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def tensor2im(input_image, imtype=np.uint8):
""""Converts a Tensor array into a numpy image array.
Parameters:
input_image (tensor) -- the input image tensor array, range(0, 1)
imtype (type) -- the desired type of the converted numpy array
"""
if not isinstance(input_image, np.ndarray):
if isinstance(input_image, torch.Tensor): # get the data from a variable
image_tensor = input_image.data
else:
return input_image
image_numpy = image_tensor.clamp(0.0, 1.0).cpu().float().numpy() # convert it into a numpy array
if image_numpy.shape[0] == 1: # grayscale to RGB
image_numpy = np.tile(image_numpy, (3, 1, 1))
image_numpy = np.transpose(image_numpy, (1, 2, 0)) * 255.0 # post-processing: transpose and scaling
else: # if it is a numpy array, do nothing
image_numpy = input_image
return image_numpy.astype(imtype)
|
"Converts a Tensor array into a numpy image array.
Parameters:
input_image (tensor) -- the input image tensor array, range(0, 1)
imtype (type) -- the desired type of the converted numpy array
|
tensor2im
|
python
|
OpenTalker/video-retalking
|
third_part/face3d/util/util.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/util/util.py
|
Apache-2.0
|
def diagnose_network(net, name='network'):
"""Calculate and print the mean of average absolute(gradients)
Parameters:
net (torch network) -- Torch network
name (str) -- the name of the network
"""
mean = 0.0
count = 0
for param in net.parameters():
if param.grad is not None:
mean += torch.mean(torch.abs(param.grad.data))
count += 1
if count > 0:
mean = mean / count
print(name)
print(mean)
|
Calculate and print the mean of average absolute(gradients)
Parameters:
net (torch network) -- Torch network
name (str) -- the name of the network
|
diagnose_network
|
python
|
OpenTalker/video-retalking
|
third_part/face3d/util/util.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/util/util.py
|
Apache-2.0
|
def save_image(image_numpy, image_path, aspect_ratio=1.0):
"""Save a numpy image to the disk
Parameters:
image_numpy (numpy array) -- input numpy array
image_path (str) -- the path of the image
"""
image_pil = Image.fromarray(image_numpy)
h, w, _ = image_numpy.shape
if aspect_ratio is None:
pass
elif aspect_ratio > 1.0:
image_pil = image_pil.resize((h, int(w * aspect_ratio)), Image.BICUBIC)
elif aspect_ratio < 1.0:
image_pil = image_pil.resize((int(h / aspect_ratio), w), Image.BICUBIC)
image_pil.save(image_path)
|
Save a numpy image to the disk
Parameters:
image_numpy (numpy array) -- input numpy array
image_path (str) -- the path of the image
|
save_image
|
python
|
OpenTalker/video-retalking
|
third_part/face3d/util/util.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/util/util.py
|
Apache-2.0
|
def print_numpy(x, val=True, shp=False):
"""Print the mean, min, max, median, std, and size of a numpy array
Parameters:
val (bool) -- if print the values of the numpy array
shp (bool) -- if print the shape of the numpy array
"""
x = x.astype(np.float64)
if shp:
print('shape,', x.shape)
if val:
x = x.flatten()
print('mean = %3.3f, min = %3.3f, max = %3.3f, median = %3.3f, std=%3.3f' % (
np.mean(x), np.min(x), np.max(x), np.median(x), np.std(x)))
|
Print the mean, min, max, median, std, and size of a numpy array
Parameters:
val (bool) -- if print the values of the numpy array
shp (bool) -- if print the shape of the numpy array
|
print_numpy
|
python
|
OpenTalker/video-retalking
|
third_part/face3d/util/util.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/util/util.py
|
Apache-2.0
|
def mkdirs(paths):
"""create empty directories if they don't exist
Parameters:
paths (str list) -- a list of directory paths
"""
if isinstance(paths, list) and not isinstance(paths, str):
for path in paths:
mkdir(path)
else:
mkdir(paths)
|
create empty directories if they don't exist
Parameters:
paths (str list) -- a list of directory paths
|
mkdirs
|
python
|
OpenTalker/video-retalking
|
third_part/face3d/util/util.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/util/util.py
|
Apache-2.0
|
def draw_landmarks(img, landmark, color='r', step=2):
"""
Return:
img -- numpy.array, (B, H, W, 3) img with landmark, RGB order, range (0, 255)
Parameters:
img -- numpy.array, (B, H, W, 3), RGB order, range (0, 255)
landmark -- numpy.array, (B, 68, 2), y direction is opposite to v direction
color -- str, 'r' or 'b' (red or blue)
"""
if color =='r':
c = np.array([255., 0, 0])
else:
c = np.array([0, 0, 255.])
_, H, W, _ = img.shape
img, landmark = img.copy(), landmark.copy()
landmark[..., 1] = H - 1 - landmark[..., 1]
landmark = np.round(landmark).astype(np.int32)
for i in range(landmark.shape[1]):
x, y = landmark[:, i, 0], landmark[:, i, 1]
for j in range(-step, step):
for k in range(-step, step):
u = np.clip(x + j, 0, W - 1)
v = np.clip(y + k, 0, H - 1)
for m in range(landmark.shape[0]):
img[m, v[m], u[m]] = c
return img
|
Return:
img -- numpy.array, (B, H, W, 3) img with landmark, RGB order, range (0, 255)
Parameters:
img -- numpy.array, (B, H, W, 3), RGB order, range (0, 255)
landmark -- numpy.array, (B, 68, 2), y direction is opposite to v direction
color -- str, 'r' or 'b' (red or blue)
|
draw_landmarks
|
python
|
OpenTalker/video-retalking
|
third_part/face3d/util/util.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/util/util.py
|
Apache-2.0
|
def save_images(webpage, visuals, image_path, aspect_ratio=1.0, width=256):
"""Save images to the disk.
Parameters:
webpage (the HTML class) -- the HTML webpage class that stores these imaegs (see html.py for more details)
visuals (OrderedDict) -- an ordered dictionary that stores (name, images (either tensor or numpy) ) pairs
image_path (str) -- the string is used to create image paths
aspect_ratio (float) -- the aspect ratio of saved images
width (int) -- the images will be resized to width x width
This function will save images stored in 'visuals' to the HTML file specified by 'webpage'.
"""
image_dir = webpage.get_image_dir()
short_path = ntpath.basename(image_path[0])
name = os.path.splitext(short_path)[0]
webpage.add_header(name)
ims, txts, links = [], [], []
for label, im_data in visuals.items():
im = util.tensor2im(im_data)
image_name = '%s/%s.png' % (label, name)
os.makedirs(os.path.join(image_dir, label), exist_ok=True)
save_path = os.path.join(image_dir, image_name)
util.save_image(im, save_path, aspect_ratio=aspect_ratio)
ims.append(image_name)
txts.append(label)
links.append(image_name)
webpage.add_images(ims, txts, links, width=width)
|
Save images to the disk.
Parameters:
webpage (the HTML class) -- the HTML webpage class that stores these imaegs (see html.py for more details)
visuals (OrderedDict) -- an ordered dictionary that stores (name, images (either tensor or numpy) ) pairs
image_path (str) -- the string is used to create image paths
aspect_ratio (float) -- the aspect ratio of saved images
width (int) -- the images will be resized to width x width
This function will save images stored in 'visuals' to the HTML file specified by 'webpage'.
|
save_images
|
python
|
OpenTalker/video-retalking
|
third_part/face3d/util/visualizer.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/util/visualizer.py
|
Apache-2.0
|
def __init__(self, opt):
"""Initialize the Visualizer class
Parameters:
opt -- stores all the experiment flags; needs to be a subclass of BaseOptions
Step 1: Cache the training/test options
Step 2: create a tensorboard writer
Step 3: create an HTML object for saving HTML filters
Step 4: create a logging file to store training losses
"""
self.opt = opt # cache the option
self.use_html = opt.isTrain and not opt.no_html
self.writer = SummaryWriter(os.path.join(opt.checkpoints_dir, 'logs', opt.name))
self.win_size = opt.display_winsize
self.name = opt.name
self.saved = False
if self.use_html: # create an HTML object at <checkpoints_dir>/web/; images will be saved under <checkpoints_dir>/web/images/
self.web_dir = os.path.join(opt.checkpoints_dir, opt.name, 'web')
self.img_dir = os.path.join(self.web_dir, 'images')
print('create web directory %s...' % self.web_dir)
util.mkdirs([self.web_dir, self.img_dir])
# create a logging file to store training losses
self.log_name = os.path.join(opt.checkpoints_dir, opt.name, 'loss_log.txt')
with open(self.log_name, "a") as log_file:
now = time.strftime("%c")
log_file.write('================ Training Loss (%s) ================\n' % now)
|
Initialize the Visualizer class
Parameters:
opt -- stores all the experiment flags; needs to be a subclass of BaseOptions
Step 1: Cache the training/test options
Step 2: create a tensorboard writer
Step 3: create an HTML object for saving HTML filters
Step 4: create a logging file to store training losses
|
__init__
|
python
|
OpenTalker/video-retalking
|
third_part/face3d/util/visualizer.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/util/visualizer.py
|
Apache-2.0
|
def display_current_results(self, visuals, total_iters, epoch, save_result):
"""Display current results on tensorboad; save current results to an HTML file.
Parameters:
visuals (OrderedDict) - - dictionary of images to display or save
total_iters (int) -- total iterations
epoch (int) - - the current epoch
save_result (bool) - - if save the current results to an HTML file
"""
for label, image in visuals.items():
self.writer.add_image(label, util.tensor2im(image), total_iters, dataformats='HWC')
if self.use_html and (save_result or not self.saved): # save images to an HTML file if they haven't been saved.
self.saved = True
# save images to the disk
for label, image in visuals.items():
image_numpy = util.tensor2im(image)
img_path = os.path.join(self.img_dir, 'epoch%.3d_%s.png' % (epoch, label))
util.save_image(image_numpy, img_path)
# update website
webpage = html.HTML(self.web_dir, 'Experiment name = %s' % self.name, refresh=0)
for n in range(epoch, 0, -1):
webpage.add_header('epoch [%d]' % n)
ims, txts, links = [], [], []
for label, image_numpy in visuals.items():
image_numpy = util.tensor2im(image)
img_path = 'epoch%.3d_%s.png' % (n, label)
ims.append(img_path)
txts.append(label)
links.append(img_path)
webpage.add_images(ims, txts, links, width=self.win_size)
webpage.save()
|
Display current results on tensorboad; save current results to an HTML file.
Parameters:
visuals (OrderedDict) - - dictionary of images to display or save
total_iters (int) -- total iterations
epoch (int) - - the current epoch
save_result (bool) - - if save the current results to an HTML file
|
display_current_results
|
python
|
OpenTalker/video-retalking
|
third_part/face3d/util/visualizer.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/util/visualizer.py
|
Apache-2.0
|
def print_current_losses(self, epoch, iters, losses, t_comp, t_data):
"""print current losses on console; also save the losses to the disk
Parameters:
epoch (int) -- current epoch
iters (int) -- current training iteration during this epoch (reset to 0 at the end of every epoch)
losses (OrderedDict) -- training losses stored in the format of (name, float) pairs
t_comp (float) -- computational time per data point (normalized by batch_size)
t_data (float) -- data loading time per data point (normalized by batch_size)
"""
message = '(epoch: %d, iters: %d, time: %.3f, data: %.3f) ' % (epoch, iters, t_comp, t_data)
for k, v in losses.items():
message += '%s: %.3f ' % (k, v)
print(message) # print the message
with open(self.log_name, "a") as log_file:
log_file.write('%s\n' % message) # save the message
|
print current losses on console; also save the losses to the disk
Parameters:
epoch (int) -- current epoch
iters (int) -- current training iteration during this epoch (reset to 0 at the end of every epoch)
losses (OrderedDict) -- training losses stored in the format of (name, float) pairs
t_comp (float) -- computational time per data point (normalized by batch_size)
t_data (float) -- data loading time per data point (normalized by batch_size)
|
print_current_losses
|
python
|
OpenTalker/video-retalking
|
third_part/face3d/util/visualizer.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/util/visualizer.py
|
Apache-2.0
|
def __init__(self, opt):
"""Initialize the Visualizer class
Parameters:
opt -- stores all the experiment flags; needs to be a subclass of BaseOptions
Step 1: Cache the training/test options
Step 2: create a tensorboard writer
Step 3: create an HTML object for saving HTML filters
Step 4: create a logging file to store training losses
"""
self.opt = opt # cache the option
self.name = opt.name
self.img_dir = os.path.join(opt.checkpoints_dir, opt.name, 'results')
if opt.phase != 'test':
self.writer = SummaryWriter(os.path.join(opt.checkpoints_dir, opt.name, 'logs'))
# create a logging file to store training losses
self.log_name = os.path.join(opt.checkpoints_dir, opt.name, 'loss_log.txt')
with open(self.log_name, "a") as log_file:
now = time.strftime("%c")
log_file.write('================ Training Loss (%s) ================\n' % now)
|
Initialize the Visualizer class
Parameters:
opt -- stores all the experiment flags; needs to be a subclass of BaseOptions
Step 1: Cache the training/test options
Step 2: create a tensorboard writer
Step 3: create an HTML object for saving HTML filters
Step 4: create a logging file to store training losses
|
__init__
|
python
|
OpenTalker/video-retalking
|
third_part/face3d/util/visualizer.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/util/visualizer.py
|
Apache-2.0
|
def display_current_results(self, visuals, total_iters, epoch, dataset='train', save_results=False, count=0, name=None,
add_image=True):
"""Display current results on tensorboad; save current results to an HTML file.
