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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "65e35379",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<All keys matched successfully>"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import os\n",
"os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'\n",
"import torch\n",
"import torch.nn as nn\n",
"import timm\n",
"torch.manual_seed(3407)\n",
"torch.cuda.manual_seed(3407)\n",
"# 定义GELU_PyTorch_Tanh类\n",
"class GELU_PyTorch_Tanh(nn.Module):\n",
" def forward(self, x):\n",
" return 0.5 * x * (1 + torch.tanh((2 / torch.pi) ** 0.5 * (x + 0.044715 * x ** 3)))\n",
"\n",
"# 定义CustomModel类\n",
"class CustomModel(nn.Module):\n",
" def __init__(self, base_model):\n",
" super(CustomModel, self).__init__()\n",
" self.base_model = base_model\n",
" \n",
" # 定义新的分类层\n",
" self.fc1 = nn.Linear(1536, 2048) # EfficientNet-B1的输出维度是1536\n",
" self.bn1 = nn.BatchNorm1d(2048)\n",
" self.dropout1 = nn.Dropout(p=0.5) \n",
" self.fc2 = nn.Linear(2048, 2048)\n",
" self.bn2 = nn.BatchNorm1d(2048)\n",
" self.gelu = GELU_PyTorch_Tanh()\n",
" self.dropout2 = nn.Dropout(p=0.5)\n",
" \n",
" self.final_fc = nn.Linear(2048, 1) # 二分类问题,输出维度为1\n",
" self.sigmoid = nn.Sigmoid()\n",
" \n",
" def forward(self, x):\n",
" x = self.base_model(x)\n",
" x = self.fc1(x)\n",
" x = self.bn1(x)\n",
" x = self.gelu(x)\n",
" x = self.dropout1(x)\n",
" x = self.fc2(x)\n",
" x = self.bn2(x)\n",
" x = self.gelu(x)\n",
" x = self.dropout2(x)\n",
" x = self.final_fc(x)\n",
" x = self.sigmoid(x)\n",
" return x\n",
"\n",
"# 创建基础模型\n",
"base_model = timm.create_model('tf_efficientnet_b3.ns_jft_in1k', pretrained=True, num_classes=0)\n",
"base_model = base_model.cuda()\n",
"\n",
"# 创建自定义模型\n",
"custom_model = CustomModel(base_model)\n",
"custom_model = custom_model.cuda()\n",
"\n",
"# 加载保存的模型权重\n",
"checkpoint_path = \"E:\\\\下载\\\\custom_model_96.64.pt\"\n",
"custom_model.load_state_dict(torch.load(checkpoint_path))"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "3f695d9a",
"metadata": {},
"outputs": [],
"source": [
"from PIL import Image\n",
"from torchvision import transforms\n",
"custom_model.eval()\n",
"transform = transforms.Compose([\n",
" transforms.Resize((300, 300)),\n",
" transforms.ToTensor(),\n",
" transforms.Normalize(\n",
" mean=[0.485, 0.456, 0.406],\n",
" std=[0.229, 0.224, 0.225]\n",
" )\n",
"])"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "a8a96208",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"Image: 296.png, probabilities: tensor([[0.9940]], device='cuda:0')\n",
"Image: 297.png, probabilities: tensor([[0.9999]], device='cuda:0')\n",
"Image: 299.png, probabilities: tensor([[0.1865]], device='cuda:0')\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Image: 301.png, probabilities: tensor([[0.9997]], device='cuda:0')\n",
"Image: 302.png, probabilities: tensor([[1.]], device='cuda:0')\n",
"Image: 303.png, probabilities: tensor([[0.9951]], device='cuda:0')\n",
"Image: 304.png, probabilities: tensor([[0.8944]], device='cuda:0')\n",
"Image: 308.png, probabilities: tensor([[0.2670]], device='cuda:0')\n",
"Image: 309.png, probabilities: tensor([[0.9981]], device='cuda:0')\n",
"Image: 314.png, probabilities: tensor([[0.2295]], device='cuda:0')\n",
"Image: 315.png, probabilities: tensor([[0.8504]], device='cuda:0')\n",
"Image: 317.png, probabilities: tensor([[0.3725]], device='cuda:0')\n",
"Image: 319.png, probabilities: tensor([[0.9968]], device='cuda:0')\n",
"Image: 32.png, probabilities: tensor([[1.0000]], device='cuda:0')\n",
"Image: 321.png, probabilities: tensor([[0.9590]], device='cuda:0')\n",
"Image: 322.png, probabilities: tensor([[0.8992]], device='cuda:0')\n",
"Image: 326.png, probabilities: tensor([[0.2035]], device='cuda:0')\n",
"Image: 329.png, probabilities: tensor([[0.6888]], device='cuda:0')\n",
"Image: 330.png, probabilities: tensor([[1.0000]], device='cuda:0')\n",
"Image: 331.png, probabilities: tensor([[0.9997]], device='cuda:0')\n",
"Image: 332.png, probabilities: tensor([[0.9604]], device='cuda:0')\n",
"Image: 333.png, probabilities: tensor([[0.2669]], device='cuda:0')\n",
"Image: 334.png, probabilities: tensor([[0.9747]], device='cuda:0')\n",
"Image: 335.png, probabilities: tensor([[0.9998]], device='cuda:0')\n",
"Image: 340.png, probabilities: tensor([[1.0000]], device='cuda:0')\n",
"Image: 341.png, probabilities: tensor([[1.0000]], device='cuda:0')\n",
"Image: 344.png, probabilities: tensor([[0.9643]], device='cuda:0')\n",
"Image: 346.png, probabilities: tensor([[0.2700]], device='cuda:0')\n",
"Image: 35.png, probabilities: tensor([[0.8739]], device='cuda:0')\n",
"Image: 352.png, probabilities: tensor([[1.0000]], device='cuda:0')\n",
