import torch from ..utils.util import * def warp_color(IA_l, IB_lab, features_B, vggnet, nonlocal_net, colornet, feature_noise=0, temperature=0.01): IA_rgb_from_gray = gray2rgb_batch(IA_l) with torch.no_grad(): A_relu1_1, A_relu2_1, A_relu3_1, A_relu4_1, A_relu5_1 = vggnet( IA_rgb_from_gray, ["r12", "r22", "r32", "r42", "r52"], preprocess=True ) B_relu1_1, B_relu2_1, B_relu3_1, B_relu4_1, B_relu5_1 = features_B # NOTE: output the feature before normalization features_A = [A_relu1_1, A_relu2_1, A_relu3_1, A_relu4_1, A_relu5_1] A_relu2_1 = feature_normalize(A_relu2_1) A_relu3_1 = feature_normalize(A_relu3_1) A_relu4_1 = feature_normalize(A_relu4_1) A_relu5_1 = feature_normalize(A_relu5_1) B_relu2_1 = feature_normalize(B_relu2_1) B_relu3_1 = feature_normalize(B_relu3_1) B_relu4_1 = feature_normalize(B_relu4_1) B_relu5_1 = feature_normalize(B_relu5_1) nonlocal_BA_lab, similarity_map = nonlocal_net( IB_lab, A_relu2_1, A_relu3_1, A_relu4_1, A_relu5_1, B_relu2_1, B_relu3_1, B_relu4_1, B_relu5_1, temperature=temperature, ) return nonlocal_BA_lab, similarity_map, features_A def frame_colorization( IA_lab, IB_lab, IA_last_lab, features_B, vggnet, nonlocal_net, colornet, joint_training=True, feature_noise=0, luminance_noise=0, temperature=0.01, ): IA_l = IA_lab[:, 0:1, :, :] if luminance_noise: IA_l = IA_l + torch.randn_like(IA_l, requires_grad=False) * luminance_noise with torch.autograd.set_grad_enabled(joint_training): nonlocal_BA_lab, similarity_map, features_A_gray = warp_color( IA_l, IB_lab, features_B, vggnet, nonlocal_net, colornet, feature_noise, temperature=temperature ) nonlocal_BA_ab = nonlocal_BA_lab[:, 1:3, :, :] color_input = torch.cat((IA_l, nonlocal_BA_ab, similarity_map, IA_last_lab), dim=1) IA_ab_predict = colornet(color_input) return IA_ab_predict, nonlocal_BA_lab, features_A_gray