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| author | Grafting Rayman <156515434+GraftingRayman@users.noreply.github.com> | 2025-01-17 11:06:44 +0000 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2025-01-17 11:06:44 +0000 |
| commit | e6bd5af6a8e306a1cdef63402a77a980a04ad6e1 (patch) | |
| tree | d0732226bbc22feedad9e834b2218d7d0b0eff54 /r_facelib/detection | |
| parent | 495ffc4777522e40941753e3b1b79c02f84b25b4 (diff) | |
| download | Comfyui-reactor-node-e6bd5af6a8e306a1cdef63402a77a980a04ad6e1.tar.gz | |
Diffstat (limited to 'r_facelib/detection')
20 files changed, 2819 insertions, 0 deletions
diff --git a/r_facelib/detection/__init__.py b/r_facelib/detection/__init__.py new file mode 100644 index 0000000..3c953bd --- /dev/null +++ b/r_facelib/detection/__init__.py @@ -0,0 +1,102 @@ +import os
+import torch
+from torch import nn
+from copy import deepcopy
+import pathlib
+
+from r_facelib.utils import load_file_from_url
+from r_facelib.utils import download_pretrained_models
+from r_facelib.detection.yolov5face.models.common import Conv
+
+from .retinaface.retinaface import RetinaFace
+from .yolov5face.face_detector import YoloDetector
+
+
+def init_detection_model(model_name, half=False, device='cuda'):
+ if 'retinaface' in model_name:
+ model = init_retinaface_model(model_name, half, device)
+ elif 'YOLOv5' in model_name:
+ model = init_yolov5face_model(model_name, device)
+ else:
+ raise NotImplementedError(f'{model_name} is not implemented.')
+
+ return model
+
+
+def init_retinaface_model(model_name, half=False, device='cuda'):
+ if model_name == 'retinaface_resnet50':
+ model = RetinaFace(network_name='resnet50', half=half)
+ model_url = 'https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_Resnet50_Final.pth'
+ elif model_name == 'retinaface_mobile0.25':
+ model = RetinaFace(network_name='mobile0.25', half=half)
+ model_url = 'https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_mobilenet0.25_Final.pth'
+ else:
+ raise NotImplementedError(f'{model_name} is not implemented.')
+
+ model_path = load_file_from_url(url=model_url, model_dir='../../models/facedetection', progress=True, file_name=None)
+ load_net = torch.load(model_path, map_location=lambda storage, loc: storage)
+ # remove unnecessary 'module.'
+ for k, v in deepcopy(load_net).items():
+ if k.startswith('module.'):
+ load_net[k[7:]] = v
+ load_net.pop(k)
+ model.load_state_dict(load_net, strict=True)
+ model.eval()
+ model = model.to(device)
+
+ return model
+
+
+def init_yolov5face_model(model_name, device='cuda'):
+ current_dir = str(pathlib.Path(__file__).parent.resolve())
+ if model_name == 'YOLOv5l':
+ model = YoloDetector(config_name=current_dir+'/yolov5face/models/yolov5l.yaml', device=device)
+ model_url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/yolov5l-face.pth'
+ elif model_name == 'YOLOv5n':
+ model = YoloDetector(config_name=current_dir+'/yolov5face/models/yolov5n.yaml', device=device)
+ model_url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/yolov5n-face.pth'
+ else:
+ raise NotImplementedError(f'{model_name} is not implemented.')
+
+ model_path = load_file_from_url(url=model_url, model_dir='../../models/facedetection', progress=True, file_name=None)
+ load_net = torch.load(model_path, map_location=lambda storage, loc: storage)
+ model.detector.load_state_dict(load_net, strict=True)
+ model.detector.eval()
+ model.detector = model.detector.to(device).float()
+
+ for m in model.detector.modules():
+ if type(m) in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU]:
+ m.inplace = True # pytorch 1.7.0 compatibility
+ elif isinstance(m, Conv):
+ m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatibility
+
+ return model
+
+
+# Download from Google Drive
+# def init_yolov5face_model(model_name, device='cuda'):
+# if model_name == 'YOLOv5l':
+# model = YoloDetector(config_name='facelib/detection/yolov5face/models/yolov5l.yaml', device=device)
+# f_id = {'yolov5l-face.pth': '131578zMA6B2x8VQHyHfa6GEPtulMCNzV'}
+# elif model_name == 'YOLOv5n':
+# model = YoloDetector(config_name='facelib/detection/yolov5face/models/yolov5n.yaml', device=device)
+# f_id = {'yolov5n-face.pth': '1fhcpFvWZqghpGXjYPIne2sw1Fy4yhw6o'}
+# else:
+# raise NotImplementedError(f'{model_name} is not implemented.')
+
+# model_path = os.path.join('../../models/facedetection', list(f_id.keys())[0])
+# if not os.path.exists(model_path):
+# download_pretrained_models(file_ids=f_id, save_path_root='../../models/facedetection')
+
+# load_net = torch.load(model_path, map_location=lambda storage, loc: storage)
+# model.detector.load_state_dict(load_net, strict=True)
+# model.detector.eval()
+# model.detector = model.detector.to(device).float()
+
+# for m in model.detector.modules():
+# if type(m) in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU]:
+# m.inplace = True # pytorch 1.7.0 compatibility
+# elif isinstance(m, Conv):
+# m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatibility
+
+# return model
\ No newline at end of file diff --git a/r_facelib/detection/align_trans.py b/r_facelib/detection/align_trans.py new file mode 100644 index 0000000..0b7374a --- /dev/null +++ b/r_facelib/detection/align_trans.py @@ -0,0 +1,219 @@ +import cv2
+import numpy as np
+
+from .matlab_cp2tform import get_similarity_transform_for_cv2
+
+# reference facial points, a list of coordinates (x,y)
+REFERENCE_FACIAL_POINTS = [[30.29459953, 51.69630051], [65.53179932, 51.50139999], [48.02519989, 71.73660278],
+ [33.54930115, 92.3655014], [62.72990036, 92.20410156]]
+
+DEFAULT_CROP_SIZE = (96, 112)
+
+
+class FaceWarpException(Exception):
+
+ def __str__(self):
+ return 'In File {}:{}'.format(__file__, super.__str__(self))
+
+
+def get_reference_facial_points(output_size=None, inner_padding_factor=0.0, outer_padding=(0, 0), default_square=False):
+ """
+ Function:
+ ----------
+ get reference 5 key points according to crop settings:
+ 0. Set default crop_size:
+ if default_square:
+ crop_size = (112, 112)
+ else:
+ crop_size = (96, 112)
+ 1. Pad the crop_size by inner_padding_factor in each side;
+ 2. Resize crop_size into (output_size - outer_padding*2),
+ pad into output_size with outer_padding;
+ 3. Output reference_5point;
+ Parameters:
+ ----------
+ @output_size: (w, h) or None
+ size of aligned face image
+ @inner_padding_factor: (w_factor, h_factor)
+ padding factor for inner (w, h)
+ @outer_padding: (w_pad, h_pad)
+ each row is a pair of coordinates (x, y)
+ @default_square: True or False
+ if True:
+ default crop_size = (112, 112)
+ else:
+ default crop_size = (96, 112);
+ !!! make sure, if output_size is not None:
+ (output_size - outer_padding)
+ = some_scale * (default crop_size * (1.0 +
+ inner_padding_factor))
+ Returns:
+ ----------
+ @reference_5point: 5x2 np.array
+ each row is a pair of transformed coordinates (x, y)
+ """
+
+ tmp_5pts = np.array(REFERENCE_FACIAL_POINTS)
+ tmp_crop_size = np.array(DEFAULT_CROP_SIZE)
+
+ # 0) make the inner region a square
+ if default_square:
+ size_diff = max(tmp_crop_size) - tmp_crop_size
+ tmp_5pts += size_diff / 2
+ tmp_crop_size += size_diff
+
+ if (output_size and output_size[0] == tmp_crop_size[0] and output_size[1] == tmp_crop_size[1]):
+
+ return tmp_5pts
+
+ if (inner_padding_factor == 0 and outer_padding == (0, 0)):
+ if output_size is None:
+ return tmp_5pts
+ else:
+ raise FaceWarpException('No paddings to do, output_size must be None or {}'.format(tmp_crop_size))
+
+ # check output size
+ if not (0 <= inner_padding_factor <= 1.0):
+ raise FaceWarpException('Not (0 <= inner_padding_factor <= 1.0)')
+
+ if ((inner_padding_factor > 0 or outer_padding[0] > 0 or outer_padding[1] > 0) and output_size is None):
+ output_size = tmp_crop_size * \
+ (1 + inner_padding_factor * 2).astype(np.int32)
+ output_size += np.array(outer_padding)
+ if not (outer_padding[0] < output_size[0] and outer_padding[1] < output_size[1]):
+ raise FaceWarpException('Not (outer_padding[0] < output_size[0] and outer_padding[1] < output_size[1])')
+
+ # 1) pad the inner region according inner_padding_factor
+ if inner_padding_factor > 0:
+ size_diff = tmp_crop_size * inner_padding_factor * 2
+ tmp_5pts += size_diff / 2
+ tmp_crop_size += np.round(size_diff).astype(np.int32)
+
+ # 2) resize the padded inner region
+ size_bf_outer_pad = np.array(output_size) - np.array(outer_padding) * 2
+
+ if size_bf_outer_pad[0] * tmp_crop_size[1] != size_bf_outer_pad[1] * tmp_crop_size[0]:
+ raise FaceWarpException('Must have (output_size - outer_padding)'
+ '= some_scale * (crop_size * (1.0 + inner_padding_factor)')
+
+ scale_factor = size_bf_outer_pad[0].astype(np.float32) / tmp_crop_size[0]
+ tmp_5pts = tmp_5pts * scale_factor
+ # size_diff = tmp_crop_size * (scale_factor - min(scale_factor))
+ # tmp_5pts = tmp_5pts + size_diff / 2
+ tmp_crop_size = size_bf_outer_pad
+
+ # 3) add outer_padding to make output_size
+ reference_5point = tmp_5pts + np.array(outer_padding)
+ tmp_crop_size = output_size
+
+ return reference_5point
+
+
+def get_affine_transform_matrix(src_pts, dst_pts):
+ """
+ Function:
+ ----------
+ get affine transform matrix 'tfm' from src_pts to dst_pts
+ Parameters:
+ ----------
+ @src_pts: Kx2 np.array
+ source points matrix, each row is a pair of coordinates (x, y)
+ @dst_pts: Kx2 np.array
+ destination points matrix, each row is a pair of coordinates (x, y)
+ Returns:
+ ----------
+ @tfm: 2x3 np.array
+ transform matrix from src_pts to dst_pts
+ """
+
+ tfm = np.float32([[1, 0, 0], [0, 1, 0]])
+ n_pts = src_pts.shape[0]
+ ones = np.ones((n_pts, 1), src_pts.dtype)
+ src_pts_ = np.hstack([src_pts, ones])
+ dst_pts_ = np.hstack([dst_pts, ones])
+
+ A, res, rank, s = np.linalg.lstsq(src_pts_, dst_pts_)
+
+ if rank == 3:
+ tfm = np.float32([[A[0, 0], A[1, 0], A[2, 0]], [A[0, 1], A[1, 1], A[2, 1]]])
+ elif rank == 2:
+ tfm = np.float32([[A[0, 0], A[1, 0], 0], [A[0, 1], A[1, 1], 0]])
+
+ return tfm
+
+
+def warp_and_crop_face(src_img, facial_pts, reference_pts=None, crop_size=(96, 112), align_type='smilarity'):
+ """
+ Function:
+ ----------
+ apply affine transform 'trans' to uv
+ Parameters:
+ ----------
+ @src_img: 3x3 np.array
+ input image
+ @facial_pts: could be
+ 1)a list of K coordinates (x,y)
+ or
+ 2) Kx2 or 2xK np.array
+ each row or col is a pair of coordinates (x, y)
+ @reference_pts: could be
+ 1) a list of K coordinates (x,y)
+ or
+ 2) Kx2 or 2xK np.array
+ each row or col is a pair of coordinates (x, y)
+ or
+ 3) None
+ if None, use default reference facial points
+ @crop_size: (w, h)
+ output face image size
+ @align_type: transform type, could be one of
+ 1) 'similarity': use similarity transform
+ 2) 'cv2_affine': use the first 3 points to do affine transform,
+ by calling cv2.getAffineTransform()
+ 3) 'affine': use all points to do affine transform
+ Returns:
+ ----------
+ @face_img: output face image with size (w, h) = @crop_size
+ """
+
+ if reference_pts is None:
+ if crop_size[0] == 96 and crop_size[1] == 112:
+ reference_pts = REFERENCE_FACIAL_POINTS
+ else:
+ default_square = False
+ inner_padding_factor = 0
+ outer_padding = (0, 0)
+ output_size = crop_size
+
+ reference_pts = get_reference_facial_points(output_size, inner_padding_factor, outer_padding,
+ default_square)
+
+ ref_pts = np.float32(reference_pts)