Parameters:
visuals (OrderedDict) - - dictionary of images to display or save
total_iters (int) -- total iterations
epoch (int) - - the current epoch
dataset (str) - - 'train' or 'val' or 'test'
"""
# if (not add_image) and (not save_results): return
for label, image in visuals.items():
for i in range(image.shape[0]):
image_numpy = util.tensor2im(image[i])
if add_image:
self.writer.add_image(label + '%s_%02d'%(dataset, i + count),
image_numpy, total_iters, dataformats='HWC')
if save_results:
save_path = os.path.join(self.img_dir, dataset, 'epoch_%s_%06d'%(epoch, total_iters))
if not os.path.isdir(save_path):
os.makedirs(save_path)
if name is not None:
img_path = os.path.join(save_path, '%s.png' % name)
else:
img_path = os.path.join(save_path, '%s_%03d.png' % (label, i + count))
util.save_image(image_numpy, img_path)
|
Display current results on tensorboad; save current results to an HTML file.
Parameters:
visuals (OrderedDict) - - dictionary of images to display or save
total_iters (int) -- total iterations
epoch (int) - - the current epoch
dataset (str) - - 'train' or 'val' or 'test'
|
display_current_results
|
python
|
OpenTalker/video-retalking
|
third_part/face3d/util/visualizer.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/util/visualizer.py
|
Apache-2.0
|
def print_current_losses(self, epoch, iters, losses, t_comp, t_data, dataset='train'):
"""print current losses on console; also save the losses to the disk
Parameters:
epoch (int) -- current epoch
iters (int) -- current training iteration during this epoch (reset to 0 at the end of every epoch)
losses (OrderedDict) -- training losses stored in the format of (name, float) pairs
t_comp (float) -- computational time per data point (normalized by batch_size)
t_data (float) -- data loading time per data point (normalized by batch_size)
"""
message = '(dataset: %s, epoch: %d, iters: %d, time: %.3f, data: %.3f) ' % (
dataset, epoch, iters, t_comp, t_data)
for k, v in losses.items():
message += '%s: %.3f ' % (k, v)
print(message) # print the message
with open(self.log_name, "a") as log_file:
log_file.write('%s\n' % message) # save the message
|
print current losses on console; also save the losses to the disk
Parameters:
epoch (int) -- current epoch
iters (int) -- current training iteration during this epoch (reset to 0 at the end of every epoch)
losses (OrderedDict) -- training losses stored in the format of (name, float) pairs
t_comp (float) -- computational time per data point (normalized by batch_size)
t_data (float) -- data loading time per data point (normalized by batch_size)
|
print_current_losses
|
python
|
OpenTalker/video-retalking
|
third_part/face3d/util/visualizer.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/util/visualizer.py
|
Apache-2.0
|
def transform(point, center, scale, resolution, invert=False):
"""Generate and affine transformation matrix.
Given a set of points, a center, a scale and a targer resolution, the
function generates and affine transformation matrix. If invert is ``True``
it will produce the inverse transformation.
Arguments:
point {torch.tensor} -- the input 2D point
center {torch.tensor or numpy.array} -- the center around which to perform the transformations
scale {float} -- the scale of the face/object
resolution {float} -- the output resolution
Keyword Arguments:
invert {bool} -- define wherever the function should produce the direct or the
inverse transformation matrix (default: {False})
"""
_pt = torch.ones(3)
_pt[0] = point[0]
_pt[1] = point[1]
h = 200.0 * scale
t = torch.eye(3)
t[0, 0] = resolution / h
t[1, 1] = resolution / h
t[0, 2] = resolution * (-center[0] / h + 0.5)
t[1, 2] = resolution * (-center[1] / h + 0.5)
if invert:
t = torch.inverse(t)
new_point = (torch.matmul(t, _pt))[0:2]
return new_point.int()
|
Generate and affine transformation matrix.
Given a set of points, a center, a scale and a targer resolution, the
function generates and affine transformation matrix. If invert is ``True``
it will produce the inverse transformation.
Arguments:
point {torch.tensor} -- the input 2D point
center {torch.tensor or numpy.array} -- the center around which to perform the transformations
scale {float} -- the scale of the face/object
resolution {float} -- the output resolution
Keyword Arguments:
invert {bool} -- define wherever the function should produce the direct or the
inverse transformation matrix (default: {False})
|
transform
|
python
|
OpenTalker/video-retalking
|
third_part/face_detection/utils.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/face_detection/utils.py
|
Apache-2.0
|
def crop(image, center, scale, resolution=256.0):
"""Center crops an image or set of heatmaps
Arguments:
image {numpy.array} -- an rgb image
center {numpy.array} -- the center of the object, usually the same as of the bounding box
scale {float} -- scale of the face
Keyword Arguments:
resolution {float} -- the size of the output cropped image (default: {256.0})
Returns:
[type] -- [description]
""" # Crop around the center point
""" Crops the image around the center. Input is expected to be an np.ndarray """
ul = transform([1, 1], center, scale, resolution, True)
br = transform([resolution, resolution], center, scale, resolution, True)
# pad = math.ceil(torch.norm((ul - br).float()) / 2.0 - (br[0] - ul[0]) / 2.0)
if image.ndim > 2:
newDim = np.array([br[1] - ul[1], br[0] - ul[0],
image.shape[2]], dtype=np.int32)
newImg = np.zeros(newDim, dtype=np.uint8)
else:
newDim = np.array([br[1] - ul[1], br[0] - ul[0]], dtype=np.int)
newImg = np.zeros(newDim, dtype=np.uint8)
ht = image.shape[0]
wd = image.shape[1]
newX = np.array(
[max(1, -ul[0] + 1), min(br[0], wd) - ul[0]], dtype=np.int32)
newY = np.array(
[max(1, -ul[1] + 1), min(br[1], ht) - ul[1]], dtype=np.int32)
oldX = np.array([max(1, ul[0] + 1), min(br[0], wd)], dtype=np.int32)
oldY = np.array([max(1, ul[1] + 1), min(br[1], ht)], dtype=np.int32)
newImg[newY[0] - 1:newY[1], newX[0] - 1:newX[1]
] = image[oldY[0] - 1:oldY[1], oldX[0] - 1:oldX[1], :]
newImg = cv2.resize(newImg, dsize=(int(resolution), int(resolution)),
interpolation=cv2.INTER_LINEAR)
return newImg
|
Center crops an image or set of heatmaps
Arguments:
image {numpy.array} -- an rgb image
center {numpy.array} -- the center of the object, usually the same as of the bounding box
scale {float} -- scale of the face
Keyword Arguments:
resolution {float} -- the size of the output cropped image (default: {256.0})
Returns:
[type] -- [description]
|
crop
|
python
|
OpenTalker/video-retalking
|
third_part/face_detection/utils.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/face_detection/utils.py
|
Apache-2.0
|
def get_preds_fromhm(hm, center=None, scale=None):
"""Obtain (x,y) coordinates given a set of N heatmaps. If the center
and the scale is provided the function will return the points also in
the original coordinate frame.
Arguments:
hm {torch.tensor} -- the predicted heatmaps, of shape [B, N, W, H]
Keyword Arguments:
center {torch.tensor} -- the center of the bounding box (default: {None})
scale {float} -- face scale (default: {None})
"""
max, idx = torch.max(
hm.view(hm.size(0), hm.size(1), hm.size(2) * hm.size(3)), 2)
idx += 1
preds = idx.view(idx.size(0), idx.size(1), 1).repeat(1, 1, 2).float()
preds[..., 0].apply_(lambda x: (x - 1) % hm.size(3) + 1)
preds[..., 1].add_(-1).div_(hm.size(2)).floor_().add_(1)
for i in range(preds.size(0)):
for j in range(preds.size(1)):
hm_ = hm[i, j, :]
pX, pY = int(preds[i, j, 0]) - 1, int(preds[i, j, 1]) - 1
if pX > 0 and pX < 63 and pY > 0 and pY < 63:
diff = torch.FloatTensor(
[hm_[pY, pX + 1] - hm_[pY, pX - 1],
hm_[pY + 1, pX] - hm_[pY - 1, pX]])
preds[i, j].add_(diff.sign_().mul_(.25))
preds.add_(-.5)
preds_orig = torch.zeros(preds.size())
if center is not None and scale is not None:
for i in range(hm.size(0)):
for j in range(hm.size(1)):
preds_orig[i, j] = transform(
preds[i, j], center, scale, hm.size(2), True)
return preds, preds_orig
|
Obtain (x,y) coordinates given a set of N heatmaps. If the center
and the scale is provided the function will return the points also in
the original coordinate frame.
Arguments:
hm {torch.tensor} -- the predicted heatmaps, of shape [B, N, W, H]
Keyword Arguments:
center {torch.tensor} -- the center of the bounding box (default: {None})
scale {float} -- face scale (default: {None})
|
get_preds_fromhm
|
python
|
OpenTalker/video-retalking
|
third_part/face_detection/utils.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/face_detection/utils.py
|
Apache-2.0
|
def get_preds_fromhm_batch(hm, centers=None, scales=None):
"""Obtain (x,y) coordinates given a set of N heatmaps. If the centers
and the scales is provided the function will return the points also in
the original coordinate frame.
Arguments:
hm {torch.tensor} -- the predicted heatmaps, of shape [B, N, W, H]
Keyword Arguments:
centers {torch.tensor} -- the centers of the bounding box (default: {None})
scales {float} -- face scales (default: {None})
"""
max, idx = torch.max(
hm.view(hm.size(0), hm.size(1), hm.size(2) * hm.size(3)), 2)
idx += 1
preds = idx.view(idx.size(0), idx.size(1), 1).repeat(1, 1, 2).float()
preds[..., 0].apply_(lambda x: (x - 1) % hm.size(3) + 1)
preds[..., 1].add_(-1).div_(hm.size(2)).floor_().add_(1)
for i in range(preds.size(0)):
for j in range(preds.size(1)):
hm_ = hm[i, j, :]
pX, pY = int(preds[i, j, 0]) - 1, int(preds[i, j, 1]) - 1
if pX > 0 and pX < 63 and pY > 0 and pY < 63:
diff = torch.FloatTensor(
[hm_[pY, pX + 1] - hm_[pY, pX - 1],
hm_[pY + 1, pX] - hm_[pY - 1, pX]])
preds[i, j].add_(diff.sign_().mul_(.25))
preds.add_(-.5)
preds_orig = torch.zeros(preds.size())
if centers is not None and scales is not None:
for i in range(hm.size(0)):
for j in range(hm.size(1)):
preds_orig[i, j] = transform(
preds[i, j], centers[i], scales[i], hm.size(2), True)
return preds, preds_orig
|
Obtain (x,y) coordinates given a set of N heatmaps. If the centers
and the scales is provided the function will return the points also in
the original coordinate frame.
Arguments:
hm {torch.tensor} -- the predicted heatmaps, of shape [B, N, W, H]
Keyword Arguments:
centers {torch.tensor} -- the centers of the bounding box (default: {None})
scales {float} -- face scales (default: {None})
|
get_preds_fromhm_batch
|
python
|
OpenTalker/video-retalking
|
third_part/face_detection/utils.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/face_detection/utils.py
|
Apache-2.0
|
def shuffle_lr(parts, pairs=None):
"""Shuffle the points left-right according to the axis of symmetry
of the object.
Arguments:
parts {torch.tensor} -- a 3D or 4D object containing the
heatmaps.
Keyword Arguments:
pairs {list of integers} -- [order of the flipped points] (default: {None})
"""
if pairs is None:
pairs = [16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0,
26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 27, 28, 29, 30, 35,
34, 33, 32, 31, 45, 44, 43, 42, 47, 46, 39, 38, 37, 36, 41,
40, 54, 53, 52, 51, 50, 49, 48, 59, 58, 57, 56, 55, 64, 63,
62, 61, 60, 67, 66, 65]
if parts.ndimension() == 3:
parts = parts[pairs, ...]
else:
parts = parts[:, pairs, ...]
return parts
|
Shuffle the points left-right according to the axis of symmetry
of the object.
Arguments:
parts {torch.tensor} -- a 3D or 4D object containing the
heatmaps.
Keyword Arguments:
pairs {list of integers} -- [order of the flipped points] (default: {None})
|
shuffle_lr
|
python
|
OpenTalker/video-retalking
|
third_part/face_detection/utils.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/face_detection/utils.py
|
Apache-2.0
|
def flip(tensor, is_label=False):
"""Flip an image or a set of heatmaps left-right
Arguments:
tensor {numpy.array or torch.tensor} -- [the input image or heatmaps]
Keyword Arguments:
is_label {bool} -- [denote wherever the input is an image or a set of heatmaps ] (default: {False})
"""
if not torch.is_tensor(tensor):
tensor = torch.from_numpy(tensor)
if is_label:
tensor = shuffle_lr(tensor).flip(tensor.ndimension() - 1)
else:
tensor = tensor.flip(tensor.ndimension() - 1)
return tensor
|
Flip an image or a set of heatmaps left-right
Arguments:
tensor {numpy.array or torch.tensor} -- [the input image or heatmaps]
Keyword Arguments:
is_label {bool} -- [denote wherever the input is an image or a set of heatmaps ] (default: {False})
|
flip
|
python
|
OpenTalker/video-retalking
|
third_part/face_detection/utils.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/face_detection/utils.py
|
Apache-2.0
|
def appdata_dir(appname=None, roaming=False):
""" appdata_dir(appname=None, roaming=False)
Get the path to the application directory, where applications are allowed
to write user specific files (e.g. configurations). For non-user specific
data, consider using common_appdata_dir().
If appname is given, a subdir is appended (and created if necessary).
If roaming is True, will prefer a roaming directory (Windows Vista/7).
