csbbbv / colormedgan Goto Github PK

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implement of "ColorMedGAN: A Semantic Colorization Framework for Medical Images"

Python 95.86% Jupyter Notebook 4.14%

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colormedgan's Issues

style/OASIS_OAS1_0152_MR1_101.png

Hi;

One of the parameters for colorization class, is a style which by default, point to this file. Do we need this file or which file to use instead, is any image from OASIS dataset will do.

What is the structure of oasisMRI/A and oasisMRI/B directory

Hi;

Below code read from colorization.py class load images from oasisMRI/A and oasisMRI/B directory , what is the structure of this directory. Is there a script to generate images to populate these dirs:

train_loader_src = utils.data_load(os.path.join('/media/user/8f28289d-aa45-46ab-b9e4-a7d4d9f08b7e/shaobo/colorization_tmp_oasis/data/', args.src_data), 'train', transform, args.batch_size, shuffle=True, drop_last=True)
train_loader_tgt = utils.data_load(os.path.join('/media/user/8f28289d-aa45-46ab-b9e4-a7d4d9f08b7e/shaobo/colorization_tmp_oasis/data/', args.tgt_data), 'train', transform, args.batch_size, shuffle=True, drop_last=True)
test_loader_src = utils.data_load(os.path.join('/media/user/8f28289d-aa45-46ab-b9e4-a7d4d9f08b7e/shaobo/colorization_tmp_oasis/data/', args.src_data), 'test', transform, 1, shuffle=True, drop_last=True)

oasisMRI/B and mask

Hi;

I downloaded the the Visible Human Project data from https://data.lhncbc.nlm.nih.gov/public/Visible-Human/Female-Images/PNG_format/head/index.html. However, I'm abit confused if mask is required, because I only found the image. See below snapshot:

oasisMRI/A structure and oasis brain data

Hi;

I download the oasis data from [https://www.oasis-brains.org/#data], I notice there are so many files and folder, for example, there are:
'PROCESSED' and 'RAW' directory and there are many files under those directories, but data_load funcation require a path to and subfolder. So I'm confused to which directory should the path point to and which subfolder.

train_loader_src = utils.data_load(os.path.join('./data', args.src_data), 'train', transform, args.batch_size, shuffle=True, drop_last=True)

def data_load(path, subfolder, transform, batch_size, shuffle=False, drop_last=True):
    dset = datasets.ImageFolder(path, transform)
    ind = dset.class_to_idx[subfolder]

    n = 0
    for i in range(dset.__len__()):
        if ind != dset.imgs[n][1]:
            del dset.imgs[n]
            n -= 1
        n += 1

    return torch.utils.data.DataLoader(dset, batch_size=batch_size, shuffle=shuffle, drop_last=drop_last)

Loss is not Decreasing

Hi;

When I run the "colorization-v2.py" with new dataset. I notice the training loss is not decreasing (and the result image is not good). I wonder if the issue that the source and target dataset is not quit similar or what is the issue.

