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import random
import torch
import torch.nn.functional as F
from torch.utils.data import Dataset
from torchvision import transforms
from torchvision.transforms.functional import crop
from models.video_model import VideoModel
from util.atlas_utils import (
load_neural_atlases_models,
get_frames_data,
get_high_res_atlas,
get_atlas_crops,
reconstruct_video_layer,
create_uv_mask,
get_masks_boundaries,
get_random_crop_params,
get_atlas_bounding_box,
)
from util.util import load_video
class AtlasDataset(Dataset):
def __init__(self, config):
self.config = config
self.device = config["device"]
self.min_size = min(self.config["resx"], self.config["resy"])
self.max_size = max(self.config["resx"], self.config["resy"])
data_folder = f"data/videos/{self.config['checkpoint_path'].split('/')[2]}"
self.original_video = load_video(
data_folder,
resize=(self.config["resy"], self.config["resx"]),
num_frames=self.config["maximum_number_of_frames"],
).to(self.device)
(
foreground_mapping,
background_mapping,
foreground_atlas_model,
background_atlas_model,
alpha_model,
) = load_neural_atlases_models(config)
(
original_background_all_uvs,
original_foreground_all_uvs,
self.all_alpha,
foreground_atlas_alpha,
) = get_frames_data(
config,
foreground_mapping,
background_mapping,
alpha_model,
)
self.background_reconstruction = reconstruct_video_layer(original_background_all_uvs, background_atlas_model)
# using original video for the foreground layer
self.foreground_reconstruction = self.original_video * self.all_alpha
(
self.background_all_uvs,
self.scaled_background_uvs,
self.background_min_u,
self.background_min_v,
self.background_max_u,
self.background_max_v,
) = self.preprocess_uv_values(
original_background_all_uvs, config["grid_atlas_resolution"], device=self.device, layer="background"
)
(
self.foreground_all_uvs,
self.scaled_foreground_uvs,
self.foreground_min_u,
self.foreground_min_v,
self.foreground_max_u,
self.foreground_max_v,
) = self.preprocess_uv_values(
original_foreground_all_uvs, config["grid_atlas_resolution"], device=self.device, layer="foreground"
)
self.background_uv_mask = create_uv_mask(
config,
background_mapping,
self.background_min_u,
self.background_min_v,
self.background_max_u,
self.background_max_v,
uv_shift=-0.5,
resolution_shift=1,
)
self.foreground_uv_mask = create_uv_mask(
config,
foreground_mapping,
self.foreground_min_u,
self.foreground_min_v,
self.foreground_max_u,
self.foreground_max_v,
uv_shift=0.5,
resolution_shift=0,
)
self.background_grid_atlas = get_high_res_atlas(
background_atlas_model,
self.background_min_v,
self.background_min_u,
self.background_max_v,
self.background_max_u,
config["grid_atlas_resolution"],
device=config["device"],
layer="background",
)
self.foreground_grid_atlas = get_high_res_atlas(
foreground_atlas_model,
self.foreground_min_v,
self.foreground_min_u,
self.foreground_max_v,
self.foreground_max_u,
config["grid_atlas_resolution"],
device=config["device"],
layer="foreground",
)
if config["return_atlas_alpha"]:
self.foreground_atlas_alpha = foreground_atlas_alpha # used for visualizations
self.cnn_min_crop_size = 2 ** self.config["num_scales"] + 1
if self.config["finetune_foreground"]:
self.mask_boundaries = get_masks_boundaries(
alpha_video=self.all_alpha.cpu(),
border=self.config["masks_border_expansion"],
threshold=self.config["mask_alpha_threshold"],
min_crop_size=self.cnn_min_crop_size,
)
self.cropped_foreground_atlas, self.foreground_atlas_bbox = get_atlas_bounding_box(
self.mask_boundaries, self.foreground_grid_atlas, self.foreground_all_uvs
)
self.step = -1
crop_transforms = transforms.Compose(
[
transforms.RandomApply(
[transforms.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1, hue=0.1)],
p=0.1,
),
]
)
self.crop_aug = crop_transforms