"""
# Define default user directory
userDir = os.getenv('FACEALIGNMENT_USERDIR', None)
if userDir is None:
userDir = os.path.expanduser('~')
if not os.path.isdir(userDir): # pragma: no cover
userDir = '/var/tmp' # issue #54
# Get system app data dir
path = None
if sys.platform.startswith('win'):
path1, path2 = os.getenv('LOCALAPPDATA'), os.getenv('APPDATA')
path = (path2 or path1) if roaming else (path1 or path2)
elif sys.platform.startswith('darwin'):
path = os.path.join(userDir, 'Library', 'Application Support')
# On Linux and as fallback
if not (path and os.path.isdir(path)):
path = userDir
# Maybe we should store things local to the executable (in case of a
# portable distro or a frozen application that wants to be portable)
prefix = sys.prefix
if getattr(sys, 'frozen', None):
prefix = os.path.abspath(os.path.dirname(sys.executable))
for reldir in ('settings', '../settings'):
localpath = os.path.abspath(os.path.join(prefix, reldir))
if os.path.isdir(localpath): # pragma: no cover
try:
open(os.path.join(localpath, 'test.write'), 'wb').close()
os.remove(os.path.join(localpath, 'test.write'))
except IOError:
pass # We cannot write in this directory
else:
path = localpath
break
# Get path specific for this app
if appname:
if path == userDir:
appname = '.' + appname.lstrip('.') # Make it a hidden directory
path = os.path.join(path, appname)
if not os.path.isdir(path): # pragma: no cover
os.mkdir(path)
# Done
return path
|
appdata_dir(appname=None, roaming=False)
Get the path to the application directory, where applications are allowed
to write user specific files (e.g. configurations). For non-user specific
data, consider using common_appdata_dir().
If appname is given, a subdir is appended (and created if necessary).
If roaming is True, will prefer a roaming directory (Windows Vista/7).
|
appdata_dir
|
python
|
OpenTalker/video-retalking
|
third_part/face_detection/utils.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/face_detection/utils.py
|
Apache-2.0
|
def detect_from_directory(self, path, extensions=['.jpg', '.png'], recursive=False, show_progress_bar=True):
"""Detects faces from all the images present in a given directory.
Arguments:
path {string} -- a string containing a path that points to the folder containing the images
Keyword Arguments:
extensions {list} -- list of string containing the extensions to be
consider in the following format: ``.extension_name`` (default:
{['.jpg', '.png']}) recursive {bool} -- option wherever to scan the
folder recursively (default: {False}) show_progress_bar {bool} --
display a progressbar (default: {True})
Example:
>>> directory = 'data'
... detected_faces = detect_from_directory(directory)
{A dictionary of [lists containing bounding boxes(x1, y1, x2, y2)]}
"""
if self.verbose:
logger = logging.getLogger(__name__)
if len(extensions) == 0:
if self.verbose:
logger.error("Expected at list one extension, but none was received.")
raise ValueError
if self.verbose:
logger.info("Constructing the list of images.")
additional_pattern = '/**/*' if recursive else '/*'
files = []
for extension in extensions:
files.extend(glob.glob(path + additional_pattern + extension, recursive=recursive))
if self.verbose:
logger.info("Finished searching for images. %s images found", len(files))
logger.info("Preparing to run the detection.")
predictions = {}
for image_path in tqdm(files, disable=not show_progress_bar):
if self.verbose:
logger.info("Running the face detector on image: %s", image_path)
predictions[image_path] = self.detect_from_image(image_path)
if self.verbose:
logger.info("The detector was successfully run on all %s images", len(files))
return predictions
|
Detects faces from all the images present in a given directory.
Arguments:
path {string} -- a string containing a path that points to the folder containing the images
Keyword Arguments:
extensions {list} -- list of string containing the extensions to be
consider in the following format: ``.extension_name`` (default:
{['.jpg', '.png']}) recursive {bool} -- option wherever to scan the
folder recursively (default: {False}) show_progress_bar {bool} --
display a progressbar (default: {True})
Example:
>>> directory = 'data'
... detected_faces = detect_from_directory(directory)
{A dictionary of [lists containing bounding boxes(x1, y1, x2, y2)]}
|
detect_from_directory
|
python
|
OpenTalker/video-retalking
|
third_part/face_detection/detection/core.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/face_detection/detection/core.py
|
Apache-2.0
|
def tensor_or_path_to_ndarray(tensor_or_path, rgb=True):
"""Convert path (represented as a string) or torch.tensor to a numpy.ndarray
Arguments:
tensor_or_path {numpy.ndarray, torch.tensor or string} -- path to the image, or the image itself
"""
if isinstance(tensor_or_path, str):
return cv2.imread(tensor_or_path) if not rgb else cv2.imread(tensor_or_path)[..., ::-1]
elif torch.is_tensor(tensor_or_path):
# Call cpu in case its coming from cuda
return tensor_or_path.cpu().numpy()[..., ::-1].copy() if not rgb else tensor_or_path.cpu().numpy()
elif isinstance(tensor_or_path, np.ndarray):
return tensor_or_path[..., ::-1].copy() if not rgb else tensor_or_path
else:
raise TypeError
|
Convert path (represented as a string) or torch.tensor to a numpy.ndarray
Arguments:
tensor_or_path {numpy.ndarray, torch.tensor or string} -- path to the image, or the image itself
|
tensor_or_path_to_ndarray
|
python
|
OpenTalker/video-retalking
|
third_part/face_detection/detection/core.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/face_detection/detection/core.py
|
Apache-2.0
|
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]
|
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]
|
encode
|
python
|
OpenTalker/video-retalking
|
third_part/face_detection/detection/sfd/bbox.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/face_detection/detection/sfd/bbox.py
|
Apache-2.0
|
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
|
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
|
decode
|
python
|
OpenTalker/video-retalking
|
third_part/face_detection/detection/sfd/bbox.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/face_detection/detection/sfd/bbox.py
|
Apache-2.0
|
def batch_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])), 2)
boxes[:, :, :2] -= boxes[:, :, 2:] / 2
boxes[:, :, 2:] += boxes[:, :, :2]
return boxes
|
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
|
batch_decode
|
python
|
OpenTalker/video-retalking
|
third_part/face_detection/detection/sfd/bbox.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/face_detection/detection/sfd/bbox.py
|
Apache-2.0
|
def get_norm_layer(norm_type='instance'):
"""Return a normalization layer
Parameters:
norm_type (str) -- the name of the normalization layer: batch | instance | none
For BatchNorm, we use learnable affine parameters and track running statistics (mean/stddev).
For InstanceNorm, we do not use learnable affine parameters. We do not track running statistics.
"""
if norm_type == 'batch':
norm_layer = functools.partial(nn.BatchNorm2d, affine=True, track_running_stats=True)
elif norm_type == 'instance':
# change default flag, make sure instance norm behave as the same in both train and eval
# https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/395
norm_layer = functools.partial(nn.InstanceNorm2d, affine=False, track_running_stats=False)
elif norm_type == 'none':
norm_layer = None
else:
raise NotImplementedError('normalization layer [%s] is not found' % norm_type)
return norm_layer
|
Return a normalization layer
Parameters:
norm_type (str) -- the name of the normalization layer: batch | instance | none
For BatchNorm, we use learnable affine parameters and track running statistics (mean/stddev).
For InstanceNorm, we do not use learnable affine parameters. We do not track running statistics.
|
get_norm_layer
|
python
|
OpenTalker/video-retalking
|
third_part/ganimation_replicate/model/model_utils.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/ganimation_replicate/model/model_utils.py
|
Apache-2.0
|
def forward(self,
styles,
conditions,
input_is_latent=False,
noise=None,
randomize_noise=True,
truncation=1,
truncation_latent=None,
inject_index=None,
return_latents=False):
"""Forward function for StyleGAN2GeneratorSFT.
Args:
styles (list[Tensor]): Sample codes of styles.
conditions (list[Tensor]): SFT conditions to generators.
input_is_latent (bool): Whether input is latent style. Default: False.
noise (Tensor | None): Input noise or None. Default: None.
randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True.
truncation (float): The truncation ratio. Default: 1.
truncation_latent (Tensor | None): The truncation latent tensor. Default: None.
inject_index (int | None): The injection index for mixing noise. Default: None.
return_latents (bool): Whether to return style latents. Default: False.
"""
# style codes -> latents with Style MLP layer
if not input_is_latent:
styles = [self.style_mlp(s) for s in styles]
# noises
if noise is None:
if randomize_noise:
noise = [None] * self.num_layers # for each style conv layer
else: # use the stored noise
noise = [getattr(self.noises, f'noise{i}') for i in range(self.num_layers)]
# style truncation
if truncation < 1:
style_truncation = []
for style in styles:
style_truncation.append(truncation_latent + truncation * (style - truncation_latent))
styles = style_truncation
# get style latents with injection
if len(styles) == 1:
inject_index = self.num_latent
if styles[0].ndim < 3:
# repeat latent code for all the layers
latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
else: # used for encoder with different latent code for each layer
latent = styles[0]
elif len(styles) == 2: # mixing noises
if inject_index is None:
inject_index = random.randint(1, self.num_latent - 1)
latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
latent2 = styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1)
latent = torch.cat([latent1, latent2], 1)
# main generation
out = self.constant_input(latent.shape[0])
out = self.style_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.style_convs[::2], self.style_convs[1::2], noise[1::2],
noise[2::2], self.to_rgbs):
out = conv1(out, latent[:, i], noise=noise1)
# the conditions may have fewer levels
if i < len(conditions):
# SFT part to combine the conditions
if self.sft_half: # only apply SFT to half of the channels
out_same, out_sft = torch.split(out, int(out.size(1) // 2), dim=1)
out_sft = out_sft * conditions[i - 1] + conditions[i]
out = torch.cat([out_same, out_sft], dim=1)
else: # apply SFT to all the channels
out = out * conditions[i - 1] + conditions[i]
out = conv2(out, latent[:, i + 1], noise=noise2)
skip = to_rgb(out, latent[:, i + 2], skip) # feature back to the rgb space
i += 2
image = skip
if return_latents:
return image, latent
else:
return image, None
|
Forward function for StyleGAN2GeneratorSFT.
Args:
styles (list[Tensor]): Sample codes of styles.
conditions (list[Tensor]): SFT conditions to generators.
input_is_latent (bool): Whether input is latent style. Default: False.
noise (Tensor | None): Input noise or None. Default: None.
randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True.
truncation (float): The truncation ratio. Default: 1.
truncation_latent (Tensor | None): The truncation latent tensor. Default: None.
inject_index (int | None): The injection index for mixing noise. Default: None.
return_latents (bool): Whether to return style latents. Default: False.
|
forward
|
python
|
OpenTalker/video-retalking
|
third_part/GFPGAN/gfpgan/archs/gfpganv1_arch.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/GFPGAN/gfpgan/archs/gfpganv1_arch.py
|
Apache-2.0
|
def forward(self, x, return_latents=False, return_rgb=True, randomize_noise=True):
"""Forward function for GFPGANv1.
Args:
x (Tensor): Input images.
return_latents (bool): Whether to return style latents. Default: False.
return_rgb (bool): Whether return intermediate rgb images. Default: True.
randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True.
"""
conditions = []
unet_skips = []
out_rgbs = []
# encoder
feat = self.conv_body_first(x)
for i in range(self.log_size - 2):
feat = self.conv_body_down[i](feat)
unet_skips.insert(0, feat)
feat = self.final_conv(feat)
# style code
style_code = self.final_linear(feat.view(feat.size(0), -1))
if self.different_w:
style_code = style_code.view(style_code.size(0), -1, self.num_style_feat)
# decode
for i in range(self.log_size - 2):
# add unet skip
feat = feat + unet_skips[i]
# ResUpLayer
feat = self.conv_body_up[i](feat)
# generate scale and shift for SFT layers
scale = self.condition_scale[i](feat)
conditions.append(scale.clone())
shift = self.condition_shift[i](feat)
conditions.append(shift.clone())
# generate rgb images
if return_rgb:
out_rgbs.append(self.toRGB[i](feat))
# decoder
image, _ = self.stylegan_decoder([style_code],
conditions,
return_latents=return_latents,
input_is_latent=self.input_is_latent,
randomize_noise=randomize_noise)
return image, out_rgbs
|
Forward function for GFPGANv1.
Args:
x (Tensor): Input images.
return_latents (bool): Whether to return style latents. Default: False.
return_rgb (bool): Whether return intermediate rgb images. Default: True.
randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True.
|
forward
|
python
|
OpenTalker/video-retalking
|
third_part/GFPGAN/gfpgan/archs/gfpganv1_arch.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/GFPGAN/gfpgan/archs/gfpganv1_arch.py
|
Apache-2.0
|
def forward(self, x, return_feats=False):
"""Forward function for FacialComponentDiscriminator.
Args:
x (Tensor): Input images.
return_feats (bool): Whether to return intermediate features. Default: False.
"""
feat = self.conv1(x)
feat = self.conv3(self.conv2(feat))
rlt_feats = []
if return_feats:
rlt_feats.append(feat.clone())
feat = self.conv5(self.conv4(feat))
if return_feats:
rlt_feats.append(feat.clone())
out = self.final_conv(feat)
if return_feats:
return out, rlt_feats
else:
return out, None
|
Forward function for FacialComponentDiscriminator.
Args:
x (Tensor): Input images.
return_feats (bool): Whether to return intermediate features. Default: False.
|
forward
|
python
|
OpenTalker/video-retalking
|
third_part/GFPGAN/gfpgan/archs/gfpganv1_arch.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/GFPGAN/gfpgan/archs/gfpganv1_arch.py
|
Apache-2.0
|
def forward(self,
styles,
conditions,
input_is_latent=False,
noise=None,
randomize_noise=True,
truncation=1,
truncation_latent=None,
inject_index=None,
return_latents=False):
"""Forward function for StyleGAN2GeneratorCSFT.
