{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# QAT 的不同训练策略\n",
"\n",
"载入库："
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"from torch import nn, jit\n",
"from torch.ao.quantization import quantize_qat\n",
"from torchvision.models.quantization import mobilenet_v2\n",
"\n",
"# 载入自定义模块\n",
"from mod import torchq\n",
"from torchq.xinet import CV\n",
"from torchq.helper import evaluate, print_size_of_model, load_model\n",
"\n",
"\n",
"def create_model(num_classes=10,\n",
" quantize=False,\n",
" pretrained=False):\n",
" '''定义模型'''\n",
" float_model = mobilenet_v2(pretrained=pretrained,\n",
" quantize=quantize)\n",
" # 匹配 ``num_classes``\n",
" float_model.classifier[1] = nn.Linear(float_model.last_channel,\n",
" num_classes)\n",
" return float_model\n",
"\n",
"\n",
"def create_float_model(num_classes,\n",
" model_path):\n",
" model = create_model(quantize=False,\n",
" num_classes=num_classes)\n",
" model = load_model(model, model_path)\n",
" return model\n"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"|===========================================================================|\n",
"| PyTorch CUDA memory summary, device ID 0 |\n",
"|---------------------------------------------------------------------------|\n",
"| CUDA OOMs: 0 | cudaMalloc retries: 0 |\n",
"|===========================================================================|\n",
"| Metric | Cur Usage | Peak Usage | Tot Alloc | Tot Freed |\n",
"|---------------------------------------------------------------------------|\n",
"| Allocated memory | 0 B | 0 B | 0 B | 0 B |\n",
"| from large pool | 0 B | 0 B | 0 B | 0 B |\n",
"| from small pool | 0 B | 0 B | 0 B | 0 B |\n",
"|---------------------------------------------------------------------------|\n",
"| Active memory | 0 B | 0 B | 0 B | 0 B |\n",
"| from large pool | 0 B | 0 B | 0 B | 0 B |\n",
"| from small pool | 0 B | 0 B | 0 B | 0 B |\n",
"|---------------------------------------------------------------------------|\n",
"| GPU reserved memory | 0 B | 0 B | 0 B | 0 B |\n",
"| from large pool | 0 B | 0 B | 0 B | 0 B |\n",
"| from small pool | 0 B | 0 B | 0 B | 0 B |\n",
"|---------------------------------------------------------------------------|\n",
"| Non-releasable memory | 0 B | 0 B | 0 B | 0 B |\n",
"| from large pool | 0 B | 0 B | 0 B | 0 B |\n",
"| from small pool | 0 B | 0 B | 0 B | 0 B |\n",
"|---------------------------------------------------------------------------|\n",
"| Allocations | 0 | 0 | 0 | 0 |\n",
"| from large pool | 0 | 0 | 0 | 0 |\n",
"| from small pool | 0 | 0 | 0 | 0 |\n",
"|---------------------------------------------------------------------------|\n",
"| Active allocs | 0 | 0 | 0 | 0 |\n",
"| from large pool | 0 | 0 | 0 | 0 |\n",
"| from small pool | 0 | 0 | 0 | 0 |\n",
"|---------------------------------------------------------------------------|\n",
"| GPU reserved segments | 0 | 0 | 0 | 0 |\n",
"| from large pool | 0 | 0 | 0 | 0 |\n",
"| from small pool | 0 | 0 | 0 | 0 |\n",
"|---------------------------------------------------------------------------|\n",
"| Non-releasable allocs | 0 | 0 | 0 | 0 |\n",
"| from large pool | 0 | 0 | 0 | 0 |\n",
"| from small pool | 0 | 0 | 0 | 0 |\n",
"|---------------------------------------------------------------------------|\n",
"| Oversize allocations | 0 | 0 | 0 | 0 |\n",
"|---------------------------------------------------------------------------|\n",
"| Oversize GPU segments | 0 | 0 | 0 | 0 |\n",
"|===========================================================================|\n",
"\n"
]
}
],
"source": [
"torch.cuda.empty_cache() # 清空 GPU 缓存\n",
"print(torch.cuda.memory_summary()) # 打印显存"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [],