+ ref_pts_shp = ref_pts.shape
+ if max(ref_pts_shp) < 3 or min(ref_pts_shp) != 2:
+ raise FaceWarpException('reference_pts.shape must be (K,2) or (2,K) and K>2')
+
+ if ref_pts_shp[0] == 2:
+ ref_pts = ref_pts.T
+
+ src_pts = np.float32(facial_pts)
+ src_pts_shp = src_pts.shape
+ if max(src_pts_shp) < 3 or min(src_pts_shp) != 2:
+ raise FaceWarpException('facial_pts.shape must be (K,2) or (2,K) and K>2')
+
+ if src_pts_shp[0] == 2:
+ src_pts = src_pts.T
+
+ if src_pts.shape != ref_pts.shape:
+ raise FaceWarpException('facial_pts and reference_pts must have the same shape')
+
+ if align_type == 'cv2_affine':
+ tfm = cv2.getAffineTransform(src_pts[0:3], ref_pts[0:3])
+ elif align_type == 'affine':
+ tfm = get_affine_transform_matrix(src_pts, ref_pts)
+ else:
+ tfm = get_similarity_transform_for_cv2(src_pts, ref_pts)
+
+ face_img = cv2.warpAffine(src_img, tfm, (crop_size[0], crop_size[1]))
+
+ return face_img
diff --git a/r_facelib/detection/matlab_cp2tform.py b/r_facelib/detection/matlab_cp2tform.py new file mode 100644 index 0000000..b1014a8 --- /dev/null +++ b/r_facelib/detection/matlab_cp2tform.py @@ -0,0 +1,317 @@ +import numpy as np
+from numpy.linalg import inv, lstsq
+from numpy.linalg import matrix_rank as rank
+from numpy.linalg import norm
+
+
+class MatlabCp2tormException(Exception):
+
+ def __str__(self):
+ return 'In File {}:{}'.format(__file__, super.__str__(self))
+
+
+def tformfwd(trans, uv):
+ """
+ Function:
+ ----------
+ apply affine transform 'trans' to uv
+
+ Parameters:
+ ----------
+ @trans: 3x3 np.array
+ transform matrix
+ @uv: Kx2 np.array
+ each row is a pair of coordinates (x, y)
+
+ Returns:
+ ----------
+ @xy: Kx2 np.array
+ each row is a pair of transformed coordinates (x, y)
+ """
+ uv = np.hstack((uv, np.ones((uv.shape[0], 1))))
+ xy = np.dot(uv, trans)
+ xy = xy[:, 0:-1]
+ return xy
+
+
+def tforminv(trans, uv):
+ """
+ Function:
+ ----------
+ apply the inverse of affine transform 'trans' to uv
+
+ Parameters:
+ ----------
+ @trans: 3x3 np.array
+ transform matrix
+ @uv: Kx2 np.array
+ each row is a pair of coordinates (x, y)
+
+ Returns:
+ ----------
+ @xy: Kx2 np.array
+ each row is a pair of inverse-transformed coordinates (x, y)
+ """
+ Tinv = inv(trans)
+ xy = tformfwd(Tinv, uv)
+ return xy
+
+
+def findNonreflectiveSimilarity(uv, xy, options=None):
+ options = {'K': 2}
+
+ K = options['K']
+ M = xy.shape[0]
+ x = xy[:, 0].reshape((-1, 1)) # use reshape to keep a column vector
+ y = xy[:, 1].reshape((-1, 1)) # use reshape to keep a column vector
+
+ tmp1 = np.hstack((x, y, np.ones((M, 1)), np.zeros((M, 1))))
+ tmp2 = np.hstack((y, -x, np.zeros((M, 1)), np.ones((M, 1))))
+ X = np.vstack((tmp1, tmp2))
+
+ u = uv[:, 0].reshape((-1, 1)) # use reshape to keep a column vector
+ v = uv[:, 1].reshape((-1, 1)) # use reshape to keep a column vector
+ U = np.vstack((u, v))
+
+ # We know that X * r = U
+ if rank(X) >= 2 * K:
+ r, _, _, _ = lstsq(X, U, rcond=-1)
+ r = np.squeeze(r)
+ else:
+ raise Exception('cp2tform:twoUniquePointsReq')
+ sc = r[0]
+ ss = r[1]
+ tx = r[2]
+ ty = r[3]
+
+ Tinv = np.array([[sc, -ss, 0], [ss, sc, 0], [tx, ty, 1]])
+ T = inv(Tinv)
+ T[:, 2] = np.array([0, 0, 1])
+
+ return T, Tinv
+
+
+def findSimilarity(uv, xy, options=None):
+ options = {'K': 2}
+
+ # uv = np.array(uv)
+ # xy = np.array(xy)
+
+ # Solve for trans1
+ trans1, trans1_inv = findNonreflectiveSimilarity(uv, xy, options)
+
+ # Solve for trans2
+
+ # manually reflect the xy data across the Y-axis
+ xyR = xy
+ xyR[:, 0] = -1 * xyR[:, 0]
+
+ trans2r, trans2r_inv = findNonreflectiveSimilarity(uv, xyR, options)
+
+ # manually reflect the tform to undo the reflection done on xyR
+ TreflectY = np.array([[-1, 0, 0], [0, 1, 0], [0, 0, 1]])
+
+ trans2 = np.dot(trans2r, TreflectY)
+
+ # Figure out if trans1 or trans2 is better
+ xy1 = tformfwd(trans1, uv)
+ norm1 = norm(xy1 - xy)
+
+ xy2 = tformfwd(trans2, uv)
+ norm2 = norm(xy2 - xy)
+
+ if norm1 <= norm2:
+ return trans1, trans1_inv
+ else:
+ trans2_inv = inv(trans2)
+ return trans2, trans2_inv
+
+
+def get_similarity_transform(src_pts, dst_pts, reflective=True):
+ """
+ Function:
+ ----------
+ Find Similarity Transform Matrix 'trans':
+ u = src_pts[:, 0]
+ v = src_pts[:, 1]
+ x = dst_pts[:, 0]
+ y = dst_pts[:, 1]
+ [x, y, 1] = [u, v, 1] * trans
+
+ Parameters:
+ ----------
+ @src_pts: Kx2 np.array
+ source points, each row is a pair of coordinates (x, y)
+ @dst_pts: Kx2 np.array
+ destination points, each row is a pair of transformed
+ coordinates (x, y)
+ @reflective: True or False
+ if True:
+ use reflective similarity transform
+ else:
+ use non-reflective similarity transform
+
+ Returns:
+ ----------
+ @trans: 3x3 np.array
+ transform matrix from uv to xy
+ trans_inv: 3x3 np.array
+ inverse of trans, transform matrix from xy to uv
+ """
+
+ if reflective:
+ trans, trans_inv = findSimilarity(src_pts, dst_pts)
+ else:
+ trans, trans_inv = findNonreflectiveSimilarity(src_pts, dst_pts)
+
+ return trans, trans_inv
+
+
+def cvt_tform_mat_for_cv2(trans):
+ """
+ Function:
+ ----------
+ Convert Transform Matrix 'trans' into 'cv2_trans' which could be
+ directly used by cv2.warpAffine():
+ u = src_pts[:, 0]
+ v = src_pts[:, 1]
+ x = dst_pts[:, 0]
+ y = dst_pts[:, 1]
+ [x, y].T = cv_trans * [u, v, 1].T
+
+ Parameters:
+ ----------
+ @trans: 3x3 np.array
+ transform matrix from uv to xy
+
+ Returns:
+ ----------
+ @cv2_trans: 2x3 np.array
+ transform matrix from src_pts to dst_pts, could be directly used
+ for cv2.warpAffine()
+ """
+ cv2_trans = trans[:, 0:2].T
+
+ return cv2_trans
+
+
+def get_similarity_transform_for_cv2(src_pts, dst_pts, reflective=True):
+ """
+ Function:
+ ----------
+ Find Similarity Transform Matrix 'cv2_trans' which could be
+ directly used by cv2.warpAffine():
+ u = src_pts[:, 0]
+ v = src_pts[:, 1]
+ x = dst_pts[:, 0]
+ y = dst_pts[:, 1]
+ [x, y].T = cv_trans * [u, v, 1].T
+
+ Parameters:
+ ----------
+ @src_pts: Kx2 np.array
+ source points, each row is a pair of coordinates (x, y)
+ @dst_pts: Kx2 np.array
+ destination points, each row is a pair of transformed
+ coordinates (x, y)
+ reflective: True or False
+ if True:
+ use reflective similarity transform
+ else:
+ use non-reflective similarity transform
+
+ Returns:
+ ----------
+ @cv2_trans: 2x3 np.array
+ transform matrix from src_pts to dst_pts, could be directly used
+ for cv2.warpAffine()
+ """
+ trans, trans_inv = get_similarity_transform(src_pts, dst_pts, reflective)
+ cv2_trans = cvt_tform_mat_for_cv2(trans)
+
+ return cv2_trans
+
+
+if __name__ == '__main__':
+ """
+ u = [0, 6, -2]
+ v = [0, 3, 5]
+ x = [-1, 0, 4]
+ y = [-1, -10, 4]
+
+ # In Matlab, run:
+ #
+ # uv = [u'; v'];
+ # xy = [x'; y'];
+ # tform_sim=cp2tform(uv,xy,'similarity');
+ #
+ # trans = tform_sim.tdata.T
+ # ans =
+ # -0.0764 -1.6190 0
+ # 1.6190 -0.0764 0
+ # -3.2156 0.0290 1.0000
+ # trans_inv = tform_sim.tdata.Tinv
+ # ans =
+ #
+ # -0.0291 0.6163 0
+ # -0.6163 -0.0291 0
+ # -0.0756 1.9826 1.0000
+ # xy_m=tformfwd(tform_sim, u,v)
+ #
+ # xy_m =
+ #
+ # -3.2156 0.0290
+ # 1.1833 -9.9143
+ # 5.0323 2.8853
+ # uv_m=tforminv(tform_sim, x,y)
+ #
+ # uv_m =
+ #
+ # 0.5698 1.3953
+ # 6.0872 2.2733
+ # -2.6570 4.3314
+ """
+ u = [0, 6, -2]
+ v = [0, 3, 5]
+ x = [-1, 0, 4]
+ y = [-1, -10, 4]
+
+ uv = np.array((u, v)).T
+ xy = np.array((x, y)).T
+
+ print('\n--->uv:')
+ print(uv)
+ print('\n--->xy:')
+ print(xy)
+
+ trans, trans_inv = get_similarity_transform(uv, xy)
+
+ print('\n--->trans matrix:')
+ print(trans)
+
+ print('\n--->trans_inv matrix:')
+ print(trans_inv)
+
+ print('\n---> apply transform to uv')
+ print('\nxy_m = uv_augmented * trans')
+ uv_aug = np.hstack((uv, np.ones((uv.shape[0], 1))))
+ xy_m = np.dot(uv_aug, trans)
+ print(xy_m)
+
+ print('\nxy_m = tformfwd(trans, uv)')
+ xy_m = tformfwd(trans, uv)
+ print(xy_m)
+
+ print('\n---> apply inverse transform to xy')
+ print('\nuv_m = xy_augmented * trans_inv')
+ xy_aug = np.hstack((xy, np.ones((xy.shape[0], 1))))
+ uv_m = np.dot(xy_aug, trans_inv)
+ print(uv_m)
+
+ print('\nuv_m = tformfwd(trans_inv, xy)')
+ uv_m = tformfwd(trans_inv, xy)
+ print(uv_m)
+
+ uv_m = tforminv(trans, xy)
+ print('\nuv_m = tforminv(trans, xy)')
+ print(uv_m)
diff --git a/r_facelib/detection/retinaface/retinaface.py b/r_facelib/detection/retinaface/retinaface.py new file mode 100644 index 0000000..5d9770a --- /dev/null +++ b/r_facelib/detection/retinaface/retinaface.py @@ -0,0 +1,389 @@ +import cv2
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from PIL import Image
+from torchvision.models._utils import IntermediateLayerGetter as IntermediateLayerGetter
+
+from modules import shared
+
+from r_facelib.detection.align_trans import get_reference_facial_points, warp_and_crop_face
+from r_facelib.detection.retinaface.retinaface_net import FPN, SSH, MobileNetV1, make_bbox_head, make_class_head, make_landmark_head
+from r_facelib.detection.retinaface.retinaface_utils import (PriorBox, batched_decode, batched_decode_landm, decode, decode_landm,
+ py_cpu_nms)
+
+#device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+if torch.cuda.is_available():
+ device = torch.device('cuda')
+elif torch.backends.mps.is_available():
+ device = torch.device('mps')
+# elif hasattr(torch,'dml'):
+# device = torch.device('dml')
+elif hasattr(torch,'dml') or hasattr(torch,'privateuseone'): # AMD
+ if shared.cmd_opts is not None: # A1111
+ if shared.cmd_opts.device_id is not None:
+ device = torch.device(f'privateuseone:{shared.cmd_opts.device_id}')
+ else:
+ device = torch.device('privateuseone:0')
+ else:
+ device = torch.device('privateuseone:0')
+else:
+ device = torch.device('cpu')
+
+
+def generate_config(network_name):
+
+ cfg_mnet = {
+ 'name': 'mobilenet0.25',
+ 'min_sizes': [[16, 32], [64, 128], [256, 512]],
+ 'steps': [8, 16, 32],
+ 'variance': [0.1, 0.2],
+ 'clip': False,
+ 'loc_weight': 2.0,
+ 'gpu_train': True,
+ 'batch_size': 32,
+ 'ngpu': 1,
+ 'epoch': 250,
+ 'decay1': 190,
+ 'decay2': 220,
+ 'image_size': 640,
+ 'return_layers': {
+ 'stage1': 1,
+ 'stage2': 2,
+ 'stage3': 3
+ },
+ 'in_channel': 32,
+ 'out_channel': 64
+ }
+
+ cfg_re50 = {
+ 'name': 'Resnet50',
+ 'min_sizes': [[16, 32], [64, 128], [256, 512]],
+ 'steps': [8, 16, 32],
+ 'variance': [0.1, 0.2],
+ 'clip': False,
+ 'loc_weight': 2.0,
+ 'gpu_train': True,
+ 'batch_size': 24,
+ 'ngpu': 4,
+ 'epoch': 100,
+ 'decay1': 70,
+ 'decay2': 90,
+ 'image_size': 840,
+ 'return_layers': {
+ 'layer2': 1,
+ 'layer3': 2,
+ 'layer4': 3
+ },
+ 'in_channel': 256,
+ 'out_channel': 256
+ }
+
+ if network_name == 'mobile0.25':
+ return cfg_mnet
+ elif network_name == 'resnet50':
+ return cfg_re50
+ else:
+ raise NotImplementedError(f'network_name={network_name}')
+
+
+class RetinaFace(nn.Module):
+
+ def __init__(self, network_name='resnet50', half=False, phase='test'):
+ super(RetinaFace, self).__init__()
+ self.half_inference = half
+ cfg = generate_config(network_name)
+ self.backbone = cfg['name']
+
+ self.model_name = f'retinaface_{network_name}'
+ self.cfg = cfg
+ self.phase = phase
+ self.target_size, self.max_size = 1600, 2150
+ self.resize, self.scale, self.scale1 = 1., None, None
+ self.mean_tensor = torch.tensor([[[[104.]], [[117.]], [[123.]]]]).to(device)
+ self.reference = get_reference_facial_points(default_square=True)
+ # Build network.
