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from enum import Enum
from typing import List, Dict, Optional
import torch
from torch import Tensor
from torch.nn import Module
from torch.nn.functional import interpolate
from tha3.nn.eyebrow_decomposer.eyebrow_decomposer_00 import EyebrowDecomposer00, \
EyebrowDecomposer00Factory, EyebrowDecomposer00Args
from tha3.nn.eyebrow_morphing_combiner.eyebrow_morphing_combiner_00 import \
EyebrowMorphingCombiner00Factory, EyebrowMorphingCombiner00Args, EyebrowMorphingCombiner00
from tha3.nn.face_morpher.face_morpher_08 import FaceMorpher08Args, FaceMorpher08Factory
from tha3.poser.general_poser_02 import GeneralPoser02
from tha3.poser.poser import PoseParameterCategory, PoseParameters
from tha3.nn.editor.editor_07 import Editor07, Editor07Args
from tha3.nn.two_algo_body_rotator.two_algo_face_body_rotator_05 import TwoAlgoFaceBodyRotator05, \
TwoAlgoFaceBodyRotator05Args
from tha3.util import torch_load
from tha3.compute.cached_computation_func import TensorListCachedComputationFunc
from tha3.compute.cached_computation_protocol import CachedComputationProtocol
from tha3.nn.nonlinearity_factory import ReLUFactory, LeakyReLUFactory
from tha3.nn.normalization import InstanceNorm2dFactory
from tha3.nn.util import BlockArgs
class Network(Enum):
eyebrow_decomposer = 1
eyebrow_morphing_combiner = 2
face_morpher = 3
two_algo_face_body_rotator = 4
editor = 5
@property
def outputs_key(self):
return f"{self.name}_outputs"
class Branch(Enum):
face_morphed_half = 1
face_morphed_full = 2
all_outputs = 3
NUM_EYEBROW_PARAMS = 12
NUM_FACE_PARAMS = 27
NUM_ROTATION_PARAMS = 6
class FiveStepPoserComputationProtocol(CachedComputationProtocol):
def __init__(self, eyebrow_morphed_image_index: int):
super().__init__()
self.eyebrow_morphed_image_index = eyebrow_morphed_image_index
self.cached_batch_0 = None
self.cached_eyebrow_decomposer_output = None
def compute_func(self) -> TensorListCachedComputationFunc:
def func(modules: Dict[str, Module],
batch: List[Tensor],
outputs: Dict[str, List[Tensor]]):
if self.cached_batch_0 is None:
new_batch_0 = True
elif batch[0].shape[0] != self.cached_batch_0.shape[0]:
new_batch_0 = True
else:
new_batch_0 = torch.max((batch[0] - self.cached_batch_0).abs()).item() > 0
if not new_batch_0:
outputs[Network.eyebrow_decomposer.outputs_key] = self.cached_eyebrow_decomposer_output
output = self.get_output(Branch.all_outputs.name, modules, batch, outputs)
if new_batch_0:
self.cached_batch_0 = batch[0]
self.cached_eyebrow_decomposer_output = outputs[Network.eyebrow_decomposer.outputs_key]
return output
return func
def compute_output(self, key: str, modules: Dict[str, Module], batch: List[Tensor],
outputs: Dict[str, List[Tensor]]) -> List[Tensor]:
if key == Network.eyebrow_decomposer.outputs_key:
input_image = batch[0][:, :, 64:192, 64 + 128:192 + 128]
return modules[Network.eyebrow_decomposer.name].forward(input_image)
elif key == Network.eyebrow_morphing_combiner.outputs_key:
eyebrow_decomposer_output = self.get_output(Network.eyebrow_decomposer.outputs_key, modules, batch, outputs)
background_layer = eyebrow_decomposer_output[EyebrowDecomposer00.BACKGROUND_LAYER_INDEX]
eyebrow_layer = eyebrow_decomposer_output[EyebrowDecomposer00.EYEBROW_LAYER_INDEX]
eyebrow_pose = batch[1][:, :NUM_EYEBROW_PARAMS]
return modules[Network.eyebrow_morphing_combiner.name].forward(
background_layer,
eyebrow_layer,
eyebrow_pose)
elif key == Network.face_morpher.outputs_key:
eyebrow_morphing_combiner_output = self.get_output(
Network.eyebrow_morphing_combiner.outputs_key, modules, batch, outputs)
eyebrow_morphed_image = eyebrow_morphing_combiner_output[self.eyebrow_morphed_image_index]
input_image = batch[0][:, :, 32:32 + 192, (32 + 128):(32 + 192 + 128)].clone()