"Image: 355.png, probabilities: tensor([[0.9999]], device='cuda:0')\n",
"Image: 356.png, probabilities: tensor([[0.9995]], device='cuda:0')\n",
"Image: 357.png, probabilities: tensor([[1.0000]], device='cuda:0')\n",
"Image: 359.png, probabilities: tensor([[0.1207]], device='cuda:0')\n",
"Image: 366.png, probabilities: tensor([[0.9998]], device='cuda:0')\n",
"Image: 37.png, probabilities: tensor([[0.9986]], device='cuda:0')\n",
"Image: 370.png, probabilities: tensor([[0.9975]], device='cuda:0')\n",
"Image: 373.png, probabilities: tensor([[0.9987]], device='cuda:0')\n",
"Image: 374.png, probabilities: tensor([[0.9889]], device='cuda:0')\n",
"Image: 377.png, probabilities: tensor([[0.9887]], device='cuda:0')\n",
"Image: 378.png, probabilities: tensor([[0.9636]], device='cuda:0')\n",
"Image: 379.png, probabilities: tensor([[0.4890]], device='cuda:0')\n",
"Image: 38.png, probabilities: tensor([[0.9850]], device='cuda:0')\n",
"Image: 380.png, probabilities: tensor([[0.1819]], device='cuda:0')\n",
"Image: 385.png, probabilities: tensor([[0.9980]], device='cuda:0')\n",
"Image: 386.png, probabilities: tensor([[0.5823]], device='cuda:0')\n",
"Image: 390.png, probabilities: tensor([[0.1392]], device='cuda:0')\n",
"Image: 395.png, probabilities: tensor([[0.9489]], device='cuda:0')\n",
"Image: 398.png, probabilities: tensor([[0.1002]], device='cuda:0')\n",
"Image: 399.png, probabilities: tensor([[0.9999]], device='cuda:0')\n",
"Image: 402.png, probabilities: tensor([[0.9971]], device='cuda:0')\n",
"Image: 403.png, probabilities: tensor([[0.2957]], device='cuda:0')\n",
"Image: 404.png, probabilities: tensor([[0.9915]], device='cuda:0')\n",
"Image: 405.png, probabilities: tensor([[0.9591]], device='cuda:0')\n",
"Image: 406.png, probabilities: tensor([[0.9929]], device='cuda:0')\n",
"Image: 408.png, probabilities: tensor([[0.9369]], device='cuda:0')\n",
"Image: 410.png, probabilities: tensor([[0.2750]], device='cuda:0')\n",
"Image: 411.png, probabilities: tensor([[0.6763]], device='cuda:0')\n",
"Image: 432.png, probabilities: tensor([[0.0976]], device='cuda:0')\n",
"Image: 436.png, probabilities: tensor([[0.3852]], device='cuda:0')\n",
"Image: 442.png, probabilities: tensor([[0.9198]], device='cuda:0')\n",
"Image: 443.png, probabilities: tensor([[0.8540]], device='cuda:0')\n",
"Image: 447.png, probabilities: tensor([[0.8477]], device='cuda:0')\n",
"Image: 45.png, probabilities: tensor([[0.9998]], device='cuda:0')\n",
"Image: 456.png, probabilities: tensor([[1.0000]], device='cuda:0')\n",
"Image: 466.png, probabilities: tensor([[0.9869]], device='cuda:0')\n",
"Image: 47.png, probabilities: tensor([[0.8141]], device='cuda:0')\n",
"Image: 472.png, probabilities: tensor([[0.9405]], device='cuda:0')\n",
"Image: 473.png, probabilities: tensor([[0.5525]], device='cuda:0')\n",
"Image: 494.png, probabilities: tensor([[0.1025]], device='cuda:0')\n",
"Image: 5.png, probabilities: tensor([[0.9850]], device='cuda:0')\n",
"Image: 50.png, probabilities: tensor([[0.9674]], device='cuda:0')\n",
"Image: 508.png, probabilities: tensor([[0.1743]], device='cuda:0')\n",
"Image: 513.png, probabilities: tensor([[0.1263]], device='cuda:0')\n",
"Image: 521.png, probabilities: tensor([[0.0868]], device='cuda:0')\n",
"Image: 528.png, probabilities: tensor([[0.7037]], device='cuda:0')\n",
"Image: 531.png, probabilities: tensor([[0.5545]], device='cuda:0')\n",
"Image: 547.png, probabilities: tensor([[0.4904]], device='cuda:0')\n",
"Image: 548.png, probabilities: tensor([[0.1058]], device='cuda:0')\n",
"Image: 549.png, probabilities: tensor([[0.9236]], device='cuda:0')\n",
"Image: 55.png, probabilities: tensor([[0.1722]], device='cuda:0')\n",
"Image: 551.png, probabilities: tensor([[0.7328]], device='cuda:0')\n",
"Image: 553.png, probabilities: tensor([[0.9970]], device='cuda:0')\n",
"Image: 56.png, probabilities: tensor([[0.9300]], device='cuda:0')\n",
"Image: 566.png, probabilities: tensor([[0.9863]], device='cuda:0')\n",
"Image: 58.png, probabilities: tensor([[0.9981]], device='cuda:0')\n",
"Image: 582.png, probabilities: tensor([[0.9995]], device='cuda:0')\n",
"Image: 60.png, probabilities: tensor([[0.9384]], device='cuda:0')\n",
"Image: 601.png, probabilities: tensor([[0.2533]], device='cuda:0')\n",
"Image: 602.png, probabilities: tensor([[0.5581]], device='cuda:0')\n",
"Image: 603.png, probabilities: tensor([[0.9950]], device='cuda:0')\n",
"Image: 606.png, probabilities: tensor([[0.2512]], device='cuda:0')\n",
"Image: 610.png, probabilities: tensor([[0.4485]], device='cuda:0')\n",
"Image: 615.png, probabilities: tensor([[0.5570]], device='cuda:0')\n",
"Image: 616.png, probabilities: tensor([[0.3093]], device='cuda:0')\n",
"Image: 623.png, probabilities: tensor([[0.9003]], device='cuda:0')\n",