+ backbone = None
+ if cfg['name'] == 'mobilenet0.25':
+ backbone = MobileNetV1()
+ self.body = IntermediateLayerGetter(backbone, cfg['return_layers'])
+ elif cfg['name'] == 'Resnet50':
+ import torchvision.models as models
+ backbone = models.resnet50(pretrained=False)
+ self.body = IntermediateLayerGetter(backbone, cfg['return_layers'])
+
+ in_channels_stage2 = cfg['in_channel']
+ in_channels_list = [
+ in_channels_stage2 * 2,
+ in_channels_stage2 * 4,
+ in_channels_stage2 * 8,
+ ]
+
+ out_channels = cfg['out_channel']
+ self.fpn = FPN(in_channels_list, out_channels)
+ self.ssh1 = SSH(out_channels, out_channels)
+ self.ssh2 = SSH(out_channels, out_channels)
+ self.ssh3 = SSH(out_channels, out_channels)
+
+ self.ClassHead = make_class_head(fpn_num=3, inchannels=cfg['out_channel'])
+ self.BboxHead = make_bbox_head(fpn_num=3, inchannels=cfg['out_channel'])
+ self.LandmarkHead = make_landmark_head(fpn_num=3, inchannels=cfg['out_channel'])
+
+ self.to(device)
+ self.eval()
+ if self.half_inference:
+ self.half()
+
+ def forward(self, inputs):
+ self.to(device)
+ out = self.body(inputs)
+
+ if self.backbone == 'mobilenet0.25' or self.backbone == 'Resnet50':
+ out = list(out.values())
+ # FPN
+ fpn = self.fpn(out)
+
+ # SSH
+ feature1 = self.ssh1(fpn[0])
+ feature2 = self.ssh2(fpn[1])
+ feature3 = self.ssh3(fpn[2])
+ features = [feature1, feature2, feature3]
+
+ bbox_regressions = torch.cat([self.BboxHead[i](feature) for i, feature in enumerate(features)], dim=1)
+ classifications = torch.cat([self.ClassHead[i](feature) for i, feature in enumerate(features)], dim=1)
+ tmp = [self.LandmarkHead[i](feature) for i, feature in enumerate(features)]
+ ldm_regressions = (torch.cat(tmp, dim=1))
+
+ if self.phase == 'train':
+ output = (bbox_regressions, classifications, ldm_regressions)
+ else:
+ output = (bbox_regressions, F.softmax(classifications, dim=-1), ldm_regressions)
+ return output
+
+ def __detect_faces(self, inputs):
+ # get scale
+ height, width = inputs.shape[2:]
+ self.scale = torch.tensor([width, height, width, height], dtype=torch.float32).to(device)
+ tmp = [width, height, width, height, width, height, width, height, width, height]
+ self.scale1 = torch.tensor(tmp, dtype=torch.float32).to(device)
+
+ # forawrd
+ inputs = inputs.to(device)
+ if self.half_inference:
+ inputs = inputs.half()
+ loc, conf, landmarks = self(inputs)
+
+ # get priorbox
+ priorbox = PriorBox(self.cfg, image_size=inputs.shape[2:])
+ priors = priorbox.forward().to(device)
+
+ return loc, conf, landmarks, priors
+
+ # single image detection
+ def transform(self, image, use_origin_size):
+ # convert to opencv format
+ if isinstance(image, Image.Image):
+ image = cv2.cvtColor(np.asarray(image), cv2.COLOR_RGB2BGR)
+ image = image.astype(np.float32)
+
+ # testing scale
+ im_size_min = np.min(image.shape[0:2])
+ im_size_max = np.max(image.shape[0:2])
+ resize = float(self.target_size) / float(im_size_min)
+
+ # prevent bigger axis from being more than max_size
+ if np.round(resize * im_size_max) > self.max_size:
+ resize = float(self.max_size) / float(im_size_max)
+ resize = 1 if use_origin_size else resize
+
+ # resize
+ if resize != 1:
+ image = cv2.resize(image, None, None, fx=resize, fy=resize, interpolation=cv2.INTER_LINEAR)
+
+ # convert to torch.tensor format
+ # image -= (104, 117, 123)
+ image = image.transpose(2, 0, 1)
+ image = torch.from_numpy(image).unsqueeze(0)
+
+ return image, resize
+
+ def detect_faces(
+ self,
+ image,
+ conf_threshold=0.8,
+ nms_threshold=0.4,
+ use_origin_size=True,
+ ):
+ """
+ Params:
+ imgs: BGR image
+ """
+ image, self.resize = self.transform(image, use_origin_size)
+ image = image.to(device)
+ if self.half_inference:
+ image = image.half()
+ image = image - self.mean_tensor
+
+ loc, conf, landmarks, priors = self.__detect_faces(image)
+
+ boxes = decode(loc.data.squeeze(0), priors.data, self.cfg['variance'])
+ boxes = boxes * self.scale / self.resize
+ boxes = boxes.cpu().numpy()
+
+ scores = conf.squeeze(0).data.cpu().numpy()[:, 1]
+
+ landmarks = decode_landm(landmarks.squeeze(0), priors, self.cfg['variance'])
+ landmarks = landmarks * self.scale1 / self.resize
+ landmarks = landmarks.cpu().numpy()
+
+ # ignore low scores
+ inds = np.where(scores > conf_threshold)[0]
+ boxes, landmarks, scores = boxes[inds], landmarks[inds], scores[inds]
+
+ # sort
+ order = scores.argsort()[::-1]
+ boxes, landmarks, scores = boxes[order], landmarks[order], scores[order]
+
+ # do NMS
+ bounding_boxes = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32, copy=False)
+ keep = py_cpu_nms(bounding_boxes, nms_threshold)
+ bounding_boxes, landmarks = bounding_boxes[keep, :], landmarks[keep]
+ # self.t['forward_pass'].toc()
+ # print(self.t['forward_pass'].average_time)
+ # import sys
+ # sys.stdout.flush()
+ return np.concatenate((bounding_boxes, landmarks), axis=1)
+
+ def __align_multi(self, image, boxes, landmarks, limit=None):
+
+ if len(boxes) < 1:
+ return [], []
+
+ if limit:
+ boxes = boxes[:limit]
+ landmarks = landmarks[:limit]
+
+ faces = []
+ for landmark in landmarks:
+ facial5points = [[landmark[2 * j], landmark[2 * j + 1]] for j in range(5)]
+
+ warped_face = warp_and_crop_face(np.array(image), facial5points, self.reference, crop_size=(112, 112))
+ faces.append(warped_face)
+
+ return np.concatenate((boxes, landmarks), axis=1), faces
+
+ def align_multi(self, img, conf_threshold=0.8, limit=None):
+
+ rlt = self.detect_faces(img, conf_threshold=conf_threshold)
+ boxes, landmarks = rlt[:, 0:5], rlt[:, 5:]
+
+ return self.__align_multi(img, boxes, landmarks, limit)
+
+ # batched detection
+ def batched_transform(self, frames, use_origin_size):
+ """
+ Arguments:
+ frames: a list of PIL.Image, or torch.Tensor(shape=[n, h, w, c],
+ type=np.float32, BGR format).
+ use_origin_size: whether to use origin size.
+ """
+ from_PIL = True if isinstance(frames[0], Image.Image) else False
+
+ # convert to opencv format
+ if from_PIL:
+ frames = [cv2.cvtColor(np.asarray(frame), cv2.COLOR_RGB2BGR) for frame in frames]
+ frames = np.asarray(frames, dtype=np.float32)
+
+ # testing scale
+ im_size_min = np.min(frames[0].shape[0:2])
+ im_size_max = np.max(frames[0].shape[0:2])
+ resize = float(self.target_size) / float(im_size_min)
+
+ # prevent bigger axis from being more than max_size
+ if np.round(resize * im_size_max) > self.max_size:
+ resize = float(self.max_size) / float(im_size_max)
+ resize = 1 if use_origin_size else resize
+
+ # resize
+ if resize != 1:
+ if not from_PIL:
+ frames = F.interpolate(frames, scale_factor=resize)
+ else:
+ frames = [
+ cv2.resize(frame, None, None, fx=resize, fy=resize, interpolation=cv2.INTER_LINEAR)
+ for frame in frames
+ ]
+
+ # convert to torch.tensor format
+ if not from_PIL:
+ frames = frames.transpose(1, 2).transpose(1, 3).contiguous()
+ else:
+ frames = frames.transpose((0, 3, 1, 2))
+ frames = torch.from_numpy(frames)
+
+ return frames, resize
+
+ def batched_detect_faces(self, frames, conf_threshold=0.8, nms_threshold=0.4, use_origin_size=True):
+ """
+ Arguments:
+ frames: a list of PIL.Image, or np.array(shape=[n, h, w, c],
+ type=np.uint8, BGR format).
+ conf_threshold: confidence threshold.
+ nms_threshold: nms threshold.
+ use_origin_size: whether to use origin size.
+ Returns:
+ final_bounding_boxes: list of np.array ([n_boxes, 5],
+ type=np.float32).
+ final_landmarks: list of np.array ([n_boxes, 10], type=np.float32).
+ """
+ # self.t['forward_pass'].tic()
+ frames, self.resize = self.batched_transform(frames, use_origin_size)
+ frames = frames.to(device)
+ frames = frames - self.mean_tensor
+
+ b_loc, b_conf, b_landmarks, priors = self.__detect_faces(frames)
+
+ final_bounding_boxes, final_landmarks = [], []
+
+ # decode
+ priors = priors.unsqueeze(0)
+ b_loc = batched_decode(b_loc, priors, self.cfg['variance']) * self.scale / self.resize
+ b_landmarks = batched_decode_landm(b_landmarks, priors, self.cfg['variance']) * self.scale1 / self.resize
+ b_conf = b_conf[:, :, 1]
+
+ # index for selection
+ b_indice = b_conf > conf_threshold
+
+ # concat
+ b_loc_and_conf = torch.cat((b_loc, b_conf.unsqueeze(-1)), dim=2).float()
+
+ for pred, landm, inds in zip(b_loc_and_conf, b_landmarks, b_indice):
+
+ # ignore low scores
+ pred, landm = pred[inds, :], landm[inds, :]
+ if pred.shape[0] == 0:
+ final_bounding_boxes.append(np.array([], dtype=np.float32))
+ final_landmarks.append(np.array([], dtype=np.float32))
+ continue
+
+ # sort
+ # order = score.argsort(descending=True)
+ # box, landm, score = box[order], landm[order], score[order]
+
+ # to CPU
+ bounding_boxes, landm = pred.cpu().numpy(), landm.cpu().numpy()
+
+ # NMS
+ keep = py_cpu_nms(bounding_boxes, nms_threshold)
+ bounding_boxes, landmarks = bounding_boxes[keep, :], landm[keep]
+
+ # append
+ final_bounding_boxes.append(bounding_boxes)
+ final_landmarks.append(landmarks)
+ # self.t['forward_pass'].toc(average=True)
+ # self.batch_time += self.t['forward_pass'].diff
+ # self.total_frame += len(frames)
+ # print(self.batch_time / self.total_frame)
+
+ return final_bounding_boxes, final_landmarks
diff --git a/r_facelib/detection/retinaface/retinaface_net.py b/r_facelib/detection/retinaface/retinaface_net.py new file mode 100644 index 0000000..c52535e --- /dev/null +++ b/r_facelib/detection/retinaface/retinaface_net.py @@ -0,0 +1,196 @@ +import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+def conv_bn(inp, oup, stride=1, leaky=0):
+ return nn.Sequential(
+ nn.Conv2d(inp, oup, 3, stride, 1, bias=False), nn.BatchNorm2d(oup),
+ nn.LeakyReLU(negative_slope=leaky, inplace=True))
+
+
+def conv_bn_no_relu(inp, oup, stride):
+ return nn.Sequential(
+ nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
+ nn.BatchNorm2d(oup),
+ )
+
+
+def conv_bn1X1(inp, oup, stride, leaky=0):
+ return nn.Sequential(
+ nn.Conv2d(inp, oup, 1, stride, padding=0, bias=False), nn.BatchNorm2d(oup),
+ nn.LeakyReLU(negative_slope=leaky, inplace=True))
+
+
+def conv_dw(inp, oup, stride, leaky=0.1):
+ return nn.Sequential(
+ nn.Conv2d(inp, inp, 3, stride, 1, groups=inp, bias=False),
+ nn.BatchNorm2d(inp),
+ nn.LeakyReLU(negative_slope=leaky, inplace=True),
+ nn.Conv2d(inp, oup, 1, 1, 0, bias=False),
+ nn.BatchNorm2d(oup),
+ nn.LeakyReLU(negative_slope=leaky, inplace=True),
+ )
+
+
+class SSH(nn.Module):
+
+ def __init__(self, in_channel, out_channel):
+ super(SSH, self).__init__()
+ assert out_channel % 4 == 0
+ leaky = 0
+ if (out_channel <= 64):
+ leaky = 0.1
+ self.conv3X3 = conv_bn_no_relu(in_channel, out_channel // 2, stride=1)
+
+ self.conv5X5_1 = conv_bn(in_channel, out_channel // 4, stride=1, leaky=leaky)
+ self.conv5X5_2 = conv_bn_no_relu(out_channel // 4, out_channel // 4, stride=1)
+
+ self.conv7X7_2 = conv_bn(out_channel // 4, out_channel // 4, stride=1, leaky=leaky)
+ self.conv7x7_3 = conv_bn_no_relu(out_channel // 4, out_channel // 4, stride=1)
+
+ def forward(self, input):
+ conv3X3 = self.conv3X3(input)
+
+ conv5X5_1 = self.conv5X5_1(input)
+ conv5X5 = self.conv5X5_2(conv5X5_1)
+
+ conv7X7_2 = self.conv7X7_2(conv5X5_1)
+ conv7X7 = self.conv7x7_3(conv7X7_2)
+
+ out = torch.cat([conv3X3, conv5X5, conv7X7], dim=1)
+ out = F.relu(out)
+ return out
+
+
+class FPN(nn.Module):
+
+ def __init__(self, in_channels_list, out_channels):
+ super(FPN, self).__init__()
+ leaky = 0
+ if (out_channels <= 64):
+ leaky = 0.1
+ self.output1 = conv_bn1X1(in_channels_list[0], out_channels, stride=1, leaky=leaky)
+ self.output2 = conv_bn1X1(in_channels_list[1], out_channels, stride=1, leaky=leaky)
+ self.output3 = conv_bn1X1(in_channels_list[2], out_channels, stride=1, leaky=leaky)
+
+ self.merge1 = conv_bn(out_channels, out_channels, leaky=leaky)
+ self.merge2 = conv_bn(out_channels, out_channels, leaky=leaky)
+
+ def forward(self, input):
+ # names = list(input.keys())
