67 lines
3.2 KiB
Python
67 lines
3.2 KiB
Python
# 输入为off原始数据，验证j&r仿真正确性
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import torch
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import numpy as np
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from icraft.xir import *
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from icraft.xrt import *
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from icraft.host_backend import *
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from icraft.buyibackend import *
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from datetime import datetime
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import sys
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sys.path.append(R'../0_PointNet')
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from utils.data import get_single_data
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CLASS= {'airplane': 0, 'bathtub': 1, 'bed': 2, 'bench': 3, 'bookshelf': 4, 'bottle': 5, 'bowl': 6, 'car': 7, 'chair': 8, 'cone': 9, 'cup': 10, 'curtain': 11, 'desk': 12, 'door': 13, 'dresser': 14, 'flower_pot': 15, 'glass_box': 16, 'guitar': 17, 'keyboard': 18, 'lamp': 19, 'laptop': 20, 'mantel': 21, 'monitor': 22, 'night_stand': 23, 'person': 24, 'piano': 25, 'plant': 26, 'radio': 27, 'range_hood': 28, 'sink': 29, 'sofa': 30, 'stairs': 31, 'stool': 32, 'table': 33, 'tent': 34, 'toilet': 35, 'tv_stand': 36, 'vase': 37, 'wardrobe': 38, 'xbox': 39}
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# ---------------------------------参数设置---------------------------------
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STAGE = 'g'
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stage_list = {
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'p':'parsed',
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'o':'optimized',
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'q':'quantized',
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'a':'adapted',
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'g':'BY',
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}
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# 路径设置
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GENERATED_JSON_FILE = "../3_deploy/modelzoo/PointNet/imodel/8/pointnet_"+stage_list[STAGE]+".json"
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GENERATED_RAW_FILE = "../3_deploy/modelzoo/PointNet/imodel/8/pointnet_"+stage_list[STAGE]+".raw"
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CLASS= {'airplane': 0, 'bathtub': 1, 'bed': 2, 'bench': 3, 'bookshelf': 4, 'bottle': 5, 'bowl': 6, 'car': 7, 'chair': 8, 'cone': 9, 'cup': 10, 'curtain': 11, 'desk': 12, 'door': 13, 'dresser': 14, 'flower_pot': 15, 'glass_box': 16, 'guitar': 17, 'keyboard': 18, 'lamp': 19, 'laptop': 20, 'mantel': 21, 'monitor': 22, 'night_stand': 23, 'person': 24, 'piano': 25, 'plant': 26, 'radio': 27, 'range_hood': 28, 'sink': 29, 'sofa': 30, 'stairs': 31, 'stool': 32, 'table': 33, 'tent': 34, 'toilet': 35, 'tv_stand': 36, 'vase': 37, 'wardrobe': 38, 'xbox': 39}
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data_path = R"./Data/ModelNet40/airplane/test/airplane_0627.off"
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# convert pointcloud data to Icraft Tensor
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_data = get_single_data(data_path) # [1024,3] np.float64
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_inputs = _data.unsqueeze(0).transpose(1,2).numpy().astype(np.float32) # [1,3,1024],np.float32
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_inputs = np.ascontiguousarray(_inputs)
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input_tensor = Tensor(_inputs, Layout("**C"))
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# 加载指令生成后的网络
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generated_network = Network.CreateFromJsonFile(GENERATED_JSON_FILE)
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generated_network.loadParamsFromFile(GENERATED_RAW_FILE)
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print('INFO: Create network!')
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# 创建Session
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session = Session.Create([HostBackend],generated_network.view(0),[HostDevice.Default()])
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session.apply()
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# 模型前向推理
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generated_output = session.forward([input_tensor])
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# check outputs
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# [1,40] : pred_results
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# [3,3] : transform_matrix_3x3
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# [64,64]: transform_matrix_64x64
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# for i in range(3):
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# out = np.array(generated_output[i])
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# print(out.shape)
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print('INFO: get forward results!')
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# 组装成检测结果
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outputs = []
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for i in range(3):
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out = np.array(generated_output[i])
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print(out.shape)
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outputs.append(torch.tensor(out))
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print("_output =",outputs[0].shape)
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# 后处理
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_, pred = torch.max(outputs[0], 1) # value,index=pred
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pred = pred.cpu().numpy()[0]
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for category in CLASS.keys():
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if CLASS[category] == pred:
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break
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print(f"TEST RESULT:\n\tcategory =",category) |