"Image: 631.png, probabilities: tensor([[0.9872]], device='cuda:0')\n",
"Image: 634.png, probabilities: tensor([[0.9161]], device='cuda:0')\n",
"Image: 636.png, probabilities: tensor([[0.7980]], device='cuda:0')\n",
"Image: 643.png, probabilities: tensor([[0.1409]], device='cuda:0')\n",
"Image: 646.png, probabilities: tensor([[0.4887]], device='cuda:0')\n",
"Image: 647.png, probabilities: tensor([[0.9980]], device='cuda:0')\n",
"Image: 65.png, probabilities: tensor([[0.9747]], device='cuda:0')\n",
"Image: 652.png, probabilities: tensor([[0.7711]], device='cuda:0')\n",
"Image: 655.png, probabilities: tensor([[0.7735]], device='cuda:0')\n",
"Image: 657.png, probabilities: tensor([[0.9389]], device='cuda:0')\n",
"Image: 658.png, probabilities: tensor([[0.9969]], device='cuda:0')\n",
"Image: 661.png, probabilities: tensor([[0.1696]], device='cuda:0')\n",
"Image: 68.png, probabilities: tensor([[0.7115]], device='cuda:0')\n",
"Image: 70.png, probabilities: tensor([[0.8401]], device='cuda:0')\n",
"Image: 71.png, probabilities: tensor([[0.9998]], device='cuda:0')\n",
"Image: 725.png, probabilities: tensor([[0.0848]], device='cuda:0')\n",
"Image: 729.png, probabilities: tensor([[0.6455]], device='cuda:0')\n",
"Image: 74.png, probabilities: tensor([[0.9886]], device='cuda:0')\n",
"Image: 75.png, probabilities: tensor([[0.9997]], device='cuda:0')\n",
"Image: 754.png, probabilities: tensor([[0.1064]], device='cuda:0')\n",
"Image: 757.png, probabilities: tensor([[0.3208]], device='cuda:0')\n",
"Image: 769.png, probabilities: tensor([[0.7946]], device='cuda:0')\n",
"Image: 772.png, probabilities: tensor([[0.1057]], device='cuda:0')\n",
"Image: 783.png, probabilities: tensor([[0.6728]], device='cuda:0')\n",
"Image: 786.png, probabilities: tensor([[0.6520]], device='cuda:0')\n",
"Image: 787.png, probabilities: tensor([[0.6209]], device='cuda:0')\n",
"Image: 79.png, probabilities: tensor([[1.0000]], device='cuda:0')\n",
"Image: 799.png, probabilities: tensor([[0.1545]], device='cuda:0')\n",
"Image: 80.png, probabilities: tensor([[0.9985]], device='cuda:0')\n",
"Image: 802.png, probabilities: tensor([[0.0917]], device='cuda:0')\n",
"Image: 804.png, probabilities: tensor([[0.5928]], device='cuda:0')\n",
"Image: 805.png, probabilities: tensor([[0.9758]], device='cuda:0')\n",
"Image: 806.png, probabilities: tensor([[0.4562]], device='cuda:0')\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Image: 809.png, probabilities: tensor([[0.9955]], device='cuda:0')\n",
"Image: 81.png, probabilities: tensor([[0.9188]], device='cuda:0')\n",
"Image: 810.png, probabilities: tensor([[0.3929]], device='cuda:0')\n",
"Image: 811.png, probabilities: tensor([[0.4093]], device='cuda:0')\n",
"Image: 813.png, probabilities: tensor([[0.8086]], device='cuda:0')\n",
"Image: 815.png, probabilities: tensor([[0.9792]], device='cuda:0')\n",
"Image: 817.png, probabilities: tensor([[0.1523]], device='cuda:0')\n",
"Image: 824.png, probabilities: tensor([[0.9964]], device='cuda:0')\n",
"Image: 825.png, probabilities: tensor([[1.0000]], device='cuda:0')\n",
"Image: 828.png, probabilities: tensor([[0.1760]], device='cuda:0')\n",
"Image: 829.png, probabilities: tensor([[0.9984]], device='cuda:0')\n",
"Image: 833.png, probabilities: tensor([[0.3641]], device='cuda:0')\n",
"Image: 835.png, probabilities: tensor([[0.6530]], device='cuda:0')\n",
"Image: 836.png, probabilities: tensor([[0.9552]], device='cuda:0')\n",
"Image: 837.png, probabilities: tensor([[0.1598]], device='cuda:0')\n",
"Image: 838.png, probabilities: tensor([[0.9871]], device='cuda:0')\n",
"Image: 840.png, probabilities: tensor([[0.7139]], device='cuda:0')\n",
"Image: 841.png, probabilities: tensor([[0.4956]], device='cuda:0')\n",
"Image: 845.png, probabilities: tensor([[0.9994]], device='cuda:0')\n",
"Image: 848.png, probabilities: tensor([[1.0000]], device='cuda:0')\n",
"Image: 854.png, probabilities: tensor([[0.0968]], device='cuda:0')\n",
"Image: 855.png, probabilities: tensor([[0.9650]], device='cuda:0')\n",
"Image: 858.png, probabilities: tensor([[0.4625]], device='cuda:0')\n",
"Image: 859.png, probabilities: tensor([[0.3299]], device='cuda:0')\n",
"Image: 866.png, probabilities: tensor([[0.0859]], device='cuda:0')\n",
"Image: 868.png, probabilities: tensor([[0.5983]], device='cuda:0')\n",
"Image: 874.png, probabilities: tensor([[0.9229]], device='cuda:0')\n",
"Image: 876.png, probabilities: tensor([[1.0000]], device='cuda:0')\n",
"Image: 879.png, probabilities: tensor([[0.1339]], device='cuda:0')\n",
"Image: 884.png, probabilities: tensor([[0.9993]], device='cuda:0')\n",
"Image: 885.png, probabilities: tensor([[0.9999]], device='cuda:0')\n",
"Image: 887.png, probabilities: tensor([[1.0000]], device='cuda:0')\n",
