Upload 53 files

videoretalking/third_part/GPEN/align_faces.py

@@ -0,0 +1,271 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Apr 24 15:43:29 2017
+@author: zhaoy
+"""
+"""
+@Modified by yangxy (yangtao9009@gmail.com)
+"""
+import cv2
+import numpy as np
+from skimage import transform as trans
+
+# 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)
+
+
+def _umeyama(src, dst, estimate_scale=True, scale=1.0):
+    """Estimate N-D similarity transformation with or without scaling.
+    Parameters
+    ----------
+    src : (M, N) array
+        Source coordinates.
+    dst : (M, N) array
+        Destination coordinates.
+    estimate_scale : bool
+        Whether to estimate scaling factor.
+    Returns
+    -------
+    T : (N + 1, N + 1)
+        The homogeneous similarity transformation matrix. The matrix contains
+        NaN values only if the problem is not well-conditioned.
+    References
+    ----------
+    .. [1] "Least-squares estimation of transformation parameters between two
+            point patterns", Shinji Umeyama, PAMI 1991, :DOI:`10.1109/34.88573`
+    """
+
+    num = src.shape[0]
+    dim = src.shape[1]
+
+    # Compute mean of src and dst.
+    src_mean = src.mean(axis=0)
+    dst_mean = dst.mean(axis=0)
+
+    # Subtract mean from src and dst.
+    src_demean = src - src_mean
+    dst_demean = dst - dst_mean
+
+    # Eq. (38).
+    A = dst_demean.T @ src_demean / num
+
+    # Eq. (39).
+    d = np.ones((dim,), dtype=np.double)
+    if np.linalg.det(A) < 0:
+        d[dim - 1] = -1
+
+    T = np.eye(dim + 1, dtype=np.double)
+
+    U, S, V = np.linalg.svd(A)
+
+    # Eq. (40) and (43).
+    rank = np.linalg.matrix_rank(A)
+    if rank == 0:
+        return np.nan * T
+    elif rank == dim - 1:
+        if np.linalg.det(U) * np.linalg.det(V) > 0:
+            T[:dim, :dim] = U @ V
+        else:
+            s = d[dim - 1]
+            d[dim - 1] = -1
+            T[:dim, :dim] = U @ np.diag(d) @ V
+            d[dim - 1] = s
+    else:
+        T[:dim, :dim] = U @ np.diag(d) @ V
+
+    if estimate_scale:
+        # Eq. (41) and (42).
+        scale = 1.0 / src_demean.var(axis=0).sum() * (S @ d)
+    else:
+        scale = scale
+
+    T[:dim, dim] = dst_mean - scale * (T[:dim, :dim] @ src_mean.T)
+    T[:dim, :dim] *= scale
+
+    return T, scale
+
+
+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):
+    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]):
+        print('output_size == DEFAULT_CROP_SIZE {}: return default reference points'.format(tmp_crop_size))
+        return tmp_5pts
+
+    if (inner_padding_factor == 0 and
+            outer_padding == (0, 0)):
+        if output_size is None:
+            print('No paddings to do: return default reference points')
+            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)
+        print('              deduced from paddings, output_size = ', output_size)
+
+    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
+    # print('---> STEP1: 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)
+
+    # print('              crop_size = ', tmp_crop_size)
+    # print('              reference_5pts = ', tmp_5pts)
+
+    # 2) resize the padded inner region
+    # print('---> STEP2: resize the padded inner region')
+    size_bf_outer_pad = np.array(output_size) - np.array(outer_padding) * 2
+    # print('              crop_size = ', tmp_crop_size)
+    # print('              size_bf_outer_pad = ', size_bf_outer_pad)
+
+    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]
+    # print('              resize scale_factor = ', scale_factor)
+    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
+    # print('              crop_size = ', tmp_crop_size)
+    # print('              reference_5pts = ', tmp_5pts)
+
+    # 3) add outer_padding to make output_size
+    reference_5point = tmp_5pts + np.array(outer_padding)
+    tmp_crop_size = output_size
+    # print('---> STEP3: add outer_padding to make output_size')
+    # print('              crop_size = ', tmp_crop_size)
+    # print('              reference_5pts = ', tmp_5pts)
+    #
+    # print('===> end get_reference_facial_points\n')
+
+    return reference_5point
+
+
+def get_affine_transform_matrix(src_pts, 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'): #smilarity cv2_affine affine
+    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 or ref_pts_shp[0] == 3:
+        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 or src_pts_shp[0] == 3:
+        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 is 'cv2_affine':
+        tfm = cv2.getAffineTransform(src_pts[0:3], ref_pts[0:3])
+        tfm_inv = cv2.getAffineTransform(ref_pts[0:3], src_pts[0:3])
+    elif align_type is 'cv2_rigid':
+        tfm, _ = cv2.estimateAffinePartial2D(src_pts[0:3], ref_pts[0:3])
+        tfm_inv, _ = cv2.estimateAffinePartial2D(ref_pts[0:3], src_pts[0:3])
+    elif align_type is 'affine':
+        tfm = get_affine_transform_matrix(src_pts, ref_pts)
+        tfm_inv = get_affine_transform_matrix(ref_pts, src_pts)
+    else:
+        params, scale = _umeyama(src_pts, ref_pts)
+        tfm = params[:2, :]
+
+        params, _ = _umeyama(ref_pts, src_pts, False, scale=1.0/scale)
+        tfm_inv = params[:2, :]
+
+    # M = cv2.getPerspectiveTransform(ref_pts[0:4], src_pts[0:4])
+    face_img = cv2.warpAffine(src_img, tfm, (crop_size[0], crop_size[1]), flags=3)
+    # face_img = cv2.warpPerspective(src_img, M, (crop_size[0], crop_size[1]), flags=cv2.INTER_LINEAR )
+
+    return face_img, tfm_inv

