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[Quantization] Improve the usage of FastDeploy tools. (#660)
* Add PaddleOCR Support * Add PaddleOCR Support * Add PaddleOCRv3 Support * Add PaddleOCRv3 Support * Update README.md * Update README.md * Update README.md * Update README.md * Add PaddleOCRv3 Support * Add PaddleOCRv3 Supports * Add PaddleOCRv3 Suport * Fix Rec diff * Remove useless functions * Remove useless comments * Add PaddleOCRv2 Support * Add PaddleOCRv3 & PaddleOCRv2 Support * remove useless parameters * Add utils of sorting det boxes * Fix code naming convention * Fix code naming convention * Fix code naming convention * Fix bug in the Classify process * Imporve OCR Readme * Fix diff in Cls model * Update Model Download Link in Readme * Fix diff in PPOCRv2 * Improve OCR readme * Imporve OCR readme * Improve OCR readme * Improve OCR readme * Imporve OCR readme * Improve OCR readme * Fix conflict * Add readme for OCRResult * Improve OCR readme * Add OCRResult readme * Improve OCR readme * Improve OCR readme * Add Model Quantization Demo * Fix Model Quantization Readme * Fix Model Quantization Readme * Add the function to do PTQ quantization * Improve quant tools readme * Improve quant tool readme * Improve quant tool readme * Add PaddleInference-GPU for OCR Rec model * Add QAT method to fastdeploy-quantization tool * Remove examples/slim for now * Move configs folder * Add Quantization Support for Classification Model * Imporve ways of importing preprocess * Upload YOLO Benchmark on readme * Upload YOLO Benchmark on readme * Upload YOLO Benchmark on readme * Improve Quantization configs and readme * Add support for multi-inputs model * Add backends and params file for YOLOv7 * Add quantized model deployment support for YOLO series * Fix YOLOv5 quantize readme * Fix YOLO quantize readme * Fix YOLO quantize readme * Improve quantize YOLO readme * Improve quantize YOLO readme * Improve quantize YOLO readme * Improve quantize YOLO readme * Improve quantize YOLO readme * Fix bug, change Fronted to ModelFormat * Change Fronted to ModelFormat * Add examples to deploy quantized paddleclas models * Fix readme * Add quantize Readme * Add quantize Readme * Add quantize Readme * Modify readme of quantization tools * Modify readme of quantization tools * Improve quantization tools readme * Improve quantization readme * Improve PaddleClas quantized model deployment readme * Add PPYOLOE-l quantized deployment examples * Improve quantization tools readme * Improve Quantize Readme * Fix conflicts * Fix conflicts * improve readme * Improve quantization tools and readme * Improve quantization tools and readme * Add quantized deployment examples for PaddleSeg model * Fix cpp readme * Fix memory leak of reader_wrapper function * Fix model file name in PaddleClas quantization examples * Update Runtime and E2E benchmark * Update Runtime and E2E benchmark * Rename quantization tools to auto compression tools * Remove PPYOLOE data when deployed on MKLDNN * Fix readme * Support PPYOLOE with OR without NMS and update readme * Update Readme * Update configs and readme * Update configs and readme * Add Paddle-TensorRT backend in quantized model deploy examples * Support PPYOLOE+ series * Add reused_input_tensors for PPYOLOE * Improve fastdeploy tools usage * improve fastdeploy tool * Improve fastdeploy auto compression tool * Improve fastdeploy auto compression tool * Improve fastdeploy auto compression tool * Improve fastdeploy auto compression tool * Improve fastdeploy auto compression tool * remove modify * Improve fastdeploy auto compression tool * Improve fastdeploy auto compression tool * Improve fastdeploy auto compression tool * Improve fastdeploy auto compression tool * Improve fastdeploy auto compression tool * Remove extra requirements for fd-auto-compress package * Imporve fastdeploy-tools package * Install fastdeploy-tools package when build fastdeploy-python * Imporve quantization readme
This commit is contained in:
yunyaoXYY
388
tools/common_tools/auto_compression/fd_auto_compress/dataset.py
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tools/common_tools/auto_compression/fd_auto_compress/dataset.py
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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import cv2
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import os
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import numpy as np
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import random
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from PIL import Image, ImageEnhance
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import paddle
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"""
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Preprocess for Yolov5/v6/v7 Series
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"""
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def generate_scale(im, target_shape):
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origin_shape = im.shape[:2]
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im_size_min = np.min(origin_shape)
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im_size_max = np.max(origin_shape)
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target_size_min = np.min(target_shape)
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target_size_max = np.max(target_shape)
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im_scale = float(target_size_min) / float(im_size_min)
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if np.round(im_scale * im_size_max) > target_size_max:
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im_scale = float(target_size_max) / float(im_size_max)
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im_scale_x = im_scale
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im_scale_y = im_scale
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return im_scale_y, im_scale_x
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def yolo_image_preprocess(img, target_shape=[640, 640]):
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# Resize image
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im_scale_y, im_scale_x = generate_scale(img, target_shape)
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img = cv2.resize(
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img,
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None,
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None,
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fx=im_scale_x,
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fy=im_scale_y,
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interpolation=cv2.INTER_LINEAR)
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# Pad
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im_h, im_w = img.shape[:2]
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h, w = target_shape[:]
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if h != im_h or w != im_w:
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canvas = np.ones((h, w, 3), dtype=np.float32)
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canvas *= np.array([114.0, 114.0, 114.0], dtype=np.float32)
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canvas[0:im_h, 0:im_w, :] = img.astype(np.float32)
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img = canvas
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img = np.transpose(img / 255, [2, 0, 1])
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return img.astype(np.float32)
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"""
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Preprocess for PaddleClas model
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"""
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def cls_resize_short(img, target_size):
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img_h, img_w = img.shape[:2]
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percent = float(target_size) / min(img_w, img_h)
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w = int(round(img_w * percent))
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h = int(round(img_h * percent))
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return cv2.resize(img, (w, h), interpolation=cv2.INTER_LINEAR)
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def crop_image(img, target_size, center):
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height, width = img.shape[:2]
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size = target_size
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if center == True:
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w_start = (width - size) // 2
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h_start = (height - size) // 2
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else:
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w_start = np.random.randint(0, width - size + 1)
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h_start = np.random.randint(0, height - size + 1)
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w_end = w_start + size
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h_end = h_start + size
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return img[h_start:h_end, w_start:w_end, :]
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def cls_image_preprocess(img):
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# resize
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img = cls_resize_short(img, target_size=256)
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# crop
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img = crop_image(img, target_size=224, center=True)
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#ToCHWImage & Normalize
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img = np.transpose(img / 255, [2, 0, 1])
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img_mean = np.array([0.485, 0.456, 0.406]).reshape((3, 1, 1))
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img_std = np.array([0.229, 0.224, 0.225]).reshape((3, 1, 1))
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img -= img_mean
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img /= img_std
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return img.astype(np.float32)
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"""
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Preprocess for PPYOLOE
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"""
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def ppdet_resize_no_keepratio(img, target_shape=[640, 640]):
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im_shape = img.shape
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resize_h, resize_w = target_shape
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im_scale_y = resize_h / im_shape[0]
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im_scale_x = resize_w / im_shape[1]
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scale_factor = np.asarray([im_scale_y, im_scale_x], dtype=np.float32)
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return cv2.resize(
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img, None, None, fx=im_scale_x, fy=im_scale_y,
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interpolation=2), scale_factor
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def ppyoloe_withNMS_image_preprocess(img):
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img, scale_factor = ppdet_resize_no_keepratio(img, target_shape=[640, 640])
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img = np.transpose(img / 255, [2, 0, 1])
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img_mean = np.array([0.485, 0.456, 0.406]).reshape((3, 1, 1))
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img_std = np.array([0.229, 0.224, 0.225]).reshape((3, 1, 1))
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img -= img_mean
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img /= img_std
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return img.astype(np.float32), scale_factor
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def ppyoloe_plus_withNMS_image_preprocess(img):
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img, scale_factor = ppdet_resize_no_keepratio(img, target_shape=[640, 640])
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img = np.transpose(img / 255, [2, 0, 1])
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return img.astype(np.float32), scale_factor
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"""
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Preprocess for PP_LiteSeg
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"""
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def ppseg_cityscapes_ptq_preprocess(img):
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#ToCHWImage & Normalize
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img = np.transpose(img / 255.0, [2, 0, 1])
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img_mean = np.array([0.5, 0.5, 0.5]).reshape((3, 1, 1))
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img_std = np.array([0.5, 0.5, 0.5]).reshape((3, 1, 1))
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img -= img_mean
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img /= img_std
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return img.astype(np.float32)
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def ResizeStepScaling(img,
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min_scale_factor=0.75,
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max_scale_factor=1.25,
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scale_step_size=0.25):
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# refer form ppseg
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if min_scale_factor == max_scale_factor:
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scale_factor = min_scale_factor