+ # input = list(input.values())
+
+ output1 = self.output1(input[0])
+ output2 = self.output2(input[1])
+ output3 = self.output3(input[2])
+
+ up3 = F.interpolate(output3, size=[output2.size(2), output2.size(3)], mode='nearest')
+ output2 = output2 + up3
+ output2 = self.merge2(output2)
+
+ up2 = F.interpolate(output2, size=[output1.size(2), output1.size(3)], mode='nearest')
+ output1 = output1 + up2
+ output1 = self.merge1(output1)
+
+ out = [output1, output2, output3]
+ return out
+
+
+class MobileNetV1(nn.Module):
+
+ def __init__(self):
+ super(MobileNetV1, self).__init__()
+ self.stage1 = nn.Sequential(
+ conv_bn(3, 8, 2, leaky=0.1), # 3
+ conv_dw(8, 16, 1), # 7
+ conv_dw(16, 32, 2), # 11
+ conv_dw(32, 32, 1), # 19
+ conv_dw(32, 64, 2), # 27
+ conv_dw(64, 64, 1), # 43
+ )
+ self.stage2 = nn.Sequential(
+ conv_dw(64, 128, 2), # 43 + 16 = 59
+ conv_dw(128, 128, 1), # 59 + 32 = 91
+ conv_dw(128, 128, 1), # 91 + 32 = 123
+ conv_dw(128, 128, 1), # 123 + 32 = 155
+ conv_dw(128, 128, 1), # 155 + 32 = 187
+ conv_dw(128, 128, 1), # 187 + 32 = 219
+ )
+ self.stage3 = nn.Sequential(
+ conv_dw(128, 256, 2), # 219 +3 2 = 241
+ conv_dw(256, 256, 1), # 241 + 64 = 301
+ )
+ self.avg = nn.AdaptiveAvgPool2d((1, 1))
+ self.fc = nn.Linear(256, 1000)
+
+ def forward(self, x):
+ x = self.stage1(x)
+ x = self.stage2(x)
+ x = self.stage3(x)
+ x = self.avg(x)
+ # x = self.model(x)
+ x = x.view(-1, 256)
+ x = self.fc(x)
+ return x
+
+
+class ClassHead(nn.Module):
+
+ def __init__(self, inchannels=512, num_anchors=3):
+ super(ClassHead, self).__init__()
+ self.num_anchors = num_anchors
+ self.conv1x1 = nn.Conv2d(inchannels, self.num_anchors * 2, kernel_size=(1, 1), stride=1, padding=0)
+
+ def forward(self, x):
+ out = self.conv1x1(x)
+ out = out.permute(0, 2, 3, 1).contiguous()
+
+ return out.view(out.shape[0], -1, 2)
+
+
+class BboxHead(nn.Module):
+
+ def __init__(self, inchannels=512, num_anchors=3):
+ super(BboxHead, self).__init__()
+ self.conv1x1 = nn.Conv2d(inchannels, num_anchors * 4, kernel_size=(1, 1), stride=1, padding=0)
+
+ def forward(self, x):
+ out = self.conv1x1(x)
+ out = out.permute(0, 2, 3, 1).contiguous()
+
+ return out.view(out.shape[0], -1, 4)
+
+
+class LandmarkHead(nn.Module):
+
+ def __init__(self, inchannels=512, num_anchors=3):
+ super(LandmarkHead, self).__init__()
+ self.conv1x1 = nn.Conv2d(inchannels, num_anchors * 10, kernel_size=(1, 1), stride=1, padding=0)
+
+ def forward(self, x):
+ out = self.conv1x1(x)
+ out = out.permute(0, 2, 3, 1).contiguous()
+
+ return out.view(out.shape[0], -1, 10)
+
+
+def make_class_head(fpn_num=3, inchannels=64, anchor_num=2):
+ classhead = nn.ModuleList()
+ for i in range(fpn_num):
+ classhead.append(ClassHead(inchannels, anchor_num))
+ return classhead
+
+
+def make_bbox_head(fpn_num=3, inchannels=64, anchor_num=2):
+ bboxhead = nn.ModuleList()
+ for i in range(fpn_num):
+ bboxhead.append(BboxHead(inchannels, anchor_num))
+ return bboxhead
+
+
+def make_landmark_head(fpn_num=3, inchannels=64, anchor_num=2):
+ landmarkhead = nn.ModuleList()
+ for i in range(fpn_num):
+ landmarkhead.append(LandmarkHead(inchannels, anchor_num))
+ return landmarkhead
diff --git a/r_facelib/detection/retinaface/retinaface_utils.py b/r_facelib/detection/retinaface/retinaface_utils.py new file mode 100644 index 0000000..f19e320 --- /dev/null +++ b/r_facelib/detection/retinaface/retinaface_utils.py @@ -0,0 +1,421 @@ +import numpy as np
+import torch
+import torchvision
+from itertools import product as product
+from math import ceil
+
+
+class PriorBox(object):
+
+ def __init__(self, cfg, image_size=None, phase='train'):
+ super(PriorBox, self).__init__()
+ self.min_sizes = cfg['min_sizes']
+ self.steps = cfg['steps']
+ self.clip = cfg['clip']
+ self.image_size = image_size
+ self.feature_maps = [[ceil(self.image_size[0] / step), ceil(self.image_size[1] / step)] for step in self.steps]
+ self.name = 's'
+
+ def forward(self):
+ anchors = []
+ for k, f in enumerate(self.feature_maps):
+ min_sizes = self.min_sizes[k]
+ for i, j in product(range(f[0]), range(f[1])):
+ for min_size in min_sizes:
+ s_kx = min_size / self.image_size[1]
+ s_ky = min_size / self.image_size[0]
+ dense_cx = [x * self.steps[k] / self.image_size[1] for x in [j + 0.5]]
+ dense_cy = [y * self.steps[k] / self.image_size[0] for y in [i + 0.5]]
+ for cy, cx in product(dense_cy, dense_cx):
+ anchors += [cx, cy, s_kx, s_ky]
+
+ # back to torch land
+ output = torch.Tensor(anchors).view(-1, 4)
+ if self.clip:
+ output.clamp_(max=1, min=0)
+ return output
+
+
+def py_cpu_nms(dets, thresh):
+ """Pure Python NMS baseline."""
+ keep = torchvision.ops.nms(
+ boxes=torch.Tensor(dets[:, :4]),
+ scores=torch.Tensor(dets[:, 4]),
+ iou_threshold=thresh,
+ )
+
+ return list(keep)
+
+
+def point_form(boxes):
+ """ Convert prior_boxes to (xmin, ymin, xmax, ymax)
+ representation for comparison to point form ground truth data.
+ Args:
+ boxes: (tensor) center-size default boxes from priorbox layers.
+ Return:
+ boxes: (tensor) Converted xmin, ymin, xmax, ymax form of boxes.
+ """
+ return torch.cat(
+ (
+ boxes[:, :2] - boxes[:, 2:] / 2, # xmin, ymin
+ boxes[:, :2] + boxes[:, 2:] / 2),
+ 1) # xmax, ymax
+
+
+def center_size(boxes):
+ """ Convert prior_boxes to (cx, cy, w, h)
+ representation for comparison to center-size form ground truth data.
+ Args:
+ boxes: (tensor) point_form boxes
+ Return:
+ boxes: (tensor) Converted xmin, ymin, xmax, ymax form of boxes.
+ """
+ return torch.cat(
+ (boxes[:, 2:] + boxes[:, :2]) / 2, # cx, cy
+ boxes[:, 2:] - boxes[:, :2],
+ 1) # w, h
+
+
+def intersect(box_a, box_b):
+ """ We resize both tensors to [A,B,2] without new malloc:
+ [A,2] -> [A,1,2] -> [A,B,2]
+ [B,2] -> [1,B,2] -> [A,B,2]
+ Then we compute the area of intersect between box_a and box_b.
+ Args:
+ box_a: (tensor) bounding boxes, Shape: [A,4].
+ box_b: (tensor) bounding boxes, Shape: [B,4].
+ Return:
+ (tensor) intersection area, Shape: [A,B].
+ """
+ A = box_a.size(0)
+ B = box_b.size(0)
+ max_xy = torch.min(box_a[:, 2:].unsqueeze(1).expand(A, B, 2), box_b[:, 2:].unsqueeze(0).expand(A, B, 2))
+ min_xy = torch.max(box_a[:, :2].unsqueeze(1).expand(A, B, 2), box_b[:, :2].unsqueeze(0).expand(A, B, 2))
+ inter = torch.clamp((max_xy - min_xy), min=0)
+ return inter[:, :, 0] * inter[:, :, 1]
+
+
+def jaccard(box_a, box_b):
+ """Compute the jaccard overlap of two sets of boxes. The jaccard overlap
+ is simply the intersection over union of two boxes. Here we operate on
+ ground truth boxes and default boxes.
+ E.g.:
+ A ∩ B / A ∪ B = A ∩ B / (area(A) + area(B) - A ∩ B)
+ Args:
+ box_a: (tensor) Ground truth bounding boxes, Shape: [num_objects,4]
+ box_b: (tensor) Prior boxes from priorbox layers, Shape: [num_priors,4]
+ Return:
+ jaccard overlap: (tensor) Shape: [box_a.size(0), box_b.size(0)]
+ """
+ inter = intersect(box_a, box_b)
+ area_a = ((box_a[:, 2] - box_a[:, 0]) * (box_a[:, 3] - box_a[:, 1])).unsqueeze(1).expand_as(inter) # [A,B]
+ area_b = ((box_b[:, 2] - box_b[:, 0]) * (box_b[:, 3] - box_b[:, 1])).unsqueeze(0).expand_as(inter) # [A,B]
+ union = area_a + area_b - inter
+ return inter / union # [A,B]
+
+
+def matrix_iou(a, b):
+ """
+ return iou of a and b, numpy version for data augenmentation
+ """
+ lt = np.maximum(a[:, np.newaxis, :2], b[:, :2])
+ rb = np.minimum(a[:, np.newaxis, 2:], b[:, 2:])
+
+ area_i = np.prod(rb - lt, axis=2) * (lt < rb).all(axis=2)
+ area_a = np.prod(a[:, 2:] - a[:, :2], axis=1)
+ area_b = np.prod(b[:, 2:] - b[:, :2], axis=1)
+ return area_i / (area_a[:, np.newaxis] + area_b - area_i)
+
+
+def matrix_iof(a, b):
+ """
+ return iof of a and b, numpy version for data augenmentation
+ """
+ lt = np.maximum(a[:, np.newaxis, :2], b[:, :2])
+ rb = np.minimum(a[:, np.newaxis, 2:], b[:, 2:])
+
+ area_i = np.prod(rb - lt, axis=2) * (lt < rb).all(axis=2)
+ area_a = np.prod(a[:, 2:] - a[:, :2], axis=1)
+ return area_i / np.maximum(area_a[:, np.newaxis], 1)
+
+
+def match(threshold, truths, priors, variances, labels, landms, loc_t, conf_t, landm_t, idx):
+ """Match each prior box with the ground truth box of the highest jaccard
+ overlap, encode the bounding boxes, then return the matched indices
+ corresponding to both confidence and location preds.
+ Args:
+ threshold: (float) The overlap threshold used when matching boxes.
+ truths: (tensor) Ground truth boxes, Shape: [num_obj, 4].
+ priors: (tensor) Prior boxes from priorbox layers, Shape: [n_priors,4].
+ variances: (tensor) Variances corresponding to each prior coord,
+ Shape: [num_priors, 4].
+ labels: (tensor) All the class labels for the image, Shape: [num_obj].
+ landms: (tensor) Ground truth landms, Shape [num_obj, 10].
+ loc_t: (tensor) Tensor to be filled w/ encoded location targets.
+ conf_t: (tensor) Tensor to be filled w/ matched indices for conf preds.
+ landm_t: (tensor) Tensor to be filled w/ encoded landm targets.
+ idx: (int) current batch index
+ Return:
+ The matched indices corresponding to 1)location 2)confidence
+ 3)landm preds.
+ """
+ # jaccard index
+ overlaps = jaccard(truths, point_form(priors))
+ # (Bipartite Matching)
+ # [1,num_objects] best prior for each ground truth
+ best_prior_overlap, best_prior_idx = overlaps.max(1, keepdim=True)
+
+ # ignore hard gt
+ valid_gt_idx = best_prior_overlap[:, 0] >= 0.2
+ best_prior_idx_filter = best_prior_idx[valid_gt_idx, :]
+ if best_prior_idx_filter.shape[0] <= 0:
+ loc_t[idx] = 0
+ conf_t[idx] = 0
+ return
+
+ # [1,num_priors] best ground truth for each prior
+ best_truth_overlap, best_truth_idx = overlaps.max(0, keepdim=True)
+ best_truth_idx.squeeze_(0)
+ best_truth_overlap.squeeze_(0)
+ best_prior_idx.squeeze_(1)
+ best_prior_idx_filter.squeeze_(1)
+ best_prior_overlap.squeeze_(1)
+ best_truth_overlap.index_fill_(0, best_prior_idx_filter, 2) # ensure best prior
+ # TODO refactor: index best_prior_idx with long tensor
+ # ensure every gt matches with its prior of max overlap
+ for j in range(best_prior_idx.size(0)): # 判别此anchor是预测哪一个boxes
+ best_truth_idx[best_prior_idx[j]] = j
+ matches = truths[best_truth_idx] # Shape: [num_priors,4] 此处为每一个anchor对应的bbox取出来
+ conf = labels[best_truth_idx] # Shape: [num_priors] 此处为每一个anchor对应的label取出来
+ conf[best_truth_overlap < threshold] = 0 # label as background overlap<0.35的全部作为负样本
+ loc = encode(matches, priors, variances)
+
+ matches_landm = landms[best_truth_idx]
+ landm = encode_landm(matches_landm, priors, variances)
+ loc_t[idx] = loc # [num_priors,4] encoded offsets to learn
+ conf_t[idx] = conf # [num_priors] top class label for each prior
+ landm_t[idx] = landm
+
+
+def encode(matched, priors, variances):
+ """Encode the variances from the priorbox layers into the ground truth boxes
+ we have matched (based on jaccard overlap) with the prior boxes.
+ Args:
+ matched: (tensor) Coords of ground truth for each prior in point-form
+ Shape: [num_priors, 4].
+ priors: (tensor) Prior boxes in center-offset form
+ Shape: [num_priors,4].
+ variances: (list[float]) Variances of priorboxes
+ Return:
+ encoded boxes (tensor), Shape: [num_priors, 4]
+ """
+
+ # dist b/t match center and prior's center
+ g_cxcy = (matched[:, :2] + matched[:, 2:]) / 2 - priors[:, :2]
+ # encode variance
+ g_cxcy /= (variances[0] * priors[:, 2:])
+ # match wh / prior wh
+ g_wh = (matched[:, 2:] - matched[:, :2]) / priors[:, 2:]
+ g_wh = torch.log(g_wh) / variances[1]
+ # return target for smooth_l1_loss
+ return torch.cat([g_cxcy, g_wh], 1) # [num_priors,4]
+
+
+def encode_landm(matched, priors, variances):
+ """Encode the variances from the priorbox layers into the ground truth boxes
+ we have matched (based on jaccard overlap) with the prior boxes.
+ Args:
+ matched: (tensor) Coords of ground truth for each prior in point-form
+ Shape: [num_priors, 10].
+ priors: (tensor) Prior boxes in center-offset form
+ Shape: [num_priors,4].