videoretalking/third_part/GPEN/face_detect/data/FDDB/img_list.txt

@@ -0,0 +1,2845 @@
+2002/08/11/big/img_591
+2002/08/26/big/img_265
+2002/07/19/big/img_423
+2002/08/24/big/img_490
+2002/08/31/big/img_17676
+2002/07/31/big/img_228
+2002/07/24/big/img_402
+2002/08/04/big/img_769
+2002/07/19/big/img_581
+2002/08/13/big/img_723
+2002/08/12/big/img_821
+2003/01/17/big/img_610
+2002/08/13/big/img_1116
+2002/08/28/big/img_19238
+2002/08/21/big/img_660
+2002/08/14/big/img_607
+2002/08/05/big/img_3708
+2002/08/19/big/img_511
+2002/08/07/big/img_1316
+2002/07/25/big/img_1047
+2002/07/23/big/img_474
+2002/07/27/big/img_970
+2002/09/02/big/img_15752
+2002/09/01/big/img_16378
+2002/09/01/big/img_16189
+2002/08/26/big/img_276
+2002/07/24/big/img_518
+2002/08/14/big/img_1027
+2002/08/24/big/img_733
+2002/08/15/big/img_249
+2003/01/15/big/img_1371
+2002/08/07/big/img_1348
+2003/01/01/big/img_331
+2002/08/23/big/img_536
+2002/07/30/big/img_224
+2002/08/10/big/img_763
+2002/08/21/big/img_293
+2002/08/15/big/img_1211
+2002/08/15/big/img_1194
+2003/01/15/big/img_390
+2002/08/06/big/img_2893
+2002/08/17/big/img_691
+2002/08/07/big/img_1695
+2002/08/16/big/img_829
+2002/07/25/big/img_201
+2002/08/23/big/img_36
+2003/01/15/big/img_763
+2003/01/15/big/img_637
+2002/08/22/big/img_592
+2002/07/25/big/img_817
+2003/01/15/big/img_1219
+2002/08/05/big/img_3508
+2002/08/15/big/img_1108
+2002/07/19/big/img_488
+2003/01/16/big/img_704
+2003/01/13/big/img_1087
+2002/08/10/big/img_670
+2002/07/24/big/img_104
+2002/08/27/big/img_19823
+2002/09/01/big/img_16229
+2003/01/13/big/img_846
+2002/08/04/big/img_412
+2002/07/22/big/img_554
+2002/08/12/big/img_331
+2002/08/02/big/img_533
+2002/08/12/big/img_259
+2002/08/18/big/img_328
+2003/01/14/big/img_630
+2002/08/05/big/img_3541
+2002/08/06/big/img_2390
+2002/08/20/big/img_150
+2002/08/02/big/img_1231
+2002/08/16/big/img_710
+2002/08/19/big/img_591
+2002/07/22/big/img_725
+2002/07/24/big/img_820
+2003/01/13/big/img_568
+2002/08/22/big/img_853
+2002/08/09/big/img_648
+2002/08/23/big/img_528
+2003/01/14/big/img_888
+2002/08/30/big/img_18201
+2002/08/13/big/img_965
+2003/01/14/big/img_660
+2002/07/19/big/img_517
+2003/01/14/big/img_406
+2002/08/30/big/img_18433
+2002/08/07/big/img_1630
+2002/08/06/big/img_2717
+2002/08/21/big/img_470
+2002/07/23/big/img_633
+2002/08/20/big/img_915
+2002/08/16/big/img_893
+2002/07/29/big/img_644
+2002/08/15/big/img_529
+2002/08/16/big/img_668
+2002/08/07/big/img_1871
+2002/07/25/big/img_192
+2002/07/31/big/img_961
+2002/08/19/big/img_738
+2002/07/31/big/img_382
+2002/08/19/big/img_298
+2003/01/17/big/img_608
+2002/08/21/big/img_514
+2002/07/23/big/img_183
+2003/01/17/big/img_536
+2002/07/24/big/img_478
+2002/08/06/big/img_2997
+2002/09/02/big/img_15380
+2002/08/07/big/img_1153
+2002/07/31/big/img_967
+2002/07/31/big/img_711
+2002/08/26/big/img_664
+2003/01/01/big/img_326
+2002/08/24/big/img_775
+2002/08/08/big/img_961
+2002/08/16/big/img_77
+2002/08/12/big/img_296
+2002/07/22/big/img_905
+2003/01/13/big/img_284
+2002/08/13/big/img_887
+2002/08/24/big/img_849
+2002/07/30/big/img_345
+2002/08/18/big/img_419
+2002/08/01/big/img_1347
+2002/08/05/big/img_3670
+2002/07/21/big/img_479
+2002/08/08/big/img_913
+2002/09/02/big/img_15828
+2002/08/30/big/img_18194
+2002/08/08/big/img_471
+2002/08/22/big/img_734
+2002/08/09/big/img_586
+2002/08/09/big/img_454
+2002/07/29/big/img_47
+2002/07/19/big/img_381
+2002/07/29/big/img_733
+2002/08/20/big/img_327
+2002/07/21/big/img_96
+2002/08/06/big/img_2680
+2002/07/25/big/img_919
+2002/07/21/big/img_158
+2002/07/22/big/img_801
+2002/07/22/big/img_567
+2002/07/24/big/img_804
+2002/07/24/big/img_690
+2003/01/15/big/img_576
+2002/08/14/big/img_335
+2003/01/13/big/img_390
+2002/08/11/big/img_258
+2002/07/23/big/img_917
+2002/08/15/big/img_525
+2003/01/15/big/img_505
+2002/07/30/big/img_886
+2003/01/16/big/img_640
+2003/01/14/big/img_642
+2003/01/17/big/img_844
+2002/08/04/big/img_571
+2002/08/29/big/img_18702
+2003/01/15/big/img_240
+2002/07/29/big/img_553
+2002/08/10/big/img_354
+2002/08/18/big/img_17
+2003/01/15/big/img_782
+2002/07/27/big/img_382
+2002/08/14/big/img_970
+2003/01/16/big/img_70
+2003/01/16/big/img_625
+2002/08/18/big/img_341
+2002/08/26/big/img_188
+2002/08/09/big/img_405
+2002/08/02/big/img_37
+2002/08/13/big/img_748
+2002/07/22/big/img_399
+2002/07/25/big/img_844
+2002/08/12/big/img_340
+2003/01/13/big/img_815
+2002/08/26/big/img_5
+2002/08/10/big/img_158
+2002/08/18/big/img_95
+2002/07/29/big/img_1297
+2003/01/13/big/img_508
+2002/09/01/big/img_16680
+2003/01/16/big/img_338
+2002/08/13/big/img_517
+2002/07/22/big/img_626
+2002/08/06/big/img_3024
+2002/07/26/big/img_499
+2003/01/13/big/img_387
+2002/08/31/big/img_18025
+2002/08/13/big/img_520
+2003/01/16/big/img_576
+2002/07/26/big/img_121
+2002/08/25/big/img_703
+2002/08/26/big/img_615
+2002/08/17/big/img_434
+2002/08/02/big/img_677
+2002/08/18/big/img_276
+2002/08/05/big/img_3672
+2002/07/26/big/img_700
+2002/07/31/big/img_277
+2003/01/14/big/img_220
+2002/08/23/big/img_232
+2002/08/31/big/img_17422
+2002/07/22/big/img_508
+2002/08/13/big/img_681
+2003/01/15/big/img_638
+2002/08/30/big/img_18408
+2003/01/14/big/img_533
+2003/01/17/big/img_12
+2002/08/28/big/img_19388
+2002/08/08/big/img_133
+2002/07/26/big/img_885
+2002/08/19/big/img_387
+2002/08/27/big/img_19976
+2002/08/26/big/img_118
+2002/08/28/big/img_19146
+2002/08/05/big/img_3259
+2002/08/15/big/img_536
+2002/07/22/big/img_279
+2002/07/22/big/img_9
+2002/08/13/big/img_301
+2002/08/15/big/img_974
+2002/08/06/big/img_2355
+2002/08/01/big/img_1526
+2002/08/03/big/img_417
+2002/08/04/big/img_407
+2002/08/15/big/img_1029
+2002/07/29/big/img_700
+2002/08/01/big/img_1463
+2002/08/31/big/img_17365
+2002/07/28/big/img_223
+2002/07/19/big/img_827
+2002/07/27/big/img_531
+2002/07/19/big/img_845
+2002/08/20/big/img_382
+2002/07/31/big/img_268
+2002/08/27/big/img_19705
+2002/08/02/big/img_830
+2002/08/23/big/img_250
+2002/07/20/big/img_777
+2002/08/21/big/img_879
+2002/08/26/big/img_20146
+2002/08/23/big/img_789
+2002/08/06/big/img_2683
+2002/08/25/big/img_576
+2002/08/09/big/img_498
+2002/08/08/big/img_384
+2002/08/26/big/img_592
+2002/07/29/big/img_1470
+2002/08/21/big/img_452
+2002/08/30/big/img_18395
+2002/08/15/big/img_215
+2002/07/21/big/img_643
+2002/07/22/big/img_209
+2003/01/17/big/img_346
+2002/08/25/big/img_658
+2002/08/21/big/img_221
+2002/08/14/big/img_60
+2003/01/17/big/img_885
+2003/01/16/big/img_482
+2002/08/19/big/img_593
+2002/08/08/big/img_233
+2002/07/30/big/img_458
+2002/07/23/big/img_384
+2003/01/15/big/img_670
+2003/01/15/big/img_267
+2002/08/26/big/img_540
+2002/07/29/big/img_552
+2002/07/30/big/img_997
+2003/01/17/big/img_377
+2002/08/21/big/img_265
+2002/08/09/big/img_561
+2002/07/31/big/img_945
+2002/09/02/big/img_15252
+2002/08/11/big/img_276
+2002/07/22/big/img_491
+2002/07/26/big/img_517
+2002/08/14/big/img_726
+2002/08/08/big/img_46
+2002/08/28/big/img_19458
+2002/08/06/big/img_2935
+2002/07/29/big/img_1392
+2002/08/13/big/img_776
+2002/08/24/big/img_616
+2002/08/14/big/img_1065
+2002/07/29/big/img_889
+2002/08/18/big/img_188
+2002/08/07/big/img_1453
+2002/08/02/big/img_760
+2002/07/28/big/img_416
+2002/08/07/big/img_1393
+2002/08/26/big/img_292
+2002/08/26/big/img_301
+2003/01/13/big/img_195
+2002/07/26/big/img_532
+2002/08/20/big/img_550
+2002/08/05/big/img_3658
+2002/08/26/big/img_738
+2002/09/02/big/img_15750
+2003/01/17/big/img_451
+2002/07/23/big/img_339
+2002/08/16/big/img_637
+2002/08/14/big/img_748
+2002/08/06/big/img_2739
+2002/07/25/big/img_482
+2002/08/19/big/img_191
+2002/08/26/big/img_537
+2003/01/15/big/img_716
+2003/01/15/big/img_767
+2002/08/02/big/img_452
+2002/08/08/big/img_1011
+2002/08/10/big/img_144
+2003/01/14/big/img_122
+2002/07/24/big/img_586
+2002/07/24/big/img_762
+2002/08/20/big/img_369
+2002/07/30/big/img_146
+2002/08/23/big/img_396
+2003/01/15/big/img_200
+2002/08/15/big/img_1183
+2003/01/14/big/img_698
+2002/08/09/big/img_792
+2002/08/06/big/img_2347
+2002/07/31/big/img_911
+2002/08/26/big/img_722
+2002/08/23/big/img_621
+2002/08/05/big/img_3790
+2003/01/13/big/img_633
+2002/08/09/big/img_224
+2002/07/24/big/img_454
+2002/07/21/big/img_202
+2002/08/02/big/img_630
+2002/08/30/big/img_18315
+2002/07/19/big/img_491
+2002/09/01/big/img_16456
+2002/08/09/big/img_242
+2002/07/25/big/img_595
+2002/07/22/big/img_522
+2002/08/01/big/img_1593
+2002/07/29/big/img_336
+2002/08/15/big/img_448
+2002/08/28/big/img_19281
+2002/07/29/big/img_342
+2002/08/12/big/img_78
+2003/01/14/big/img_525
+2002/07/28/big/img_147
+2002/08/11/big/img_353
+2002/08/22/big/img_513
+2002/08/04/big/img_721
+2002/08/17/big/img_247
+2003/01/14/big/img_891
+2002/08/20/big/img_853
+2002/07/19/big/img_414
+2002/08/01/big/img_1530
+2003/01/14/big/img_924
+2002/08/22/big/img_468
+2002/08/18/big/img_354
+2002/08/30/big/img_18193
+2002/08/23/big/img_492
+2002/08/15/big/img_871
+2002/08/12/big/img_494
+2002/08/06/big/img_2470
+2002/07/23/big/img_923
+2002/08/26/big/img_155
+2002/08/08/big/img_669
+2002/07/23/big/img_404
+2002/08/28/big/img_19421
+2002/08/29/big/img_18993
+2002/08/25/big/img_416
+2003/01/17/big/img_434
+2002/07/29/big/img_1370
+2002/07/28/big/img_483
+2002/08/11/big/img_50
+2002/08/10/big/img_404
+2002/09/02/big/img_15057
+2003/01/14/big/img_911
+2002/09/01/big/img_16697
+2003/01/16/big/img_665
+2002/09/01/big/img_16708
+2002/08/22/big/img_612
+2002/08/28/big/img_19471
+2002/08/02/big/img_198
+2003/01/16/big/img_527
+2002/08/22/big/img_209
+2002/08/30/big/img_18205
+2003/01/14/big/img_114
+2003/01/14/big/img_1028
+2003/01/16/big/img_894
+2003/01/14/big/img_837
+2002/07/30/big/img_9
+2002/08/06/big/img_2821
+2002/08/04/big/img_85
+2003/01/13/big/img_884
+2002/07/22/big/img_570
+2002/08/07/big/img_1773
+2002/07/26/big/img_208
+2003/01/17/big/img_946
+2002/07/19/big/img_930
+2003/01/01/big/img_698
+2003/01/17/big/img_612
+2002/07/19/big/img_372
+2002/07/30/big/img_721
+2003/01/14/big/img_649
+2002/08/19/big/img_4
+2002/07/25/big/img_1024
+2003/01/15/big/img_601
+2002/08/30/big/img_18470
+2002/07/22/big/img_29
+2002/08/07/big/img_1686
+2002/07/20/big/img_294
+2002/08/14/big/img_800
+2002/08/19/big/img_353
+2002/08/19/big/img_350
+2002/08/05/big/img_3392
+2002/08/09/big/img_622
+2003/01/15/big/img_236
+2002/08/11/big/img_643
+2002/08/05/big/img_3458
+2002/08/12/big/img_413
+2002/08/22/big/img_415
+2002/08/13/big/img_635
+2002/08/07/big/img_1198
+2002/08/04/big/img_873
+2002/08/12/big/img_407
+2003/01/15/big/img_346
+2002/08/02/big/img_275
+2002/08/17/big/img_997
+2002/08/21/big/img_958
+2002/08/20/big/img_579
+2002/07/29/big/img_142
+2003/01/14/big/img_1115
+2002/08/16/big/img_365
+2002/07/29/big/img_1414
+2002/08/17/big/img_489
+2002/08/13/big/img_1010
+2002/07/31/big/img_276
+2002/07/25/big/img_1000
+2002/08/23/big/img_524
+2002/08/28/big/img_19147
+2003/01/13/big/img_433
+2002/08/20/big/img_205
+2003/01/01/big/img_458
+2002/07/29/big/img_1449
+2003/01/16/big/img_696
+2002/08/28/big/img_19296
+2002/08/29/big/img_18688
+2002/08/21/big/img_767
+2002/08/20/big/img_532
+2002/08/26/big/img_187
+2002/07/26/big/img_183
+2002/07/27/big/img_890
+2003/01/13/big/img_576
+2002/07/30/big/img_15
+2002/07/31/big/img_889
+2002/08/31/big/img_17759
+2003/01/14/big/img_1114
+2002/07/19/big/img_445
+2002/08/03/big/img_593
+2002/07/24/big/img_750
+2002/07/30/big/img_133
+2002/08/25/big/img_671
+2002/07/20/big/img_351
+2002/08/31/big/img_17276
+2002/08/05/big/img_3231
+2002/09/02/big/img_15882
+2002/08/14/big/img_115
+2002/08/02/big/img_1148
+2002/07/25/big/img_936
+2002/07/31/big/img_639
+2002/08/04/big/img_427
+2002/08/22/big/img_843
+2003/01/17/big/img_17
+2003/01/13/big/img_690
+2002/08/13/big/img_472
+2002/08/09/big/img_425
+2002/08/05/big/img_3450
+2003/01/17/big/img_439
+2002/08/13/big/img_539
+2002/07/28/big/img_35
+2002/08/16/big/img_241
+2002/08/06/big/img_2898
+2003/01/16/big/img_429
+2002/08/05/big/img_3817
+2002/08/27/big/img_19919
+2002/07/19/big/img_422
+2002/08/15/big/img_560
+2002/07/23/big/img_750
+2002/07/30/big/img_353
+2002/08/05/big/img_43
+2002/08/23/big/img_305
+2002/08/01/big/img_2137
+2002/08/30/big/img_18097
+2002/08/01/big/img_1389
+2002/08/02/big/img_308
+2003/01/14/big/img_652
+2002/08/01/big/img_1798
+2003/01/14/big/img_732
+2003/01/16/big/img_294
+2002/08/26/big/img_213
+2002/07/24/big/img_842
+2003/01/13/big/img_630
+2003/01/13/big/img_634
+2002/08/06/big/img_2285
+2002/08/01/big/img_2162
+2002/08/30/big/img_18134
+2002/08/02/big/img_1045
+2002/08/01/big/img_2143
+2002/07/25/big/img_135
+2002/07/20/big/img_645
+2002/08/05/big/img_3666
+2002/08/14/big/img_523
+2002/08/04/big/img_425
+2003/01/14/big/img_137
+2003/01/01/big/img_176
+2002/08/15/big/img_505
+2002/08/24/big/img_386
+2002/08/05/big/img_3187
+2002/08/15/big/img_419
+2003/01/13/big/img_520
+2002/08/04/big/img_444
+2002/08/26/big/img_483
+2002/08/05/big/img_3449
+2002/08/30/big/img_18409
+2002/08/28/big/img_19455
+2002/08/27/big/img_20090
+2002/07/23/big/img_625
+2002/08/24/big/img_205
+2002/08/08/big/img_938
+2003/01/13/big/img_527
+2002/08/07/big/img_1712
+2002/07/24/big/img_801
+2002/08/09/big/img_579
+2003/01/14/big/img_41
+2003/01/15/big/img_1130
+2002/07/21/big/img_672
+2002/08/07/big/img_1590
+2003/01/01/big/img_532
+2002/08/02/big/img_529
+2002/08/05/big/img_3591
+2002/08/23/big/img_5
+2003/01/14/big/img_882
+2002/08/28/big/img_19234
+2002/07/24/big/img_398
+2003/01/14/big/img_592
+2002/08/22/big/img_548
+2002/08/12/big/img_761
+2003/01/16/big/img_497
+2002/08/18/big/img_133
+2002/08/08/big/img_874
+2002/07/19/big/img_247
+2002/08/15/big/img_170
+2002/08/27/big/img_19679
+2002/08/20/big/img_246
+2002/08/24/big/img_358
+2002/07/29/big/img_599
+2002/08/01/big/img_1555
+2002/07/30/big/img_491
+2002/07/30/big/img_371
+2003/01/16/big/img_682
+2002/07/25/big/img_619
+2003/01/15/big/img_587
+2002/08/02/big/img_1212
+2002/08/01/big/img_2152
+2002/07/25/big/img_668
+2003/01/16/big/img_574
+2002/08/28/big/img_19464
+2002/08/11/big/img_536
+2002/07/24/big/img_201
+2002/08/05/big/img_3488
+2002/07/25/big/img_887
+2002/07/22/big/img_789
+2002/07/30/big/img_432
+2002/08/16/big/img_166
+2002/09/01/big/img_16333
+2002/07/26/big/img_1010
+2002/07/21/big/img_793
+2002/07/22/big/img_720
+2002/07/31/big/img_337
+2002/07/27/big/img_185
+2002/08/23/big/img_440
+2002/07/31/big/img_801
+2002/07/25/big/img_478
+2003/01/14/big/img_171
+2002/08/07/big/img_1054
+2002/09/02/big/img_15659
+2002/07/29/big/img_1348
+2002/08/09/big/img_337
+2002/08/26/big/img_684
+2002/07/31/big/img_537
+2002/08/15/big/img_808
+2003/01/13/big/img_740
+2002/08/07/big/img_1667
+2002/08/03/big/img_404
+2002/08/06/big/img_2520
+2002/07/19/big/img_230
+2002/07/19/big/img_356
+2003/01/16/big/img_627
+2002/08/04/big/img_474
+2002/07/29/big/img_833
+2002/07/25/big/img_176
+2002/08/01/big/img_1684
+2002/08/21/big/img_643
+2002/08/27/big/img_19673
+2002/08/02/big/img_838
+2002/08/06/big/img_2378
+2003/01/15/big/img_48
+2002/07/30/big/img_470
+2002/08/15/big/img_963
+2002/08/24/big/img_444
+2002/08/16/big/img_662
+2002/08/15/big/img_1209
+2002/07/24/big/img_25
+2002/08/06/big/img_2740
+2002/07/29/big/img_996
+2002/08/31/big/img_18074
+2002/08/04/big/img_343
+2003/01/17/big/img_509
+2003/01/13/big/img_726
+2002/08/07/big/img_1466
+2002/07/26/big/img_307
+2002/08/10/big/img_598
+2002/08/13/big/img_890
+2002/08/14/big/img_997
+2002/07/19/big/img_392
+2002/08/02/big/img_475
+2002/08/29/big/img_19038
+2002/07/29/big/img_538
+2002/07/29/big/img_502
+2002/08/02/big/img_364
+2002/08/31/big/img_17353
+2002/08/08/big/img_539
+2002/08/01/big/img_1449
+2002/07/22/big/img_363
+2002/08/02/big/img_90
+2002/09/01/big/img_16867
+2002/08/05/big/img_3371
+2002/07/30/big/img_342
+2002/08/07/big/img_1363
+2002/08/22/big/img_790
+2003/01/15/big/img_404
+2002/08/05/big/img_3447
+2002/09/01/big/img_16167
+2003/01/13/big/img_840
+2002/08/22/big/img_1001
+2002/08/09/big/img_431
+2002/07/27/big/img_618
+2002/07/31/big/img_741
+2002/07/30/big/img_964
+2002/07/25/big/img_86
+2002/07/29/big/img_275
+2002/08/21/big/img_921
+2002/07/26/big/img_892
+2002/08/21/big/img_663
+2003/01/13/big/img_567
+2003/01/14/big/img_719
+2002/07/28/big/img_251
+2003/01/15/big/img_1123
+2002/07/29/big/img_260
+2002/08/24/big/img_337
+2002/08/01/big/img_1914
+2002/08/13/big/img_373
+2003/01/15/big/img_589
+2002/08/13/big/img_906
+2002/07/26/big/img_270
+2002/08/26/big/img_313
+2002/08/25/big/img_694
+2003/01/01/big/img_327
+2002/07/23/big/img_261
+2002/08/26/big/img_642
+2002/07/29/big/img_918
+2002/07/23/big/img_455
+2002/07/24/big/img_612
+2002/07/23/big/img_534
+2002/07/19/big/img_534
+2002/07/19/big/img_726
+2002/08/01/big/img_2146
+2002/08/02/big/img_543
+2003/01/16/big/img_777
+2002/07/30/big/img_484
+2002/08/13/big/img_1161
+2002/07/21/big/img_390
+2002/08/06/big/img_2288
+2002/08/21/big/img_677
+2002/08/13/big/img_747
+2002/08/15/big/img_1248
+2002/07/31/big/img_416
+2002/09/02/big/img_15259
+2002/08/16/big/img_781
+2002/08/24/big/img_754
+2002/07/24/big/img_803
+2002/08/20/big/img_609
+2002/08/28/big/img_19571
+2002/09/01/big/img_16140
+2002/08/26/big/img_769
+2002/07/20/big/img_588
+2002/08/02/big/img_898
+2002/07/21/big/img_466
+2002/08/14/big/img_1046
+2002/07/25/big/img_212
+2002/08/26/big/img_353
+2002/08/19/big/img_810
+2002/08/31/big/img_17824
+2002/08/12/big/img_631
+2002/07/19/big/img_828
+2002/07/24/big/img_130
+2002/08/25/big/img_580
+2002/07/31/big/img_699
+2002/07/23/big/img_808
+2002/07/31/big/img_377
+2003/01/16/big/img_570
+2002/09/01/big/img_16254
+2002/07/21/big/img_471
+2002/08/01/big/img_1548
+2002/08/18/big/img_252
+2002/08/19/big/img_576
+2002/08/20/big/img_464
+2002/07/27/big/img_735
+2002/08/21/big/img_589
+2003/01/15/big/img_1192
+2002/08/09/big/img_302
+2002/07/31/big/img_594
+2002/08/23/big/img_19
+2002/08/29/big/img_18819
+2002/08/19/big/img_293
+2002/07/30/big/img_331
+2002/08/23/big/img_607
+2002/07/30/big/img_363
+2002/08/16/big/img_766
+2003/01/13/big/img_481
+2002/08/06/big/img_2515
+2002/09/02/big/img_15913
+2002/09/02/big/img_15827
+2002/09/02/big/img_15053
+2002/08/07/big/img_1576
+2002/07/23/big/img_268
+2002/08/21/big/img_152
+2003/01/15/big/img_578
+2002/07/21/big/img_589
+2002/07/20/big/img_548
+2002/08/27/big/img_19693
+2002/08/31/big/img_17252
+2002/07/31/big/img_138
+2002/07/23/big/img_372
+2002/08/16/big/img_695
+2002/07/27/big/img_287
+2002/08/15/big/img_315
+2002/08/10/big/img_361
+2002/07/29/big/img_899
+2002/08/13/big/img_771
+2002/08/21/big/img_92
+2003/01/15/big/img_425
+2003/01/16/big/img_450
+2002/09/01/big/img_16942
+2002/08/02/big/img_51
+2002/09/02/big/img_15379
+2002/08/24/big/img_147
+2002/08/30/big/img_18122
+2002/07/26/big/img_950
+2002/08/07/big/img_1400
+2002/08/17/big/img_468
+2002/08/15/big/img_470
+2002/07/30/big/img_318
+2002/07/22/big/img_644
+2002/08/27/big/img_19732
+2002/07/23/big/img_601
+2002/08/26/big/img_398
+2002/08/21/big/img_428
+2002/08/06/big/img_2119
+2002/08/29/big/img_19103
+2003/01/14/big/img_933
+2002/08/11/big/img_674
+2002/08/28/big/img_19420
+2002/08/03/big/img_418
+2002/08/17/big/img_312
+2002/07/25/big/img_1044
+2003/01/17/big/img_671
+2002/08/30/big/img_18297
+2002/07/25/big/img_755
+2002/07/23/big/img_471
+2002/08/21/big/img_39
+2002/07/26/big/img_699
+2003/01/14/big/img_33
+2002/07/31/big/img_411
+2002/08/16/big/img_645
+2003/01/17/big/img_116
+2002/09/02/big/img_15903
+2002/08/20/big/img_120
+2002/08/22/big/img_176
+2002/07/29/big/img_1316
+2002/08/27/big/img_19914
+2002/07/22/big/img_719
+2002/08/28/big/img_19239
+2003/01/13/big/img_385
+2002/08/08/big/img_525
+2002/07/19/big/img_782
+2002/08/13/big/img_843
+2002/07/30/big/img_107
+2002/08/11/big/img_752
+2002/07/29/big/img_383
+2002/08/26/big/img_249
+2002/08/29/big/img_18860
+2002/07/30/big/img_70
+2002/07/26/big/img_194
+2002/08/15/big/img_530
+2002/08/08/big/img_816
+2002/07/31/big/img_286
+2003/01/13/big/img_294
+2002/07/31/big/img_251
+2002/07/24/big/img_13
+2002/08/31/big/img_17938
+2002/07/22/big/img_642
+2003/01/14/big/img_728
+2002/08/18/big/img_47
+2002/08/22/big/img_306
+2002/08/20/big/img_348
+2002/08/15/big/img_764
+2002/08/08/big/img_163
+2002/07/23/big/img_531
+2002/07/23/big/img_467
+2003/01/16/big/img_743
+2003/01/13/big/img_535
+2002/08/02/big/img_523
+2002/08/22/big/img_120
+2002/08/11/big/img_496
+2002/08/29/big/img_19075
+2002/08/08/big/img_465
+2002/08/09/big/img_790
+2002/08/19/big/img_588
+2002/08/23/big/img_407
+2003/01/17/big/img_435
+2002/08/24/big/img_398
+2002/08/27/big/img_19899
+2003/01/15/big/img_335
+2002/08/13/big/img_493
+2002/09/02/big/img_15460
+2002/07/31/big/img_470
+2002/08/05/big/img_3550
+2002/07/28/big/img_123
+2002/08/01/big/img_1498
+2002/08/04/big/img_504
+2003/01/17/big/img_427
+2002/08/27/big/img_19708
+2002/07/27/big/img_861
+2002/07/25/big/img_685
+2002/07/31/big/img_207
+2003/01/14/big/img_745
+2002/08/31/big/img_17756
+2002/08/24/big/img_288
+2002/08/18/big/img_181
+2002/08/10/big/img_520
+2002/08/25/big/img_705
+2002/08/23/big/img_226
+2002/08/04/big/img_727
+2002/07/24/big/img_625
+2002/08/28/big/img_19157
+2002/08/23/big/img_586
+2002/07/31/big/img_232
+2003/01/13/big/img_240
+2003/01/14/big/img_321
+2003/01/15/big/img_533
+2002/07/23/big/img_480
+2002/07/24/big/img_371
+2002/08/21/big/img_702
+2002/08/31/big/img_17075
+2002/09/02/big/img_15278
+2002/07/29/big/img_246
+2003/01/15/big/img_829
+2003/01/15/big/img_1213
+2003/01/16/big/img_441
+2002/08/14/big/img_921
+2002/07/23/big/img_425
+2002/08/15/big/img_296
+2002/07/19/big/img_135
+2002/07/26/big/img_402
+2003/01/17/big/img_88
+2002/08/20/big/img_872
+2002/08/13/big/img_1110
+2003/01/16/big/img_1040
+2002/07/23/big/img_9
+2002/08/13/big/img_700
+2002/08/16/big/img_371
+2002/08/27/big/img_19966
+2003/01/17/big/img_391
+2002/08/18/big/img_426
+2002/08/01/big/img_1618
+2002/07/21/big/img_754
+2003/01/14/big/img_1101
+2003/01/16/big/img_1022
+2002/07/22/big/img_275
+2002/08/24/big/img_86
+2002/08/17/big/img_582
+2003/01/15/big/img_765
+2003/01/17/big/img_449
+2002/07/28/big/img_265
+2003/01/13/big/img_552
+2002/07/28/big/img_115
+2003/01/16/big/img_56
+2002/08/02/big/img_1232
+2003/01/17/big/img_925
+2002/07/22/big/img_445
+2002/07/25/big/img_957
+2002/07/20/big/img_589
+2002/08/31/big/img_17107
+2002/07/29/big/img_483
+2002/08/14/big/img_1063
+2002/08/07/big/img_1545
+2002/08/14/big/img_680
+2002/09/01/big/img_16694
+2002/08/14/big/img_257
+2002/08/11/big/img_726
+2002/07/26/big/img_681
+2002/07/25/big/img_481
+2003/01/14/big/img_737
+2002/08/28/big/img_19480
+2003/01/16/big/img_362
+2002/08/27/big/img_19865
+2003/01/01/big/img_547
+2002/09/02/big/img_15074
+2002/08/01/big/img_1453
+2002/08/22/big/img_594
+2002/08/28/big/img_19263
+2002/08/13/big/img_478
+2002/07/29/big/img_1358
+2003/01/14/big/img_1022
+2002/08/16/big/img_450
+2002/08/02/big/img_159
+2002/07/26/big/img_781
+2003/01/13/big/img_601
+2002/08/20/big/img_407
+2002/08/15/big/img_468
+2002/08/31/big/img_17902
+2002/08/16/big/img_81
+2002/07/25/big/img_987
+2002/07/25/big/img_500
+2002/08/02/big/img_31
+2002/08/18/big/img_538
+2002/08/08/big/img_54
+2002/07/23/big/img_686
+2002/07/24/big/img_836
+2003/01/17/big/img_734
+2002/08/16/big/img_1055
+2003/01/16/big/img_521
+2002/07/25/big/img_612
+2002/08/22/big/img_778
+2002/08/03/big/img_251
+2002/08/12/big/img_436
+2002/08/23/big/img_705
+2002/07/28/big/img_243
+2002/07/25/big/img_1029
+2002/08/20/big/img_287
+2002/08/29/big/img_18739
+2002/08/05/big/img_3272
+2002/07/27/big/img_214
+2003/01/14/big/img_5
+2002/08/01/big/img_1380
+2002/08/29/big/img_19097
+2002/07/30/big/img_486
+2002/08/29/big/img_18707
+2002/08/10/big/img_559
+2002/08/15/big/img_365
+2002/08/09/big/img_525
+2002/08/10/big/img_689
+2002/07/25/big/img_502
+2002/08/03/big/img_667
+2002/08/10/big/img_855
+2002/08/10/big/img_706
+2002/08/18/big/img_603
+2003/01/16/big/img_1055
+2002/08/31/big/img_17890
+2002/08/15/big/img_761
+2003/01/15/big/img_489
+2002/08/26/big/img_351
+2002/08/01/big/img_1772
+2002/08/31/big/img_17729
+2002/07/25/big/img_609
+2003/01/13/big/img_539
+2002/07/27/big/img_686
+2002/07/31/big/img_311
+2002/08/22/big/img_799
+2003/01/16/big/img_936
+2002/08/31/big/img_17813
+2002/08/04/big/img_862
+2002/08/09/big/img_332
+2002/07/20/big/img_148
+2002/08/12/big/img_426
+2002/07/24/big/img_69
+2002/07/27/big/img_685
+2002/08/02/big/img_480
+2002/08/26/big/img_154
+2002/07/24/big/img_598
+2002/08/01/big/img_1881
+2002/08/20/big/img_667
+2003/01/14/big/img_495
+2002/07/21/big/img_744
+2002/07/30/big/img_150
+2002/07/23/big/img_924
+2002/08/08/big/img_272
+2002/07/23/big/img_310
+2002/07/25/big/img_1011
+2002/09/02/big/img_15725
+2002/07/19/big/img_814
+2002/08/20/big/img_936
+2002/07/25/big/img_85
+2002/08/24/big/img_662
+2002/08/09/big/img_495
+2003/01/15/big/img_196
+2002/08/16/big/img_707
+2002/08/28/big/img_19370
+2002/08/06/big/img_2366
+2002/08/06/big/img_3012
+2002/08/01/big/img_1452
+2002/07/31/big/img_742
+2002/07/27/big/img_914
+2003/01/13/big/img_290
+2002/07/31/big/img_288
+2002/08/02/big/img_171
+2002/08/22/big/img_191
+2002/07/27/big/img_1066
+2002/08/12/big/img_383
+2003/01/17/big/img_1018
+2002/08/01/big/img_1785
+2002/08/11/big/img_390
+2002/08/27/big/img_20037
+2002/08/12/big/img_38
+2003/01/15/big/img_103
+2002/08/26/big/img_31
+2002/08/18/big/img_660
+2002/07/22/big/img_694
+2002/08/15/big/img_24
+2002/07/27/big/img_1077
+2002/08/01/big/img_1943
+2002/07/22/big/img_292
+2002/09/01/big/img_16857
+2002/07/22/big/img_892
+2003/01/14/big/img_46
+2002/08/09/big/img_469
+2002/08/09/big/img_414
+2003/01/16/big/img_40
+2002/08/28/big/img_19231
+2002/07/27/big/img_978
+2002/07/23/big/img_475
+2002/07/25/big/img_92
+2002/08/09/big/img_799
+2002/07/25/big/img_491
+2002/08/03/big/img_654
+2003/01/15/big/img_687
+2002/08/11/big/img_478
+2002/08/07/big/img_1664
+2002/08/20/big/img_362
+2002/08/01/big/img_1298
+2003/01/13/big/img_500
+2002/08/06/big/img_2896
+2002/08/30/big/img_18529
+2002/08/16/big/img_1020
+2002/07/29/big/img_892
+2002/08/29/big/img_18726
+2002/07/21/big/img_453
+2002/08/17/big/img_437
+2002/07/19/big/img_665
+2002/07/22/big/img_440
+2002/07/19/big/img_582
+2002/07/21/big/img_233
+2003/01/01/big/img_82
+2002/07/25/big/img_341
+2002/07/29/big/img_864
+2002/08/02/big/img_276
+2002/08/29/big/img_18654
+2002/07/27/big/img_1024
+2002/08/19/big/img_373
+2003/01/15/big/img_241
+2002/07/25/big/img_84
+2002/08/13/big/img_834
+2002/08/10/big/img_511
+2002/08/01/big/img_1627
+2002/08/08/big/img_607
+2002/08/06/big/img_2083
+2002/08/01/big/img_1486
+2002/08/08/big/img_700
+2002/08/01/big/img_1954
+2002/08/21/big/img_54
+2002/07/30/big/img_847
+2002/08/28/big/img_19169
+2002/07/21/big/img_549
+2002/08/03/big/img_693
+2002/07/31/big/img_1002
+2003/01/14/big/img_1035
+2003/01/16/big/img_622
+2002/07/30/big/img_1201
+2002/08/10/big/img_444
+2002/07/31/big/img_374
+2002/08/21/big/img_301
+2002/08/13/big/img_1095
+2003/01/13/big/img_288
+2002/07/25/big/img_232
+2003/01/13/big/img_967
+2002/08/26/big/img_360
+2002/08/05/big/img_67
+2002/08/29/big/img_18969
+2002/07/28/big/img_16
+2002/08/16/big/img_515
+2002/07/20/big/img_708
+2002/08/18/big/img_178
+2003/01/15/big/img_509
+2002/07/25/big/img_430
+2002/08/21/big/img_738
+2002/08/16/big/img_886
+2002/09/02/big/img_15605
+2002/09/01/big/img_16242
+2002/08/24/big/img_711
+2002/07/25/big/img_90
+2002/08/09/big/img_491
+2002/07/30/big/img_534
+2003/01/13/big/img_474
+2002/08/25/big/img_510
+2002/08/15/big/img_555
+2002/08/02/big/img_775
+2002/07/23/big/img_975
+2002/08/19/big/img_229
+2003/01/17/big/img_860
+2003/01/02/big/img_10
+2002/07/23/big/img_542
+2002/08/06/big/img_2535
+2002/07/22/big/img_37
+2002/08/06/big/img_2342
+2002/08/25/big/img_515
+2002/08/25/big/img_336
+2002/08/18/big/img_837
+2002/08/21/big/img_616
+2003/01/17/big/img_24
+2002/07/26/big/img_936
+2002/08/14/big/img_896
+2002/07/29/big/img_465
+2002/07/31/big/img_543
+2002/08/01/big/img_1411
+2002/08/02/big/img_423
+2002/08/21/big/img_44
+2002/07/31/big/img_11
+2003/01/15/big/img_628
+2003/01/15/big/img_605
+2002/07/30/big/img_571
+2002/07/23/big/img_428
+2002/08/15/big/img_942
+2002/07/26/big/img_531
+2003/01/16/big/img_59
+2002/08/02/big/img_410
+2002/07/31/big/img_230
+2002/08/19/big/img_806
+2003/01/14/big/img_462
+2002/08/16/big/img_370
+2002/08/13/big/img_380
+2002/08/16/big/img_932
+2002/07/19/big/img_393
+2002/08/20/big/img_764
+2002/08/15/big/img_616
+2002/07/26/big/img_267
+2002/07/27/big/img_1069
+2002/08/14/big/img_1041
+2003/01/13/big/img_594
+2002/09/01/big/img_16845
+2002/08/09/big/img_229
+2003/01/16/big/img_639
+2002/08/19/big/img_398
+2002/08/18/big/img_978
+2002/08/24/big/img_296
+2002/07/29/big/img_415
+2002/07/30/big/img_923
+2002/08/18/big/img_575
+2002/08/22/big/img_182
+2002/07/25/big/img_806
+2002/07/22/big/img_49
+2002/07/29/big/img_989
+2003/01/17/big/img_789
+2003/01/15/big/img_503
+2002/09/01/big/img_16062
+2003/01/17/big/img_794
+2002/08/15/big/img_564
+2003/01/15/big/img_222
+2002/08/01/big/img_1656
+2003/01/13/big/img_432
+2002/07/19/big/img_426
+2002/08/17/big/img_244
+2002/08/13/big/img_805
+2002/09/02/big/img_15067
+2002/08/11/big/img_58
+2002/08/22/big/img_636
+2002/07/22/big/img_416
+2002/08/13/big/img_836
+2002/08/26/big/img_363
+2002/07/30/big/img_917
+2003/01/14/big/img_206
+2002/08/12/big/img_311
+2002/08/31/big/img_17623
+2002/07/29/big/img_661
+2003/01/13/big/img_417
+2002/08/02/big/img_463
+2002/08/02/big/img_669
+2002/08/26/big/img_670
+2002/08/02/big/img_375
+2002/07/19/big/img_209
+2002/08/08/big/img_115
+2002/08/21/big/img_399
+2002/08/20/big/img_911
+2002/08/07/big/img_1212
+2002/08/20/big/img_578
+2002/08/22/big/img_554
+2002/08/21/big/img_484
+2002/07/25/big/img_450
+2002/08/03/big/img_542
+2002/08/15/big/img_561
+2002/07/23/big/img_360
+2002/08/30/big/img_18137
+2002/07/25/big/img_250
+2002/08/03/big/img_647
+2002/08/20/big/img_375
+2002/08/14/big/img_387
+2002/09/01/big/img_16990
+2002/08/28/big/img_19341
+2003/01/15/big/img_239
+2002/08/20/big/img_528
+2002/08/12/big/img_130
+2002/09/02/big/img_15108
+2003/01/15/big/img_372
+2002/08/16/big/img_678
+2002/08/04/big/img_623
+2002/07/23/big/img_477
+2002/08/28/big/img_19590
+2003/01/17/big/img_978
+2002/09/01/big/img_16692
+2002/07/20/big/img_109
+2002/08/06/big/img_2660
+2003/01/14/big/img_464
+2002/08/09/big/img_618
+2002/07/22/big/img_722
+2002/08/25/big/img_419
+2002/08/03/big/img_314
+2002/08/25/big/img_40
+2002/07/27/big/img_430
+2002/08/10/big/img_569
+2002/08/23/big/img_398
+2002/07/23/big/img_893
+2002/08/16/big/img_261
+2002/08/06/big/img_2668
+2002/07/22/big/img_835
+2002/09/02/big/img_15093
+2003/01/16/big/img_65
+2002/08/21/big/img_448
+2003/01/14/big/img_351
+2003/01/17/big/img_133
+2002/07/28/big/img_493
+2003/01/15/big/img_640
+2002/09/01/big/img_16880
+2002/08/15/big/img_350
+2002/08/20/big/img_624
+2002/08/25/big/img_604
+2002/08/06/big/img_2200
+2002/08/23/big/img_290
+2002/08/13/big/img_1152
+2003/01/14/big/img_251
+2002/08/02/big/img_538
+2002/08/22/big/img_613
+2003/01/13/big/img_351
+2002/08/18/big/img_368
+2002/07/23/big/img_392
+2002/07/25/big/img_198
+2002/07/25/big/img_418
+2002/08/26/big/img_614
+2002/07/23/big/img_405
+2003/01/14/big/img_445
+2002/07/25/big/img_326
+2002/08/10/big/img_734
+2003/01/14/big/img_530
+2002/08/08/big/img_561
+2002/08/29/big/img_18990
+2002/08/10/big/img_576
+2002/07/29/big/img_1494
+2002/07/19/big/img_198
+2002/08/10/big/img_562
+2002/07/22/big/img_901
+2003/01/14/big/img_37
+2002/09/02/big/img_15629
+2003/01/14/big/img_58
+2002/08/01/big/img_1364
+2002/07/27/big/img_636
+2003/01/13/big/img_241
+2002/09/01/big/img_16988
+2003/01/13/big/img_560
+2002/08/09/big/img_533
+2002/07/31/big/img_249
+2003/01/17/big/img_1007
+2002/07/21/big/img_64
+2003/01/13/big/img_537
+2003/01/15/big/img_606
+2002/08/18/big/img_651
+2002/08/24/big/img_405
+2002/07/26/big/img_837
+2002/08/09/big/img_562
+2002/08/01/big/img_1983
+2002/08/03/big/img_514
+2002/07/29/big/img_314
+2002/08/12/big/img_493
+2003/01/14/big/img_121
+2003/01/14/big/img_479
+2002/08/04/big/img_410
+2002/07/22/big/img_607
+2003/01/17/big/img_417
+2002/07/20/big/img_547
+2002/08/13/big/img_396
+2002/08/31/big/img_17538
+2002/08/13/big/img_187
+2002/08/12/big/img_328
+2003/01/14/big/img_569
+2002/07/27/big/img_1081
+2002/08/14/big/img_504
+2002/08/23/big/img_785
+2002/07/26/big/img_339
+2002/08/07/big/img_1156
+2002/08/07/big/img_1456
+2002/08/23/big/img_378
+2002/08/27/big/img_19719
+2002/07/31/big/img_39
+2002/07/31/big/img_883
+2003/01/14/big/img_676
+2002/07/29/big/img_214
+2002/07/26/big/img_669
+2002/07/25/big/img_202
+2002/08/08/big/img_259
+2003/01/17/big/img_943
+2003/01/15/big/img_512
+2002/08/05/big/img_3295
+2002/08/27/big/img_19685
+2002/08/08/big/img_277
+2002/08/30/big/img_18154
+2002/07/22/big/img_663
+2002/08/29/big/img_18914
+2002/07/31/big/img_908
+2002/08/27/big/img_19926
+2003/01/13/big/img_791
+2003/01/15/big/img_827
+2002/08/18/big/img_878
+2002/08/14/big/img_670
+2002/07/20/big/img_182
+2002/08/15/big/img_291
+2002/08/06/big/img_2600
+2002/07/23/big/img_587
+2002/08/14/big/img_577
+2003/01/15/big/img_585
+2002/07/30/big/img_310
+2002/08/03/big/img_658
+2002/08/10/big/img_157
+2002/08/19/big/img_811
+2002/07/29/big/img_1318
+2002/08/04/big/img_104
+2002/07/30/big/img_332
+2002/07/24/big/img_789
+2002/07/29/big/img_516
+2002/07/23/big/img_843
+2002/08/01/big/img_1528
+2002/08/13/big/img_798
+2002/08/07/big/img_1729
+2002/08/28/big/img_19448
+2003/01/16/big/img_95
+2002/08/12/big/img_473
+2002/07/27/big/img_269
+2003/01/16/big/img_621
+2002/07/29/big/img_772
+2002/07/24/big/img_171
+2002/07/19/big/img_429
+2002/08/07/big/img_1933
+2002/08/27/big/img_19629
+2002/08/05/big/img_3688
+2002/08/07/big/img_1691
+2002/07/23/big/img_600
+2002/07/29/big/img_666
+2002/08/25/big/img_566
+2002/08/06/big/img_2659
+2002/08/29/big/img_18929
+2002/08/16/big/img_407
+2002/08/18/big/img_774
+2002/08/19/big/img_249
+2002/08/06/big/img_2427
+2002/08/29/big/img_18899
+2002/08/01/big/img_1818
+2002/07/31/big/img_108
+2002/07/29/big/img_500
+2002/08/11/big/img_115
+2002/07/19/big/img_521
+2002/08/02/big/img_1163
+2002/07/22/big/img_62
+2002/08/13/big/img_466
+2002/08/21/big/img_956
+2002/08/23/big/img_602
+2002/08/20/big/img_858
+2002/07/25/big/img_690
+2002/07/19/big/img_130
+2002/08/04/big/img_874
+2002/07/26/big/img_489
+2002/07/22/big/img_548
+2002/08/10/big/img_191
+2002/07/25/big/img_1051
+2002/08/18/big/img_473
+2002/08/12/big/img_755
+2002/08/18/big/img_413
+2002/08/08/big/img_1044
+2002/08/17/big/img_680
+2002/08/26/big/img_235
+2002/08/20/big/img_330
+2002/08/22/big/img_344
+2002/08/09/big/img_593
+2002/07/31/big/img_1006
+2002/08/14/big/img_337
+2002/08/16/big/img_728
+2002/07/24/big/img_834
+2002/08/04/big/img_552
+2002/09/02/big/img_15213
+2002/07/25/big/img_725
+2002/08/30/big/img_18290
+2003/01/01/big/img_475
+2002/07/27/big/img_1083
+2002/08/29/big/img_18955
+2002/08/31/big/img_17232