input_image[:, :, 32:32 + 128, 32:32 + 128] = eyebrow_morphed_image
face_pose = batch[1][:, NUM_EYEBROW_PARAMS:NUM_EYEBROW_PARAMS + NUM_FACE_PARAMS]
return modules[Network.face_morpher.name].forward(input_image, face_pose)
elif key == Branch.face_morphed_full.name:
face_morpher_output = self.get_output(Network.face_morpher.outputs_key, modules, batch, outputs)
face_morphed_image = face_morpher_output[0]
input_image = batch[0].clone()
input_image[:, :, 32:32 + 192, 32 + 128:32 + 192 + 128] = face_morphed_image
return [input_image]
elif key == Branch.face_morphed_half.name:
face_morphed_full = self.get_output(Branch.face_morphed_full.name, modules, batch, outputs)[0]
return [
interpolate(face_morphed_full, size=(256, 256), mode='bilinear', align_corners=False)
]
elif key == Network.two_algo_face_body_rotator.outputs_key:
face_morphed_half = self.get_output(Branch.face_morphed_half.name, modules, batch, outputs)[0]
rotation_pose = batch[1][:, NUM_EYEBROW_PARAMS + NUM_FACE_PARAMS:]
return modules[Network.two_algo_face_body_rotator.name].forward(face_morphed_half, rotation_pose)
elif key == Network.editor.outputs_key:
input_original_image = self.get_output(Branch.face_morphed_full.name, modules, batch, outputs)[0]
rotator_outputs = self.get_output(
Network.two_algo_face_body_rotator.outputs_key, modules, batch, outputs)
half_warped_image = rotator_outputs[TwoAlgoFaceBodyRotator05.WARPED_IMAGE_INDEX]
full_warped_image = interpolate(
half_warped_image, size=(512, 512), mode='bilinear', align_corners=False)
half_grid_change = rotator_outputs[TwoAlgoFaceBodyRotator05.GRID_CHANGE_INDEX]
full_grid_change = interpolate(
half_grid_change, size=(512, 512), mode='bilinear', align_corners=False)
rotation_pose = batch[1][:, NUM_EYEBROW_PARAMS + NUM_FACE_PARAMS:]
return modules[Network.editor.name].forward(
input_original_image, full_warped_image, full_grid_change, rotation_pose)
elif key == Branch.all_outputs.name:
editor_output = self.get_output(Network.editor.outputs_key, modules, batch, outputs)
rotater_output = self.get_output(Network.two_algo_face_body_rotator.outputs_key, modules, batch, outputs)
face_morpher_output = self.get_output(Network.face_morpher.outputs_key, modules, batch, outputs)
eyebrow_morphing_combiner_output = self.get_output(
Network.eyebrow_morphing_combiner.outputs_key, modules, batch, outputs)
eyebrow_decomposer_output = self.get_output(
Network.eyebrow_decomposer.outputs_key, modules, batch, outputs)
output = editor_output \
+ rotater_output \
+ face_morpher_output \
+ eyebrow_morphing_combiner_output \
+ eyebrow_decomposer_output
return output
else:
raise RuntimeError("Unsupported key: " + key)
def load_eyebrow_decomposer(file_name: str):
factory = EyebrowDecomposer00Factory(
EyebrowDecomposer00Args(
image_size=128,
image_channels=4,
start_channels=64,
bottleneck_image_size=16,
num_bottleneck_blocks=6,
max_channels=512,
block_args=BlockArgs(
initialization_method='he',
use_spectral_norm=False,
normalization_layer_factory=InstanceNorm2dFactory(),
nonlinearity_factory=ReLUFactory(inplace=True))))
print("Loading the eyebrow decomposer ... ", end="")
module = factory.create()
module.load_state_dict(torch_load(file_name))
print("DONE!!!")
return module
def load_eyebrow_morphing_combiner(file_name: str):
factory = EyebrowMorphingCombiner00Factory(
EyebrowMorphingCombiner00Args(
image_size=128,
image_channels=4,
start_channels=64,
num_pose_params=12,
bottleneck_image_size=16,
num_bottleneck_blocks=6,
max_channels=512,
block_args=BlockArgs(
initialization_method='he',
use_spectral_norm=False,
normalization_layer_factory=InstanceNorm2dFactory(),
nonlinearity_factory=ReLUFactory(inplace=True))))
print("Loading the eyebrow morphing conbiner ... ", end="")
module = factory.create()
module.load_state_dict(torch_load(file_name))
print("DONE!!!")