Args:
styles (list[Tensor]): Sample codes of styles.
conditions (list[Tensor]): SFT conditions to generators.
input_is_latent (bool): Whether input is latent style. Default: False.
noise (Tensor | None): Input noise or None. Default: None.
randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True.
truncation (float): The truncation ratio. Default: 1.
truncation_latent (Tensor | None): The truncation latent tensor. Default: None.
inject_index (int | None): The injection index for mixing noise. Default: None.
return_latents (bool): Whether to return style latents. Default: False.
"""
# style codes -> latents with Style MLP layer
if not input_is_latent:
styles = [self.style_mlp(s) for s in styles]
# noises
if noise is None:
if randomize_noise:
noise = [None] * self.num_layers # for each style conv layer
else: # use the stored noise
noise = [getattr(self.noises, f'noise{i}') for i in range(self.num_layers)]
# style truncation
if truncation < 1:
style_truncation = []
for style in styles:
style_truncation.append(truncation_latent + truncation * (style - truncation_latent))
styles = style_truncation
# get style latents with injection
if len(styles) == 1:
inject_index = self.num_latent
if styles[0].ndim < 3:
# repeat latent code for all the layers
latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
else: # used for encoder with different latent code for each layer
latent = styles[0]
elif len(styles) == 2: # mixing noises
if inject_index is None:
inject_index = random.randint(1, self.num_latent - 1)
latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
latent2 = styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1)
latent = torch.cat([latent1, latent2], 1)
# main generation
out = self.constant_input(latent.shape[0])
out = self.style_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.style_convs[::2], self.style_convs[1::2], noise[1::2],
noise[2::2], self.to_rgbs):
out = conv1(out, latent[:, i], noise=noise1)
# the conditions may have fewer levels
if i < len(conditions):
# SFT part to combine the conditions
if self.sft_half: # only apply SFT to half of the channels
out_same, out_sft = torch.split(out, int(out.size(1) // 2), dim=1)
out_sft = out_sft * conditions[i - 1] + conditions[i]
out = torch.cat([out_same, out_sft], dim=1)
else: # apply SFT to all the channels
out = out * conditions[i - 1] + conditions[i]
out = conv2(out, latent[:, i + 1], noise=noise2)
skip = to_rgb(out, latent[:, i + 2], skip) # feature back to the rgb space
i += 2
image = skip
if return_latents:
return image, latent
else:
return image, None
|
Forward function for StyleGAN2GeneratorCSFT.
Args:
styles (list[Tensor]): Sample codes of styles.
conditions (list[Tensor]): SFT conditions to generators.
input_is_latent (bool): Whether input is latent style. Default: False.
noise (Tensor | None): Input noise or None. Default: None.
randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True.
truncation (float): The truncation ratio. Default: 1.
truncation_latent (Tensor | None): The truncation latent tensor. Default: None.
inject_index (int | None): The injection index for mixing noise. Default: None.
return_latents (bool): Whether to return style latents. Default: False.
|
forward
|
python
|
OpenTalker/video-retalking
|
third_part/GFPGAN/gfpgan/archs/gfpganv1_clean_arch.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/GFPGAN/gfpgan/archs/gfpganv1_clean_arch.py
|
Apache-2.0
|
def forward(self, x, return_latents=False, return_rgb=True, randomize_noise=True):
"""Forward function for GFPGANv1Clean.
Args:
x (Tensor): Input images.
return_latents (bool): Whether to return style latents. Default: False.
return_rgb (bool): Whether return intermediate rgb images. Default: True.
randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True.
"""
conditions = []
unet_skips = []
out_rgbs = []
# encoder
feat = F.leaky_relu_(self.conv_body_first(x), negative_slope=0.2)
for i in range(self.log_size - 2):
feat = self.conv_body_down[i](feat)
unet_skips.insert(0, feat)
feat = F.leaky_relu_(self.final_conv(feat), negative_slope=0.2)
# style code
style_code = self.final_linear(feat.view(feat.size(0), -1))
if self.different_w:
style_code = style_code.view(style_code.size(0), -1, self.num_style_feat)
# decode
for i in range(self.log_size - 2):
# add unet skip
feat = feat + unet_skips[i]
# ResUpLayer
feat = self.conv_body_up[i](feat)
# generate scale and shift for SFT layers
scale = self.condition_scale[i](feat)
conditions.append(scale.clone())
shift = self.condition_shift[i](feat)
conditions.append(shift.clone())
# generate rgb images
if return_rgb:
out_rgbs.append(self.toRGB[i](feat))
# decoder
image, _ = self.stylegan_decoder([style_code],
conditions,
return_latents=return_latents,
input_is_latent=self.input_is_latent,
randomize_noise=randomize_noise)
return image, out_rgbs
|
Forward function for GFPGANv1Clean.
Args:
x (Tensor): Input images.
return_latents (bool): Whether to return style latents. Default: False.
return_rgb (bool): Whether return intermediate rgb images. Default: True.
randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True.
|
forward
|
python
|
OpenTalker/video-retalking
|
third_part/GFPGAN/gfpgan/archs/gfpganv1_clean_arch.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/GFPGAN/gfpgan/archs/gfpganv1_clean_arch.py
|
Apache-2.0
|
def forward(self,
styles,
conditions,
input_is_latent=False,
noise=None,
randomize_noise=True,
truncation=1,
truncation_latent=None,
inject_index=None,
return_latents=False):
"""Forward function for StyleGAN2GeneratorBilinearSFT.
Args:
styles (list[Tensor]): Sample codes of styles.
conditions (list[Tensor]): SFT conditions to generators.
input_is_latent (bool): Whether input is latent style. Default: False.
noise (Tensor | None): Input noise or None. Default: None.
randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True.
truncation (float): The truncation ratio. Default: 1.
truncation_latent (Tensor | None): The truncation latent tensor. Default: None.
inject_index (int | None): The injection index for mixing noise. Default: None.
return_latents (bool): Whether to return style latents. Default: False.
"""
# style codes -> latents with Style MLP layer
if not input_is_latent:
styles = [self.style_mlp(s) for s in styles]
# noises
if noise is None:
if randomize_noise:
noise = [None] * self.num_layers # for each style conv layer
else: # use the stored noise
noise = [getattr(self.noises, f'noise{i}') for i in range(self.num_layers)]
# style truncation
if truncation < 1:
style_truncation = []
for style in styles:
style_truncation.append(truncation_latent + truncation * (style - truncation_latent))
styles = style_truncation
# get style latents with injection
if len(styles) == 1:
inject_index = self.num_latent
if styles[0].ndim < 3:
# repeat latent code for all the layers
latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
else: # used for encoder with different latent code for each layer
latent = styles[0]
elif len(styles) == 2: # mixing noises
if inject_index is None:
inject_index = random.randint(1, self.num_latent - 1)
latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
latent2 = styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1)
latent = torch.cat([latent1, latent2], 1)
# main generation
out = self.constant_input(latent.shape[0])
out = self.style_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.style_convs[::2], self.style_convs[1::2], noise[1::2],
noise[2::2], self.to_rgbs):
out = conv1(out, latent[:, i], noise=noise1)
# the conditions may have fewer levels
if i < len(conditions):
# SFT part to combine the conditions
if self.sft_half: # only apply SFT to half of the channels
out_same, out_sft = torch.split(out, int(out.size(1) // 2), dim=1)
out_sft = out_sft * conditions[i - 1] + conditions[i]
out = torch.cat([out_same, out_sft], dim=1)
else: # apply SFT to all the channels
out = out * conditions[i - 1] + conditions[i]
out = conv2(out, latent[:, i + 1], noise=noise2)
skip = to_rgb(out, latent[:, i + 2], skip) # feature back to the rgb space
i += 2
image = skip
if return_latents:
return image, latent
else:
return image, None
|
Forward function for StyleGAN2GeneratorBilinearSFT.
Args:
styles (list[Tensor]): Sample codes of styles.
conditions (list[Tensor]): SFT conditions to generators.
input_is_latent (bool): Whether input is latent style. Default: False.
noise (Tensor | None): Input noise or None. Default: None.
randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True.
truncation (float): The truncation ratio. Default: 1.
truncation_latent (Tensor | None): The truncation latent tensor. Default: None.
inject_index (int | None): The injection index for mixing noise. Default: None.
return_latents (bool): Whether to return style latents. Default: False.
|
forward
|
python
|
OpenTalker/video-retalking
|
third_part/GFPGAN/gfpgan/archs/gfpgan_bilinear_arch.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/GFPGAN/gfpgan/archs/gfpgan_bilinear_arch.py
|
Apache-2.0
|
def forward(self, x, return_latents=False, return_rgb=True, randomize_noise=True):
"""Forward function for GFPGANBilinear.
Args:
x (Tensor): Input images.
return_latents (bool): Whether to return style latents. Default: False.
return_rgb (bool): Whether return intermediate rgb images. Default: True.
randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True.
"""
conditions = []
unet_skips = []
out_rgbs = []
# encoder
feat = self.conv_body_first(x)
for i in range(self.log_size - 2):
feat = self.conv_body_down[i](feat)
unet_skips.insert(0, feat)
feat = self.final_conv(feat)
# style code
style_code = self.final_linear(feat.view(feat.size(0), -1))
if self.different_w:
style_code = style_code.view(style_code.size(0), -1, self.num_style_feat)
# decode
for i in range(self.log_size - 2):
# add unet skip
feat = feat + unet_skips[i]
# ResUpLayer
feat = self.conv_body_up[i](feat)
# generate scale and shift for SFT layers
scale = self.condition_scale[i](feat)
conditions.append(scale.clone())
shift = self.condition_shift[i](feat)
conditions.append(shift.clone())
# generate rgb images
if return_rgb:
out_rgbs.append(self.toRGB[i](feat))
# decoder
image, _ = self.stylegan_decoder([style_code],
conditions,
return_latents=return_latents,
input_is_latent=self.input_is_latent,
randomize_noise=randomize_noise)
return image, out_rgbs
|
Forward function for GFPGANBilinear.
Args:
x (Tensor): Input images.
return_latents (bool): Whether to return style latents. Default: False.
return_rgb (bool): Whether return intermediate rgb images. Default: True.
randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True.
|
forward
|
python
|
OpenTalker/video-retalking
|
third_part/GFPGAN/gfpgan/archs/gfpgan_bilinear_arch.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/GFPGAN/gfpgan/archs/gfpgan_bilinear_arch.py
|
Apache-2.0
|
def forward(self, x, style):
"""Forward function.
Args:
x (Tensor): Tensor with shape (b, c, h, w).
style (Tensor): Tensor with shape (b, num_style_feat).
Returns:
Tensor: Modulated tensor after convolution.
"""
b, c, h, w = x.shape # c = c_in
# weight modulation
style = self.modulation(style).view(b, 1, c, 1, 1)
# self.weight: (1, c_out, c_in, k, k); style: (b, 1, c, 1, 1)
weight = self.scale * self.weight * style # (b, c_out, c_in, k, k)
if self.demodulate:
demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + self.eps)
weight = weight * demod.view(b, self.out_channels, 1, 1, 1)
weight = weight.view(b * self.out_channels, c, self.kernel_size, self.kernel_size)
if self.sample_mode == 'upsample':
x = F.interpolate(x, scale_factor=2, mode=self.interpolation_mode, align_corners=self.align_corners)
elif self.sample_mode == 'downsample':
x = F.interpolate(x, scale_factor=0.5, mode=self.interpolation_mode, align_corners=self.align_corners)
b, c, h, w = x.shape
x = x.view(1, b * c, h, w)
# weight: (b*c_out, c_in, k, k), groups=b
out = F.conv2d(x, weight, padding=self.padding, groups=b)
out = out.view(b, self.out_channels, *out.shape[2:4])
return out
|
Forward function.
Args:
x (Tensor): Tensor with shape (b, c, h, w).
style (Tensor): Tensor with shape (b, num_style_feat).
Returns:
Tensor: Modulated tensor after convolution.
|
forward
|
python
|
OpenTalker/video-retalking
|
third_part/GFPGAN/gfpgan/archs/stylegan2_bilinear_arch.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/GFPGAN/gfpgan/archs/stylegan2_bilinear_arch.py
|
Apache-2.0
|
def forward(self, x, style, skip=None):
"""Forward function.
Args:
x (Tensor): Feature tensor with shape (b, c, h, w).
style (Tensor): Tensor with shape (b, num_style_feat).
skip (Tensor): Base/skip tensor. Default: None.
Returns:
Tensor: RGB images.
"""
out = self.modulated_conv(x, style)
out = out + self.bias
if skip is not None:
if self.upsample:
skip = F.interpolate(
skip, scale_factor=2, mode=self.interpolation_mode, align_corners=self.align_corners)
out = out + skip
return out
|
Forward function.
Args:
x (Tensor): Feature tensor with shape (b, c, h, w).
style (Tensor): Tensor with shape (b, num_style_feat).
skip (Tensor): Base/skip tensor. Default: None.
Returns:
Tensor: RGB images.
|
forward
|
python
|
OpenTalker/video-retalking
|
third_part/GFPGAN/gfpgan/archs/stylegan2_bilinear_arch.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/GFPGAN/gfpgan/archs/stylegan2_bilinear_arch.py
|
Apache-2.0
|
def forward(self,
styles,
input_is_latent=False,
noise=None,
randomize_noise=True,
truncation=1,
truncation_latent=None,
inject_index=None,
return_latents=False):
"""Forward function for StyleGAN2Generator.
Args:
styles (list[Tensor]): Sample codes of styles.
input_is_latent (bool): Whether input is latent style.