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elif scale_step_size == 0:
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scale_factor = np.random.uniform(min_scale_factor, max_scale_factor)
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else:
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num_steps = int((max_scale_factor - min_scale_factor) / scale_step_size
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+ 1)
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scale_factors = np.linspace(min_scale_factor, max_scale_factor,
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num_steps).tolist()
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np.random.shuffle(scale_factors)
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scale_factor = scale_factors[0]
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w = int(round(scale_factor * img.shape[1]))
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h = int(round(scale_factor * img.shape[0]))
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img = cv2.resize(img, (w, h), interpolation=cv2.INTER_LINEAR)
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return img
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def RandomPaddingCrop(img,
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crop_size=(512, 512),
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im_padding_value=(127.5, 127.5, 127.5),
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label_padding_value=255):
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if isinstance(crop_size, list) or isinstance(crop_size, tuple):
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if len(crop_size) != 2:
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raise ValueError(
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'Type of `crop_size` is list or tuple. It should include 2 elements, but it is {}'
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.format(crop_size))
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else:
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raise TypeError(
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"The type of `crop_size` is invalid. It should be list or tuple, but it is {}"
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.format(type(crop_size)))
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if isinstance(crop_size, int):
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crop_width = crop_size
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crop_height = crop_size
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else:
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crop_width = crop_size[0]
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crop_height = crop_size[1]
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img_height = img.shape[0]
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img_width = img.shape[1]
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if img_height == crop_height and img_width == crop_width:
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return img
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else:
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pad_height = max(crop_height - img_height, 0)
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pad_width = max(crop_width - img_width, 0)
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if (pad_height > 0 or pad_width > 0):
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img = cv2.copyMakeBorder(
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img,
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0,
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pad_height,
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0,
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pad_width,
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cv2.BORDER_CONSTANT,
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value=im_padding_value)
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img_height = img.shape[0]
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img_width = img.shape[1]
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if crop_height > 0 and crop_width > 0:
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h_off = np.random.randint(img_height - crop_height + 1)
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w_off = np.random.randint(img_width - crop_width + 1)
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img = img[h_off:(crop_height + h_off), w_off:(w_off + crop_width
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), :]
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return img
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def RandomHorizontalFlip(img, prob=0.5):
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if random.random() < prob:
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if len(img.shape) == 3:
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img = img[:, ::-1, :]
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elif len(img.shape) == 2:
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img = img[:, ::-1]
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return img
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else:
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return img
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def brightness(im, brightness_lower, brightness_upper):
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brightness_delta = np.random.uniform(brightness_lower, brightness_upper)
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im = ImageEnhance.Brightness(im).enhance(brightness_delta)
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return im
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def contrast(im, contrast_lower, contrast_upper):
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contrast_delta = np.random.uniform(contrast_lower, contrast_upper)
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im = ImageEnhance.Contrast(im).enhance(contrast_delta)
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return im
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def saturation(im, saturation_lower, saturation_upper):
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saturation_delta = np.random.uniform(saturation_lower, saturation_upper)
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im = ImageEnhance.Color(im).enhance(saturation_delta)
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return im
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def hue(im, hue_lower, hue_upper):
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hue_delta = np.random.uniform(hue_lower, hue_upper)
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im = np.array(im.convert('HSV'))
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im[:, :, 0] = im[:, :, 0] + hue_delta
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im = Image.fromarray(im, mode='HSV').convert('RGB')