return module
def load_face_morpher(file_name: str):
factory = FaceMorpher08Factory(
FaceMorpher08Args(
image_size=192,
image_channels=4,
num_expression_params=27,
start_channels=64,
bottleneck_image_size=24,
num_bottleneck_blocks=6,
max_channels=512,
block_args=BlockArgs(
initialization_method='he',
use_spectral_norm=False,
normalization_layer_factory=InstanceNorm2dFactory(),
nonlinearity_factory=ReLUFactory(inplace=False))))
print("Loading the face morpher ... ", end="")
module = factory.create()
module.load_state_dict(torch_load(file_name))
print("DONE!!!")
return module
def load_two_algo_generator(file_name) -> Module:
module = TwoAlgoFaceBodyRotator05(
TwoAlgoFaceBodyRotator05Args(
image_size=256,
image_channels=4,
start_channels=64,
num_pose_params=6,
bottleneck_image_size=32,
num_bottleneck_blocks=6,
max_channels=512,
upsample_mode='nearest',
block_args=BlockArgs(
initialization_method='he',
use_spectral_norm=False,
normalization_layer_factory=InstanceNorm2dFactory(),
nonlinearity_factory=LeakyReLUFactory(inplace=False, negative_slope=0.1))))
print("Loading the face-body rotator ... ", end="")
module.load_state_dict(torch_load(file_name))
print("DONE!!!")
return module
def load_editor(file_name) -> Module:
module = Editor07(
Editor07Args(
image_size=512,
image_channels=4,
num_pose_params=6,
start_channels=32,
bottleneck_image_size=64,
num_bottleneck_blocks=6,
max_channels=512,
upsampling_mode='nearest',
block_args=BlockArgs(
initialization_method='he',
use_spectral_norm=False,
normalization_layer_factory=InstanceNorm2dFactory(),
nonlinearity_factory=LeakyReLUFactory(inplace=False, negative_slope=0.1))))
print("Loading the combiner ... ", end="")
module.load_state_dict(torch_load(file_name))
print("DONE!!!")
return module
def get_pose_parameters():
return PoseParameters.Builder() \
.add_parameter_group("eyebrow_troubled", PoseParameterCategory.EYEBROW, arity=2) \
.add_parameter_group("eyebrow_angry", PoseParameterCategory.EYEBROW, arity=2) \
.add_parameter_group("eyebrow_lowered", PoseParameterCategory.EYEBROW, arity=2) \
.add_parameter_group("eyebrow_raised", PoseParameterCategory.EYEBROW, arity=2) \
.add_parameter_group("eyebrow_happy", PoseParameterCategory.EYEBROW, arity=2) \
.add_parameter_group("eyebrow_serious", PoseParameterCategory.EYEBROW, arity=2) \
.add_parameter_group("eye_wink", PoseParameterCategory.EYE, arity=2) \
.add_parameter_group("eye_happy_wink", PoseParameterCategory.EYE, arity=2) \
.add_parameter_group("eye_surprised", PoseParameterCategory.EYE, arity=2) \
.add_parameter_group("eye_relaxed", PoseParameterCategory.EYE, arity=2) \
.add_parameter_group("eye_unimpressed", PoseParameterCategory.EYE, arity=2) \
.add_parameter_group("eye_raised_lower_eyelid", PoseParameterCategory.EYE, arity=2) \
.add_parameter_group("iris_small", PoseParameterCategory.IRIS_MORPH, arity=2) \
.add_parameter_group("mouth_aaa", PoseParameterCategory.MOUTH, arity=1, default_value=1.0) \
.add_parameter_group("mouth_iii", PoseParameterCategory.MOUTH, arity=1) \
.add_parameter_group("mouth_uuu", PoseParameterCategory.MOUTH, arity=1) \
.add_parameter_group("mouth_eee", PoseParameterCategory.MOUTH, arity=1) \
.add_parameter_group("mouth_ooo", PoseParameterCategory.MOUTH, arity=1) \
.add_parameter_group("mouth_delta", PoseParameterCategory.MOUTH, arity=1) \
.add_parameter_group("mouth_lowered_corner", PoseParameterCategory.MOUTH, arity=2) \
.add_parameter_group("mouth_raised_corner", PoseParameterCategory.MOUTH, arity=2) \
.add_parameter_group("mouth_smirk", PoseParameterCategory.MOUTH, arity=1) \