Default: False.
noise (Tensor | None): Input noise or None. Default: None.
randomize_noise (bool): Randomize noise, used when 'noise' is
False. Default: True.
truncation (float): TODO. Default: 1.
truncation_latent (Tensor | None): TODO. Default: None.
inject_index (int | None): The injection index for mixing noise.
Default: None.
return_latents (bool): Whether to return style latents.
Default: False.
"""
# style codes -> latents with Style MLP layer
if not input_is_latent:
styles = [self.style_mlp(s) for s in styles]
# noises
if noise is None:
if randomize_noise:
noise = [None] * self.num_layers # for each style conv layer
else: # use the stored noise
noise = [getattr(self.noises, f'noise{i}') for i in range(self.num_layers)]
# style truncation
if truncation < 1:
style_truncation = []
for style in styles:
style_truncation.append(truncation_latent + truncation * (style - truncation_latent))
styles = style_truncation
# get style latent with injection
if len(styles) == 1:
inject_index = self.num_latent
if styles[0].ndim < 3:
# repeat latent code for all the layers
latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
else: # used for encoder with different latent code for each layer
latent = styles[0]
elif len(styles) == 2: # mixing noises
if inject_index is None:
inject_index = random.randint(1, self.num_latent - 1)
latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
latent2 = styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1)
latent = torch.cat([latent1, latent2], 1)
# main generation
out = self.constant_input(latent.shape[0])
out = self.style_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.style_convs[::2], self.style_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
|
Forward function for StyleGAN2Generator.
Args:
styles (list[Tensor]): Sample codes of styles.
input_is_latent (bool): Whether input is latent style.
Default: False.
noise (Tensor | None): Input noise or None. Default: None.
randomize_noise (bool): Randomize noise, used when 'noise' is
False. Default: True.
truncation (float): TODO. Default: 1.
truncation_latent (Tensor | None): TODO. Default: None.
inject_index (int | None): The injection index for mixing noise.
Default: None.
return_latents (bool): Whether to return style latents.
Default: False.
|
forward
|
python
|
OpenTalker/video-retalking
|
third_part/GFPGAN/gfpgan/archs/stylegan2_bilinear_arch.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/GFPGAN/gfpgan/archs/stylegan2_bilinear_arch.py
|
Apache-2.0
|
def forward(self, x, style):
"""Forward function.
Args:
x (Tensor): Tensor with shape (b, c, h, w).
style (Tensor): Tensor with shape (b, num_style_feat).
Returns:
Tensor: Modulated tensor after convolution.
"""
b, c, h, w = x.shape # c = c_in
# weight modulation
style = self.modulation(style).view(b, 1, c, 1, 1)
# self.weight: (1, c_out, c_in, k, k); style: (b, 1, c, 1, 1)
weight = self.weight * style # (b, c_out, c_in, k, k)
if self.demodulate:
demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + self.eps)
weight = weight * demod.view(b, self.out_channels, 1, 1, 1)
weight = weight.view(b * self.out_channels, c, self.kernel_size, self.kernel_size)
# upsample or downsample if necessary
if self.sample_mode == 'upsample':
x = F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=False)
elif self.sample_mode == 'downsample':
x = F.interpolate(x, scale_factor=0.5, mode='bilinear', align_corners=False)
b, c, h, w = x.shape
x = x.view(1, b * c, h, w)
# weight: (b*c_out, c_in, k, k), groups=b
out = F.conv2d(x, weight, padding=self.padding, groups=b)
out = out.view(b, self.out_channels, *out.shape[2:4])
return out
|
Forward function.
Args:
x (Tensor): Tensor with shape (b, c, h, w).
style (Tensor): Tensor with shape (b, num_style_feat).
Returns:
Tensor: Modulated tensor after convolution.
|
forward
|
python
|
OpenTalker/video-retalking
|
third_part/GFPGAN/gfpgan/archs/stylegan2_clean_arch.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/GFPGAN/gfpgan/archs/stylegan2_clean_arch.py
|
Apache-2.0
|
def forward(self, x, style, skip=None):
"""Forward function.
Args:
x (Tensor): Feature tensor with shape (b, c, h, w).
style (Tensor): Tensor with shape (b, num_style_feat).
skip (Tensor): Base/skip tensor. Default: None.
Returns:
Tensor: RGB images.
"""
out = self.modulated_conv(x, style)
out = out + self.bias
if skip is not None:
if self.upsample:
skip = F.interpolate(skip, scale_factor=2, mode='bilinear', align_corners=False)
out = out + skip
return out
|
Forward function.
Args:
x (Tensor): Feature tensor with shape (b, c, h, w).
style (Tensor): Tensor with shape (b, num_style_feat).
skip (Tensor): Base/skip tensor. Default: None.
Returns:
Tensor: RGB images.
|
forward
|
python
|
OpenTalker/video-retalking
|
third_part/GFPGAN/gfpgan/archs/stylegan2_clean_arch.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/GFPGAN/gfpgan/archs/stylegan2_clean_arch.py
|
Apache-2.0
|
def forward(self,
styles,
input_is_latent=False,
noise=None,
randomize_noise=True,
truncation=1,
truncation_latent=None,
inject_index=None,
return_latents=False):
"""Forward function for StyleGAN2GeneratorClean.
Args:
styles (list[Tensor]): Sample codes of styles.
input_is_latent (bool): Whether input is latent style. Default: False.
noise (Tensor | None): Input noise or None. Default: None.
randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True.
truncation (float): The truncation ratio. Default: 1.
truncation_latent (Tensor | None): The truncation latent tensor. Default: None.
inject_index (int | None): The injection index for mixing noise. Default: None.
return_latents (bool): Whether to return style latents. Default: False.
"""
# style codes -> latents with Style MLP layer
if not input_is_latent:
styles = [self.style_mlp(s) for s in styles]
# noises
if noise is None:
if randomize_noise:
noise = [None] * self.num_layers # for each style conv layer
else: # use the stored noise
noise = [getattr(self.noises, f'noise{i}') for i in range(self.num_layers)]
# style truncation
if truncation < 1:
style_truncation = []
for style in styles:
style_truncation.append(truncation_latent + truncation * (style - truncation_latent))
styles = style_truncation
# get style latents with injection
if len(styles) == 1:
inject_index = self.num_latent
if styles[0].ndim < 3:
# repeat latent code for all the layers
latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
else: # used for encoder with different latent code for each layer
latent = styles[0]
elif len(styles) == 2: # mixing noises
if inject_index is None:
inject_index = random.randint(1, self.num_latent - 1)
latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
latent2 = styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1)
latent = torch.cat([latent1, latent2], 1)
# main generation
out = self.constant_input(latent.shape[0])
out = self.style_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.style_convs[::2], self.style_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) # feature back to the rgb space
i += 2
image = skip
if return_latents:
return image, latent
else:
return image, None
|
Forward function for StyleGAN2GeneratorClean.
Args:
styles (list[Tensor]): Sample codes of styles.
input_is_latent (bool): Whether input is latent style. Default: False.
noise (Tensor | None): Input noise or None. Default: None.
randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True.
truncation (float): The truncation ratio. Default: 1.
truncation_latent (Tensor | None): The truncation latent tensor. Default: None.
inject_index (int | None): The injection index for mixing noise. Default: None.
return_latents (bool): Whether to return style latents. Default: False.
|
forward
|
python
|
OpenTalker/video-retalking
|
third_part/GFPGAN/gfpgan/archs/stylegan2_clean_arch.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/GFPGAN/gfpgan/archs/stylegan2_clean_arch.py
|
Apache-2.0
|
def color_jitter(img, shift):
"""jitter color: randomly jitter the RGB values, in numpy formats"""
jitter_val = np.random.uniform(-shift, shift, 3).astype(np.float32)
img = img + jitter_val
img = np.clip(img, 0, 1)
return img
|
jitter color: randomly jitter the RGB values, in numpy formats
|
color_jitter
|
python
|
OpenTalker/video-retalking
|
third_part/GFPGAN/gfpgan/data/ffhq_degradation_dataset.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/GFPGAN/gfpgan/data/ffhq_degradation_dataset.py
|
Apache-2.0
|
def color_jitter_pt(img, brightness, contrast, saturation, hue):
"""jitter color: randomly jitter the brightness, contrast, saturation, and hue, in torch Tensor formats"""
fn_idx = torch.randperm(4)
for fn_id in fn_idx:
if fn_id == 0 and brightness is not None:
brightness_factor = torch.tensor(1.0).uniform_(brightness[0], brightness[1]).item()
img = adjust_brightness(img, brightness_factor)
if fn_id == 1 and contrast is not None:
contrast_factor = torch.tensor(1.0).uniform_(contrast[0], contrast[1]).item()
img = adjust_contrast(img, contrast_factor)
if fn_id == 2 and saturation is not None:
saturation_factor = torch.tensor(1.0).uniform_(saturation[0], saturation[1]).item()
img = adjust_saturation(img, saturation_factor)
if fn_id == 3 and hue is not None:
hue_factor = torch.tensor(1.0).uniform_(hue[0], hue[1]).item()
img = adjust_hue(img, hue_factor)
return img
|
jitter color: randomly jitter the brightness, contrast, saturation, and hue, in torch Tensor formats
|
color_jitter_pt
|
python
|
OpenTalker/video-retalking
|
third_part/GFPGAN/gfpgan/data/ffhq_degradation_dataset.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/GFPGAN/gfpgan/data/ffhq_degradation_dataset.py
|
Apache-2.0
|
def get_component_coordinates(self, index, status):
"""Get facial component (left_eye, right_eye, mouth) coordinates from a pre-loaded pth file"""
components_bbox = self.components_list[f'{index:08d}']
if status[0]: # hflip
# exchange right and left eye
tmp = components_bbox['left_eye']
components_bbox['left_eye'] = components_bbox['right_eye']
components_bbox['right_eye'] = tmp
# modify the width coordinate
components_bbox['left_eye'][0] = self.out_size - components_bbox['left_eye'][0]
components_bbox['right_eye'][0] = self.out_size - components_bbox['right_eye'][0]
components_bbox['mouth'][0] = self.out_size - components_bbox['mouth'][0]
# get coordinates
locations = []
for part in ['left_eye', 'right_eye', 'mouth']:
mean = components_bbox[part][0:2]
half_len = components_bbox[part][2]
if 'eye' in part:
half_len *= self.eye_enlarge_ratio
loc = np.hstack((mean - half_len + 1, mean + half_len))
loc = torch.from_numpy(loc).float()
locations.append(loc)
return locations
|
Get facial component (left_eye, right_eye, mouth) coordinates from a pre-loaded pth file
|
get_component_coordinates
|
python
|
OpenTalker/video-retalking
|
third_part/GFPGAN/gfpgan/data/ffhq_degradation_dataset.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/GFPGAN/gfpgan/data/ffhq_degradation_dataset.py
|
Apache-2.0
|
def construct_img_pyramid(self):
"""Construct image pyramid for intermediate restoration loss"""
pyramid_gt = [self.gt]
down_img = self.gt
for _ in range(0, self.log_size - 3):
down_img = F.interpolate(down_img, scale_factor=0.5, mode='bilinear', align_corners=False)
pyramid_gt.insert(0, down_img)
return pyramid_gt
|
Construct image pyramid for intermediate restoration loss
|
construct_img_pyramid
|
python
|
OpenTalker/video-retalking
|
third_part/GFPGAN/gfpgan/models/gfpgan_model.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/GFPGAN/gfpgan/models/gfpgan_model.py
|
Apache-2.0
|
def _gram_mat(self, x):
"""Calculate Gram matrix.
Args:
x (torch.Tensor): Tensor with shape of (n, c, h, w).
Returns:
torch.Tensor: Gram matrix.
"""
n, c, h, w = x.size()
features = x.view(n, c, w * h)
features_t = features.transpose(1, 2)
gram = features.bmm(features_t) / (c * h * w)
return gram
|
Calculate Gram matrix.
Args:
x (torch.Tensor): Tensor with shape of (n, c, h, w).
Returns:
torch.Tensor: Gram matrix.
|
_gram_mat
|
python
|
OpenTalker/video-retalking
|
third_part/GFPGAN/gfpgan/models/gfpgan_model.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/GFPGAN/gfpgan/models/gfpgan_model.py
|
Apache-2.0
|
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
|
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`
|
_umeyama
|
python
|
OpenTalker/video-retalking
|
third_part/GPEN/align_faces.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/GPEN/align_faces.py
|
Apache-2.0
|
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()}
|
Old style model is stored with all names of parameters sharing common prefix 'module.'
|
remove_prefix
|
python
|
OpenTalker/video-retalking
|
third_part/GPEN/face_detect/retinaface_detection.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/GPEN/face_detect/retinaface_detection.py
|
Apache-2.0
|
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)
|
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
|
detection_collate
|
python
|
OpenTalker/video-retalking
|
third_part/GPEN/face_detect/data/wider_face.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/GPEN/face_detect/data/wider_face.py
|
Apache-2.0
|
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'])
|
:param cfg: Network related settings.
