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https://github.com/iperov/DeepFaceLab.git
synced 2025-07-06 04:52:13 -07:00
removed use_float16 option
fix multigpu training
This commit is contained in:
Colombo
parent
5fe5fa131c
commit
9fd49ee3f0
3 changed files with 36 additions and 37 deletions
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@ -100,12 +100,15 @@ class SampleProcessor(object):
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target_face_type = t
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elif t >= SPTF.MODE_BEGIN and t < SPTF.MODE_END:
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mode_type = t
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if mode_type == SPTF.MODE_FACE_MASK_HULL and not is_face_sample:
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raise ValueError("MODE_FACE_MASK_HULL applicable only for face samples")
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if mode_type == SPTF.MODE_FACE_MASK_STRUCT and not is_face_sample:
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raise ValueError("MODE_FACE_MASK_STRUCT applicable only for face samples")
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if is_face_sample:
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if target_face_type == SPTF.NONE:
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raise ValueError("target face type must be defined for face samples")
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can_warp = (img_type==SPTF.IMG_WARPED or img_type==SPTF.IMG_WARPED_TRANSFORMED)
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can_transform = (img_type==SPTF.IMG_WARPED_TRANSFORMED or img_type==SPTF.IMG_TRANSFORMED)
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@ -135,17 +138,17 @@ class SampleProcessor(object):
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if mode_type == SPTF.MODE_FACE_MASK_HULL:
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if sample.eyebrows_expand_mod is not None:
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mask = LandmarksProcessor.get_image_hull_mask (sample_bgr.shape, sample.landmarks, eyebrows_expand_mod=sample.eyebrows_expand_mod )
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img = LandmarksProcessor.get_image_hull_mask (sample_bgr.shape, sample.landmarks, eyebrows_expand_mod=sample.eyebrows_expand_mod )
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else:
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mask = LandmarksProcessor.get_image_hull_mask (sample_bgr.shape, sample.landmarks)
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img = LandmarksProcessor.get_image_hull_mask (sample_bgr.shape, sample.landmarks)
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if sample.ie_polys is not None:
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sample.ie_polys.overlay_mask(mask)
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sample.ie_polys.overlay_mask(img)
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elif mode_type == SPTF.MODE_FACE_MASK_STRUCT:
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if sample.eyebrows_expand_mod is not None:
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mask = LandmarksProcessor.get_face_struct_mask (sample_bgr.shape, sample.landmarks, eyebrows_expand_mod=sample.eyebrows_expand_mod )
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img = LandmarksProcessor.get_face_struct_mask (sample_bgr.shape, sample.landmarks, eyebrows_expand_mod=sample.eyebrows_expand_mod )
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else:
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mask = LandmarksProcessor.get_face_struct_mask (sample_bgr.shape, sample.landmarks)
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img = LandmarksProcessor.get_face_struct_mask (sample_bgr.shape, sample.landmarks)
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else:
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img = sample_bgr
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if motion_blur is not None:
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@ -162,50 +165,49 @@ class SampleProcessor(object):
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if np.random.randint(100) < chance:
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img = cv2.GaussianBlur(img, ( np.random.randint( kernel_max_size )*2+1 ,) *2 , 0)
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if is_face_sample and target_face_type != SPTF.NONE:
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if is_face_sample:
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target_ft = SampleProcessor.SPTF_FACETYPE_TO_FACETYPE[target_face_type]
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if target_ft > sample.face_type:
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raise Exception ('sample %s type %s does not match model requirement %s. Consider extract necessary type of faces.' % (sample.filename, sample.face_type, target_ft) )
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if sample.face_type == FaceType.MARK_ONLY:
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mat = LandmarksProcessor.get_transform_mat (sample.landmarks, sample.shape[0], target_ft), (sample.shape[0],sample.shape[0])
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mat = LandmarksProcessor.get_transform_mat (sample.landmarks, sample.shape[0], target_ft)
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if mode_type == SPTF.MODE_FACE_MASK_HULL or mode_type == SPTF.MODE_FACE_MASK_STRUCT:
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mask = cv2.warpAffine( mask, mat, flags=cv2.INTER_CUBIC )
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mask = imagelib.warp_by_params (params, mask, can_warp, can_transform, can_flip=True, border_replicate=False)
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mask = cv2.resize( mask, (resolution,resolution), cv2.INTER_CUBIC )[...,None]