"Image: 890.png, probabilities: tensor([[0.9813]], device='cuda:0')\n",
"Image: 891.png, probabilities: tensor([[0.3142]], device='cuda:0')\n",
"Image: 892.png, probabilities: tensor([[0.9999]], device='cuda:0')\n",
"Image: 894.png, probabilities: tensor([[0.9982]], device='cuda:0')\n",
"Image: 895.png, probabilities: tensor([[0.5560]], device='cuda:0')\n",
"Image: 898.png, probabilities: tensor([[0.0945]], device='cuda:0')\n",
"Image: 899.png, probabilities: tensor([[0.2587]], device='cuda:0')\n",
"Image: 9.png, probabilities: tensor([[1.0000]], device='cuda:0')\n",
"Image: 900.png, probabilities: tensor([[0.9961]], device='cuda:0')\n",
"Image: 902.png, probabilities: tensor([[0.9995]], device='cuda:0')\n",
"Image: 905.png, probabilities: tensor([[0.9998]], device='cuda:0')\n",
"Image: 906.png, probabilities: tensor([[0.9997]], device='cuda:0')\n",
"Image: 907.png, probabilities: tensor([[0.3080]], device='cuda:0')\n",
"Image: 908.png, probabilities: tensor([[0.0736]], device='cuda:0')\n",
"Image: 909.png, probabilities: tensor([[1.0000]], device='cuda:0')\n",
"Image: 918.png, probabilities: tensor([[0.8783]], device='cuda:0')\n",
"Image: 919.png, probabilities: tensor([[0.9930]], device='cuda:0')\n",
"Image: 922.png, probabilities: tensor([[0.5057]], device='cuda:0')\n",
"Image: 925.png, probabilities: tensor([[0.3462]], device='cuda:0')\n",
"Image: 926.png, probabilities: tensor([[0.0856]], device='cuda:0')\n",
"Image: 929.png, probabilities: tensor([[0.5666]], device='cuda:0')\n",
"Image: 93.png, probabilities: tensor([[1.0000]], device='cuda:0')\n",
"Image: 934.png, probabilities: tensor([[0.6791]], device='cuda:0')\n",
"Image: 935.png, probabilities: tensor([[0.0844]], device='cuda:0')\n",
"Image: 939.png, probabilities: tensor([[1.0000]], device='cuda:0')\n",
"Image: 94.png, probabilities: tensor([[0.9857]], device='cuda:0')\n",
"Image: 941.png, probabilities: tensor([[1.0000]], device='cuda:0')\n",
"Image: 944.png, probabilities: tensor([[0.6272]], device='cuda:0')\n",
"Image: 945.png, probabilities: tensor([[0.1469]], device='cuda:0')\n",
"Image: 946.png, probabilities: tensor([[0.9304]], device='cuda:0')\n",
"Image: 949.png, probabilities: tensor([[0.4188]], device='cuda:0')\n",
"Image: 95.png, probabilities: tensor([[0.9998]], device='cuda:0')\n",
"Image: 951.png, probabilities: tensor([[0.9993]], device='cuda:0')\n",
"Image: 952.png, probabilities: tensor([[1.0000]], device='cuda:0')\n",
"Image: 954.png, probabilities: tensor([[0.3126]], device='cuda:0')\n",
"Image: 959.png, probabilities: tensor([[0.5024]], device='cuda:0')\n",
"Image: 961.png, probabilities: tensor([[0.9981]], device='cuda:0')\n",
"Image: 962.png, probabilities: tensor([[0.4659]], device='cuda:0')\n",
"Image: 968.png, probabilities: tensor([[0.9481]], device='cuda:0')\n",
"Image: 972.png, probabilities: tensor([[0.9998]], device='cuda:0')\n",
"Image: 973.png, probabilities: tensor([[0.9747]], device='cuda:0')\n",
"Image: 976.png, probabilities: tensor([[0.9994]], device='cuda:0')\n",
"Image: 978.png, probabilities: tensor([[1.0000]], device='cuda:0')\n",
"Image: 979.png, probabilities: tensor([[0.8647]], device='cuda:0')\n",
"Image: 987.png, probabilities: tensor([[0.9550]], device='cuda:0')\n",
"Image: 989.png, probabilities: tensor([[0.9988]], device='cuda:0')\n",
"Image: 99.png, probabilities: tensor([[0.7989]], device='cuda:0')\n",
"Image: 990.png, probabilities: tensor([[0.5491]], device='cuda:0')\n",
"Image: 991.png, probabilities: tensor([[0.9999]], device='cuda:0')\n",
"Image: 996.png, probabilities: tensor([[0.9424]], device='cuda:0')\n",
"Image: 997.png, probabilities: tensor([[0.5517]], device='cuda:0')\n",
"Image: 999.png, probabilities: tensor([[0.7581]], device='cuda:0')\n",
"Number of outputs greater than 0.07: 581\n"
]
}
],
"source": [
"#probabilities_list = []\n",
"image_folder = r\"E:\\qq文件\\任务2\\提取图片\"\n",
"image_files = [f for f in os.listdir(image_folder) if os.path.isfile(os.path.join(image_folder, f))]\n",
"\n",
"count_above_threshold = 0 # 计数大于0.5的输出数量\n",
"\n",
"for image_file in image_files:\n",
" image_path = os.path.join(image_folder, image_file)\n",
" image = Image.open(image_path).convert('RGB')\n",
" input_tensor = transform(image).unsqueeze(0) # 增加batch维度\n",
" input_tensor = input_tensor.cuda() # 将输入张量转移到GPU\n",
"\n",
" with torch.no_grad():\n",
" output = custom_model(input_tensor)\n",
" \n",
" if output.item() >= 0.07:\n",
" count_above_threshold += 1\n",
" print(f\"Image: {image_file}, probabilities: {output}\")\n",
"\n",
"print(f\"Number of outputs greater than 0.07: {count_above_threshold}\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "142a9b6f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Image: 0.png, probabilities: tensor([[0.9870]], device='cuda:0')\n",