+2002/08/08/big/img_480
+2002/08/01/big/img_1311
+2002/07/30/big/img_745
+2002/08/03/big/img_649
+2002/08/12/big/img_193
+2002/07/29/big/img_228
+2002/07/25/big/img_836
+2002/08/20/big/img_400
+2002/07/30/big/img_507
+2002/09/02/big/img_15072
+2002/07/26/big/img_658
+2002/07/28/big/img_503
+2002/08/05/big/img_3814
+2002/08/24/big/img_745
+2003/01/13/big/img_817
+2002/08/08/big/img_579
+2002/07/22/big/img_251
+2003/01/13/big/img_689
+2002/07/25/big/img_407
+2002/08/13/big/img_1050
+2002/08/14/big/img_733
+2002/07/24/big/img_82
+2003/01/17/big/img_288
+2003/01/15/big/img_475
+2002/08/14/big/img_620
+2002/08/21/big/img_167
+2002/07/19/big/img_300
+2002/07/26/big/img_219
+2002/08/01/big/img_1468
+2002/07/23/big/img_260
+2002/08/09/big/img_555
+2002/07/19/big/img_160
+2002/08/02/big/img_1060
+2003/01/14/big/img_149
+2002/08/15/big/img_346
+2002/08/24/big/img_597
+2002/08/22/big/img_502
+2002/08/30/big/img_18228
+2002/07/21/big/img_766
+2003/01/15/big/img_841
+2002/07/24/big/img_516
+2002/08/02/big/img_265
+2002/08/15/big/img_1243
+2003/01/15/big/img_223
+2002/08/04/big/img_236
+2002/07/22/big/img_309
+2002/07/20/big/img_656
+2002/07/31/big/img_412
+2002/09/01/big/img_16462
+2003/01/16/big/img_431
+2002/07/22/big/img_793
+2002/08/15/big/img_877
+2002/07/26/big/img_282
+2002/07/25/big/img_529
+2002/08/24/big/img_613
+2003/01/17/big/img_700
+2002/08/06/big/img_2526
+2002/08/24/big/img_394
+2002/08/21/big/img_521
+2002/08/25/big/img_560
+2002/07/29/big/img_966
+2002/07/25/big/img_448
+2003/01/13/big/img_782
+2002/08/21/big/img_296
+2002/09/01/big/img_16755
+2002/08/05/big/img_3552
+2002/09/02/big/img_15823
+2003/01/14/big/img_193
+2002/07/21/big/img_159
+2002/08/02/big/img_564
+2002/08/16/big/img_300
+2002/07/19/big/img_269
+2002/08/13/big/img_676
+2002/07/28/big/img_57
+2002/08/05/big/img_3318
+2002/07/31/big/img_218
+2002/08/21/big/img_898
+2002/07/29/big/img_109
+2002/07/19/big/img_854
+2002/08/23/big/img_311
+2002/08/14/big/img_318
+2002/07/25/big/img_523
+2002/07/21/big/img_678
+2003/01/17/big/img_690
+2002/08/28/big/img_19503
+2002/08/18/big/img_251
+2002/08/22/big/img_672
+2002/08/20/big/img_663
+2002/08/02/big/img_148
+2002/09/02/big/img_15580
+2002/07/25/big/img_778
+2002/08/14/big/img_565
+2002/08/12/big/img_374
+2002/08/13/big/img_1018
+2002/08/20/big/img_474
+2002/08/25/big/img_33
+2002/08/02/big/img_1190
+2002/08/08/big/img_864
+2002/08/14/big/img_1071
+2002/08/30/big/img_18103
+2002/08/18/big/img_533
+2003/01/16/big/img_650
+2002/07/25/big/img_108
+2002/07/26/big/img_81
+2002/07/27/big/img_543
+2002/07/29/big/img_521
+2003/01/13/big/img_434
+2002/08/26/big/img_674
+2002/08/06/big/img_2932
+2002/08/07/big/img_1262
+2003/01/15/big/img_201
+2003/01/16/big/img_673
+2002/09/02/big/img_15988
+2002/07/29/big/img_1306
+2003/01/14/big/img_1072
+2002/08/30/big/img_18232
+2002/08/05/big/img_3711
+2002/07/23/big/img_775
+2002/08/01/big/img_16
+2003/01/16/big/img_630
+2002/08/22/big/img_695
+2002/08/14/big/img_51
+2002/08/14/big/img_782
+2002/08/24/big/img_742
+2003/01/14/big/img_512
+2003/01/15/big/img_1183
+2003/01/15/big/img_714
+2002/08/01/big/img_2078
+2002/07/31/big/img_682
+2002/09/02/big/img_15687
+2002/07/26/big/img_518
+2002/08/27/big/img_19676
+2002/09/02/big/img_15969
+2002/08/02/big/img_931
+2002/08/25/big/img_508
+2002/08/29/big/img_18616
+2002/07/22/big/img_839
+2002/07/28/big/img_313
+2003/01/14/big/img_155
+2002/08/02/big/img_1105
+2002/08/09/big/img_53
+2002/08/16/big/img_469
+2002/08/15/big/img_502
+2002/08/20/big/img_575
+2002/07/25/big/img_138
+2003/01/16/big/img_579
+2002/07/19/big/img_352
+2003/01/14/big/img_762
+2003/01/01/big/img_588
+2002/08/02/big/img_981
+2002/08/21/big/img_447
+2002/09/01/big/img_16151
+2003/01/14/big/img_769
+2002/08/23/big/img_461
+2002/08/17/big/img_240
+2002/09/02/big/img_15220
+2002/07/19/big/img_408
+2002/09/02/big/img_15496
+2002/07/29/big/img_758
+2002/08/28/big/img_19392
+2002/08/06/big/img_2723
+2002/08/31/big/img_17752
+2002/08/23/big/img_469
+2002/08/13/big/img_515
+2002/09/02/big/img_15551
+2002/08/03/big/img_462
+2002/07/24/big/img_613
+2002/07/22/big/img_61
+2002/08/08/big/img_171
+2002/08/21/big/img_177
+2003/01/14/big/img_105
+2002/08/02/big/img_1017
+2002/08/22/big/img_106
+2002/07/27/big/img_542
+2002/07/21/big/img_665
+2002/07/23/big/img_595
+2002/08/04/big/img_657
+2002/08/29/big/img_19002
+2003/01/15/big/img_550
+2002/08/14/big/img_662
+2002/07/20/big/img_425
+2002/08/30/big/img_18528
+2002/07/26/big/img_611
+2002/07/22/big/img_849
+2002/08/07/big/img_1655
+2002/08/21/big/img_638
+2003/01/17/big/img_732
+2003/01/01/big/img_496
+2002/08/18/big/img_713
+2002/08/08/big/img_109
+2002/07/27/big/img_1008
+2002/07/20/big/img_559
+2002/08/16/big/img_699
+2002/08/31/big/img_17702
+2002/07/31/big/img_1013
+2002/08/01/big/img_2027
+2002/08/02/big/img_1001
+2002/08/03/big/img_210
+2002/08/01/big/img_2087
+2003/01/14/big/img_199
+2002/07/29/big/img_48
+2002/07/19/big/img_727
+2002/08/09/big/img_249
+2002/08/04/big/img_632
+2002/08/22/big/img_620
+2003/01/01/big/img_457
+2002/08/05/big/img_3223
+2002/07/27/big/img_240
+2002/07/25/big/img_797
+2002/08/13/big/img_430
+2002/07/25/big/img_615
+2002/08/12/big/img_28
+2002/07/30/big/img_220
+2002/07/24/big/img_89
+2002/08/21/big/img_357
+2002/08/09/big/img_590
+2003/01/13/big/img_525
+2002/08/17/big/img_818
+2003/01/02/big/img_7
+2002/07/26/big/img_636
+2003/01/13/big/img_1122
+2002/07/23/big/img_810
+2002/08/20/big/img_888
+2002/07/27/big/img_3
+2002/08/15/big/img_451
+2002/09/02/big/img_15787
+2002/07/31/big/img_281
+2002/08/05/big/img_3274
+2002/08/07/big/img_1254
+2002/07/31/big/img_27
+2002/08/01/big/img_1366
+2002/07/30/big/img_182
+2002/08/27/big/img_19690
+2002/07/29/big/img_68
+2002/08/23/big/img_754
+2002/07/30/big/img_540
+2002/08/27/big/img_20063
+2002/08/14/big/img_471
+2002/08/02/big/img_615
+2002/07/30/big/img_186
+2002/08/25/big/img_150
+2002/07/27/big/img_626
+2002/07/20/big/img_225
+2003/01/15/big/img_1252
+2002/07/19/big/img_367
+2003/01/15/big/img_582
+2002/08/09/big/img_572
+2002/08/08/big/img_428
+2003/01/15/big/img_639
+2002/08/28/big/img_19245
+2002/07/24/big/img_321
+2002/08/02/big/img_662
+2002/08/08/big/img_1033
+2003/01/17/big/img_867
+2002/07/22/big/img_652
+2003/01/14/big/img_224
+2002/08/18/big/img_49
+2002/07/26/big/img_46
+2002/08/31/big/img_18021
+2002/07/25/big/img_151
+2002/08/23/big/img_540
+2002/08/25/big/img_693
+2002/07/23/big/img_340
+2002/07/28/big/img_117
+2002/09/02/big/img_15768
+2002/08/26/big/img_562
+2002/07/24/big/img_480
+2003/01/15/big/img_341
+2002/08/10/big/img_783
+2002/08/20/big/img_132
+2003/01/14/big/img_370
+2002/07/20/big/img_720
+2002/08/03/big/img_144
+2002/08/20/big/img_538
+2002/08/01/big/img_1745
+2002/08/11/big/img_683
+2002/08/03/big/img_328
+2002/08/10/big/img_793
+2002/08/14/big/img_689
+2002/08/02/big/img_162
+2003/01/17/big/img_411
+2002/07/31/big/img_361
+2002/08/15/big/img_289
+2002/08/08/big/img_254
+2002/08/15/big/img_996
+2002/08/20/big/img_785
+2002/07/24/big/img_511
+2002/08/06/big/img_2614
+2002/08/29/big/img_18733
+2002/08/17/big/img_78
+2002/07/30/big/img_378
+2002/08/31/big/img_17947
+2002/08/26/big/img_88
+2002/07/30/big/img_558
+2002/08/02/big/img_67
+2003/01/14/big/img_325
+2002/07/29/big/img_1357
+2002/07/19/big/img_391
+2002/07/30/big/img_307
+2003/01/13/big/img_219
+2002/07/24/big/img_807
+2002/08/23/big/img_543
+2002/08/29/big/img_18620
+2002/07/22/big/img_769
+2002/08/26/big/img_503
+2002/07/30/big/img_78
+2002/08/14/big/img_1036
+2002/08/09/big/img_58
+2002/07/24/big/img_616
+2002/08/02/big/img_464
+2002/07/26/big/img_576
+2002/07/22/big/img_273
+2003/01/16/big/img_470
+2002/07/29/big/img_329
+2002/07/30/big/img_1086
+2002/07/31/big/img_353
+2002/09/02/big/img_15275
+2003/01/17/big/img_555
+2002/08/26/big/img_212
+2002/08/01/big/img_1692
+2003/01/15/big/img_600
+2002/07/29/big/img_825
+2002/08/08/big/img_68
+2002/08/10/big/img_719
+2002/07/31/big/img_636
+2002/07/29/big/img_325
+2002/07/21/big/img_515
+2002/07/22/big/img_705
+2003/01/13/big/img_818
+2002/08/09/big/img_486
+2002/08/22/big/img_141
+2002/07/22/big/img_303
+2002/08/09/big/img_393
+2002/07/29/big/img_963
+2002/08/02/big/img_1215
+2002/08/19/big/img_674
+2002/08/12/big/img_690
+2002/08/21/big/img_637
+2002/08/21/big/img_841
+2002/08/24/big/img_71
+2002/07/25/big/img_596
+2002/07/24/big/img_864
+2002/08/18/big/img_293
+2003/01/14/big/img_657
+2002/08/15/big/img_411
+2002/08/16/big/img_348
+2002/08/05/big/img_3157
+2002/07/20/big/img_663
+2003/01/13/big/img_654
+2003/01/16/big/img_433
+2002/08/30/big/img_18200
+2002/08/12/big/img_226
+2003/01/16/big/img_491
+2002/08/08/big/img_666
+2002/07/19/big/img_576
+2003/01/15/big/img_776
+2003/01/16/big/img_899
+2002/07/19/big/img_397
+2002/08/14/big/img_44
+2003/01/15/big/img_762
+2002/08/02/big/img_982
+2002/09/02/big/img_15234
+2002/08/17/big/img_556
+2002/08/21/big/img_410
+2002/08/21/big/img_386
+2002/07/19/big/img_690
+2002/08/05/big/img_3052
+2002/08/14/big/img_219
+2002/08/16/big/img_273
+2003/01/15/big/img_752
+2002/08/08/big/img_184
+2002/07/31/big/img_743
+2002/08/23/big/img_338
+2003/01/14/big/img_1055
+2002/08/05/big/img_3405
+2003/01/15/big/img_17
+2002/08/03/big/img_141
+2002/08/14/big/img_549
+2002/07/27/big/img_1034
+2002/07/31/big/img_932
+2002/08/30/big/img_18487
+2002/09/02/big/img_15814
+2002/08/01/big/img_2086
+2002/09/01/big/img_16535
+2002/07/22/big/img_500
+2003/01/13/big/img_400
+2002/08/25/big/img_607
+2002/08/30/big/img_18384
+2003/01/14/big/img_951
+2002/08/13/big/img_1150
+2002/08/08/big/img_1022
+2002/08/10/big/img_428
+2002/08/28/big/img_19242
+2002/08/05/big/img_3098
+2002/07/23/big/img_400
+2002/08/26/big/img_365
+2002/07/20/big/img_318
+2002/08/13/big/img_740
+2003/01/16/big/img_37
+2002/08/26/big/img_274
+2002/08/02/big/img_205
+2002/08/21/big/img_695
+2002/08/06/big/img_2289
+2002/08/20/big/img_794
+2002/08/18/big/img_438
+2002/08/07/big/img_1380
+2002/08/02/big/img_737
+2002/08/07/big/img_1651
+2002/08/15/big/img_1238
+2002/08/01/big/img_1681
+2002/08/06/big/img_3017
+2002/07/23/big/img_706
+2002/07/31/big/img_392
+2002/08/09/big/img_539
+2002/07/29/big/img_835
+2002/08/26/big/img_723
+2002/08/28/big/img_19235
+2003/01/16/big/img_353
+2002/08/10/big/img_150
+2002/08/29/big/img_19025
+2002/08/21/big/img_310
+2002/08/10/big/img_823
+2002/07/26/big/img_981
+2002/08/11/big/img_288
+2002/08/19/big/img_534
+2002/08/21/big/img_300
+2002/07/31/big/img_49
+2002/07/30/big/img_469
+2002/08/28/big/img_19197
+2002/08/25/big/img_205
+2002/08/10/big/img_390
+2002/08/23/big/img_291
+2002/08/26/big/img_230
+2002/08/18/big/img_76
+2002/07/23/big/img_409
+2002/08/14/big/img_1053
+2003/01/14/big/img_291
+2002/08/10/big/img_503
+2002/08/27/big/img_19928
+2002/08/03/big/img_563
+2002/08/17/big/img_250
+2002/08/06/big/img_2381
+2002/08/17/big/img_948
+2002/08/06/big/img_2710
+2002/07/22/big/img_696
+2002/07/31/big/img_670
+2002/08/12/big/img_594
+2002/07/29/big/img_624
+2003/01/17/big/img_934
+2002/08/03/big/img_584
+2002/08/22/big/img_1003
+2002/08/05/big/img_3396
+2003/01/13/big/img_570
+2002/08/02/big/img_219
+2002/09/02/big/img_15774
+2002/08/16/big/img_818
+2002/08/23/big/img_402
+2003/01/14/big/img_552
+2002/07/29/big/img_71
+2002/08/05/big/img_3592
+2002/08/16/big/img_80
+2002/07/27/big/img_672
+2003/01/13/big/img_470
+2003/01/16/big/img_702
+2002/09/01/big/img_16130
+2002/08/08/big/img_240
+2002/09/01/big/img_16338
+2002/07/26/big/img_312
+2003/01/14/big/img_538
+2002/07/20/big/img_695
+2002/08/30/big/img_18098
+2002/08/25/big/img_259
+2002/08/16/big/img_1042
+2002/08/09/big/img_837
+2002/08/31/big/img_17760
+2002/07/31/big/img_14
+2002/08/09/big/img_361
+2003/01/16/big/img_107
+2002/08/14/big/img_124
+2002/07/19/big/img_463
+2003/01/15/big/img_275
+2002/07/25/big/img_1151
+2002/07/29/big/img_1501
+2002/08/27/big/img_19889
+2002/08/29/big/img_18603
+2003/01/17/big/img_601
+2002/08/25/big/img_355
+2002/08/08/big/img_297
+2002/08/20/big/img_290
+2002/07/31/big/img_195
+2003/01/01/big/img_336
+2002/08/18/big/img_369
+2002/07/25/big/img_621
+2002/08/11/big/img_508
+2003/01/14/big/img_458
+2003/01/15/big/img_795
+2002/08/12/big/img_498
+2002/08/01/big/img_1734
+2002/08/02/big/img_246
+2002/08/16/big/img_565
+2002/08/11/big/img_475
+2002/08/22/big/img_408
+2002/07/28/big/img_78
+2002/07/21/big/img_81
+2003/01/14/big/img_697
+2002/08/14/big/img_661
+2002/08/15/big/img_507
+2002/08/19/big/img_55
+2002/07/22/big/img_152
+2003/01/14/big/img_470
+2002/08/03/big/img_379
+2002/08/22/big/img_506
+2003/01/16/big/img_966
+2002/08/18/big/img_698
+2002/08/24/big/img_528
+2002/08/23/big/img_10
+2002/08/01/big/img_1655
+2002/08/22/big/img_953
+2002/07/19/big/img_630
+2002/07/22/big/img_889
+2002/08/16/big/img_351
+2003/01/16/big/img_83
+2002/07/19/big/img_805
+2002/08/14/big/img_704
+2002/07/19/big/img_389
+2002/08/31/big/img_17765
+2002/07/29/big/img_606
+2003/01/17/big/img_939
+2002/09/02/big/img_15081
+2002/08/21/big/img_181
+2002/07/29/big/img_1321
+2002/07/21/big/img_497
+2002/07/20/big/img_539
+2002/08/24/big/img_119
+2002/08/01/big/img_1281
+2002/07/26/big/img_207
+2002/07/26/big/img_432
+2002/07/27/big/img_1006
+2002/08/05/big/img_3087
+2002/08/14/big/img_252
+2002/08/14/big/img_798
+2002/07/24/big/img_538
+2002/09/02/big/img_15507
+2002/08/08/big/img_901
+2003/01/14/big/img_557
+2002/08/07/big/img_1819
+2002/08/04/big/img_470
+2002/08/01/big/img_1504
+2002/08/16/big/img_1070
+2002/08/16/big/img_372
+2002/08/23/big/img_416
+2002/08/30/big/img_18208
+2002/08/01/big/img_2043
+2002/07/22/big/img_385
+2002/08/22/big/img_466
+2002/08/21/big/img_869
+2002/08/28/big/img_19429
+2002/08/02/big/img_770
+2002/07/23/big/img_433
+2003/01/14/big/img_13
+2002/07/27/big/img_953
+2002/09/02/big/img_15728
+2002/08/01/big/img_1361
+2002/08/29/big/img_18897
+2002/08/26/big/img_534
+2002/08/11/big/img_121
+2002/08/26/big/img_20130
+2002/07/31/big/img_363
+2002/08/13/big/img_978
+2002/07/25/big/img_835
+2002/08/02/big/img_906
+2003/01/14/big/img_548
+2002/07/30/big/img_80
+2002/07/26/big/img_982
+2003/01/16/big/img_99
+2002/08/19/big/img_362
+2002/08/24/big/img_376
+2002/08/07/big/img_1264
+2002/07/27/big/img_938
+2003/01/17/big/img_535
+2002/07/26/big/img_457
+2002/08/08/big/img_848
+2003/01/15/big/img_859
+2003/01/15/big/img_622
+2002/07/30/big/img_403
+2002/07/29/big/img_217
+2002/07/26/big/img_891
+2002/07/24/big/img_70
+2002/08/25/big/img_619
+2002/08/05/big/img_3375
+2002/08/01/big/img_2160
+2002/08/06/big/img_2227
+2003/01/14/big/img_117
+2002/08/14/big/img_227
+2002/08/13/big/img_565
+2002/08/19/big/img_625
+2002/08/03/big/img_812
+2002/07/24/big/img_41
+2002/08/16/big/img_235
+2002/07/29/big/img_759
+2002/07/21/big/img_433
+2002/07/29/big/img_190
+2003/01/16/big/img_435
+2003/01/13/big/img_708
+2002/07/30/big/img_57
+2002/08/22/big/img_162
+2003/01/01/big/img_558
+2003/01/15/big/img_604
+2002/08/16/big/img_935
+2002/08/20/big/img_394
+2002/07/28/big/img_465
+2002/09/02/big/img_15534
+2002/08/16/big/img_87
+2002/07/22/big/img_469
+2002/08/12/big/img_245
+2003/01/13/big/img_236
+2002/08/06/big/img_2736
+2002/08/03/big/img_348
+2003/01/14/big/img_218
+2002/07/26/big/img_232
+2003/01/15/big/img_244
+2002/07/25/big/img_1121
+2002/08/01/big/img_1484
+2002/07/26/big/img_541
+2002/08/07/big/img_1244
+2002/07/31/big/img_3
+2002/08/30/big/img_18437
+2002/08/29/big/img_19094
+2002/08/01/big/img_1355
+2002/08/19/big/img_338
+2002/07/19/big/img_255
+2002/07/21/big/img_76
+2002/08/25/big/img_199
+2002/08/12/big/img_740
+2002/07/30/big/img_852
+2002/08/15/big/img_599
+2002/08/23/big/img_254
+2002/08/19/big/img_125
+2002/07/24/big/img_2
+2002/08/04/big/img_145
+2002/08/05/big/img_3137
+2002/07/28/big/img_463
+2003/01/14/big/img_801
+2002/07/23/big/img_366
+2002/08/26/big/img_600
+2002/08/26/big/img_649
+2002/09/02/big/img_15849
+2002/07/26/big/img_248
+2003/01/13/big/img_200
+2002/08/07/big/img_1794
+2002/08/31/big/img_17270
+2002/08/23/big/img_608
+2003/01/13/big/img_837
+2002/08/23/big/img_581
+2002/08/20/big/img_754
+2002/08/18/big/img_183
+2002/08/20/big/img_328
+2002/07/22/big/img_494
+2002/07/29/big/img_399
+2002/08/28/big/img_19284
+2002/08/08/big/img_566
+2002/07/25/big/img_376
+2002/07/23/big/img_138
+2002/07/25/big/img_435
+2002/08/17/big/img_685
+2002/07/19/big/img_90
+2002/07/20/big/img_716
+2002/08/31/big/img_17458
+2002/08/26/big/img_461
+2002/07/25/big/img_355
+2002/08/06/big/img_2152
+2002/07/27/big/img_932
+2002/07/23/big/img_232
+2002/08/08/big/img_1020
+2002/07/31/big/img_366
+2002/08/06/big/img_2667
+2002/08/21/big/img_465
+2002/08/15/big/img_305
+2002/08/02/big/img_247
+2002/07/28/big/img_46
+2002/08/27/big/img_19922
+2002/08/23/big/img_643
+2003/01/13/big/img_624
+2002/08/23/big/img_625
+2002/08/05/big/img_3787
+2003/01/13/big/img_627
+2002/09/01/big/img_16381
+2002/08/05/big/img_3668
+2002/07/21/big/img_535
+2002/08/27/big/img_19680
+2002/07/22/big/img_413
+2002/07/29/big/img_481
+2003/01/15/big/img_496
+2002/07/23/big/img_701
+2002/08/29/big/img_18670
+2002/07/28/big/img_319
+2003/01/14/big/img_517
+2002/07/26/big/img_256
+2003/01/16/big/img_593
+2002/07/30/big/img_956
+2002/07/30/big/img_667
+2002/07/25/big/img_100
+2002/08/11/big/img_570
+2002/07/26/big/img_745
+2002/08/04/big/img_834
+2002/08/25/big/img_521
+2002/08/01/big/img_2148
+2002/09/02/big/img_15183
+2002/08/22/big/img_514
+2002/08/23/big/img_477
+2002/07/23/big/img_336
+2002/07/26/big/img_481
+2002/08/20/big/img_409
+2002/07/23/big/img_918
+2002/08/09/big/img_474
+2002/08/02/big/img_929
+2002/08/31/big/img_17932
+2002/08/19/big/img_161
+2002/08/09/big/img_667
+2002/07/31/big/img_805
+2002/09/02/big/img_15678
+2002/08/31/big/img_17509
+2002/08/29/big/img_18998
+2002/07/23/big/img_301
+2002/08/07/big/img_1612
+2002/08/06/big/img_2472
+2002/07/23/big/img_466
+2002/08/27/big/img_19634
+2003/01/16/big/img_16
+2002/08/14/big/img_193
+2002/08/21/big/img_340
+2002/08/27/big/img_19799
+2002/08/01/big/img_1345
+2002/08/07/big/img_1448
+2002/08/11/big/img_324
+2003/01/16/big/img_754
+2002/08/13/big/img_418
+2003/01/16/big/img_544
+2002/08/19/big/img_135
+2002/08/10/big/img_455
+2002/08/10/big/img_693
+2002/08/31/big/img_17967
+2002/08/28/big/img_19229
+2002/08/04/big/img_811
+2002/09/01/big/img_16225
+2003/01/16/big/img_428
+2002/09/02/big/img_15295
+2002/07/26/big/img_108
+2002/07/21/big/img_477
+2002/08/07/big/img_1354
+2002/08/23/big/img_246
+2002/08/16/big/img_652
+2002/07/27/big/img_553
+2002/07/31/big/img_346
+2002/08/04/big/img_537
+2002/08/08/big/img_498
+2002/08/29/big/img_18956
+2003/01/13/big/img_922
+2002/08/31/big/img_17425
+2002/07/26/big/img_438
+2002/08/19/big/img_185
+2003/01/16/big/img_33
+2002/08/10/big/img_252
+2002/07/29/big/img_598
+2002/08/27/big/img_19820
+2002/08/06/big/img_2664
+2002/08/20/big/img_705
+2003/01/14/big/img_816
+2002/08/03/big/img_552
+2002/07/25/big/img_561
+2002/07/25/big/img_934
+2002/08/01/big/img_1893
+2003/01/14/big/img_746
+2003/01/16/big/img_519
+2002/08/03/big/img_681
+2002/07/24/big/img_808
+2002/08/14/big/img_803
+2002/08/25/big/img_155
+2002/07/30/big/img_1107
+2002/08/29/big/img_18882
+2003/01/15/big/img_598
+2002/08/19/big/img_122
+2002/07/30/big/img_428
+2002/07/24/big/img_684
+2002/08/22/big/img_192
+2002/08/22/big/img_543
+2002/08/07/big/img_1318
+2002/08/18/big/img_25
+2002/07/26/big/img_583
+2002/07/20/big/img_464
+2002/08/19/big/img_664
+2002/08/24/big/img_861
+2002/09/01/big/img_16136
+2002/08/22/big/img_400
+2002/08/12/big/img_445
+2003/01/14/big/img_174
+2002/08/27/big/img_19677
+2002/08/31/big/img_17214
+2002/08/30/big/img_18175
+2003/01/17/big/img_402
+2002/08/06/big/img_2396
+2002/08/18/big/img_448
+2002/08/21/big/img_165
+2002/08/31/big/img_17609
+2003/01/01/big/img_151
+2002/08/26/big/img_372
+2002/09/02/big/img_15994
+2002/07/26/big/img_660
+2002/09/02/big/img_15197
+2002/07/29/big/img_258
+2002/08/30/big/img_18525
+2003/01/13/big/img_368
+2002/07/29/big/img_1538
+2002/07/21/big/img_787
+2002/08/18/big/img_152
+2002/08/06/big/img_2379
+2003/01/17/big/img_864
+2002/08/27/big/img_19998
+2002/08/01/big/img_1634
+2002/07/25/big/img_414
+2002/08/22/big/img_627
+2002/08/07/big/img_1669
+2002/08/16/big/img_1052
+2002/08/31/big/img_17796
+2002/08/18/big/img_199
+2002/09/02/big/img_15147
+2002/08/09/big/img_460
+2002/08/14/big/img_581
+2002/08/30/big/img_18286
+2002/07/26/big/img_337
+2002/08/18/big/img_589
+2003/01/14/big/img_866
+2002/07/20/big/img_624
+2002/08/01/big/img_1801
+2002/07/24/big/img_683
+2002/08/09/big/img_725
+2003/01/14/big/img_34
+2002/07/30/big/img_144
+2002/07/30/big/img_706
+2002/08/08/big/img_394
+2002/08/19/big/img_619
+2002/08/06/big/img_2703
+2002/08/29/big/img_19034
+2002/07/24/big/img_67
+2002/08/27/big/img_19841
+2002/08/19/big/img_427
+2003/01/14/big/img_333
+2002/09/01/big/img_16406
+2002/07/19/big/img_882
+2002/08/17/big/img_238
+2003/01/14/big/img_739
+2002/07/22/big/img_151
+2002/08/21/big/img_743
+2002/07/25/big/img_1048
+2002/07/30/big/img_395
+2003/01/13/big/img_584
+2002/08/13/big/img_742
+2002/08/13/big/img_1168
+2003/01/14/big/img_147
+2002/07/26/big/img_803
+2002/08/05/big/img_3298
+2002/08/07/big/img_1451
+2002/08/16/big/img_424
+2002/07/29/big/img_1069
+2002/09/01/big/img_16735
+2002/07/21/big/img_637
+2003/01/14/big/img_585
+2002/08/02/big/img_358
+2003/01/13/big/img_358
+2002/08/14/big/img_198
+2002/08/17/big/img_935
+2002/08/04/big/img_42
+2002/08/30/big/img_18245
+2002/07/25/big/img_158
+2002/08/22/big/img_744
+2002/08/06/big/img_2291
+2002/08/05/big/img_3044
+2002/07/30/big/img_272
+2002/08/23/big/img_641
+2002/07/24/big/img_797
+2002/07/30/big/img_392
+2003/01/14/big/img_447
+2002/07/31/big/img_898
+2002/08/06/big/img_2812
+2002/08/13/big/img_564
+2002/07/22/big/img_43
+2002/07/26/big/img_634
+2002/07/19/big/img_843
+2002/08/26/big/img_58
+2002/07/21/big/img_375
+2002/08/25/big/img_729
+2002/07/19/big/img_561
+2003/01/15/big/img_884
+2002/07/25/big/img_891
+2002/08/09/big/img_558
+2002/08/26/big/img_587
+2002/08/13/big/img_1146
+2002/09/02/big/img_15153
+2002/07/26/big/img_316
+2002/08/01/big/img_1940
+2002/08/26/big/img_90
+2003/01/13/big/img_347
+2002/07/25/big/img_520
+2002/08/29/big/img_18718
+2002/08/28/big/img_19219
+2002/08/13/big/img_375
+2002/07/20/big/img_719
+2002/08/31/big/img_17431
+2002/07/28/big/img_192
+2002/08/26/big/img_259
+2002/08/18/big/img_484
+2002/07/29/big/img_580
+2002/07/26/big/img_84
+2002/08/02/big/img_302
+2002/08/31/big/img_17007
+2003/01/15/big/img_543
+2002/09/01/big/img_16488
+2002/08/22/big/img_798
+2002/07/30/big/img_383
+2002/08/04/big/img_668
+2002/08/13/big/img_156
+2002/08/07/big/img_1353
+2002/07/25/big/img_281
+2003/01/14/big/img_587
+2003/01/15/big/img_524
+2002/08/19/big/img_726
+2002/08/21/big/img_709
+2002/08/26/big/img_465
+2002/07/31/big/img_658
+2002/08/28/big/img_19148
+2002/07/23/big/img_423
+2002/08/16/big/img_758
+2002/08/22/big/img_523
+2002/08/16/big/img_591
+2002/08/23/big/img_845
+2002/07/26/big/img_678
+2002/08/09/big/img_806
+2002/08/06/big/img_2369
+2002/07/29/big/img_457
+2002/07/19/big/img_278
+2002/08/30/big/img_18107
+2002/07/26/big/img_444
+2002/08/20/big/img_278
+2002/08/26/big/img_92
+2002/08/26/big/img_257
+2002/07/25/big/img_266
+2002/08/05/big/img_3829
+2002/07/26/big/img_757
+2002/07/29/big/img_1536
+2002/08/09/big/img_472
+2003/01/17/big/img_480
+2002/08/28/big/img_19355
+2002/07/26/big/img_97
+2002/08/06/big/img_2503
+2002/07/19/big/img_254
+2002/08/01/big/img_1470
+2002/08/21/big/img_42
+2002/08/20/big/img_217
+2002/08/06/big/img_2459
+2002/07/19/big/img_552
+2002/08/13/big/img_717
+2002/08/12/big/img_586
+2002/08/20/big/img_411
+2003/01/13/big/img_768
+2002/08/07/big/img_1747
+2002/08/15/big/img_385
+2002/08/01/big/img_1648
+2002/08/15/big/img_311
+2002/08/21/big/img_95
+2002/08/09/big/img_108
+2002/08/21/big/img_398
+2002/08/17/big/img_340
+2002/08/14/big/img_474
+2002/08/13/big/img_294
+2002/08/24/big/img_840
+2002/08/09/big/img_808
+2002/08/23/big/img_491
+2002/07/28/big/img_33
+2003/01/13/big/img_664
+2002/08/02/big/img_261
+2002/08/09/big/img_591
+2002/07/26/big/img_309
+2003/01/14/big/img_372
+2002/08/19/big/img_581
+2002/08/19/big/img_168
+2002/08/26/big/img_422
+2002/07/24/big/img_106
+2002/08/01/big/img_1936
+2002/08/05/big/img_3764
+2002/08/21/big/img_266
+2002/08/31/big/img_17968
+2002/08/01/big/img_1941
+2002/08/15/big/img_550
+2002/08/14/big/img_13
+2002/07/30/big/img_171
+2003/01/13/big/img_490
+2002/07/25/big/img_427
+2002/07/19/big/img_770
+2002/08/12/big/img_759
+2003/01/15/big/img_1360
+2002/08/05/big/img_3692
+2003/01/16/big/img_30
+2002/07/25/big/img_1026
+2002/07/22/big/img_288
+2002/08/29/big/img_18801
+2002/07/24/big/img_793
+2002/08/13/big/img_178
+2002/08/06/big/img_2322
+2003/01/14/big/img_560
+2002/08/18/big/img_408
+2003/01/16/big/img_915
+2003/01/16/big/img_679
+2002/08/07/big/img_1552
+2002/08/29/big/img_19050
+2002/08/01/big/img_2172
+2002/07/31/big/img_30
+2002/07/30/big/img_1019
+2002/07/30/big/img_587
+2003/01/13/big/img_773
+2002/07/30/big/img_410
+2002/07/28/big/img_65
+2002/08/05/big/img_3138
+2002/07/23/big/img_541
+2002/08/22/big/img_963
+2002/07/27/big/img_657
+2002/07/30/big/img_1051
+2003/01/16/big/img_150
+2002/07/31/big/img_519
+2002/08/01/big/img_1961
+2002/08/05/big/img_3752
+2002/07/23/big/img_631
+2003/01/14/big/img_237
+2002/07/28/big/img_21
+2002/07/22/big/img_813
+2002/08/05/big/img_3563
+2003/01/17/big/img_620
+2002/07/19/big/img_523
+2002/07/30/big/img_904
+2002/08/29/big/img_18642
+2002/08/11/big/img_492
+2002/08/01/big/img_2130
+2002/07/25/big/img_618
+2002/08/17/big/img_305
+2003/01/16/big/img_520
+2002/07/26/big/img_495
+2002/08/17/big/img_164
+2002/08/03/big/img_440
+2002/07/24/big/img_441
+2002/08/06/big/img_2146
+2002/08/11/big/img_558
+2002/08/02/big/img_545
+2002/08/31/big/img_18090
+2003/01/01/big/img_136
+2002/07/25/big/img_1099
+2003/01/13/big/img_728
+2003/01/16/big/img_197
+2002/07/26/big/img_651
+2002/08/11/big/img_676
+2003/01/15/big/img_10
+2002/08/21/big/img_250
+2002/08/14/big/img_325
+2002/08/04/big/img_390
+2002/07/24/big/img_554
+2003/01/16/big/img_333
+2002/07/31/big/img_922
+2002/09/02/big/img_15586
+2003/01/16/big/img_184
+2002/07/22/big/img_766
+2002/07/21/big/img_608
+2002/08/07/big/img_1578
+2002/08/17/big/img_961
+2002/07/27/big/img_324
+2002/08/05/big/img_3765
+2002/08/23/big/img_462
+2003/01/16/big/img_382
+2002/08/27/big/img_19838
+2002/08/01/big/img_1505
+2002/08/21/big/img_662
+2002/08/14/big/img_605
+2002/08/19/big/img_816
+2002/07/29/big/img_136
+2002/08/20/big/img_719
+2002/08/06/big/img_2826
+2002/08/10/big/img_630
+2003/01/17/big/img_973
+2002/08/14/big/img_116
+2002/08/02/big/img_666
+2002/08/21/big/img_710
+2002/08/05/big/img_55
+2002/07/31/big/img_229
+2002/08/01/big/img_1549
+2002/07/23/big/img_432
+2002/07/21/big/img_430
+2002/08/21/big/img_549
+2002/08/08/big/img_985
+2002/07/20/big/img_610
+2002/07/23/big/img_978
+2002/08/23/big/img_219
+2002/07/25/big/img_175
+2003/01/15/big/img_230
+2002/08/23/big/img_385
+2002/07/31/big/img_879
+2002/08/12/big/img_495
+2002/08/22/big/img_499
+2002/08/30/big/img_18322
+2002/08/15/big/img_795
+2002/08/13/big/img_835
+2003/01/17/big/img_930
+2002/07/30/big/img_873
+2002/08/11/big/img_257
+2002/07/31/big/img_593
+2002/08/21/big/img_916
+2003/01/13/big/img_814
+2002/07/25/big/img_722
+2002/08/16/big/img_379
+2002/07/31/big/img_497
+2002/07/22/big/img_602
+2002/08/21/big/img_642
+2002/08/21/big/img_614
+2002/08/23/big/img_482
+2002/07/29/big/img_603
+2002/08/13/big/img_705
+2002/07/23/big/img_833
+2003/01/14/big/img_511
+2002/07/24/big/img_376
+2002/08/17/big/img_1030
+2002/08/05/big/img_3576
+2002/08/16/big/img_540
+2002/07/22/big/img_630
+2002/08/10/big/img_180
+2002/08/14/big/img_905
+2002/08/29/big/img_18777
+2002/08/22/big/img_693
+2003/01/16/big/img_933
+2002/08/20/big/img_555
+2002/08/15/big/img_549
+2003/01/14/big/img_830
+2003/01/16/big/img_64
+2002/08/27/big/img_19670
+2002/08/22/big/img_729
+2002/07/27/big/img_981
+2002/08/09/big/img_458
+2003/01/17/big/img_884
+2002/07/25/big/img_639
+2002/08/31/big/img_18008
+2002/08/22/big/img_249
+2002/08/17/big/img_971
+2002/08/04/big/img_308
+2002/07/28/big/img_362
+2002/08/12/big/img_142
+2002/08/26/big/img_61
+2002/08/14/big/img_422
+2002/07/19/big/img_607
+2003/01/15/big/img_717
+2002/08/01/big/img_1475
+2002/08/29/big/img_19061
+2003/01/01/big/img_346
+2002/07/20/big/img_315
+2003/01/15/big/img_756
+2002/08/15/big/img_879
+2002/08/08/big/img_615
+2003/01/13/big/img_431
+2002/08/05/big/img_3233
+2002/08/24/big/img_526
+2003/01/13/big/img_717
+2002/09/01/big/img_16408
+2002/07/22/big/img_217
+2002/07/31/big/img_960
+2002/08/21/big/img_610
+2002/08/05/big/img_3753
+2002/08/03/big/img_151
+2002/08/21/big/img_267
+2002/08/01/big/img_2175
+2002/08/04/big/img_556
+2002/08/21/big/img_527
+2002/09/02/big/img_15800
+2002/07/27/big/img_156
+2002/07/20/big/img_590
+2002/08/15/big/img_700
+2002/08/08/big/img_444
+2002/07/25/big/img_94
+2002/07/24/big/img_778
+2002/08/14/big/img_694
+2002/07/20/big/img_666
+2002/08/02/big/img_200
+2002/08/02/big/img_578
+2003/01/17/big/img_332
+2002/09/01/big/img_16352
+2002/08/27/big/img_19668
+2002/07/23/big/img_823
+2002/08/13/big/img_431
+2003/01/16/big/img_463
+2002/08/27/big/img_19711
+2002/08/23/big/img_154
+2002/07/31/big/img_360
+2002/08/23/big/img_555
+2002/08/10/big/img_561
+2003/01/14/big/img_550
+2002/08/07/big/img_1370
+2002/07/30/big/img_1184
+2002/08/01/big/img_1445
+2002/08/23/big/img_22
+2002/07/30/big/img_606
+2003/01/17/big/img_271
+2002/08/31/big/img_17316
+2002/08/16/big/img_973
+2002/07/26/big/img_77
+2002/07/20/big/img_788
+2002/08/06/big/img_2426
+2002/08/07/big/img_1498
+2002/08/16/big/img_358
+2002/08/06/big/img_2851
+2002/08/12/big/img_359
+2002/08/01/big/img_1521
+2002/08/02/big/img_709
+2002/08/20/big/img_935
+2002/08/12/big/img_188
+2002/08/24/big/img_411
+2002/08/22/big/img_680
+2002/08/06/big/img_2480
+2002/07/20/big/img_627
+2002/07/30/big/img_214
+2002/07/25/big/img_354
+2002/08/02/big/img_636
+2003/01/15/big/img_661
+2002/08/07/big/img_1327
+2002/08/01/big/img_2108
+2002/08/31/big/img_17919
+2002/08/29/big/img_18768
+2002/08/05/big/img_3840
+2002/07/26/big/img_242
+2003/01/14/big/img_451
+2002/08/20/big/img_923
+2002/08/27/big/img_19908
+2002/08/16/big/img_282
+2002/08/19/big/img_440
+2003/01/01/big/img_230
+2002/08/08/big/img_212
+2002/07/20/big/img_443
+2002/08/25/big/img_635
+2003/01/13/big/img_1169
+2002/07/26/big/img_998
+2002/08/15/big/img_995
+2002/08/06/big/img_3002
+2002/07/29/big/img_460
+2003/01/14/big/img_925
+2002/07/23/big/img_539
+2002/08/16/big/img_694
+2003/01/13/big/img_459
+2002/07/23/big/img_249
+2002/08/20/big/img_539
+2002/08/04/big/img_186
+2002/08/26/big/img_264
+2002/07/22/big/img_704
+2002/08/25/big/img_277
+2002/08/22/big/img_988
+2002/07/29/big/img_504
+2002/08/05/big/img_3600
+2002/08/30/big/img_18380
+2003/01/14/big/img_937
+2002/08/21/big/img_254
+2002/08/10/big/img_130
+2002/08/20/big/img_339
+2003/01/14/big/img_428
+2002/08/20/big/img_889
+2002/08/31/big/img_17637
+2002/07/26/big/img_644
+2002/09/01/big/img_16776
+2002/08/06/big/img_2239
+2002/08/06/big/img_2646
+2003/01/13/big/img_491
+2002/08/10/big/img_579
+2002/08/21/big/img_713
+2002/08/22/big/img_482
+2002/07/22/big/img_167
+2002/07/24/big/img_539
+2002/08/14/big/img_721
+2002/07/25/big/img_389
+2002/09/01/big/img_16591
+2002/08/13/big/img_543
+2003/01/14/big/img_432
+2002/08/09/big/img_287
+2002/07/26/big/img_126
+2002/08/23/big/img_412
+2002/08/15/big/img_1034
+2002/08/28/big/img_19485
+2002/07/31/big/img_236
+2002/07/30/big/img_523
+2002/07/19/big/img_141
+2003/01/17/big/img_957
+2002/08/04/big/img_81
+2002/07/25/big/img_206
+2002/08/15/big/img_716
+2002/08/13/big/img_403
+2002/08/15/big/img_685
+2002/07/26/big/img_884
+2002/07/19/big/img_499
+2002/07/23/big/img_772
+2002/07/27/big/img_752
+2003/01/14/big/img_493
+2002/08/25/big/img_664
+2002/07/31/big/img_334
+2002/08/26/big/img_678
+2002/09/01/big/img_16541
+2003/01/14/big/img_347
+2002/07/23/big/img_187
+2002/07/30/big/img_1163
+2002/08/05/big/img_35
+2002/08/22/big/img_944
+2002/08/07/big/img_1239
+2002/07/29/big/img_1215
+2002/08/03/big/img_312
+2002/08/05/big/img_3523
+2002/07/29/big/img_218
+2002/08/13/big/img_672
+2002/08/16/big/img_205
+2002/08/17/big/img_594
+2002/07/29/big/img_1411
+2002/07/30/big/img_942
+2003/01/16/big/img_312
+2002/08/08/big/img_312
+2002/07/25/big/img_15
+2002/08/09/big/img_839
+2002/08/01/big/img_2069
+2002/08/31/big/img_17512
+2002/08/01/big/img_3
+2002/07/31/big/img_320
+2003/01/15/big/img_1265
+2002/08/14/big/img_563
+2002/07/31/big/img_167
+2002/08/20/big/img_374
+2002/08/13/big/img_406
+2002/08/08/big/img_625
+2002/08/02/big/img_314
+2002/08/27/big/img_19964
+2002/09/01/big/img_16670
+2002/07/31/big/img_599
+2002/08/29/big/img_18906
+2002/07/24/big/img_373
+2002/07/26/big/img_513
+2002/09/02/big/img_15497
+2002/08/19/big/img_117
+2003/01/01/big/img_158
+2002/08/24/big/img_178
+2003/01/13/big/img_935
+2002/08/13/big/img_609
+2002/08/30/big/img_18341
+2002/08/25/big/img_674
+2003/01/13/big/img_209
+2002/08/13/big/img_258
+2002/08/05/big/img_3543
+2002/08/07/big/img_1970
+2002/08/06/big/img_3004
+2003/01/17/big/img_487
+2002/08/24/big/img_873
+2002/08/29/big/img_18730
+2002/08/09/big/img_375
+2003/01/16/big/img_751
+2002/08/02/big/img_603
+2002/08/19/big/img_325
+2002/09/01/big/img_16420
+2002/08/05/big/img_3633
+2002/08/21/big/img_516
+2002/07/19/big/img_501
+2002/07/26/big/img_688
+2002/07/24/big/img_256
+2002/07/25/big/img_438
+2002/07/31/big/img_1017
+2002/08/22/big/img_512
+2002/07/21/big/img_543
+2002/08/08/big/img_223
+2002/08/19/big/img_189
+2002/08/12/big/img_630
+2002/07/30/big/img_958
+2002/07/28/big/img_208
+2002/08/31/big/img_17691
+2002/07/22/big/img_542
+2002/07/19/big/img_741
+2002/07/19/big/img_158
+2002/08/15/big/img_399
+2002/08/01/big/img_2159
+2002/08/14/big/img_455
+2002/08/17/big/img_1011
+2002/08/26/big/img_744
+2002/08/12/big/img_624
+2003/01/17/big/img_821
+2002/08/16/big/img_980
+2002/07/28/big/img_281
+2002/07/25/big/img_171
+2002/08/03/big/img_116
+2002/07/22/big/img_467
+2002/07/31/big/img_750
+2002/07/26/big/img_435
+2002/07/19/big/img_822
+2002/08/13/big/img_626
+2002/08/11/big/img_344
+2002/08/02/big/img_473
+2002/09/01/big/img_16817
+2002/08/01/big/img_1275
+2002/08/28/big/img_19270
+2002/07/23/big/img_607
+2002/08/09/big/img_316
+2002/07/29/big/img_626
+2002/07/24/big/img_824
+2002/07/22/big/img_342
+2002/08/08/big/img_794
+2002/08/07/big/img_1209
+2002/07/19/big/img_18
+2002/08/25/big/img_634
+2002/07/24/big/img_730
+2003/01/17/big/img_356
+2002/07/23/big/img_305
+2002/07/30/big/img_453
+2003/01/13/big/img_972
+2002/08/06/big/img_2610
+2002/08/29/big/img_18920
+2002/07/31/big/img_123
+2002/07/26/big/img_979
+2002/08/24/big/img_635
+2002/08/05/big/img_3704
+2002/08/07/big/img_1358
+2002/07/22/big/img_306
+2002/08/13/big/img_619
+2002/08/02/big/img_366