training start!
/tmp/ipykernel_32362/2251178119.py:222: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  input_tensor = torch.tensor(input_tensor).cuda()
/tmp/ipykernel_32362/2251178119.py:199: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  input_tensor = torch.tensor(input_tensor).cuda()
[1/300] - time: 317.32, G_loss: 3207.841, G_identity_loss: 41.411, G_GAN_loss: 1.206, G_cycle_loss: 1166.547, D_loss: 0.718,edge_lossAB:305.701,edge_lossBA:517.383
[2/300] - time: 313.98, G_loss: 3179.180, G_identity_loss: 41.041, G_GAN_loss: 1.493, G_cycle_loss: 1157.893, D_loss: 0.043,edge_lossAB:305.372,edge_lossBA:505.499
[3/300] - time: 311.25, G_loss: 3190.971, G_identity_loss: 41.144, G_GAN_loss: 1.528, G_cycle_loss: 1161.091, D_loss: 0.064,edge_lossAB:305.300,edge_lossBA:511.119
[4/300] - time: 310.40, G_loss: 3213.715, G_identity_loss: 41.396, G_GAN_loss: 1.579, G_cycle_loss: 1170.092, D_loss: 0.036,edge_lossAB:305.284,edge_lossBA:514.884
[5/300] - time: 318.63, G_loss: 3182.718, G_identity_loss: 40.951, G_GAN_loss: 1.499, G_cycle_loss: 1158.106, D_loss: 0.033,edge_lossAB:305.216,edge_lossBA:509.095
[6/300] - time: 318.43, G_loss: 3200.436, G_identity_loss: 41.102, G_GAN_loss: 1.347, G_cycle_loss: 1164.378, D_loss: 0.013,edge_lossAB:305.176,edge_lossBA:514.640
[7/300] - time: 314.86, G_loss: 3217.338, G_identity_loss: 41.281, G_GAN_loss: 1.545, G_cycle_loss: 1170.220, D_loss: 0.004,edge_lossAB:305.189,edge_lossBA:519.346
[8/300] - time: 316.70, G_loss: 3202.906, G_identity_loss: 41.088, G_GAN_loss: 1.454, G_cycle_loss: 1165.202, D_loss: 0.005,edge_lossAB:305.142,edge_lossBA:515.462
[9/300] - time: 312.62, G_loss: 3206.784, G_identity_loss: 41.111, G_GAN_loss: 1.593, G_cycle_loss: 1167.109, D_loss: 0.002,edge_lossAB:305.082,edge_lossBA:515.213
[10/300] - time: 308.82, G_loss: 3198.395, G_identity_loss: 41.027, G_GAN_loss: 1.513, G_cycle_loss: 1163.688, D_loss: 0.007,edge_lossAB:305.148,edge_lossBA:513.488
[11/300] - time: 316.53, G_loss: 3207.627, G_identity_loss: 41.126, G_GAN_loss: 1.634, G_cycle_loss: 1166.874, D_loss: 0.118,edge_lossAB:305.211,edge_lossBA:516.340
[12/300] - time: 314.78, G_loss: 3222.334, G_identity_loss: 41.294, G_GAN_loss: 1.628, G_cycle_loss: 1172.039, D_loss: 0.282,edge_lossAB:305.097,edge_lossBA:520.259
[13/300] - time: 312.54, G_loss: 3208.740, G_identity_loss: 41.129, G_GAN_loss: 1.677, G_cycle_loss: 1167.481, D_loss: 0.005,edge_lossAB:305.186,edge_lossBA:515.833
[14/300] - time: 312.70, G_loss: 3198.559, G_identity_loss: 40.958, G_GAN_loss: 1.660, G_cycle_loss: 1162.785, D_loss: 0.002,edge_lossAB:305.247,edge_lossBA:516.036
[15/300] - time: 315.77, G_loss: 3205.798, G_identity_loss: 41.074, G_GAN_loss: 1.806, G_cycle_loss: 1166.488, D_loss: 0.068,edge_lossAB:305.155,edge_lossBA:514.752
[16/300] - time: 314.16, G_loss: 3187.306, G_identity_loss: 40.842, G_GAN_loss: 1.646, G_cycle_loss: 1160.229, D_loss: 0.007,edge_lossAB:305.257,edge_lossBA:509.344
[17/300] - time: 315.81, G_loss: 3210.980, G_identity_loss: 41.140, G_GAN_loss: 1.633, G_cycle_loss: 1168.849, D_loss: 0.002,edge_lossAB:305.266,edge_lossBA:515.213
[18/300] - time: 311.29, G_loss: 3215.051, G_identity_loss: 41.180, G_GAN_loss: 1.648, G_cycle_loss: 1169.344, D_loss: 0.001,edge_lossAB:305.195,edge_lossBA:518.579
[19/300] - time: 307.72, G_loss: 3193.194, G_identity_loss: 40.884, G_GAN_loss: 1.631, G_cycle_loss: 1162.326, D_loss: 0.001,edge_lossAB:305.247,edge_lossBA:511.056
[20/300] - time: 308.99, G_loss: 3202.393, G_identity_loss: 41.025, G_GAN_loss: 1.547, G_cycle_loss: 1165.242, D_loss: 0.001,edge_lossAB:305.341,edge_lossBA:514.024
[21/300] - time: 312.40, G_loss: 3214.748, G_identity_loss: 41.164, G_GAN_loss: 1.535, G_cycle_loss: 1169.189, D_loss: 0.001,edge_lossAB:305.399,edge_lossBA:518.372
[22/300] - time: 316.83, G_loss: 3212.618, G_identity_loss: 41.129, G_GAN_loss: 1.518, G_cycle_loss: 1169.187, D_loss: 0.001,edge_lossAB:305.434,edge_lossBA:516.139
[23/300] - time: 320.54, G_loss: 3197.307, G_identity_loss: 40.946, G_GAN_loss: 1.486, G_cycle_loss: 1162.910, D_loss: 0.000,edge_lossAB:305.457,edge_lossBA:513.772
[24/300] - time: 312.48, G_loss: 3189.521, G_identity_loss: 40.843, G_GAN_loss: 1.485, G_cycle_loss: 1160.436, D_loss: 0.000,edge_lossAB:305.465,edge_lossBA:511.382
[25/300] - time: 314.36, G_loss: 3201.155, G_identity_loss: 40.984, G_GAN_loss: 1.481, G_cycle_loss: 1164.983, D_loss: 0.000,edge_lossAB:305.476,edge_lossBA:513.529
[26/300] - time: 319.07, G_loss: 3195.194, G_identity_loss: 40.953, G_GAN_loss: 1.573, G_cycle_loss: 1162.428, D_loss: 0.005,edge_lossAB:305.412,edge_lossBA:513.016
[27/300] - time: 313.97, G_loss: 3207.933, G_identity_loss: 41.072, G_GAN_loss: 1.617, G_cycle_loss: 1166.128, D_loss: 0.002,edge_lossAB:305.220,edge_lossBA:518.418
[28/300] - time: 320.90, G_loss: 3224.590, G_identity_loss: 41.289, G_GAN_loss: 1.622, G_cycle_loss: 1173.127, D_loss: 0.000,edge_lossAB:305.141,edge_lossBA:520.537
[29/300] - time: 316.49, G_loss: 3202.150, G_identity_loss: 41.006, G_GAN_loss: 1.646, G_cycle_loss: 1165.928, D_loss: 0.000,edge_lossAB:305.245,edge_lossBA:512.683
[30/300] - time: 320.58, G_loss: 3205.837, G_identity_loss: 41.049, G_GAN_loss: 1.803, G_cycle_loss: 1166.792, D_loss: 0.002,edge_lossAB:305.310,edge_lossBA:514.402
[31/300] - time: 313.99, G_loss: 3204.593, G_identity_loss: 41.025, G_GAN_loss: 1.792, G_cycle_loss: 1165.923, D_loss: 0.001,edge_lossAB:305.196,edge_lossBA:515.028
[32/300] - time: 310.80, G_loss: 3192.099, G_identity_loss: 40.853, G_GAN_loss: 1.698, G_cycle_loss: 1161.419, D_loss: 0.000,edge_lossAB:305.272,edge_lossBA:511.785
[33/300] - time: 311.47, G_loss: 3208.148, G_identity_loss: 41.059, G_GAN_loss: 1.667, G_cycle_loss: 1166.684, D_loss: 0.000,edge_lossAB:305.260,edge_lossBA:517.084
[34/300] - time: 321.20, G_loss: 3192.482, G_identity_loss: 40.877, G_GAN_loss: 1.647, G_cycle_loss: 1161.804, D_loss: 0.000,edge_lossAB:305.235,edge_lossBA:511.480
[35/300] - time: 308.90, G_loss: 3215.950, G_identity_loss: 41.177, G_GAN_loss: 1.641, G_cycle_loss: 1170.287, D_loss: 0.000,edge_lossAB:305.198,edge_lossBA:517.754
[36/300] - time: 314.18, G_loss: 3204.143, G_identity_loss: 41.015, G_GAN_loss: 1.634, G_cycle_loss: 1165.544, D_loss: 0.006,edge_lossAB:305.233,edge_lossBA:515.290
[37/300] - time: 309.02, G_loss: 3200.775, G_identity_loss: 40.980, G_GAN_loss: 1.586, G_cycle_loss: 1165.518, D_loss: 0.000,edge_lossAB:305.213,edge_lossBA:512.040
[38/300] - time: 317.21, G_loss: 3201.883, G_identity_loss: 40.988, G_GAN_loss: 1.571, G_cycle_loss: 1164.989, D_loss: 0.000,edge_lossAB:305.199,edge_lossBA:514.454
[39/300] - time: 311.74, G_loss: 3202.527, G_identity_loss: 41.007, G_GAN_loss: 1.565, G_cycle_loss: 1164.978, D_loss: 0.000,edge_lossAB:305.200,edge_lossBA:514.866
[40/300] - time: 319.19, G_loss: 3199.643, G_identity_loss: 40.968, G_GAN_loss: 1.561, G_cycle_loss: 1164.212, D_loss: 0.000,edge_lossAB:305.193,edge_lossBA:514.003
[41/300] - time: 320.65, G_loss: 3198.583, G_identity_loss: 40.980, G_GAN_loss: 1.546, G_cycle_loss: 1163.577, D_loss: 0.153,edge_lossAB:305.226,edge_lossBA:514.165
[42/300] - time: 311.49, G_loss: 3186.606, G_identity_loss: 40.801, G_GAN_loss: 1.433, G_cycle_loss: 1159.495, D_loss: 0.083,edge_lossAB:305.245,edge_lossBA:510.465
[43/300] - time: 307.47, G_loss: 3189.355, G_identity_loss: 40.849, G_GAN_loss: 1.440, G_cycle_loss: 1160.651, D_loss: 0.001,edge_lossAB:305.231,edge_lossBA:510.475
[44/300] - time: 317.77, G_loss: 3208.742, G_identity_loss: 41.083, G_GAN_loss: 1.548, G_cycle_loss: 1167.338, D_loss: 0.000,edge_lossAB:305.290,edge_lossBA:516.308
[45/300] - time: 314.47, G_loss: 3219.012, G_identity_loss: 41.183, G_GAN_loss: 1.633, G_cycle_loss: 1171.049, D_loss: 0.000,edge_lossAB:305.323,edge_lossBA:518.595
[46/300] - time: 319.87, G_loss: 3186.758, G_identity_loss: 40.819, G_GAN_loss: 1.663, G_cycle_loss: 1160.232, D_loss: 0.000,edge_lossAB:305.346,edge_lossBA:509.137
[47/300] - time: 312.19, G_loss: 3186.264, G_identity_loss: 40.768, G_GAN_loss: 1.681, G_cycle_loss: 1159.653, D_loss: 0.000,edge_lossAB:305.222,edge_lossBA:509.923
[48/300] - time: 318.71, G_loss: 3197.524, G_identity_loss: 40.922, G_GAN_loss: 1.660, G_cycle_loss: 1163.592, D_loss: 0.000,edge_lossAB:305.202,edge_lossBA:512.917
[49/300] - time: 315.61, G_loss: 3207.812, G_identity_loss: 41.054, G_GAN_loss: 1.665, G_cycle_loss: 1167.736, D_loss: 0.000,edge_lossAB:305.214,edge_lossBA:514.891
[50/300] - time: 314.30, G_loss: 3174.044, G_identity_loss: 40.628, G_GAN_loss: 1.628, G_cycle_loss: 1155.125, D_loss: 0.000,edge_lossAB:305.226,edge_lossBA:506.867
