add tinypose demo (#1179)

demo/auto_compression/detection/keypoint_utils.py

+
+# Copyright (c) 2021 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 logging
+import os
+import json
+from collections import defaultdict, OrderedDict
+import numpy as np
+from pycocotools.coco import COCO
+from pycocotools.cocoeval import COCOeval
+from scipy.io import loadmat, savemat
+import cv2
+from paddleslim.common import get_logger
+logger = get_logger(__name__, level=logging.INFO)
+
+def get_affine_mat_kernel(h, w, s, inv=False):
+    if w < h:
+        w_ = s
+        h_ = int(np.ceil((s / w * h) / 64.) * 64)
+        scale_w = w
+        scale_h = h_ / w_ * w
+
+    else:
+        h_ = s
+        w_ = int(np.ceil((s / h * w) / 64.) * 64)
+        scale_h = h
+        scale_w = w_ / h_ * h
+
+    center = np.array([np.round(w / 2.), np.round(h / 2.)])
+
+    size_resized = (w_, h_)
+    trans = get_affine_transform(
+        center, np.array([scale_w, scale_h]), 0, size_resized, inv=inv)
+
+    return trans, size_resized
+
+
+def get_affine_transform(center,
+                         input_size,
+                         rot,
+                         output_size,
+                         shift=(0., 0.),
+                         inv=False):
+    """Get the affine transform matrix, given the center/scale/rot/output_size.
+    Args:
+        center (np.ndarray[2, ]): Center of the bounding box (x, y).
+        input_size (np.ndarray[2, ]): Size of input feature (width, height).
+        rot (float): Rotation angle (degree).
+        output_size (np.ndarray[2, ]): Size of the destination heatmaps.
+        shift (0-100%): Shift translation ratio wrt the width/height.
+            Default (0., 0.).
+        inv (bool): Option to inverse the affine transform direction.
+            (inv=False: src->dst or inv=True: dst->src)
+    Returns:
+        np.ndarray: The transform matrix.
+    """
+    assert len(center) == 2
+    assert len(output_size) == 2
+    assert len(shift) == 2
+
+    if not isinstance(input_size, (np.ndarray, list)):
+        input_size = np.array([input_size, input_size], dtype=np.float32)
+    scale_tmp = input_size
+
+    shift = np.array(shift)
+    src_w = scale_tmp[0]
+    dst_w = output_size[0]
+    dst_h = output_size[1]
+
+    rot_rad = np.pi * rot / 180
+    src_dir = rotate_point([0., src_w * -0.5], rot_rad)
+    dst_dir = np.array([0., dst_w * -0.5])
+
+    src = np.zeros((3, 2), dtype=np.float32)
+
+    src[0, :] = center + scale_tmp * shift
+    src[1, :] = center + src_dir + scale_tmp * shift
+    src[2, :] = _get_3rd_point(src[0, :], src[1, :])
+
+    dst = np.zeros((3, 2), dtype=np.float32)
+    dst[0, :] = [dst_w * 0.5, dst_h * 0.5]
+    dst[1, :] = np.array([dst_w * 0.5, dst_h * 0.5]) + dst_dir
+    dst[2, :] = _get_3rd_point(dst[0, :], dst[1, :])
+
+    if inv:
+        trans = cv2.getAffineTransform(np.float32(dst), np.float32(src))
+    else:
+        trans = cv2.getAffineTransform(np.float32(src), np.float32(dst))
+
+    return trans
+
+
+def get_warp_matrix(theta, size_input, size_dst, size_target):
+    """This code is based on
+        https://github.com/open-mmlab/mmpose/blob/master/mmpose/core/post_processing/post_transforms.py
+        Calculate the transformation matrix under the constraint of unbiased.
+    Paper ref: Huang et al. The Devil is in the Details: Delving into Unbiased
+    Data Processing for Human Pose Estimation (CVPR 2020).