videoretalking/third_part/GPEN/face_detect/data/__init__.py

@@ -0,0 +1,3 @@
+from .wider_face import WiderFaceDetection, detection_collate
+from .data_augment import *
+from .config import *

videoretalking/third_part/GPEN/face_detect/data/config.py

@@ -0,0 +1,42 @@
+# config.py
+
+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,
+    'pretrain': False,
+    '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,
+    'pretrain': False,
+    'return_layers': {'layer2': 1, 'layer3': 2, 'layer4': 3},
+    'in_channel': 256,
+    'out_channel': 256
+}
+

videoretalking/third_part/GPEN/face_detect/data/data_augment.py

@@ -0,0 +1,237 @@
+import cv2
+import numpy as np
+import random
+from face_detect.utils.box_utils import matrix_iof
+
+
+def _crop(image, boxes, labels, landm, img_dim):
+    height, width, _ = image.shape
+    pad_image_flag = True
+
+    for _ in range(250):
+        """
+        if random.uniform(0, 1) <= 0.2:
+            scale = 1.0
+        else:
+            scale = random.uniform(0.3, 1.0)
+        """
+        PRE_SCALES = [0.3, 0.45, 0.6, 0.8, 1.0]
+        scale = random.choice(PRE_SCALES)
+        short_side = min(width, height)
+        w = int(scale * short_side)
+        h = w
+
+        if width == w:
+            l = 0
+        else:
+            l = random.randrange(width - w)
+        if height == h:
+            t = 0
+        else:
+            t = random.randrange(height - h)
+        roi = np.array((l, t, l + w, t + h))
+
+        value = matrix_iof(boxes, roi[np.newaxis])
+        flag = (value >= 1)
+        if not flag.any():
+            continue
+
+        centers = (boxes[:, :2] + boxes[:, 2:]) / 2
+        mask_a = np.logical_and(roi[:2] < centers, centers < roi[2:]).all(axis=1)
+        boxes_t = boxes[mask_a].copy()
+        labels_t = labels[mask_a].copy()
+        landms_t = landm[mask_a].copy()
+        landms_t = landms_t.reshape([-1, 5, 2])
+
+        if boxes_t.shape[0] == 0:
+            continue
+
+        image_t = image[roi[1]:roi[3], roi[0]:roi[2]]
+
+        boxes_t[:, :2] = np.maximum(boxes_t[:, :2], roi[:2])
+        boxes_t[:, :2] -= roi[:2]
+        boxes_t[:, 2:] = np.minimum(boxes_t[:, 2:], roi[2:])
+        boxes_t[:, 2:] -= roi[:2]
+
+        # landm
+        landms_t[:, :, :2] = landms_t[:, :, :2] - roi[:2]
+        landms_t[:, :, :2] = np.maximum(landms_t[:, :, :2], np.array([0, 0]))
+        landms_t[:, :, :2] = np.minimum(landms_t[:, :, :2], roi[2:] - roi[:2])
+        landms_t = landms_t.reshape([-1, 10])
+
+
+	# make sure that the cropped image contains at least one face > 16 pixel at training image scale
+        b_w_t = (boxes_t[:, 2] - boxes_t[:, 0] + 1) / w * img_dim
+        b_h_t = (boxes_t[:, 3] - boxes_t[:, 1] + 1) / h * img_dim
+        mask_b = np.minimum(b_w_t, b_h_t) > 0.0
+        boxes_t = boxes_t[mask_b]
+        labels_t = labels_t[mask_b]
+        landms_t = landms_t[mask_b]
+
+        if boxes_t.shape[0] == 0:
+            continue
+
+        pad_image_flag = False
+
+        return image_t, boxes_t, labels_t, landms_t, pad_image_flag
+    return image, boxes, labels, landm, pad_image_flag
+
+
+def _distort(image):
+
+    def _convert(image, alpha=1, beta=0):
+        tmp = image.astype(float) * alpha + beta
+        tmp[tmp < 0] = 0
+        tmp[tmp > 255] = 255
+        image[:] = tmp
+
+    image = image.copy()
+
+    if random.randrange(2):
+
+        #brightness distortion
+        if random.randrange(2):
+            _convert(image, beta=random.uniform(-32, 32))
+
+        #contrast distortion
+        if random.randrange(2):
+            _convert(image, alpha=random.uniform(0.5, 1.5))
+
+        image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
+
+        #saturation distortion
+        if random.randrange(2):
+            _convert(image[:, :, 1], alpha=random.uniform(0.5, 1.5))
+
+        #hue distortion
+        if random.randrange(2):
+            tmp = image[:, :, 0].astype(int) + random.randint(-18, 18)
+            tmp %= 180
+            image[:, :, 0] = tmp
+
+        image = cv2.cvtColor(image, cv2.COLOR_HSV2BGR)
+
+    else:
+
+        #brightness distortion
+        if random.randrange(2):
+            _convert(image, beta=random.uniform(-32, 32))
+
+        image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
+
+        #saturation distortion
+        if random.randrange(2):
+            _convert(image[:, :, 1], alpha=random.uniform(0.5, 1.5))
+
+        #hue distortion
+        if random.randrange(2):
+            tmp = image[:, :, 0].astype(int) + random.randint(-18, 18)
+            tmp %= 180
+            image[:, :, 0] = tmp
+
+        image = cv2.cvtColor(image, cv2.COLOR_HSV2BGR)
+
+        #contrast distortion
+        if random.randrange(2):
+            _convert(image, alpha=random.uniform(0.5, 1.5))
+
+    return image
+
+
+def _expand(image, boxes, fill, p):
+    if random.randrange(2):
+        return image, boxes
+
+    height, width, depth = image.shape
+
+    scale = random.uniform(1, p)
+    w = int(scale * width)
+    h = int(scale * height)
+
+    left = random.randint(0, w - width)
+    top = random.randint(0, h - height)
+
+    boxes_t = boxes.copy()
+    boxes_t[:, :2] += (left, top)
+    boxes_t[:, 2:] += (left, top)
+    expand_image = np.empty(
+        (h, w, depth),
+        dtype=image.dtype)
+    expand_image[:, :] = fill
+    expand_image[top:top + height, left:left + width] = image
+    image = expand_image
+
+    return image, boxes_t
+
+
+def _mirror(image, boxes, landms):
+    _, width, _ = image.shape
+    if random.randrange(2):
+        image = image[:, ::-1]
+        boxes = boxes.copy()
+        boxes[:, 0::2] = width - boxes[:, 2::-2]
+
+        # landm
+        landms = landms.copy()
+        landms = landms.reshape([-1, 5, 2])
+        landms[:, :, 0] = width - landms[:, :, 0]
+        tmp = landms[:, 1, :].copy()
+        landms[:, 1, :] = landms[:, 0, :]
+        landms[:, 0, :] = tmp
+        tmp1 = landms[:, 4, :].copy()
+        landms[:, 4, :] = landms[:, 3, :]
+        landms[:, 3, :] = tmp1
+        landms = landms.reshape([-1, 10])
+
+    return image, boxes, landms
+
+
+def _pad_to_square(image, rgb_mean, pad_image_flag):
+    if not pad_image_flag:
+        return image
+    height, width, _ = image.shape
+    long_side = max(width, height)
+    image_t = np.empty((long_side, long_side, 3), dtype=image.dtype)
+    image_t[:, :] = rgb_mean
+    image_t[0:0 + height, 0:0 + width] = image
+    return image_t
+
+
+def _resize_subtract_mean(image, insize, rgb_mean):
+    interp_methods = [cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_NEAREST, cv2.INTER_LANCZOS4]
+    interp_method = interp_methods[random.randrange(5)]
+    image = cv2.resize(image, (insize, insize), interpolation=interp_method)
+    image = image.astype(np.float32)
+    image -= rgb_mean
+    return image.transpose(2, 0, 1)
+
+
+class preproc(object):
+
+    def __init__(self, img_dim, rgb_means):
+        self.img_dim = img_dim
+        self.rgb_means = rgb_means
+
+    def __call__(self, image, targets):
+        assert targets.shape[0] > 0, "this image does not have gt"
+
+        boxes = targets[:, :4].copy()
+        labels = targets[:, -1].copy()
+        landm = targets[:, 4:-1].copy()
+
+        image_t, boxes_t, labels_t, landm_t, pad_image_flag = _crop(image, boxes, labels, landm, self.img_dim)
+        image_t = _distort(image_t)
+        image_t = _pad_to_square(image_t,self.rgb_means, pad_image_flag)
+        image_t, boxes_t, landm_t = _mirror(image_t, boxes_t, landm_t)
+        height, width, _ = image_t.shape
+        image_t = _resize_subtract_mean(image_t, self.img_dim, self.rgb_means)
+        boxes_t[:, 0::2] /= width
+        boxes_t[:, 1::2] /= height
+
+        landm_t[:, 0::2] /= width
+        landm_t[:, 1::2] /= height
+
+        labels_t = np.expand_dims(labels_t, 1)
+        targets_t = np.hstack((boxes_t, landm_t, labels_t))
+
+        return image_t, targets_t

videoretalking/third_part/GPEN/face_detect/data/wider_face.py

@@ -0,0 +1,101 @@
+import os
+import os.path
+import sys
+import torch
+import torch.utils.data as data
+import cv2
+import numpy as np
+
+class WiderFaceDetection(data.Dataset):
+    def __init__(self, txt_path, preproc=None):
+        self.preproc = preproc
+        self.imgs_path = []
+        self.words = []
+        f = open(txt_path,'r')
+        lines = f.readlines()
+        isFirst = True
+        labels = []
+        for line in lines:
+            line = line.rstrip()
+            if line.startswith('#'):
+                if isFirst is True:
+                    isFirst = False
+                else:
+                    labels_copy = labels.copy()
+                    self.words.append(labels_copy)
+                    labels.clear()
+                path = line[2:]
+                path = txt_path.replace('label.txt','images/') + path
+                self.imgs_path.append(path)
+            else:
+                line = line.split(' ')
+                label = [float(x) for x in line]
+                labels.append(label)
+
+        self.words.append(labels)
+
+    def __len__(self):
+        return len(self.imgs_path)
+
+    def __getitem__(self, index):
+        img = cv2.imread(self.imgs_path[index])
+        height, width, _ = img.shape
+
+        labels = self.words[index]
+        annotations = np.zeros((0, 15))
+        if len(labels) == 0:
+            return annotations
+        for idx, label in enumerate(labels):
+            annotation = np.zeros((1, 15))
+            # bbox
+            annotation[0, 0] = label[0]  # x1
+            annotation[0, 1] = label[1]  # y1
+            annotation[0, 2] = label[0] + label[2]  # x2
+            annotation[0, 3] = label[1] + label[3]  # y2
+
+            # landmarks
+            annotation[0, 4] = label[4]    # l0_x
+            annotation[0, 5] = label[5]    # l0_y
+            annotation[0, 6] = label[7]    # l1_x
+            annotation[0, 7] = label[8]    # l1_y
+            annotation[0, 8] = label[10]   # l2_x
+            annotation[0, 9] = label[11]   # l2_y
+            annotation[0, 10] = label[13]  # l3_x
+            annotation[0, 11] = label[14]  # l3_y
+            annotation[0, 12] = label[16]  # l4_x
+            annotation[0, 13] = label[17]  # l4_y
+            if (annotation[0, 4]<0):
+                annotation[0, 14] = -1
+            else:
+                annotation[0, 14] = 1
+
+            annotations = np.append(annotations, annotation, axis=0)
+        target = np.array(annotations)
+        if self.preproc is not None:
+            img, target = self.preproc(img, target)
+
+        return torch.from_numpy(img), target
+
+def detection_collate(batch):
+    """Custom collate fn for dealing with batches of images that have a different
+    number of associated object annotations (bounding boxes).
+
+    Arguments:
+        batch: (tuple) A tuple of tensor images and lists of annotations
+
+    Return:
+        A tuple containing:
+            1) (tensor) batch of images stacked on their 0 dim
+            2) (list of tensors) annotations for a given image are stacked on 0 dim
+    """
+    targets = []
+    imgs = []
+    for _, sample in enumerate(batch):
+        for _, tup in enumerate(sample):
+            if torch.is_tensor(tup):
+                imgs.append(tup)
+            elif isinstance(tup, type(np.empty(0))):
+                annos = torch.from_numpy(tup).float()
+                targets.append(annos)
+
+    return (torch.stack(imgs, 0), targets)

videoretalking/third_part/GPEN/face_detect/facemodels/net.py

@@ -0,0 +1,137 @@
+import time
+import torch
+import torch.nn as nn
+import torchvision.models._utils as _utils
+import torchvision.models as models
+import torch.nn.functional as F
+from torch.autograd import Variable
+
+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
+

videoretalking/third_part/GPEN/face_detect/facemodels/retinaface.py

@@ -0,0 +1,127 @@
+import torch
+import torch.nn as nn
+import torchvision.models.detection.backbone_utils as backbone_utils
+import torchvision.models._utils as _utils
+import torch.nn.functional as F
+from collections import OrderedDict
+
+from face_detect.facemodels.net import MobileNetV1 as MobileNetV1
+from face_detect.facemodels.net import FPN as FPN
+from face_detect.facemodels.net import SSH as SSH
+
+
+
+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)
+
+class RetinaFace(nn.Module):
+    def __init__(self, cfg = None, phase = 'train'):
+        """
+        :param cfg:  Network related settings.
+        :param phase: train or test.
+        """
+        super(RetinaFace,self).__init__()
+        self.phase = phase
+        backbone = None
+        if cfg['name'] == 'mobilenet0.25':
+            backbone = MobileNetV1()
+            if cfg['pretrain']:
+                checkpoint = torch.load("./weights/mobilenetV1X0.25_pretrain.tar", map_location=torch.device('cpu'))
+                from collections import OrderedDict
+                new_state_dict = OrderedDict()
+                for k, v in checkpoint['state_dict'].items():
+                    name = k[7:]  # remove module.
+                    new_state_dict[name] = v
+                # load params
+                backbone.load_state_dict(new_state_dict)
+        elif cfg['name'] == 'Resnet50':
+            import torchvision.models as models
+            backbone = models.resnet50(pretrained=cfg['pretrain'])
+
+        self.body = _utils.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 = self._make_class_head(fpn_num=3, inchannels=cfg['out_channel'])
+        self.BboxHead = self._make_bbox_head(fpn_num=3, inchannels=cfg['out_channel'])
+        self.LandmarkHead = self._make_landmark_head(fpn_num=3, inchannels=cfg['out_channel'])
+
+    def _make_class_head(self,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(self,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(self,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
+
+    def forward(self,inputs):
+        out = self.body(inputs)
+
+        # 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)
+        ldm_regressions = torch.cat([self.LandmarkHead[i](feature) for i, feature in enumerate(features)], 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

videoretalking/third_part/GPEN/face_detect/layers/__init__.py

@@ -0,0 +1,2 @@
+from .functions import *
+from .modules import *

videoretalking/third_part/GPEN/face_detect/layers/functions/prior_box.py

@@ -0,0 +1,34 @@
+import torch
+from itertools import product as product
+import numpy as np
+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

videoretalking/third_part/GPEN/face_detect/layers/modules/__init__.py

@@ -0,0 +1,3 @@
+from .multibox_loss import MultiBoxLoss
+
+__all__ = ['MultiBoxLoss']

videoretalking/third_part/GPEN/face_detect/layers/modules/multibox_loss.py

@@ -0,0 +1,125 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.autograd import Variable
+from face_detect.utils.box_utils import match, log_sum_exp
+from face_detect.data import cfg_mnet
+GPU = cfg_mnet['gpu_train']
+
+class MultiBoxLoss(nn.Module):
+    """SSD Weighted Loss Function
+    Compute Targets:
+        1) Produce Confidence Target Indices by matching  ground truth boxes
+           with (default) 'priorboxes' that have jaccard index > threshold parameter
+           (default threshold: 0.5).
+        2) Produce localization target by 'encoding' variance into offsets of ground
+           truth boxes and their matched  'priorboxes'.
+        3) Hard negative mining to filter the excessive number of negative examples
+           that comes with using a large number of default bounding boxes.
+           (default negative:positive ratio 3:1)
+    Objective Loss:
+        L(x,c,l,g) = (Lconf(x, c) + αLloc(x,l,g)) / N
+        Where, Lconf is the CrossEntropy Loss and Lloc is the SmoothL1 Loss
+        weighted by α which is set to 1 by cross val.
+        Args:
+            c: class confidences,
+            l: predicted boxes,
+            g: ground truth boxes
+            N: number of matched default boxes
+        See: https://arxiv.org/pdf/1512.02325.pdf for more details.
+    """
+
+    def __init__(self, num_classes, overlap_thresh, prior_for_matching, bkg_label, neg_mining, neg_pos, neg_overlap, encode_target):
+        super(MultiBoxLoss, self).__init__()
+        self.num_classes = num_classes
+        self.threshold = overlap_thresh
+        self.background_label = bkg_label
+        self.encode_target = encode_target
+        self.use_prior_for_matching = prior_for_matching
+        self.do_neg_mining = neg_mining
+        self.negpos_ratio = neg_pos
+        self.neg_overlap = neg_overlap
+        self.variance = [0.1, 0.2]
+
+    def forward(self, predictions, priors, targets):
+        """Multibox Loss
+        Args:
+            predictions (tuple): A tuple containing loc preds, conf preds,
+            and prior boxes from SSD net.
+                conf shape: torch.size(batch_size,num_priors,num_classes)
+                loc shape: torch.size(batch_size,num_priors,4)
+                priors shape: torch.size(num_priors,4)
+
+            ground_truth (tensor): Ground truth boxes and labels for a batch,
+                shape: [batch_size,num_objs,5] (last idx is the label).
+        """
+
+        loc_data, conf_data, landm_data = predictions
+        priors = priors
+        num = loc_data.size(0)
+        num_priors = (priors.size(0))
+
+        # match priors (default boxes) and ground truth boxes
+        loc_t = torch.Tensor(num, num_priors, 4)
+        landm_t = torch.Tensor(num, num_priors, 10)
+        conf_t = torch.LongTensor(num, num_priors)
+        for idx in range(num):
+            truths = targets[idx][:, :4].data
+            labels = targets[idx][:, -1].data
+            landms = targets[idx][:, 4:14].data
+            defaults = priors.data
+            match(self.threshold, truths, defaults, self.variance, labels, landms, loc_t, conf_t, landm_t, idx)
+        if GPU:
+            loc_t = loc_t.cuda()
+            conf_t = conf_t.cuda()
+            landm_t = landm_t.cuda()
+
+        zeros = torch.tensor(0).cuda()
+        # landm Loss (Smooth L1)
+        # Shape: [batch,num_priors,10]
+        pos1 = conf_t > zeros
+        num_pos_landm = pos1.long().sum(1, keepdim=True)
+        N1 = max(num_pos_landm.data.sum().float(), 1)
+        pos_idx1 = pos1.unsqueeze(pos1.dim()).expand_as(landm_data)
+        landm_p = landm_data[pos_idx1].view(-1, 10)
+        landm_t = landm_t[pos_idx1].view(-1, 10)
+        loss_landm = F.smooth_l1_loss(landm_p, landm_t, reduction='sum')
+
+
+        pos = conf_t != zeros
+        conf_t[pos] = 1
+
+        # Localization Loss (Smooth L1)
+        # Shape: [batch,num_priors,4]
+        pos_idx = pos.unsqueeze(pos.dim()).expand_as(loc_data)
+        loc_p = loc_data[pos_idx].view(-1, 4)
+        loc_t = loc_t[pos_idx].view(-1, 4)
+        loss_l = F.smooth_l1_loss(loc_p, loc_t, reduction='sum')
+
+        # Compute max conf across batch for hard negative mining
+        batch_conf = conf_data.view(-1, self.num_classes)
+        loss_c = log_sum_exp(batch_conf) - batch_conf.gather(1, conf_t.view(-1, 1))
+
+        # Hard Negative Mining
+        loss_c[pos.view(-1, 1)] = 0 # filter out pos boxes for now
+        loss_c = loss_c.view(num, -1)
+        _, loss_idx = loss_c.sort(1, descending=True)
+        _, idx_rank = loss_idx.sort(1)
+        num_pos = pos.long().sum(1, keepdim=True)
+        num_neg = torch.clamp(self.negpos_ratio*num_pos, max=pos.size(1)-1)
+        neg = idx_rank < num_neg.expand_as(idx_rank)
+
+        # Confidence Loss Including Positive and Negative Examples
+        pos_idx = pos.unsqueeze(2).expand_as(conf_data)
+        neg_idx = neg.unsqueeze(2).expand_as(conf_data)
+        conf_p = conf_data[(pos_idx+neg_idx).gt(0)].view(-1,self.num_classes)
+        targets_weighted = conf_t[(pos+neg).gt(0)]
+        loss_c = F.cross_entropy(conf_p, targets_weighted, reduction='sum')
+
+        # Sum of losses: L(x,c,l,g) = (Lconf(x, c) + αLloc(x,l,g)) / N
+        N = max(num_pos.data.sum().float(), 1)
+        loss_l /= N
+        loss_c /= N
+        loss_landm /= N1
+
+        return loss_l, loss_c, loss_landm

videoretalking/third_part/GPEN/face_detect/retinaface_detection.py

@@ -0,0 +1,193 @@
+'''
+@paper: GAN Prior Embedded Network for Blind Face Restoration in the Wild (CVPR2021)
+@author: yangxy (yangtao9009@gmail.com)
+'''
+import os
+import torch
+import torch.backends.cudnn as cudnn
+import numpy as np
+from face_detect.data import cfg_re50
+from face_detect.layers.functions.prior_box import PriorBox
+from face_detect.utils.nms.py_cpu_nms import py_cpu_nms
+import cv2
+from face_detect.facemodels.retinaface import RetinaFace
+from face_detect.utils.box_utils import decode, decode_landm
+import time
+import torch.nn.functional as F
+
+
+class RetinaFaceDetection(object):
+    def __init__(self, base_dir, device='cuda', network='RetinaFace-R50'):
+        torch.set_grad_enabled(False)
+        cudnn.benchmark = True
+        self.pretrained_path = os.path.join(base_dir, network+'.pth')
+        self.device = device #torch.cuda.current_device()
+        self.cfg = cfg_re50
+        self.net = RetinaFace(cfg=self.cfg, phase='test')
+        self.load_model()
+        self.net = self.net.to(device)
+
+        self.mean = torch.tensor([[[[104]], [[117]], [[123]]]]).to(device)
+
+    def check_keys(self, pretrained_state_dict):
+        ckpt_keys = set(pretrained_state_dict.keys())
+        model_keys = set(self.net.state_dict().keys())
+        used_pretrained_keys = model_keys & ckpt_keys
+        unused_pretrained_keys = ckpt_keys - model_keys
+        missing_keys = model_keys - ckpt_keys
+        assert len(used_pretrained_keys) > 0, 'load NONE from pretrained checkpoint'
+        return True
+
+    def remove_prefix(self, state_dict, prefix):
+        ''' Old style model is stored with all names of parameters sharing common prefix 'module.' '''
+        f = lambda x: x.split(prefix, 1)[-1] if x.startswith(prefix) else x
+        return {f(key): value for key, value in state_dict.items()}
+
+    def load_model(self, load_to_cpu=False):
+        #if load_to_cpu:
+        #    pretrained_dict = torch.load(self.pretrained_path, map_location=lambda storage, loc: storage)
+        #else:
+        #    pretrained_dict = torch.load(self.pretrained_path, map_location=lambda storage, loc: storage.cuda())
+        pretrained_dict = torch.load(self.pretrained_path, map_location=torch.device('cpu'))
+        if "state_dict" in pretrained_dict.keys():
+            pretrained_dict = self.remove_prefix(pretrained_dict['state_dict'], 'module.')
+        else:
+            pretrained_dict = self.remove_prefix(pretrained_dict, 'module.')
+        self.check_keys(pretrained_dict)
+        self.net.load_state_dict(pretrained_dict, strict=False)
+        self.net.eval()
+    
+    def detect(self, img_raw, resize=1, confidence_threshold=0.9, nms_threshold=0.4, top_k=5000, keep_top_k=750, save_image=False):
+        img = np.float32(img_raw)
+
+        im_height, im_width = img.shape[:2]
+        ss = 1.0
+        # tricky
+        if max(im_height, im_width) > 1500:
+            ss = 1000.0/max(im_height, im_width)
+            img = cv2.resize(img, (0,0), fx=ss, fy=ss)
+            im_height, im_width = img.shape[:2]
+
+        scale = torch.Tensor([img.shape[1], img.shape[0], img.shape[1], img.shape[0]])
+        img -= (104, 117, 123)
+        img = img.transpose(2, 0, 1)
+        img = torch.from_numpy(img).unsqueeze(0)
+        img = img.to(self.device)
+        scale = scale.to(self.device)
+        
+        with torch.no_grad():
+            loc, conf, landms = self.net(img)  # forward pass
+
+        priorbox = PriorBox(self.cfg, image_size=(im_height, im_width))
+        priors = priorbox.forward()
+        priors = priors.to(self.device)
+        prior_data = priors.data
+        boxes = decode(loc.data.squeeze(0), prior_data, self.cfg['variance'])
+        boxes = boxes * scale / resize
+        boxes = boxes.cpu().numpy()
+        scores = conf.squeeze(0).data.cpu().numpy()[:, 1]
+        landms = decode_landm(landms.data.squeeze(0), prior_data, self.cfg['variance'])
+        scale1 = torch.Tensor([img.shape[3], img.shape[2], img.shape[3], img.shape[2],
+                               img.shape[3], img.shape[2], img.shape[3], img.shape[2],
+                               img.shape[3], img.shape[2]])
+        scale1 = scale1.to(self.device)
+        landms = landms * scale1 / resize
+        landms = landms.cpu().numpy()
+
+        # ignore low scores
+        inds = np.where(scores > confidence_threshold)[0]
+        boxes = boxes[inds]
+        landms = landms[inds]
+        scores = scores[inds]
+
+        # keep top-K before NMS
+        order = scores.argsort()[::-1][:top_k]
+        boxes = boxes[order]
+        landms = landms[order]
+        scores = scores[order]
+
+        # do NMS
+        dets = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32, copy=False)
+        keep = py_cpu_nms(dets, nms_threshold)
+        # keep = nms(dets, nms_threshold,force_cpu=args.cpu)
+        dets = dets[keep, :]
+        landms = landms[keep]
+
+        # keep top-K faster NMS
+        dets = dets[:keep_top_k, :]
+        landms = landms[:keep_top_k, :]
+
+        # sort faces(delete)
+        '''
+        fscores = [det[4] for det in dets]
+        sorted_idx = sorted(range(len(fscores)), key=lambda k:fscores[k], reverse=False) # sort index
+        tmp = [landms[idx] for idx in sorted_idx]
+        landms = np.asarray(tmp)
+        '''
+        
+        landms = landms.reshape((-1, 5, 2))
+        landms = landms.transpose((0, 2, 1))
+        landms = landms.reshape(-1, 10, )
+        return dets/ss, landms/ss
+
+    def detect_tensor(self, img, resize=1, confidence_threshold=0.9, nms_threshold=0.4, top_k=5000, keep_top_k=750, save_image=False):
+        im_height, im_width = img.shape[-2:]
+        ss = 1000/max(im_height, im_width)
+        img = F.interpolate(img, scale_factor=ss)
+        im_height, im_width = img.shape[-2:]
+        scale = torch.Tensor([im_width, im_height, im_width, im_height]).to(self.device)
+        img -= self.mean
+
+        loc, conf, landms = self.net(img)  # forward pass
+
+        priorbox = PriorBox(self.cfg, image_size=(im_height, im_width))
+        priors = priorbox.forward()
+        priors = priors.to(self.device)
+        prior_data = priors.data
+        boxes = decode(loc.data.squeeze(0), prior_data, self.cfg['variance'])
+        boxes = boxes * scale / resize
+        boxes = boxes.cpu().numpy()
+        scores = conf.squeeze(0).data.cpu().numpy()[:, 1]
+        landms = decode_landm(landms.data.squeeze(0), prior_data, self.cfg['variance'])
+        scale1 = torch.Tensor([img.shape[3], img.shape[2], img.shape[3], img.shape[2],
+                               img.shape[3], img.shape[2], img.shape[3], img.shape[2],
+                               img.shape[3], img.shape[2]])
+        scale1 = scale1.to(self.device)
+        landms = landms * scale1 / resize
+        landms = landms.cpu().numpy()
+
+        # ignore low scores
+        inds = np.where(scores > confidence_threshold)[0]
+        boxes = boxes[inds]
+        landms = landms[inds]
+        scores = scores[inds]
+
+        # keep top-K before NMS
+        order = scores.argsort()[::-1][:top_k]
+        boxes = boxes[order]
+        landms = landms[order]
+        scores = scores[order]
+
+        # do NMS
+        dets = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32, copy=False)
+        keep = py_cpu_nms(dets, nms_threshold)
+        # keep = nms(dets, nms_threshold,force_cpu=args.cpu)
+        dets = dets[keep, :]
+        landms = landms[keep]
+
+        # keep top-K faster NMS
+        dets = dets[:keep_top_k, :]
+        landms = landms[:keep_top_k, :]
+
+        # sort faces(delete)
+        '''
+        fscores = [det[4] for det in dets]
+        sorted_idx = sorted(range(len(fscores)), key=lambda k:fscores[k], reverse=False) # sort index
+        tmp = [landms[idx] for idx in sorted_idx]
+        landms = np.asarray(tmp)
+        '''
+        
+        landms = landms.reshape((-1, 5, 2))
+        landms = landms.transpose((0, 2, 1))
+        landms = landms.reshape(-1, 10, )
+        return dets/ss, landms/ss