[51/300] - time: 314.47, G_loss: 3196.855, G_identity_loss: 40.912, G_GAN_loss: 1.505, G_cycle_loss: 1163.226, D_loss: 0.000,edge_lossAB:305.290,edge_lossBA:512.877
[52/300] - time: 314.49, G_loss: 3206.667, G_identity_loss: 41.019, G_GAN_loss: 1.515, G_cycle_loss: 1166.304, D_loss: 0.000,edge_lossAB:305.368,edge_lossBA:516.256
[53/300] - time: 317.09, G_loss: 3201.222, G_identity_loss: 40.969, G_GAN_loss: 1.509, G_cycle_loss: 1164.134, D_loss: 0.000,edge_lossAB:305.338,edge_lossBA:515.768
[54/300] - time: 316.23, G_loss: 3191.872, G_identity_loss: 40.844, G_GAN_loss: 1.500, G_cycle_loss: 1160.808, D_loss: 0.004,edge_lossAB:305.391,edge_lossBA:513.110
[55/300] - time: 312.52, G_loss: 3198.930, G_identity_loss: 40.945, G_GAN_loss: 1.757, G_cycle_loss: 1163.944, D_loss: 0.000,edge_lossAB:305.297,edge_lossBA:513.791
[56/300] - time: 321.42, G_loss: 3201.074, G_identity_loss: 40.972, G_GAN_loss: 1.962, G_cycle_loss: 1165.059, D_loss: 0.000,edge_lossAB:305.092,edge_lossBA:513.056
[57/300] - time: 313.45, G_loss: 3213.315, G_identity_loss: 41.138, G_GAN_loss: 1.713, G_cycle_loss: 1168.632, D_loss: 0.000,edge_lossAB:305.339,edge_lossBA:518.060
[58/300] - time: 315.15, G_loss: 3212.619, G_identity_loss: 41.108, G_GAN_loss: 1.657, G_cycle_loss: 1168.523, D_loss: 0.000,edge_lossAB:305.317,edge_lossBA:517.620
[59/300] - time: 314.54, G_loss: 3205.483, G_identity_loss: 41.002, G_GAN_loss: 1.629, G_cycle_loss: 1166.656, D_loss: 0.001,edge_lossAB:305.289,edge_lossBA:514.283
[60/300] - time: 314.89, G_loss: 3220.228, G_identity_loss: 41.163, G_GAN_loss: 1.590, G_cycle_loss: 1171.346, D_loss: 0.000,edge_lossAB:305.326,edge_lossBA:519.604
[61/300] - time: 321.28, G_loss: 3187.626, G_identity_loss: 40.743, G_GAN_loss: 1.577, G_cycle_loss: 1159.935, D_loss: 0.000,edge_lossAB:305.293,edge_lossBA:510.242
[62/300] - time: 319.54, G_loss: 3207.534, G_identity_loss: 40.979, G_GAN_loss: 1.571, G_cycle_loss: 1166.925, D_loss: 0.000,edge_lossAB:305.264,edge_lossBA:515.970
[63/300] - time: 314.51, G_loss: 3200.944, G_identity_loss: 40.897, G_GAN_loss: 1.576, G_cycle_loss: 1164.735, D_loss: 0.000,edge_lossAB:305.259,edge_lossBA:514.155
[64/300] - time: 311.25, G_loss: 3188.744, G_identity_loss: 40.792, G_GAN_loss: 1.671, G_cycle_loss: 1160.911, D_loss: 0.025,edge_lossAB:305.250,edge_lossBA:509.422
[65/300] - time: 313.22, G_loss: 3205.431, G_identity_loss: 41.043, G_GAN_loss: 1.991, G_cycle_loss: 1166.859, D_loss: 0.001,edge_lossAB:304.984,edge_lossBA:513.972
[66/300] - time: 309.63, G_loss: 3206.666, G_identity_loss: 40.990, G_GAN_loss: 1.990, G_cycle_loss: 1166.715, D_loss: 0.000,edge_lossAB:304.970,edge_lossBA:515.768
[67/300] - time: 312.41, G_loss: 3211.357, G_identity_loss: 41.035, G_GAN_loss: 1.992, G_cycle_loss: 1168.108, D_loss: 0.000,edge_lossAB:304.959,edge_lossBA:517.369
[68/300] - time: 313.28, G_loss: 3201.682, G_identity_loss: 40.908, G_GAN_loss: 1.995, G_cycle_loss: 1165.890, D_loss: 0.000,edge_lossAB:304.956,edge_lossBA:512.325
[69/300] - time: 320.05, G_loss: 3203.386, G_identity_loss: 40.927, G_GAN_loss: 1.995, G_cycle_loss: 1165.030, D_loss: 0.000,edge_lossAB:304.948,edge_lossBA:516.073
[70/300] - time: 324.18, G_loss: 3202.572, G_identity_loss: 40.924, G_GAN_loss: 1.996, G_cycle_loss: 1165.932, D_loss: 0.000,edge_lossAB:304.937,edge_lossBA:512.822
[71/300] - time: 311.08, G_loss: 3217.365, G_identity_loss: 41.094, G_GAN_loss: 1.996, G_cycle_loss: 1170.492, D_loss: 0.000,edge_lossAB:304.930,edge_lossBA:518.537
[72/300] - time: 310.48, G_loss: 3216.173, G_identity_loss: 41.080, G_GAN_loss: 1.997, G_cycle_loss: 1169.956, D_loss: 0.000,edge_lossAB:304.931,edge_lossBA:518.184
[73/300] - time: 318.84, G_loss: 3205.089, G_identity_loss: 40.925, G_GAN_loss: 1.997, G_cycle_loss: 1165.282, D_loss: 0.000,edge_lossAB:304.930,edge_lossBA:516.899
[74/300] - time: 314.19, G_loss: 3209.185, G_identity_loss: 41.087, G_GAN_loss: 1.998, G_cycle_loss: 1167.198, D_loss: 0.000,edge_lossAB:304.928,edge_lossBA:517.104
[75/300] - time: 315.34, G_loss: 3184.426, G_identity_loss: 40.798, G_GAN_loss: 1.999, G_cycle_loss: 1158.948, D_loss: 0.000,edge_lossAB:305.113,edge_lossBA:508.739
[76/300] - time: 315.55, G_loss: 3188.078, G_identity_loss: 40.819, G_GAN_loss: 1.999, G_cycle_loss: 1160.497, D_loss: 0.000,edge_lossAB:305.003,edge_lossBA:509.734
[77/300] - time: 311.76, G_loss: 3201.553, G_identity_loss: 40.904, G_GAN_loss: 1.999, G_cycle_loss: 1163.608, D_loss: 0.000,edge_lossAB:304.980,edge_lossBA:516.937
[78/300] - time: 312.22, G_loss: 3203.120, G_identity_loss: 40.933, G_GAN_loss: 1.999, G_cycle_loss: 1166.006, D_loss: 0.000,edge_lossAB:304.966,edge_lossBA:513.590
[79/300] - time: 310.39, G_loss: 3217.034, G_identity_loss: 41.092, G_GAN_loss: 1.998, G_cycle_loss: 1170.428, D_loss: 0.000,edge_lossAB:304.957,edge_lossBA:517.900
[80/300] - time: 311.62, G_loss: 3199.521, G_identity_loss: 40.853, G_GAN_loss: 1.998, G_cycle_loss: 1163.376, D_loss: 0.000,edge_lossAB:304.949,edge_lossBA:515.506
[81/300] - time: 314.62, G_loss: 3213.534, G_identity_loss: 41.042, G_GAN_loss: 1.999, G_cycle_loss: 1168.795, D_loss: 0.000,edge_lossAB:304.942,edge_lossBA:518.367
[82/300] - time: 319.87, G_loss: 3204.780, G_identity_loss: 40.950, G_GAN_loss: 1.890, G_cycle_loss: 1165.635, D_loss: 0.002,edge_lossAB:305.090,edge_lossBA:516.278
[83/300] - time: 313.48, G_loss: 3180.336, G_identity_loss: 40.629, G_GAN_loss: 1.998, G_cycle_loss: 1157.672, D_loss: 0.000,edge_lossAB:304.959,edge_lossBA:507.720
[84/300] - time: 309.02, G_loss: 3203.258, G_identity_loss: 40.933, G_GAN_loss: 1.998, G_cycle_loss: 1166.557, D_loss: 0.000,edge_lossAB:304.931,edge_lossBA:511.985
[85/300] - time: 321.14, G_loss: 3202.033, G_identity_loss: 40.899, G_GAN_loss: 1.997, G_cycle_loss: 1165.489, D_loss: 0.000,edge_lossAB:304.924,edge_lossBA:513.149
[86/300] - time: 325.58, G_loss: 3207.098, G_identity_loss: 40.982, G_GAN_loss: 1.994, G_cycle_loss: 1168.651, D_loss: 0.000,edge_lossAB:304.921,edge_lossBA:511.626
[87/300] - time: 315.48, G_loss: 3204.183, G_identity_loss: 40.912, G_GAN_loss: 1.998, G_cycle_loss: 1165.928, D_loss: 0.000,edge_lossAB:304.916,edge_lossBA:515.143
[88/300] - time: 318.18, G_loss: 3200.282, G_identity_loss: 40.880, G_GAN_loss: 1.997, G_cycle_loss: 1164.625, D_loss: 0.000,edge_lossAB:304.923,edge_lossBA:513.552
[89/300] - time: 317.41, G_loss: 3202.946, G_identity_loss: 40.915, G_GAN_loss: 1.997, G_cycle_loss: 1166.086, D_loss: 0.000,edge_lossAB:304.914,edge_lossBA:513.082
[90/300] - time: 314.85, G_loss: 3196.218, G_identity_loss: 40.814, G_GAN_loss: 1.989, G_cycle_loss: 1162.318, D_loss: 0.000,edge_lossAB:304.929,edge_lossBA:513.816
[91/300] - time: 317.47, G_loss: 3200.280, G_identity_loss: 40.853, G_GAN_loss: 1.831, G_cycle_loss: 1164.122, D_loss: 0.001,edge_lossAB:305.108,edge_lossBA:514.390
[92/300] - time: 314.58, G_loss: 3207.527, G_identity_loss: 40.960, G_GAN_loss: 1.999, G_cycle_loss: 1167.318, D_loss: 0.000,edge_lossAB:304.936,edge_lossBA:514.729
[93/300] - time: 313.76, G_loss: 3191.434, G_identity_loss: 40.750, G_GAN_loss: 1.999, G_cycle_loss: 1161.239, D_loss: 0.000,edge_lossAB:304.915,edge_lossBA:511.650
[94/300] - time: 311.65, G_loss: 3195.407, G_identity_loss: 40.808, G_GAN_loss: 1.999, G_cycle_loss: 1162.982, D_loss: 0.000,edge_lossAB:304.911,edge_lossBA:512.135
[95/300] - time: 313.76, G_loss: 3184.662, G_identity_loss: 40.676, G_GAN_loss: 1.999, G_cycle_loss: 1158.485, D_loss: 0.000,edge_lossAB:304.910,edge_lossBA:510.758
[96/300] - time: 312.01, G_loss: 3209.366, G_identity_loss: 40.984, G_GAN_loss: 1.999, G_cycle_loss: 1168.863, D_loss: 0.000,edge_lossAB:304.909,edge_lossBA:513.514
[97/300] - time: 316.69, G_loss: 3217.411, G_identity_loss: 41.075, G_GAN_loss: 1.999, G_cycle_loss: 1170.687, D_loss: 0.000,edge_lossAB:304.909,edge_lossBA:518.110
[98/300] - time: 314.17, G_loss: 3202.874, G_identity_loss: 40.903, G_GAN_loss: 1.999, G_cycle_loss: 1165.675, D_loss: 0.000,edge_lossAB:304.909,edge_lossBA:513.978
[99/300] - time: 313.86, G_loss: 3215.202, G_identity_loss: 41.040, G_GAN_loss: 1.999, G_cycle_loss: 1169.076, D_loss: 0.000,edge_lossAB:304.908,edge_lossBA:519.229
[100/300] - time: 312.41, G_loss: 3195.294, G_identity_loss: 40.845, G_GAN_loss: 1.999, G_cycle_loss: 1162.731, D_loss: 0.000,edge_lossAB:304.965,edge_lossBA:512.237
[101/300] - time: 315.57, G_loss: 3186.913, G_identity_loss: 40.793, G_GAN_loss: 2.000, G_cycle_loss: 1160.228, D_loss: 0.000,edge_lossAB:304.947,edge_lossBA:509.054
