size of train loader is: 90
predictions are: tensor([[-0.0380, -0.1871, 0.0729, -0.3570, -0.2153, 0.3066, 1.1273, -0.0558],
[-0.0316, -0.1876, 0.0317, -0.3613, -0.2333, 0.3023, 1.0940, -0.0665],
[-0.0700, -0.1882, 0.0068, -0.3201, -0.1884, 0.2953, 1.0516, -0.0567],
[-0.0844, -0.2009, 0.0573, -0.3166, -0.2597, 0.3127, 1.0343, -0.0573],
[-0.0486, -0.2333, 0.0535, -0.3245, -0.2310, 0.2818, 1.0590, -0.0716],
[-0.0240, -0.1989, 0.0572, -0.3135, -0.2435, 0.2912, 1.0612, -0.0560],
[-0.0942, -0.2439, 0.0277, -0.3147, -0.2368, 0.2978, 1.0110, -0.0874],
[-0.0356, -0.2285, 0.0064, -0.3179, -0.2432, 0.3083, 1.0300, -0.0756]],
device='cuda:0', grad_fn=<AddmmBackward>)
landmarks are: tensor([[501.9200, 240.1600, 691.0000, 358.0000, 295.0000, 294.0000, 488.6482,
279.6466],
[495.6300, 246.0600, 692.0000, 235.0000, 286.0000, 242.0000, 464.0000,
339.0000],
[488.7100, 240.8900, 613.4007, 218.3425, 281.0000, 220.0000, 415.9966,
338.4796],
[502.5721, 245.4983, 640.0000, 131.0000, 360.0000, 143.0000, 542.9840,
321.8463],
[505.1393, 246.4364, 700.0000, 306.0000, 303.0000, 294.0000, 569.6925,
351.8367],
[501.0900, 244.0100, 724.0000, 251.0000, 302.0000, 276.0000, 504.6415,
291.7443],
[495.9500, 244.2800, 608.0000, 127.0000, 323.0000, 166.0000, 491.0000,
333.0000],
[490.2500, 241.3400, 699.0000, 304.0000, 398.6197, 313.8339, 429.1374,
303.8483]], device='cuda:0')
loss_train_step before backward: tensor(166475.6875, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(166475.6875, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: 166475.6875
step: 1
running loss: 166475.6875
Train Steps: 1/90 Loss: 166475.6875 predictions are: tensor([[ 0.1848, -0.1262, 0.4091, -0.2304, -0.1060, 0.4372, 1.3852, 0.0739],
[ 0.2051, -0.0935, 0.4141, -0.2483, -0.0607, 0.4669, 1.4023, 0.1099],
[ 0.1923, -0.1570, 0.3770, -0.2265, -0.0891, 0.4168, 1.3499, 0.0978],
[ 0.1799, -0.0961, 0.3741, -0.2188, -0.0891, 0.4474, 1.3451, 0.1094],
[ 0.1413, -0.0899, 0.3942, -0.2284, -0.1005, 0.4032, 1.3716, 0.0928],
[ 0.1852, -0.1177, 0.3888, -0.2247, -0.0988, 0.4586, 1.3553, 0.0956],
[ 0.1285, -0.1051, 0.3694, -0.2425, -0.1180, 0.3835, 1.3341, 0.1009],
[ 0.1393, -0.1126, 0.3555, -0.2547, -0.0628, 0.4297, 1.3280, 0.1162]],
device='cuda:0', grad_fn=<AddmmBackward>)
landmarks are: tensor([[506.5656, 247.0619, 739.0000, 256.0000, 321.0000, 284.0000, 601.9836,
326.1537],
[507.1360, 244.8729, 674.0000, 325.0000, 308.0000, 290.0000, 586.7878,
345.5895],
[491.8400, 243.1800, 700.0000, 273.0000, 387.9706, 313.0692, 469.0000,
334.0000],
[486.7600, 240.2900, 672.0000, 259.0000, 301.0000, 285.0000, 438.0412,
303.4578],
[503.9984, 248.0000, 683.0000, 130.0000, 447.0000, 135.0000, 591.2198,
324.7654],
[490.2500, 241.3400, 699.0000, 304.0000, 398.6197, 313.8339, 429.1374,
303.8483],
[ nan, nan, 682.0000, 133.0000, 433.0000, 142.0000, 589.3204,
328.9302],
[489.1657, 239.8694, 565.0000, 143.0000, 323.0000, 117.0000, 425.5759,
299.5533]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 2
running loss: nan