videoretalking/third_part/GPEN/face_detect/utils/box_utils.py

@@ -0,0 +1,330 @@
+import torch
+import numpy as np
+
+
+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 mathing 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/ endcoded location targets.
+        conf_t: (tensor) Tensor to be filled w/ matched indices for conf preds.
+        landm_t: (tensor) Tensor to be filled w/ endcoded 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
+    """
+    landms = torch.cat((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:],
+                        ), dim=1)
+    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
+
+

videoretalking/third_part/GPEN/face_detect/utils/nms/py_cpu_nms.py

@@ -0,0 +1,38 @@
+# --------------------------------------------------------
+# Fast R-CNN
+# Copyright (c) 2015 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Ross Girshick
+# --------------------------------------------------------
+
+import numpy as np
+
+def py_cpu_nms(dets, thresh):
+    """Pure Python NMS baseline."""
+    x1 = dets[:, 0]
+    y1 = dets[:, 1]
+    x2 = dets[:, 2]
+    y2 = dets[:, 3]
+    scores = dets[:, 4]
+
+    areas = (x2 - x1 + 1) * (y2 - y1 + 1)
+    order = scores.argsort()[::-1]
+
+    keep = []
+    while order.size > 0:
+        i = order[0]
+        keep.append(i)
+        xx1 = np.maximum(x1[i], x1[order[1:]])
+        yy1 = np.maximum(y1[i], y1[order[1:]])
+        xx2 = np.minimum(x2[i], x2[order[1:]])
+        yy2 = np.minimum(y2[i], y2[order[1:]])
+
+        w = np.maximum(0.0, xx2 - xx1 + 1)
+        h = np.maximum(0.0, yy2 - yy1 + 1)
+        inter = w * h
+        ovr = inter / (areas[i] + areas[order[1:]] - inter)
+
+        inds = np.where(ovr <= thresh)[0]
+        order = order[inds + 1]
+
+    return keep

videoretalking/third_part/GPEN/face_detect/utils/timer.py

@@ -0,0 +1,40 @@
+# --------------------------------------------------------
+# Fast R-CNN
+# Copyright (c) 2015 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Ross Girshick
+# --------------------------------------------------------
+
+import time
+
+
+class Timer(object):
+    """A simple timer."""
+    def __init__(self):
+        self.total_time = 0.
+        self.calls = 0
+        self.start_time = 0.
+        self.diff = 0.
+        self.average_time = 0.
+
+    def tic(self):
+        # using time.time instead of time.clock because time time.clock
+        # does not normalize for multithreading
+        self.start_time = time.time()
+
+    def toc(self, average=True):
+        self.diff = time.time() - self.start_time
+        self.total_time += self.diff
+        self.calls += 1
+        self.average_time = self.total_time / self.calls
+        if average:
+            return self.average_time
+        else:
+            return self.diff
+
+    def clear(self):
+        self.total_time = 0.
+        self.calls = 0
+        self.start_time = 0.
+        self.diff = 0.
+        self.average_time = 0.

videoretalking/third_part/GPEN/face_model/face_gan.py

@@ -0,0 +1,55 @@
+'''
+@paper: GAN Prior Embedded Network for Blind Face Restoration in the Wild (CVPR2021)
+@author: yangxy (yangtao9009@gmail.com)
+'''
+import torch
+import os
+import cv2
+import glob
+import numpy as np
+from torch import nn
+import torch.nn.functional as F
+from torchvision import transforms, utils
+from face_model.gpen_model import FullGenerator
+
+class FaceGAN(object):
+    def __init__(self, base_dir='./', size=512, model=None, channel_multiplier=2, narrow=1, is_norm=True, device='cuda'):
+        self.mfile = os.path.join(base_dir, model+'.pth')
+        self.n_mlp = 8
+        self.device = device
+        self.is_norm = is_norm
+        self.resolution = size
+        self.load_model(channel_multiplier, narrow)
+
+    def load_model(self, channel_multiplier=2, narrow=1):
+        self.model = FullGenerator(self.resolution, 512, self.n_mlp, channel_multiplier, narrow=narrow, device=self.device)
+        pretrained_dict = torch.load(self.mfile, map_location=torch.device('cpu'))
+        self.model.load_state_dict(pretrained_dict)
+        self.model.to(self.device)
+        self.model.eval()
+
+    def process(self, img):
+        img = cv2.resize(img, (self.resolution, self.resolution))
+        img_t = self.img2tensor(img)
+
+        with torch.no_grad():
+            out, __ = self.model(img_t)
+
+        out = self.tensor2img(out)
+
+        return out
+
+    def img2tensor(self, img):
+        img_t = torch.from_numpy(img).to(self.device)/255.
+        if self.is_norm:
+            img_t = (img_t - 0.5) / 0.5
+        img_t = img_t.permute(2, 0, 1).unsqueeze(0).flip(1) # BGR->RGB
+        return img_t
+
+    def tensor2img(self, img_t, pmax=255.0, imtype=np.uint8):
+        if self.is_norm:
+            img_t = img_t * 0.5 + 0.5
+        img_t = img_t.squeeze(0).permute(1, 2, 0).flip(2) # RGB->BGR
+        img_np = np.clip(img_t.float().cpu().numpy(), 0, 1) * pmax
+
+        return img_np.astype(imtype)

videoretalking/third_part/GPEN/face_model/gpen_model.py

@@ -0,0 +1,746 @@
+'''
+@paper: GAN Prior Embedded Network for Blind Face Restoration in the Wild (CVPR2021)
+@author: yangxy (yangtao9009@gmail.com)
+'''
+import math
+import random
+import functools
+import operator
+import itertools
+
+import torch
+from torch import nn
+from torch.nn import functional as F
+from torch.autograd import Function
+
+from face_model.op import FusedLeakyReLU, fused_leaky_relu, upfirdn2d
+
+class PixelNorm(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, input):
+        return input * torch.rsqrt(torch.mean(input ** 2, dim=1, keepdim=True) + 1e-8)
+
+
+def make_kernel(k):
+    k = torch.tensor(k, dtype=torch.float32)
+
+    if k.ndim == 1:
+        k = k[None, :] * k[:, None]
+
+    k /= k.sum()
+
+    return k
+
+
+class Upsample(nn.Module):
+    def __init__(self, kernel, factor=2, device='cpu'):
+        super().__init__()
+
+        self.factor = factor
+        kernel = make_kernel(kernel) * (factor ** 2)
+        self.register_buffer('kernel', kernel)
+
+        p = kernel.shape[0] - factor
+
+        pad0 = (p + 1) // 2 + factor - 1
+        pad1 = p // 2
+
+        self.pad = (pad0, pad1)
+        self.device = device
+
+    def forward(self, input):
+        out = upfirdn2d(input, self.kernel, up=self.factor, down=1, pad=self.pad, device=self.device)
+
+        return out
+
+
+class Downsample(nn.Module):
+    def __init__(self, kernel, factor=2, device='cpu'):
+        super().__init__()
+
+        self.factor = factor
+        kernel = make_kernel(kernel)
+        self.register_buffer('kernel', kernel)
+
+        p = kernel.shape[0] - factor
+
+        pad0 = (p + 1) // 2
+        pad1 = p // 2
+
+        self.pad = (pad0, pad1)
+        self.device = device
+
+    def forward(self, input):
+        out = upfirdn2d(input, self.kernel, up=1, down=self.factor, pad=self.pad, device=self.device)
+
+        return out
+
+
+class Blur(nn.Module):
+    def __init__(self, kernel, pad, upsample_factor=1, device='cpu'):
+        super().__init__()
+
+        kernel = make_kernel(kernel)
+
+        if upsample_factor > 1:
+            kernel = kernel * (upsample_factor ** 2)
+
+        self.register_buffer('kernel', kernel)
+
+        self.pad = pad
+        self.device = device
+
+    def forward(self, input):
+        out = upfirdn2d(input, self.kernel, pad=self.pad, device=self.device)
+
+        return out
+
+
+class EqualConv2d(nn.Module):
+    def __init__(
+        self, in_channel, out_channel, kernel_size, stride=1, padding=0, bias=True
+    ):
+        super().__init__()
+
+        self.weight = nn.Parameter(
+            torch.randn(out_channel, in_channel, kernel_size, kernel_size)
+        )
+        self.scale = 1 / math.sqrt(in_channel * kernel_size ** 2)
+
+        self.stride = stride
+        self.padding = padding
+
+        if bias:
+            self.bias = nn.Parameter(torch.zeros(out_channel))
+
+        else:
+            self.bias = None
+
+    def forward(self, input):
+        out = F.conv2d(
+            input,
+            self.weight * self.scale,
+            bias=self.bias,
+            stride=self.stride,
+            padding=self.padding,
+        )
+
+        return out
+
+    def __repr__(self):
+        return (
+            f'{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]},'
+            f' {self.weight.shape[2]}, stride={self.stride}, padding={self.padding})'
+        )
+
+
+class EqualLinear(nn.Module):
+    def __init__(
+        self, in_dim, out_dim, bias=True, bias_init=0, lr_mul=1, activation=None, device='cpu'
+    ):
+        super().__init__()
+
+        self.weight = nn.Parameter(torch.randn(out_dim, in_dim).div_(lr_mul))
+
+        if bias:
+            self.bias = nn.Parameter(torch.zeros(out_dim).fill_(bias_init))
+
+        else:
+            self.bias = None
+
+        self.activation = activation
+        self.device = device
+
+        self.scale = (1 / math.sqrt(in_dim)) * lr_mul
+        self.lr_mul = lr_mul
+
+    def forward(self, input):
+        if self.activation:
+            out = F.linear(input, self.weight * self.scale)
+            out = fused_leaky_relu(out, self.bias * self.lr_mul, device=self.device)
+
+        else:
+            out = F.linear(input, self.weight * self.scale, bias=self.bias * self.lr_mul)
+
+        return out
+
+    def __repr__(self):
+        return (
+            f'{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]})'
+        )
+
+
+class ScaledLeakyReLU(nn.Module):
+    def __init__(self, negative_slope=0.2):
+        super().__init__()
+
+        self.negative_slope = negative_slope
+
+    def forward(self, input):
+        out = F.leaky_relu(input, negative_slope=self.negative_slope)
+
+        return out * math.sqrt(2)
+
+
+class ModulatedConv2d(nn.Module):
+    def __init__(
+        self,
+        in_channel,
+        out_channel,
+        kernel_size,
+        style_dim,
+        demodulate=True,
+        upsample=False,
+        downsample=False,
+        blur_kernel=[1, 3, 3, 1],
+        device='cpu'
+    ):
+        super().__init__()
+
+        self.eps = 1e-8
+        self.kernel_size = kernel_size
+        self.in_channel = in_channel
+        self.out_channel = out_channel
+        self.upsample = upsample
+        self.downsample = downsample
+
+        if upsample:
+            factor = 2
+            p = (len(blur_kernel) - factor) - (kernel_size - 1)
+            pad0 = (p + 1) // 2 + factor - 1
+            pad1 = p // 2 + 1
+
+            self.blur = Blur(blur_kernel, pad=(pad0, pad1), upsample_factor=factor, device=device)
+
+        if downsample:
+            factor = 2
+            p = (len(blur_kernel) - factor) + (kernel_size - 1)
+            pad0 = (p + 1) // 2
+            pad1 = p // 2
+
+            self.blur = Blur(blur_kernel, pad=(pad0, pad1), device=device)
+
+        fan_in = in_channel * kernel_size ** 2
+        self.scale = 1 / math.sqrt(fan_in)
+        self.padding = kernel_size // 2
+
+        self.weight = nn.Parameter(
+            torch.randn(1, out_channel, in_channel, kernel_size, kernel_size)
+        )
+
+        self.modulation = EqualLinear(style_dim, in_channel, bias_init=1)
+
+        self.demodulate = demodulate
+
+    def __repr__(self):
+        return (
+            f'{self.__class__.__name__}({self.in_channel}, {self.out_channel}, {self.kernel_size}, '
+            f'upsample={self.upsample}, downsample={self.downsample})'
+        )
+
+    def forward(self, input, style):
+        batch, in_channel, height, width = input.shape
+
+        style = self.modulation(style).view(batch, 1, in_channel, 1, 1)
+        weight = self.scale * self.weight * style
+
+        if self.demodulate:
+            demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + 1e-8)
+            weight = weight * demod.view(batch, self.out_channel, 1, 1, 1)
+
+        weight = weight.view(
+            batch * self.out_channel, in_channel, self.kernel_size, self.kernel_size
+        )
+
+        if self.upsample:
+            input = input.view(1, batch * in_channel, height, width)
+            weight = weight.view(
+                batch, self.out_channel, in_channel, self.kernel_size, self.kernel_size
+            )
+            weight = weight.transpose(1, 2).reshape(
+                batch * in_channel, self.out_channel, self.kernel_size, self.kernel_size
+            )
+            out = F.conv_transpose2d(input, weight, padding=0, stride=2, groups=batch)
+            _, _, height, width = out.shape
+            out = out.view(batch, self.out_channel, height, width)
+            out = self.blur(out)
+
+        elif self.downsample:
+            input = self.blur(input)
+            _, _, height, width = input.shape
+            input = input.view(1, batch * in_channel, height, width)
+            out = F.conv2d(input, weight, padding=0, stride=2, groups=batch)
+            _, _, height, width = out.shape
+            out = out.view(batch, self.out_channel, height, width)
+
+        else:
+            input = input.view(1, batch * in_channel, height, width)
+            out = F.conv2d(input, weight, padding=self.padding, groups=batch)
+            _, _, height, width = out.shape
+            out = out.view(batch, self.out_channel, height, width)
+
+        return out
+
+
+class NoiseInjection(nn.Module):
+    def __init__(self, isconcat=True):
+        super().__init__()
+
+        self.isconcat = isconcat
+        self.weight = nn.Parameter(torch.zeros(1))
+
+    def forward(self, image, noise=None):
+        if noise is None:
+            batch, _, height, width = image.shape
+            noise = image.new_empty(batch, 1, height, width).normal_()
+
+        if self.isconcat:
+            return torch.cat((image, self.weight * noise), dim=1)
+        else:
+            return image + self.weight * noise
+
+
+class ConstantInput(nn.Module):
+    def __init__(self, channel, size=4):
+        super().__init__()
+
+        self.input = nn.Parameter(torch.randn(1, channel, size, size))
+
+    def forward(self, input):
+        batch = input.shape[0]
+        out = self.input.repeat(batch, 1, 1, 1)
+
+        return out
+
+
+class StyledConv(nn.Module):
+    def __init__(
+        self,
+        in_channel,
+        out_channel,
+        kernel_size,
+        style_dim,
+        upsample=False,
+        blur_kernel=[1, 3, 3, 1],
+        demodulate=True,
+        isconcat=True,
+        device='cpu'
+    ):
+        super().__init__()
+
+        self.conv = ModulatedConv2d(
+            in_channel,
+            out_channel,
+            kernel_size,
+            style_dim,
+            upsample=upsample,
+            blur_kernel=blur_kernel,
+            demodulate=demodulate,
+            device=device
+        )
+
+        self.noise = NoiseInjection(isconcat)
+        #self.bias = nn.Parameter(torch.zeros(1, out_channel, 1, 1))
+        #self.activate = ScaledLeakyReLU(0.2)
+        feat_multiplier = 2 if isconcat else 1
+        self.activate = FusedLeakyReLU(out_channel*feat_multiplier, device=device)
+
+    def forward(self, input, style, noise=None):
+        out = self.conv(input, style)
+        out = self.noise(out, noise=noise)
+        # out = out + self.bias
+        out = self.activate(out)
+
+        return out
+
+
+class ToRGB(nn.Module):
+    def __init__(self, in_channel, style_dim, upsample=True, blur_kernel=[1, 3, 3, 1], device='cpu'):
+        super().__init__()
+
+        if upsample:
+            self.upsample = Upsample(blur_kernel, device=device)
+
+        self.conv = ModulatedConv2d(in_channel, 3, 1, style_dim, demodulate=False, device=device)
+        self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1))
+
+    def forward(self, input, style, skip=None):
+        out = self.conv(input, style)
+        out = out + self.bias
+
+        if skip is not None:
+            skip = self.upsample(skip)
+
+            out = out + skip
+
+        return out
+
+class Generator(nn.Module):
+    def __init__(
+        self,
+        size,
+        style_dim,
+        n_mlp,
+        channel_multiplier=2,
+        blur_kernel=[1, 3, 3, 1],
+        lr_mlp=0.01,
+        isconcat=True,
+        narrow=1,
+        device='cpu'
+    ):
+        super().__init__()
+
+        self.size = size
+        self.n_mlp = n_mlp
+        self.style_dim = style_dim
+        self.feat_multiplier = 2 if isconcat else 1
+
+        layers = [PixelNorm()]
+
+        for i in range(n_mlp):
+            layers.append(
+                EqualLinear(
+                    style_dim, style_dim, lr_mul=lr_mlp, activation='fused_lrelu', device=device
+                )
+            )
+
+        self.style = nn.Sequential(*layers)
+
+        self.channels = {
+            4: int(512 * narrow),
+            8: int(512 * narrow),
+            16: int(512 * narrow),
+            32: int(512 * narrow),
+            64: int(256 * channel_multiplier * narrow),
+            128: int(128 * channel_multiplier * narrow),
+            256: int(64 * channel_multiplier * narrow),
+            512: int(32 * channel_multiplier * narrow),
+            1024: int(16 * channel_multiplier * narrow)
+        }
+
+        self.input = ConstantInput(self.channels[4])
+        self.conv1 = StyledConv(
+            self.channels[4], self.channels[4], 3, style_dim, blur_kernel=blur_kernel, isconcat=isconcat, device=device
+        )
+        self.to_rgb1 = ToRGB(self.channels[4]*self.feat_multiplier, style_dim, upsample=False, device=device)
+
+        self.log_size = int(math.log(size, 2))
+
+        self.convs = nn.ModuleList()
+        self.upsamples = nn.ModuleList()
+        self.to_rgbs = nn.ModuleList()
+
+        in_channel = self.channels[4]
+
+        for i in range(3, self.log_size + 1):
+            out_channel = self.channels[2 ** i]
+
+            self.convs.append(
+                StyledConv(
+                    in_channel*self.feat_multiplier,
+                    out_channel,
+                    3,
+                    style_dim,
+                    upsample=True,
+                    blur_kernel=blur_kernel,
+                    isconcat=isconcat,
+                    device=device
+                )
+            )
+
+            self.convs.append(
+                StyledConv(
+                    out_channel*self.feat_multiplier, out_channel, 3, style_dim, blur_kernel=blur_kernel, isconcat=isconcat, device=device
+                )
+            )
+
+            self.to_rgbs.append(ToRGB(out_channel*self.feat_multiplier, style_dim, device=device))
+
+            in_channel = out_channel
+
+        self.n_latent = self.log_size * 2 - 2
+
+    def make_noise(self):
+        device = self.input.input.device
+
+        noises = [torch.randn(1, 1, 2 ** 2, 2 ** 2, device=device)]
+
+        for i in range(3, self.log_size + 1):
+            for _ in range(2):
+                noises.append(torch.randn(1, 1, 2 ** i, 2 ** i, device=device))
+
+        return noises
+
+    def mean_latent(self, n_latent):
+        latent_in = torch.randn(
+            n_latent, self.style_dim, device=self.input.input.device
+        )
+        latent = self.style(latent_in).mean(0, keepdim=True)
+
+        return latent
+
+    def get_latent(self, input):
+        return self.style(input)
+
+    def forward(
+        self,
+        styles,
+        return_latents=False,
+        inject_index=None,
+        truncation=1,
+        truncation_latent=None,
+        input_is_latent=False,
+        noise=None,
+    ):
+        if not input_is_latent:
+            styles = [self.style(s) for s in styles]
+
+        if noise is None:
+            '''
+            noise = [None] * (2 * (self.log_size - 2) + 1)
+            '''
+            noise = []
+            batch = styles[0].shape[0]
+            for i in range(self.n_mlp + 1):
+                size = 2 ** (i+2)
+                noise.append(torch.randn(batch, self.channels[size], size, size, device=styles[0].device))
+            
+        if truncation < 1:
+            style_t = []
+
+            for style in styles:
+                style_t.append(
+                    truncation_latent + truncation * (style - truncation_latent)
+                )
+
+            styles = style_t
+
+        if len(styles) < 2:
+            inject_index = self.n_latent
+
+            latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
+
+        else:
+            if inject_index is None:
+                inject_index = random.randint(1, self.n_latent - 1)
+
+            latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
+            latent2 = styles[1].unsqueeze(1).repeat(1, self.n_latent - inject_index, 1)
+
+            latent = torch.cat([latent, latent2], 1)
+
+        out = self.input(latent)
+        out = self.conv1(out, latent[:, 0], noise=noise[0])
+
+        skip = self.to_rgb1(out, latent[:, 1])
+
+        i = 1
+        for conv1, conv2, noise1, noise2, to_rgb in zip(
+            self.convs[::2], self.convs[1::2], noise[1::2], noise[2::2], self.to_rgbs
+        ):
+            out = conv1(out, latent[:, i], noise=noise1)
+            out = conv2(out, latent[:, i + 1], noise=noise2)
+            skip = to_rgb(out, latent[:, i + 2], skip)
+
+            i += 2
+
+        image = skip
+
+        if return_latents:
+            return image, latent
+
+        else:
+            return image, None
+
+class ConvLayer(nn.Sequential):
+    def __init__(
+        self,
+        in_channel,
+        out_channel,
+        kernel_size,
+        downsample=False,
+        blur_kernel=[1, 3, 3, 1],
+        bias=True,
+        activate=True,
+        device='cpu'
+    ):
+        layers = []
+
+        if downsample:
+            factor = 2
+            p = (len(blur_kernel) - factor) + (kernel_size - 1)
+            pad0 = (p + 1) // 2
+            pad1 = p // 2
+
+            layers.append(Blur(blur_kernel, pad=(pad0, pad1), device=device))
+
+            stride = 2
+            self.padding = 0
+
+        else:
+            stride = 1
+            self.padding = kernel_size // 2
+
+        layers.append(
+            EqualConv2d(
+                in_channel,
+                out_channel,
+                kernel_size,
+                padding=self.padding,
+                stride=stride,
+                bias=bias and not activate,
+            )
+        )
+
+        if activate:
+            if bias:
+                layers.append(FusedLeakyReLU(out_channel, device=device))
+
+            else:
+                layers.append(ScaledLeakyReLU(0.2))
+
+        super().__init__(*layers)
+
+
+class ResBlock(nn.Module):
+    def __init__(self, in_channel, out_channel, blur_kernel=[1, 3, 3, 1], device='cpu'):
+        super().__init__()
+
+        self.conv1 = ConvLayer(in_channel, in_channel, 3, device=device)
+        self.conv2 = ConvLayer(in_channel, out_channel, 3, downsample=True, device=device)
+
+        self.skip = ConvLayer(
+            in_channel, out_channel, 1, downsample=True, activate=False, bias=False
+        )
+
+    def forward(self, input):
+        out = self.conv1(input)
+        out = self.conv2(out)
+
+        skip = self.skip(input)
+        out = (out + skip) / math.sqrt(2)
+
+        return out
+
+class FullGenerator(nn.Module):
+    def __init__(
+        self,
+        size,
+        style_dim,
+        n_mlp,
+        channel_multiplier=2,
+        blur_kernel=[1, 3, 3, 1],
+        lr_mlp=0.01,
+        isconcat=True,
+        narrow=1,
+        device='cpu'
+    ):
+        super().__init__()
+        channels = {
+            4: int(512 * narrow),
+            8: int(512 * narrow),
+            16: int(512 * narrow),
+            32: int(512 * narrow),
+            64: int(256 * channel_multiplier * narrow),
+            128: int(128 * channel_multiplier * narrow),
+            256: int(64 * channel_multiplier * narrow),
+            512: int(32 * channel_multiplier * narrow),
+            1024: int(16 * channel_multiplier * narrow)
+        }
+
+        self.log_size = int(math.log(size, 2))
+        self.generator = Generator(size, style_dim, n_mlp, channel_multiplier=channel_multiplier, blur_kernel=blur_kernel, lr_mlp=lr_mlp, isconcat=isconcat, narrow=narrow, device=device)
+        
+        conv = [ConvLayer(3, channels[size], 1, device=device)]
+        self.ecd0 = nn.Sequential(*conv)
+        in_channel = channels[size]
+
+        self.names = ['ecd%d'%i for i in range(self.log_size-1)]
+        for i in range(self.log_size, 2, -1):
+            out_channel = channels[2 ** (i - 1)]
+            #conv = [ResBlock(in_channel, out_channel, blur_kernel)]
+            conv = [ConvLayer(in_channel, out_channel, 3, downsample=True, device=device)] 
+            setattr(self, self.names[self.log_size-i+1], nn.Sequential(*conv))
+            in_channel = out_channel
+        self.final_linear = nn.Sequential(EqualLinear(channels[4] * 4 * 4, style_dim, activation='fused_lrelu', device=device))
+
+    def forward(self,
+        inputs,
+        return_latents=False,
+        inject_index=None,
+        truncation=1,
+        truncation_latent=None,
+        input_is_latent=False,
+    ):
+        noise = []
+        for i in range(self.log_size-1):
+            ecd = getattr(self, self.names[i])
+            inputs = ecd(inputs)
+            noise.append(inputs)
+            
+        inputs = inputs.view(inputs.shape[0], -1)
+        outs = self.final_linear(inputs)
+        noise = list(itertools.chain.from_iterable(itertools.repeat(x, 2) for x in noise))[::-1]
+        outs = self.generator([outs], return_latents, inject_index, truncation, truncation_latent, input_is_latent, noise=noise[1:])
+        return outs
+
+class Discriminator(nn.Module):
+    def __init__(self, size, channel_multiplier=2, blur_kernel=[1, 3, 3, 1], narrow=1, device='cpu'):
+        super().__init__()
+
+        channels = {
+            4: int(512 * narrow),
+            8: int(512 * narrow),
+            16: int(512 * narrow),
+            32: int(512 * narrow),
+            64: int(256 * channel_multiplier * narrow),
+            128: int(128 * channel_multiplier * narrow),
+            256: int(64 * channel_multiplier * narrow),
+            512: int(32 * channel_multiplier * narrow),
+            1024: int(16 * channel_multiplier * narrow)
+        }
+
+        convs = [ConvLayer(3, channels[size], 1, device=device)]
+
+        log_size = int(math.log(size, 2))
+
+        in_channel = channels[size]
+
+        for i in range(log_size, 2, -1):
+            out_channel = channels[2 ** (i - 1)]
+
+            convs.append(ResBlock(in_channel, out_channel, blur_kernel, device=device))
+
+            in_channel = out_channel
+
+        self.convs = nn.Sequential(*convs)
+
+        self.stddev_group = 4
+        self.stddev_feat = 1
+
+        self.final_conv = ConvLayer(in_channel + 1, channels[4], 3, device=device)
+        self.final_linear = nn.Sequential(
+            EqualLinear(channels[4] * 4 * 4, channels[4], activation='fused_lrelu', device=device),
+            EqualLinear(channels[4], 1),
+        )
+
+    def forward(self, input):
+        out = self.convs(input)
+
+        batch, channel, height, width = out.shape
+        group = min(batch, self.stddev_group)
+        stddev = out.view(
+            group, -1, self.stddev_feat, channel // self.stddev_feat, height, width
+        )
+        stddev = torch.sqrt(stddev.var(0, unbiased=False) + 1e-8)
+        stddev = stddev.mean([2, 3, 4], keepdims=True).squeeze(2)
+        stddev = stddev.repeat(group, 1, height, width)
+        out = torch.cat([out, stddev], 1)
+
+        out = self.final_conv(out)
+
+        out = out.view(batch, -1)
+        out = self.final_linear(out)
+        return out

videoretalking/third_part/GPEN/face_model/op/__init__.py

@@ -0,0 +1,2 @@
+from .fused_act import FusedLeakyReLU, fused_leaky_relu
+from .upfirdn2d import upfirdn2d