[102/300] - time: 320.25, G_loss: 3201.938, G_identity_loss: 40.918, G_GAN_loss: 2.000, G_cycle_loss: 1164.284, D_loss: 0.000,edge_lossAB:304.928,edge_lossBA:516.067
[103/300] - time: 317.31, G_loss: 3199.668, G_identity_loss: 40.871, G_GAN_loss: 2.000, G_cycle_loss: 1164.737, D_loss: 0.000,edge_lossAB:304.920,edge_lossBA:513.206
[104/300] - time: 313.95, G_loss: 3220.214, G_identity_loss: 41.127, G_GAN_loss: 2.000, G_cycle_loss: 1171.899, D_loss: 0.000,edge_lossAB:304.917,edge_lossBA:518.394
[105/300] - time: 316.29, G_loss: 3202.860, G_identity_loss: 40.905, G_GAN_loss: 2.000, G_cycle_loss: 1165.153, D_loss: 0.000,edge_lossAB:304.914,edge_lossBA:515.435
[106/300] - time: 311.86, G_loss: 3212.385, G_identity_loss: 41.018, G_GAN_loss: 1.961, G_cycle_loss: 1168.645, D_loss: 0.001,edge_lossAB:304.934,edge_lossBA:517.007
[107/300] - time: 314.66, G_loss: 3200.857, G_identity_loss: 40.875, G_GAN_loss: 1.737, G_cycle_loss: 1164.390, D_loss: 0.000,edge_lossAB:305.038,edge_lossBA:514.735
[108/300] - time: 308.84, G_loss: 3195.257, G_identity_loss: 40.825, G_GAN_loss: 1.999, G_cycle_loss: 1163.568, D_loss: 0.000,edge_lossAB:304.926,edge_lossBA:510.439
[109/300] - time: 312.60, G_loss: 3192.224, G_identity_loss: 40.759, G_GAN_loss: 1.999, G_cycle_loss: 1161.108, D_loss: 0.000,edge_lossAB:304.917,edge_lossBA:512.669
[110/300] - time: 316.91, G_loss: 3207.202, G_identity_loss: 40.941, G_GAN_loss: 1.999, G_cycle_loss: 1166.918, D_loss: 0.000,edge_lossAB:304.918,edge_lossBA:515.732
[111/300] - time: 313.23, G_loss: 3218.876, G_identity_loss: 41.110, G_GAN_loss: 1.999, G_cycle_loss: 1171.948, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:516.877
[112/300] - time: 316.65, G_loss: 3216.833, G_identity_loss: 41.080, G_GAN_loss: 1.999, G_cycle_loss: 1170.745, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:517.569
[113/300] - time: 319.38, G_loss: 3202.281, G_identity_loss: 40.882, G_GAN_loss: 1.999, G_cycle_loss: 1164.954, D_loss: 0.000,edge_lossAB:304.911,edge_lossBA:514.974
[114/300] - time: 311.60, G_loss: 3203.459, G_identity_loss: 40.911, G_GAN_loss: 1.998, G_cycle_loss: 1165.592, D_loss: 0.000,edge_lossAB:304.933,edge_lossBA:514.920
[115/300] - time: 319.43, G_loss: 3210.896, G_identity_loss: 41.014, G_GAN_loss: 1.953, G_cycle_loss: 1168.440, D_loss: 0.001,edge_lossAB:304.920,edge_lossBA:516.252
[116/300] - time: 311.13, G_loss: 3215.709, G_identity_loss: 41.071, G_GAN_loss: 1.943, G_cycle_loss: 1169.771, D_loss: 0.000,edge_lossAB:304.927,edge_lossBA:518.506
[117/300] - time: 309.84, G_loss: 3212.418, G_identity_loss: 41.022, G_GAN_loss: 1.999, G_cycle_loss: 1168.030, D_loss: 0.000,edge_lossAB:304.909,edge_lossBA:519.064
[118/300] - time: 313.00, G_loss: 3178.677, G_identity_loss: 40.602, G_GAN_loss: 2.000, G_cycle_loss: 1157.039, D_loss: 0.000,edge_lossAB:304.909,edge_lossBA:507.763
[119/300] - time: 319.44, G_loss: 3205.323, G_identity_loss: 40.934, G_GAN_loss: 2.000, G_cycle_loss: 1166.209, D_loss: 0.000,edge_lossAB:304.908,edge_lossBA:515.115
[120/300] - time: 316.52, G_loss: 3192.845, G_identity_loss: 40.779, G_GAN_loss: 2.000, G_cycle_loss: 1161.297, D_loss: 0.000,edge_lossAB:304.908,edge_lossBA:513.220
[121/300] - time: 314.39, G_loss: 3204.376, G_identity_loss: 40.935, G_GAN_loss: 2.000, G_cycle_loss: 1166.103, D_loss: 0.000,edge_lossAB:304.909,edge_lossBA:514.420
[122/300] - time: 312.83, G_loss: 3204.999, G_identity_loss: 40.938, G_GAN_loss: 2.000, G_cycle_loss: 1166.912, D_loss: 0.000,edge_lossAB:304.909,edge_lossBA:513.744
[123/300] - time: 315.14, G_loss: 3201.907, G_identity_loss: 40.913, G_GAN_loss: 1.999, G_cycle_loss: 1165.693, D_loss: 0.000,edge_lossAB:304.909,edge_lossBA:512.689
[124/300] - time: 311.39, G_loss: 3199.100, G_identity_loss: 40.850, G_GAN_loss: 1.999, G_cycle_loss: 1163.347, D_loss: 0.000,edge_lossAB:304.909,edge_lossBA:515.119
[125/300] - time: 319.53, G_loss: 3185.850, G_identity_loss: 40.702, G_GAN_loss: 1.999, G_cycle_loss: 1159.546, D_loss: 0.000,edge_lossAB:304.910,edge_lossBA:509.569
[126/300] - time: 310.80, G_loss: 3198.693, G_identity_loss: 40.846, G_GAN_loss: 1.999, G_cycle_loss: 1163.388, D_loss: 0.000,edge_lossAB:304.914,edge_lossBA:514.344
[127/300] - time: 313.19, G_loss: 3186.647, G_identity_loss: 40.696, G_GAN_loss: 1.999, G_cycle_loss: 1159.505, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:510.736
[128/300] - time: 321.30, G_loss: 3199.689, G_identity_loss: 40.872, G_GAN_loss: 1.999, G_cycle_loss: 1163.534, D_loss: 0.000,edge_lossAB:304.910,edge_lossBA:515.124
[129/300] - time: 315.60, G_loss: 3198.842, G_identity_loss: 40.864, G_GAN_loss: 1.999, G_cycle_loss: 1164.690, D_loss: 0.000,edge_lossAB:304.910,edge_lossBA:511.625
[130/300] - time: 311.17, G_loss: 3190.684, G_identity_loss: 40.757, G_GAN_loss: 1.999, G_cycle_loss: 1161.577, D_loss: 0.000,edge_lossAB:304.910,edge_lossBA:510.095
[131/300] - time: 312.57, G_loss: 3198.808, G_identity_loss: 40.861, G_GAN_loss: 1.999, G_cycle_loss: 1164.239, D_loss: 0.000,edge_lossAB:304.911,edge_lossBA:512.634
[132/300] - time: 317.20, G_loss: 3191.323, G_identity_loss: 40.767, G_GAN_loss: 1.999, G_cycle_loss: 1162.159, D_loss: 0.000,edge_lossAB:304.912,edge_lossBA:509.650
[133/300] - time: 315.24, G_loss: 3212.236, G_identity_loss: 41.027, G_GAN_loss: 1.999, G_cycle_loss: 1169.144, D_loss: 0.000,edge_lossAB:304.909,edge_lossBA:516.432
[134/300] - time: 311.12, G_loss: 3180.627, G_identity_loss: 40.611, G_GAN_loss: 1.999, G_cycle_loss: 1156.987, D_loss: 0.000,edge_lossAB:304.911,edge_lossBA:509.889
[135/300] - time: 312.23, G_loss: 3196.810, G_identity_loss: 40.845, G_GAN_loss: 1.763, G_cycle_loss: 1162.676, D_loss: 0.001,edge_lossAB:305.049,edge_lossBA:514.525
[136/300] - time: 305.49, G_loss: 3197.114, G_identity_loss: 40.820, G_GAN_loss: 1.896, G_cycle_loss: 1162.512, D_loss: 0.000,edge_lossAB:305.018,edge_lossBA:515.204
[137/300] - time: 320.98, G_loss: 3200.427, G_identity_loss: 40.893, G_GAN_loss: 1.999, G_cycle_loss: 1165.265, D_loss: 0.000,edge_lossAB:304.922,edge_lossBA:512.262
[138/300] - time: 311.31, G_loss: 3204.328, G_identity_loss: 40.923, G_GAN_loss: 1.999, G_cycle_loss: 1166.632, D_loss: 0.000,edge_lossAB:304.916,edge_lossBA:513.190
[139/300] - time: 315.91, G_loss: 3198.721, G_identity_loss: 40.849, G_GAN_loss: 1.999, G_cycle_loss: 1163.835, D_loss: 0.000,edge_lossAB:304.914,edge_lossBA:513.572
[140/300] - time: 312.44, G_loss: 3200.868, G_identity_loss: 40.875, G_GAN_loss: 1.999, G_cycle_loss: 1164.376, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:514.531
[141/300] - time: 318.28, G_loss: 3190.993, G_identity_loss: 40.742, G_GAN_loss: 1.999, G_cycle_loss: 1160.925, D_loss: 0.000,edge_lossAB:304.914,edge_lossBA:511.859
[142/300] - time: 310.76, G_loss: 3193.307, G_identity_loss: 40.789, G_GAN_loss: 1.999, G_cycle_loss: 1161.724, D_loss: 0.000,edge_lossAB:304.914,edge_lossBA:512.476
[143/300] - time: 318.50, G_loss: 3207.506, G_identity_loss: 40.952, G_GAN_loss: 1.999, G_cycle_loss: 1166.887, D_loss: 0.000,edge_lossAB:304.914,edge_lossBA:516.154
[144/300] - time: 316.87, G_loss: 3214.722, G_identity_loss: 41.059, G_GAN_loss: 2.000, G_cycle_loss: 1169.913, D_loss: 0.000,edge_lossAB:304.912,edge_lossBA:517.182
[145/300] - time: 311.40, G_loss: 3224.683, G_identity_loss: 41.191, G_GAN_loss: 2.000, G_cycle_loss: 1173.797, D_loss: 0.000,edge_lossAB:304.911,edge_lossBA:518.819
[146/300] - time: 318.64, G_loss: 3197.179, G_identity_loss: 40.833, G_GAN_loss: 2.000, G_cycle_loss: 1163.429, D_loss: 0.000,edge_lossAB:304.912,edge_lossBA:513.184
[147/300] - time: 313.89, G_loss: 3185.987, G_identity_loss: 40.702, G_GAN_loss: 2.000, G_cycle_loss: 1159.724, D_loss: 0.000,edge_lossAB:304.911,edge_lossBA:509.414
[148/300] - time: 314.17, G_loss: 3202.270, G_identity_loss: 40.915, G_GAN_loss: 1.881, G_cycle_loss: 1165.672, D_loss: 0.002,edge_lossAB:305.298,edge_lossBA:513.137
[149/300] - time: 312.94, G_loss: 3203.318, G_identity_loss: 40.912, G_GAN_loss: 1.946, G_cycle_loss: 1165.323, D_loss: 0.000,edge_lossAB:305.059,edge_lossBA:515.173
[150/300] - time: 310.63, G_loss: 3208.509, G_identity_loss: 40.962, G_GAN_loss: 2.000, G_cycle_loss: 1166.994, D_loss: 0.000,edge_lossAB:304.921,edge_lossBA:516.929
[151/300] - time: 313.23, G_loss: 3208.907, G_identity_loss: 40.982, G_GAN_loss: 2.000, G_cycle_loss: 1167.672, D_loss: 0.000,edge_lossAB:304.914,edge_lossBA:516.045
[152/300] - time: 314.02, G_loss: 3216.105, G_identity_loss: 41.077, G_GAN_loss: 2.000, G_cycle_loss: 1170.335, D_loss: 0.000,edge_lossAB:304.914,edge_lossBA:517.726