+    Args:
+        theta (float): Rotation angle in degrees.
+        size_input (np.ndarray): Size of input image [w, h].
+        size_dst (np.ndarray): Size of output image [w, h].
+        size_target (np.ndarray): Size of ROI in input plane [w, h].
+    Returns:
+        matrix (np.ndarray): A matrix for transformation.
+    """
+    theta = np.deg2rad(theta)
+    matrix = np.zeros((2, 3), dtype=np.float32)
+    scale_x = size_dst[0] / size_target[0]
+    scale_y = size_dst[1] / size_target[1]
+    matrix[0, 0] = np.cos(theta) * scale_x
+    matrix[0, 1] = -np.sin(theta) * scale_x
+    matrix[0, 2] = scale_x * (
+        -0.5 * size_input[0] * np.cos(theta) + 0.5 * size_input[1] *
+        np.sin(theta) + 0.5 * size_target[0])
+    matrix[1, 0] = np.sin(theta) * scale_y
+    matrix[1, 1] = np.cos(theta) * scale_y
+    matrix[1, 2] = scale_y * (
+        -0.5 * size_input[0] * np.sin(theta) - 0.5 * size_input[1] *
+        np.cos(theta) + 0.5 * size_target[1])
+    return matrix
+
+
+def _get_3rd_point(a, b):
+    """To calculate the affine matrix, three pairs of points are required. This
+    function is used to get the 3rd point, given 2D points a & b.
+    The 3rd point is defined by rotating vector `a - b` by 90 degrees
+    anticlockwise, using b as the rotation center.
+    Args:
+        a (np.ndarray): point(x,y)
+        b (np.ndarray): point(x,y)
+    Returns:
+        np.ndarray: The 3rd point.
+    """
+    assert len(
+        a) == 2, 'input of _get_3rd_point should be point with length of 2'
+    assert len(
+        b) == 2, 'input of _get_3rd_point should be point with length of 2'
+    direction = a - b
+    third_pt = b + np.array([-direction[1], direction[0]], dtype=np.float32)
+
+    return third_pt
+
+
+def rotate_point(pt, angle_rad):
+    """Rotate a point by an angle.
+    Args:
+        pt (list[float]): 2 dimensional point to be rotated
+        angle_rad (float): rotation angle by radian
+    Returns:
+        list[float]: Rotated point.
+    """
+    assert len(pt) == 2
+    sn, cs = np.sin(angle_rad), np.cos(angle_rad)
+    new_x = pt[0] * cs - pt[1] * sn
+    new_y = pt[0] * sn + pt[1] * cs
+    rotated_pt = [new_x, new_y]
+
+    return rotated_pt
+
+
+def transpred(kpts, h, w, s):
+    trans, _ = get_affine_mat_kernel(h, w, s, inv=True)
+
+    return warp_affine_joints(kpts[..., :2].copy(), trans)
+
+
+def warp_affine_joints(joints, mat):
+    """Apply affine transformation defined by the transform matrix on the
+    joints.
+    Args:
+        joints (np.ndarray[..., 2]): Origin coordinate of joints.
+        mat (np.ndarray[3, 2]): The affine matrix.
+    Returns:
+        matrix (np.ndarray[..., 2]): Result coordinate of joints.