:param phase: train or test.
|
__init__
|
python
|
OpenTalker/video-retalking
|
third_part/GPEN/face_detect/facemodels/retinaface.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/GPEN/face_detect/facemodels/retinaface.py
|
Apache-2.0
|
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
|
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.
|
point_form
|
python
|
OpenTalker/video-retalking
|
third_part/GPEN/face_detect/utils/box_utils.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/GPEN/face_detect/utils/box_utils.py
|
Apache-2.0
|
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
|
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.
|
center_size
|
python
|
OpenTalker/video-retalking
|
third_part/GPEN/face_detect/utils/box_utils.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/GPEN/face_detect/utils/box_utils.py
|
Apache-2.0
|
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]
|
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].
|
intersect
|
python
|
OpenTalker/video-retalking
|
third_part/GPEN/face_detect/utils/box_utils.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/GPEN/face_detect/utils/box_utils.py
|
Apache-2.0
|
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)
|
return iou of a and b, numpy version for data augenmentation
|
matrix_iou
|
python
|
OpenTalker/video-retalking
|
third_part/GPEN/face_detect/utils/box_utils.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/GPEN/face_detect/utils/box_utils.py
|
Apache-2.0
|
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)
|
return iof of a and b, numpy version for data augenmentation
|
matrix_iof
|
python
|
OpenTalker/video-retalking
|
third_part/GPEN/face_detect/utils/box_utils.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/GPEN/face_detect/utils/box_utils.py
|
Apache-2.0
|
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]
|
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]
|
encode
|
python
|
OpenTalker/video-retalking
|
third_part/GPEN/face_detect/utils/box_utils.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/GPEN/face_detect/utils/box_utils.py
|
Apache-2.0
|
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
|
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]
|
encode_landm
|
python
|
OpenTalker/video-retalking
|
third_part/GPEN/face_detect/utils/box_utils.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/GPEN/face_detect/utils/box_utils.py
|
Apache-2.0
|
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
|
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
|
decode
|
python
|
OpenTalker/video-retalking
|
third_part/GPEN/face_detect/utils/box_utils.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/GPEN/face_detect/utils/box_utils.py
|
Apache-2.0
|
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
|
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
|
decode_landm
|
python
|
OpenTalker/video-retalking
|
third_part/GPEN/face_detect/utils/box_utils.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/GPEN/face_detect/utils/box_utils.py
|
Apache-2.0
|
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
|
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
|
log_sum_exp
|
python
|
OpenTalker/video-retalking
|
third_part/GPEN/face_detect/utils/box_utils.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/GPEN/face_detect/utils/box_utils.py
|
Apache-2.0
|
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
|
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.
|
nms
|
python
|
OpenTalker/video-retalking
|
third_part/GPEN/face_detect/utils/box_utils.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/GPEN/face_detect/utils/box_utils.py
|
Apache-2.0
|
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
|
Cap a number to ensure positivity
:param num: positive or negative number
:returns: (overflow, capped_number)
|
positive_cap
|
python
|
OpenTalker/video-retalking
|
third_part/GPEN/face_morpher/facemorpher/aligner.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/GPEN/face_morpher/facemorpher/aligner.py
|
Apache-2.0
|
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
|
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.
|
roi_coordinates
|
python
|
OpenTalker/video-retalking
|
third_part/GPEN/face_morpher/facemorpher/aligner.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/GPEN/face_morpher/facemorpher/aligner.py
|
Apache-2.0
|
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
|
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
|
scaling_factor
|
python
|
OpenTalker/video-retalking
|
third_part/GPEN/face_morpher/facemorpher/aligner.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/GPEN/face_morpher/facemorpher/aligner.py
|
Apache-2.0
|
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))
|
Resize image with the provided scaling factor
:param img: image to be resized
:param scale: scaling factor for resizing the image
|
resize_image
|
python
|
OpenTalker/video-retalking
|
third_part/GPEN/face_morpher/facemorpher/aligner.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/GPEN/face_morpher/facemorpher/aligner.py
|
Apache-2.0
|
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)
|
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
|
resize_align
|
python
|
OpenTalker/video-retalking
|
third_part/GPEN/face_morpher/facemorpher/aligner.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/GPEN/face_morpher/facemorpher/aligner.py
|
Apache-2.0
|
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
|
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
|
mask_from_points
|
python
|
OpenTalker/video-retalking
|
third_part/GPEN/face_morpher/facemorpher/blender.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/GPEN/face_morpher/facemorpher/blender.py
|
Apache-2.0
|
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
|
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
|
overlay_image
|
python
|
OpenTalker/video-retalking
|
third_part/GPEN/face_morpher/facemorpher/blender.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/GPEN/face_morpher/facemorpher/blender.py
|
Apache-2.0
|
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
|
Apply mask to supplied image
:param img: max 3 channel image
:param mask: [0-255] values in mask
:returns: new image with mask applied
|
apply_mask
|
python
|
OpenTalker/video-retalking
|
third_part/GPEN/face_morpher/facemorpher/blender.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/GPEN/face_morpher/facemorpher/blender.py
|
Apache-2.0
|
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]]
|
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
|
boundary_points
|
python
|
OpenTalker/video-retalking
|
third_part/GPEN/face_morpher/facemorpher/locator.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/GPEN/face_morpher/facemorpher/locator.py
|
Apache-2.0
|
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 []
|
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
|
face_points_dlib
|
python
|
OpenTalker/video-retalking
|
third_part/GPEN/face_morpher/facemorpher/locator.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/GPEN/face_morpher/facemorpher/locator.py
|
Apache-2.0
|
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)
|
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
|
weighted_average_points
|
python
|
OpenTalker/video-retalking
|
third_part/GPEN/face_morpher/facemorpher/locator.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/GPEN/face_morpher/facemorpher/locator.py
|
Apache-2.0
|
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()
|
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
|
morph
|
python
|
OpenTalker/video-retalking
|
third_part/GPEN/face_morpher/facemorpher/morpher.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/GPEN/face_morpher/facemorpher/morpher.py
|
Apache-2.0
|
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()
|
Create a morph sequence from multiple images in imgpaths
:param imgpaths: array or generator of image paths
|
morpher
|
python
|
OpenTalker/video-retalking
|
third_part/GPEN/face_morpher/facemorpher/morpher.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/GPEN/face_morpher/facemorpher/morpher.py
|
Apache-2.0
|
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
|
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
|
bilinear_interpolate
|
python
|
OpenTalker/video-retalking
|
third_part/GPEN/face_morpher/facemorpher/warper.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/GPEN/face_morpher/facemorpher/warper.py
|
Apache-2.0
|
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)
|
x,y grid coordinates within the ROI of supplied points
:param points: points to generate grid coordinates
:returns: array of (x, y) coordinates
|
grid_coordinates
|
python
|
OpenTalker/video-retalking
|
third_part/GPEN/face_morpher/facemorpher/warper.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/GPEN/face_morpher/facemorpher/warper.py
|
Apache-2.0
|
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
|
Warp each triangle from the src_image only within the
ROI of the destination image (points in dst_points).
|
process_warp
|
python
|
OpenTalker/video-retalking
|
third_part/GPEN/face_morpher/facemorpher/warper.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/GPEN/face_morpher/facemorpher/warper.py
|
Apache-2.0
|
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
|
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
|
triangular_affine_matrices
|
python
|
OpenTalker/video-retalking
|
third_part/GPEN/face_morpher/facemorpher/warper.py
|
https://github.com/OpenTalker/video-retalking/blob/master/third_part/GPEN/face_morpher/facemorpher/warper.py
|
Apache-2.0
|
def get_landmark(filepath, predictor, detector=None, fa=None):
"""get landmark with dlib
:return: np.array shape=(68, 2)
"""
if fa is not None:
image = io.imread(filepath)
lms, _, bboxes = fa.get_landmarks(image, return_bboxes=True)
if len(lms) == 0:
return None
return lms[0]
if detector is None:
detector = dlib.get_frontal_face_detector()
if isinstance(filepath, PIL.Image.Image):
img = np.array(filepath)
else:
img = dlib.load_rgb_image(filepath)
dets = detector(img)
for k, d in enumerate(dets):
shape = predictor(img, d)
break
else:
return None
t = list(shape.parts())
a = []
for tt in t:
a.append([tt.x, tt.y])
lm = np.array(a)
return lm
|
get landmark with dlib
:return: np.array shape=(68, 2)
|
get_landmark
|
python
|
OpenTalker/video-retalking
|
utils/alignment_stit.py
|
https://github.com/OpenTalker/video-retalking/blob/master/utils/alignment_stit.py
|
Apache-2.0
|
def align_face(filepath_or_image, predictor, output_size, detector=None,
enable_padding=False, scale=1.0):
"""
:param filepath: str
:return: PIL Image
"""
c, x, y = compute_transform(filepath_or_image, predictor, detector=detector,
scale=scale)
quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y])
img = crop_image(filepath_or_image, output_size, quad, enable_padding=enable_padding)
# Return aligned image.
return img
|
:param filepath: str
:return: PIL Image
|
align_face
|
python
|
OpenTalker/video-retalking
|
utils/alignment_stit.py
|
https://github.com/OpenTalker/video-retalking/blob/master/utils/alignment_stit.py
|
Apache-2.0
|
def num_frames(length, fsize, fshift):
"""Compute number of time frames of spectrogram
"""
pad = (fsize - fshift)
if length % fshift == 0:
M = (length + pad * 2 - fsize) // fshift + 1
else:
M = (length + pad * 2 - fsize) // fshift + 2
return M
|
Compute number of time frames of spectrogram
|
num_frames
|
python
|
OpenTalker/video-retalking
|
utils/audio.py
|
https://github.com/OpenTalker/video-retalking/blob/master/utils/audio.py
|
Apache-2.0
|
def get_landmark(self, img_np):
"""get landmark with dlib
:return: np.array shape=(68, 2)
"""
detector = dlib.get_frontal_face_detector()
dets = detector(img_np, 1)
if len(dets) == 0:
return None
d = dets[0]
# Get the landmarks/parts for the face in box d.
shape = self.predictor(img_np, d)
t = list(shape.parts())
a = []
for tt in t:
a.append([tt.x, tt.y])
lm = np.array(a)
return lm
|
get landmark with dlib
:return: np.array shape=(68, 2)
|
get_landmark
|
python
|
OpenTalker/video-retalking
|
utils/ffhq_preprocess.py
|
https://github.com/OpenTalker/video-retalking/blob/master/utils/ffhq_preprocess.py
|
Apache-2.0
|
def align_face(self, img, lm, output_size=1024):
"""
:param filepath: str
:return: PIL Image
"""
lm_chin = lm[0: 17] # left-right
lm_eyebrow_left = lm[17: 22] # left-right
lm_eyebrow_right = lm[22: 27] # left-right
lm_nose = lm[27: 31] # top-down
lm_nostrils = lm[31: 36] # top-down
lm_eye_left = lm[36: 42] # left-clockwise
lm_eye_right = lm[42: 48] # left-clockwise
lm_mouth_outer = lm[48: 60] # left-clockwise
lm_mouth_inner = lm[60: 68] # left-clockwise
# Calculate auxiliary vectors.
eye_left = np.mean(lm_eye_left, axis=0)
eye_right = np.mean(lm_eye_right, axis=0)
eye_avg = (eye_left + eye_right) * 0.5
eye_to_eye = eye_right - eye_left
mouth_left = lm_mouth_outer[0]
mouth_right = lm_mouth_outer[6]
mouth_avg = (mouth_left + mouth_right) * 0.5
eye_to_mouth = mouth_avg - eye_avg
# Choose oriented crop rectangle.
x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1]
x /= np.hypot(*x)
x *= max(np.hypot(*eye_to_eye) * 2.0, np.hypot(*eye_to_mouth) * 1.8)
y = np.flipud(x) * [-1, 1]
c = eye_avg + eye_to_mouth * 0.1
quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y])
qsize = np.hypot(*x) * 2
# Shrink.
shrink = int(np.floor(qsize / output_size * 0.5))
if shrink > 1:
rsize = (int(np.rint(float(img.size[0]) / shrink)), int(np.rint(float(img.size[1]) / shrink)))
img = img.resize(rsize, Image.ANTIALIAS)
quad /= shrink
qsize /= shrink
# Crop.
border = max(int(np.rint(qsize * 0.1)), 3)
crop = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))),
int(np.ceil(max(quad[:, 1]))))
crop = (max(crop[0] - border, 0), max(crop[1] - border, 0), min(crop[2] + border, img.size[0]),
min(crop[3] + border, img.size[1]))
if crop[2] - crop[0] < img.size[0] or crop[3] - crop[1] < img.size[1]:
quad -= crop[0:2]
# Transform.
quad = (quad + 0.5).flatten()
lx = max(min(quad[0], quad[2]), 0)
ly = max(min(quad[1], quad[7]), 0)
rx = min(max(quad[4], quad[6]), img.size[0])
ry = min(max(quad[3], quad[5]), img.size[0])
# Save aligned image.
return crop, [lx, ly, rx, ry]
|
:param filepath: str
:return: PIL Image
|
align_face
|
python
|
OpenTalker/video-retalking
|
utils/ffhq_preprocess.py
|
https://github.com/OpenTalker/video-retalking/blob/master/utils/ffhq_preprocess.py
|
Apache-2.0
|
def convert_flow_to_deformation(flow):
r"""convert flow fields to deformations.
Args:
flow (tensor): Flow field obtained by the model
Returns:
deformation (tensor): The deformation used for warping
"""
b,c,h,w = flow.shape
flow_norm = 2 * torch.cat([flow[:,:1,...]/(w-1),flow[:,1:,...]/(h-1)], 1)
grid = make_coordinate_grid(flow)
deformation = grid + flow_norm.permute(0,2,3,1)
return deformation
|
convert flow fields to deformations.
Args:
flow (tensor): Flow field obtained by the model
Returns:
deformation (tensor): The deformation used for warping
|
convert_flow_to_deformation
|
python
|
OpenTalker/video-retalking
|
utils/flow_util.py
|
https://github.com/OpenTalker/video-retalking/blob/master/utils/flow_util.py
|
Apache-2.0
|
def make_coordinate_grid(flow):
r"""obtain coordinate grid with the same size as the flow filed.