[153/300] - time: 311.56, G_loss: 3215.386, G_identity_loss: 41.045, G_GAN_loss: 2.000, G_cycle_loss: 1170.064, D_loss: 0.000,edge_lossAB:304.914,edge_lossBA:517.535
[154/300] - time: 311.68, G_loss: 3204.571, G_identity_loss: 40.923, G_GAN_loss: 2.000, G_cycle_loss: 1166.875, D_loss: 0.000,edge_lossAB:304.914,edge_lossBA:512.892
[155/300] - time: 312.06, G_loss: 3213.138, G_identity_loss: 41.025, G_GAN_loss: 2.000, G_cycle_loss: 1169.366, D_loss: 0.000,edge_lossAB:304.914,edge_lossBA:516.495
[156/300] - time: 314.49, G_loss: 3196.739, G_identity_loss: 40.825, G_GAN_loss: 2.000, G_cycle_loss: 1162.974, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:513.087
[157/300] - time: 307.59, G_loss: 3202.449, G_identity_loss: 40.900, G_GAN_loss: 2.000, G_cycle_loss: 1165.856, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:513.155
[158/300] - time: 317.79, G_loss: 3196.252, G_identity_loss: 40.812, G_GAN_loss: 2.000, G_cycle_loss: 1163.047, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:513.049
[159/300] - time: 311.27, G_loss: 3203.467, G_identity_loss: 40.906, G_GAN_loss: 2.000, G_cycle_loss: 1166.063, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:513.830
[160/300] - time: 320.13, G_loss: 3192.569, G_identity_loss: 40.763, G_GAN_loss: 2.000, G_cycle_loss: 1162.260, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:510.655
[161/300] - time: 310.66, G_loss: 3200.180, G_identity_loss: 40.870, G_GAN_loss: 2.000, G_cycle_loss: 1165.036, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:512.902
[162/300] - time: 312.46, G_loss: 3218.599, G_identity_loss: 41.077, G_GAN_loss: 2.000, G_cycle_loss: 1171.175, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:518.215
[163/300] - time: 318.68, G_loss: 3201.749, G_identity_loss: 40.879, G_GAN_loss: 2.000, G_cycle_loss: 1165.040, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:514.090
[164/300] - time: 315.67, G_loss: 3199.155, G_identity_loss: 40.845, G_GAN_loss: 2.000, G_cycle_loss: 1164.159, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:513.460
[165/300] - time: 315.55, G_loss: 3184.847, G_identity_loss: 40.656, G_GAN_loss: 2.000, G_cycle_loss: 1159.176, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:509.258
[166/300] - time: 312.16, G_loss: 3200.749, G_identity_loss: 40.849, G_GAN_loss: 2.000, G_cycle_loss: 1164.458, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:514.375
[167/300] - time: 314.53, G_loss: 3195.611, G_identity_loss: 40.806, G_GAN_loss: 2.000, G_cycle_loss: 1163.356, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:511.860
[168/300] - time: 311.21, G_loss: 3205.075, G_identity_loss: 40.887, G_GAN_loss: 2.000, G_cycle_loss: 1165.594, D_loss: 0.000,edge_lossAB:304.912,edge_lossBA:516.345
[169/300] - time: 312.46, G_loss: 3202.161, G_identity_loss: 40.878, G_GAN_loss: 2.000, G_cycle_loss: 1165.249, D_loss: 0.000,edge_lossAB:304.912,edge_lossBA:513.851
[170/300] - time: 314.21, G_loss: 3219.003, G_identity_loss: 41.090, G_GAN_loss: 2.000, G_cycle_loss: 1170.589, D_loss: 0.000,edge_lossAB:304.912,edge_lossBA:520.207
[171/300] - time: 309.94, G_loss: 3207.055, G_identity_loss: 40.905, G_GAN_loss: 2.000, G_cycle_loss: 1165.944, D_loss: 0.000,edge_lossAB:304.912,edge_lossBA:517.759
[172/300] - time: 313.66, G_loss: 3199.385, G_identity_loss: 40.841, G_GAN_loss: 2.000, G_cycle_loss: 1164.687, D_loss: 0.000,edge_lossAB:304.912,edge_lossBA:512.341
[173/300] - time: 311.21, G_loss: 3188.645, G_identity_loss: 40.702, G_GAN_loss: 2.000, G_cycle_loss: 1160.642, D_loss: 0.000,edge_lossAB:304.912,edge_lossBA:510.434
[174/300] - time: 317.12, G_loss: 3209.006, G_identity_loss: 40.963, G_GAN_loss: 2.000, G_cycle_loss: 1168.107, D_loss: 0.000,edge_lossAB:304.912,edge_lossBA:514.608
[175/300] - time: 310.64, G_loss: 3206.970, G_identity_loss: 40.920, G_GAN_loss: 2.000, G_cycle_loss: 1166.743, D_loss: 0.000,edge_lossAB:304.912,edge_lossBA:516.297
[176/300] - time: 317.35, G_loss: 3224.988, G_identity_loss: 41.156, G_GAN_loss: 2.000, G_cycle_loss: 1173.297, D_loss: 0.000,edge_lossAB:304.912,edge_lossBA:520.144
[177/300] - time: 314.72, G_loss: 3196.984, G_identity_loss: 40.799, G_GAN_loss: 2.000, G_cycle_loss: 1163.599, D_loss: 0.000,edge_lossAB:304.912,edge_lossBA:512.382
[178/300] - time: 322.12, G_loss: 3189.699, G_identity_loss: 40.714, G_GAN_loss: 2.000, G_cycle_loss: 1160.347, D_loss: 0.000,edge_lossAB:304.912,edge_lossBA:511.882
[179/300] - time: 313.10, G_loss: 3230.572, G_identity_loss: 41.224, G_GAN_loss: 2.000, G_cycle_loss: 1175.557, D_loss: 0.000,edge_lossAB:304.912,edge_lossBA:520.881
[180/300] - time: 316.43, G_loss: 3192.967, G_identity_loss: 40.762, G_GAN_loss: 2.000, G_cycle_loss: 1161.926, D_loss: 0.000,edge_lossAB:304.912,edge_lossBA:512.389
[181/300] - time: 314.66, G_loss: 3188.955, G_identity_loss: 40.720, G_GAN_loss: 2.000, G_cycle_loss: 1160.968, D_loss: 0.000,edge_lossAB:304.912,edge_lossBA:509.878
[182/300] - time: 316.80, G_loss: 3203.657, G_identity_loss: 40.889, G_GAN_loss: 2.000, G_cycle_loss: 1165.030, D_loss: 0.000,edge_lossAB:304.912,edge_lossBA:515.866
[183/300] - time: 311.72, G_loss: 3211.504, G_identity_loss: 40.984, G_GAN_loss: 2.000, G_cycle_loss: 1168.231, D_loss: 0.000,edge_lossAB:304.912,edge_lossBA:517.341
[184/300] - time: 317.10, G_loss: 3182.684, G_identity_loss: 40.627, G_GAN_loss: 2.000, G_cycle_loss: 1158.153, D_loss: 0.000,edge_lossAB:304.912,edge_lossBA:509.417
[185/300] - time: 314.32, G_loss: 3198.699, G_identity_loss: 40.839, G_GAN_loss: 2.000, G_cycle_loss: 1165.095, D_loss: 0.000,edge_lossAB:304.912,edge_lossBA:510.888
[186/300] - time: 313.74, G_loss: 3202.334, G_identity_loss: 40.860, G_GAN_loss: 2.000, G_cycle_loss: 1164.585, D_loss: 0.000,edge_lossAB:304.912,edge_lossBA:516.000
[187/300] - time: 314.76, G_loss: 3203.477, G_identity_loss: 40.887, G_GAN_loss: 2.000, G_cycle_loss: 1165.379, D_loss: 0.000,edge_lossAB:304.912,edge_lossBA:515.181
[188/300] - time: 313.65, G_loss: 3191.964, G_identity_loss: 40.748, G_GAN_loss: 2.000, G_cycle_loss: 1161.860, D_loss: 0.000,edge_lossAB:304.912,edge_lossBA:511.378
[189/300] - time: 316.29, G_loss: 3212.606, G_identity_loss: 40.987, G_GAN_loss: 2.000, G_cycle_loss: 1168.580, D_loss: 0.000,edge_lossAB:304.912,edge_lossBA:517.356
[190/300] - time: 311.45, G_loss: 3210.730, G_identity_loss: 40.957, G_GAN_loss: 2.000, G_cycle_loss: 1167.890, D_loss: 0.000,edge_lossAB:304.912,edge_lossBA:517.361
[191/300] - time: 316.27, G_loss: 3198.491, G_identity_loss: 40.814, G_GAN_loss: 2.000, G_cycle_loss: 1163.716, D_loss: 0.000,edge_lossAB:304.912,edge_lossBA:513.842
[192/300] - time: 312.34, G_loss: 3206.418, G_identity_loss: 40.917, G_GAN_loss: 2.000, G_cycle_loss: 1166.386, D_loss: 0.000,edge_lossAB:304.912,edge_lossBA:515.849
[193/300] - time: 308.55, G_loss: 3209.299, G_identity_loss: 40.949, G_GAN_loss: 2.000, G_cycle_loss: 1167.524, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:517.100
[194/300] - time: 310.20, G_loss: 3179.971, G_identity_loss: 40.587, G_GAN_loss: 2.000, G_cycle_loss: 1156.395, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:510.426
[195/300] - time: 321.34, G_loss: 3203.764, G_identity_loss: 40.877, G_GAN_loss: 2.000, G_cycle_loss: 1165.420, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:515.364
[196/300] - time: 309.77, G_loss: 3198.205, G_identity_loss: 40.817, G_GAN_loss: 2.000, G_cycle_loss: 1164.204, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:512.243
[197/300] - time: 313.09, G_loss: 3196.721, G_identity_loss: 40.809, G_GAN_loss: 2.000, G_cycle_loss: 1163.806, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:511.948
[198/300] - time: 308.18, G_loss: 3185.319, G_identity_loss: 40.649, G_GAN_loss: 2.000, G_cycle_loss: 1159.337, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:509.832
[199/300] - time: 314.04, G_loss: 3210.303, G_identity_loss: 40.973, G_GAN_loss: 2.000, G_cycle_loss: 1168.006, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:516.592
[200/300] - time: 319.22, G_loss: 3193.274, G_identity_loss: 40.758, G_GAN_loss: 2.000, G_cycle_loss: 1162.506, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:511.093
[201/300] - time: 310.17, G_loss: 3201.585, G_identity_loss: 40.870, G_GAN_loss: 2.000, G_cycle_loss: 1164.839, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:514.491
[202/300] - time: 314.61, G_loss: 3215.825, G_identity_loss: 41.036, G_GAN_loss: 2.000, G_cycle_loss: 1169.808, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:518.527