"Image: 1.png, probabilities: tensor([[0.9999]], device='cuda:0')\n",
"Image: 100.png, probabilities: tensor([[0.7772]], device='cuda:0')\n",
"Image: 1000.png, probabilities: tensor([[0.9953]], device='cuda:0')\n",
"Image: 1001.png, probabilities: tensor([[1.0000]], device='cuda:0')\n",
"Image: 1002.png, probabilities: tensor([[0.9997]], device='cuda:0')\n",
"Image: 1003.png, probabilities: tensor([[0.9995]], device='cuda:0')\n",
"Image: 1004.png, probabilities: tensor([[1.0000]], device='cuda:0')\n",
"Image: 1005.png, probabilities: tensor([[0.9994]], device='cuda:0')\n",
"Image: 1006.png, probabilities: tensor([[1.0000]], device='cuda:0')\n",
"Image: 1007.png, probabilities: tensor([[0.9998]], device='cuda:0')\n",
"Image: 1008.png, probabilities: tensor([[0.3588]], device='cuda:0')\n",
"Image: 101.png, probabilities: tensor([[0.9978]], device='cuda:0')\n",
"Image: 1010.png, probabilities: tensor([[0.9953]], device='cuda:0')\n",
"Image: 1012.png, probabilities: tensor([[0.4747]], device='cuda:0')\n",
"Image: 1013.png, probabilities: tensor([[0.8764]], device='cuda:0')\n",
"Image: 1017.png, probabilities: tensor([[0.3660]], device='cuda:0')\n",
"Image: 1018.png, probabilities: tensor([[0.9980]], device='cuda:0')\n",
"Image: 1019.png, probabilities: tensor([[1.0000]], device='cuda:0')\n",
"Image: 102.png, probabilities: tensor([[0.9619]], device='cuda:0')\n",
"Image: 1021.png, probabilities: tensor([[0.9998]], device='cuda:0')\n",
"Image: 1022.png, probabilities: tensor([[1.0000]], device='cuda:0')\n",
"Image: 1023.png, probabilities: tensor([[0.9999]], device='cuda:0')\n",
"Image: 1025.png, probabilities: tensor([[0.1526]], device='cuda:0')\n",
"Image: 1027.png, probabilities: tensor([[0.1112]], device='cuda:0')\n",
"Image: 1029.png, probabilities: tensor([[0.9998]], device='cuda:0')\n",
"Image: 103.png, probabilities: tensor([[0.9975]], device='cuda:0')\n",
"Image: 1030.png, probabilities: tensor([[1.0000]], device='cuda:0')\n",
"Image: 1031.png, probabilities: tensor([[0.9871]], device='cuda:0')\n",
"Image: 1033.png, probabilities: tensor([[0.7425]], device='cuda:0')\n",
"Image: 1034.png, probabilities: tensor([[0.8482]], device='cuda:0')\n",
"Image: 1035.png, probabilities: tensor([[1.0000]], device='cuda:0')\n",
"Image: 1036.png, probabilities: tensor([[0.8803]], device='cuda:0')\n",
"Image: 1037.png, probabilities: tensor([[0.9883]], device='cuda:0')\n",
"Image: 1038.png, probabilities: tensor([[0.8936]], device='cuda:0')\n",
"Image: 1039.png, probabilities: tensor([[0.6551]], device='cuda:0')\n",
"Image: 104.png, probabilities: tensor([[0.9625]], device='cuda:0')\n",
"Image: 1040.png, probabilities: tensor([[0.6643]], device='cuda:0')\n",
"Image: 1041.png, probabilities: tensor([[0.7153]], device='cuda:0')\n",
"Image: 1044.png, probabilities: tensor([[0.8434]], device='cuda:0')\n",
"Image: 1045.png, probabilities: tensor([[0.7953]], device='cuda:0')\n",
"Image: 1046.png, probabilities: tensor([[0.9999]], device='cuda:0')\n",
"Image: 1047.png, probabilities: tensor([[0.9726]], device='cuda:0')\n",
"Image: 1048.png, probabilities: tensor([[0.9999]], device='cuda:0')\n",
"Image: 1049.png, probabilities: tensor([[1.0000]], device='cuda:0')\n",
"Image: 105.png, probabilities: tensor([[0.9905]], device='cuda:0')\n",
"Image: 1050.png, probabilities: tensor([[0.9679]], device='cuda:0')\n",
"Image: 1051.png, probabilities: tensor([[0.1384]], device='cuda:0')\n",
"Image: 1053.png, probabilities: tensor([[0.2259]], device='cuda:0')\n",
"Image: 1054.png, probabilities: tensor([[0.5965]], device='cuda:0')\n",
"Image: 1055.png, probabilities: tensor([[0.9966]], device='cuda:0')\n",
"Image: 1056.png, probabilities: tensor([[0.9997]], device='cuda:0')\n",
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"Image: 82.png, probabilities: tensor([[0.9570]], device='cuda:0')\n",
"Image: 821.png, probabilities: tensor([[0.9455]], device='cuda:0')\n",
"Image: 822.png, probabilities: tensor([[0.9647]], device='cuda:0')\n",
"Image: 823.png, probabilities: tensor([[0.3889]], device='cuda:0')\n",
"Image: 824.png, probabilities: tensor([[0.9964]], device='cuda:0')\n",
"Image: 825.png, probabilities: tensor([[1.0000]], device='cuda:0')\n",
"Image: 826.png, probabilities: tensor([[0.9886]], device='cuda:0')\n",
"Image: 827.png, probabilities: tensor([[0.8574]], device='cuda:0')\n",
"Image: 828.png, probabilities: tensor([[0.1760]], device='cuda:0')\n",
"Image: 829.png, probabilities: tensor([[0.9984]], device='cuda:0')\n",
"Image: 83.png, probabilities: tensor([[0.9652]], device='cuda:0')\n",
"Image: 830.png, probabilities: tensor([[0.8191]], device='cuda:0')\n",