"source": [
"# 设置 warnings\n",
"import warnings\n",
"warnings.filterwarnings(\n",
" action='ignore',\n",
" category=DeprecationWarning,\n",
" module='.*'\n",
")\n",
"warnings.filterwarnings(\n",
" action='ignore',\n",
" module='torch.ao.quantization'\n",
")\n",
"\n",
"\n",
"def print_info(model, model_type, num_eval, criterion):\n",
" '''打印信息'''\n",
" print_size_of_model(model)\n",
" top1, top5 = evaluate(model, criterion, test_iter)\n",
" print(f'\\n{model_type}：\\n\\t'\n",
" f'在 {num_eval} 张图片上评估 accuracy 为: {top1.avg:2.5f}')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"超参数设置："
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [],
"source": [
"saved_model_dir = 'models/'\n",
"float_model_file = 'mobilenet_pretrained_float.pth'\n",
"# scripted_qat_model_file = 'mobilenet_qat_scripted_quantized.pth'\n",
"# 超参数\n",
"float_model_path = saved_model_dir + float_model_file\n",
"batch_size = 16\n",
"num_classes = 10\n",
"num_epochs = 50\n",
"learning_rate = 5e-5\n",
"ylim = [0.8, 1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"加载数据集："
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Files already downloaded and verified\n",
"Files already downloaded and verified\n"
]
}
],
"source": [
"# 为了 cifar10 匹配 ImageNet，需要将其 resize 到 224\n",
"train_iter, test_iter = CV.load_data_cifar10(batch_size=batch_size,\n",
" resize=224)\n",
"num_eval = sum(len(ys) for _, ys in test_iter)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"打印浮点模型信息："
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"模型大小：9.187789 MB\n",
"\n",
"浮点模型：\n",
"\t在 10000 张图片上评估 accuracy 为: 94.91000\n"
]
}
],
"source": [
"float_model = create_float_model(num_classes, float_model_path)\n",
"model_type = '浮点模型'\n",
"criterion = nn.CrossEntropyLoss(reduction=\"none\")\n",
"print_info(float_model, model_type, num_eval, criterion)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"普通策略："
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"loss 0.013, train acc 0.996, test acc 0.950\n",
"352.4 examples/sec on cuda:1\n"
]
},
{
"data": {
"image/svg+xml": "\n\n\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"ename": "",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[1;31mThe Kernel crashed while executing code in the the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click here for more info. View Jupyter log for further details."
]
}
],
"source": [
"num_epochs = 1\n",
"ylim = [0.85, 1]\n",
"device = 'cuda:1'\n",
"param_group = True\n",
"\n",
"# 量化参数\n",
"is_freeze = False\n",
"is_quantized_acc = False\n",
"need_qconfig = True # 做一些 QAT 的量化配置工作\n",
"\n",
"# 提供位置参数\n",
"args = [train_iter,\n",
" test_iter,\n",
" learning_rate,\n",
" num_epochs,\n",
" device,\n",
" is_freeze,\n",
" is_quantized_acc,\n",
" need_qconfig,\n",
" param_group,\n",
" ylim]\n",
"\n",
"qat_model = create_float_model(num_classes, float_model_path)\n",
"qat_model.fuse_model(is_qat=True)\n",
"quantized_model = quantize_qat(qat_model, CV.train_fine_tuning, args)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"torch.float32\n",
"torch.float32\n",
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"torch.float32\n"
]
}
],
"source": [
"for name, param in quantized_model.features.named_parameters():\n",
" print(param.dtype)"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"QuantizableMobileNetV2(\n",
" (features): Sequential(\n",
" (0): ConvNormActivation(\n",
" (0): ConvBnReLU2d(\n",
" (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
" (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (2): ReLU()\n",
" )\n",
" (1): Identity()\n",
" (2): Identity()\n",
" )\n",
" (1): QuantizableInvertedResidual(\n",