videoretalking/third_part/GPEN/face_model/op/fused_act.py

@@ -0,0 +1,96 @@
+import os
+import platform
+
+import torch
+from torch import nn
+import torch.nn.functional as F
+from torch.autograd import Function
+from torch.utils.cpp_extension import load, _import_module_from_library
+
+# if running GPEN without cuda, please comment line 11-19
+if platform.system() == 'Linux' and torch.cuda.is_available():
+    module_path = os.path.dirname(__file__)
+    fused = load(
+        'fused',
+        sources=[
+            os.path.join(module_path, 'fused_bias_act.cpp'),
+            os.path.join(module_path, 'fused_bias_act_kernel.cu'),
+        ],
+    )
+
+
+#fused = _import_module_from_library('fused', '/tmp/torch_extensions/fused', True)
+
+
+class FusedLeakyReLUFunctionBackward(Function):
+    @staticmethod
+    def forward(ctx, grad_output, out, negative_slope, scale):
+        ctx.save_for_backward(out)
+        ctx.negative_slope = negative_slope
+        ctx.scale = scale
+
+        empty = grad_output.new_empty(0)
+
+        grad_input = fused.fused_bias_act(
+            grad_output, empty, out, 3, 1, negative_slope, scale
+        )
+
+        dim = [0]
+
+        if grad_input.ndim > 2:
+            dim += list(range(2, grad_input.ndim))
+
+        grad_bias = grad_input.sum(dim).detach()
+
+        return grad_input, grad_bias
+
+    @staticmethod
+    def backward(ctx, gradgrad_input, gradgrad_bias):
+        out, = ctx.saved_tensors
+        gradgrad_out = fused.fused_bias_act(
+            gradgrad_input, gradgrad_bias, out, 3, 1, ctx.negative_slope, ctx.scale
+        )
+
+        return gradgrad_out, None, None, None
+
+
+class FusedLeakyReLUFunction(Function):
+    @staticmethod
+    def forward(ctx, input, bias, negative_slope, scale):
+        empty = input.new_empty(0)
+        out = fused.fused_bias_act(input, bias, empty, 3, 0, negative_slope, scale)
+        ctx.save_for_backward(out)
+        ctx.negative_slope = negative_slope
+        ctx.scale = scale
+
+        return out
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        out, = ctx.saved_tensors
+
+        grad_input, grad_bias = FusedLeakyReLUFunctionBackward.apply(
+            grad_output, out, ctx.negative_slope, ctx.scale
+        )
+
+        return grad_input, grad_bias, None, None
+
+
+class FusedLeakyReLU(nn.Module):
+    def __init__(self, channel, negative_slope=0.2, scale=2 ** 0.5, device='cpu'):
+        super().__init__()
+
+        self.bias = nn.Parameter(torch.zeros(channel))
+        self.negative_slope = negative_slope
+        self.scale = scale
+        self.device = device
+
+    def forward(self, input):
+        return fused_leaky_relu(input, self.bias, self.negative_slope, self.scale, self.device)
+
+
+def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5, device='cpu'):
+    if platform.system() == 'Linux' and torch.cuda.is_available() and device != 'cpu':
+        return FusedLeakyReLUFunction.apply(input, bias, negative_slope, scale)
+    else:
+        return scale * F.leaky_relu(input + bias.view((1, -1)+(1,)*(len(input.shape)-2)), negative_slope=negative_slope)

videoretalking/third_part/GPEN/face_model/op/fused_bias_act.cpp

@@ -0,0 +1,21 @@
+#include <torch/extension.h>
+
+
+torch::Tensor fused_bias_act_op(const torch::Tensor& input, const torch::Tensor& bias, const torch::Tensor& refer,
+    int act, int grad, float alpha, float scale);
+
+#define CHECK_CUDA(x) TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor")
+#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
+#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)
+
+torch::Tensor fused_bias_act(const torch::Tensor& input, const torch::Tensor& bias, const torch::Tensor& refer,
+    int act, int grad, float alpha, float scale) {
+    CHECK_CUDA(input);
+    CHECK_CUDA(bias);
+
+    return fused_bias_act_op(input, bias, refer, act, grad, alpha, scale);
+}
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+    m.def("fused_bias_act", &fused_bias_act, "fused bias act (CUDA)");
+}

videoretalking/third_part/GPEN/face_model/op/fused_bias_act_kernel.cu

@@ -0,0 +1,99 @@
+// Copyright (c) 2019, NVIDIA Corporation. All rights reserved.
+//
+// This work is made available under the Nvidia Source Code License-NC.
+// To view a copy of this license, visit
+// https://nvlabs.github.io/stylegan2/license.html
+
+#include <torch/types.h>
+
+#include <ATen/ATen.h>
+#include <ATen/AccumulateType.h>
+#include <ATen/cuda/CUDAContext.h>
+#include <ATen/cuda/CUDAApplyUtils.cuh>
+
+#include <cuda.h>
+#include <cuda_runtime.h>
+
+
+template <typename scalar_t>
+static __global__ void fused_bias_act_kernel(scalar_t* out, const scalar_t* p_x, const scalar_t* p_b, const scalar_t* p_ref,
+    int act, int grad, scalar_t alpha, scalar_t scale, int loop_x, int size_x, int step_b, int size_b, int use_bias, int use_ref) {
+    int xi = blockIdx.x * loop_x * blockDim.x + threadIdx.x;
+
+    scalar_t zero = 0.0;
+
+    for (int loop_idx = 0; loop_idx < loop_x && xi < size_x; loop_idx++, xi += blockDim.x) {
+        scalar_t x = p_x[xi];
+
+        if (use_bias) {
+            x += p_b[(xi / step_b) % size_b];
+        }
+
+        scalar_t ref = use_ref ? p_ref[xi] : zero;
+
+        scalar_t y;
+
+        switch (act * 10 + grad) {
+            default:
+            case 10: y = x; break;
+            case 11: y = x; break;
+            case 12: y = 0.0; break;
+
+            case 30: y = (x > 0.0) ? x : x * alpha; break;
+            case 31: y = (ref > 0.0) ? x : x * alpha; break;
+            case 32: y = 0.0; break;
+        }
+
+        out[xi] = y * scale;
+    }
+}
+
+
+torch::Tensor fused_bias_act_op(const torch::Tensor& input, const torch::Tensor& bias, const torch::Tensor& refer,
+    int act, int grad, float alpha, float scale) {
+    int curDevice = -1;
+    cudaGetDevice(&curDevice);
+    cudaStream_t stream = at::cuda::getCurrentCUDAStream(curDevice);
+
+    auto x = input.contiguous();
+    auto b = bias.contiguous();
+    auto ref = refer.contiguous();
+
+    int use_bias = b.numel() ? 1 : 0;
+    int use_ref = ref.numel() ? 1 : 0;
+
+    int size_x = x.numel();
+    int size_b = b.numel();
+    int step_b = 1;
+
+    for (int i = 1 + 1; i < x.dim(); i++) {
+        step_b *= x.size(i);
+    }
+
+    int loop_x = 4;
+    int block_size = 4 * 32;
+    int grid_size = (size_x - 1) / (loop_x * block_size) + 1;
+
+    auto y = torch::empty_like(x);
+
+    AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "fused_bias_act_kernel", [&] {
+        fused_bias_act_kernel<scalar_t><<<grid_size, block_size, 0, stream>>>(
+            y.data_ptr<scalar_t>(),
+            x.data_ptr<scalar_t>(),
+            b.data_ptr<scalar_t>(),
+            ref.data_ptr<scalar_t>(),
+            act,
+            grad,
+            alpha,
+            scale,
+            loop_x,
+            size_x,
+            step_b,
+            size_b,
+            use_bias,
+            use_ref
+        );
+    });
+
+    return y;
+}

videoretalking/third_part/GPEN/face_model/op/upfirdn2d.cpp

@@ -0,0 +1,23 @@
+#include <torch/extension.h>
+
+
+torch::Tensor upfirdn2d_op(const torch::Tensor& input, const torch::Tensor& kernel,
+                            int up_x, int up_y, int down_x, int down_y,
+                            int pad_x0, int pad_x1, int pad_y0, int pad_y1);
+
+#define CHECK_CUDA(x) TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor")
+#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
+#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)
+
+torch::Tensor upfirdn2d(const torch::Tensor& input, const torch::Tensor& kernel,
+                        int up_x, int up_y, int down_x, int down_y,
+                        int pad_x0, int pad_x1, int pad_y0, int pad_y1) {
+    CHECK_CUDA(input);
+    CHECK_CUDA(kernel);
+
+    return upfirdn2d_op(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1);
+}
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+    m.def("upfirdn2d", &upfirdn2d, "upfirdn2d (CUDA)");
+}

videoretalking/third_part/GPEN/face_model/op/upfirdn2d.py

@@ -0,0 +1,194 @@
+import os
+import platform
+
+import torch
+import torch.nn.functional as F
+from torch.autograd import Function
+from torch.utils.cpp_extension import load, _import_module_from_library
+
+# if running GPEN without cuda, please comment line 10-18
+if platform.system() == 'Linux' and torch.cuda.is_available():
+    module_path = os.path.dirname(__file__)
+    upfirdn2d_op = load(
+        'upfirdn2d',
+        sources=[
+            os.path.join(module_path, 'upfirdn2d.cpp'),
+            os.path.join(module_path, 'upfirdn2d_kernel.cu'),
+        ],
+    )
+
+
+#upfirdn2d_op = _import_module_from_library('upfirdn2d', '/tmp/torch_extensions/upfirdn2d', True)
+
+class UpFirDn2dBackward(Function):
+    @staticmethod
+    def forward(
+        ctx, grad_output, kernel, grad_kernel, up, down, pad, g_pad, in_size, out_size
+    ):
+
+        up_x, up_y = up
+        down_x, down_y = down
+        g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1 = g_pad
+
+        grad_output = grad_output.reshape(-1, out_size[0], out_size[1], 1)
+
+        grad_input = upfirdn2d_op.upfirdn2d(
+            grad_output,
+            grad_kernel,
+            down_x,
+            down_y,
+            up_x,
+            up_y,
+            g_pad_x0,
+            g_pad_x1,
+            g_pad_y0,
+            g_pad_y1,
+        )
+        grad_input = grad_input.view(in_size[0], in_size[1], in_size[2], in_size[3])
+
+        ctx.save_for_backward(kernel)
+
+        pad_x0, pad_x1, pad_y0, pad_y1 = pad
+
+        ctx.up_x = up_x
+        ctx.up_y = up_y
+        ctx.down_x = down_x
+        ctx.down_y = down_y
+        ctx.pad_x0 = pad_x0
+        ctx.pad_x1 = pad_x1
+        ctx.pad_y0 = pad_y0
+        ctx.pad_y1 = pad_y1
+        ctx.in_size = in_size
+        ctx.out_size = out_size
+
+        return grad_input
+
+    @staticmethod
+    def backward(ctx, gradgrad_input):
+        kernel, = ctx.saved_tensors
+
+        gradgrad_input = gradgrad_input.reshape(-1, ctx.in_size[2], ctx.in_size[3], 1)
+
+        gradgrad_out = upfirdn2d_op.upfirdn2d(
+            gradgrad_input,
+            kernel,
+            ctx.up_x,
+            ctx.up_y,
+            ctx.down_x,
+            ctx.down_y,
+            ctx.pad_x0,
+            ctx.pad_x1,
+            ctx.pad_y0,
+            ctx.pad_y1,
+        )
+        # gradgrad_out = gradgrad_out.view(ctx.in_size[0], ctx.out_size[0], ctx.out_size[1], ctx.in_size[3])
+        gradgrad_out = gradgrad_out.view(
+            ctx.in_size[0], ctx.in_size[1], ctx.out_size[0], ctx.out_size[1]
+        )
+
+        return gradgrad_out, None, None, None, None, None, None, None, None
+
+
+class UpFirDn2d(Function):
+    @staticmethod
+    def forward(ctx, input, kernel, up, down, pad):
+        up_x, up_y = up
+        down_x, down_y = down
+        pad_x0, pad_x1, pad_y0, pad_y1 = pad
+
+        kernel_h, kernel_w = kernel.shape
+        batch, channel, in_h, in_w = input.shape
+        ctx.in_size = input.shape
+
+        input = input.reshape(-1, in_h, in_w, 1)
+
+        ctx.save_for_backward(kernel, torch.flip(kernel, [0, 1]))
+
+        out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1
+        out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1
+        ctx.out_size = (out_h, out_w)
+
+        ctx.up = (up_x, up_y)
+        ctx.down = (down_x, down_y)
+        ctx.pad = (pad_x0, pad_x1, pad_y0, pad_y1)
+
+        g_pad_x0 = kernel_w - pad_x0 - 1
+        g_pad_y0 = kernel_h - pad_y0 - 1
+        g_pad_x1 = in_w * up_x - out_w * down_x + pad_x0 - up_x + 1
+        g_pad_y1 = in_h * up_y - out_h * down_y + pad_y0 - up_y + 1
+
+        ctx.g_pad = (g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1)
+
+        out = upfirdn2d_op.upfirdn2d(
+            input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1
+        )
+        # out = out.view(major, out_h, out_w, minor)
+        out = out.view(-1, channel, out_h, out_w)
+
+        return out
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        kernel, grad_kernel = ctx.saved_tensors
+
+        grad_input = UpFirDn2dBackward.apply(
+            grad_output,
+            kernel,
+            grad_kernel,
+            ctx.up,
+            ctx.down,
+            ctx.pad,
+            ctx.g_pad,
+            ctx.in_size,
+            ctx.out_size,
+        )
+
+        return grad_input, None, None, None, None
+
+
+def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0), device='cpu'):
+    if platform.system() == 'Linux' and torch.cuda.is_available() and device != 'cpu':
+        out = UpFirDn2d.apply(
+            input, kernel, (up, up), (down, down), (pad[0], pad[1], pad[0], pad[1])
+        )
+    else:
+        out = upfirdn2d_native(input, kernel, up, up, down, down, pad[0], pad[1], pad[0], pad[1])
+
+    return out
+
+
+def upfirdn2d_native(
+    input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1
+):
+    input = input.permute(0, 2, 3, 1)
+    _, in_h, in_w, minor = input.shape
+    kernel_h, kernel_w = kernel.shape
+    out = input.view(-1, in_h, 1, in_w, 1, minor)
+    out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1])
+    out = out.view(-1, in_h * up_y, in_w * up_x, minor)
+
+    out = F.pad(
+        out, [0, 0, max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)]
+    )
+    out = out[
+        :,
+        max(-pad_y0, 0) : out.shape[1] - max(-pad_y1, 0),
+        max(-pad_x0, 0) : out.shape[2] - max(-pad_x1, 0),
+        :,
+    ]
+
+    out = out.permute(0, 3, 1, 2)
+    out = out.reshape(
+        [-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1]
+    )
+    w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w)
+    out = F.conv2d(out, w)
+    out = out.reshape(
+        -1,
+        minor,
+        in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1,
+        in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1,
+    )
+    # out = out.permute(0, 2, 3, 1)
+    return out[:, :, ::down_y, ::down_x]
+

videoretalking/third_part/GPEN/face_model/op/upfirdn2d_kernel.cu

@@ -0,0 +1,272 @@
+// Copyright (c) 2019, NVIDIA Corporation. All rights reserved.
+//
+// This work is made available under the Nvidia Source Code License-NC.
+// To view a copy of this license, visit
+// https://nvlabs.github.io/stylegan2/license.html
+
+#include <torch/types.h>
+
+#include <ATen/ATen.h>
+#include <ATen/AccumulateType.h>
+#include <ATen/cuda/CUDAContext.h>
+#include <ATen/cuda/CUDAApplyUtils.cuh>
+
+#include <cuda.h>
+#include <cuda_runtime.h>
+
+
+static __host__ __device__ __forceinline__ int floor_div(int a, int b) {
+    int c = a / b;
+
+    if (c * b > a) {
+        c--;
+    }
+
+    return c;
+}
+
+
+struct UpFirDn2DKernelParams {
+    int up_x;
+    int up_y;
+    int down_x;
+    int down_y;
+    int pad_x0;
+    int pad_x1;
+    int pad_y0;
+    int pad_y1;
+
+    int major_dim;
+    int in_h;
+    int in_w;
+    int minor_dim;
+    int kernel_h;
+    int kernel_w;
+    int out_h;
+    int out_w;
+    int loop_major;
+    int loop_x;
+};
+
+
+template <typename scalar_t, int up_x, int up_y, int down_x, int down_y, int kernel_h, int kernel_w, int tile_out_h, int tile_out_w>
+__global__ void upfirdn2d_kernel(scalar_t* out, const scalar_t* input, const scalar_t* kernel, const UpFirDn2DKernelParams p) {
+    const int tile_in_h = ((tile_out_h - 1) * down_y + kernel_h - 1) / up_y + 1;
+    const int tile_in_w = ((tile_out_w - 1) * down_x + kernel_w - 1) / up_x + 1;
+
+    __shared__ volatile float sk[kernel_h][kernel_w];
+    __shared__ volatile float sx[tile_in_h][tile_in_w];
+
+    int minor_idx = blockIdx.x;
+    int tile_out_y = minor_idx / p.minor_dim;
+    minor_idx -= tile_out_y * p.minor_dim;
+    tile_out_y *= tile_out_h;
+    int tile_out_x_base = blockIdx.y * p.loop_x * tile_out_w;
+    int major_idx_base = blockIdx.z * p.loop_major;
+
+    if (tile_out_x_base >= p.out_w | tile_out_y >= p.out_h | major_idx_base >= p.major_dim) {
+        return;
+    }
+
+    for (int tap_idx = threadIdx.x; tap_idx < kernel_h * kernel_w; tap_idx += blockDim.x) {
+        int ky = tap_idx / kernel_w;
+        int kx = tap_idx - ky * kernel_w;
+        scalar_t v = 0.0;
+
+        if (kx < p.kernel_w & ky < p.kernel_h) {
+            v = kernel[(p.kernel_h - 1 - ky) * p.kernel_w + (p.kernel_w - 1 - kx)];
+        }
+
+        sk[ky][kx] = v;
+    }
+
+    for (int loop_major = 0, major_idx = major_idx_base; loop_major < p.loop_major & major_idx < p.major_dim; loop_major++, major_idx++) {
+        for (int loop_x = 0, tile_out_x = tile_out_x_base; loop_x < p.loop_x & tile_out_x < p.out_w; loop_x++, tile_out_x += tile_out_w) {
+            int tile_mid_x = tile_out_x * down_x + up_x - 1 - p.pad_x0;
+            int tile_mid_y = tile_out_y * down_y + up_y - 1 - p.pad_y0;
+            int tile_in_x = floor_div(tile_mid_x, up_x);
+            int tile_in_y = floor_div(tile_mid_y, up_y);
+
+            __syncthreads();
+
+            for (int in_idx = threadIdx.x; in_idx < tile_in_h * tile_in_w; in_idx += blockDim.x) {
+                int rel_in_y = in_idx / tile_in_w;
+                int rel_in_x = in_idx - rel_in_y * tile_in_w;
+                int in_x = rel_in_x + tile_in_x;
+                int in_y = rel_in_y + tile_in_y;
+
+                scalar_t v = 0.0;
+
+                if (in_x >= 0 & in_y >= 0 & in_x < p.in_w & in_y < p.in_h) {
+                    v = input[((major_idx * p.in_h + in_y) * p.in_w + in_x) * p.minor_dim + minor_idx];
+                }
+
+                sx[rel_in_y][rel_in_x] = v;
+            }
+
+            __syncthreads();
+            for (int out_idx = threadIdx.x; out_idx < tile_out_h * tile_out_w; out_idx += blockDim.x) {
+                int rel_out_y = out_idx / tile_out_w;
+                int rel_out_x = out_idx - rel_out_y * tile_out_w;
+                int out_x = rel_out_x + tile_out_x;
+                int out_y = rel_out_y + tile_out_y;
+
+                int mid_x = tile_mid_x + rel_out_x * down_x;
+                int mid_y = tile_mid_y + rel_out_y * down_y;
+                int in_x = floor_div(mid_x, up_x);
+                int in_y = floor_div(mid_y, up_y);
+                int rel_in_x = in_x - tile_in_x;
+                int rel_in_y = in_y - tile_in_y;
+                int kernel_x = (in_x + 1) * up_x - mid_x - 1;
+                int kernel_y = (in_y + 1) * up_y - mid_y - 1;
+
+                scalar_t v = 0.0;
+
+                #pragma unroll
+                for (int y = 0; y < kernel_h / up_y; y++)
+                    #pragma unroll
+                    for (int x = 0; x < kernel_w / up_x; x++)
+                        v += sx[rel_in_y + y][rel_in_x + x] * sk[kernel_y + y * up_y][kernel_x + x * up_x];
+
+                if (out_x < p.out_w & out_y < p.out_h) {
+                    out[((major_idx * p.out_h + out_y) * p.out_w + out_x) * p.minor_dim + minor_idx] = v;
+                }
+            }
+        }
+    }
+}
+
+
+torch::Tensor upfirdn2d_op(const torch::Tensor& input, const torch::Tensor& kernel,
+    int up_x, int up_y, int down_x, int down_y,
+    int pad_x0, int pad_x1, int pad_y0, int pad_y1) {
+    int curDevice = -1;
+    cudaGetDevice(&curDevice);
+    cudaStream_t stream = at::cuda::getCurrentCUDAStream(curDevice);
+
+    UpFirDn2DKernelParams p;
+
+    auto x = input.contiguous();
+    auto k = kernel.contiguous();
+
+    p.major_dim = x.size(0);
+    p.in_h = x.size(1);
+    p.in_w = x.size(2);
+    p.minor_dim = x.size(3);
+    p.kernel_h = k.size(0);
+    p.kernel_w = k.size(1);
+    p.up_x = up_x;
+    p.up_y = up_y;
+    p.down_x = down_x;
+    p.down_y = down_y;
+    p.pad_x0 = pad_x0;
+    p.pad_x1 = pad_x1;
+    p.pad_y0 = pad_y0;
+    p.pad_y1 = pad_y1;
+
+    p.out_h = (p.in_h * p.up_y + p.pad_y0 + p.pad_y1 - p.kernel_h + p.down_y) / p.down_y;
+    p.out_w = (p.in_w * p.up_x + p.pad_x0 + p.pad_x1 - p.kernel_w + p.down_x) / p.down_x;
+
+    auto out = at::empty({p.major_dim, p.out_h, p.out_w, p.minor_dim}, x.options());
+
+    int mode = -1;
+
+    int tile_out_h;
+    int tile_out_w;
+
+    if (p.up_x == 1 && p.up_y == 1 && p.down_x == 1 && p.down_y == 1 && p.kernel_h <= 4 && p.kernel_w <= 4) {
+        mode = 1;
+        tile_out_h = 16;
+        tile_out_w = 64;
+    }
+
+    if (p.up_x == 1 && p.up_y == 1 && p.down_x == 1 && p.down_y == 1 && p.kernel_h <= 3 && p.kernel_w <= 3) {
+        mode = 2;
+        tile_out_h = 16;
+        tile_out_w = 64;
+    }
+
+    if (p.up_x == 2 && p.up_y == 2 && p.down_x == 1 && p.down_y == 1 && p.kernel_h <= 4 && p.kernel_w <= 4) {
+        mode = 3;
+        tile_out_h = 16;
+        tile_out_w = 64;
+    }
+
+    if (p.up_x == 2 && p.up_y == 2 && p.down_x == 1 && p.down_y == 1 && p.kernel_h <= 2 && p.kernel_w <= 2) {
+        mode = 4;
+        tile_out_h = 16;
+        tile_out_w = 64;
+    }
+
+    if (p.up_x == 1 && p.up_y == 1 && p.down_x == 2 && p.down_y == 2 && p.kernel_h <= 4 && p.kernel_w <= 4) {
+        mode = 5;
+        tile_out_h = 8;
+        tile_out_w = 32;
+    }
+
+    if (p.up_x == 1 && p.up_y == 1 && p.down_x == 2 && p.down_y == 2 && p.kernel_h <= 2 && p.kernel_w <= 2) {
+        mode = 6;
+        tile_out_h = 8;
+        tile_out_w = 32;
+    }
+
+    dim3 block_size;
+    dim3 grid_size;
+
+    if (tile_out_h > 0 && tile_out_w) {
+        p.loop_major = (p.major_dim - 1) / 16384 + 1;
+        p.loop_x = 1;
+        block_size = dim3(32 * 8, 1, 1);
+        grid_size = dim3(((p.out_h - 1) / tile_out_h + 1) * p.minor_dim,
+                         (p.out_w - 1) / (p.loop_x * tile_out_w) + 1,
+                         (p.major_dim - 1) / p.loop_major + 1);
+    }
+
+    AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "upfirdn2d_cuda", [&] {
+        switch (mode) {
+        case 1:
+            upfirdn2d_kernel<scalar_t, 1, 1, 1, 1, 4, 4, 16, 64><<<grid_size, block_size, 0, stream>>>(
+                out.data_ptr<scalar_t>(), x.data_ptr<scalar_t>(), k.data_ptr<scalar_t>(), p
+            );
+
+            break;
+
+        case 2:
+            upfirdn2d_kernel<scalar_t, 1, 1, 1, 1, 3, 3, 16, 64><<<grid_size, block_size, 0, stream>>>(
+                out.data_ptr<scalar_t>(), x.data_ptr<scalar_t>(), k.data_ptr<scalar_t>(), p
+            );
+
+            break;
+
+        case 3:
+            upfirdn2d_kernel<scalar_t, 2, 2, 1, 1, 4, 4, 16, 64><<<grid_size, block_size, 0, stream>>>(
+                out.data_ptr<scalar_t>(), x.data_ptr<scalar_t>(), k.data_ptr<scalar_t>(), p
+            );
+
+            break;
+
+        case 4:
+            upfirdn2d_kernel<scalar_t, 2, 2, 1, 1, 2, 2, 16, 64><<<grid_size, block_size, 0, stream>>>(
+                out.data_ptr<scalar_t>(), x.data_ptr<scalar_t>(), k.data_ptr<scalar_t>(), p
+            );
+
+            break;
+
+        case 5:
+            upfirdn2d_kernel<scalar_t, 1, 1, 2, 2, 4, 4, 8, 32><<<grid_size, block_size, 0, stream>>>(
+                out.data_ptr<scalar_t>(), x.data_ptr<scalar_t>(), k.data_ptr<scalar_t>(), p
+            );
+
+            break;
+
+        case 6:
+            upfirdn2d_kernel<scalar_t, 1, 1, 2, 2, 4, 4, 8, 32><<<grid_size, block_size, 0, stream>>>(
+                out.data_ptr<scalar_t>(), x.data_ptr<scalar_t>(), k.data_ptr<scalar_t>(), p
+            );
+
+            break;
+        }
+    });
+
+    return out;
+}

videoretalking/third_part/GPEN/face_morpher/.gitignore

@@ -0,0 +1,3 @@
+*.pyc
+*.swp
+MANIFEST

videoretalking/third_part/GPEN/face_morpher/README.rst

@@ -0,0 +1,260 @@
+Face Morpher
+============
+
+| Warp, average and morph human faces!
+| Scripts will automatically detect frontal faces and skip images if
+  none is detected.
+
+Built with Python, `dlib`_, Numpy, Scipy, dlib.
+
+| Supported on Python 2.7, Python 3.6+
+| Tested on macOS Mojave and 64bit Linux (dockerized).
+
+Requirements
+--------------
+-  ``pip install -r requirements.txt``
+- Download `http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2` and extract file.
+- Export environment variable ``DLIB_DATA_DIR`` to the folder where ``shape_predictor_68_face_landmarks.dat`` is located. Default ``data``. E.g ``export DLIB_DATA_DIR=/Downloads/data``
+
+Either:
+
+-  `Use as local command-line utility`_
+-  `Use as pip library`_
+-  `Try out in a docker container`_
+
+.. _`Use as local command-line utility`:
+
+Use as local command-line utility
+---------------------------------
+::
+
+    $ git clone https://github.com/alyssaq/face_morpher
+
+Morphing Faces
+--------------
+
+Morph from a source to destination image:
+
+::
+
+    python facemorpher/morpher.py --src=<src_imgpath> --dest=<dest_imgpath> --plot
+
+Morph through a series of images in a folder:
+
+::
+
+    python facemorpher/morpher.py --images=<folder> --out_video=out.avi
+
+All options listed in ``morpher.py`` (pasted below):
+
+::
+
+    Morph from source to destination face or
+    Morph through all images in a folder
+
+    Usage:
+        morpher.py (--src=<src_path> --dest=<dest_path> | --images=<folder>)
+                [--width=<width>] [--height=<height>]
+                [--num=<num_frames>] [--fps=<frames_per_second>]
+                [--out_frames=<folder>] [--out_video=<filename>]
+                [--plot] [--background=(black|transparent|average)]
+
+    Options:
+        -h, --help              Show this screen.
+        --src=<src_imgpath>     Filepath to source image (.jpg, .jpeg, .png)
+        --dest=<dest_imgpath>   Filepath to destination image (.jpg, .jpeg, .png)
+        --images=<folder>       Folderpath to images
+        --width=<width>         Custom width of the images/video [default: 500]
+        --height=<height>       Custom height of the images/video [default: 600]
+        --num=<num_frames>      Number of morph frames [default: 20]
+        --fps=<fps>             Number frames per second for the video [default: 10]
+        --out_frames=<folder>   Folder path to save all image frames
+        --out_video=<filename>  Filename to save a video
+        --plot                  Flag to plot images to result.png [default: False]
+        --background=<bg>       Background of images to be one of (black|transparent|average) [default: black]
+        --version               Show version.
+
+Averaging Faces
+---------------
+
+Average faces from all images in a folder:
+
+::
+
+    python facemorpher/averager.py --images=<images_folder> --out=average.png
+
+All options listed in ``averager.py`` (pasted below):
+
+::
+
+    Face averager
+
+    Usage:
+        averager.py --images=<images_folder> [--blur] [--plot]
+                [--background=(black|transparent|average)]
+                [--width=<width>] [--height=<height>]
+                [--out=<filename>] [--destimg=<filename>]
+
+    Options:
+        -h, --help             Show this screen.
+        --images=<folder>      Folder to images (.jpg, .jpeg, .png)
+        --blur                 Flag to blur edges of image [default: False]
+        --width=<width>        Custom width of the images/video [default: 500]
+        --height=<height>      Custom height of the images/video [default: 600]
+        --out=<filename>       Filename to save the average face [default: result.png]
+        --destimg=<filename>   Destination face image to overlay average face
+        --plot                 Flag to display the average face [default: False]
+        --background=<bg>      Background of image to be one of (black|transparent|average) [default: black]
+        --version              Show version.
+
+Steps (facemorpher folder)
+--------------------------
+
+1. Locator
+^^^^^^^^^^
+
+-  Locates face points
+-  For a different locator, return an array of (x, y) control face
+   points
+
+2. Aligner
+^^^^^^^^^^
+
+-  Align faces by resizing, centering and cropping to given size
+
+3. Warper
+^^^^^^^^^
+
+-  Given 2 images and its face points, warp one image to the other
+-  Triangulates face points
+-  Affine transforms each triangle with bilinear interpolation
+
+4a. Morpher
+^^^^^^^^^^^
+
+-  Morph between 2 or more images
+
+4b. Averager
+^^^^^^^^^^^^
+
+-  Average faces from 2 or more images
+
+Blender
+^^^^^^^
+
+Optional blending of warped image:
+
+-  Weighted average
+-  Alpha feathering
+-  Poisson blend
+
+Examples - `Being John Malkovich`_
+----------------------------------
+
+Create a morphing video between the 2 images:
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+| ``> python facemorpher/morpher.py --src=alyssa.jpg --dest=john_malkovich.jpg``
+| ``--out_video=out.avi``
+
+(out.avi played and recorded as gif)
+
+.. figure:: https://raw.github.com/alyssaq/face_morpher/master/examples/being_john_malvokich.gif
+   :alt: gif
+
+Save the frames to a folder:
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+| ``> python facemorpher/morpher.py --src=alyssa.jpg --dest=john_malkovich.jpg``
+| ``--out_frames=out_folder --num=30``
+
+Plot the frames:
+^^^^^^^^^^^^^^^^
+
+| ``> python facemorpher/morpher.py --src=alyssa.jpg --dest=john_malkovich.jpg``
+| ``--num=12 --plot``
+
+.. figure:: https://raw.github.com/alyssaq/face_morpher/master/examples/plot.png
+   :alt: plot
+
+Average all face images in a folder:
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+85 images used
+
+| ``> python facemorpher/averager.py --images=images --blur --background=transparent``
+| ``--width=220 --height=250``
+
+.. figure:: https://raw.github.com/alyssaq/face_morpher/master/examples/average_faces.png
+   :alt: average\_faces
+
+.. _`Use as pip library`:
+
+Use as pip library
+---------------------------------
+::
+
+    $ pip install facemorpher
+
+Examples
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+Additional options are exactly the same as the command line
+
+::
+
+    import facemorpher
+
+    # Get a list of image paths in a folder
+    imgpaths = facemorpher.list_imgpaths('imagefolder')
+
+    # To morph, supply an array of face images:
+    facemorpher.morpher(imgpaths, plot=True)
+
+    # To average, supply an array of face images:
+    facemorpher.averager(['image1.png', 'image2.png'], plot=True)
+
+
+Once pip installed, 2 binaries are also available as a command line utility:
+
+::
+
+    $ facemorpher --src=<src_imgpath> --dest=<dest_imgpath> --plot
+    $ faceaverager --images=<images_folder> --plot
+
+Try out in a docker container
+---------------------------------
+Mount local folder to `/images` in docker container, run it and enter a bash session.
+--rm removes the container when you close it.
+::
+
+    $ docker run -v  /Users/alyssa/Desktop/images:/images --name py3 --rm -it jjanzic/docker-python3-opencv bash
+
+Once you're in the container, install ``facemorpher`` and try the examples listed above
+::
+
+    root@0dad0912ebbe:/# pip install facemorpher
+    root@0dad0912ebbe:/# facemorpher --src=<img1> --dest=<img2> --plot
+
+Documentation
+-------------
+
+http://alyssaq.github.io/face_morpher
+
+Build & publish Docs
+^^^^^^^^^^^^^^^^^^^^
+
+::
+
+    ./scripts/publish_ghpages.sh
+
+License
+-------
+`MIT`_
+
+.. _Being John Malkovich: http://www.rottentomatoes.com/m/being_john_malkovich
+.. _Mac installation steps: https://gist.github.com/alyssaq/f60393545173379e0f3f#file-4-opencv3-with-python3-md
+.. _MIT: http://alyssaq.github.io/mit-license
+.. _OpenCV: http://opencv.org
+.. _Homebrew: https://brew.sh
+.. _source: https://github.com/opencv/opencv
+.. _dlib: http://dlib.net