"Image: 831.png, probabilities: tensor([[0.5931]], device='cuda:0')\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Image: 832.png, probabilities: tensor([[0.7373]], device='cuda:0')\n",
"Image: 833.png, probabilities: tensor([[0.3641]], device='cuda:0')\n",
"Image: 834.png, probabilities: tensor([[0.8001]], device='cuda:0')\n",
"Image: 835.png, probabilities: tensor([[0.6530]], device='cuda:0')\n",
"Image: 836.png, probabilities: tensor([[0.9552]], device='cuda:0')\n",
"Image: 837.png, probabilities: tensor([[0.1598]], device='cuda:0')\n",
"Image: 838.png, probabilities: tensor([[0.9871]], device='cuda:0')\n",
"Image: 839.png, probabilities: tensor([[0.9956]], device='cuda:0')\n",
"Image: 84.png, probabilities: tensor([[1.0000]], device='cuda:0')\n",
"Image: 840.png, probabilities: tensor([[0.7139]], device='cuda:0')\n",
"Image: 841.png, probabilities: tensor([[0.4956]], device='cuda:0')\n",
"Image: 842.png, probabilities: tensor([[0.9958]], device='cuda:0')\n",
"Image: 843.png, probabilities: tensor([[0.1795]], device='cuda:0')\n",
"Image: 845.png, probabilities: tensor([[0.9994]], device='cuda:0')\n",
"Image: 846.png, probabilities: tensor([[0.9997]], device='cuda:0')\n",
"Image: 847.png, probabilities: tensor([[0.9865]], device='cuda:0')\n",
"Image: 848.png, probabilities: tensor([[1.0000]], device='cuda:0')\n",
"Image: 849.png, probabilities: tensor([[0.1230]], device='cuda:0')\n",
"Image: 85.png, probabilities: tensor([[0.1523]], device='cuda:0')\n",
"Image: 850.png, probabilities: tensor([[0.1356]], device='cuda:0')\n",
"Image: 852.png, probabilities: tensor([[0.9836]], device='cuda:0')\n",
"Image: 853.png, probabilities: tensor([[0.4158]], device='cuda:0')\n",
"Image: 854.png, probabilities: tensor([[0.0968]], device='cuda:0')\n",
"Image: 855.png, probabilities: tensor([[0.9650]], device='cuda:0')\n",
"Image: 856.png, probabilities: tensor([[0.3099]], device='cuda:0')\n",
"Image: 857.png, probabilities: tensor([[0.2750]], device='cuda:0')\n",
"Image: 858.png, probabilities: tensor([[0.4625]], device='cuda:0')\n",
"Image: 859.png, probabilities: tensor([[0.3299]], device='cuda:0')\n",
"Image: 86.png, probabilities: tensor([[0.9760]], device='cuda:0')\n",
"Image: 860.png, probabilities: tensor([[0.9272]], device='cuda:0')\n",
"Image: 862.png, probabilities: tensor([[1.0000]], device='cuda:0')\n",
"Image: 866.png, probabilities: tensor([[0.0859]], device='cuda:0')\n",
"Image: 867.png, probabilities: tensor([[0.9415]], device='cuda:0')\n",
"Image: 868.png, probabilities: tensor([[0.5983]], device='cuda:0')\n",
"Image: 869.png, probabilities: tensor([[0.8496]], device='cuda:0')\n",
"Image: 870.png, probabilities: tensor([[0.7020]], device='cuda:0')\n",
"Image: 871.png, probabilities: tensor([[0.7364]], device='cuda:0')\n",
"Image: 872.png, probabilities: tensor([[0.9998]], device='cuda:0')\n",
"Image: 873.png, probabilities: tensor([[1.0000]], device='cuda:0')\n",
"Image: 874.png, probabilities: tensor([[0.9229]], device='cuda:0')\n",
"Image: 875.png, probabilities: tensor([[0.0887]], device='cuda:0')\n",
"Image: 876.png, probabilities: tensor([[1.0000]], device='cuda:0')\n",
"Image: 879.png, probabilities: tensor([[0.1339]], device='cuda:0')\n",
"Image: 88.png, probabilities: tensor([[0.4853]], device='cuda:0')\n",
"Image: 880.png, probabilities: tensor([[0.7385]], device='cuda:0')\n",
"Image: 881.png, probabilities: tensor([[0.8786]], device='cuda:0')\n",
"Image: 884.png, probabilities: tensor([[0.9993]], device='cuda:0')\n",
"Image: 885.png, probabilities: tensor([[0.9999]], device='cuda:0')\n",
"Image: 887.png, probabilities: tensor([[1.0000]], device='cuda:0')\n",
"Image: 888.png, probabilities: tensor([[1.0000]], device='cuda:0')\n",
"Image: 889.png, probabilities: tensor([[0.9145]], device='cuda:0')\n",
"Image: 890.png, probabilities: tensor([[0.9813]], device='cuda:0')\n",
"Image: 891.png, probabilities: tensor([[0.3142]], device='cuda:0')\n",
"Image: 892.png, probabilities: tensor([[0.9999]], device='cuda:0')\n",
"Image: 894.png, probabilities: tensor([[0.9982]], device='cuda:0')\n",
"Image: 895.png, probabilities: tensor([[0.5560]], device='cuda:0')\n",
"Image: 897.png, probabilities: tensor([[0.9999]], device='cuda:0')\n",
"Image: 898.png, probabilities: tensor([[0.0945]], device='cuda:0')\n",
"Image: 899.png, probabilities: tensor([[0.2587]], device='cuda:0')\n",
"Image: 9.png, probabilities: tensor([[1.0000]], device='cuda:0')\n",
"Image: 90.png, probabilities: tensor([[0.1438]], device='cuda:0')\n",
"Image: 900.png, probabilities: tensor([[0.9961]], device='cuda:0')\n",
"Image: 902.png, probabilities: tensor([[0.9995]], device='cuda:0')\n",
"Image: 903.png, probabilities: tensor([[0.8771]], device='cuda:0')\n",