Train Steps: 2/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[ nan, nan, 586.7780, 154.1319, 303.0000, 160.0000, 405.3121,
334.6727],
[503.4279, 238.9313, 696.0000, 318.0000, 301.0000, 283.0000, 563.9941,
317.8241],
[493.4800, 246.5000, 545.7030, 163.3048, 306.0000, 153.0000, 444.0000,
343.0000],
[503.1426, 241.1203, 649.0000, 328.0000, 310.0000, 301.0000, 589.3204,
319.9065],
[502.8574, 238.6186, 723.0000, 284.0000, 312.0000, 249.0000, 565.8936,
319.2123],
[497.2200, 247.1100, 615.0000, 138.0000, 336.0000, 137.0000, 474.0000,
319.0000],
[495.3700, 238.8200, 566.2405, 164.9726, 340.0000, 126.0000, 436.0000,
347.0000],
[507.9918, 247.0619, 669.0000, 163.0000, 388.0000, 102.0000, 515.2408,
310.1886]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 3
running loss: nan
Train Steps: 3/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[496.2400, 244.3000, 711.3916, 211.3691, 302.6665, 181.0179, 471.8022,
328.6678],
[501.1459, 243.3093, 674.0000, 166.0000, 354.0000, 166.0000, 563.9941,
335.1775],
[488.6800, 242.1600, 575.0000, 105.0000, 308.0000, 153.0000, 469.0000,
334.0000],
[502.8574, 243.6220, 735.0000, 260.0000, 294.0000, 250.0000, 562.7277,
331.7068],
[502.2400, 255.1600, 710.0000, 301.0000, 329.0000, 165.0000, 433.0551,
371.7868],
[490.0000, 239.9400, 700.0000, 293.0000, 380.0000, 282.0000, 442.6711,
337.0366],
[502.2869, 242.9966, 642.0000, 132.0000, 345.0000, 164.0000, 545.6324,
319.2123],
[495.9200, 243.9100, 607.8350, 143.0867, 345.3565, 118.8599, 474.6164,
313.2611]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 4
running loss: nan
Train Steps: 4/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[495.8200, 244.5800, 635.6002, 147.2001, 358.0301, 112.2785, 471.7639,
320.9747],
[490.0000, 239.9400, 700.0000, 293.0000, 380.0000, 282.0000, 442.6711,
337.0366],
[508.2770, 247.6873, 731.0000, 212.0000, 375.0000, 195.0000, 571.5920,
359.4722],
[495.8100, 239.8400, 686.5782, 321.6804, 329.3477, 300.9463, 475.3770,
308.0492],
[501.8300, 248.5600, 700.0000, 342.0000, 319.0000, 283.0000, 481.0000,
328.0000],
[490.2000, 245.0600, 699.0000, 281.0000, 289.0000, 222.0000, 396.7645,
323.8376],
[487.6300, 240.1400, 682.6197, 310.0718, 402.5010, 305.6570, 410.0000,
326.0000],
[496.8000, 249.8500, 576.1289, 175.8134, 322.0000, 149.0000, 455.0000,
334.0000]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 5
running loss: nan
Train Steps: 5/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[499.2800, 248.5500, 715.0000, 279.0000, 326.0000, 321.0000, 500.0000,
333.0000],
[501.0600, 243.8700, 723.0000, 259.0000, 287.0000, 273.0000, 506.0000,
315.0000],
[482.6800, 240.6500, 588.0000, 152.0000, 275.0000, 202.0000, 441.2465,
305.0196],
[ nan, nan, 617.9645, 156.6336, 294.0000, 164.0000, 433.0000,
310.0000],
[498.3700, 249.1100, 607.0000, 137.0000, 321.0000, 173.0000, 496.0000,
346.0000],
[501.9300, 256.1800, 715.0000, 298.0000, 284.0000, 257.0000, 456.0000,
344.0000],
[495.6600, 245.6500, 605.0000, 169.0000, 315.0000, 191.0000, 481.0000,
371.0000],
[493.5900, 246.1400, 597.4271, 221.6781, 277.0000, 226.0000, 419.0314,