videoretalking/third_part/GPEN/face_morpher/facemorpher/__init__.py

@@ -0,0 +1,9 @@
+"""
+Face Morpher module init code
+"""
+from .morpher import morpher, list_imgpaths
+from .averager import averager
+
+__all__ = ['list_imgpaths',
+           'morpher',
+           'averager']

videoretalking/third_part/GPEN/face_morpher/facemorpher/aligner.py

@@ -0,0 +1,102 @@
+"""
+Align face and image sizes
+"""
+import cv2
+import numpy as np
+
+def positive_cap(num):
+  """ Cap a number to ensure positivity
+
+  :param num: positive or negative number
+  :returns: (overflow, capped_number)
+  """
+  if num < 0:
+    return 0, abs(num)
+  else:
+    return num, 0
+
+def roi_coordinates(rect, size, scale):
+  """ Align the rectangle into the center and return the top-left coordinates
+  within the new size. If rect is smaller, we add borders.
+
+  :param rect: (x, y, w, h) bounding rectangle of the face
+  :param size: (width, height) are the desired dimensions
+  :param scale: scaling factor of the rectangle to be resized
+  :returns: 4 numbers. Top-left coordinates of the aligned ROI.
+    (x, y, border_x, border_y). All values are > 0.
+  """
+  rectx, recty, rectw, recth = rect
+  new_height, new_width = size
+  mid_x = int((rectx + rectw/2) * scale)
+  mid_y = int((recty + recth/2) * scale)
+  roi_x = mid_x - int(new_width/2)
+  roi_y = mid_y - int(new_height/2)
+
+  roi_x, border_x = positive_cap(roi_x)
+  roi_y, border_y = positive_cap(roi_y)
+  return roi_x, roi_y, border_x, border_y
+
+def scaling_factor(rect, size):
+  """ Calculate the scaling factor for the current image to be
+      resized to the new dimensions
+
+  :param rect: (x, y, w, h) bounding rectangle of the face
+  :param size: (width, height) are the desired dimensions
+  :returns: floating point scaling factor
+  """
+  new_height, new_width = size
+  rect_h, rect_w = rect[2:]
+  height_ratio = rect_h / new_height
+  width_ratio = rect_w / new_width
+  scale = 1
+  if height_ratio > width_ratio:
+    new_recth = 0.8 * new_height
+    scale = new_recth / rect_h
+  else:
+    new_rectw = 0.8 * new_width
+    scale = new_rectw / rect_w
+  return scale
+
+def resize_image(img, scale):
+  """ Resize image with the provided scaling factor
+
+  :param img: image to be resized
+  :param scale: scaling factor for resizing the image
+  """
+  cur_height, cur_width = img.shape[:2]
+  new_scaled_height = int(scale * cur_height)
+  new_scaled_width = int(scale * cur_width)
+
+  return cv2.resize(img, (new_scaled_width, new_scaled_height))
+
+def resize_align(img, points, size):
+  """ Resize image and associated points, align face to the center
+    and crop to the desired size
+
+  :param img: image to be resized
+  :param points: *m* x 2 array of points
+  :param size: (height, width) tuple of new desired size
+  """
+  new_height, new_width = size
+
+  # Resize image based on bounding rectangle
+  rect = cv2.boundingRect(np.array([points], np.int32))
+  scale = scaling_factor(rect, size)
+  img = resize_image(img, scale)
+
+  # Align bounding rect to center
+  cur_height, cur_width = img.shape[:2]
+  roi_x, roi_y, border_x, border_y = roi_coordinates(rect, size, scale)
+  roi_h = np.min([new_height-border_y, cur_height-roi_y])
+  roi_w = np.min([new_width-border_x, cur_width-roi_x])
+
+  # Crop to supplied size
+  crop = np.zeros((new_height, new_width, 3), img.dtype)
+  crop[border_y:border_y+roi_h, border_x:border_x+roi_w] = (
+     img[roi_y:roi_y+roi_h, roi_x:roi_x+roi_w])
+
+  # Scale and align face points to the crop
+  points[:, 0] = (points[:, 0] * scale) + (border_x - roi_x)
+  points[:, 1] = (points[:, 1] * scale) + (border_y - roi_y)
+
+  return (crop, points)

videoretalking/third_part/GPEN/face_morpher/facemorpher/averager.py

@@ -0,0 +1,123 @@
+"""
+::
+
+  Face averager
+
+  Usage:
+    averager.py --images=<images_folder> [--blur] [--plot]
+              [--background=(black|transparent|average)]
+              [--width=<width>] [--height=<height>]
+              [--out=<filename>] [--destimg=<filename>]
+
+  Options:
+    -h, --help             Show this screen.
+    --images=<folder>      Folder to images (.jpg, .jpeg, .png)
+    --blur                 Flag to blur edges of image [default: False]
+    --width=<width>        Custom width of the images/video [default: 500]
+    --height=<height>      Custom height of the images/video [default: 600]
+    --out=<filename>       Filename to save the average face [default: result.png]
+    --destimg=<filename>   Destination face image to overlay average face
+    --plot                 Flag to display the average face [default: False]
+    --background=<bg>      Background of image to be one of (black|transparent|average) [default: black]
+    --version              Show version.
+"""
+
+from docopt import docopt
+import os
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.image as mpimg
+
+from facemorpher import locator
+from facemorpher import aligner
+from facemorpher import warper
+from facemorpher import blender
+from facemorpher import plotter
+
+def list_imgpaths(imgfolder):
+  for fname in os.listdir(imgfolder):
+    if (fname.lower().endswith('.jpg') or
+       fname.lower().endswith('.png') or
+       fname.lower().endswith('.jpeg')):
+      yield os.path.join(imgfolder, fname)
+
+def sharpen(img):
+  blured = cv2.GaussianBlur(img, (0, 0), 2.5)
+  return cv2.addWeighted(img, 1.4, blured, -0.4, 0)
+
+def load_image_points(path, size):
+  img = cv2.imread(path)
+  points = locator.face_points(img)
+
+  if len(points) == 0:
+    print('No face in %s' % path)
+    return None, None
+  else:
+    return aligner.resize_align(img, points, size)
+
+def averager(imgpaths, dest_filename=None, width=500, height=600, background='black',
+             blur_edges=False, out_filename='result.png', plot=False):
+
+  size = (height, width)
+
+  images = []
+  point_set = []
+  for path in imgpaths:
+    img, points = load_image_points(path, size)
+    if img is not None:
+      images.append(img)
+      point_set.append(points)
+
+  if len(images) == 0:
+    raise FileNotFoundError('Could not find any valid images.' +
+                            ' Supported formats are .jpg, .png, .jpeg')
+
+  if dest_filename is not None:
+    dest_img, dest_points = load_image_points(dest_filename, size)
+    if dest_img is None or dest_points is None:
+      raise Exception('No face or detected face points in dest img: ' + dest_filename)
+  else:
+    dest_img = np.zeros(images[0].shape, np.uint8)
+    dest_points = locator.average_points(point_set)
+
+  num_images = len(images)
+  result_images = np.zeros(images[0].shape, np.float32)
+  for i in range(num_images):
+    result_images += warper.warp_image(images[i], point_set[i],
+                                       dest_points, size, np.float32)
+
+  result_image = np.uint8(result_images / num_images)
+  face_indexes = np.nonzero(result_image)
+  dest_img[face_indexes] = result_image[face_indexes]
+
+  mask = blender.mask_from_points(size, dest_points)
+  if blur_edges:
+    blur_radius = 10
+    mask = cv2.blur(mask, (blur_radius, blur_radius))
+
+  if background in ('transparent', 'average'):
+    dest_img = np.dstack((dest_img, mask))
+
+    if background == 'average':
+      average_background = locator.average_points(images)
+      dest_img = blender.overlay_image(dest_img, mask, average_background)
+
+  print('Averaged {} images'.format(num_images))
+  plt = plotter.Plotter(plot, num_images=1, out_filename=out_filename)
+  plt.save(dest_img)
+  plt.plot_one(dest_img)
+  plt.show()
+
+def main():
+  args = docopt(__doc__, version='Face Averager 1.0')
+  try:
+    averager(list_imgpaths(args['--images']), args['--destimg'],
+             int(args['--width']), int(args['--height']),
+             args['--background'], args['--blur'], args['--out'], args['--plot'])
+  except Exception as e:
+    print(e)
+
+
+if __name__ == "__main__":
+  main()

videoretalking/third_part/GPEN/face_morpher/facemorpher/blender.py

@@ -0,0 +1,133 @@
+import cv2
+import numpy as np
+import scipy.sparse
+
+def mask_from_points(size, points):
+  """ Create a mask of supplied size from supplied points
+  :param size: tuple of output mask size
+  :param points: array of [x, y] points
+  :returns: mask of values 0 and 255 where
+            255 indicates the convex hull containing the points
+  """
+  radius = 10  # kernel size
+  kernel = np.ones((radius, radius), np.uint8)
+
+  mask = np.zeros(size, np.uint8)
+  cv2.fillConvexPoly(mask, cv2.convexHull(points), 255)
+  mask = cv2.erode(mask, kernel)
+
+  return mask
+
+def overlay_image(foreground_image, mask, background_image):
+  """ Overlay foreground image onto the background given a mask
+  :param foreground_image: foreground image points
+  :param mask: [0-255] values in mask
+  :param background_image: background image points
+  :returns: image with foreground where mask > 0 overlaid on background image
+  """
+  foreground_pixels = mask > 0
+  background_image[..., :3][foreground_pixels] = foreground_image[..., :3][foreground_pixels]
+  return background_image
+
+def apply_mask(img, mask):
+  """ Apply mask to supplied image
+  :param img: max 3 channel image
+  :param mask: [0-255] values in mask
+  :returns: new image with mask applied
+  """
+  masked_img = np.copy(img)
+  num_channels = 3
+  for c in range(num_channels):
+    masked_img[..., c] = img[..., c] * (mask / 255)
+
+  return masked_img
+
+def weighted_average(img1, img2, percent=0.5):
+  if percent <= 0:
+    return img2
+  elif percent >= 1:
+    return img1
+  else:
+    return cv2.addWeighted(img1, percent, img2, 1-percent, 0)
+
+def alpha_feathering(src_img, dest_img, img_mask, blur_radius=15):
+  mask = cv2.blur(img_mask, (blur_radius, blur_radius))
+  mask = mask / 255.0
+
+  result_img = np.empty(src_img.shape, np.uint8)
+  for i in range(3):
+    result_img[..., i] = src_img[..., i] * mask + dest_img[..., i] * (1-mask)
+
+  return result_img
+
+def poisson_blend(img_source, dest_img, img_mask, offset=(0, 0)):
+  # http://opencv.jp/opencv2-x-samples/poisson-blending
+  img_target = np.copy(dest_img)
+  import pyamg
+  # compute regions to be blended
+  region_source = (
+    max(-offset[0], 0),
+    max(-offset[1], 0),
+    min(img_target.shape[0] - offset[0], img_source.shape[0]),
+    min(img_target.shape[1] - offset[1], img_source.shape[1]))
+  region_target = (
+    max(offset[0], 0),
+    max(offset[1], 0),
+    min(img_target.shape[0], img_source.shape[0] + offset[0]),
+    min(img_target.shape[1], img_source.shape[1] + offset[1]))
+  region_size = (region_source[2] - region_source[0],
+                 region_source[3] - region_source[1])
+
+  # clip and normalize mask image
+  img_mask = img_mask[region_source[0]:region_source[2],
+                      region_source[1]:region_source[3]]
+
+  # create coefficient matrix
+  coff_mat = scipy.sparse.identity(np.prod(region_size), format='lil')
+  for y in range(region_size[0]):
+    for x in range(region_size[1]):
+      if img_mask[y, x]:
+        index = x + y * region_size[1]
+        coff_mat[index, index] = 4
+        if index + 1 < np.prod(region_size):
+          coff_mat[index, index + 1] = -1
+        if index - 1 >= 0:
+          coff_mat[index, index - 1] = -1
+        if index + region_size[1] < np.prod(region_size):
+          coff_mat[index, index + region_size[1]] = -1
+        if index - region_size[1] >= 0:
+          coff_mat[index, index - region_size[1]] = -1
+  coff_mat = coff_mat.tocsr()
+
+  # create poisson matrix for b
+  poisson_mat = pyamg.gallery.poisson(img_mask.shape)
+  # for each layer (ex. RGB)
+  for num_layer in range(img_target.shape[2]):
+    # get subimages
+    t = img_target[region_target[0]:region_target[2],
+                   region_target[1]:region_target[3], num_layer]
+    s = img_source[region_source[0]:region_source[2],
+                   region_source[1]:region_source[3], num_layer]
+    t = t.flatten()
+    s = s.flatten()
+
+    # create b
+    b = poisson_mat * s
+    for y in range(region_size[0]):
+      for x in range(region_size[1]):
+        if not img_mask[y, x]:
+          index = x + y * region_size[1]
+          b[index] = t[index]
+
+    # solve Ax = b
+    x = pyamg.solve(coff_mat, b, verb=False, tol=1e-10)
+
+    # assign x to target image
+    x = np.reshape(x, region_size)
+    x[x > 255] = 255
+    x[x < 0] = 0
+    x = np.array(x, img_target.dtype)
+    img_target[region_target[0]:region_target[2],
+               region_target[1]:region_target[3], num_layer] = x
+
+  return img_target

videoretalking/third_part/GPEN/face_morpher/facemorpher/locator.py

@@ -0,0 +1,112 @@
+"""
+Locate face points
+"""
+
+import cv2
+import numpy as np
+import os.path as path
+import dlib
+import os
+
+
+DATA_DIR = os.environ.get(
+  'DLIB_DATA_DIR',
+  path.join(path.dirname(path.dirname(path.realpath(__file__))), 'data')
+)
+dlib_detector = dlib.get_frontal_face_detector()
+dlib_predictor = dlib.shape_predictor(path.join(DATA_DIR, 'shape_predictor_68_face_landmarks.dat'))
+
+def boundary_points(points, width_percent=0.1, height_percent=0.1):
+  """ Produce additional boundary points
+  :param points: *m* x 2 array of x,y points
+  :param width_percent: [-1, 1] percentage of width to taper inwards. Negative for opposite direction
+  :param height_percent: [-1, 1] percentage of height to taper downwards. Negative for opposite direction
+  :returns: 2 additional points at the top corners
+  """
+  x, y, w, h = cv2.boundingRect(np.array([points], np.int32))
+  spacerw = int(w * width_percent)
+  spacerh = int(h * height_percent)
+  return [[x+spacerw, y+spacerh],
+          [x+w-spacerw, y+spacerh]]
+
+
+def face_points(img, add_boundary_points=True):
+  return face_points_dlib(img, add_boundary_points)
+
+def face_points_dlib(img, add_boundary_points=True):
+  """ Locates 68 face points using dlib (http://dlib.net)
+    Requires shape_predictor_68_face_landmarks.dat to be in face_morpher/data
+    Download at: http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2
+  :param img: an image array
+  :param add_boundary_points: bool to add additional boundary points
+  :returns: Array of x,y face points. Empty array if no face found
+  """
+  try:
+    points = []
+    rgbimg = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    rects = dlib_detector(rgbimg, 1)
+
+    if rects and len(rects) > 0:
+      # We only take the first found face
+      shapes = dlib_predictor(rgbimg, rects[0])
+      points = np.array([(shapes.part(i).x, shapes.part(i).y) for i in range(68)], np.int32)
+
+      if add_boundary_points:
+        # Add more points inwards and upwards as dlib only detects up to eyebrows
+        points = np.vstack([
+          points,
+          boundary_points(points, 0.1, -0.03),
+          boundary_points(points, 0.13, -0.05),
+          boundary_points(points, 0.15, -0.08),
+          boundary_points(points, 0.33, -0.12)])
+
+    return points
+  except Exception as e:
+    print(e)
+    return []
+
+def face_points_stasm(img, add_boundary_points=True):
+  import stasm
+  """ Locates 77 face points using stasm (http://www.milbo.users.sonic.net/stasm)
+
+  :param img: an image array
+  :param add_boundary_points: bool to add 2 additional points
+  :returns: Array of x,y face points. Empty array if no face found
+  """
+  try:
+    points = stasm.search_single(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY))
+  except Exception as e:
+    print('Failed finding face points: ', e)
+    return []
+
+  points = points.astype(np.int32)
+  if len(points) == 0:
+    return points
+
+  if add_boundary_points:
+    return np.vstack([points, boundary_points(points)])
+
+  return points
+
+def average_points(point_set):
+  """ Averages a set of face points from images
+
+  :param point_set: *n* x *m* x 2 array of face points. \\
+  *n* = number of images. *m* = number of face points per image
+  """
+  return np.mean(point_set, 0).astype(np.int32)
+
+def weighted_average_points(start_points, end_points, percent=0.5):
+  """ Weighted average of two sets of supplied points
+
+  :param start_points: *m* x 2 array of start face points.
+  :param end_points: *m* x 2 array of end face points.
+  :param percent: [0, 1] percentage weight on start_points
+  :returns: *m* x 2 array of weighted average points
+  """
+  if percent <= 0:
+    return end_points
+  elif percent >= 1:
+    return start_points
+  else:
+    return np.asarray(start_points*percent + end_points*(1-percent), np.int32)

videoretalking/third_part/GPEN/face_morpher/facemorpher/morpher.py

@@ -0,0 +1,153 @@
+"""
+::
+
+  Morph from source to destination face or
+  Morph through all images in a folder
+
+  Usage:
+    morpher.py (--src=<src_path> --dest=<dest_path> | --images=<folder>)
+              [--width=<width>] [--height=<height>]
+              [--num=<num_frames>] [--fps=<frames_per_second>]
+              [--out_frames=<folder>] [--out_video=<filename>]
+              [--plot] [--background=(black|transparent|average)]
+
+  Options:
+    -h, --help              Show this screen.
+    --src=<src_imgpath>     Filepath to source image (.jpg, .jpeg, .png)
+    --dest=<dest_imgpath>   Filepath to destination image (.jpg, .jpeg, .png)
+    --images=<folder>       Folderpath to images
+    --width=<width>         Custom width of the images/video [default: 500]
+    --height=<height>       Custom height of the images/video [default: 600]
+    --num=<num_frames>      Number of morph frames [default: 20]
+    --fps=<fps>             Number frames per second for the video [default: 10]
+    --out_frames=<folder>   Folder path to save all image frames
+    --out_video=<filename>  Filename to save a video
+    --plot                  Flag to plot images to result.png [default: False]
+    --background=<bg>       Background of images to be one of (black|transparent|average) [default: black]
+    --version               Show version.
+"""
+from docopt import docopt
+import os
+import numpy as np
+import cv2
+
+from facemorpher import locator
+from facemorpher import aligner
+from facemorpher import warper
+from facemorpher import blender
+from facemorpher import plotter
+from facemorpher import videoer
+
+def verify_args(args):
+  if args['--images'] is None:
+    valid = os.path.isfile(args['--src']) & os.path.isfile(args['--dest'])
+    if not valid:
+      print('--src=%s or --dest=%s file does not exist. Double check the supplied paths' % (
+        args['--src'], args['--dest']))
+      exit(1)
+  else:
+    valid = os.path.isdir(args['--images'])
+    if not valid:
+      print('--images=%s is not a valid directory' % args['--images'])
+      exit(1)
+
+def load_image_points(path, size):
+  img = cv2.imread(path)
+  points = locator.face_points(img)
+
+  if len(points) == 0:
+    print('No face in %s' % path)
+    return None, None
+  else:
+    return aligner.resize_align(img, points, size)
+
+def load_valid_image_points(imgpaths, size):
+  for path in imgpaths:
+    img, points = load_image_points(path, size)
+    if img is not None:
+      print(path)
+      yield (img, points)
+
+def list_imgpaths(images_folder=None, src_image=None, dest_image=None):
+  if images_folder is None:
+    yield src_image
+    yield dest_image
+  else:
+    for fname in os.listdir(images_folder):
+      if (fname.lower().endswith('.jpg') or
+         fname.lower().endswith('.png') or
+         fname.lower().endswith('.jpeg')):
+        yield os.path.join(images_folder, fname)
+
+def morph(src_img, src_points, dest_img, dest_points,
+          video, width=500, height=600, num_frames=20, fps=10,
+          out_frames=None, out_video=None, plot=False, background='black'):
+  """
+  Create a morph sequence from source to destination image
+
+  :param src_img: ndarray source image
+  :param src_points: source image array of x,y face points
+  :param dest_img: ndarray destination image
+  :param dest_points: destination image array of x,y face points
+  :param video: facemorpher.videoer.Video object
+  """
+  size = (height, width)
+  stall_frames = np.clip(int(fps*0.15), 1, fps)  # Show first & last longer
+  plt = plotter.Plotter(plot, num_images=num_frames, out_folder=out_frames)
+  num_frames -= (stall_frames * 2)  # No need to process src and dest image
+
+  plt.plot_one(src_img)
+  video.write(src_img, 1)
+
+  # Produce morph frames!
+  for percent in np.linspace(1, 0, num=num_frames):
+    points = locator.weighted_average_points(src_points, dest_points, percent)
+    src_face = warper.warp_image(src_img, src_points, points, size)
+    end_face = warper.warp_image(dest_img, dest_points, points, size)
+    average_face = blender.weighted_average(src_face, end_face, percent)
+
+    if background in ('transparent', 'average'):
+      mask = blender.mask_from_points(average_face.shape[:2], points)
+      average_face = np.dstack((average_face, mask))
+
+      if background == 'average':
+        average_background = blender.weighted_average(src_img, dest_img, percent)
+        average_face = blender.overlay_image(average_face, mask, average_background)
+
+    plt.plot_one(average_face)
+    plt.save(average_face)
+    video.write(average_face)
+
+  plt.plot_one(dest_img)
+  video.write(dest_img, stall_frames)
+  plt.show()
+
+def morpher(imgpaths, width=500, height=600, num_frames=20, fps=10,
+            out_frames=None, out_video=None, plot=False, background='black'):
+  """
+  Create a morph sequence from multiple images in imgpaths
+
+  :param imgpaths: array or generator of image paths
+  """
+  video = videoer.Video(out_video, fps, width, height)
+  images_points_gen = load_valid_image_points(imgpaths, (height, width))
+  src_img, src_points = next(images_points_gen)
+  for dest_img, dest_points in images_points_gen:
+    morph(src_img, src_points, dest_img, dest_points, video,
+          width, height, num_frames, fps, out_frames, out_video, plot, background)
+    src_img, src_points = dest_img, dest_points
+  video.end()
+
+def main():
+  args = docopt(__doc__, version='Face Morpher 1.0')
+  verify_args(args)
+
+  morpher(list_imgpaths(args['--images'], args['--src'], args['--dest']),
+          int(args['--width']), int(args['--height']),
+          int(args['--num']), int(args['--fps']),
+          args['--out_frames'], args['--out_video'],
+          args['--plot'], args['--background'])
+
+
+if __name__ == "__main__":
+  main()

videoretalking/third_part/GPEN/face_morpher/facemorpher/plotter.py

@@ -0,0 +1,90 @@
+"""
+Plot and save images
+"""
+
+import matplotlib.pyplot as plt
+import matplotlib.image as mpimg
+import os.path
+import numpy as np
+import cv2
+
+def bgr2rgb(img):
+  # OpenCV's BGR to RGB
+  rgb = np.copy(img)
+  rgb[..., 0], rgb[..., 2] = img[..., 2], img[..., 0]
+  return rgb
+
+def check_do_plot(func):
+  def inner(self, *args, **kwargs):
+    if self.do_plot:
+      func(self, *args, **kwargs)
+
+  return inner
+
+def check_do_save(func):
+  def inner(self, *args, **kwargs):
+    if self.do_save:
+      func(self, *args, **kwargs)
+
+  return inner
+
+class Plotter(object):
+  def __init__(self, plot=True, rows=0, cols=0, num_images=0, out_folder=None, out_filename=None):
+    self.save_counter = 1
+    self.plot_counter = 1
+    self.do_plot = plot
+    self.do_save = out_filename is not None
+    self.out_filename = out_filename
+    self.set_filepath(out_folder)
+
+    if (rows + cols) == 0 and num_images > 0:
+      # Auto-calculate the number of rows and cols for the figure
+      self.rows = np.ceil(np.sqrt(num_images / 2.0))
+      self.cols = np.ceil(num_images / self.rows)
+    else:
+      self.rows = rows
+      self.cols = cols
+
+  def set_filepath(self, folder):
+    if folder is None:
+      self.filepath = None
+      return
+
+    if not os.path.exists(folder):
+      os.makedirs(folder)
+    self.filepath = os.path.join(folder, 'frame{0:03d}.png')
+    self.do_save = True
+
+  @check_do_save
+  def save(self, img, filename=None):
+    if self.filepath:
+      filename = self.filepath.format(self.save_counter)
+      self.save_counter += 1
+    elif filename is None:
+      filename = self.out_filename
+
+    mpimg.imsave(filename, bgr2rgb(img))
+    print(filename + ' saved')
+
+  @check_do_plot
+  def plot_one(self, img):
+    p = plt.subplot(self.rows, self.cols, self.plot_counter)
+    p.axes.get_xaxis().set_visible(False)
+    p.axes.get_yaxis().set_visible(False)
+    plt.imshow(bgr2rgb(img))
+    self.plot_counter += 1
+
+  @check_do_plot
+  def show(self):
+    plt.gcf().subplots_adjust(hspace=0.05, wspace=0,
+                              left=0, bottom=0, right=1, top=0.98)
+    plt.axis('off')
+    #plt.show()
+    plt.savefig('result.png')
+
+  @check_do_plot
+  def plot_mesh(self, points, tri, color='k'):
+    """ plot triangles """
+    for tri_indices in tri.simplices:
+      t_ext = [tri_indices[0], tri_indices[1], tri_indices[2], tri_indices[0]]
+      plt.plot(points[t_ext, 0], points[t_ext, 1], color)

videoretalking/third_part/GPEN/face_morpher/facemorpher/videoer.py

@@ -0,0 +1,36 @@
+"""
+Create a video with image frames
+"""
+
+import cv2
+import numpy as np
+
+
+def check_write_video(func):
+  def inner(self, *args, **kwargs):
+    if self.video:
+      return func(self, *args, **kwargs)
+    else:
+      pass
+  return inner
+
+
+class Video(object):
+  def __init__(self, filename, fps, w, h):
+    self.filename = filename
+
+    if filename is None:
+      self.video = None
+    else:
+      fourcc = cv2.VideoWriter_fourcc(*'MJPG')
+      self.video = cv2.VideoWriter(filename, fourcc, fps, (w, h), True)
+
+  @check_write_video
+  def write(self, img, num_times=1):
+    for i in range(num_times):
+      self.video.write(img[..., :3])
+
+  @check_write_video
+  def end(self):
+    print(self.filename + ' saved')
+    self.video.release()

videoretalking/third_part/GPEN/face_morpher/facemorpher/warper.py

@@ -0,0 +1,139 @@
+import numpy as np
+import scipy.spatial as spatial
+
+def bilinear_interpolate(img, coords):
+  """ Interpolates over every image channel
+  http://en.wikipedia.org/wiki/Bilinear_interpolation
+
+  :param img: max 3 channel image
+  :param coords: 2 x _m_ array. 1st row = xcoords, 2nd row = ycoords
+  :returns: array of interpolated pixels with same shape as coords
+  """
+  int_coords = np.int32(coords)
+  x0, y0 = int_coords
+  dx, dy = coords - int_coords
+
+  # 4 Neighbour pixels
+  q11 = img[y0, x0]
+  q21 = img[y0, x0+1]
+  q12 = img[y0+1, x0]
+  q22 = img[y0+1, x0+1]
+
+  btm = q21.T * dx + q11.T * (1 - dx)
+  top = q22.T * dx + q12.T * (1 - dx)
+  inter_pixel = top * dy + btm * (1 - dy)
+
+  return inter_pixel.T
+
+def grid_coordinates(points):
+  """ x,y grid coordinates within the ROI of supplied points
+
+  :param points: points to generate grid coordinates
+  :returns: array of (x, y) coordinates
+  """
+  xmin = np.min(points[:, 0])
+  xmax = np.max(points[:, 0]) + 1
+  ymin = np.min(points[:, 1])
+  ymax = np.max(points[:, 1]) + 1
+  return np.asarray([(x, y) for y in range(ymin, ymax)
+                     for x in range(xmin, xmax)], np.uint32)
+
+def process_warp(src_img, result_img, tri_affines, dst_points, delaunay):
+  """
+  Warp each triangle from the src_image only within the
+  ROI of the destination image (points in dst_points).
+  """
+  roi_coords = grid_coordinates(dst_points)
+  # indices to vertices. -1 if pixel is not in any triangle
+  roi_tri_indices = delaunay.find_simplex(roi_coords)
+
+  for simplex_index in range(len(delaunay.simplices)):
+    coords = roi_coords[roi_tri_indices == simplex_index]
+    num_coords = len(coords)
+    out_coords = np.dot(tri_affines[simplex_index],
+                        np.vstack((coords.T, np.ones(num_coords))))
+    x, y = coords.T
+    result_img[y, x] = bilinear_interpolate(src_img, out_coords)
+
+  return None
+
+def triangular_affine_matrices(vertices, src_points, dest_points):
+  """
+  Calculate the affine transformation matrix for each
+  triangle (x,y) vertex from dest_points to src_points
+
+  :param vertices: array of triplet indices to corners of triangle
+  :param src_points: array of [x, y] points to landmarks for source image
+  :param dest_points: array of [x, y] points to landmarks for destination image
+  :returns: 2 x 3 affine matrix transformation for a triangle
+  """
+  ones = [1, 1, 1]
+  for tri_indices in vertices:
+    src_tri = np.vstack((src_points[tri_indices, :].T, ones))
+    dst_tri = np.vstack((dest_points[tri_indices, :].T, ones))
+    mat = np.dot(src_tri, np.linalg.inv(dst_tri))[:2, :]
+    yield mat
+
+def warp_image(src_img, src_points, dest_points, dest_shape, dtype=np.uint8):
+  # Resultant image will not have an alpha channel
+  num_chans = 3
+  src_img = src_img[:, :, :3]
+
+  rows, cols = dest_shape[:2]
+  result_img = np.zeros((rows, cols, num_chans), dtype)
+
+  delaunay = spatial.Delaunay(dest_points)
+  tri_affines = np.asarray(list(triangular_affine_matrices(
+    delaunay.simplices, src_points, dest_points)))
+
+  process_warp(src_img, result_img, tri_affines, dest_points, delaunay)
+
+  return result_img
+
+def test_local():
+  from functools import partial
+  import cv2
+  import scipy.misc
+  import locator
+  import aligner
+  from matplotlib import pyplot as plt
+
+  # Load source image
+  face_points_func = partial(locator.face_points, '../data')
+  base_path = '../females/Screenshot 2015-03-04 17.11.12.png'
+  src_path = '../females/BlDmB5QCYAAY8iw.jpg'
+  src_img = cv2.imread(src_path)
+
+  # Define control points for warps
+  src_points = face_points_func(src_path)
+  base_img = cv2.imread(base_path)
+  base_points = face_points_func(base_path)
+
+  size = (600, 500)
+  src_img, src_points = aligner.resize_align(src_img, src_points, size)
+  base_img, base_points = aligner.resize_align(base_img, base_points, size)
+  result_points = locator.weighted_average_points(src_points, base_points, 0.2)
+
+  # Perform transform
+  dst_img1 = warp_image(src_img, src_points, result_points, size)
+  dst_img2 = warp_image(base_img, base_points, result_points, size)
+
+  import blender
+  ave = blender.weighted_average(dst_img1, dst_img2, 0.6)
+  mask = blender.mask_from_points(size, result_points)
+  blended_img = blender.poisson_blend(dst_img1, dst_img2, mask)
+
+  plt.subplot(2, 2, 1)
+  plt.imshow(ave)
+  plt.subplot(2, 2, 2)
+  plt.imshow(dst_img1)
+  plt.subplot(2, 2, 3)
+  plt.imshow(dst_img2)
+  plt.subplot(2, 2, 4)
+
+  plt.imshow(blended_img)
+  plt.show()
+
+
+if __name__ == "__main__":
+  test_local()

videoretalking/third_part/GPEN/face_morpher/requirements.txt

@@ -0,0 +1,5 @@
+numpy
+scipy
+matplotlib
+docopt
+dlib

videoretalking/third_part/GPEN/face_morpher/scripts/make_docs.sh

@@ -0,0 +1,18 @@
+#!/bin/bash
+
+rm -rf docs
+# reStructuredText in python files to rst. Documentation in docs folder
+sphinx-apidoc -A "Alyssa Quek" -f -F -o docs facemorpher/
+
+cd docs
+
+# Append module path to end of conf file
+echo "" >> conf.py
+echo "import os" >> conf.py
+echo "import sys" >> conf.py
+echo "sys.path.insert(0, os.path.abspath('../'))" >> conf.py
+echo "sys.path.insert(0, os.path.abspath('../facemorpher'))" >> conf.py
+
+# Make sphinx documentation
+make html
+cd ..