"Image: 904.png, probabilities: tensor([[0.0886]], device='cuda:0')\n",
"Image: 905.png, probabilities: tensor([[0.9998]], device='cuda:0')\n",
"Image: 906.png, probabilities: tensor([[0.9997]], device='cuda:0')\n",
"Image: 907.png, probabilities: tensor([[0.3080]], device='cuda:0')\n",
"Image: 908.png, probabilities: tensor([[0.0736]], device='cuda:0')\n",
"Image: 909.png, probabilities: tensor([[1.0000]], device='cuda:0')\n",
"Image: 91.png, probabilities: tensor([[0.9993]], device='cuda:0')\n",
"Image: 910.png, probabilities: tensor([[0.9988]], device='cuda:0')\n",
"Image: 911.png, probabilities: tensor([[0.9731]], device='cuda:0')\n",
"Image: 912.png, probabilities: tensor([[0.6966]], device='cuda:0')\n",
"Image: 913.png, probabilities: tensor([[0.9782]], device='cuda:0')\n",
"Image: 915.png, probabilities: tensor([[0.0828]], device='cuda:0')\n",
"Image: 916.png, probabilities: tensor([[1.0000]], device='cuda:0')\n",
"Image: 917.png, probabilities: tensor([[0.2300]], device='cuda:0')\n",
"Image: 918.png, probabilities: tensor([[0.8783]], device='cuda:0')\n",
"Image: 919.png, probabilities: tensor([[0.9930]], device='cuda:0')\n",
"Image: 92.png, probabilities: tensor([[0.4636]], device='cuda:0')\n",
"Image: 921.png, probabilities: tensor([[0.6411]], device='cuda:0')\n",
"Image: 922.png, probabilities: tensor([[0.5057]], device='cuda:0')\n",
"Image: 925.png, probabilities: tensor([[0.3462]], device='cuda:0')\n",
"Image: 926.png, probabilities: tensor([[0.0856]], device='cuda:0')\n",
"Image: 927.png, probabilities: tensor([[0.8358]], device='cuda:0')\n",
"Image: 929.png, probabilities: tensor([[0.5666]], device='cuda:0')\n",
"Image: 93.png, probabilities: tensor([[1.0000]], device='cuda:0')\n",
"Image: 931.png, probabilities: tensor([[0.9999]], device='cuda:0')\n",
"Image: 932.png, probabilities: tensor([[0.9911]], device='cuda:0')\n",
"Image: 934.png, probabilities: tensor([[0.6791]], device='cuda:0')\n",
"Image: 935.png, probabilities: tensor([[0.0844]], device='cuda:0')\n",
"Image: 936.png, probabilities: tensor([[0.9442]], device='cuda:0')\n",
"Image: 937.png, probabilities: tensor([[0.1311]], device='cuda:0')\n",
"Image: 938.png, probabilities: tensor([[0.3800]], device='cuda:0')\n",
"Image: 939.png, probabilities: tensor([[1.0000]], device='cuda:0')\n",
"Image: 94.png, probabilities: tensor([[0.9857]], device='cuda:0')\n",
"Image: 940.png, probabilities: tensor([[0.9999]], device='cuda:0')\n",
"Image: 941.png, probabilities: tensor([[1.0000]], device='cuda:0')\n",
"Image: 942.png, probabilities: tensor([[0.1564]], device='cuda:0')\n",
"Image: 943.png, probabilities: tensor([[0.3817]], device='cuda:0')\n",
"Image: 944.png, probabilities: tensor([[0.6272]], device='cuda:0')\n",
"Image: 945.png, probabilities: tensor([[0.1469]], device='cuda:0')\n",
"Image: 946.png, probabilities: tensor([[0.9304]], device='cuda:0')\n",
"Image: 947.png, probabilities: tensor([[0.1642]], device='cuda:0')\n",
"Image: 948.png, probabilities: tensor([[0.9583]], device='cuda:0')\n",
"Image: 949.png, probabilities: tensor([[0.4188]], device='cuda:0')\n",
"Image: 95.png, probabilities: tensor([[0.9998]], device='cuda:0')\n",
"Image: 950.png, probabilities: tensor([[0.4057]], device='cuda:0')\n",
"Image: 951.png, probabilities: tensor([[0.9993]], device='cuda:0')\n",
"Image: 952.png, probabilities: tensor([[1.0000]], device='cuda:0')\n",
"Image: 953.png, probabilities: tensor([[0.9703]], device='cuda:0')\n",
"Image: 954.png, probabilities: tensor([[0.3126]], device='cuda:0')\n",
"Image: 955.png, probabilities: tensor([[0.8071]], device='cuda:0')\n",
"Image: 956.png, probabilities: tensor([[0.3739]], device='cuda:0')\n",
"Image: 958.png, probabilities: tensor([[0.9663]], device='cuda:0')\n",
"Image: 959.png, probabilities: tensor([[0.5024]], device='cuda:0')\n",
"Image: 96.png, probabilities: tensor([[0.9172]], device='cuda:0')\n",
"Image: 961.png, probabilities: tensor([[0.9981]], device='cuda:0')\n",
"Image: 962.png, probabilities: tensor([[0.4659]], device='cuda:0')\n",
"Image: 963.png, probabilities: tensor([[0.9875]], device='cuda:0')\n",
"Image: 966.png, probabilities: tensor([[0.8276]], device='cuda:0')\n",
"Image: 968.png, probabilities: tensor([[0.9481]], device='cuda:0')\n",
"Image: 969.png, probabilities: tensor([[0.9942]], device='cuda:0')\n",
"Image: 97.png, probabilities: tensor([[0.3735]], device='cuda:0')\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Image: 970.png, probabilities: tensor([[0.9907]], device='cuda:0')\n",
"Image: 971.png, probabilities: tensor([[0.9974]], device='cuda:0')\n",
"Image: 972.png, probabilities: tensor([[0.9998]], device='cuda:0')\n",
"Image: 973.png, probabilities: tensor([[0.9747]], device='cuda:0')\n",