+ variances: (list[float]) Variances of priorboxes
+ Return:
+ encoded landm (tensor), Shape: [num_priors, 10]
+ """
+
+ # dist b/t match center and prior's center
+ matched = torch.reshape(matched, (matched.size(0), 5, 2))
+ priors_cx = priors[:, 0].unsqueeze(1).expand(matched.size(0), 5).unsqueeze(2)
+ priors_cy = priors[:, 1].unsqueeze(1).expand(matched.size(0), 5).unsqueeze(2)
+ priors_w = priors[:, 2].unsqueeze(1).expand(matched.size(0), 5).unsqueeze(2)
+ priors_h = priors[:, 3].unsqueeze(1).expand(matched.size(0), 5).unsqueeze(2)
+ priors = torch.cat([priors_cx, priors_cy, priors_w, priors_h], dim=2)
+ g_cxcy = matched[:, :, :2] - priors[:, :, :2]
+ # encode variance
+ g_cxcy /= (variances[0] * priors[:, :, 2:])
+ # g_cxcy /= priors[:, :, 2:]
+ g_cxcy = g_cxcy.reshape(g_cxcy.size(0), -1)
+ # return target for smooth_l1_loss
+ return g_cxcy
+
+
+# Adapted from https://github.com/Hakuyume/chainer-ssd
+def decode(loc, priors, variances):
+ """Decode locations from predictions using priors to undo
+ the encoding we did for offset regression at train time.
+ Args:
+ loc (tensor): location predictions for loc layers,
+ Shape: [num_priors,4]
+ priors (tensor): Prior boxes in center-offset form.
+ Shape: [num_priors,4].
+ variances: (list[float]) Variances of priorboxes
+ Return:
+ decoded bounding box predictions
+ """
+
+ boxes = torch.cat((priors[:, :2] + loc[:, :2] * variances[0] * priors[:, 2:],
+ priors[:, 2:] * torch.exp(loc[:, 2:] * variances[1])), 1)
+ boxes[:, :2] -= boxes[:, 2:] / 2
+ boxes[:, 2:] += boxes[:, :2]
+ return boxes
+
+
+def decode_landm(pre, priors, variances):
+ """Decode landm from predictions using priors to undo
+ the encoding we did for offset regression at train time.
+ Args:
+ pre (tensor): landm predictions for loc layers,
+ Shape: [num_priors,10]
+ priors (tensor): Prior boxes in center-offset form.
+ Shape: [num_priors,4].
+ variances: (list[float]) Variances of priorboxes
+ Return:
+ decoded landm predictions
+ """
+ tmp = (
+ priors[:, :2] + pre[:, :2] * variances[0] * priors[:, 2:],
+ priors[:, :2] + pre[:, 2:4] * variances[0] * priors[:, 2:],
+ priors[:, :2] + pre[:, 4:6] * variances[0] * priors[:, 2:],
+ priors[:, :2] + pre[:, 6:8] * variances[0] * priors[:, 2:],
+ priors[:, :2] + pre[:, 8:10] * variances[0] * priors[:, 2:],
+ )
+ landms = torch.cat(tmp, dim=1)
+ return landms
+
+
+def batched_decode(b_loc, priors, variances):
+ """Decode locations from predictions using priors to undo
+ the encoding we did for offset regression at train time.
+ Args:
+ b_loc (tensor): location predictions for loc layers,
+ Shape: [num_batches,num_priors,4]
+ priors (tensor): Prior boxes in center-offset form.
+ Shape: [1,num_priors,4].
+ variances: (list[float]) Variances of priorboxes
+ Return:
+ decoded bounding box predictions
+ """
+ boxes = (
+ priors[:, :, :2] + b_loc[:, :, :2] * variances[0] * priors[:, :, 2:],
+ priors[:, :, 2:] * torch.exp(b_loc[:, :, 2:] * variances[1]),
+ )
+ boxes = torch.cat(boxes, dim=2)
+
+ boxes[:, :, :2] -= boxes[:, :, 2:] / 2
+ boxes[:, :, 2:] += boxes[:, :, :2]
+ return boxes
+
+
+def batched_decode_landm(pre, priors, variances):
+ """Decode landm from predictions using priors to undo
+ the encoding we did for offset regression at train time.
+ Args:
+ pre (tensor): landm predictions for loc layers,
+ Shape: [num_batches,num_priors,10]
+ priors (tensor): Prior boxes in center-offset form.
+ Shape: [1,num_priors,4].
+ variances: (list[float]) Variances of priorboxes
+ Return:
+ decoded landm predictions
+ """
+ landms = (
+ priors[:, :, :2] + pre[:, :, :2] * variances[0] * priors[:, :, 2:],
+ priors[:, :, :2] + pre[:, :, 2:4] * variances[0] * priors[:, :, 2:],
+ priors[:, :, :2] + pre[:, :, 4:6] * variances[0] * priors[:, :, 2:],
+ priors[:, :, :2] + pre[:, :, 6:8] * variances[0] * priors[:, :, 2:],
+ priors[:, :, :2] + pre[:, :, 8:10] * variances[0] * priors[:, :, 2:],
+ )
+ landms = torch.cat(landms, dim=2)
+ return landms
+
+
+def log_sum_exp(x):
+ """Utility function for computing log_sum_exp while determining
+ This will be used to determine unaveraged confidence loss across
+ all examples in a batch.
+ Args:
+ x (Variable(tensor)): conf_preds from conf layers
+ """
+ x_max = x.data.max()
+ return torch.log(torch.sum(torch.exp(x - x_max), 1, keepdim=True)) + x_max
+
+
+# Original author: Francisco Massa:
+# https://github.com/fmassa/object-detection.torch
+# Ported to PyTorch by Max deGroot (02/01/2017)
+def nms(boxes, scores, overlap=0.5, top_k=200):
+ """Apply non-maximum suppression at test time to avoid detecting too many
+ overlapping bounding boxes for a given object.
+ Args:
+ boxes: (tensor) The location preds for the img, Shape: [num_priors,4].
+ scores: (tensor) The class predscores for the img, Shape:[num_priors].
+ overlap: (float) The overlap thresh for suppressing unnecessary boxes.
+ top_k: (int) The Maximum number of box preds to consider.
+ Return:
+ The indices of the kept boxes with respect to num_priors.
+ """
+
+ keep = torch.Tensor(scores.size(0)).fill_(0).long()
+ if boxes.numel() == 0:
+ return keep
+ x1 = boxes[:, 0]
+ y1 = boxes[:, 1]
+ x2 = boxes[:, 2]
+ y2 = boxes[:, 3]
+ area = torch.mul(x2 - x1, y2 - y1)
+ v, idx = scores.sort(0) # sort in ascending order
+ # I = I[v >= 0.01]
+ idx = idx[-top_k:] # indices of the top-k largest vals
+ xx1 = boxes.new()
+ yy1 = boxes.new()
+ xx2 = boxes.new()
+ yy2 = boxes.new()
+ w = boxes.new()
+ h = boxes.new()
+
+ # keep = torch.Tensor()
+ count = 0
+ while idx.numel() > 0:
+ i = idx[-1] # index of current largest val
+ # keep.append(i)
+ keep[count] = i
+ count += 1
+ if idx.size(0) == 1:
+ break
+ idx = idx[:-1] # remove kept element from view
+ # load bboxes of next highest vals
+ torch.index_select(x1, 0, idx, out=xx1)
+ torch.index_select(y1, 0, idx, out=yy1)
+ torch.index_select(x2, 0, idx, out=xx2)
+ torch.index_select(y2, 0, idx, out=yy2)
+ # store element-wise max with next highest score
+ xx1 = torch.clamp(xx1, min=x1[i])
+ yy1 = torch.clamp(yy1, min=y1[i])
+ xx2 = torch.clamp(xx2, max=x2[i])
+ yy2 = torch.clamp(yy2, max=y2[i])
+ w.resize_as_(xx2)
+ h.resize_as_(yy2)
+ w = xx2 - xx1
+ h = yy2 - yy1
+ # check sizes of xx1 and xx2.. after each iteration
+ w = torch.clamp(w, min=0.0)
+ h = torch.clamp(h, min=0.0)
+ inter = w * h
+ # IoU = i / (area(a) + area(b) - i)
+ rem_areas = torch.index_select(area, 0, idx) # load remaining areas)
+ union = (rem_areas - inter) + area[i]
+ IoU = inter / union # store result in iou
+ # keep only elements with an IoU <= overlap
+ idx = idx[IoU.le(overlap)]
+ return keep, count
diff --git a/r_facelib/detection/yolov5face/__init__.py b/r_facelib/detection/yolov5face/__init__.py new file mode 100644 index 0000000..e69de29 --- /dev/null +++ b/r_facelib/detection/yolov5face/__init__.py diff --git a/r_facelib/detection/yolov5face/face_detector.py b/r_facelib/detection/yolov5face/face_detector.py new file mode 100644 index 0000000..ca6d8e3 --- /dev/null +++ b/r_facelib/detection/yolov5face/face_detector.py @@ -0,0 +1,141 @@ +import copy
+from pathlib import Path
+
+import cv2
+import numpy as np
+import torch
+from torch import torch_version
+
+from r_facelib.detection.yolov5face.models.common import Conv
+from r_facelib.detection.yolov5face.models.yolo import Model
+from r_facelib.detection.yolov5face.utils.datasets import letterbox
+from r_facelib.detection.yolov5face.utils.general import (
+ check_img_size,
+ non_max_suppression_face,
+ scale_coords,
+ scale_coords_landmarks,
+)
+
+print(f"Torch version: {torch.__version__}")
+IS_HIGH_VERSION = torch_version.__version__ >= "1.9.0"
+
+def isListempty(inList):
+ if isinstance(inList, list): # Is a list
+ return all(map(isListempty, inList))
+ return False # Not a list
+
+class YoloDetector:
+ def __init__(
+ self,
+ config_name,
+ min_face=10,
+ target_size=None,
+ device='cuda',
+ ):
+ """
+ config_name: name of .yaml config with network configuration from models/ folder.
+ min_face : minimal face size in pixels.
+ target_size : target size of smaller image axis (choose lower for faster work). e.g. 480, 720, 1080.
+ None for original resolution.
+ """
+ self._class_path = Path(__file__).parent.absolute()
+ self.target_size = target_size
+ self.min_face = min_face
+ self.detector = Model(cfg=config_name)
+ self.device = device
+
+
+ def _preprocess(self, imgs):
+ """
+ Preprocessing image before passing through the network. Resize and conversion to torch tensor.
+ """
+ pp_imgs = []
+ for img in imgs:
+ h0, w0 = img.shape[:2] # orig hw
+ if self.target_size:
+ r = self.target_size / min(h0, w0) # resize image to img_size
+ if r < 1:
+ img = cv2.resize(img, (int(w0 * r), int(h0 * r)), interpolation=cv2.INTER_LINEAR)
+
+ imgsz = check_img_size(max(img.shape[:2]), s=self.detector.stride.max()) # check img_size
+ img = letterbox(img, new_shape=imgsz)[0]
+ pp_imgs.append(img)
+ pp_imgs = np.array(pp_imgs)
+ pp_imgs = pp_imgs.transpose(0, 3, 1, 2)
+ pp_imgs = torch.from_numpy(pp_imgs).to(self.device)
+ pp_imgs = pp_imgs.float() # uint8 to fp16/32
+ return pp_imgs / 255.0 # 0 - 255 to 0.0 - 1.0
+
+ def _postprocess(self, imgs, origimgs, pred, conf_thres, iou_thres):
+ """
+ Postprocessing of raw pytorch model output.
+ Returns:
+ bboxes: list of arrays with 4 coordinates of bounding boxes with format x1,y1,x2,y2.
+ points: list of arrays with coordinates of 5 facial keypoints (eyes, nose, lips corners).
+ """
+ bboxes = [[] for _ in range(len(origimgs))]
+ landmarks = [[] for _ in range(len(origimgs))]
+
+ pred = non_max_suppression_face(pred, conf_thres, iou_thres)
+
+ for image_id, origimg in enumerate(origimgs):
+ img_shape = origimg.shape
+ image_height, image_width = img_shape[:2]
+ gn = torch.tensor(img_shape)[[1, 0, 1, 0]] # normalization gain whwh
+ gn_lks = torch.tensor(img_shape)[[1, 0, 1, 0, 1, 0, 1, 0, 1, 0]] # normalization gain landmarks
+ det = pred[image_id].cpu()
+ scale_coords(imgs[image_id].shape[1:], det[:, :4], img_shape).round()
+ scale_coords_landmarks(imgs[image_id].shape[1:], det[:, 5:15], img_shape).round()
+
+ for j in range(det.size()[0]):
+ box = (det[j, :4].view(1, 4) / gn).view(-1).tolist()
+ box = list(
+ map(int, [box[0] * image_width, box[1] * image_height, box[2] * image_width, box[3] * image_height])
+ )
+ if box[3] - box[1] < self.min_face:
+ continue
+ lm = (det[j, 5:15].view(1, 10) / gn_lks).view(-1).tolist()
+ lm = list(map(int, [i * image_width if j % 2 == 0 else i * image_height for j, i in enumerate(lm)]))
+ lm = [lm[i : i + 2] for i in range(0, len(lm), 2)]
+ bboxes[image_id].append(box)
+ landmarks[image_id].append(lm)
+ return bboxes, landmarks
+
+ def detect_faces(self, imgs, conf_thres=0.7, iou_thres=0.5):
+ """
+ Get bbox coordinates and keypoints of faces on original image.
+ Params:
+ imgs: image or list of images to detect faces on with BGR order (convert to RGB order for inference)
+ conf_thres: confidence threshold for each prediction
+ iou_thres: threshold for NMS (filter of intersecting bboxes)
+ Returns:
+ bboxes: list of arrays with 4 coordinates of bounding boxes with format x1,y1,x2,y2.
+ points: list of arrays with coordinates of 5 facial keypoints (eyes, nose, lips corners).