Args:
flow (tensor): Flow field obtained by the model
Returns:
grid (tensor): The grid with the same size as the input flow
"""
b,c,h,w = flow.shape
x = torch.arange(w).to(flow)
y = torch.arange(h).to(flow)
x = (2 * (x / (w - 1)) - 1)
y = (2 * (y / (h - 1)) - 1)
yy = y.view(-1, 1).repeat(1, w)
xx = x.view(1, -1).repeat(h, 1)
meshed = torch.cat([xx.unsqueeze_(2), yy.unsqueeze_(2)], 2)
meshed = meshed.expand(b, -1, -1, -1)
return meshed
|
obtain coordinate grid with the same size as the flow filed.
Args:
flow (tensor): Flow field obtained by the model
Returns:
grid (tensor): The grid with the same size as the input flow
|
make_coordinate_grid
|
python
|
OpenTalker/video-retalking
|
utils/flow_util.py
|
https://github.com/OpenTalker/video-retalking/blob/master/utils/flow_util.py
|
Apache-2.0
|
def warp_image(source_image, deformation):
r"""warp the input image according to the deformation
Args:
source_image (tensor): source images to be warped
deformation (tensor): deformations used to warp the images; value in range (-1, 1)
Returns:
output (tensor): the warped images
"""
_, h_old, w_old, _ = deformation.shape
_, _, h, w = source_image.shape
if h_old != h or w_old != w:
deformation = deformation.permute(0, 3, 1, 2)
deformation = torch.nn.functional.interpolate(deformation, size=(h, w), mode='bilinear')
deformation = deformation.permute(0, 2, 3, 1)
return torch.nn.functional.grid_sample(source_image, deformation)
|
warp the input image according to the deformation
Args:
source_image (tensor): source images to be warped
deformation (tensor): deformations used to warp the images; value in range (-1, 1)
Returns:
output (tensor): the warped images
|
warp_image
|
python
|
OpenTalker/video-retalking
|
utils/flow_util.py
|
https://github.com/OpenTalker/video-retalking/blob/master/utils/flow_util.py
|
Apache-2.0
|
def split_coeff(coeffs):
"""
Return:
coeffs_dict -- a dict of torch.tensors
Parameters:
coeffs -- torch.tensor, size (B, 256)
"""
id_coeffs = coeffs[:, :80]
exp_coeffs = coeffs[:, 80: 144]
tex_coeffs = coeffs[:, 144: 224]
angles = coeffs[:, 224: 227]
gammas = coeffs[:, 227: 254]
translations = coeffs[:, 254:]
return {
'id': id_coeffs,
'exp': exp_coeffs,
'tex': tex_coeffs,
'angle': angles,
'gamma': gammas,
'trans': translations
}
|
Return:
coeffs_dict -- a dict of torch.tensors
Parameters:
coeffs -- torch.tensor, size (B, 256)
|
split_coeff
|
python
|
OpenTalker/video-retalking
|
utils/inference_utils.py
|
https://github.com/OpenTalker/video-retalking/blob/master/utils/inference_utils.py
|
Apache-2.0
|
def compute_density_for_timestep_sampling(
weighting_scheme: str, batch_size: int, logit_mean: float = None, logit_std: float = None, mode_scale: float = None
):
"""Compute the density for sampling the timesteps when doing SD3 training.
Courtesy: This was contributed by Rafie Walker in https://github.com/huggingface/diffusers/pull/8528.
SD3 paper reference: https://arxiv.org/abs/2403.03206v1.
"""
if weighting_scheme == "logit_normal":
# See 3.1 in the SD3 paper ($rf/lognorm(0.00,1.00)$).
u = torch.normal(mean=logit_mean, std=logit_std, size=(batch_size,), device="cpu")
u = torch.nn.functional.sigmoid(u)
elif weighting_scheme == "mode":
u = torch.rand(size=(batch_size,), device="cpu")
u = 1 - u - mode_scale * (torch.cos(math.pi * u / 2) ** 2 - 1 + u)
else:
u = torch.rand(size=(batch_size,), device="cpu")
return u
|
Compute the density for sampling the timesteps when doing SD3 training.
Courtesy: This was contributed by Rafie Walker in https://github.com/huggingface/diffusers/pull/8528.
SD3 paper reference: https://arxiv.org/abs/2403.03206v1.
|
compute_density_for_timestep_sampling
|
python
|
memoavatar/memo
|
finetune.py
|
https://github.com/memoavatar/memo/blob/master/finetune.py
|
Apache-2.0
|
def compute_loss_weighting_for_sd3(weighting_scheme: str, sigmas=None):
"""Computes loss weighting scheme for SD3 training.
Courtesy: This was contributed by Rafie Walker in https://github.com/huggingface/diffusers/pull/8528.
SD3 paper reference: https://arxiv.org/abs/2403.03206v1.
"""
if weighting_scheme == "sigma_sqrt":
weighting = (sigmas**-2.0).float()
elif weighting_scheme == "cosmap":
bot = 1 - 2 * sigmas + 2 * sigmas**2
weighting = 2 / (math.pi * bot)
else:
weighting = torch.ones_like(sigmas)
return weighting
|
Computes loss weighting scheme for SD3 training.
Courtesy: This was contributed by Rafie Walker in https://github.com/huggingface/diffusers/pull/8528.
SD3 paper reference: https://arxiv.org/abs/2403.03206v1.
|
compute_loss_weighting_for_sd3
|
python
|
memoavatar/memo
|
finetune.py
|
https://github.com/memoavatar/memo/blob/master/finetune.py
|
Apache-2.0
|
def set_use_npu_flash_attention(self, use_npu_flash_attention: bool) -> None:
r"""
Set whether to use npu flash attention from `torch_npu` or not.
"""
if use_npu_flash_attention:
processor = AttnProcessorNPU()
else:
# set attention processor
# We use the AttnProcessor2_0 by default when torch 2.x is used which uses
# torch.nn.functional.scaled_dot_product_attention for native Flash/memory_efficient_attention
# but only if it has the default `scale` argument. TODO remove scale_qk check when we move to torch 2.1
processor = (
AttnProcessor2_0() if hasattr(F, "scaled_dot_product_attention") and self.scale_qk else AttnProcessor()
)
self.set_processor(processor)
|
Set whether to use npu flash attention from `torch_npu` or not.
|
set_use_npu_flash_attention
|
python
|
memoavatar/memo
|
memo/models/attention_processor.py
|
https://github.com/memoavatar/memo/blob/master/memo/models/attention_processor.py
|
Apache-2.0
|
def set_use_memory_efficient_attention_xformers(
self,
use_memory_efficient_attention_xformers: bool,
attention_op: Optional[Callable] = None,
) -> None:
r"""
Set whether to use memory efficient attention from `xformers` or not.
Args:
use_memory_efficient_attention_xformers (`bool`):
Whether to use memory efficient attention from `xformers` or not.
attention_op (`Callable`, *optional*):
The attention operation to use. Defaults to `None` which uses the default attention operation from
`xformers`.
"""
is_custom_diffusion = hasattr(self, "processor") and isinstance(
self.processor,
(
CustomDiffusionAttnProcessor,
CustomDiffusionXFormersAttnProcessor,
CustomDiffusionAttnProcessor2_0,
),
)
is_joint_diffusion = hasattr(self, "processor") and isinstance(
self.processor,
(JointAttnProcessor2_0),
)
is_added_kv_processor = hasattr(self, "processor") and isinstance(
self.processor,
(
AttnAddedKVProcessor,
AttnAddedKVProcessor2_0,
SlicedAttnAddedKVProcessor,
XFormersAttnAddedKVProcessor,
),
)
if use_memory_efficient_attention_xformers:
if is_added_kv_processor and is_custom_diffusion:
raise NotImplementedError(
f"Memory efficient attention is currently not supported for custom diffusion for attention processor type {self.processor}"
)
if not is_xformers_available():
raise ModuleNotFoundError(
(
"Refer to https://github.com/facebookresearch/xformers for more information on how to install"
" xformers"
),
name="xformers",
)
elif not torch.cuda.is_available():
raise ValueError(
"torch.cuda.is_available() should be True but is False. xformers' memory efficient attention is"
" only available for GPU "
)
else:
try:
# Make sure we can run the memory efficient attention
_ = xformers.ops.memory_efficient_attention(
torch.randn((1, 2, 40), device="cuda"),
torch.randn((1, 2, 40), device="cuda"),
torch.randn((1, 2, 40), device="cuda"),
)
except Exception as e:
raise e
if is_custom_diffusion:
processor = CustomDiffusionXFormersAttnProcessor(
train_kv=self.processor.train_kv,
train_q_out=self.processor.train_q_out,
hidden_size=self.processor.hidden_size,
cross_attention_dim=self.processor.cross_attention_dim,
attention_op=attention_op,
)
processor.load_state_dict(self.processor.state_dict())
if hasattr(self.processor, "to_k_custom_diffusion"):
processor.to(self.processor.to_k_custom_diffusion.weight.device)
elif is_added_kv_processor:
# TODO(Patrick, Suraj, William) - currently xformers doesn't work for UnCLIP
# which uses this type of cross attention ONLY because the attention mask of format
# [0, ..., -10.000, ..., 0, ...,] is not supported
# throw warning
logger.info(
"Memory efficient attention with `xformers` might currently not work correctly if an attention mask is required for the attention operation."
)
processor = XFormersAttnAddedKVProcessor(attention_op=attention_op)
elif is_joint_diffusion:
processor = JointAttnProcessor2_0()
else:
processor = XFormersAttnProcessor(attention_op=attention_op)
else:
if is_custom_diffusion:
attn_processor_class = (
CustomDiffusionAttnProcessor2_0
if hasattr(F, "scaled_dot_product_attention")
else CustomDiffusionAttnProcessor
)
processor = attn_processor_class(
train_kv=self.processor.train_kv,
train_q_out=self.processor.train_q_out,
hidden_size=self.processor.hidden_size,
cross_attention_dim=self.processor.cross_attention_dim,
)
processor.load_state_dict(self.processor.state_dict())
if hasattr(self.processor, "to_k_custom_diffusion"):
processor.to(self.processor.to_k_custom_diffusion.weight.device)
else:
# set attention processor
# We use the AttnProcessor2_0 by default when torch 2.x is used which uses
# torch.nn.functional.scaled_dot_product_attention for native Flash/memory_efficient_attention
# but only if it has the default `scale` argument. TODO remove scale_qk check when we move to torch 2.1
processor = (
AttnProcessor2_0()
if hasattr(F, "scaled_dot_product_attention") and self.scale_qk
else AttnProcessor()
)
self.set_processor(processor)
|
Set whether to use memory efficient attention from `xformers` or not.
Args:
use_memory_efficient_attention_xformers (`bool`):
Whether to use memory efficient attention from `xformers` or not.
attention_op (`Callable`, *optional*):
The attention operation to use. Defaults to `None` which uses the default attention operation from
`xformers`.
|
set_use_memory_efficient_attention_xformers
|
python
|
memoavatar/memo
|
memo/models/attention_processor.py
|
https://github.com/memoavatar/memo/blob/master/memo/models/attention_processor.py
|
Apache-2.0
|
def set_attention_slice(self, slice_size: int) -> None:
r"""
Set the slice size for attention computation.
Args:
slice_size (`int`):
The slice size for attention computation.
"""
if slice_size is not None and slice_size > self.sliceable_head_dim:
raise ValueError(f"slice_size {slice_size} has to be smaller or equal to {self.sliceable_head_dim}.")
if slice_size is not None and self.added_kv_proj_dim is not None:
processor = SlicedAttnAddedKVProcessor(slice_size)
elif slice_size is not None:
processor = SlicedAttnProcessor(slice_size)
elif self.added_kv_proj_dim is not None:
processor = AttnAddedKVProcessor()
else:
# set attention processor
# We use the AttnProcessor2_0 by default when torch 2.x is used which uses
# torch.nn.functional.scaled_dot_product_attention for native Flash/memory_efficient_attention
# but only if it has the default `scale` argument. TODO remove scale_qk check when we move to torch 2.1
processor = (
AttnProcessor2_0() if hasattr(F, "scaled_dot_product_attention") and self.scale_qk else AttnProcessor()
)
self.set_processor(processor)
|
Set the slice size for attention computation.
Args:
slice_size (`int`):
The slice size for attention computation.
|
set_attention_slice
|
python
|
memoavatar/memo
|
memo/models/attention_processor.py
|
https://github.com/memoavatar/memo/blob/master/memo/models/attention_processor.py
|
Apache-2.0
|
def set_processor(self, processor: "AttnProcessor") -> None:
r"""
Set the attention processor to use.
Args:
processor (`AttnProcessor`):
The attention processor to use.
"""
# if current processor is in `self._modules` and if passed `processor` is not, we need to
# pop `processor` from `self._modules`
if (
hasattr(self, "processor")
and isinstance(self.processor, torch.nn.Module)
and not isinstance(processor, torch.nn.Module)
):
logger.info(f"You are removing possibly trained weights of {self.processor} with {processor}")
self._modules.pop("processor")
self.processor = processor
|
Set the attention processor to use.
Args:
processor (`AttnProcessor`):
The attention processor to use.
|
set_processor
|
python
|
memoavatar/memo
|
memo/models/attention_processor.py
|
https://github.com/memoavatar/memo/blob/master/memo/models/attention_processor.py
|
Apache-2.0
|
def forward(
self,
hidden_states: torch.Tensor,
encoder_hidden_states: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
**cross_attention_kwargs,
) -> torch.Tensor:
r"""
The forward method of the `Attention` class.
Args:
hidden_states (`torch.Tensor`):
The hidden states of the query.
encoder_hidden_states (`torch.Tensor`, *optional*):
The hidden states of the encoder.
attention_mask (`torch.Tensor`, *optional*):
The attention mask to use. If `None`, no mask is applied.