self.dist = self.config["center_frame_distance"]
@staticmethod
def preprocess_uv_values(layer_uv_values, resolution, device="cuda", layer="background"):
if layer == "background":
shift = 1
else:
shift = 0
uv_values = (layer_uv_values + shift) * resolution
min_u, min_v = uv_values.reshape(-1, 2).min(dim=0).values.long()
uv_values -= torch.tensor([min_u, min_v], device=device)
max_u, max_v = uv_values.reshape(-1, 2).max(dim=0).values.ceil().long()
edge_size = torch.tensor([max_u, max_v], device=device)
scaled_uv_values = ((uv_values.reshape(-1, 2) / edge_size) * 2 - 1).unsqueeze(1).unsqueeze(0)
return uv_values, scaled_uv_values, min_u, min_v, max_u, max_v
def get_random_crop_data(self, crop_size):
t = random.randint(0, self.config["maximum_number_of_frames"] - 1)
y_start, x_start, h_crop, w_crop = get_random_crop_params((self.config["resx"], self.config["resy"]), crop_size)
return y_start, x_start, h_crop, w_crop, t
def get_global_crops_multi(self):
foreground_atlas_crops = []
background_atlas_crops = []
foreground_uvs = []
background_uvs = []
background_alpha_crops = []
foreground_alpha_crops = []
original_background_crops = []
original_foreground_crops = []
output_dict = {}
t = random.randint(self.dist, self.config["maximum_number_of_frames"] - 1 - self.dist)
flip = torch.rand(1) < self.config["flip_p"]
if self.config["finetune_foreground"]:
for cur_frame in [t - self.dist, t, t + self.dist]:
y_start, x_start, frame_h, frame_w = self.mask_boundaries[cur_frame].tolist()
crop_size = (
max(
random.randint(round(self.config["crops_min_cover"] * frame_h), frame_h),
self.cnn_min_crop_size,
),
max(
random.randint(round(self.config["crops_min_cover"] * frame_w), frame_w),
self.cnn_min_crop_size,
),
)
y_crop, x_crop, h_crop, w_crop = get_random_crop_params((frame_w, frame_h), crop_size)
foreground_uv = self.foreground_all_uvs[
cur_frame,
y_start + y_crop : y_start + y_crop + h_crop,
x_start + x_crop : x_start + x_crop + w_crop,
]
alpha = self.all_alpha[
[cur_frame],
:,
y_start + y_crop : y_start + y_crop + h_crop,
x_start + x_crop : x_start + x_crop + w_crop,
]
original_foreground_crop = self.foreground_reconstruction[
[cur_frame],
:,
y_start + y_crop : y_start + y_crop + h_crop,
x_start + x_crop : x_start + x_crop + w_crop,
]
original_foreground_crop = self.crop_aug(original_foreground_crop)
foreground_alpha_crops.append(alpha.flip(-1) if flip else alpha)
foreground_uvs.append(foreground_uv) # not scaled
original_foreground_crops.append(
original_foreground_crop.flip(-1) if flip else original_foreground_crop
)
foreground_min_vals = torch.tensor(
[self.config["grid_atlas_resolution"]] * 2, device=self.device, dtype=torch.long
)
foreground_max_vals = torch.tensor([0] * 2, device=self.device, dtype=torch.long)
for uv_values in foreground_uvs:
min_uv = uv_values.amin(dim=[0, 1]).long()
max_uv = uv_values.amax(dim=[0, 1]).ceil().long()
foreground_min_vals = torch.minimum(foreground_min_vals, min_uv)
foreground_max_vals = torch.maximum(foreground_max_vals, max_uv)
h_v = foreground_max_vals[1] - foreground_min_vals[1]
w_u = foreground_max_vals[0] - foreground_min_vals[0]
foreground_atlas_crop = crop(
self.foreground_grid_atlas,
foreground_min_vals[1],
foreground_min_vals[0],
h_v,
w_u,
)
foreground_atlas_crop = self.crop_aug(foreground_atlas_crop)
for i, uv_values in enumerate(foreground_uvs):
foreground_uvs[i] = (
2 * (uv_values - foreground_min_vals) / (foreground_max_vals - foreground_min_vals) - 1
).unsqueeze(0)
if flip:
foreground_uvs[i][:, :, :, 0] = -foreground_uvs[i][:, :, :, 0]
foreground_uvs[i] = foreground_uvs[i].flip(-2)
foreground_atlas_crops.append(foreground_atlas_crop.flip(-1) if flip else foreground_atlas_crop)
elif self.config["finetune_background"]:
crop_size = (
random.randint(round(self.config["crops_min_cover"] * self.min_size), self.min_size),