+    """
+    joints = np.array(joints)
+    shape = joints.shape
+    joints = joints.reshape(-1, 2)
+    return np.dot(np.concatenate(
+        (joints, joints[:, 0:1] * 0 + 1), axis=1),
+                  mat.T).reshape(shape)
+
+
+def affine_transform(pt, t):
+    new_pt = np.array([pt[0], pt[1], 1.]).T
+    new_pt = np.dot(t, new_pt)
+    return new_pt[:2]
+
+
+def transform_preds(coords, center, scale, output_size):
+    target_coords = np.zeros(coords.shape)
+    trans = get_affine_transform(center, scale * 200, 0, output_size, inv=1)
+    for p in range(coords.shape[0]):
+        target_coords[p, 0:2] = affine_transform(coords[p, 0:2], trans)
+    return target_coords
+
+
+def oks_iou(g, d, a_g, a_d, sigmas=None, in_vis_thre=None):
+    if not isinstance(sigmas, np.ndarray):
+        sigmas = np.array([
+            .26, .25, .25, .35, .35, .79, .79, .72, .72, .62, .62, 1.07, 1.07,
+            .87, .87, .89, .89
+        ]) / 10.0
+    vars = (sigmas * 2)**2
+    xg = g[0::3]
+    yg = g[1::3]
+    vg = g[2::3]
+    ious = np.zeros((d.shape[0]))
+    for n_d in range(0, d.shape[0]):
+        xd = d[n_d, 0::3]
+        yd = d[n_d, 1::3]
+        vd = d[n_d, 2::3]
+        dx = xd - xg
+        dy = yd - yg
+        e = (dx**2 + dy**2) / vars / ((a_g + a_d[n_d]) / 2 + np.spacing(1)) / 2
+        if in_vis_thre is not None:
+            ind = list(vg > in_vis_thre) and list(vd > in_vis_thre)
+            e = e[ind]
+        ious[n_d] = np.sum(np.exp(-e)) / e.shape[0] if e.shape[0] != 0 else 0.0
+    return ious
+
+def oks_nms(kpts_db, thresh, sigmas=None, in_vis_thre=None):
+    """greedily select boxes with high confidence and overlap with current maximum <= thresh
+    rule out overlap >= thresh
+    Args:
+        kpts_db (list): The predicted keypoints within the image
+        thresh (float): The threshold to select the boxes
+        sigmas (np.array): The variance to calculate the oks iou
+            Default: None
+        in_vis_thre (float): The threshold to select the high confidence boxes
+            Default: None
+    Return:
+        keep (list): indexes to keep
+    """
+
+    if len(kpts_db) == 0:
+        return []
+
+    scores = np.array([kpts_db[i]['score'] for i in range(len(kpts_db))])
+    kpts = np.array(
+        [kpts_db[i]['keypoints'].flatten() for i in range(len(kpts_db))])
+    areas = np.array([kpts_db[i]['area'] for i in range(len(kpts_db))])
+
+    order = scores.argsort()[::-1]
+
+    keep = []
+    while order.size > 0:
+        i = order[0]
+        keep.append(i)
+
+        oks_ovr = oks_iou(kpts[i], kpts[order[1:]], areas[i], areas[order[1:]],
+                          sigmas, in_vis_thre)
+
+        inds = np.where(oks_ovr <= thresh)[0]
+        order = order[inds + 1]
+
+    return keep
+
+def rescore(overlap, scores, thresh, type='gaussian'):
+    assert overlap.shape[0] == scores.shape[0]
+    if type == 'linear':
+        inds = np.where(overlap >= thresh)[0]
+        scores[inds] = scores[inds] * (1 - overlap[inds])
+    else:
+        scores = scores * np.exp(-overlap**2 / thresh)
+
+    return scores
+
+
+def soft_oks_nms(kpts_db, thresh, sigmas=None, in_vis_thre=None):
+    """greedily select boxes with high confidence and overlap with current maximum <= thresh
+    rule out overlap >= thresh
+    Args:
+        kpts_db (list): The predicted keypoints within the image
+        thresh (float): The threshold to select the boxes
+        sigmas (np.array): The variance to calculate the oks iou
+            Default: None
+        in_vis_thre (float): The threshold to select the high confidence boxes
+            Default: None
+    Return:
+        keep (list): indexes to keep
+    """
+
+    if len(kpts_db) == 0:
+        return []
+
+    scores = np.array([kpts_db[i]['score'] for i in range(len(kpts_db))])
+    kpts = np.array(
+        [kpts_db[i]['keypoints'].flatten() for i in range(len(kpts_db))])
+    areas = np.array([kpts_db[i]['area'] for i in range(len(kpts_db))])
+
+    order = scores.argsort()[::-1]