[203/300] - time: 311.91, G_loss: 3209.317, G_identity_loss: 40.944, G_GAN_loss: 2.000, G_cycle_loss: 1167.833, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:516.042
[204/300] - time: 311.20, G_loss: 3215.717, G_identity_loss: 41.021, G_GAN_loss: 2.000, G_cycle_loss: 1168.799, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:520.891
[205/300] - time: 316.99, G_loss: 3193.044, G_identity_loss: 40.741, G_GAN_loss: 2.000, G_cycle_loss: 1161.509, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:512.777
[206/300] - time: 308.60, G_loss: 3202.759, G_identity_loss: 40.878, G_GAN_loss: 2.000, G_cycle_loss: 1165.662, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:514.195
[207/300] - time: 310.75, G_loss: 3197.150, G_identity_loss: 40.790, G_GAN_loss: 2.000, G_cycle_loss: 1163.463, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:512.472
[208/300] - time: 314.17, G_loss: 3211.071, G_identity_loss: 40.977, G_GAN_loss: 2.000, G_cycle_loss: 1168.525, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:516.299
[209/300] - time: 314.63, G_loss: 3179.138, G_identity_loss: 40.578, G_GAN_loss: 2.000, G_cycle_loss: 1156.921, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:508.690
[210/300] - time: 315.93, G_loss: 3215.460, G_identity_loss: 41.040, G_GAN_loss: 2.000, G_cycle_loss: 1170.151, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:517.180
[211/300] - time: 319.94, G_loss: 3221.856, G_identity_loss: 41.113, G_GAN_loss: 2.000, G_cycle_loss: 1171.769, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:520.302
[212/300] - time: 320.95, G_loss: 3206.968, G_identity_loss: 40.926, G_GAN_loss: 2.000, G_cycle_loss: 1166.478, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:516.632
[213/300] - time: 317.47, G_loss: 3210.360, G_identity_loss: 40.972, G_GAN_loss: 2.000, G_cycle_loss: 1167.554, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:517.552
[214/300] - time: 310.87, G_loss: 3201.970, G_identity_loss: 40.861, G_GAN_loss: 2.000, G_cycle_loss: 1164.911, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:514.533
[215/300] - time: 311.55, G_loss: 3222.906, G_identity_loss: 41.121, G_GAN_loss: 2.000, G_cycle_loss: 1172.039, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:520.923
[216/300] - time: 319.07, G_loss: 3192.408, G_identity_loss: 40.743, G_GAN_loss: 2.000, G_cycle_loss: 1161.254, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:512.944
[217/300] - time: 317.43, G_loss: 3187.416, G_identity_loss: 40.694, G_GAN_loss: 2.000, G_cycle_loss: 1161.577, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:506.470
[218/300] - time: 320.05, G_loss: 3187.418, G_identity_loss: 40.684, G_GAN_loss: 2.000, G_cycle_loss: 1159.371, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:511.526
[219/300] - time: 318.78, G_loss: 3193.751, G_identity_loss: 40.763, G_GAN_loss: 2.000, G_cycle_loss: 1162.600, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:510.864
[220/300] - time: 313.64, G_loss: 3205.839, G_identity_loss: 40.915, G_GAN_loss: 2.000, G_cycle_loss: 1166.705, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:514.529
[221/300] - time: 310.86, G_loss: 3197.498, G_identity_loss: 40.809, G_GAN_loss: 2.000, G_cycle_loss: 1163.615, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:512.768
[222/300] - time: 311.41, G_loss: 3203.182, G_identity_loss: 40.868, G_GAN_loss: 2.000, G_cycle_loss: 1164.601, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:516.354
[223/300] - time: 315.53, G_loss: 3207.167, G_identity_loss: 40.941, G_GAN_loss: 2.000, G_cycle_loss: 1167.307, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:514.998
[224/300] - time: 312.03, G_loss: 3198.468, G_identity_loss: 40.804, G_GAN_loss: 2.000, G_cycle_loss: 1163.254, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:514.321
[225/300] - time: 311.43, G_loss: 3194.636, G_identity_loss: 40.793, G_GAN_loss: 2.000, G_cycle_loss: 1163.560, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:509.926
[226/300] - time: 312.40, G_loss: 3179.859, G_identity_loss: 40.601, G_GAN_loss: 2.000, G_cycle_loss: 1157.043, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:509.225
[227/300] - time: 316.39, G_loss: 3191.670, G_identity_loss: 40.718, G_GAN_loss: 2.000, G_cycle_loss: 1159.867, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:515.124
[228/300] - time: 310.92, G_loss: 3198.722, G_identity_loss: 40.829, G_GAN_loss: 2.000, G_cycle_loss: 1164.442, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:512.635
[229/300] - time: 317.12, G_loss: 3197.396, G_identity_loss: 40.817, G_GAN_loss: 2.000, G_cycle_loss: 1163.292, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:513.625
[230/300] - time: 314.26, G_loss: 3213.462, G_identity_loss: 41.006, G_GAN_loss: 2.000, G_cycle_loss: 1169.263, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:517.064
[231/300] - time: 311.25, G_loss: 3207.539, G_identity_loss: 40.924, G_GAN_loss: 2.000, G_cycle_loss: 1166.295, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:517.509
[232/300] - time: 316.70, G_loss: 3208.698, G_identity_loss: 40.945, G_GAN_loss: 2.000, G_cycle_loss: 1167.487, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:515.843
[233/300] - time: 313.64, G_loss: 3203.011, G_identity_loss: 40.863, G_GAN_loss: 2.000, G_cycle_loss: 1164.865, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:515.489
[234/300] - time: 314.85, G_loss: 3210.768, G_identity_loss: 40.974, G_GAN_loss: 2.000, G_cycle_loss: 1168.568, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:516.215
[235/300] - time: 312.22, G_loss: 3210.793, G_identity_loss: 40.991, G_GAN_loss: 2.000, G_cycle_loss: 1169.749, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:513.366
[236/300] - time: 313.62, G_loss: 3196.943, G_identity_loss: 40.798, G_GAN_loss: 2.000, G_cycle_loss: 1163.905, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:511.277
[237/300] - time: 318.29, G_loss: 3211.329, G_identity_loss: 40.980, G_GAN_loss: 2.000, G_cycle_loss: 1168.350, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:516.862
[238/300] - time: 313.35, G_loss: 3198.332, G_identity_loss: 40.831, G_GAN_loss: 2.000, G_cycle_loss: 1164.128, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:512.791
[239/300] - time: 311.28, G_loss: 3186.782, G_identity_loss: 40.686, G_GAN_loss: 2.000, G_cycle_loss: 1160.032, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:509.339
[240/300] - time: 316.03, G_loss: 3188.948, G_identity_loss: 40.689, G_GAN_loss: 2.000, G_cycle_loss: 1160.271, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:511.047
[241/300] - time: 311.23, G_loss: 3186.480, G_identity_loss: 40.675, G_GAN_loss: 2.000, G_cycle_loss: 1160.095, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:509.250
[242/300] - time: 314.71, G_loss: 3195.457, G_identity_loss: 40.782, G_GAN_loss: 2.000, G_cycle_loss: 1162.461, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:513.168
[243/300] - time: 310.55, G_loss: 3207.386, G_identity_loss: 40.926, G_GAN_loss: 2.000, G_cycle_loss: 1166.903, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:516.063
[244/300] - time: 318.12, G_loss: 3195.983, G_identity_loss: 40.781, G_GAN_loss: 2.000, G_cycle_loss: 1162.693, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:513.090
[245/300] - time: 313.38, G_loss: 3212.038, G_identity_loss: 40.989, G_GAN_loss: 2.000, G_cycle_loss: 1169.079, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:515.994
[246/300] - time: 314.91, G_loss: 3195.242, G_identity_loss: 40.782, G_GAN_loss: 2.000, G_cycle_loss: 1162.267, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:513.301
[247/300] - time: 317.84, G_loss: 3206.615, G_identity_loss: 40.909, G_GAN_loss: 2.000, G_cycle_loss: 1166.189, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:516.529
[248/300] - time: 311.01, G_loss: 3185.723, G_identity_loss: 40.660, G_GAN_loss: 2.000, G_cycle_loss: 1159.314, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:509.965
[249/300] - time: 314.15, G_loss: 3192.971, G_identity_loss: 40.754, G_GAN_loss: 2.000, G_cycle_loss: 1161.528, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:512.506
[250/300] - time: 310.61, G_loss: 3199.997, G_identity_loss: 40.839, G_GAN_loss: 2.000, G_cycle_loss: 1164.020, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:514.959
[251/300] - time: 314.63, G_loss: 3200.886, G_identity_loss: 40.846, G_GAN_loss: 2.000, G_cycle_loss: 1165.049, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:513.302