" (conv): Sequential(\n",
" (0): ConvNormActivation(\n",
" (0): ConvBnReLU2d(\n",
" (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n",
" (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (2): ReLU()\n",
" )\n",
" (1): Identity()\n",
" (2): Identity()\n",
" )\n",
" (1): ConvBn2d(\n",
" (0): Conv2d(32, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (2): Identity()\n",
" )\n",
" (skip_add): FloatFunctional(\n",
" (activation_post_process): Identity()\n",
" )\n",
" )\n",
" (2): QuantizableInvertedResidual(\n",
" (conv): Sequential(\n",
" (0): ConvNormActivation(\n",
" (0): ConvBnReLU2d(\n",
" (0): Conv2d(16, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (2): ReLU()\n",
" )\n",
" (1): Identity()\n",
" (2): Identity()\n",
" )\n",
" (1): ConvNormActivation(\n",
" (0): ConvBnReLU2d(\n",
" (0): Conv2d(96, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=96, bias=False)\n",
" (1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (2): ReLU()\n",
" )\n",
" (1): Identity()\n",
" (2): Identity()\n",
" )\n",
" (2): ConvBn2d(\n",
" (0): Conv2d(96, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (1): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (3): Identity()\n",
" )\n",
" (skip_add): FloatFunctional(\n",
" (activation_post_process): Identity()\n",
" )\n",
" )\n",
" (3): QuantizableInvertedResidual(\n",
" (conv): Sequential(\n",
" (0): ConvNormActivation(\n",
" (0): ConvBnReLU2d(\n",
" (0): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (2): ReLU()\n",
" )\n",
" (1): Identity()\n",
" (2): Identity()\n",
" )\n",
" (1): ConvNormActivation(\n",
" (0): ConvBnReLU2d(\n",
" (0): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)\n",
" (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (2): ReLU()\n",
" )\n",
" (1): Identity()\n",
" (2): Identity()\n",
" )\n",
" (2): ConvBn2d(\n",
" (0): Conv2d(144, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (1): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (3): Identity()\n",
" )\n",
" (skip_add): FloatFunctional(\n",
" (activation_post_process): Identity()\n",
" )\n",
" )\n",
" (4): QuantizableInvertedResidual(\n",
" (conv): Sequential(\n",
" (0): ConvNormActivation(\n",
" (0): ConvBnReLU2d(\n",
" (0): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (2): ReLU()\n",
" )\n",
" (1): Identity()\n",
" (2): Identity()\n",
" )\n",
" (1): ConvNormActivation(\n",
" (0): ConvBnReLU2d(\n",
" (0): Conv2d(144, 144, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=144, bias=False)\n",
" (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (2): ReLU()\n",
" )\n",
" (1): Identity()\n",
" (2): Identity()\n",
" )\n",
" (2): ConvBn2d(\n",
" (0): Conv2d(144, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (3): Identity()\n",
" )\n",
" (skip_add): FloatFunctional(\n",
" (activation_post_process): Identity()\n",
" )\n",
" )\n",
" (5): QuantizableInvertedResidual(\n",
" (conv): Sequential(\n",
" (0): ConvNormActivation(\n",
" (0): ConvBnReLU2d(\n",
" (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (2): ReLU()\n",
" )\n",
" (1): Identity()\n",
" (2): Identity()\n",
" )\n",
" (1): ConvNormActivation(\n",
" (0): ConvBnReLU2d(\n",
" (0): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)\n",
" (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (2): ReLU()\n",
" )\n",
" (1): Identity()\n",
" (2): Identity()\n",
" )\n",
" (2): ConvBn2d(\n",
" (0): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (3): Identity()\n",
" )\n",
" (skip_add): FloatFunctional(\n",
" (activation_post_process): Identity()\n",
" )\n",
" )\n",
" (6): QuantizableInvertedResidual(\n",
" (conv): Sequential(\n",
" (0): ConvNormActivation(\n",
" (0): ConvBnReLU2d(\n",