+    scores = scores[order]
+
+    # max_dets = order.size
+    max_dets = 20
+    keep = np.zeros(max_dets, dtype=np.intp)
+    keep_cnt = 0
+    while order.size > 0 and keep_cnt < max_dets:
+        i = order[0]
+
+        oks_ovr = oks_iou(kpts[i], kpts[order[1:]], areas[i], areas[order[1:]],
+                          sigmas, in_vis_thre)
+
+        order = order[1:]
+        scores = rescore(oks_ovr, scores[1:], thresh)
+
+        tmp = scores.argsort()[::-1]
+        order = order[tmp]
+        scores = scores[tmp]
+
+        keep[keep_cnt] = i
+        keep_cnt += 1
+
+    keep = keep[:keep_cnt]
+
+    return keep
+
+
+class HRNetPostProcess(object):
+    def __init__(self, use_dark=True):
+        self.use_dark = use_dark
+
+    def get_max_preds(self, heatmaps):
+        '''get predictions from score maps
+        Args:
+            heatmaps: numpy.ndarray([batch_size, num_joints, height, width])
+        Returns:
+            preds: numpy.ndarray([batch_size, num_joints, 2]), keypoints coords
+            maxvals: numpy.ndarray([batch_size, num_joints, 2]), the maximum confidence of the keypoints
+        '''
+        assert isinstance(heatmaps,
+                          np.ndarray), 'heatmaps should be numpy.ndarray'
+        assert heatmaps.ndim == 4, 'batch_images should be 4-ndim'
+
+        batch_size = heatmaps.shape[0]
+        num_joints = heatmaps.shape[1]
+        width = heatmaps.shape[3]
+        heatmaps_reshaped = heatmaps.reshape((batch_size, num_joints, -1))
+        idx = np.argmax(heatmaps_reshaped, 2)
+        maxvals = np.amax(heatmaps_reshaped, 2)
+
+        maxvals = maxvals.reshape((batch_size, num_joints, 1))
+        idx = idx.reshape((batch_size, num_joints, 1))
+
+        preds = np.tile(idx, (1, 1, 2)).astype(np.float32)
+
+        preds[:, :, 0] = (preds[:, :, 0]) % width
+        preds[:, :, 1] = np.floor((preds[:, :, 1]) / width)
+
+        pred_mask = np.tile(np.greater(maxvals, 0.0), (1, 1, 2))
+        pred_mask = pred_mask.astype(np.float32)
+
+        preds *= pred_mask
+
+        return preds, maxvals
+
+    def gaussian_blur(self, heatmap, kernel):
+        border = (kernel - 1) // 2
+        batch_size = heatmap.shape[0]
+        num_joints = heatmap.shape[1]
+        height = heatmap.shape[2]
+        width = heatmap.shape[3]
+        for i in range(batch_size):
+            for j in range(num_joints):
+                origin_max = np.max(heatmap[i, j])
+                dr = np.zeros((height + 2 * border, width + 2 * border))
+                dr[border:-border, border:-border] = heatmap[i, j].copy()
+                dr = cv2.GaussianBlur(dr, (kernel, kernel), 0)
+                heatmap[i, j] = dr[border:-border, border:-border].copy()
+                heatmap[i, j] *= origin_max / np.max(heatmap[i, j])
+        return heatmap
+
+    def dark_parse(self, hm, coord):
+        heatmap_height = hm.shape[0]
+        heatmap_width = hm.shape[1]
+        px = int(coord[0])
+        py = int(coord[1])
+        if 1 < px < heatmap_width - 2 and 1 < py < heatmap_height - 2:
+            dx = 0.5 * (hm[py][px + 1] - hm[py][px - 1])
+            dy = 0.5 * (hm[py + 1][px] - hm[py - 1][px])
+            dxx = 0.25 * (hm[py][px + 2] - 2 * hm[py][px] + hm[py][px - 2])
+            dxy = 0.25 * (hm[py+1][px+1] - hm[py-1][px+1] - hm[py+1][px-1] \
+                + hm[py-1][px-1])
+            dyy = 0.25 * (
+                hm[py + 2 * 1][px] - 2 * hm[py][px] + hm[py - 2 * 1][px])
+            derivative = np.matrix([[dx], [dy]])
+            hessian = np.matrix([[dxx, dxy], [dxy, dyy]])
+            if dxx * dyy - dxy**2 != 0:
+                hessianinv = hessian.I
+                offset = -hessianinv * derivative
+                offset = np.squeeze(np.array(offset.T), axis=0)
+                coord += offset
+        return coord
+
+    def dark_postprocess(self, hm, coords, kernelsize):
+        '''DARK postpocessing, Zhang et al. Distribution-Aware Coordinate
+        Representation for Human Pose Estimation (CVPR 2020).
