update tinypose act demo (PaddlePaddle#1227)

demo/auto_compression/detection/configs/tinypose_qat_dis.yaml

@@ -1,7 +1,8 @@
 Global:
+  arch: 'keypoint'
   reader_config: configs/tinypose_reader.yml
   input_list: ['image']
-  Evaluation: False
+  Evaluation: True
   model_dir: ./tinypose_128x96
   model_filename: model.pdmodel
   params_filename: model.pdiparams
@@ -13,19 +14,21 @@ Distillation:
   -  conv2d_441.tmp_0
 
 Quantization:
-  activation_quantize_type: 'range_abs_max'
-  weight_quantize_type: 'abs_max'
+  use_pact: true
+  activation_quantize_type: 'moving_average_abs_max'
+  weight_quantize_type: 'channel_wise_abs_max'  # 'abs_max' is layer wise quant
   quantize_op_types:
   - conv2d
   - depthwise_conv2d
 
 TrainConfig:
-  epochs: 1
+  train_iter: 30000
   eval_iter: 1000
-  learning_rate: 0.0001
+  learning_rate: 
+    type: CosineAnnealingDecay 
+    learning_rate: 0.015
+    T_max: 30000
   optimizer_builder:
-    optimizer: 
-      type: SGD
-    weight_decay: 4.0e-05
-  #origin_metric: 0.291
-
+    optimizer:
+      type: Momentum
+    weight_decay: 0.00002

demo/auto_compression/detection/configs/tinypose_reader.yml

@@ -45,4 +45,4 @@ EvalReader:
         std: *global_std
         is_scale: true
     - Permute: {}
-  batch_size: 4
+  batch_size: 16

demo/auto_compression/detection/eval.py

@@ -19,8 +19,9 @@
 import paddle
 from ppdet.core.workspace import load_config, merge_config
 from ppdet.core.workspace import create
-from ppdet.metrics import COCOMetric, VOCMetric
+from ppdet.metrics import COCOMetric, VOCMetric, KeyPointTopDownCOCOEval
 from paddleslim.auto_compression.config_helpers import load_config as load_slim_config
+from keypoint_utils import keypoint_post_process
 
 
 def argsparser():
@@ -99,12 +100,16 @@ def eval():
                        fetch_list=fetch_targets,
                        return_numpy=False)
         res = {}
-        for out in outs:
-            v = np.array(out)
-            if len(v.shape) > 1:
-                res['bbox'] = v
-            else:
-                res['bbox_num'] = v
+        if 'arch' in global_config and global_config['arch'] == 'keypoint':
+            res = keypoint_post_process(data, data_input, exe, val_program,
+                                        fetch_targets, outs)
+        else:
+            for out in outs:
+                v = np.array(out)
+                if len(v.shape) > 1:
+                    res['bbox'] = v
+                else:
+                    res['bbox_num'] = v
         metric.update(data_all, res)
         if batch_id % 100 == 0:
             print('Eval iter:', batch_id)
@@ -135,6 +140,10 @@ def main():
             label_list=dataset.get_label_list(),
             class_num=reader_cfg['num_classes'],
             map_type=reader_cfg['map_type'])
+    elif reader_cfg['metric'] == 'KeyPointTopDownCOCOEval':
+        anno_file = dataset.get_anno()
+        metric = KeyPointTopDownCOCOEval(anno_file,
+                                         len(dataset), 17, 'output_eval')
     else:
         raise ValueError("metric currently only supports COCO and VOC.")
     global_config['metric'] = metric

demo/auto_compression/detection/keypoint_utils.py

@@ -11,39 +11,30 @@
 # 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
 import numpy as np
-from pycocotools.coco import COCO
-from pycocotools.cocoeval import COCOeval
-from scipy.io import loadmat, savemat
 import cv2
+import copy
 from paddleslim.common import get_logger
 
 logger = get_logger(__name__, level=logging.INFO)
 
+__all__ = ['keypoint_post_process']
 
-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
+def flip_back(output_flipped, matched_parts):
+    assert output_flipped.ndim == 4,\
+            'output_flipped should be [batch_size, num_joints, height, width]'
 
-    center = np.array([np.round(w / 2.), np.round(h / 2.)])
+    output_flipped = output_flipped[:, :, :, ::-1]
 
-    size_resized = (w_, h_)
-    trans = get_affine_transform(
-        center, np.array([scale_w, scale_h]), 0, size_resized, inv=inv)
+    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 trans, size_resized
+    return output_flipped
 
 
 def get_affine_transform(center,
@@ -101,37 +92,6 @@ def get_affine_transform(center,
     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.
@@ -170,29 +130,6 @@ def rotate_point(pt, angle_rad):
     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)
@@ -207,130 +144,6 @@ def transform_preds(coords, center, scale, output_size):
     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
@@ -468,3 +281,27 @@ def __call__(self, output, center, scale):
                     maxvals, axis=1)
         ]]
         return outputs
+
+
+def keypoint_post_process(data, data_input, exe, val_program, fetch_targets,
+                          outs):
+    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.
+    post_process = HRNetPostProcess()
+    outputs = post_process(hrnet_outputs, center, scale)
+    return {'keypoint': outputs}