349.3644]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 6
running loss: nan
Train Steps: 6/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[500.2902, 239.5567, 719.0000, 286.0000, 319.0000, 331.0000, 556.3961,
317.1299],
[502.0400, 245.7100, 659.0000, 135.0000, 373.0000, 107.0000, 493.7134,
292.1411],
[498.8640, 237.9932, 694.0000, 324.0000, 309.0000, 271.0000, 466.0000,
312.0000],
[495.8700, 247.7900, 701.0000, 247.0000, 292.0000, 294.0000, 456.5611,
306.1910],
[502.2869, 242.9966, 642.0000, 132.0000, 345.0000, 164.0000, 545.6324,
319.2123],
[489.9400, 241.8100, 692.0000, 292.0000, 399.9338, 306.3606, 410.9215,
346.3862],
[ nan, nan, 567.7618, 140.7894, 340.0000, 111.0000, 414.0000,
335.0000],
[491.4477, 242.0584, 704.0000, 290.0000, 361.0000, 322.0000, 423.0828,
305.8005]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 7
running loss: nan
Train Steps: 7/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[496.0500, 238.2000, 673.0000, 297.0000, 353.0000, 311.0000, 487.0000,
324.0000],
[484.2800, 242.0800, 551.7881, 114.9384, 320.0000, 127.0000, 435.1920,
310.0955],
[492.5886, 237.3677, 665.8701, 248.2718, 278.2819, 265.8849, 473.0849,
307.8573],
[491.8400, 243.1800, 700.0000, 273.0000, 387.9706, 313.0692, 469.0000,
334.0000],
[508.8475, 246.1237, 692.0000, 179.0000, 391.0000, 120.0000, 536.1351,
327.5420],
[491.3300, 247.6000, 606.0000, 184.0000, 275.0000, 263.0000, 462.2595,
312.4382],
[489.1657, 239.8694, 565.0000, 143.0000, 323.0000, 117.0000, 425.5759,
299.5533],
[496.3100, 240.7500, 646.0000, 144.0000, 345.0000, 123.0000, 464.0000,
309.0000]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 8
running loss: nan
Train Steps: 8/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[500.0300, 247.8700, 695.0000, 179.0000, 342.0000, 142.0000, 493.0000,
322.0000],
[495.8700, 247.7900, 701.0000, 247.0000, 292.0000, 294.0000, 456.5611,
306.1910],
[507.1360, 246.1237, 727.0000, 286.0000, 314.0000, 317.0000, 600.7172,
323.3772],
[491.6200, 238.9700, 696.0000, 301.0000, 352.0000, 288.0000, 430.0000,
345.0000],
[492.9800, 236.9300, 707.0000, 271.0000, 340.0000, 311.0000, 467.0000,
330.0000],
[485.8600, 235.9000, 669.0000, 349.0000, 354.0000, 307.0000, 416.3159,
289.0111],
[493.1500, 247.1400, 633.0000, 159.0000, 283.0000, 210.0000, 449.0819,
302.2865],
[503.1426, 241.4330, 727.0000, 274.0000, 315.0000, 338.0000, 564.6272,
336.5657]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 9
running loss: nan
Train Steps: 9/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[499.7197, 240.4949, 700.5271, 305.2639, 328.0690, 323.5423, 531.4897,
308.0097],
[488.1700, 239.9200, 700.0000, 308.0000, 306.0000, 285.0000, 451.0000,
305.0000],
[502.4100, 246.0400, 724.0000, 272.0000, 302.0000, 193.0000, 507.0098,
294.9176],
[496.1400, 238.9700, 684.3768, 325.7215, 307.3357, 262.1889, 469.2918,
323.8934],
[488.7100, 240.8900, 613.4007, 218.3425, 281.0000, 220.0000, 415.9966,
338.4796],
[507.7065, 245.1856, 635.0000, 330.0000, 317.0000, 292.0000, 587.4209,
342.1188],
[502.0016, 241.4330, 688.0000, 137.0000, 428.0000, 108.0000, 565.8936,