+        '''
+
+        hm = self.gaussian_blur(hm, kernelsize)
+        hm = np.maximum(hm, 1e-10)
+        hm = np.log(hm)
+        for n in range(coords.shape[0]):
+            for p in range(coords.shape[1]):
+                coords[n, p] = self.dark_parse(hm[n][p], coords[n][p])
+        return coords
+
+    def get_final_preds(self, heatmaps, center, scale, kernelsize=3):
+        """the highest heatvalue location with a quarter offset in the
+        direction from the highest response to the second highest response.
+        Args:
+            heatmaps (numpy.ndarray): The predicted heatmaps
+            center (numpy.ndarray): The boxes center
+            scale (numpy.ndarray): The scale factor
+        Returns:
+            preds: numpy.ndarray([batch_size, num_joints, 2]), keypoints coords
+            maxvals: numpy.ndarray([batch_size, num_joints, 1]), the maximum confidence of the keypoints
+        """
+        coords, maxvals = self.get_max_preds(heatmaps)
+
+        heatmap_height = heatmaps.shape[2]
+        heatmap_width = heatmaps.shape[3]
+
+        if self.use_dark:
+            coords = self.dark_postprocess(heatmaps, coords, kernelsize)
+        else:
+            for n in range(coords.shape[0]):
+                for p in range(coords.shape[1]):
+                    hm = heatmaps[n][p]
+                    px = int(math.floor(coords[n][p][0] + 0.5))
+                    py = int(math.floor(coords[n][p][1] + 0.5))
+                    if 1 < px < heatmap_width - 1 and 1 < py < heatmap_height - 1:
+                        diff = np.array([
+                            hm[py][px + 1] - hm[py][px - 1],
+                            hm[py + 1][px] - hm[py - 1][px]
+                        ])
+                        coords[n][p] += np.sign(diff) * .25
+        preds = coords.copy()
+
+        # Transform back
+        for i in range(coords.shape[0]):
+            preds[i] = transform_preds(coords[i], center[i], scale[i],
+                                       [heatmap_width, heatmap_height])
+
+        return preds, maxvals
+
+    def __call__(self, output, center, scale):
+        preds, maxvals = self.get_final_preds(np.array(output), center, scale)
+        outputs = [[
+            np.concatenate(
+                (preds, maxvals), axis=-1), np.mean(
+                    maxvals, axis=1)
+        ]]
+        return outputs
+    

demo/auto_compression/detection/run_tinypose.py

+# 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 os
+import sys
+import numpy as np
+import argparse
+import paddle
+import copy
+import cv2
+from ppdet.core.workspace import load_config, merge_config
+from ppdet.core.workspace import create
+from ppdet.metrics import KeyPointTopDownCOCOEval
+from paddleslim.auto_compression.config_helpers import load_config as load_slim_config
+from paddleslim.auto_compression import AutoCompression
+from paddleslim.quant import quant_post_static
+from keypoint_utils import HRNetPostProcess, transform_preds
+
+def argsparser():
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument(
+        '--config_path',
+        type=str,
+        default=None,
+        help="path of compression strategy config.",
+        required=True)
+    parser.add_argument(
+        '--save_dir',
+        type=str,
+        default='output',
+        help="directory to save compressed model.")
