81 lines
3.1 KiB
Python
81 lines
3.1 KiB
Python
# from imports import *
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import torch
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from utils.tensor_lab2rgb import tensor_lab2rgb
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from utils.util import *
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if torch.cuda.is_available():
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device = torch.device("cuda")
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else:
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device = troch.device("cpu")
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def warp_color(IA_l, IB_lab, features_B, vggnet, nonlocal_net, colornet, feature_noise=0, temperature=0.01):
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"""
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joint_train=True, enable grad; otherwise disable grad
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"""
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# change to rgb for feature extraction
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IA_rgb_from_gray = gray2rgb_batch(IA_l)
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with torch.no_grad():
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A_relu1_1, A_relu2_1, A_relu3_1, A_relu4_1, A_relu5_1 = vggnet(
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IA_rgb_from_gray, ["r12", "r22", "r32", "r42", "r52"], preprocess=True
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)
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B_relu1_1, B_relu2_1, B_relu3_1, B_relu4_1, B_relu5_1 = features_B
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# NOTE: output the feature before normalization
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features_A = [A_relu1_1, A_relu2_1, A_relu3_1, A_relu4_1, A_relu5_1]
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A_relu2_1 = feature_normalize(A_relu2_1)
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A_relu3_1 = feature_normalize(A_relu3_1)
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A_relu4_1 = feature_normalize(A_relu4_1)
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A_relu5_1 = feature_normalize(A_relu5_1)
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B_relu2_1 = feature_normalize(B_relu2_1)
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B_relu3_1 = feature_normalize(B_relu3_1)
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B_relu4_1 = feature_normalize(B_relu4_1)
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B_relu5_1 = feature_normalize(B_relu5_1)
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# if feature_noise > 0:
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# A_relu2_1 = A_relu2_1 + torch.randn_like(A_relu2_1, requires_grad=False) * feature_noise
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# B_relu2_1 = B_relu2_1 + torch.randn_like(B_relu2_1, requires_grad=False) * feature_noise
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# A_relu3_1 = A_relu3_1 + torch.randn_like(A_relu3_1, requires_grad=False) * feature_noise
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# B_relu3_1 = B_relu3_1 + torch.randn_like(B_relu3_1, requires_grad=False) * feature_noise
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# A_relu4_1 = A_relu4_1 + torch.randn_like(A_relu4_1, requires_grad=False) * feature_noise
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# B_relu4_1 = B_relu4_1 + torch.randn_like(B_relu4_1, requires_grad=False) * feature_noise
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# A_relu5_1 = A_relu5_1 + torch.randn_like(A_relu5_1, requires_grad=False) * feature_noise
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# B_relu5_1 = B_relu5_1 + torch.randn_like(B_relu5_1, requires_grad=False) * feature_noise
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nonlocal_BA_lab, similarity_map = nonlocal_net(
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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=0.01
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)
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return nonlocal_BA_lab, similarity_map, features_A
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def frame_colorization(
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IA_lab,
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IB_lab,
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IA_last_lab,
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features_B,
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vggnet,
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nonlocal_net,
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colornet,
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joint_training=True,
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feature_noise=0,
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luminance_noise=0,
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temperature=0.01,
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):
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# change to rgb for feature extraction
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IA_l = IA_lab[:, 0:1, :, :]
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if luminance_noise:
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IA_l = IA_l + torch.randn_like(IA_l, requires_grad=False) * luminance_noise
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with torch.autograd.set_grad_enabled(joint_training):
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nonlocal_BA_lab, similarity_map, features_A_gray = warp_color(
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IA_l, IB_lab, features_B, vggnet, nonlocal_net, colornet, feature_noise, temperature=temperature
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)
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nonlocal_BA_ab = nonlocal_BA_lab[:, 1:3, :, :]
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color_input = torch.cat((IA_l, nonlocal_BA_ab, similarity_map, IA_last_lab), dim=1)
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IA_ab_predict = colornet(color_input)
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return IA_ab_predict, nonlocal_BA_lab, features_A_gray
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