324.7654],
[496.2100, 245.7600, 709.0000, 256.0000, 283.0000, 247.0000, 482.0000,
339.0000]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 10
running loss: nan
Train Steps: 10/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[ nan, nan, 700.0000, 148.0000, 411.0000, 157.0000, 590.5867,
333.0951],
[507.7065, 249.8763, 731.0000, 239.0000, 310.0000, 259.0000, 597.5515,
328.2361],
[ nan, nan, 601.0000, 127.0000, 343.0000, 120.0000, 448.0000,
337.0000],
[498.3700, 249.1100, 607.0000, 137.0000, 321.0000, 173.0000, 496.0000,
346.0000],
[490.2700, 247.0800, 691.0000, 320.0000, 370.0000, 316.0000, 415.4624,
328.5230],
[497.2200, 247.1100, 615.0000, 138.0000, 336.0000, 137.0000, 474.0000,
319.0000],
[506.5656, 249.8763, 728.0000, 201.0000, 335.0000, 221.0000, 595.0188,
331.7068],
[497.7500, 237.4100, 707.0000, 301.0000, 315.0000, 276.0000, 472.0000,
301.0000]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 11
running loss: nan
Train Steps: 11/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[487.5800, 238.2200, 695.0000, 286.0000, 388.7642, 292.7355, 415.2475,
296.4297],
[492.8739, 241.4330, 693.0000, 281.0000, 394.7994, 321.8399, 485.0000,
334.0000],
[489.9400, 241.8100, 692.0000, 292.0000, 399.9338, 306.3606, 410.9215,
346.3862],
[494.3001, 239.5567, 714.0048, 287.3219, 310.3356, 293.9974, 483.3020,
316.7523],
[497.5500, 246.8200, 654.0000, 169.0000, 314.0000, 167.0000, 472.0000,
321.0000],
[496.3700, 240.9300, 668.0000, 163.0000, 319.0000, 153.0000, 463.0000,
308.0000],
[489.8800, 244.1100, 665.1248, 300.0650, 299.0000, 279.0000, 413.3255,
324.1305],
[501.2500, 244.2100, 697.0000, 336.0000, 297.0000, 287.0000, 462.0000,
366.0000]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 12
running loss: nan
Train Steps: 12/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[ nan, nan, 610.0000, 146.0000, 297.0000, 172.0000, 418.8090,
313.2191],
[507.1360, 244.8729, 674.0000, 325.0000, 308.0000, 290.0000, 586.7878,
345.5895],
[ nan, nan, 559.3947, 167.4743, 316.0000, 143.0000, 438.6643,
349.1448],
[500.5700, 242.0600, 663.0000, 140.0000, 314.0000, 163.0000, 506.4223,
294.0870],
[497.0400, 240.0600, 617.0000, 127.0000, 347.0000, 108.0000, 468.0000,
311.0000],
[ nan, nan, 638.5021, 191.6575, 290.0000, 190.0000, 403.1752,
333.7941],
[501.9300, 256.1800, 715.0000, 298.0000, 284.0000, 257.0000, 456.0000,
344.0000],
[509.1327, 248.6254, 690.0000, 185.0000, 393.0000, 120.0000, 515.8740,
316.4358]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 13
running loss: nan
Train Steps: 13/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[496.0200, 244.2800, 587.0000, 115.0000, 336.0000, 147.0000, 492.0000,
331.0000],
[504.5688, 242.6839, 642.0000, 350.0000, 302.0000, 292.0000, 551.3309,
327.5420],
[487.4300, 239.4100, 672.0000, 260.0000, 295.0000, 278.0000, 444.8080,
339.3793],
[ nan, nan, 727.0000, 227.0000, 365.0000, 157.0000, 539.3008,
334.4833],
[507.1360, 249.5636, 672.0000, 337.0000, 306.0000, 249.0000, 587.4209,
344.8954],
[488.5952, 242.9966, 696.0000, 291.0000, 357.5752, 290.8813, 403.4423,