+    parser.add_argument(
+        '--devices',
+        type=str,
+        default='gpu',
+        help="which device used to compress.")
+    parser.add_argument(
+        '--eval', type=bool, default=False, help="whether to run evaluation.")
+    parser.add_argument(
+        '--quant', type=bool, default=False, help="whether to run evaluation.")
+    return parser
+
+
+def print_arguments(args):
+    print('-----------  Running Arguments -----------')
+    for arg, value in sorted(vars(args).items()):
+        print('%s: %s' % (arg, value))
+    print('------------------------------------------')
+
+
+def reader_wrapper(reader, input_list):
+    def gen():
+        for data in reader:
+            in_dict = {}
+            for input_name in input_list:
+                in_dict[input_name] = data[input_name]
+            yield in_dict
+
+    return gen
+
+def flip_back(output_flipped, matched_parts):
+    assert output_flipped.ndim == 4,\
+            'output_flipped should be [batch_size, num_joints, height, width]'
+
+    output_flipped = output_flipped[:, :, :, ::-1]
+
+    for pair in matched_parts:
+        tmp = output_flipped[:, pair[0], :, :].copy()
+        output_flipped[:, pair[0], :, :] = output_flipped[:, pair[1], :, :]
+        output_flipped[:, pair[1], :, :] = tmp
+
+    return output_flipped
+
+def eval(config):
+
+    place = paddle.CUDAPlace(0) if FLAGS.devices == 'gpu' else paddle.CPUPlace()
+    exe = paddle.static.Executor(place)
+
+    val_program, feed_target_names, fetch_targets = paddle.fluid.io.load_inference_model(
+        config["model_dir"],
+        exe,
+        model_filename=config["model_filename"],
+        params_filename=config["params_filename"], )
+    dataset.check_or_download_dataset()
+    anno_file = dataset.get_anno()
+    metric = KeyPointTopDownCOCOEval(anno_file, len(dataset), 17, 'output_eval')
+    post_process = HRNetPostProcess()
+    for batch_id, data in enumerate(val_loader):
+        data_all = {k: np.array(v) for k, v in data.items()}
+        data_input = {}
+        for k, v in data.items():
+            if k in config['input_list']:
+                data_input[k] = np.array(v)
+        outs = exe.run(val_program,
+                       feed=data_input,
+                       fetch_list=fetch_targets,
+                       return_numpy=False)
+        
+        data_input['image'] = np.flip(data_input['image'], [3])        
+        output_flipped = exe.run(val_program,
+                                 feed=data_input,
+                                 fetch_list=fetch_targets,
+                                 return_numpy=False)
+        
+        output_flipped = np.array(output_flipped[0])
+        flip_perm = [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12], [13, 14], [15, 16]]
+        output_flipped = flip_back(output_flipped, flip_perm)
+        output_flipped[:, :, :, 1:] = copy.copy(output_flipped)[:, :, :, 0:-1]
+        hrnet_outputs = (np.array(outs[0]) + output_flipped) * 0.5
+        imshape = (np.array(data['im_shape'])
+                   )[:, ::-1] if 'im_shape' in data else None
+        center = np.array(data['center']) if 'center' in data else np.round(imshape / 2.)
+        scale = np.array(data['scale']) if 'scale' in data else imshape / 200.