+ """
+ # Pass input images through face detector
+ images = imgs if isinstance(imgs, list) else [imgs]
+ images = [cv2.cvtColor(img, cv2.COLOR_BGR2RGB) for img in images]
+ origimgs = copy.deepcopy(images)
+
+ images = self._preprocess(images)
+
+ if IS_HIGH_VERSION:
+ with torch.inference_mode(): # for pytorch>=1.9
+ pred = self.detector(images)[0]
+ else:
+ with torch.no_grad(): # for pytorch<1.9
+ pred = self.detector(images)[0]
+
+ bboxes, points = self._postprocess(images, origimgs, pred, conf_thres, iou_thres)
+
+ # return bboxes, points
+ if not isListempty(points):
+ bboxes = np.array(bboxes).reshape(-1,4)
+ points = np.array(points).reshape(-1,10)
+ padding = bboxes[:,0].reshape(-1,1)
+ return np.concatenate((bboxes, padding, points), axis=1)
+ else:
+ return None
+
+ def __call__(self, *args):
+ return self.predict(*args)
diff --git a/r_facelib/detection/yolov5face/models/__init__.py b/r_facelib/detection/yolov5face/models/__init__.py new file mode 100644 index 0000000..e69de29 --- /dev/null +++ b/r_facelib/detection/yolov5face/models/__init__.py diff --git a/r_facelib/detection/yolov5face/models/common.py b/r_facelib/detection/yolov5face/models/common.py new file mode 100644 index 0000000..96894d5 --- /dev/null +++ b/r_facelib/detection/yolov5face/models/common.py @@ -0,0 +1,299 @@ +# This file contains modules common to various models
+
+import math
+
+import numpy as np
+import torch
+from torch import nn
+
+from r_facelib.detection.yolov5face.utils.datasets import letterbox
+from r_facelib.detection.yolov5face.utils.general import (
+ make_divisible,
+ non_max_suppression,
+ scale_coords,
+ xyxy2xywh,
+)
+
+
+def autopad(k, p=None): # kernel, padding
+ # Pad to 'same'
+ if p is None:
+ p = k // 2 if isinstance(k, int) else [x // 2 for x in k] # auto-pad
+ return p
+
+
+def channel_shuffle(x, groups):
+ batchsize, num_channels, height, width = x.data.size()
+ channels_per_group = torch.div(num_channels, groups, rounding_mode="trunc")
+
+ # reshape
+ x = x.view(batchsize, groups, channels_per_group, height, width)
+ x = torch.transpose(x, 1, 2).contiguous()
+
+ # flatten
+ return x.view(batchsize, -1, height, width)
+
+
+def DWConv(c1, c2, k=1, s=1, act=True):
+ # Depthwise convolution
+ return Conv(c1, c2, k, s, g=math.gcd(c1, c2), act=act)
+
+
+class Conv(nn.Module):
+ # Standard convolution
+ def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups
+ super().__init__()
+ self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False)
+ self.bn = nn.BatchNorm2d(c2)
+ self.act = nn.SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity())
+
+ def forward(self, x):
+ return self.act(self.bn(self.conv(x)))
+
+ def fuseforward(self, x):
+ return self.act(self.conv(x))
+
+
+class StemBlock(nn.Module):
+ def __init__(self, c1, c2, k=3, s=2, p=None, g=1, act=True):
+ super().__init__()
+ self.stem_1 = Conv(c1, c2, k, s, p, g, act)
+ self.stem_2a = Conv(c2, c2 // 2, 1, 1, 0)
+ self.stem_2b = Conv(c2 // 2, c2, 3, 2, 1)
+ self.stem_2p = nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True)
+ self.stem_3 = Conv(c2 * 2, c2, 1, 1, 0)
+
+ def forward(self, x):
+ stem_1_out = self.stem_1(x)
+ stem_2a_out = self.stem_2a(stem_1_out)
+ stem_2b_out = self.stem_2b(stem_2a_out)
+ stem_2p_out = self.stem_2p(stem_1_out)
+ return self.stem_3(torch.cat((stem_2b_out, stem_2p_out), 1))
+
+
+class Bottleneck(nn.Module):
+ # Standard bottleneck
+ def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_out, shortcut, groups, expansion
+ super().__init__()
+ c_ = int(c2 * e) # hidden channels
+ self.cv1 = Conv(c1, c_, 1, 1)
+ self.cv2 = Conv(c_, c2, 3, 1, g=g)
+ self.add = shortcut and c1 == c2
+
+ def forward(self, x):
+ return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
+
+
+class BottleneckCSP(nn.Module):
+ # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
+ def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
+ super().__init__()
+ c_ = int(c2 * e) # hidden channels
+ self.cv1 = Conv(c1, c_, 1, 1)
+ self.cv2 = nn.Conv2d(c1, c_, 1, 1, bias=False)
+ self.cv3 = nn.Conv2d(c_, c_, 1, 1, bias=False)
+ self.cv4 = Conv(2 * c_, c2, 1, 1)
+ self.bn = nn.BatchNorm2d(2 * c_) # applied to cat(cv2, cv3)
+ self.act = nn.LeakyReLU(0.1, inplace=True)
+ self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))
+
+ def forward(self, x):
+ y1 = self.cv3(self.m(self.cv1(x)))
+ y2 = self.cv2(x)
+ return self.cv4(self.act(self.bn(torch.cat((y1, y2), dim=1))))
+
+
+class C3(nn.Module):
+ # CSP Bottleneck with 3 convolutions
+ def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
+ super().__init__()
+ c_ = int(c2 * e) # hidden channels
+ self.cv1 = Conv(c1, c_, 1, 1)
+ self.cv2 = Conv(c1, c_, 1, 1)
+ self.cv3 = Conv(2 * c_, c2, 1) # act=FReLU(c2)
+ self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))
+
+ def forward(self, x):
+ return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), dim=1))
+
+
+class ShuffleV2Block(nn.Module):
+ def __init__(self, inp, oup, stride):
+ super().__init__()
+
+ if not 1 <= stride <= 3:
+ raise ValueError("illegal stride value")
+ self.stride = stride
+
+ branch_features = oup // 2
+
+ if self.stride > 1:
+ self.branch1 = nn.Sequential(
+ self.depthwise_conv(inp, inp, kernel_size=3, stride=self.stride, padding=1),
+ nn.BatchNorm2d(inp),
+ nn.Conv2d(inp, branch_features, kernel_size=1, stride=1, padding=0, bias=False),
+ nn.BatchNorm2d(branch_features),
+ nn.SiLU(),
+ )
+ else:
+ self.branch1 = nn.Sequential()
+
+ self.branch2 = nn.Sequential(
+ nn.Conv2d(
+ inp if (self.stride > 1) else branch_features,
+ branch_features,
+ kernel_size=1,
+ stride=1,
+ padding=0,
+ bias=False,
+ ),
+ nn.BatchNorm2d(branch_features),
+ nn.SiLU(),
+ self.depthwise_conv(branch_features, branch_features, kernel_size=3, stride=self.stride, padding=1),
+ nn.BatchNorm2d(branch_features),
+ nn.Conv2d(branch_features, branch_features, kernel_size=1, stride=1, padding=0, bias=False),
+ nn.BatchNorm2d(branch_features),
+ nn.SiLU(),
+ )
+
+ @staticmethod
+ def depthwise_conv(i, o, kernel_size, stride=1, padding=0, bias=False):
+ return nn.Conv2d(i, o, kernel_size, stride, padding, bias=bias, groups=i)
+
+ def forward(self, x):
+ if self.stride == 1:
+ x1, x2 = x.chunk(2, dim=1)
+ out = torch.cat((x1, self.branch2(x2)), dim=1)
+ else:
+ out = torch.cat((self.branch1(x), self.branch2(x)), dim=1)
+ out = channel_shuffle(out, 2)
+ return out
+
+
+class SPP(nn.Module):
+ # Spatial pyramid pooling layer used in YOLOv3-SPP
+ def __init__(self, c1, c2, k=(5, 9, 13)):
+ super().__init__()
+ c_ = c1 // 2 # hidden channels
+ self.cv1 = Conv(c1, c_, 1, 1)
+ self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1)
+ self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k])
+
+ def forward(self, x):
+ x = self.cv1(x)
+ return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1))
+
+
+class Focus(nn.Module):
+ # Focus wh information into c-space
+ def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups
+ super().__init__()
+ self.conv = Conv(c1 * 4, c2, k, s, p, g, act)
+
+ def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2)
+ return self.conv(torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1))
+
+
+class Concat(nn.Module):
+ # Concatenate a list of tensors along dimension
+ def __init__(self, dimension=1):
+ super().__init__()
+ self.d = dimension
+
+ def forward(self, x):
+ return torch.cat(x, self.d)
+
+
+class NMS(nn.Module):
+ # Non-Maximum Suppression (NMS) module
+ conf = 0.25 # confidence threshold
+ iou = 0.45 # IoU threshold
+ classes = None # (optional list) filter by class
+
+ def forward(self, x):
+ return non_max_suppression(x[0], conf_thres=self.conf, iou_thres=self.iou, classes=self.classes)
+
+
+class AutoShape(nn.Module):
+ # input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS
+ img_size = 640 # inference size (pixels)
+ conf = 0.25 # NMS confidence threshold
+ iou = 0.45 # NMS IoU threshold
+ classes = None # (optional list) filter by class
+
+ def __init__(self, model):
+ super().__init__()
+ self.model = model.eval()
+
+ def autoshape(self):
+ print("autoShape already enabled, skipping... ") # model already converted to model.autoshape()
+ return self
+
+ def forward(self, imgs, size=640, augment=False, profile=False):
+ # Inference from various sources. For height=720, width=1280, RGB images example inputs are:
+ # OpenCV: = cv2.imread('image.jpg')[:,:,::-1] # HWC BGR to RGB x(720,1280,3)
+ # PIL: = Image.open('image.jpg') # HWC x(720,1280,3)
+ # numpy: = np.zeros((720,1280,3)) # HWC
+ # torch: = torch.zeros(16,3,720,1280) # BCHW
+ # multiple: = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...] # list of images
+
+ p = next(self.model.parameters()) # for device and type
+ if isinstance(imgs, torch.Tensor): # torch
+ return self.model(imgs.to(p.device).type_as(p), augment, profile) # inference
+
+ # Pre-process
+ n, imgs = (len(imgs), imgs) if isinstance(imgs, list) else (1, [imgs]) # number of images, list of images
+ shape0, shape1 = [], [] # image and inference shapes
+ for i, im in enumerate(imgs):
+ im = np.array(im) # to numpy
+ if im.shape[0] < 5: # image in CHW
+ im = im.transpose((1, 2, 0)) # reverse dataloader .transpose(2, 0, 1)
+ im = im[:, :, :3] if im.ndim == 3 else np.tile(im[:, :, None], 3) # enforce 3ch input
+ s = im.shape[:2] # HWC
+ shape0.append(s) # image shape
+ g = size / max(s) # gain
+ shape1.append([y * g for y in s])
+ imgs[i] = im # update
+ shape1 = [make_divisible(x, int(self.stride.max())) for x in np.stack(shape1, 0).max(0)] # inference shape
+ x = [letterbox(im, new_shape=shape1, auto=False)[0] for im in imgs] # pad
+ x = np.stack(x, 0) if n > 1 else x[0][None] # stack
+ x = np.ascontiguousarray(x.transpose((0, 3, 1, 2))) # BHWC to BCHW
+ x = torch.from_numpy(x).to(p.device).type_as(p) / 255.0 # uint8 to fp16/32
+
+ # Inference
+ with torch.no_grad():
+ y = self.model(x, augment, profile)[0] # forward
+ y = non_max_suppression(y, conf_thres=self.conf, iou_thres=self.iou, classes=self.classes) # NMS
+
+ # Post-process
+ for i in range(n):
+ scale_coords(shape1, y[i][:, :4], shape0[i])
+
+ return Detections(imgs, y, self.names)
+
+
+class Detections:
+ # detections class for YOLOv5 inference results
+ def __init__(self, imgs, pred, names=None):
+ super().__init__()
+ d = pred[0].device # device
+ gn = [torch.tensor([*(im.shape[i] for i in [1, 0, 1, 0]), 1.0, 1.0], device=d) for im in imgs] # normalizations
+ self.imgs = imgs # list of images as numpy arrays
+ self.pred = pred # list of tensors pred[0] = (xyxy, conf, cls)
+ self.names = names # class names
+ self.xyxy = pred # xyxy pixels
+ self.xywh = [xyxy2xywh(x) for x in pred] # xywh pixels
+ self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)] # xyxy normalized
+ self.xywhn = [x / g for x, g in zip(self.xywh, gn)] # xywh normalized
+ self.n = len(self.pred)
+
+ def __len__(self):
+ return self.n
+
+ def tolist(self):
+ # return a list of Detections objects, i.e. 'for result in results.tolist():'
+ x = [Detections([self.imgs[i]], [self.pred[i]], self.names) for i in range(self.n)]
+ for d in x:
+ for k in ["imgs", "pred", "xyxy", "xyxyn", "xywh", "xywhn"]:
+ setattr(d, k, getattr(d, k)[0]) # pop out of list
+ return x
diff --git a/r_facelib/detection/yolov5face/models/experimental.py b/r_facelib/detection/yolov5face/models/experimental.py new file mode 100644 index 0000000..bdf7aea --- /dev/null +++ b/r_facelib/detection/yolov5face/models/experimental.py @@ -0,0 +1,45 @@ +# # This file contains experimental modules
+
+import numpy as np
+import torch
+from torch import nn
+
+from r_facelib.detection.yolov5face.models.common import Conv
+
+
+class CrossConv(nn.Module):
+ # Cross Convolution Downsample
+ def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False):
+ # ch_in, ch_out, kernel, stride, groups, expansion, shortcut
+ super().__init__()
+ c_ = int(c2 * e) # hidden channels
+ self.cv1 = Conv(c1, c_, (1, k), (1, s))
+ self.cv2 = Conv(c_, c2, (k, 1), (s, 1), g=g)
+ self.add = shortcut and c1 == c2
+
+ def forward(self, x):
+ return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
+
+
+class MixConv2d(nn.Module):
+ # Mixed Depthwise Conv https://arxiv.org/abs/1907.09595
+ def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True):
+ super().__init__()
+ groups = len(k)
+ if equal_ch: # equal c_ per group
+ i = torch.linspace(0, groups - 1e-6, c2).floor() # c2 indices
+ c_ = [(i == g).sum() for g in range(groups)] # intermediate channels
+ else: # equal weight.numel() per group
+ b = [c2] + [0] * groups
+ a = np.eye(groups + 1, groups, k=-1)
+ a -= np.roll(a, 1, axis=1)
+ a *= np.array(k) ** 2
+ a[0] = 1
+ c_ = np.linalg.lstsq(a, b, rcond=None)[0].round() # solve for equal weight indices, ax = b
+