random.randint(round(self.config["crops_min_cover"] * self.max_size), self.max_size),
)
crop_data = self.get_random_crop_data(crop_size)
y, x, h, w, _ = crop_data
background_uv = self.background_all_uvs[[t - self.dist, t, t + self.dist], y : y + h, x : x + w]
original_background_crop = self.background_reconstruction[
[t - self.dist, t, t + self.dist], :, y : y + h, x : x + w
]
alpha = self.all_alpha[[t - self.dist, t, t + self.dist], :, y : y + h, x : x + w]
original_background_crop = self.crop_aug(original_background_crop)
original_background_crops = [
el.unsqueeze(0).flip(-1) if flip else el.unsqueeze(0) for el in original_background_crop
]
background_alpha_crops = [el.unsqueeze(0).flip(-1) if flip else el.unsqueeze(0) for el in alpha]
background_atlas_crop, background_min_vals, background_max_vals = get_atlas_crops(
background_uv,
self.background_grid_atlas,
self.crop_aug,
)
background_uv = 2 * (background_uv - background_min_vals) / (background_max_vals - background_min_vals) - 1
if flip:
background_uv[:, :, :, 0] = -background_uv[:, :, :, 0]
background_uv = background_uv.flip(-2)
background_atlas_crops = [
el.unsqueeze(0).flip(-1) if flip else el.unsqueeze(0) for el in background_atlas_crop
]
background_uvs = [el.unsqueeze(0) for el in background_uv]
if self.config["finetune_foreground"]:
output_dict["foreground_alpha"] = foreground_alpha_crops
output_dict["foreground_uvs"] = foreground_uvs
output_dict["original_foreground_crops"] = original_foreground_crops
output_dict["foreground_atlas_crops"] = foreground_atlas_crops
elif self.config["finetune_background"]:
output_dict["background_alpha"] = background_alpha_crops
output_dict["background_uvs"] = background_uvs
output_dict["original_background_crops"] = original_background_crops
output_dict["background_atlas_crops"] = background_atlas_crops
return output_dict
@torch.no_grad()
def render_video_from_atlas(self, model, layer="background", foreground_padding_mode="replicate"):
if layer == "background":
grid_atlas = self.background_grid_atlas
all_uvs = self.scaled_background_uvs
uv_mask = self.background_uv_mask
else:
grid_atlas = self.cropped_foreground_atlas
full_grid_atlas = self.foreground_grid_atlas
all_uvs = self.scaled_foreground_uvs
uv_mask = crop(self.foreground_uv_mask, *self.foreground_atlas_bbox)
atlas_edit_only = model.netG(grid_atlas)
edited_atlas_dict = model.render(atlas_edit_only, bg_image=grid_atlas)
if layer == "foreground":
atlas_edit_only = torch.nn.functional.pad(
atlas_edit_only,
pad=(
self.foreground_atlas_bbox[1],
full_grid_atlas.shape[-1] - (self.foreground_atlas_bbox[1] + self.foreground_atlas_bbox[3]),
self.foreground_atlas_bbox[0],
full_grid_atlas.shape[-2] - (self.foreground_atlas_bbox[0] + self.foreground_atlas_bbox[2]),
),
mode=foreground_padding_mode,
)
edit = F.grid_sample(
atlas_edit_only, all_uvs, mode="bilinear", align_corners=self.config["align_corners"]
).clamp(min=0.0, max=1.0)
edit = edit.squeeze().t() # shape (batch, 3)
edit = (
edit.reshape(self.config["maximum_number_of_frames"], self.config["resy"], self.config["resx"], 4)
.permute(0, 3, 1, 2)
.clamp(min=0.0, max=1.0)
)
edit_dict = model.render(edit, bg_image=self.original_video)
return edited_atlas_dict, edit_dict, uv_mask
def get_whole_atlas(self):
if self.config["finetune_foreground"]:
atlas = self.cropped_foreground_atlas
else:
atlas = self.background_grid_atlas
atlas = VideoModel.resize_crops(atlas, 3)
return atlas
def __getitem__(self, index):
self.step += 1
sample = {"step": self.step}
sample["global_crops"] = self.get_global_crops_multi()
if self.config["input_entire_atlas"] and ((self.step + 1) % self.config["entire_atlas_every"] == 0):
sample["input_image"] = self.get_whole_atlas()
return sample
def __len__(self):
return 1
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