" (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (2): ReLU()\n",
" )\n",
" (1): Identity()\n",
" (2): Identity()\n",
" )\n",
" (1): ConvNormActivation(\n",
" (0): ConvBnReLU2d(\n",
" (0): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)\n",
" (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (2): ReLU()\n",
" )\n",
" (1): Identity()\n",
" (2): Identity()\n",
" )\n",
" (2): ConvBn2d(\n",
" (0): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (3): Identity()\n",
" )\n",
" (skip_add): FloatFunctional(\n",
" (activation_post_process): Identity()\n",
" )\n",
" )\n",
" (7): QuantizableInvertedResidual(\n",
" (conv): Sequential(\n",
" (0): ConvNormActivation(\n",
" (0): ConvBnReLU2d(\n",
" (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (2): ReLU()\n",
" )\n",
" (1): Identity()\n",
" (2): Identity()\n",
" )\n",
" (1): ConvNormActivation(\n",
" (0): ConvBnReLU2d(\n",
" (0): Conv2d(192, 192, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=192, bias=False)\n",
" (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (2): ReLU()\n",
" )\n",
" (1): Identity()\n",
" (2): Identity()\n",
" )\n",
" (2): ConvBn2d(\n",
" (0): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (3): Identity()\n",
" )\n",
" (skip_add): FloatFunctional(\n",
" (activation_post_process): Identity()\n",
" )\n",
" )\n",
" (8): QuantizableInvertedResidual(\n",
" (conv): Sequential(\n",
" (0): ConvNormActivation(\n",
" (0): ConvBnReLU2d(\n",
" (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (2): ReLU()\n",
" )\n",
" (1): Identity()\n",
" (2): Identity()\n",
" )\n",
" (1): ConvNormActivation(\n",
" (0): ConvBnReLU2d(\n",
" (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)\n",
" (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (2): ReLU()\n",
" )\n",
" (1): Identity()\n",
" (2): Identity()\n",
" )\n",
" (2): ConvBn2d(\n",
" (0): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (3): Identity()\n",
" )\n",
" (skip_add): FloatFunctional(\n",
" (activation_post_process): Identity()\n",
" )\n",
" )\n",
" (9): QuantizableInvertedResidual(\n",
" (conv): Sequential(\n",
" (0): ConvNormActivation(\n",
" (0): ConvBnReLU2d(\n",
" (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (2): ReLU()\n",
" )\n",
" (1): Identity()\n",
" (2): Identity()\n",
" )\n",
" (1): ConvNormActivation(\n",
" (0): ConvBnReLU2d(\n",
" (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)\n",
" (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (2): ReLU()\n",
" )\n",
" (1): Identity()\n",
" (2): Identity()\n",
" )\n",
" (2): ConvBn2d(\n",
" (0): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (3): Identity()\n",
" )\n",
" (skip_add): FloatFunctional(\n",
" (activation_post_process): Identity()\n",
" )\n",
" )\n",
" (10): QuantizableInvertedResidual(\n",
" (conv): Sequential(\n",
" (0): ConvNormActivation(\n",
" (0): ConvBnReLU2d(\n",
" (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (2): ReLU()\n",
" )\n",
" (1): Identity()\n",
" (2): Identity()\n",
" )\n",
" (1): ConvNormActivation(\n",
" (0): ConvBnReLU2d(\n",
" (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)\n",
" (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (2): ReLU()\n",
" )\n",
" (1): Identity()\n",
" (2): Identity()\n",
" )\n",
" (2): ConvBn2d(\n",
" (0): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (3): Identity()\n",
" )\n",
" (skip_add): FloatFunctional(\n",
" (activation_post_process): Identity()\n",
" )\n",
" )\n",
" (11): QuantizableInvertedResidual(\n",
" (conv): Sequential(\n",
" (0): ConvNormActivation(\n",
" (0): ConvBnReLU2d(\n",
" (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (2): ReLU()\n",
" )\n",
