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from typing import Optional, List
import torch
from matplotlib import pyplot
from torch import Tensor
from torch.nn import Module, Sequential, Tanh, Sigmoid
from tha3.nn.image_processing_util import GridChangeApplier, apply_color_change
from tha3.nn.common.resize_conv_unet import ResizeConvUNet, ResizeConvUNetArgs
from tha3.util import numpy_linear_to_srgb
from tha3.module.module_factory import ModuleFactory
from tha3.nn.conv import create_conv3_from_block_args, create_conv3
from tha3.nn.nonlinearity_factory import ReLUFactory
from tha3.nn.normalization import InstanceNorm2dFactory
from tha3.nn.util import BlockArgs
class Editor07Args:
def __init__(self,
image_size: int = 512,
image_channels: int = 4,
num_pose_params: int = 6,
start_channels: int = 32,
bottleneck_image_size=32,
num_bottleneck_blocks=6,
max_channels: int = 512,
upsampling_mode: str = 'nearest',
block_args: Optional[BlockArgs] = None,
use_separable_convolution: bool = False):
if block_args is None:
block_args = BlockArgs(
normalization_layer_factory=InstanceNorm2dFactory(),
nonlinearity_factory=ReLUFactory(inplace=False))
self.block_args = block_args
self.upsampling_mode = upsampling_mode
self.max_channels = max_channels
self.num_bottleneck_blocks = num_bottleneck_blocks
self.bottleneck_image_size = bottleneck_image_size
self.start_channels = start_channels
self.num_pose_params = num_pose_params
self.image_channels = image_channels
self.image_size = image_size
self.use_separable_convolution = use_separable_convolution
class Editor07(Module):
def __init__(self, args: Editor07Args):
super().__init__()
self.args = args
self.body = ResizeConvUNet(ResizeConvUNetArgs(
image_size=args.image_size,
input_channels=2 * args.image_channels + args.num_pose_params + 2,
start_channels=args.start_channels,
bottleneck_image_size=args.bottleneck_image_size,
num_bottleneck_blocks=args.num_bottleneck_blocks,
max_channels=args.max_channels,
upsample_mode=args.upsampling_mode,
block_args=args.block_args,
use_separable_convolution=args.use_separable_convolution))
self.color_change_creator = Sequential(
create_conv3_from_block_args(
in_channels=self.args.start_channels,
out_channels=self.args.image_channels,
bias=True,
block_args=self.args.block_args),
Tanh())
self.alpha_creator = Sequential(
create_conv3_from_block_args(
in_channels=self.args.start_channels,
out_channels=self.args.image_channels,
bias=True,
block_args=self.args.block_args),
Sigmoid())
self.grid_change_creator = create_conv3(
in_channels=self.args.start_channels,
out_channels=2,
bias=False,
initialization_method='zero',
use_spectral_norm=False)
self.grid_change_applier = GridChangeApplier()
def forward(self,
input_original_image: Tensor,
input_warped_image: Tensor,
input_grid_change: Tensor,
pose: Tensor,
*args) -> List[Tensor]:
n, c = pose.shape
pose = pose.view(n, c, 1, 1).repeat(1, 1, self.args.image_size, self.args.image_size)
feature = torch.cat([input_original_image, input_warped_image, input_grid_change, pose], dim=1)
feature = self.body.forward(feature)[-1]
output_grid_change = input_grid_change + self.grid_change_creator(feature)
output_color_change = self.color_change_creator(feature)
output_color_change_alpha = self.alpha_creator(feature)
output_warped_image = self.grid_change_applier.apply(output_grid_change, input_original_image)
output_color_changed = apply_color_change(output_color_change_alpha, output_color_change, output_warped_image)
return [
output_color_changed,
output_color_change_alpha,
output_color_change,
output_warped_image,
output_grid_change,
]
COLOR_CHANGED_IMAGE_INDEX = 0
COLOR_CHANGE_ALPHA_INDEX = 1
COLOR_CHANGE_IMAGE_INDEX = 2
WARPED_IMAGE_INDEX = 3
GRID_CHANGE_INDEX = 4
OUTPUT_LENGTH = 5
class Editor07Factory(ModuleFactory):
def __init__(self, args: Editor07Args):
super().__init__()
self.args = args
def create(self) -> Module:
return Editor07(self.args)
def show_image(pytorch_image):
numpy_image = ((pytorch_image + 1.0) / 2.0).squeeze(0).numpy()
numpy_image[0:3, :, :] = numpy_linear_to_srgb(numpy_image[0:3, :, :])
c, h, w = numpy_image.shape
numpy_image = numpy_image.reshape((c, h * w)).transpose().reshape((h, w, c))
pyplot.imshow(numpy_image)
pyplot.show()
if __name__ == "__main__":
cuda = torch.device('cuda')
image_size = 512
image_channels = 4
num_pose_params = 6
args = Editor07Args(
image_size=512,
image_channels=4,
start_channels=32,
num_pose_params=6,
bottleneck_image_size=32,
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=ReLUFactory(inplace=False)))
module = Editor07(args).to(cuda)
image_count = 1
input_image = torch.zeros(image_count, 4, image_size, image_size, device=cuda)
direct_image = torch.zeros(image_count, 4, image_size, image_size, device=cuda)
warped_image = torch.zeros(image_count, 4, image_size, image_size, device=cuda)
grid_change = torch.zeros(image_count, 2, image_size, image_size, device=cuda)
pose = torch.zeros(image_count, num_pose_params, device=cuda)
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()
module.forward(input_image, warped_image, grid_change, 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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