+        outputs = post_process(hrnet_outputs, center, scale)
+        outputs = {'keypoint': outputs}
+        metric.update(data_all, outputs)
+        if batch_id % 100 == 0:
+            print('Eval iter:', batch_id)
+    metric.accumulate()
+    metric.log()
+    metric.reset()
+
+
+def eval_function(exe, compiled_test_program, test_feed_names, test_fetch_list):
+    dataset.check_or_download_dataset()
+    anno_file = dataset.get_anno()
+    metric = KeyPointTopDownCOCOEval(anno_file, len(dataset), 17, 'output_eval')
+    post_process = HRNetPostProcess()
+    for batch_id, data in enumerate(val_loader):
+        data_all = {k: np.array(v) for k, v in data.items()}
+        data_input = {}
+        for k, v in data.items():
+            if k in test_feed_names:
+                data_input[k] = np.array(v)
+        outs = exe.run(compiled_test_program,
+                       feed=data_input,
+                       fetch_list=test_fetch_list,
+                       return_numpy=False)
+        
+        data_input['image'] = np.flip(data_input['image'], [3])        
+        output_flipped = exe.run(compiled_test_program,
+                                 feed=data_input,
+                                 fetch_list=test_fetch_list,
+                                 return_numpy=False)
+        
+        output_flipped = np.array(output_flipped[0])
+        flip_perm = [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12], [13, 14], [15, 16]]
+        output_flipped = flip_back(output_flipped, flip_perm)
+        output_flipped[:, :, :, 1:] = copy.copy(output_flipped)[:, :, :, 0:-1]
+        hrnet_outputs = (np.array(outs[0]) + output_flipped) * 0.5
+        imshape = (np.array(data['im_shape'])
+                   )[:, ::-1] if 'im_shape' in data else None
+        center = np.array(data['center']) if 'center' in data else np.round(imshape / 2.)
+        scale = np.array(data['scale']) if 'scale' in data else imshape / 200.
+        outputs = post_process(hrnet_outputs, center, scale)
+        outputs = {'keypoint': outputs}
+
+        metric.update(data_all, outputs)
+        if batch_id % 100 == 0:
+            print('Eval iter:', batch_id)
+    metric.accumulate()
+    metric.log()
+    map_res = metric.get_results()
+    metric.reset()
+    return map_res['keypoint'][0]
+
+
+def main():
+    all_config = load_slim_config(FLAGS.config_path)
+    global global_config
+    assert "Global" in all_config, f"Key 'Global' not found in config file. \n{all_config}"
+    global_config = all_config["Global"]
+    reader_cfg = load_config(global_config['reader_config'])
+    
+    train_loader = create('EvalReader')(reader_cfg['TrainDataset'],
+                                        reader_cfg['worker_num'],
+                                        return_list=True)
+    train_loader = reader_wrapper(train_loader, global_config['input_list'])
+
+    global dataset
+    dataset = reader_cfg['EvalDataset']
+    global val_loader
+    val_loader = create('EvalReader')(reader_cfg['EvalDataset'],
+                                      reader_cfg['worker_num'],
+                                      return_list=True)
+
+    if FLAGS.eval:
+        eval(global_config)
+        sys.exit(0)
+
+    if 'Evaluation' in global_config.keys() and global_config['Evaluation']:
+        eval_func = eval_function
+    else:
+        eval_func = None
+
+    ac = AutoCompression(
+        model_dir=global_config["model_dir"],
+        model_filename=global_config["model_filename"],
+        params_filename=global_config["params_filename"],
+        save_dir=FLAGS.save_dir,
+        config=all_config,
+        train_dataloader=train_loader,
+        eval_callback=eval_func)
+
+    ac.compress()
+
+
+if __name__ == '__main__':
+    paddle.enable_static()
+    parser = argsparser()
+    FLAGS = parser.parse_args()
+    print_arguments(FLAGS)
+
+    assert FLAGS.devices in ['cpu', 'gpu', 'xpu', 'npu']
+    paddle.set_device(FLAGS.devices)
+
+    main()