" (1): Identity()\n",
" (2): Identity()\n",
" )\n",
" (1): ConvNormActivation(\n",
" (0): ConvBnReLU2d(\n",
" (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)\n",
" (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (2): ReLU()\n",
" )\n",
" (1): Identity()\n",
" (2): Identity()\n",
" )\n",
" (2): ConvBn2d(\n",
" (0): Conv2d(384, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (3): Identity()\n",
" )\n",
" (skip_add): FloatFunctional(\n",
" (activation_post_process): Identity()\n",
" )\n",
" )\n",
" (12): QuantizableInvertedResidual(\n",
" (conv): Sequential(\n",
" (0): ConvNormActivation(\n",
" (0): ConvBnReLU2d(\n",
" (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (2): ReLU()\n",
" )\n",
" (1): Identity()\n",
" (2): Identity()\n",
" )\n",
" (1): ConvNormActivation(\n",
" (0): ConvBnReLU2d(\n",
" (0): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)\n",
" (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (2): ReLU()\n",
" )\n",
" (1): Identity()\n",
" (2): Identity()\n",
" )\n",
" (2): ConvBn2d(\n",
" (0): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (3): Identity()\n",
" )\n",
" (skip_add): FloatFunctional(\n",
" (activation_post_process): Identity()\n",
" )\n",
" )\n",
" (13): QuantizableInvertedResidual(\n",
" (conv): Sequential(\n",
" (0): ConvNormActivation(\n",
" (0): ConvBnReLU2d(\n",
" (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (2): ReLU()\n",
" )\n",
" (1): Identity()\n",
" (2): Identity()\n",
" )\n",
" (1): ConvNormActivation(\n",
" (0): ConvBnReLU2d(\n",
" (0): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)\n",
" (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (2): ReLU()\n",
" )\n",
" (1): Identity()\n",
" (2): Identity()\n",
" )\n",
" (2): ConvBn2d(\n",
" (0): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (3): Identity()\n",
" )\n",
" (skip_add): FloatFunctional(\n",
" (activation_post_process): Identity()\n",
" )\n",
" )\n",
" (14): QuantizableInvertedResidual(\n",
" (conv): Sequential(\n",
" (0): ConvNormActivation(\n",
" (0): ConvBnReLU2d(\n",
" (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (2): ReLU()\n",
" )\n",
" (1): Identity()\n",
" (2): Identity()\n",
" )\n",
" (1): ConvNormActivation(\n",
" (0): ConvBnReLU2d(\n",
" (0): Conv2d(576, 576, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=576, bias=False)\n",
" (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (2): ReLU()\n",
" )\n",
" (1): Identity()\n",
" (2): Identity()\n",
" )\n",
" (2): ConvBn2d(\n",
" (0): Conv2d(576, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (3): Identity()\n",
" )\n",
" (skip_add): FloatFunctional(\n",
" (activation_post_process): Identity()\n",
" )\n",
" )\n",
" (15): QuantizableInvertedResidual(\n",
" (conv): Sequential(\n",
" (0): ConvNormActivation(\n",
" (0): ConvBnReLU2d(\n",
" (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (2): ReLU()\n",
" )\n",
" (1): Identity()\n",
" (2): Identity()\n",
" )\n",
" (1): ConvNormActivation(\n",
" (0): ConvBnReLU2d(\n",
" (0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)\n",
" (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (2): ReLU()\n",
" )\n",
" (1): Identity()\n",
" (2): Identity()\n",
" )\n",
" (2): ConvBn2d(\n",
" (0): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (3): Identity()\n",
" )\n",
" (skip_add): FloatFunctional(\n",
" (activation_post_process): Identity()\n",
" )\n",
" )\n",
" (16): QuantizableInvertedResidual(\n",
" (conv): Sequential(\n",
" (0): ConvNormActivation(\n",
" (0): ConvBnReLU2d(\n",
" (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (2): ReLU()\n",
" )\n",
" (1): Identity()\n",
" (2): Identity()\n",
" )\n",
" (1): ConvNormActivation(\n",