**cross_attention_kwargs:
Additional keyword arguments to pass along to the cross attention.
Returns:
`torch.Tensor`: The output of the attention layer.
"""
# The `Attention` class can call different attention processors / attention functions
# here we simply pass along all tensors to the selected processor class
# For standard processors that are defined here, `**cross_attention_kwargs` is empty
attn_parameters = set(inspect.signature(self.processor.__call__).parameters.keys())
quiet_attn_parameters = {"ip_adapter_masks"}
unused_kwargs = [
k for k, _ in cross_attention_kwargs.items() if k not in attn_parameters and k not in quiet_attn_parameters
]
if len(unused_kwargs) > 0:
logger.warning(
f"cross_attention_kwargs {unused_kwargs} are not expected by {self.processor.__class__.__name__} and will be ignored."
)
cross_attention_kwargs = {k: w for k, w in cross_attention_kwargs.items() if k in attn_parameters}
return self.processor(
self,
hidden_states,
encoder_hidden_states=encoder_hidden_states,
attention_mask=attention_mask,
**cross_attention_kwargs,
)
|
The forward method of the `Attention` class.
Args:
hidden_states (`torch.Tensor`):
The hidden states of the query.
encoder_hidden_states (`torch.Tensor`, *optional*):
The hidden states of the encoder.
attention_mask (`torch.Tensor`, *optional*):
The attention mask to use. If `None`, no mask is applied.
**cross_attention_kwargs:
Additional keyword arguments to pass along to the cross attention.
Returns:
`torch.Tensor`: The output of the attention layer.
|
forward
|
python
|
memoavatar/memo
|
memo/models/attention_processor.py
|
https://github.com/memoavatar/memo/blob/master/memo/models/attention_processor.py
|
Apache-2.0
|
def batch_to_head_dim(self, tensor: torch.Tensor) -> torch.Tensor:
r"""
Reshape the tensor from `[batch_size, seq_len, dim]` to `[batch_size // heads, seq_len, dim * heads]`. `heads`
is the number of heads initialized while constructing the `Attention` class.
Args:
tensor (`torch.Tensor`): The tensor to reshape.
Returns:
`torch.Tensor`: The reshaped tensor.
"""
head_size = self.heads
batch_size, seq_len, dim = tensor.shape
tensor = tensor.reshape(batch_size // head_size, head_size, seq_len, dim)
tensor = tensor.permute(0, 2, 1, 3).reshape(batch_size // head_size, seq_len, dim * head_size)
return tensor
|
Reshape the tensor from `[batch_size, seq_len, dim]` to `[batch_size // heads, seq_len, dim * heads]`. `heads`
is the number of heads initialized while constructing the `Attention` class.
Args:
tensor (`torch.Tensor`): The tensor to reshape.
Returns:
`torch.Tensor`: The reshaped tensor.
|
batch_to_head_dim
|
python
|
memoavatar/memo
|
memo/models/attention_processor.py
|
https://github.com/memoavatar/memo/blob/master/memo/models/attention_processor.py
|
Apache-2.0
|
def head_to_batch_dim(self, tensor: torch.Tensor, out_dim: int = 3) -> torch.Tensor:
r"""
Reshape the tensor from `[batch_size, seq_len, dim]` to `[batch_size, seq_len, heads, dim // heads]` `heads` is
the number of heads initialized while constructing the `Attention` class.
Args:
tensor (`torch.Tensor`): The tensor to reshape.
out_dim (`int`, *optional*, defaults to `3`): The output dimension of the tensor. If `3`, the tensor is
reshaped to `[batch_size * heads, seq_len, dim // heads]`.
Returns:
`torch.Tensor`: The reshaped tensor.
"""
head_size = self.heads
if tensor.ndim == 3:
batch_size, seq_len, dim = tensor.shape
extra_dim = 1
else:
batch_size, extra_dim, seq_len, dim = tensor.shape
tensor = tensor.reshape(batch_size, seq_len * extra_dim, head_size, dim // head_size)
tensor = tensor.permute(0, 2, 1, 3)
if out_dim == 3:
tensor = tensor.reshape(batch_size * head_size, seq_len * extra_dim, dim // head_size)
return tensor
|
Reshape the tensor from `[batch_size, seq_len, dim]` to `[batch_size, seq_len, heads, dim // heads]` `heads` is
the number of heads initialized while constructing the `Attention` class.
Args:
tensor (`torch.Tensor`): The tensor to reshape.
out_dim (`int`, *optional*, defaults to `3`): The output dimension of the tensor. If `3`, the tensor is
reshaped to `[batch_size * heads, seq_len, dim // heads]`.
Returns:
`torch.Tensor`: The reshaped tensor.
|
head_to_batch_dim
|
python
|
memoavatar/memo
|
memo/models/attention_processor.py
|
https://github.com/memoavatar/memo/blob/master/memo/models/attention_processor.py
|
Apache-2.0
|
def get_attention_scores(
self,
query: torch.Tensor,
key: torch.Tensor,
attention_mask: torch.Tensor = None,
) -> torch.Tensor:
r"""
Compute the attention scores.
Args:
query (`torch.Tensor`): The query tensor.
key (`torch.Tensor`): The key tensor.
attention_mask (`torch.Tensor`, *optional*): The attention mask to use. If `None`, no mask is applied.
Returns:
`torch.Tensor`: The attention probabilities/scores.
"""
dtype = query.dtype
if self.upcast_attention:
query = query.float()
key = key.float()
if attention_mask is None:
baddbmm_input = torch.empty(
query.shape[0],
query.shape[1],
key.shape[1],
dtype=query.dtype,
device=query.device,
)
beta = 0
else:
baddbmm_input = attention_mask
beta = 1
attention_scores = torch.baddbmm(
baddbmm_input,
query,
key.transpose(-1, -2),
beta=beta,
alpha=self.scale,
)
del baddbmm_input
if self.upcast_softmax:
attention_scores = attention_scores.float()
attention_probs = attention_scores.softmax(dim=-1)
del attention_scores
attention_probs = attention_probs.to(dtype)
return attention_probs
|
Compute the attention scores.
Args:
query (`torch.Tensor`): The query tensor.
key (`torch.Tensor`): The key tensor.
attention_mask (`torch.Tensor`, *optional*): The attention mask to use. If `None`, no mask is applied.
Returns:
`torch.Tensor`: The attention probabilities/scores.
|
get_attention_scores
|
python
|
memoavatar/memo
|
memo/models/attention_processor.py
|
https://github.com/memoavatar/memo/blob/master/memo/models/attention_processor.py
|
Apache-2.0
|
def prepare_attention_mask(
self,
attention_mask: torch.Tensor,
target_length: int,
batch_size: int,
out_dim: int = 3,
) -> torch.Tensor:
r"""
Prepare the attention mask for the attention computation.
Args:
attention_mask (`torch.Tensor`):
The attention mask to prepare.
target_length (`int`):
The target length of the attention mask. This is the length of the attention mask after padding.
batch_size (`int`):
The batch size, which is used to repeat the attention mask.
out_dim (`int`, *optional*, defaults to `3`):
The output dimension of the attention mask. Can be either `3` or `4`.
Returns:
`torch.Tensor`: The prepared attention mask.
"""
head_size = self.heads
if attention_mask is None:
return attention_mask
current_length: int = attention_mask.shape[-1]
if current_length != target_length:
if attention_mask.device.type == "mps":
# HACK: MPS: Does not support padding by greater than dimension of input tensor.
# Instead, we can manually construct the padding tensor.
padding_shape = (
attention_mask.shape[0],
attention_mask.shape[1],
target_length,
)
padding = torch.zeros(
padding_shape,
dtype=attention_mask.dtype,
device=attention_mask.device,
)
attention_mask = torch.cat([attention_mask, padding], dim=2)
else:
# TODO: for pipelines such as stable-diffusion, padding cross-attn mask:
# we want to instead pad by (0, remaining_length), where remaining_length is:
# remaining_length: int = target_length - current_length
# TODO: re-enable tests/models/test_models_unet_2d_condition.py#test_model_xattn_padding
attention_mask = F.pad(attention_mask, (0, target_length), value=0.0)
if out_dim == 3:
if attention_mask.shape[0] < batch_size * head_size:
attention_mask = attention_mask.repeat_interleave(head_size, dim=0)
elif out_dim == 4:
attention_mask = attention_mask.unsqueeze(1)
attention_mask = attention_mask.repeat_interleave(head_size, dim=1)
return attention_mask
|
Prepare the attention mask for the attention computation.
Args:
attention_mask (`torch.Tensor`):
The attention mask to prepare.
target_length (`int`):
The target length of the attention mask. This is the length of the attention mask after padding.
batch_size (`int`):
The batch size, which is used to repeat the attention mask.
out_dim (`int`, *optional*, defaults to `3`):
The output dimension of the attention mask. Can be either `3` or `4`.
Returns:
`torch.Tensor`: The prepared attention mask.
|
prepare_attention_mask
|
python
|
memoavatar/memo
|
memo/models/attention_processor.py
|
https://github.com/memoavatar/memo/blob/master/memo/models/attention_processor.py
|
Apache-2.0
|
def norm_encoder_hidden_states(self, encoder_hidden_states: torch.Tensor) -> torch.Tensor:
r"""
Normalize the encoder hidden states. Requires `self.norm_cross` to be specified when constructing the
`Attention` class.
Args:
encoder_hidden_states (`torch.Tensor`): Hidden states of the encoder.
Returns:
`torch.Tensor`: The normalized encoder hidden states.
"""
assert self.norm_cross is not None, "self.norm_cross must be defined to call self.norm_encoder_hidden_states"
if isinstance(self.norm_cross, nn.LayerNorm):
encoder_hidden_states = self.norm_cross(encoder_hidden_states)
elif isinstance(self.norm_cross, nn.GroupNorm):
# Group norm norms along the channels dimension and expects
# input to be in the shape of (N, C, *). In this case, we want
# to norm along the hidden dimension, so we need to move
# (batch_size, sequence_length, hidden_size) ->
# (batch_size, hidden_size, sequence_length)
encoder_hidden_states = encoder_hidden_states.transpose(1, 2)
encoder_hidden_states = self.norm_cross(encoder_hidden_states)
encoder_hidden_states = encoder_hidden_states.transpose(1, 2)
else:
assert False
return encoder_hidden_states
|
Normalize the encoder hidden states. Requires `self.norm_cross` to be specified when constructing the
`Attention` class.
Args:
encoder_hidden_states (`torch.Tensor`): Hidden states of the encoder.
Returns:
`torch.Tensor`: The normalized encoder hidden states.
|
norm_encoder_hidden_states
|
python
|
memoavatar/memo
|
memo/models/attention_processor.py
|
https://github.com/memoavatar/memo/blob/master/memo/models/attention_processor.py
|
Apache-2.0
|
def attn_processors(self) -> Dict[str, AttentionProcessor]:
r"""
Returns:
`dict` of attention processors: A dictionary containing all attention processors used in the model with
indexed by its weight name.
"""
# set recursively
processors = {}
def fn_recursive_add_processors(
name: str,
module: torch.nn.Module,
processors: Dict[str, AttentionProcessor],
):
if hasattr(module, "get_processor"):
processors[f"{name}.processor"] = module.get_processor(return_deprecated_lora=True)
for sub_name, child in module.named_children():
fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
return processors
for name, module in self.named_children():
fn_recursive_add_processors(name, module, processors)
return processors
|
Returns:
`dict` of attention processors: A dictionary containing all attention processors used in the model with
indexed by its weight name.
|
attn_processors
|
python
|
memoavatar/memo
|
memo/models/unet_2d_condition.py
|
https://github.com/memoavatar/memo/blob/master/memo/models/unet_2d_condition.py
|
Apache-2.0
|
def set_attn_processor(
self,
processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]],
_remove_lora=False,
):
r"""
Sets the attention processor to use to compute attention.
Parameters:
processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
The instantiated processor class or a dictionary of processor classes that will be set as the processor
for **all** `Attention` layers.
If `processor` is a dict, the key needs to define the path to the corresponding cross attention
processor. This is strongly recommended when setting trainable attention processors.
"""
count = len(self.attn_processors.keys())
if isinstance(processor, dict) and len(processor) != count:
raise ValueError(
f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
)
def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
if hasattr(module, "set_processor"):
if not isinstance(processor, dict):
module.set_processor(processor, _remove_lora=_remove_lora)
else:
module.set_processor(processor.pop(f"{name}.processor"), _remove_lora=_remove_lora)
for sub_name, child in module.named_children():
fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
for name, module in self.named_children():
fn_recursive_attn_processor(name, module, processor)
|
Sets the attention processor to use to compute attention.
Parameters:
processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
The instantiated processor class or a dictionary of processor classes that will be set as the processor
for **all** `Attention` layers.
If `processor` is a dict, the key needs to define the path to the corresponding cross attention
processor. This is strongly recommended when setting trainable attention processors.
|
set_attn_processor
|
python
|
memoavatar/memo
|
memo/models/unet_2d_condition.py
|
https://github.com/memoavatar/memo/blob/master/memo/models/unet_2d_condition.py
|
Apache-2.0
|
def set_default_attn_processor(self):
"""
Disables custom attention processors and sets the default attention implementation.
"""
if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
processor = AttnAddedKVProcessor()
elif all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
processor = AttnProcessor()
else:
raise ValueError(
f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}"
)
self.set_attn_processor(processor, _remove_lora=True)
|
Disables custom attention processors and sets the default attention implementation.
|
set_default_attn_processor
|
python
|
memoavatar/memo
|
memo/models/unet_2d_condition.py
|
https://github.com/memoavatar/memo/blob/master/memo/models/unet_2d_condition.py
|
Apache-2.0
|
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