[252/300] - time: 319.48, G_loss: 3216.536, G_identity_loss: 41.044, G_GAN_loss: 2.000, G_cycle_loss: 1170.375, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:517.816
[253/300] - time: 315.06, G_loss: 3201.822, G_identity_loss: 40.865, G_GAN_loss: 2.000, G_cycle_loss: 1165.471, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:513.070
[254/300] - time: 320.59, G_loss: 3199.675, G_identity_loss: 40.841, G_GAN_loss: 2.000, G_cycle_loss: 1163.941, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:514.407
[255/300] - time: 316.37, G_loss: 3208.138, G_identity_loss: 40.942, G_GAN_loss: 2.000, G_cycle_loss: 1166.967, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:516.530
[256/300] - time: 316.05, G_loss: 3217.380, G_identity_loss: 41.047, G_GAN_loss: 2.000, G_cycle_loss: 1170.635, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:518.457
[257/300] - time: 320.49, G_loss: 3208.100, G_identity_loss: 40.926, G_GAN_loss: 2.000, G_cycle_loss: 1166.811, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:516.997
[258/300] - time: 312.86, G_loss: 3211.058, G_identity_loss: 40.992, G_GAN_loss: 2.000, G_cycle_loss: 1168.751, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:515.607
[259/300] - time: 310.95, G_loss: 3190.746, G_identity_loss: 40.706, G_GAN_loss: 2.000, G_cycle_loss: 1161.415, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:511.016
[260/300] - time: 316.82, G_loss: 3174.656, G_identity_loss: 40.517, G_GAN_loss: 2.000, G_cycle_loss: 1154.915, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:508.213
[261/300] - time: 311.81, G_loss: 3193.401, G_identity_loss: 40.750, G_GAN_loss: 2.000, G_cycle_loss: 1161.293, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:513.453
[262/300] - time: 311.41, G_loss: 3189.250, G_identity_loss: 40.708, G_GAN_loss: 2.000, G_cycle_loss: 1160.770, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:510.888
[263/300] - time: 312.91, G_loss: 3203.948, G_identity_loss: 40.886, G_GAN_loss: 2.000, G_cycle_loss: 1165.721, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:515.379
[264/300] - time: 316.69, G_loss: 3196.510, G_identity_loss: 40.809, G_GAN_loss: 2.000, G_cycle_loss: 1163.845, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:511.381
[265/300] - time: 313.31, G_loss: 3199.960, G_identity_loss: 40.830, G_GAN_loss: 2.000, G_cycle_loss: 1163.522, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:515.683
[266/300] - time: 308.05, G_loss: 3210.680, G_identity_loss: 40.976, G_GAN_loss: 2.000, G_cycle_loss: 1168.401, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:516.142
[267/300] - time: 308.55, G_loss: 3210.930, G_identity_loss: 40.973, G_GAN_loss: 2.000, G_cycle_loss: 1167.717, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:518.020
[268/300] - time: 316.43, G_loss: 3206.642, G_identity_loss: 40.934, G_GAN_loss: 2.000, G_cycle_loss: 1166.568, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:516.242
[269/300] - time: 317.61, G_loss: 3211.186, G_identity_loss: 40.977, G_GAN_loss: 2.000, G_cycle_loss: 1168.168, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:517.112
[270/300] - time: 312.11, G_loss: 3209.327, G_identity_loss: 40.939, G_GAN_loss: 2.000, G_cycle_loss: 1167.521, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:516.378
[271/300] - time: 316.19, G_loss: 3192.041, G_identity_loss: 40.731, G_GAN_loss: 2.000, G_cycle_loss: 1161.116, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:512.464
[272/300] - time: 314.47, G_loss: 3202.779, G_identity_loss: 40.882, G_GAN_loss: 2.000, G_cycle_loss: 1165.783, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:513.512
[273/300] - time: 310.65, G_loss: 3186.317, G_identity_loss: 40.661, G_GAN_loss: 2.000, G_cycle_loss: 1158.793, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:512.284
[274/300] - time: 309.05, G_loss: 3209.675, G_identity_loss: 40.963, G_GAN_loss: 2.000, G_cycle_loss: 1167.527, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:516.964
[275/300] - time: 312.84, G_loss: 3196.682, G_identity_loss: 40.793, G_GAN_loss: 2.000, G_cycle_loss: 1163.184, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:512.759
[276/300] - time: 310.59, G_loss: 3193.559, G_identity_loss: 40.760, G_GAN_loss: 2.000, G_cycle_loss: 1161.381, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:513.649
[277/300] - time: 307.43, G_loss: 3190.866, G_identity_loss: 40.734, G_GAN_loss: 2.000, G_cycle_loss: 1160.809, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:511.875
[278/300] - time: 315.07, G_loss: 3192.636, G_identity_loss: 40.736, G_GAN_loss: 2.000, G_cycle_loss: 1161.037, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:513.653
[279/300] - time: 314.78, G_loss: 3208.419, G_identity_loss: 40.963, G_GAN_loss: 2.000, G_cycle_loss: 1167.693, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:515.206
[280/300] - time: 321.62, G_loss: 3199.456, G_identity_loss: 40.823, G_GAN_loss: 2.000, G_cycle_loss: 1163.731, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:514.997
[281/300] - time: 318.12, G_loss: 3201.593, G_identity_loss: 40.866, G_GAN_loss: 2.000, G_cycle_loss: 1164.856, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:514.441
[282/300] - time: 316.24, G_loss: 3212.967, G_identity_loss: 41.004, G_GAN_loss: 2.000, G_cycle_loss: 1168.864, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:517.561
[283/300] - time: 311.56, G_loss: 3218.361, G_identity_loss: 41.052, G_GAN_loss: 2.000, G_cycle_loss: 1171.093, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:518.330
[284/300] - time: 316.41, G_loss: 3185.106, G_identity_loss: 40.657, G_GAN_loss: 2.000, G_cycle_loss: 1159.705, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:508.787
[285/300] - time: 320.22, G_loss: 3189.461, G_identity_loss: 40.705, G_GAN_loss: 2.000, G_cycle_loss: 1160.518, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:511.045
[286/300] - time: 310.62, G_loss: 3188.745, G_identity_loss: 40.687, G_GAN_loss: 2.000, G_cycle_loss: 1160.520, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:510.659
[287/300] - time: 316.59, G_loss: 3196.103, G_identity_loss: 40.784, G_GAN_loss: 2.000, G_cycle_loss: 1162.469, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:514.166
[288/300] - time: 314.07, G_loss: 3212.186, G_identity_loss: 40.989, G_GAN_loss: 2.000, G_cycle_loss: 1168.866, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:516.656
[289/300] - time: 308.21, G_loss: 3201.938, G_identity_loss: 40.869, G_GAN_loss: 2.000, G_cycle_loss: 1165.169, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:514.405
[290/300] - time: 315.65, G_loss: 3214.478, G_identity_loss: 41.022, G_GAN_loss: 2.000, G_cycle_loss: 1169.931, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:516.579
[291/300] - time: 320.32, G_loss: 3209.864, G_identity_loss: 40.954, G_GAN_loss: 2.000, G_cycle_loss: 1167.553, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:517.348
[292/300] - time: 312.55, G_loss: 3199.218, G_identity_loss: 40.829, G_GAN_loss: 2.000, G_cycle_loss: 1164.056, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:514.053
[293/300] - time: 318.39, G_loss: 3198.288, G_identity_loss: 40.813, G_GAN_loss: 2.000, G_cycle_loss: 1163.485, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:514.243
[294/300] - time: 321.30, G_loss: 3191.958, G_identity_loss: 40.727, G_GAN_loss: 2.000, G_cycle_loss: 1160.905, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:513.178
[295/300] - time: 314.09, G_loss: 3211.538, G_identity_loss: 40.969, G_GAN_loss: 2.000, G_cycle_loss: 1168.029, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:518.022
[296/300] - time: 319.93, G_loss: 3208.206, G_identity_loss: 40.945, G_GAN_loss: 2.000, G_cycle_loss: 1167.903, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:514.479
[297/300] - time: 316.07, G_loss: 3212.931, G_identity_loss: 41.021, G_GAN_loss: 2.000, G_cycle_loss: 1169.687, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:515.471
[298/300] - time: 315.81, G_loss: 3190.096, G_identity_loss: 40.705, G_GAN_loss: 2.000, G_cycle_loss: 1161.828, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:509.383
[299/300] - time: 313.81, G_loss: 3206.557, G_identity_loss: 40.918, G_GAN_loss: 2.000, G_cycle_loss: 1166.935, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:515.104
[300/300] - time: 314.93, G_loss: 3210.988, G_identity_loss: 40.985, G_GAN_loss: 2.000, G_cycle_loss: 1168.369, D_loss: 0.000,edge_lossAB:304.913,edge_lossBA:516.793