.add_parameter_group("iris_rotation_x", PoseParameterCategory.IRIS_ROTATION, arity=1, range=(-1.0, 1.0)) \
.add_parameter_group("iris_rotation_y", PoseParameterCategory.IRIS_ROTATION, arity=1, range=(-1.0, 1.0)) \
.add_parameter_group("head_x", PoseParameterCategory.FACE_ROTATION, arity=1, range=(-1.0, 1.0)) \
.add_parameter_group("head_y", PoseParameterCategory.FACE_ROTATION, arity=1, range=(-1.0, 1.0)) \
.add_parameter_group("neck_z", PoseParameterCategory.FACE_ROTATION, arity=1, range=(-1.0, 1.0)) \
.add_parameter_group("body_y", PoseParameterCategory.BODY_ROTATION, arity=1, range=(-1.0, 1.0)) \
.add_parameter_group("body_z", PoseParameterCategory.BODY_ROTATION, arity=1, range=(-1.0, 1.0)) \
.add_parameter_group("breathing", PoseParameterCategory.BREATHING, arity=1, range=(0.0, 1.0)) \
.build()
def create_poser(
device: torch.device,
module_file_names: Optional[Dict[str, str]] = None,
eyebrow_morphed_image_index: int = EyebrowMorphingCombiner00.EYEBROW_IMAGE_NO_COMBINE_ALPHA_INDEX,
default_output_index: int = 0) -> GeneralPoser02:
if module_file_names is None:
module_file_names = {}
if Network.eyebrow_decomposer.name not in module_file_names:
dir = "data/models/standard_float"
file_name = dir + "/eyebrow_decomposer.pt"
module_file_names[Network.eyebrow_decomposer.name] = file_name
if Network.eyebrow_morphing_combiner.name not in module_file_names:
dir = "data/models/standard_float"
file_name = dir + "/eyebrow_morphing_combiner.pt"
module_file_names[Network.eyebrow_morphing_combiner.name] = file_name
if Network.face_morpher.name not in module_file_names:
dir = "data/models/standard_float"
file_name = dir + "/face_morpher.pt"
module_file_names[Network.face_morpher.name] = file_name
if Network.two_algo_face_body_rotator.name not in module_file_names:
dir = "data/models/standard_float"
file_name = dir + "/two_algo_face_body_rotator.pt"
module_file_names[Network.two_algo_face_body_rotator.name] = file_name
if Network.editor.name not in module_file_names:
dir = "data/models/standard_float"
file_name = dir + "/editor.pt"
module_file_names[Network.editor.name] = file_name
loaders = {
Network.eyebrow_decomposer.name:
lambda: load_eyebrow_decomposer(module_file_names[Network.eyebrow_decomposer.name]),
Network.eyebrow_morphing_combiner.name:
lambda: load_eyebrow_morphing_combiner(module_file_names[Network.eyebrow_morphing_combiner.name]),
Network.face_morpher.name:
lambda: load_face_morpher(module_file_names[Network.face_morpher.name]),
Network.two_algo_face_body_rotator.name:
lambda: load_two_algo_generator(module_file_names[Network.two_algo_face_body_rotator.name]),
Network.editor.name:
lambda: load_editor(module_file_names[Network.editor.name]),
}
return GeneralPoser02(
image_size=512,
module_loaders=loaders,
pose_parameters=get_pose_parameters().get_pose_parameter_groups(),
output_list_func=FiveStepPoserComputationProtocol(eyebrow_morphed_image_index).compute_func(),
subrect=None,
device=device,
output_length=29,
default_output_index=default_output_index)
if __name__ == "__main__":
device = torch.device('cuda')
poser = create_poser(device)
image = torch.zeros(1, 4, 512, 512, device=device)
pose = torch.zeros(1, 45, device=device)
repeat = 100
acc = 0.0
for i in range(repeat + 2):
start = torch.cuda.Event(enable_timing=True)
end = torch.cuda.Event(enable_timing=True)
start.record()
poser.pose(image, pose)
end.record()
torch.cuda.synchronize()
if i >= 2:
elapsed_time = start.elapsed_time(end)
print("%d:" % i, elapsed_time)
acc = acc + elapsed_time
print("average:", acc / repeat)
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