" (0): ConvBnReLU2d(\n",
" (0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)\n",
" (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (2): ReLU()\n",
" )\n",
" (1): Identity()\n",
" (2): Identity()\n",
" )\n",
" (2): ConvBn2d(\n",
" (0): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (3): Identity()\n",
" )\n",
" (skip_add): FloatFunctional(\n",
" (activation_post_process): Identity()\n",
" )\n",
" )\n",
" (17): QuantizableInvertedResidual(\n",
" (conv): Sequential(\n",
" (0): ConvNormActivation(\n",
" (0): ConvBnReLU2d(\n",
" (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (2): ReLU()\n",
" )\n",
" (1): Identity()\n",
" (2): Identity()\n",
" )\n",
" (1): ConvNormActivation(\n",
" (0): ConvBnReLU2d(\n",
" (0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)\n",
" (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (2): ReLU()\n",
" )\n",
" (1): Identity()\n",
" (2): Identity()\n",
" )\n",
" (2): ConvBn2d(\n",
" (0): Conv2d(960, 320, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" (3): Identity()\n",
" )\n",
" (skip_add): FloatFunctional(\n",
" (activation_post_process): Identity()\n",
" )\n",
" )\n",
" (18): ConvNormActivation(\n",
" (0): ConvBnReLU2d(\n",
" (0): Conv2d(320, 1280, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (1): BatchNorm2d(1280, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (2): ReLU()\n",
" )\n",
" (1): Identity()\n",
" (2): Identity()\n",
" )\n",
" )\n",
" (classifier): Sequential(\n",
" (0): Dropout(p=0.2, inplace=False)\n",
" (1): Linear(in_features=1280, out_features=10, bias=True)\n",
" )\n",
" (quant): QuantStub()\n",
" (dequant): DeQuantStub()\n",
")"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"quantized_model"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"qmodel_file = 'mobilenet_quantization_quantized.pth'\n",
"torch.save(quantized_model.state_dict(), saved_model_dir + qmodel_file)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"冻结前几次训练的量化器以及观测器："
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"loss 0.004, train acc 0.999, test acc 0.951\n",
"237.7 examples/sec on cuda:2\n"
]
},
{
"data": {
"image/svg+xml": "\n\n\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"args[5] = True\n",
"args[6] = False\n",
"qat_model = create_float_model(num_classes, float_model_path)\n",
"quantized_model = quantize_qat(qat_model, CV.train_fine_tuning, args)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"输出量化精度："
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"loss 0.004, train acc 0.999, test acc 0.953\n",
"239.0 examples/sec on cuda:2\n"
]
},
{
"data": {
"image/svg+xml": "\n\n\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"args[6] = True\n",
"args[5] = False\n",
"qat_model = create_float_model(num_classes, float_model_path)\n",
"quantized_model = quantize_qat(qat_model, CV.train_fine_tuning, args)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"冻结前几次训练的观测器并且生成量化精度："
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"loss 0.004, train acc 0.999, test acc 0.951\n",
"238.1 examples/sec on cuda:2\n"
]
},
{
"data": {
"image/svg+xml": "\n\n\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"args[5] = True\n",
"args[6] = True\n",
"qat_model = create_float_model(num_classes, float_model_path)\n",
"quantized_model = quantize_qat(qat_model, CV.train_fine_tuning, args)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"interpreter": {
"hash": "ccd751c8c176f1a7084878738c6c59984a17d1189ffe2fae146e3d74e2010826"
},
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"name": "ipython",
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
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