add damn it ppq

Author: Your Name

Diff for: deploy/quant_atom/.gitignore

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+Output/

Diff for: deploy/quant_atom/qt_ppq_sinst.py

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+"""
+
+Examples on how to quantize with PPQ
+
+"""
+from typing import Iterable
+
+from loguru import logger
+import torch
+from torch.utils.data import DataLoader
+from ppq import BaseGraph, QuantizationSettingFactory, TargetPlatform
+from ppq import graphwise_error_analyse, layerwise_error_analyse
+from ppq.api import (
+    export_ppq_graph,
+    quantize_onnx_model
+)
+import sys
+from torchvision import transforms
+import torchvision
+import torch
+from atomquant.onnx.dataloader import get_calib_dataloader_coco
+import os
+import cv2
+import numpy as np
+import onnxruntime as ort
+from torchvision.datasets.coco import CocoDetection
+from alfred.dl.torch.common import device
+
+
+def preprocess_func(img, target):
+    w = 640
+    h = 640
+    a = cv2.resize(img, (w, h))
+    a_t = np.array(a).astype(np.float32)
+    boxes = []
+    for t in target:
+        boxes.append(t["bbox"])
+    target = np.array(boxes)
+    a_t = torch.as_tensor(a_t)
+    target = torch.as_tensor(target)
+    return a_t, target
+
+
+def collate_fn(batch):
+    images, targets = zip(*batch)
+    if isinstance(images[0], torch.Tensor):
+        images = torch.stack(images)
+        targets = torch.stack(targets)
+    else:
+        images = np.array(images)
+    return images
+
+
+if __name__ == "__main__":
+    ONNX_PATH = sys.argv[1]
+
+    coco_root = os.path.expanduser("~/data/coco/images/val2017")
+    anno_f = os.path.expanduser(
+        "~/data/coco/annotations/instances_val2017_val_val_train.json"
+    )
+
+    # coco_ds = CocoDetection(coco_root, anno_f, )
+
+    session = ort.InferenceSession(ONNX_PATH)
+    input_name = session.get_inputs()[0].name
+
+    calib_dataloader = get_calib_dataloader_coco(
+        coco_root,
+        anno_f,
+        preprocess_func=preprocess_func,
+        input_names=input_name,
+        bs=1,
+        max_step=50,
+        collate_fn=collate_fn
+    )
+
+    REQUIRE_ANALYSE = False
+    BATCHSIZE = 1
+    # INPUT_SHAPE = [3, 224, 224]
+    INPUT_SHAPE = [640, 640, 3]
+    DEVICE = "cuda"  
+    PLATFORM = (
+        TargetPlatform.ORT_OOS_INT8
+    ) 
+    EXECUTING_DEVICE = "cpu"  # 'cuda' or 'cpu'.
+
+    # create a setting for quantizing your network with PPL CUDA.
+    # quant_setting = QuantizationSettingFactory.pplcuda_setting()
+    quant_setting = QuantizationSettingFactory.default_setting()
+    quant_setting.equalization = True  # use layerwise equalization algorithm.
+    quant_setting.dispatcher = (
+        "conservative"  # dispatch this network in conservertive way.
+    )
+
+    
+    # quantize your model.
+    quantized = quantize_onnx_model(
+        onnx_import_file=ONNX_PATH,
+        calib_dataloader=calib_dataloader.dataloader_holder,
+        calib_steps=120,
+        input_shape=[BATCHSIZE] + INPUT_SHAPE,
+        setting=quant_setting,
+        # collate_fn=collate_fn,
+        platform=PLATFORM,
+        device=DEVICE,
+        verbose=0,
+    )
+
+    # Quantization Result is a PPQ BaseGraph instance.
+    assert isinstance(quantized, BaseGraph)
+
+    try:
+        if REQUIRE_ANALYSE:
+            print("正计算网络量化误差(SNR)，最后一层的误差应小于 0.1 以保证量化精度:")
+            reports = graphwise_error_analyse(
+                graph=quantized,
+                running_device=EXECUTING_DEVICE,
+                steps=32,
+                dataloader=calib_dataloader.dataloader_holder,
+                collate_fn=lambda x: x.to(EXECUTING_DEVICE),
+            )
+            for op, snr in reports.items():
+                if snr > 0.1:
+                    logger.warning(f"层 {op} 的累计量化误差显著，请考虑进行优化")
+            print("正计算逐层量化误差(SNR)，每一层的独立量化误差应小于 0.1 以保证量化精度:")
+            layerwise_error_analyse(
+                graph=quantized,
+                running_device=EXECUTING_DEVICE,
+                interested_outputs=None,
+                dataloader=calib_dataloader.dataloader_holder,
+                collate_fn=lambda x: x.to(EXECUTING_DEVICE),
+            )
+    except Exception as e:
+        logger.warning('analyse got some error, but that is OK, pass it.')
+
+
+    # EXPORT_TARGET = TargetPlatform.ORT_OOS_INT8
+    EXPORT_TARGET = TargetPlatform.TRT_INT8
+    # EXPORT_TARGET = TargetPlatform.TRT_INT8
+    os.makedirs('Output/', exist_ok=True)
+    # export quantized graph.
+    export_ppq_graph(
+        graph=quantized,
+        platform=EXPORT_TARGET,
+        graph_save_to=f"Output/quantized_{EXPORT_TARGET}.onnx",
+        config_save_to=f"Output/quantized_{EXPORT_TARGET}.json",
+    )
+
+