Add Quantization Function. (#256)

* 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

tools/quantization/fdquant/dataset.py

@@ -0,0 +1,150 @@
+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import cv2
+import os
+import numpy as np
+import paddle
+
+
+def generate_scale(im, target_shape):
+    origin_shape = im.shape[:2]
+    im_size_min = np.min(origin_shape)
+    im_size_max = np.max(origin_shape)
+    target_size_min = np.min(target_shape)
+    target_size_max = np.max(target_shape)
+    im_scale = float(target_size_min) / float(im_size_min)
+    if np.round(im_scale * im_size_max) > target_size_max:
+        im_scale = float(target_size_max) / float(im_size_max)
+    im_scale_x = im_scale
+    im_scale_y = im_scale
+
+    return im_scale_y, im_scale_x
+
+
+def yolo_image_preprocess(img, target_shape=[640, 640]):
+    # Resize image
+    im_scale_y, im_scale_x = generate_scale(img, target_shape)
+    img = cv2.resize(
+        img,
+        None,
+        None,
+        fx=im_scale_x,
+        fy=im_scale_y,
+        interpolation=cv2.INTER_LINEAR)
+    # Pad
+    im_h, im_w = img.shape[:2]
+    h, w = target_shape[:]
+    if h != im_h or w != im_w:
+        canvas = np.ones((h, w, 3), dtype=np.float32)
+        canvas *= np.array([114.0, 114.0, 114.0], dtype=np.float32)
+        canvas[0:im_h, 0:im_w, :] = img.astype(np.float32)
+        img = canvas
+    img = np.transpose(img / 255, [2, 0, 1])
+
+    return img.astype(np.float32)
+
+
+def cls_resize_short(img, target_size):
+
+    img_h, img_w = img.shape[:2]
+    percent = float(target_size) / min(img_w, img_h)
+    w = int(round(img_w * percent))
+    h = int(round(img_h * percent))
+
+    return cv2.resize(img, (w, h), interpolation=cv2.INTER_LINEAR)
+
+
+def crop_image(img, target_size, center):
+
+    height, width = img.shape[:2]
+    size = target_size
+
+    if center == True:
+        w_start = (width - size) // 2
+        h_start = (height - size) // 2
+    else:
+        w_start = np.random.randint(0, width - size + 1)
+        h_start = np.random.randint(0, height - size + 1)
+    w_end = w_start + size
+    h_end = h_start + size
+
+    return img[h_start:h_end, w_start:w_end, :]
+
+
+def cls_image_preprocess(img):
+
+    # resize
+    img = cls_resize_short(img, target_size=256)
+    # crop
+    img = crop_image(img, target_size=224, center=True)
+
+    #ToCHWImage & Normalize
+    img = np.transpose(img / 255, [2, 0, 1])
+
+    img_mean = np.array([0.485, 0.456, 0.406]).reshape((3, 1, 1))
+    img_std = np.array([0.229, 0.224, 0.225]).reshape((3, 1, 1))
+    img -= img_mean
+    img /= img_std
+
+    return img.astype(np.float32)
+
+
+def ppdet_resize_no_keepratio(img, target_shape=[640, 640]):
+    im_shape = img.shape
+
+    resize_h, resize_w = target_shape
+    im_scale_y = resize_h / im_shape[0]
+    im_scale_x = resize_w / im_shape[1]
+
+    scale_factor = np.asarray([im_scale_y, im_scale_x], dtype=np.float32)
+    return cv2.resize(
+        img, None, None, fx=im_scale_x, fy=im_scale_y,
+        interpolation=2), scale_factor
+
+
+def ppdet_normliaze(img, is_scale=True):
+
+    mean = [0.485, 0.456, 0.406]
+    std = [0.229, 0.224, 0.225]
+    img = img.astype(np.float32, copy=False)
+
+    if is_scale:
+        scale = 1.0 / 255.0
+        img *= scale
+
+    mean = np.array(mean)[np.newaxis, np.newaxis, :]
+    std = np.array(std)[np.newaxis, np.newaxis, :]
+    img -= mean
+    img /= std
+    return img
+
+
+def hwc_to_chw(img):
+    img = img.transpose((2, 0, 1))
+    return img
+
+
+def ppdet_image_preprocess(img):
+
+    img, scale_factor = ppdet_resize_no_keepratio(img, target_shape=[640, 640])
+
+    img = np.transpose(img / 255, [2, 0, 1])
+
+    img_mean = np.array([0.485, 0.456, 0.406]).reshape((3, 1, 1))
+    img_std = np.array([0.229, 0.224, 0.225]).reshape((3, 1, 1))
+    img -= img_mean
+    img /= img_std
+
+    return img.astype(np.float32), scale_factor