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# -*- coding: utf-8 -*-
# Copyright (c) Alibaba, Inc. and its affiliates.
import numpy as np
import torch
import torch.nn as nn
from einops import rearrange
from .utils import convert_to_torch
norm_layer = nn.InstanceNorm2d
class ResidualBlock(nn.Module):
def __init__(self, in_features):
super(ResidualBlock, self).__init__()
conv_block = [
nn.ReflectionPad2d(1),
nn.Conv2d(in_features, in_features, 3),
norm_layer(in_features),
nn.ReLU(inplace=True),
nn.ReflectionPad2d(1),
nn.Conv2d(in_features, in_features, 3),
norm_layer(in_features)
]
self.conv_block = nn.Sequential(*conv_block)
def forward(self, x):
return x + self.conv_block(x)
class ContourInference(nn.Module):
def __init__(self, input_nc, output_nc, n_residual_blocks=9, sigmoid=True):
super(ContourInference, self).__init__()
# Initial convolution block
model0 = [
nn.ReflectionPad2d(3),
nn.Conv2d(input_nc, 64, 7),
norm_layer(64),
nn.ReLU(inplace=True)
]
self.model0 = nn.Sequential(*model0)
# Downsampling
model1 = []
in_features = 64
out_features = in_features * 2
for _ in range(2):
model1 += [
nn.Conv2d(in_features, out_features, 3, stride=2, padding=1),
norm_layer(out_features),
nn.ReLU(inplace=True)
]
in_features = out_features
out_features = in_features * 2
self.model1 = nn.Sequential(*model1)
model2 = []
# Residual blocks
for _ in range(n_residual_blocks):
model2 += [ResidualBlock(in_features)]
self.model2 = nn.Sequential(*model2)
# Upsampling
model3 = []
out_features = in_features // 2
for _ in range(2):
model3 += [
nn.ConvTranspose2d(in_features,
out_features,
3,
stride=2,
padding=1,
output_padding=1),
norm_layer(out_features),
nn.ReLU(inplace=True)
]
in_features = out_features
out_features = in_features // 2
self.model3 = nn.Sequential(*model3)
# Output layer
model4 = [nn.ReflectionPad2d(3), nn.Conv2d(64, output_nc, 7)]
if sigmoid:
model4 += [nn.Sigmoid()]
self.model4 = nn.Sequential(*model4)
def forward(self, x, cond=None):
out = self.model0(x)
out = self.model1(out)
out = self.model2(out)
out = self.model3(out)
out = self.model4(out)
return out
class ScribbleAnnotator:
def __init__(self, cfg, device=None):
input_nc = cfg.get('INPUT_NC', 3)
output_nc = cfg.get('OUTPUT_NC', 1)
n_residual_blocks = cfg.get('N_RESIDUAL_BLOCKS', 3)
sigmoid = cfg.get('SIGMOID', True)
pretrained_model = cfg['PRETRAINED_MODEL']
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") if device is None else device
self.model = ContourInference(input_nc, output_nc, n_residual_blocks,
sigmoid)
self.model.load_state_dict(torch.load(pretrained_model, weights_only=True))
self.model = self.model.eval().requires_grad_(False).to(self.device)
@torch.no_grad()
@torch.inference_mode()
@torch.autocast('cuda', enabled=False)
def forward(self, image):
is_batch = False if len(image.shape) == 3 else True
image = convert_to_torch(image)
if len(image.shape) == 3:
image = rearrange(image, 'h w c -> 1 c h w')
image = image.float().div(255).to(self.device)
contour_map = self.model(image)
contour_map = (contour_map.squeeze(dim=1) * 255.0).clip(
0, 255).cpu().numpy().astype(np.uint8)
contour_map = contour_map[..., None].repeat(3, -1)
if not is_batch:
contour_map = contour_map.squeeze()
return contour_map
class ScribbleVideoAnnotator(ScribbleAnnotator):
def forward(self, frames):
ret_frames = []
for frame in frames:
anno_frame = super().forward(np.array(frame))
ret_frames.append(anno_frame)
return ret_frames |