+ self.m = nn.ModuleList([nn.Conv2d(c1, int(c_[g]), k[g], s, k[g] // 2, bias=False) for g in range(groups)])
+ self.bn = nn.BatchNorm2d(c2)
+ self.act = nn.LeakyReLU(0.1, inplace=True)
+
+ def forward(self, x):
+ return x + self.act(self.bn(torch.cat([m(x) for m in self.m], 1)))
diff --git a/r_facelib/detection/yolov5face/models/yolo.py b/r_facelib/detection/yolov5face/models/yolo.py new file mode 100644 index 0000000..02479dc --- /dev/null +++ b/r_facelib/detection/yolov5face/models/yolo.py @@ -0,0 +1,235 @@ +import math
+from copy import deepcopy
+from pathlib import Path
+
+import torch
+import yaml # for torch hub
+from torch import nn
+
+from r_facelib.detection.yolov5face.models.common import (
+ C3,
+ NMS,
+ SPP,
+ AutoShape,
+ Bottleneck,
+ BottleneckCSP,
+ Concat,
+ Conv,
+ DWConv,
+ Focus,
+ ShuffleV2Block,
+ StemBlock,
+)
+from r_facelib.detection.yolov5face.models.experimental import CrossConv, MixConv2d
+from r_facelib.detection.yolov5face.utils.autoanchor import check_anchor_order
+from r_facelib.detection.yolov5face.utils.general import make_divisible
+from r_facelib.detection.yolov5face.utils.torch_utils import copy_attr, fuse_conv_and_bn
+
+
+class Detect(nn.Module):
+ stride = None # strides computed during build
+ export = False # onnx export
+
+ def __init__(self, nc=80, anchors=(), ch=()): # detection layer
+ super().__init__()
+ self.nc = nc # number of classes
+ self.no = nc + 5 + 10 # number of outputs per anchor
+
+ self.nl = len(anchors) # number of detection layers
+ self.na = len(anchors[0]) // 2 # number of anchors
+ self.grid = [torch.zeros(1)] * self.nl # init grid
+ a = torch.tensor(anchors).float().view(self.nl, -1, 2)
+ self.register_buffer("anchors", a) # shape(nl,na,2)
+ self.register_buffer("anchor_grid", a.clone().view(self.nl, 1, -1, 1, 1, 2)) # shape(nl,1,na,1,1,2)
+ self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch) # output conv
+
+ def forward(self, x):
+ z = [] # inference output
+ if self.export:
+ for i in range(self.nl):
+ x[i] = self.m[i](x[i])
+ return x
+ for i in range(self.nl):
+ x[i] = self.m[i](x[i]) # conv
+ bs, _, ny, nx = x[i].shape # x(bs,255,20,20) to x(bs,3,20,20,85)
+ x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()
+
+ if not self.training: # inference
+ if self.grid[i].shape[2:4] != x[i].shape[2:4]:
+ self.grid[i] = self._make_grid(nx, ny).to(x[i].device)
+
+ y = torch.full_like(x[i], 0)
+ y[..., [0, 1, 2, 3, 4, 15]] = x[i][..., [0, 1, 2, 3, 4, 15]].sigmoid()
+ y[..., 5:15] = x[i][..., 5:15]
+
+ y[..., 0:2] = (y[..., 0:2] * 2.0 - 0.5 + self.grid[i].to(x[i].device)) * self.stride[i] # xy
+ y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh
+
+ y[..., 5:7] = (
+ y[..., 5:7] * self.anchor_grid[i] + self.grid[i].to(x[i].device) * self.stride[i]
+ ) # landmark x1 y1
+ y[..., 7:9] = (
+ y[..., 7:9] * self.anchor_grid[i] + self.grid[i].to(x[i].device) * self.stride[i]
+ ) # landmark x2 y2
+ y[..., 9:11] = (
+ y[..., 9:11] * self.anchor_grid[i] + self.grid[i].to(x[i].device) * self.stride[i]
+ ) # landmark x3 y3
+ y[..., 11:13] = (
+ y[..., 11:13] * self.anchor_grid[i] + self.grid[i].to(x[i].device) * self.stride[i]
+ ) # landmark x4 y4
+ y[..., 13:15] = (
+ y[..., 13:15] * self.anchor_grid[i] + self.grid[i].to(x[i].device) * self.stride[i]
+ ) # landmark x5 y5
+
+ z.append(y.view(bs, -1, self.no))
+
+ return x if self.training else (torch.cat(z, 1), x)
+
+ @staticmethod
+ def _make_grid(nx=20, ny=20):
+ # yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)], indexing="ij") # for pytorch>=1.10
+ yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)])
+ return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float()
+
+
+class Model(nn.Module):
+ def __init__(self, cfg="yolov5s.yaml", ch=3, nc=None): # model, input channels, number of classes
+ super().__init__()
+ self.yaml_file = Path(cfg).name
+ with Path(cfg).open(encoding="utf8") as f:
+ self.yaml = yaml.safe_load(f) # model dict
+
+ # Define model
+ ch = self.yaml["ch"] = self.yaml.get("ch", ch) # input channels
+ if nc and nc != self.yaml["nc"]:
+ self.yaml["nc"] = nc # override yaml value
+
+ self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch]) # model, savelist
+ self.names = [str(i) for i in range(self.yaml["nc"])] # default names
+
+ # Build strides, anchors
+ m = self.model[-1] # Detect()
+ if isinstance(m, Detect):
+ s = 128 # 2x min stride
+ m.stride = torch.tensor([s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))]) # forward
+ m.anchors /= m.stride.view(-1, 1, 1)
+ check_anchor_order(m)
+ self.stride = m.stride
+ self._initialize_biases() # only run once
+
+ def forward(self, x):
+ return self.forward_once(x) # single-scale inference, train
+
+ def forward_once(self, x):
+ y = [] # outputs
+ for m in self.model:
+ if m.f != -1: # if not from previous layer
+ x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f] # from earlier layers
+
+ x = m(x) # run
+ y.append(x if m.i in self.save else None) # save output
+
+ return x
+
+ def _initialize_biases(self, cf=None): # initialize biases into Detect(), cf is class frequency
+ # https://arxiv.org/abs/1708.02002 section 3.3
+ m = self.model[-1] # Detect() module
+ for mi, s in zip(m.m, m.stride): # from
+ b = mi.bias.view(m.na, -1) # conv.bias(255) to (3,85)
+ b.data[:, 4] += math.log(8 / (640 / s) ** 2) # obj (8 objects per 640 image)
+ b.data[:, 5:] += math.log(0.6 / (m.nc - 0.99)) if cf is None else torch.log(cf / cf.sum()) # cls
+ mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)
+
+ def _print_biases(self):
+ m = self.model[-1] # Detect() module
+ for mi in m.m: # from
+ b = mi.bias.detach().view(m.na, -1).T # conv.bias(255) to (3,85)
+ print(("%6g Conv2d.bias:" + "%10.3g" * 6) % (mi.weight.shape[1], *b[:5].mean(1).tolist(), b[5:].mean()))
+
+ def fuse(self): # fuse model Conv2d() + BatchNorm2d() layers
+ print("Fusing layers... ")
+ for m in self.model.modules():
+ if isinstance(m, Conv) and hasattr(m, "bn"):
+ m.conv = fuse_conv_and_bn(m.conv, m.bn) # update conv
+ delattr(m, "bn") # remove batchnorm
+ m.forward = m.fuseforward # update forward
+ elif type(m) is nn.Upsample:
+ m.recompute_scale_factor = None # torch 1.11.0 compatibility
+ return self
+
+ def nms(self, mode=True): # add or remove NMS module
+ present = isinstance(self.model[-1], NMS) # last layer is NMS
+ if mode and not present:
+ print("Adding NMS... ")
+ m = NMS() # module
+ m.f = -1 # from
+ m.i = self.model[-1].i + 1 # index
+ self.model.add_module(name=str(m.i), module=m) # add
+ self.eval()
+ elif not mode and present:
+ print("Removing NMS... ")
+ self.model = self.model[:-1] # remove
+ return self
+
+ def autoshape(self): # add autoShape module
+ print("Adding autoShape... ")
+ m = AutoShape(self) # wrap model
+ copy_attr(m, self, include=("yaml", "nc", "hyp", "names", "stride"), exclude=()) # copy attributes
+ return m
+
+
+def parse_model(d, ch): # model_dict, input_channels(3)
+ anchors, nc, gd, gw = d["anchors"], d["nc"], d["depth_multiple"], d["width_multiple"]
+ na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors # number of anchors
+ no = na * (nc + 5) # number of outputs = anchors * (classes + 5)
+
+ layers, save, c2 = [], [], ch[-1] # layers, savelist, ch out
+ for i, (f, n, m, args) in enumerate(d["backbone"] + d["head"]): # from, number, module, args
+ m = eval(m) if isinstance(m, str) else m # eval strings
+ for j, a in enumerate(args):
+ try:
+ args[j] = eval(a) if isinstance(a, str) else a # eval strings
+ except:
+ pass
+
+ n = max(round(n * gd), 1) if n > 1 else n # depth gain
+ if m in [
+ Conv,
+ Bottleneck,
+ SPP,
+ DWConv,
+ MixConv2d,
+ Focus,
+ CrossConv,
+ BottleneckCSP,
+ C3,
+ ShuffleV2Block,
+ StemBlock,
+ ]:
+ c1, c2 = ch[f], args[0]
+
+ c2 = make_divisible(c2 * gw, 8) if c2 != no else c2
+
+ args = [c1, c2, *args[1:]]
+ if m in [BottleneckCSP, C3]:
+ args.insert(2, n)
+ n = 1
+ elif m is nn.BatchNorm2d:
+ args = [ch[f]]
+ elif m is Concat:
+ c2 = sum(ch[-1 if x == -1 else x + 1] for x in f)
+ elif m is Detect:
+ args.append([ch[x + 1] for x in f])
+ if isinstance(args[1], int): # number of anchors
+ args[1] = [list(range(args[1] * 2))] * len(f)
+ else:
+ c2 = ch[f]
+
+ m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args) # module
+ t = str(m)[8:-2].replace("__main__.", "") # module type
+ np = sum(x.numel() for x in m_.parameters()) # number params
+ m_.i, m_.f, m_.type, m_.np = i, f, t, np # attach index, 'from' index, type, number params
+ save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist
+ layers.append(m_)
+ ch.append(c2)
+ return nn.Sequential(*layers), sorted(save)
diff --git a/r_facelib/detection/yolov5face/models/yolov5l.yaml b/r_facelib/detection/yolov5face/models/yolov5l.yaml new file mode 100644 index 0000000..98a9e2c --- /dev/null +++ b/r_facelib/detection/yolov5face/models/yolov5l.yaml @@ -0,0 +1,47 @@ +# parameters
+nc: 1 # number of classes
+depth_multiple: 1.0 # model depth multiple
+width_multiple: 1.0 # layer channel multiple
+
+# anchors
+anchors:
+ - [4,5, 8,10, 13,16] # P3/8
+ - [23,29, 43,55, 73,105] # P4/16
+ - [146,217, 231,300, 335,433] # P5/32
+
+# YOLOv5 backbone
+backbone:
+ # [from, number, module, args]
+ [[-1, 1, StemBlock, [64, 3, 2]], # 0-P1/2
+ [-1, 3, C3, [128]],
+ [-1, 1, Conv, [256, 3, 2]], # 2-P3/8
+ [-1, 9, C3, [256]],
+ [-1, 1, Conv, [512, 3, 2]], # 4-P4/16
+ [-1, 9, C3, [512]],
+ [-1, 1, Conv, [1024, 3, 2]], # 6-P5/32
+ [-1, 1, SPP, [1024, [3,5,7]]],
+ [-1, 3, C3, [1024, False]], # 8
+ ]
+
+# YOLOv5 head
+head:
+ [[-1, 1, Conv, [512, 1, 1]],
+ [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+ [[-1, 5], 1, Concat, [1]], # cat backbone P4
+ [-1, 3, C3, [512, False]], # 12
+
+ [-1, 1, Conv, [256, 1, 1]],
+ [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+ [[-1, 3], 1, Concat, [1]], # cat backbone P3
+ [-1, 3, C3, [256, False]], # 16 (P3/8-small)
+
+ [-1, 1, Conv, [256, 3, 2]],
+ [[-1, 13], 1, Concat, [1]], # cat head P4
+ [-1, 3, C3, [512, False]], # 19 (P4/16-medium)
+
+ [-1, 1, Conv, [512, 3, 2]],
+ [[-1, 9], 1, Concat, [1]], # cat head P5
+ [-1, 3, C3, [1024, False]], # 22 (P5/32-large)
+
+ [[16, 19, 22], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
+ ]
\ No newline at end of file diff --git a/r_facelib/detection/yolov5face/models/yolov5n.yaml b/r_facelib/detection/yolov5face/models/yolov5n.yaml new file mode 100644 index 0000000..0a03fb0 --- /dev/null +++ b/r_facelib/detection/yolov5face/models/yolov5n.yaml @@ -0,0 +1,45 @@ +# parameters
+nc: 1 # number of classes
+depth_multiple: 1.0 # model depth multiple
+width_multiple: 1.0 # layer channel multiple
+
+# anchors
+anchors:
+ - [4,5, 8,10, 13,16] # P3/8
+ - [23,29, 43,55, 73,105] # P4/16
+ - [146,217, 231,300, 335,433] # P5/32
+
+# YOLOv5 backbone
+backbone:
+ # [from, number, module, args]
+ [[-1, 1, StemBlock, [32, 3, 2]], # 0-P2/4
+ [-1, 1, ShuffleV2Block, [128, 2]], # 1-P3/8
+ [-1, 3, ShuffleV2Block, [128, 1]], # 2
+ [-1, 1, ShuffleV2Block, [256, 2]], # 3-P4/16
+ [-1, 7, ShuffleV2Block, [256, 1]], # 4
+ [-1, 1, ShuffleV2Block, [512, 2]], # 5-P5/32
+ [-1, 3, ShuffleV2Block, [512, 1]], # 6
+ ]
+
+# YOLOv5 head
+head:
+ [[-1, 1, Conv, [128, 1, 1]],
+ [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+ [[-1, 4], 1, Concat, [1]], # cat backbone P4
+ [-1, 1, C3, [128, False]], # 10
+
+ [-1, 1, Conv, [128, 1, 1]],
+ [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+ [[-1, 2], 1, Concat, [1]], # cat backbone P3
+ [-1, 1, C3, [128, False]], # 14 (P3/8-small)
+
+ [-1, 1, Conv, [128, 3, 2]],
+ [[-1, 11], 1, Concat, [1]], # cat head P4
+ [-1, 1, C3, [128, False]], # 17 (P4/16-medium)
+
+ [-1, 1, Conv, [128, 3, 2]],
+ [[-1, 7], 1, Concat, [1]], # cat head P5
+ [-1, 1, C3, [128, False]], # 20 (P5/32-large)
+
+ [[14, 17, 20], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
+ ]
diff --git a/r_facelib/detection/yolov5face/utils/__init__.py b/r_facelib/detection/yolov5face/utils/__init__.py new file mode 100644 index 0000000..e69de29 --- /dev/null +++ b/r_facelib/detection/yolov5face/utils/__init__.py diff --git a/r_facelib/detection/yolov5face/utils/autoanchor.py b/r_facelib/detection/yolov5face/utils/autoanchor.py new file mode 100644 index 0000000..cb0de89 --- /dev/null +++ b/r_facelib/detection/yolov5face/utils/autoanchor.py @@ -0,0 +1,12 @@ +# Auto-anchor utils
+
+
+def check_anchor_order(m):
+ # Check anchor order against stride order for YOLOv5 Detect() module m, and correct if necessary
+ a = m.anchor_grid.prod(-1).view(-1) # anchor area
+ da = a[-1] - a[0] # delta a
+ ds = m.stride[-1] - m.stride[0] # delta s
+ if da.sign() != ds.sign(): # same order
+ print("Reversing anchor order")
+ m.anchors[:] = m.anchors.flip(0)
+ m.anchor_grid[:] = m.anchor_grid.flip(0)