videoretalking/third_part/GPEN/face_morpher/scripts/publish_ghpages.sh

@@ -0,0 +1,29 @@
+#!/bin/bash          
+
+# delete previous gh-pages
+git branch -D gh-pages
+git push origin :gh-pages
+
+git checkout -b gh-pages
+git rebase master
+git reset HEAD
+
+# make docs
+./scripts/make_docs.sh
+
+# Add docs
+mv docs/_build/html/*.html .
+git add *.html
+mv docs/_build/html/*.js .
+git add *.js
+mv docs/_build/html/_static/ _static
+git add _static
+
+touch .nojekyll
+git add .nojekyll
+
+# Publish to gh-pages
+git commit -m "docs"
+git push origin gh-pages
+
+git checkout master

videoretalking/third_part/GPEN/face_morpher/setup.cfg

@@ -0,0 +1,7 @@
+[pep8]
+ignore = E111,E114,E226,E302,E41,E121,E701
+max-line-length = 100
+
+[flake8]
+ignore = E111,E114,E226,E302,E41,E121,E701
+max-line-length = 100

videoretalking/third_part/GPEN/face_morpher/setup.py

@@ -0,0 +1,30 @@
+from setuptools import setup, find_packages
+
+# To test locally: python setup.py sdist bdist_wheel
+# To upload to pypi: twine upload dist/*
+
+setup(
+  name='facemorpher',
+  version='5.2.dev0',
+  author='Alyssa Quek',
+  author_email='alyssaquek@gmail.com',
+  description=('Warp, morph and average human faces!'),
+  keywords='face morphing, averaging, warping',
+  url='https://github.com/alyssaq/face_morpher',
+  license='MIT',
+  packages=find_packages(),
+  install_requires=[
+    'docopt',
+    'numpy',
+    'scipy',
+    'matplotlib',
+    'dlib'
+  ],
+  entry_points={'console_scripts': [
+      'facemorpher=facemorpher.morpher:main',
+      'faceaverager=facemorpher.averager:main'
+    ]
+  },
+  data_files=[('readme', ['README.rst'])],
+  long_description=open('README.rst').read(),
+)

videoretalking/third_part/GPEN/face_parse/blocks.py

@@ -0,0 +1,127 @@
+# -*- coding: utf-8 -*-
+import torch
+import torch.nn as nn
+from torch.nn.parameter import Parameter
+from torch.nn import functional as F
+import numpy as np
+
+class NormLayer(nn.Module):
+    """Normalization Layers.
+    ------------
+    # Arguments
+        - channels: input channels, for batch norm and instance norm.
+        - input_size: input shape without batch size, for layer norm.
+    """
+    def __init__(self, channels, normalize_shape=None, norm_type='bn', ref_channels=None):
+        super(NormLayer, self).__init__()
+        norm_type = norm_type.lower()
+        self.norm_type = norm_type
+        if norm_type == 'bn':
+            self.norm = nn.BatchNorm2d(channels, affine=True)
+        elif norm_type == 'in':
+            self.norm = nn.InstanceNorm2d(channels, affine=False)
+        elif norm_type == 'gn':
+            self.norm = nn.GroupNorm(32, channels, affine=True)
+        elif norm_type == 'pixel':
+            self.norm = lambda x: F.normalize(x, p=2, dim=1)
+        elif norm_type == 'layer':
+            self.norm = nn.LayerNorm(normalize_shape)
+        elif norm_type == 'none':
+            self.norm = lambda x: x*1.0
+        else:
+            assert 1==0, 'Norm type {} not support.'.format(norm_type)
+
+    def forward(self, x, ref=None):
+        if self.norm_type == 'spade':
+            return self.norm(x, ref)
+        else:
+            return self.norm(x)
+
+
+class ReluLayer(nn.Module):
+    """Relu Layer.
+    ------------
+    # Arguments
+        - relu type: type of relu layer, candidates are
+            - ReLU
+            - LeakyReLU: default relu slope 0.2
+            - PRelu 
+            - SELU
+            - none: direct pass
+    """
+    def __init__(self, channels, relu_type='relu'):
+        super(ReluLayer, self).__init__()
+        relu_type = relu_type.lower()
+        if relu_type == 'relu':
+            self.func = nn.ReLU(True)
+        elif relu_type == 'leakyrelu':
+            self.func = nn.LeakyReLU(0.2, inplace=True)
+        elif relu_type == 'prelu':
+            self.func = nn.PReLU(channels)
+        elif relu_type == 'selu':
+            self.func = nn.SELU(True)
+        elif relu_type == 'none':
+            self.func = lambda x: x*1.0
+        else:
+            assert 1==0, 'Relu type {} not support.'.format(relu_type)
+
+    def forward(self, x):
+        return self.func(x)
+
+
+class ConvLayer(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size=3, scale='none', norm_type='none', relu_type='none', use_pad=True, bias=True):
+        super(ConvLayer, self).__init__()
+        self.use_pad = use_pad
+        self.norm_type = norm_type
+        if norm_type in ['bn']:
+            bias = False
+        
+        stride = 2 if scale == 'down' else 1
+
+        self.scale_func = lambda x: x
+        if scale == 'up':
+            self.scale_func = lambda x: nn.functional.interpolate(x, scale_factor=2, mode='nearest')
+
+        self.reflection_pad = nn.ReflectionPad2d(int(np.ceil((kernel_size - 1.)/2))) 
+        self.conv2d = nn.Conv2d(in_channels, out_channels, kernel_size, stride, bias=bias)
+
+        self.relu = ReluLayer(out_channels, relu_type)
+        self.norm = NormLayer(out_channels, norm_type=norm_type)
+
+    def forward(self, x):
+        out = self.scale_func(x)
+        if self.use_pad:
+            out = self.reflection_pad(out)
+        out = self.conv2d(out)
+        out = self.norm(out)
+        out = self.relu(out)
+        return out
+
+
+class ResidualBlock(nn.Module):
+    """
+    Residual block recommended in: http://torch.ch/blog/2016/02/04/resnets.html
+    """
+    def __init__(self, c_in, c_out, relu_type='prelu', norm_type='bn', scale='none'):
+        super(ResidualBlock, self).__init__()
+
+        if scale == 'none' and c_in == c_out:
+            self.shortcut_func = lambda x: x
+        else:
+            self.shortcut_func = ConvLayer(c_in, c_out, 3, scale)
+        
+        scale_config_dict = {'down': ['none', 'down'], 'up': ['up', 'none'], 'none': ['none', 'none']}
+        scale_conf = scale_config_dict[scale]
+
+        self.conv1 = ConvLayer(c_in, c_out, 3, scale_conf[0], norm_type=norm_type, relu_type=relu_type) 
+        self.conv2 = ConvLayer(c_out, c_out, 3, scale_conf[1], norm_type=norm_type, relu_type='none')
+  
+    def forward(self, x):
+        identity = self.shortcut_func(x)
+
+        res = self.conv1(x)
+        res = self.conv2(res)
+        return identity + res
+        
+

videoretalking/third_part/GPEN/face_parse/face_parsing.py

@@ -0,0 +1,148 @@
+'''
+@paper: GAN Prior Embedded Network for Blind Face Restoration in the Wild (CVPR2021)
+@author: yangxy (yangtao9009@gmail.com)
+'''
+import os
+import cv2
+import torch
+import numpy as np
+from face_parse.parse_model import ParseNet
+import torch.nn.functional as F
+
+from face_parse.model import BiSeNet
+import torchvision.transforms as transforms
+
+class FaceParse(object):
+    def __init__(self, base_dir='./', model='ParseNet-latest', device='cuda', mask_map = [0, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 0, 0, 0, 0, 0, 0]):
+        self.mfile = os.path.join(base_dir, model+'.pth')
+        self.size = 512
+        self.device = device
+
+        '''
+        0: 'background' 1: 'skin'   2: 'nose'
+        3: 'eye_g'  4: 'l_eye'  5: 'r_eye'
+        6: 'l_brow' 7: 'r_brow' 8: 'l_ear'
+        9: 'r_ear'  10: 'mouth' 11: 'u_lip'
+        12: 'l_lip' 13: 'hair'  14: 'hat'
+        15: 'ear_r' 16: 'neck_l'    17: 'neck'
+        18: 'cloth'
+        '''
+        # self.MASK_COLORMAP = [[0, 0, 0], [204, 0, 0], [76, 153, 0], [204, 204, 0], [51, 51, 255], [204, 0, 204], [0, 255, 255], [255, 204, 204], [102, 51, 0], [255, 0, 0], [102, 204, 0], [255, 255, 0], [0, 0, 153], [0, 0, 204], [255, 51, 153], [0, 204, 204], [0, 51, 0], [255, 153, 51], [0, 204, 0]]
+        #self.#MASK_COLORMAP = [[0, 0, 0], [204, 0, 0], [76, 153, 0], [204, 204, 0], [51, 51, 255], [204, 0, 204], [0, 255, 255], [255, 204, 204], [102, 51, 0], [255, 0, 0], [102, 204, 0], [255, 255, 0], [0, 0, 153], [0, 0, 204], [255, 51, 153], [0, 204, 204], [0, 51, 0], [255, 153, 51], [0, 204, 0]] = [[0, 0, 0], [204, 0, 0], [76, 153, 0], [204, 204, 0], [51, 51, 255], [204, 0, 204], [0, 255, 255], [255, 204, 204], [102, 51, 0], [255, 0, 0], [102, 204, 0], [255, 255, 0], [0, 0, 153], [0, 0, 204], [255, 51, 153], [0, 204, 204], [0, 51, 0], [0, 0, 0], [0, 0, 0]]
+        # self.MASK_COLORMAP = [0, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 0, 255, 0, 0, 0]
+        self.MASK_COLORMAP = mask_map
+
+        self.load_model()
+
+    def load_model(self):
+        self.faceparse = ParseNet(self.size, self.size, 32, 64, 19, norm_type='bn', relu_type='LeakyReLU', ch_range=[32, 256])
+        self.faceparse.load_state_dict(torch.load(self.mfile, map_location=torch.device('cpu')))
+        self.faceparse.to(self.device)
+        self.faceparse.eval()
+
+    def process(self, im, masks=[0, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 0, 0, 0, 0, 0, 0]):
+        im = cv2.resize(im, (self.size, self.size))
+        imt = self.img2tensor(im)
+        with torch.no_grad():
+            pred_mask, sr_img_tensor = self.faceparse(imt)  # (1, 19, 512, 512)
+        mask = self.tenor2mask(pred_mask, masks)
+
+        return mask
+
+    def process_tensor(self, imt):
+        imt = F.interpolate(imt.flip(1)*2-1, (self.size, self.size))
+        pred_mask, sr_img_tensor = self.faceparse(imt)
+
+        mask = pred_mask.argmax(dim=1)
+        for idx, color in enumerate(self.MASK_COLORMAP):
+            mask = torch.where(mask==idx, color, mask)
+        #mask = mask.repeat(3, 1, 1).unsqueeze(0) #.cpu().float().numpy()
+        mask = mask.unsqueeze(0)
+
+        return mask
+
+    def img2tensor(self, img):
+        img = img[..., ::-1] # BGR to RGB
+        img = img / 255. * 2 - 1
+        img_tensor = torch.from_numpy(img.transpose(2, 0, 1)).unsqueeze(0).to(self.device)
+        return img_tensor.float()
+
+    def tenor2mask(self, tensor, masks):
+        if len(tensor.shape) < 4:
+            tensor = tensor.unsqueeze(0)
+        if tensor.shape[1] > 1:
+            tensor = tensor.argmax(dim=1) 
+
+        tensor = tensor.squeeze(1).data.cpu().numpy()   # (1, 512, 512)
+        color_maps = []
+        for t in tensor:
+            #tmp_img = np.zeros(tensor.shape[1:] + (3,))
+            tmp_img = np.zeros(tensor.shape[1:])
+            for idx, color in enumerate(masks):
+                tmp_img[t == idx] = color
+            color_maps.append(tmp_img.astype(np.uint8))
+        return color_maps
+
+
+
+class FaceParse_v2(object):
+    def __init__(self, device='cuda', mask_map = [0, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 0, 0, 0, 0, 0, 0]):
+        self.mfile = '/apdcephfs/private_quincheng/Expression/face-parsing.PyTorch/res/cp/79999_iter.pth'
+        self.size = 512
+        self.device = device
+
+        '''
+        0: 'background' 1: 'skin'   2: 'nose'
+        3: 'eye_g'  4: 'l_eye'  5: 'r_eye'
+        6: 'l_brow' 7: 'r_brow' 8: 'l_ear'
+        9: 'r_ear'  10: 'mouth' 11: 'u_lip'
+        12: 'l_lip' 13: 'hair'  14: 'hat'
+        15: 'ear_r' 16: 'neck_l'    17: 'neck'
+        18: 'cloth'
+        '''
+        # self.MASK_COLORMAP = [[0, 0, 0], [204, 0, 0], [76, 153, 0], [204, 204, 0], [51, 51, 255], [204, 0, 204], [0, 255, 255], [255, 204, 204], [102, 51, 0], [255, 0, 0], [102, 204, 0], [255, 255, 0], [0, 0, 153], [0, 0, 204], [255, 51, 153], [0, 204, 204], [0, 51, 0], [255, 153, 51], [0, 204, 0]]
+        #self.#MASK_COLORMAP = [[0, 0, 0], [204, 0, 0], [76, 153, 0], [204, 204, 0], [51, 51, 255], [204, 0, 204], [0, 255, 255], [255, 204, 204], [102, 51, 0], [255, 0, 0], [102, 204, 0], [255, 255, 0], [0, 0, 153], [0, 0, 204], [255, 51, 153], [0, 204, 204], [0, 51, 0], [255, 153, 51], [0, 204, 0]] = [[0, 0, 0], [204, 0, 0], [76, 153, 0], [204, 204, 0], [51, 51, 255], [204, 0, 204], [0, 255, 255], [255, 204, 204], [102, 51, 0], [255, 0, 0], [102, 204, 0], [255, 255, 0], [0, 0, 153], [0, 0, 204], [255, 51, 153], [0, 204, 204], [0, 51, 0], [0, 0, 0], [0, 0, 0]]
+        # self.MASK_COLORMAP = [0, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 0, 255, 0, 0, 0]
+        self.MASK_COLORMAP = mask_map
+        self.load_model()
+        self.to_tensor = transforms.Compose([
+            transforms.ToTensor(),
+            transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
+        ])
+
+    def load_model(self):
+        self.faceparse = BiSeNet(n_classes=19)
+        self.faceparse.load_state_dict(torch.load(self.mfile))
+        self.faceparse.to(self.device)
+        self.faceparse.eval()
+
+    def process(self, im, masks=[0, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 0, 0, 0, 0, 0, 0]):
+        im = cv2.resize(im[...,::-1], (self.size, self.size))
+        im = self.to_tensor(im)
+        imt = torch.unsqueeze(im, 0).to(self.device)
+        with torch.no_grad():
+            pred_mask = self.faceparse(imt)[0]
+        mask = self.tenor2mask(pred_mask, masks)
+        return mask
+
+    # def img2tensor(self, img):
+    #     img = img[..., ::-1] # BGR to RGB
+    #     img = img / 255. * 2 - 1
+    #     img_tensor = torch.from_numpy(img.transpose(2, 0, 1)).unsqueeze(0).to(self.device)
+    #     return img_tensor.float()
+
+    def tenor2mask(self, tensor, masks):
+        if len(tensor.shape) < 4:
+            tensor = tensor.unsqueeze(0)
+        if tensor.shape[1] > 1:
+            tensor = tensor.argmax(dim=1) 
+
+        tensor = tensor.squeeze(1).data.cpu().numpy()
+        color_maps = []
+        for t in tensor:
+            #tmp_img = np.zeros(tensor.shape[1:] + (3,))
+            tmp_img = np.zeros(tensor.shape[1:])
+            for idx, color in enumerate(masks):
+                tmp_img[t == idx] = color
+            color_maps.append(tmp_img.astype(np.uint8))
+        return color_maps

videoretalking/third_part/GPEN/face_parse/model.py

@@ -0,0 +1,283 @@
+#!/usr/bin/python
+# -*- encoding: utf-8 -*-
+
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torchvision
+
+from .resnet import Resnet18
+# from modules.bn import InPlaceABNSync as BatchNorm2d
+
+
+class ConvBNReLU(nn.Module):
+    def __init__(self, in_chan, out_chan, ks=3, stride=1, padding=1, *args, **kwargs):
+        super(ConvBNReLU, self).__init__()
+        self.conv = nn.Conv2d(in_chan,
+                out_chan,
+                kernel_size = ks,
+                stride = stride,
+                padding = padding,
+                bias = False)
+        self.bn = nn.BatchNorm2d(out_chan)
+        self.init_weight()
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = F.relu(self.bn(x))
+        return x
+
+    def init_weight(self):
+        for ly in self.children():
+            if isinstance(ly, nn.Conv2d):
+                nn.init.kaiming_normal_(ly.weight, a=1)
+                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
+
+class BiSeNetOutput(nn.Module):
+    def __init__(self, in_chan, mid_chan, n_classes, *args, **kwargs):
+        super(BiSeNetOutput, self).__init__()
+        self.conv = ConvBNReLU(in_chan, mid_chan, ks=3, stride=1, padding=1)
+        self.conv_out = nn.Conv2d(mid_chan, n_classes, kernel_size=1, bias=False)
+        self.init_weight()
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.conv_out(x)
+        return x
+
+    def init_weight(self):
+        for ly in self.children():
+            if isinstance(ly, nn.Conv2d):
+                nn.init.kaiming_normal_(ly.weight, a=1)
+                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
+
+    def get_params(self):
+        wd_params, nowd_params = [], []
+        for name, module in self.named_modules():
+            if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
+                wd_params.append(module.weight)
+                if not module.bias is None:
+                    nowd_params.append(module.bias)
+            elif isinstance(module, nn.BatchNorm2d):
+                nowd_params += list(module.parameters())
+        return wd_params, nowd_params
+
+
+class AttentionRefinementModule(nn.Module):
+    def __init__(self, in_chan, out_chan, *args, **kwargs):
+        super(AttentionRefinementModule, self).__init__()
+        self.conv = ConvBNReLU(in_chan, out_chan, ks=3, stride=1, padding=1)
+        self.conv_atten = nn.Conv2d(out_chan, out_chan, kernel_size= 1, bias=False)
+        self.bn_atten = nn.BatchNorm2d(out_chan)
+        self.sigmoid_atten = nn.Sigmoid()
+        self.init_weight()
+
+    def forward(self, x):
+        feat = self.conv(x)
+        atten = F.avg_pool2d(feat, feat.size()[2:])
+        atten = self.conv_atten(atten)
+        atten = self.bn_atten(atten)
+        atten = self.sigmoid_atten(atten)
+        out = torch.mul(feat, atten)
+        return out
+
+    def init_weight(self):
+        for ly in self.children():
+            if isinstance(ly, nn.Conv2d):
+                nn.init.kaiming_normal_(ly.weight, a=1)
+                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
+
+
+class ContextPath(nn.Module):
+    def __init__(self, *args, **kwargs):
+        super(ContextPath, self).__init__()
+        self.resnet = Resnet18()
+        self.arm16 = AttentionRefinementModule(256, 128)
+        self.arm32 = AttentionRefinementModule(512, 128)
+        self.conv_head32 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1)
+        self.conv_head16 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1)
+        self.conv_avg = ConvBNReLU(512, 128, ks=1, stride=1, padding=0)
+
+        self.init_weight()
+
+    def forward(self, x):
+        H0, W0 = x.size()[2:]
+        feat8, feat16, feat32 = self.resnet(x)
+        H8, W8 = feat8.size()[2:]
+        H16, W16 = feat16.size()[2:]
+        H32, W32 = feat32.size()[2:]
+
+        avg = F.avg_pool2d(feat32, feat32.size()[2:])
+        avg = self.conv_avg(avg)
+        avg_up = F.interpolate(avg, (H32, W32), mode='nearest')
+
+        feat32_arm = self.arm32(feat32)
+        feat32_sum = feat32_arm + avg_up
+        feat32_up = F.interpolate(feat32_sum, (H16, W16), mode='nearest')
+        feat32_up = self.conv_head32(feat32_up)
+
+        feat16_arm = self.arm16(feat16)
+        feat16_sum = feat16_arm + feat32_up
+        feat16_up = F.interpolate(feat16_sum, (H8, W8), mode='nearest')
+        feat16_up = self.conv_head16(feat16_up)
+
+        return feat8, feat16_up, feat32_up  # x8, x8, x16
+
+    def init_weight(self):
+        for ly in self.children():
+            if isinstance(ly, nn.Conv2d):
+                nn.init.kaiming_normal_(ly.weight, a=1)
+                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
+
+    def get_params(self):
+        wd_params, nowd_params = [], []
+        for name, module in self.named_modules():
+            if isinstance(module, (nn.Linear, nn.Conv2d)):
+                wd_params.append(module.weight)
+                if not module.bias is None:
+                    nowd_params.append(module.bias)
+            elif isinstance(module, nn.BatchNorm2d):
+                nowd_params += list(module.parameters())
+        return wd_params, nowd_params
+
+
+### This is not used, since I replace this with the resnet feature with the same size
+class SpatialPath(nn.Module):
+    def __init__(self, *args, **kwargs):
+        super(SpatialPath, self).__init__()
+        self.conv1 = ConvBNReLU(3, 64, ks=7, stride=2, padding=3)
+        self.conv2 = ConvBNReLU(64, 64, ks=3, stride=2, padding=1)
+        self.conv3 = ConvBNReLU(64, 64, ks=3, stride=2, padding=1)
+        self.conv_out = ConvBNReLU(64, 128, ks=1, stride=1, padding=0)
+        self.init_weight()
+
+    def forward(self, x):
+        feat = self.conv1(x)
+        feat = self.conv2(feat)
+        feat = self.conv3(feat)
+        feat = self.conv_out(feat)
+        return feat
+
+    def init_weight(self):
+        for ly in self.children():
+            if isinstance(ly, nn.Conv2d):
+                nn.init.kaiming_normal_(ly.weight, a=1)
+                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
+
+    def get_params(self):
+        wd_params, nowd_params = [], []
+        for name, module in self.named_modules():
+            if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
+                wd_params.append(module.weight)
+                if not module.bias is None:
+                    nowd_params.append(module.bias)
+            elif isinstance(module, nn.BatchNorm2d):
+                nowd_params += list(module.parameters())
+        return wd_params, nowd_params
+
+
+class FeatureFusionModule(nn.Module):
+    def __init__(self, in_chan, out_chan, *args, **kwargs):
+        super(FeatureFusionModule, self).__init__()
+        self.convblk = ConvBNReLU(in_chan, out_chan, ks=1, stride=1, padding=0)
+        self.conv1 = nn.Conv2d(out_chan,
+                out_chan//4,
+                kernel_size = 1,
+                stride = 1,
+                padding = 0,
+                bias = False)
+        self.conv2 = nn.Conv2d(out_chan//4,
+                out_chan,
+                kernel_size = 1,
+                stride = 1,
+                padding = 0,
+                bias = False)
+        self.relu = nn.ReLU(inplace=True)
+        self.sigmoid = nn.Sigmoid()
+        self.init_weight()
+
+    def forward(self, fsp, fcp):
+        fcat = torch.cat([fsp, fcp], dim=1)
+        feat = self.convblk(fcat)
+        atten = F.avg_pool2d(feat, feat.size()[2:])
+        atten = self.conv1(atten)
+        atten = self.relu(atten)
+        atten = self.conv2(atten)
+        atten = self.sigmoid(atten)
+        feat_atten = torch.mul(feat, atten)
+        feat_out = feat_atten + feat
+        return feat_out
+
+    def init_weight(self):
+        for ly in self.children():
+            if isinstance(ly, nn.Conv2d):
+                nn.init.kaiming_normal_(ly.weight, a=1)
+                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
+
+    def get_params(self):
+        wd_params, nowd_params = [], []
+        for name, module in self.named_modules():
+            if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
+                wd_params.append(module.weight)
+                if not module.bias is None:
+                    nowd_params.append(module.bias)
+            elif isinstance(module, nn.BatchNorm2d):
+                nowd_params += list(module.parameters())
+        return wd_params, nowd_params
+
+
+class BiSeNet(nn.Module):
+    def __init__(self, n_classes, *args, **kwargs):
+        super(BiSeNet, self).__init__()
+        self.cp = ContextPath()
+        ## here self.sp is deleted
+        self.ffm = FeatureFusionModule(256, 256)
+        self.conv_out = BiSeNetOutput(256, 256, n_classes)
+        self.conv_out16 = BiSeNetOutput(128, 64, n_classes)
+        self.conv_out32 = BiSeNetOutput(128, 64, n_classes)
+        self.init_weight()
+
+    def forward(self, x):
+        H, W = x.size()[2:]
+        feat_res8, feat_cp8, feat_cp16 = self.cp(x)  # here return res3b1 feature
+        feat_sp = feat_res8  # use res3b1 feature to replace spatial path feature
+        feat_fuse = self.ffm(feat_sp, feat_cp8)
+
+        feat_out = self.conv_out(feat_fuse)
+        feat_out16 = self.conv_out16(feat_cp8)
+        feat_out32 = self.conv_out32(feat_cp16)
+
+        feat_out = F.interpolate(feat_out, (H, W), mode='bilinear', align_corners=True)
+        feat_out16 = F.interpolate(feat_out16, (H, W), mode='bilinear', align_corners=True)
+        feat_out32 = F.interpolate(feat_out32, (H, W), mode='bilinear', align_corners=True)
+        return feat_out, feat_out16, feat_out32
+
+    def init_weight(self):
+        for ly in self.children():
+            if isinstance(ly, nn.Conv2d):
+                nn.init.kaiming_normal_(ly.weight, a=1)
+                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
+
+    def get_params(self):
+        wd_params, nowd_params, lr_mul_wd_params, lr_mul_nowd_params = [], [], [], []
+        for name, child in self.named_children():
+            child_wd_params, child_nowd_params = child.get_params()
+            if isinstance(child, FeatureFusionModule) or isinstance(child, BiSeNetOutput):
+                lr_mul_wd_params += child_wd_params
+                lr_mul_nowd_params += child_nowd_params
+            else:
+                wd_params += child_wd_params
+                nowd_params += child_nowd_params
+        return wd_params, nowd_params, lr_mul_wd_params, lr_mul_nowd_params
+
+
+if __name__ == "__main__":
+    net = BiSeNet(19)
+    net.cuda()
+    net.eval()
+    in_ten = torch.randn(16, 3, 640, 480).cuda()
+    out, out16, out32 = net(in_ten)
+    print(out.shape)
+
+    net.get_params()

videoretalking/third_part/GPEN/face_parse/parse_model.py

@@ -0,0 +1,77 @@
+'''
+@Created by chaofengc (chaofenghust@gmail.com)
+
+@Modified by yangxy (yangtao9009@gmail.com)
+'''
+
+from face_parse.blocks import *
+import torch
+from torch import nn
+import numpy as np
+
+def define_P(in_size=512, out_size=512, min_feat_size=32, relu_type='LeakyReLU', isTrain=False, weight_path=None):
+    net = ParseNet(in_size, out_size, min_feat_size, 64, 19, norm_type='bn', relu_type=relu_type, ch_range=[32, 256])
+    if not isTrain:
+        net.eval()  
+    if weight_path is not None:
+        net.load_state_dict(torch.load(weight_path))
+    return net
+
+
+class ParseNet(nn.Module):
+    def __init__(self,
+                in_size=128,
+                out_size=128,
+                min_feat_size=32,
+                base_ch=64,
+                parsing_ch=19,
+                res_depth=10,
+                relu_type='prelu',
+                norm_type='bn',
+                ch_range=[32, 512],
+                ):
+        super().__init__()
+        self.res_depth = res_depth
+        act_args = {'norm_type': norm_type, 'relu_type': relu_type}
+        min_ch, max_ch = ch_range
+
+        ch_clip = lambda x: max(min_ch, min(x, max_ch))
+        min_feat_size = min(in_size, min_feat_size)
+
+        down_steps = int(np.log2(in_size//min_feat_size))
+        up_steps = int(np.log2(out_size//min_feat_size))
+
+        # =============== define encoder-body-decoder ==================== 
+        self.encoder = []
+        self.encoder.append(ConvLayer(3, base_ch, 3, 1))
+        head_ch = base_ch
+        for i in range(down_steps):
+            cin, cout = ch_clip(head_ch), ch_clip(head_ch * 2)
+            self.encoder.append(ResidualBlock(cin, cout, scale='down', **act_args))
+            head_ch = head_ch * 2
+
+        self.body = []
+        for i in range(res_depth):
+            self.body.append(ResidualBlock(ch_clip(head_ch), ch_clip(head_ch), **act_args))
+
+        self.decoder = []
+        for i in range(up_steps):
+            cin, cout = ch_clip(head_ch), ch_clip(head_ch // 2)
+            self.decoder.append(ResidualBlock(cin, cout, scale='up', **act_args))
+            head_ch = head_ch // 2
+
+        self.encoder = nn.Sequential(*self.encoder)
+        self.body = nn.Sequential(*self.body)
+        self.decoder = nn.Sequential(*self.decoder)
+        self.out_img_conv = ConvLayer(ch_clip(head_ch), 3)
+        self.out_mask_conv = ConvLayer(ch_clip(head_ch), parsing_ch)
+
+    def forward(self, x):
+        feat = self.encoder(x)
+        x = feat + self.body(feat)
+        x = self.decoder(x)
+        out_img = self.out_img_conv(x) 
+        out_mask = self.out_mask_conv(x)
+        return out_mask, out_img
+
+