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return im
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def sharpness(im, sharpness_lower, sharpness_upper):
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sharpness_delta = np.random.uniform(sharpness_lower, sharpness_upper)
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im = ImageEnhance.Sharpness(im).enhance(sharpness_delta)
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return im
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def RandomDistort(img,
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brightness_range=0.5,
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brightness_prob=0.5,
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contrast_range=0.5,
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contrast_prob=0.5,
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saturation_range=0.5,
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saturation_prob=0.5,
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hue_range=18,
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hue_prob=0.5,
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sharpness_range=0.5,
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sharpness_prob=0):
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brightness_lower = 1 - brightness_range
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brightness_upper = 1 + brightness_range
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contrast_lower = 1 - contrast_range
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contrast_upper = 1 + contrast_range
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saturation_lower = 1 - saturation_range
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saturation_upper = 1 + saturation_range
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hue_lower = -hue_range
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hue_upper = hue_range
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sharpness_lower = 1 - sharpness_range
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sharpness_upper = 1 + sharpness_range
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ops = [brightness, contrast, saturation, hue, sharpness]
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random.shuffle(ops)
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params_dict = {
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'brightness': {
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'brightness_lower': brightness_lower,
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'brightness_upper': brightness_upper
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},
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'contrast': {
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'contrast_lower': contrast_lower,
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'contrast_upper': contrast_upper
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},
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'saturation': {
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'saturation_lower': saturation_lower,
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'saturation_upper': saturation_upper
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},
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'hue': {
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'hue_lower': hue_lower,
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'hue_upper': hue_upper
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},
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'sharpness': {
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'sharpness_lower': sharpness_lower,
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'sharpness_upper': sharpness_upper,
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}
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}
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prob_dict = {
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'brightness': brightness_prob,
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'contrast': contrast_prob,
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'saturation': saturation_prob,
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'hue': hue_prob,
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'sharpness': sharpness_prob
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}
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img = img.astype('uint8')
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img = Image.fromarray(img)
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for id in range(len(ops)):
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params = params_dict[ops[id].__name__]
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prob = prob_dict[ops[id].__name__]
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params['im'] = img
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if np.random.uniform(0, 1) < prob:
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img = ops[id](**params)
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img = np.asarray(img).astype('float32')
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return img
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def ppseg_cityscapes_qat_preprocess(img):
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min_scale_factor = 0.5
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max_scale_factor = 2.0
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scale_step_size = 0.25
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crop_size = (1024, 512)
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brightness_range = 0.5
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contrast_range = 0.5
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saturation_range = 0.5
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img = ResizeStepScaling(
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img, min_scale_factor=0.5, max_scale_factor=2.0, scale_step_size=0.25)
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img = RandomPaddingCrop(img, crop_size=(1024, 512))
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img = RandomHorizontalFlip(img)
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img = RandomDistort(
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img, brightness_range=0.5, contrast_range=0.5, saturation_range=0.5)
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img = np.transpose(img / 255.0, [2, 0, 1])
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img_mean = np.array([0.5, 0.5, 0.5]).reshape((3, 1, 1))
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img_std = np.array([0.5, 0.5, 0.5]).reshape((3, 1, 1))
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img -= img_mean
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img /= img_std
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return img.astype(np.float32)
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