -a ${LOG_FILE}
	echo "模型路径: ${MODEL_PATH}" \| tee -a ${LOG_FILE}
	echo "日志文件: ${LOG_FILE}" \| tee -a ${LOG_FILE}
	echo "启动时间: $(date)" \| tee -a ${LOG_FILE}
	echo "=====================================" \| tee -a ${LOG_FILE}

	# 检查GPU状态
	echo "检查GPU状态..." \| tee -a ${LOG_FILE}
	nvidia-smi \| tee -a ${LOG_FILE}

	# 设置环境变量优化
	export PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:512
	export OMP_NUM_THREADS=8

	echo "开始模型测试..." \| tee -a ${LOG_FILE}

	# 执行测试
	python3 -u main.py \
	--mode test \
	--model_path ${MODEL_PATH} \
	--model_epoch ${TEST_EPOCH} \
	--sampling_type ${SAMPLING_TYPE} \
	--ddim_steps 50 \
	--ddim_eta 0.0 \
	--device_id ${DEVICE_ID} \
	--exp_name ${EXP_NAME}_test \
	--debug \
	2>&1 \| tee -a ${LOG_FILE}

	EXIT_CODE=${PIPESTATUS[0]}

	echo "=====================================" \| tee -a ${LOG_FILE}
	if [ ${EXIT_CODE} -eq 0 ]; then
	echo "✅ 模型测试成功完成！" \| tee -a ${LOG_FILE}
	else
	echo "❌ 模型测试失败，退出码: ${EXIT_CODE}" \| tee -a ${LOG_FILE}
	fi
	echo "完成时间: $(date)" \| tee -a ${LOG_FILE}
	echo "=====================================" \| tee -a ${LOG_FILE}

	# 显示测试结果位置
	if [ ${EXIT_CODE} -eq 0 ]; then
	echo "" \| tee -a ${LOG_FILE}
	echo "📊 测试结果已保存到:" \| tee -a ${LOG_FILE}
	echo " 日志文件: ${LOG_FILE}" \| tee -a ${LOG_FILE}
	echo " 结果目录: ${EXP_DIR}/results/" \| tee -a ${LOG_FILE}
	echo "" \| tee -a ${LOG_FILE}
	echo "📈 查看测试指标:" \| tee -a ${LOG_FILE}
	echo " MTD (平均轨迹距离): 衡量生成轨迹的空间精度" \| tee -a ${LOG_FILE}
	echo " MPPE (平均位置预测误差): 衡量位置预测准确性" \| tee -a ${LOG_FILE}
	echo " TC@X (轨迹覆盖率): 在X米阈值内的预测准确率" \| tee -a ${LOG_FILE}
	echo "" \| tee -a ${LOG_FILE}
	fi

	exit ${EXIT_CODE}