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img = cv2.warpAffine( img, mat, (sample.shape[0],sample.shape[0]), flags=cv2.INTER_CUBIC )
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img = imagelib.warp_by_params (params, img, can_warp, can_transform, can_flip=True, border_replicate=False)
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img = cv2.resize( img, (resolution,resolution), cv2.INTER_CUBIC )[...,None]
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else:
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img = cv2.warpAffine( img, mat, flags=cv2.INTER_CUBIC )
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img = cv2.warpAffine( img, mat, (sample.shape[0],sample.shape[0]), flags=cv2.INTER_CUBIC )
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img = imagelib.warp_by_params (params, img, can_warp, can_transform, can_flip=True, border_replicate=True)
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img = cv2.resize( img, (resolution,resolution), cv2.INTER_CUBIC )
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else:
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mat = LandmarksProcessor.get_transform_mat (sample.landmarks, resolution, target_ft)
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if mode_type == SPTF.MODE_FACE_MASK_HULL or mode_type == SPTF.MODE_FACE_MASK_STRUCT:
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mask = imagelib.warp_by_params (params, mask, can_warp, can_transform, can_flip=True, border_replicate=False)
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mask = cv2.warpAffine( mask, mat, (resolution,resolution), borderMode=cv2.BORDER_CONSTANT, flags=cv2.INTER_CUBIC )[...,None]
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img = imagelib.warp_by_params (params, img, can_warp, can_transform, can_flip=True, border_replicate=False)
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img = cv2.warpAffine( img, mat, (resolution,resolution), borderMode=cv2.BORDER_CONSTANT, flags=cv2.INTER_CUBIC )[...,None]
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else:
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img = imagelib.warp_by_params (params, img, can_warp, can_transform, can_flip=True, border_replicate=True)
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img = cv2.warpAffine( img, mat, (resolution,resolution), borderMode=cv2.BORDER_REPLICATE, flags=cv2.INTER_CUBIC )
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img = cv2.warpAffine( img, mat, (resolution,resolution), borderMode=cv2.BORDER_REPLICATE, flags=cv2.INTER_CUBIC )
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else:
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img = imagelib.warp_by_params (params, img, can_warp, can_transform, can_flip=True, border_replicate=True)
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img = cv2.resize( img, (resolution,resolution), cv2.INTER_CUBIC )
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if mode_type == SPTF.MODE_FACE_MASK_HULL or mode_type == SPTF.MODE_FACE_MASK_STRUCT:
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out_sample = np.clip(mask, 0, 1).astype(np.float32)
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out_sample = np.clip(img.astype(np.float32), 0, 1)
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else:
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img = np.clip(img, 0, 1).astype(np.float32)
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img = np.clip(img.astype(np.float32), 0, 1)
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if ct_mode is not None and ct_sample is not None:
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if ct_sample_bgr is None:
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ct_sample_bgr = ct_sample.load_bgr()
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img = imagelib.color_transfer (ct_mode,
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img,
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cv2.resize( ct_sample_bgr, (resolution,resolution), cv2.INTER_LINEAR ) )
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img = imagelib.color_transfer (ct_mode,
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img,
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cv2.resize( ct_sample_bgr, (resolution,resolution), cv2.INTER_LINEAR ) )
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if mode_type == SPTF.MODE_BGR:
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out_sample = img
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elif mode_type == SPTF.MODE_BGR_SHUFFLE:
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@ -225,14 +227,14 @@ class SampleProcessor(object):
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out_sample = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)[...,None]
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elif mode_type == SPTF.MODE_GGG:
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out_sample = np.repeat ( np.expand_dims(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY),-1), (3,), -1)
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if not debug:
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if normalize_tanh:
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out_sample = np.clip (out_sample * 2.0 - 1.0, -1.0, 1.0)
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if data_format == "NCHW":
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out_sample = np.transpose(out_sample, (2,0,1) )
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out_sample = np.transpose(out_sample, (2,0,1) )
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outputs_sample.append ( out_sample )
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outputs += [outputs_sample]
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