Avg one epoch time: 314.51, total 300 epochs time: 94745.18

what is "conference_images" directory in colorization.py

Hi;

In below code in colorization.py, it load image from "conference_images" directory, what are those images?

C_list = os.listdir('./conference_images')
for path in C_list:
    img = Image.open(os.path.join('./conference_images',path))
    img = img.convert('RGB')
    img = transform(img)
    img = img.detach().to(torch.device('cpu')).squeeze().permute(1, 2, 0).type(torch.uint8).numpy()

    hash_code = phash(img)
    conference_code.append(hash_code)
    conference_path.append(path)
print("phash dict created!")

ValueError: Floating point image RGB values must be in the 0..1 range.

Hi;

I used a different dataset, but I got the below error. Any clue where could be the issue. Also, why do you add 1 and divide my 2 before saving it. (+ 1) / 2 )

[1/8] - time: 313.78, G_loss: 3210.494, G_identity_loss: 41.789, G_GAN_loss: 1.541, G_cycle_loss: 1167.457, D_loss: 0.663,edge_lossAB:305.678,edge_lossBA:516.925
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Input In [30], in <cell line: 346>()
    654         path = os.path.join('/tmp/ahmed/results',args.name + '_results', 'Colorization', str(epoch+1) + '_epoch_' + args.name + '_test_' + str(n) + '.png')
    656         old_i=(result.cpu().numpy().transpose(1, 2, 0) + 1) / 2
--> 658         plt.imsave(path, old_i )
    660 lr_scheduler_G.step()
    661 lr_scheduler_D_A.step()

File /home/software/python/AnacondaPython3.9/lib/python3.9/site-packages/matplotlib/pyplot.py:2144, in imsave(fname, arr, **kwargs)
   2142 @_copy_docstring_and_deprecators(matplotlib.image.imsave)
   2143 def imsave(fname, arr, **kwargs):
-> 2144     return matplotlib.image.imsave(fname, arr, **kwargs)

File /home/software/python/AnacondaPython3.9/lib/python3.9/site-packages/matplotlib/image.py:1641, in imsave(fname, arr, vmin, vmax, cmap, format, origin, dpi, metadata, pil_kwargs)
   1639     rgba = arr
   1640 else:
-> 1641     rgba = sm.to_rgba(arr, bytes=True)
   1642 if pil_kwargs is None:
   1643     pil_kwargs = {}

File /home/software/python/AnacondaPython3.9/lib/python3.9/site-packages/matplotlib/cm.py:437, in ScalarMappable.to_rgba(self, x, alpha, bytes, norm)
    435 if xx.dtype.kind == 'f':
    436     if norm and (xx.max() > 1 or xx.min() < 0):
--> 437         raise ValueError("Floating point image RGB values "
    438                          "must be in the 0..1 range.")
    439     if bytes:
    440         xx = (xx * 255).astype(np.uint8)

ValueError: Floating point image RGB values must be in the 0..1 range.

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