"Image: 974.png, probabilities: tensor([[0.8266]], device='cuda:0')\n",
"Image: 975.png, probabilities: tensor([[0.9842]], device='cuda:0')\n",
"Image: 976.png, probabilities: tensor([[0.9994]], device='cuda:0')\n",
"Image: 977.png, probabilities: tensor([[0.9891]], device='cuda:0')\n",
"Image: 978.png, probabilities: tensor([[1.0000]], device='cuda:0')\n",
"Image: 979.png, probabilities: tensor([[0.8647]], device='cuda:0')\n",
"Image: 98.png, probabilities: tensor([[0.9980]], device='cuda:0')\n",
"Image: 981.png, probabilities: tensor([[1.0000]], device='cuda:0')\n",
"Image: 982.png, probabilities: tensor([[0.9880]], device='cuda:0')\n",
"Image: 983.png, probabilities: tensor([[0.9217]], device='cuda:0')\n",
"Image: 984.png, probabilities: tensor([[0.5426]], device='cuda:0')\n",
"Image: 985.png, probabilities: tensor([[0.9996]], device='cuda:0')\n",
"Image: 987.png, probabilities: tensor([[0.9550]], device='cuda:0')\n",
"Image: 988.png, probabilities: tensor([[1.0000]], device='cuda:0')\n",
"Image: 989.png, probabilities: tensor([[0.9988]], device='cuda:0')\n",
"Image: 99.png, probabilities: tensor([[0.7989]], device='cuda:0')\n",
"Image: 990.png, probabilities: tensor([[0.5491]], device='cuda:0')\n",
"Image: 991.png, probabilities: tensor([[0.9999]], device='cuda:0')\n",
"Image: 994.png, probabilities: tensor([[1.0000]], device='cuda:0')\n",
"Image: 996.png, probabilities: tensor([[0.9424]], device='cuda:0')\n",
"Image: 997.png, probabilities: tensor([[0.5517]], device='cuda:0')\n",
"Image: 998.png, probabilities: tensor([[0.9832]], device='cuda:0')\n",
"Image: 999.png, probabilities: tensor([[0.7581]], device='cuda:0')\n",
"Number of outputs greater than 0.07: 1635\n"
]
}
],
"source": [
"image_folder = r\"E:\\qq文件\\任务2\\任务2\"\n",
"image_files = [f for f in os.listdir(image_folder) if os.path.isfile(os.path.join(image_folder, f))]\n",
"\n",
"count_above_threshold = 0 # 计数大于0.5的输出数量\n",
"\n",
"for image_file in image_files:\n",
" image_path = os.path.join(image_folder, image_file)\n",
" image = Image.open(image_path).convert('RGB')\n",
" input_tensor = transform(image).unsqueeze(0) # 增加batch维度\n",
" input_tensor = input_tensor.cuda() # 将输入张量转移到GPU\n",
"\n",
" with torch.no_grad():\n",
" output = custom_model(input_tensor)\n",
" \n",
" if output.item() >= 0.07:\n",
" count_above_threshold += 1\n",
" print(f\"Image: {image_file}, probabilities: {output}\")\n",
"\n",
"print(f\"Number of outputs greater than 0.07: {count_above_threshold}\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "f943aa30",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Classification results saved to classification_results.csv\n"
]
}
],
"source": [
"import pandas as pd\n",
"image_folder = r\"E:\\qq文件\\任务2\\任务2\"\n",
"image_files = [f for f in os.listdir(image_folder) if os.path.isfile(os.path.join(image_folder, f))]\n",
"\n",
"results = [] # 用于存储结果的列表\n",
"\n",
"for image_file in image_files:\n",
" image_path = os.path.join(image_folder, image_file)\n",
" image = Image.open(image_path).convert('RGB')\n",
" input_tensor = transform(image).unsqueeze(0) # 增加batch维度\n",
" input_tensor = input_tensor.cuda() # 将输入张量转移到GPU\n",
"\n",
" with torch.no_grad():\n",
" output = custom_model(input_tensor)\n",
"\n",
" classification = 1 if output.item() >= 0.07 else 0 # 根据阈值进行分类\n",
" results.append([image_file, classification])\n",
"\n",
"# 将结果保存到CSV文件\n",
"df = pd.DataFrame(results, columns=['Image Name', 'Classification'])\n",
"df.to_csv('classification_results_0.07.csv', index=False) \n",
"\n",
"print(\"Classification results saved to classification_results.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "333ab682",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"\n",
"def sort_by_number(filename):\n",
" \"\"\"提取文件名中的数字部分。\"\"\"\n",
" return int(filename.split('.')[0])\n",
"\n",
"def remove_png(filename):\n",
" \"\"\"移除 .png 后缀\"\"\"\n",
" return filename[:-4]\n",
"\n",
"# 读取 CSV 文件, 指定列名和标题行\n",
"df = pd.read_csv('classification_results_0.07.csv', names=['Image Name', 'Classification'], header=0) \n",
"\n",
"# 移除 .png 后缀\n",
"df['Image Name'] = df['Image Name'].apply(remove_png) \n",
"\n",
"# 按照数字大小排序\n",
"df['number'] = df['Image Name'].apply(sort_by_number)\n",
"df.sort_values(by='number', inplace=True)\n",
"\n",
"# 删除辅助列\n",
"df.drop('number', axis=1, inplace=True) \n",
"\n",
"# 修改标题\n",
"df.columns = ['id', 'label'] \n",
"\n",
"# 保存排序后的文件列表\n",
"df.to_csv('sorted_file_list.csv', header=True, index=False) # 保存时保留标题行"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "81f3c5a7",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.12"
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"nbformat": 4,
"nbformat_minor": 5
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