diff --git a/r_facelib/detection/yolov5face/utils/datasets.py b/r_facelib/detection/yolov5face/utils/datasets.py new file mode 100644 index 0000000..a72609b --- /dev/null +++ b/r_facelib/detection/yolov5face/utils/datasets.py @@ -0,0 +1,35 @@ +import cv2
+import numpy as np
+
+
+def letterbox(img, new_shape=(640, 640), color=(114, 114, 114), auto=True, scale_fill=False, scaleup=True):
+ # Resize image to a 32-pixel-multiple rectangle https://github.com/ultralytics/yolov3/issues/232
+ shape = img.shape[:2] # current shape [height, width]
+ if isinstance(new_shape, int):
+ new_shape = (new_shape, new_shape)
+
+ # Scale ratio (new / old)
+ r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
+ if not scaleup: # only scale down, do not scale up (for better test mAP)
+ r = min(r, 1.0)
+
+ # Compute padding
+ ratio = r, r # width, height ratios
+ new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
+ dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding
+ if auto: # minimum rectangle
+ dw, dh = np.mod(dw, 64), np.mod(dh, 64) # wh padding
+ elif scale_fill: # stretch
+ dw, dh = 0.0, 0.0
+ new_unpad = (new_shape[1], new_shape[0])
+ ratio = new_shape[1] / shape[1], new_shape[0] / shape[0] # width, height ratios
+
+ dw /= 2 # divide padding into 2 sides
+ dh /= 2
+
+ if shape[::-1] != new_unpad: # resize
+ img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)
+ top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
+ left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
+ img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border
+ return img, ratio, (dw, dh)
diff --git a/r_facelib/detection/yolov5face/utils/extract_ckpt.py b/r_facelib/detection/yolov5face/utils/extract_ckpt.py new file mode 100644 index 0000000..e6bde00 --- /dev/null +++ b/r_facelib/detection/yolov5face/utils/extract_ckpt.py @@ -0,0 +1,5 @@ +import torch
+import sys
+sys.path.insert(0,'./facelib/detection/yolov5face')
+model = torch.load('facelib/detection/yolov5face/yolov5n-face.pt', map_location='cpu')['model']
+torch.save(model.state_dict(),'../../models/facedetection')
\ No newline at end of file diff --git a/r_facelib/detection/yolov5face/utils/general.py b/r_facelib/detection/yolov5face/utils/general.py new file mode 100644 index 0000000..618d2f3 --- /dev/null +++ b/r_facelib/detection/yolov5face/utils/general.py @@ -0,0 +1,271 @@ +import math
+import time
+
+import numpy as np
+import torch
+import torchvision
+
+
+def check_img_size(img_size, s=32):
+ # Verify img_size is a multiple of stride s
+ new_size = make_divisible(img_size, int(s)) # ceil gs-multiple
+ # if new_size != img_size:
+ # print(f"WARNING: --img-size {img_size:g} must be multiple of max stride {s:g}, updating to {new_size:g}")
+ return new_size
+
+
+def make_divisible(x, divisor):
+ # Returns x evenly divisible by divisor
+ return math.ceil(x / divisor) * divisor
+
+
+def xyxy2xywh(x):
+ # Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] where xy1=top-left, xy2=bottom-right
+ y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
+ y[:, 0] = (x[:, 0] + x[:, 2]) / 2 # x center
+ y[:, 1] = (x[:, 1] + x[:, 3]) / 2 # y center
+ y[:, 2] = x[:, 2] - x[:, 0] # width
+ y[:, 3] = x[:, 3] - x[:, 1] # height
+ return y
+
+
+def xywh2xyxy(x):
+ # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
+ y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
+ y[:, 0] = x[:, 0] - x[:, 2] / 2 # top left x
+ y[:, 1] = x[:, 1] - x[:, 3] / 2 # top left y
+ y[:, 2] = x[:, 0] + x[:, 2] / 2 # bottom right x
+ y[:, 3] = x[:, 1] + x[:, 3] / 2 # bottom right y
+ return y
+
+
+def scale_coords(img1_shape, coords, img0_shape, ratio_pad=None):
+ # Rescale coords (xyxy) from img1_shape to img0_shape
+ if ratio_pad is None: # calculate from img0_shape
+ gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new
+ pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding
+ else:
+ gain = ratio_pad[0][0]
+ pad = ratio_pad[1]
+
+ coords[:, [0, 2]] -= pad[0] # x padding
+ coords[:, [1, 3]] -= pad[1] # y padding
+ coords[:, :4] /= gain
+ clip_coords(coords, img0_shape)
+ return coords
+
+
+def clip_coords(boxes, img_shape):
+ # Clip bounding xyxy bounding boxes to image shape (height, width)
+ boxes[:, 0].clamp_(0, img_shape[1]) # x1
+ boxes[:, 1].clamp_(0, img_shape[0]) # y1
+ boxes[:, 2].clamp_(0, img_shape[1]) # x2
+ boxes[:, 3].clamp_(0, img_shape[0]) # y2
+
+
+def box_iou(box1, box2):
+ # https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py
+ """
+ Return intersection-over-union (Jaccard index) of boxes.
+ Both sets of boxes are expected to be in (x1, y1, x2, y2) format.
+ Arguments:
+ box1 (Tensor[N, 4])
+ box2 (Tensor[M, 4])
+ Returns:
+ iou (Tensor[N, M]): the NxM matrix containing the pairwise
+ IoU values for every element in boxes1 and boxes2
+ """
+
+ def box_area(box):
+ return (box[2] - box[0]) * (box[3] - box[1])
+
+ area1 = box_area(box1.T)
+ area2 = box_area(box2.T)
+
+ inter = (torch.min(box1[:, None, 2:], box2[:, 2:]) - torch.max(box1[:, None, :2], box2[:, :2])).clamp(0).prod(2)
+ return inter / (area1[:, None] + area2 - inter)
+
+
+def non_max_suppression_face(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, labels=()):
+ """Performs Non-Maximum Suppression (NMS) on inference results
+ Returns:
+ detections with shape: nx6 (x1, y1, x2, y2, conf, cls)
+ """
+
+ nc = prediction.shape[2] - 15 # number of classes
+ xc = prediction[..., 4] > conf_thres # candidates
+
+ # Settings
+ # (pixels) maximum box width and height
+ max_wh = 4096
+ time_limit = 10.0 # seconds to quit after
+ redundant = True # require redundant detections
+ multi_label = nc > 1 # multiple labels per box (adds 0.5ms/img)
+ merge = False # use merge-NMS
+
+ t = time.time()
+ output = [torch.zeros((0, 16), device=prediction.device)] * prediction.shape[0]
+ for xi, x in enumerate(prediction): # image index, image inference
+ # Apply constraints
+ x = x[xc[xi]] # confidence
+
+ # Cat apriori labels if autolabelling
+ if labels and len(labels[xi]):
+ label = labels[xi]
+ v = torch.zeros((len(label), nc + 15), device=x.device)
+ v[:, :4] = label[:, 1:5] # box
+ v[:, 4] = 1.0 # conf
+ v[range(len(label)), label[:, 0].long() + 15] = 1.0 # cls
+ x = torch.cat((x, v), 0)
+
+ # If none remain process next image
+ if not x.shape[0]:
+ continue
+
+ # Compute conf
+ x[:, 15:] *= x[:, 4:5] # conf = obj_conf * cls_conf
+
+ # Box (center x, center y, width, height) to (x1, y1, x2, y2)
+ box = xywh2xyxy(x[:, :4])
+
+ # Detections matrix nx6 (xyxy, conf, landmarks, cls)
+ if multi_label:
+ i, j = (x[:, 15:] > conf_thres).nonzero(as_tuple=False).T
+ x = torch.cat((box[i], x[i, j + 15, None], x[:, 5:15], j[:, None].float()), 1)
+ else: # best class only
+ conf, j = x[:, 15:].max(1, keepdim=True)
+ x = torch.cat((box, conf, x[:, 5:15], j.float()), 1)[conf.view(-1) > conf_thres]
+
+ # Filter by class
+ if classes is not None:
+ x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)]
+
+ # If none remain process next image
+ n = x.shape[0] # number of boxes
+ if not n:
+ continue
+
+ # Batched NMS
+ c = x[:, 15:16] * (0 if agnostic else max_wh) # classes
+ boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores
+ i = torchvision.ops.nms(boxes, scores, iou_thres) # NMS
+
+ if merge and (1 < n < 3e3): # Merge NMS (boxes merged using weighted mean)
+ # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)
+ iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix
+ weights = iou * scores[None] # box weights
+ x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True) # merged boxes
+ if redundant:
+ i = i[iou.sum(1) > 1] # require redundancy
+
+ output[xi] = x[i]
+ if (time.time() - t) > time_limit:
+ break # time limit exceeded
+
+ return output
+
+
+def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, labels=()):
+ """Performs Non-Maximum Suppression (NMS) on inference results
+
+ Returns:
+ detections with shape: nx6 (x1, y1, x2, y2, conf, cls)
+ """
+
+ nc = prediction.shape[2] - 5 # number of classes
+ xc = prediction[..., 4] > conf_thres # candidates
+
+ # Settings
+ # (pixels) maximum box width and height
+ max_wh = 4096
+ time_limit = 10.0 # seconds to quit after
+ redundant = True # require redundant detections
+ multi_label = nc > 1 # multiple labels per box (adds 0.5ms/img)
+ merge = False # use merge-NMS
+
+ t = time.time()
+ output = [torch.zeros((0, 6), device=prediction.device)] * prediction.shape[0]
+ for xi, x in enumerate(prediction): # image index, image inference
+ x = x[xc[xi]] # confidence
+
+ # Cat apriori labels if autolabelling
+ if labels and len(labels[xi]):
+ label_id = labels[xi]
+ v = torch.zeros((len(label_id), nc + 5), device=x.device)
+ v[:, :4] = label_id[:, 1:5] # box
+ v[:, 4] = 1.0 # conf
+ v[range(len(label_id)), label_id[:, 0].long() + 5] = 1.0 # cls
+ x = torch.cat((x, v), 0)
+
+ # If none remain process next image
+ if not x.shape[0]:
+ continue
+
+ # Compute conf
+ x[:, 5:] *= x[:, 4:5] # conf = obj_conf * cls_conf
+
+ # Box (center x, center y, width, height) to (x1, y1, x2, y2)
+ box = xywh2xyxy(x[:, :4])
+
+ # Detections matrix nx6 (xyxy, conf, cls)
+ if multi_label:
+ i, j = (x[:, 5:] > conf_thres).nonzero(as_tuple=False).T
+ x = torch.cat((box[i], x[i, j + 5, None], j[:, None].float()), 1)
+ else: # best class only
+ conf, j = x[:, 5:].max(1, keepdim=True)
+ x = torch.cat((box, conf, j.float()), 1)[conf.view(-1) > conf_thres]
+
+ # Filter by class
+ if classes is not None:
+ x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)]
+
+ # Check shape
+ n = x.shape[0] # number of boxes
+ if not n: # no boxes
+ continue
+
+ x = x[x[:, 4].argsort(descending=True)] # sort by confidence
+
+ # Batched NMS
+ c = x[:, 5:6] * (0 if agnostic else max_wh) # classes
+ boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores
+ i = torchvision.ops.nms(boxes, scores, iou_thres) # NMS
+ if merge and (1 < n < 3e3): # Merge NMS (boxes merged using weighted mean)
+ # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)
+ iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix
+ weights = iou * scores[None] # box weights
+ x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True) # merged boxes
+ if redundant:
+ i = i[iou.sum(1) > 1] # require redundancy
+
+ output[xi] = x[i]
+ if (time.time() - t) > time_limit:
+ print(f"WARNING: NMS time limit {time_limit}s exceeded")
+ break # time limit exceeded
+
+ return output
+
+
+def scale_coords_landmarks(img1_shape, coords, img0_shape, ratio_pad=None):
+ # Rescale coords (xyxy) from img1_shape to img0_shape
+ if ratio_pad is None: # calculate from img0_shape
+ gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new
+ pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding
+ else:
+ gain = ratio_pad[0][0]
+ pad = ratio_pad[1]
+
+ coords[:, [0, 2, 4, 6, 8]] -= pad[0] # x padding
+ coords[:, [1, 3, 5, 7, 9]] -= pad[1] # y padding
+ coords[:, :10] /= gain
+ coords[:, 0].clamp_(0, img0_shape[1]) # x1
+ coords[:, 1].clamp_(0, img0_shape[0]) # y1
+ coords[:, 2].clamp_(0, img0_shape[1]) # x2
+ coords[:, 3].clamp_(0, img0_shape[0]) # y2
+ coords[:, 4].clamp_(0, img0_shape[1]) # x3
+ coords[:, 5].clamp_(0, img0_shape[0]) # y3
+ coords[:, 6].clamp_(0, img0_shape[1]) # x4
+ coords[:, 7].clamp_(0, img0_shape[0]) # y4
+ coords[:, 8].clamp_(0, img0_shape[1]) # x5
+ coords[:, 9].clamp_(0, img0_shape[0]) # y5
+ return coords
diff --git a/r_facelib/detection/yolov5face/utils/torch_utils.py b/r_facelib/detection/yolov5face/utils/torch_utils.py new file mode 100644 index 0000000..f702962 --- /dev/null +++ b/r_facelib/detection/yolov5face/utils/torch_utils.py @@ -0,0 +1,40 @@ +import torch
+from torch import nn
+
+
+def fuse_conv_and_bn(conv, bn):
+ # Fuse convolution and batchnorm layers https://tehnokv.com/posts/fusing-batchnorm-and-conv/
+ fusedconv = (
+ nn.Conv2d(
+ conv.in_channels,
+ conv.out_channels,
+ kernel_size=conv.kernel_size,
+ stride=conv.stride,
+ padding=conv.padding,
+ groups=conv.groups,
+ bias=True,
+ )
+ .requires_grad_(False)
+ .to(conv.weight.device)
+ )
+
+ # prepare filters
+ w_conv = conv.weight.clone().view(conv.out_channels, -1)
+ w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var)))
+ fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.size()))
+
+ # prepare spatial bias
+ b_conv = torch.zeros(conv.weight.size(0), device=conv.weight.device) if conv.bias is None else conv.bias
+ b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps))
+ fusedconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn)
+
+ return fusedconv
+
+
+def copy_attr(a, b, include=(), exclude=()):
+ # Copy attributes from b to a, options to only include [...] and to exclude [...]
+ for k, v in b.__dict__.items():
+ if (include and k not in include) or k.startswith("_") or k in exclude:
+ continue
+
+ setattr(a, k, v)
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