325.8875],
[506.6800, 242.2100, 691.0000, 344.0000, 321.0000, 283.0000, 509.5424,
296.3059],
[495.9600, 245.1500, 673.1207, 178.4619, 329.3477, 136.4104, 469.4820,
323.4764]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 14
running loss: nan
Train Steps: 14/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[500.5754, 243.6220, 664.0000, 140.0000, 375.0000, 155.0000, 563.9941,
337.9540],
[501.8700, 246.5800, 712.0000, 229.0000, 335.0000, 130.0000, 468.6673,
290.0746],
[497.0800, 247.8600, 675.0000, 213.0000, 281.0000, 264.0000, 457.2733,
307.3623],
[503.9984, 240.8076, 715.0000, 321.0000, 294.0000, 276.0000, 516.5071,
298.3883],
[492.8739, 244.5602, 712.0000, 280.0000, 330.0000, 355.0000, 501.0000,
322.0000],
[484.6700, 238.7000, 663.0000, 216.0000, 272.0000, 243.0000, 442.3150,
327.6658],
[496.2700, 244.6800, 704.0000, 305.0000, 312.0000, 300.0000, 488.0000,
335.0000],
[486.3200, 237.8300, 593.6238, 177.4812, 285.0000, 175.0000, 428.0689,
298.7724]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 15
running loss: nan
Train Steps: 15/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[498.8640, 244.8729, 686.0000, 180.0000, 297.0000, 182.0000, 444.0000,
338.0000],
[ nan, nan, 578.0000, 130.0000, 319.0000, 137.0000, 434.1235,
310.8764],
[487.7700, 239.9900, 586.0000, 160.0000, 276.0000, 211.0000, 422.7267,
302.6769],
[489.9400, 241.8100, 692.0000, 292.0000, 399.9338, 306.3606, 410.9215,
346.3862],
[498.3100, 251.9000, 613.0000, 162.0000, 376.0000, 128.0000, 454.0000,
347.0000],
[497.2500, 245.9600, 578.0000, 122.0000, 335.0000, 133.0000, 478.0000,
317.0000],
[499.6200, 246.9000, 696.0000, 293.0000, 370.0000, 331.0000, 488.0000,
313.0000],
[489.7400, 240.3700, 708.0000, 253.0000, 327.0000, 331.0000, 485.0000,
331.0000]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 16
running loss: nan
Train Steps: 16/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[494.5853, 238.9313, 603.2948, 142.7309, 316.7463, 167.4912, 486.3070,
323.3411],
[494.1100, 234.6700, 699.0000, 253.0000, 279.0000, 242.0000, 482.0000,
289.0000],
[500.7300, 247.9500, 656.0000, 139.0000, 402.0000, 92.0000, 500.0116,
296.8201],
[492.8739, 242.3712, 602.0000, 128.0000, 330.0000, 124.0000, 463.0000,
307.0000],
[494.2300, 243.5200, 602.0000, 135.0000, 345.0000, 107.0000, 432.3427,
314.3904],
[496.0500, 246.9500, 698.0000, 284.0000, 296.0000, 193.0000, 430.9181,
346.0170],
[492.0181, 236.1169, 695.7136, 309.4855, 371.7718, 319.7672, 483.3020,
309.1750],
[502.0016, 240.8076, 708.0000, 170.0000, 398.0000, 134.0000, 564.6272,
320.6006]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 17
running loss: nan
Train Steps: 17/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[495.9600, 234.7900, 703.0000, 313.0000, 326.0000, 306.0000, 484.8128,
294.7599],
[497.0900, 250.5100, 584.0000, 173.0000, 347.0000, 130.0000, 455.8488,
346.4075],
[494.7200, 244.7300, 668.0000, 222.0000, 294.0000, 173.0000, 425.0000,
347.0000],
[496.2100, 244.5800, 688.8793, 172.7032, 324.0115, 153.2296, 472.5629,
329.7797],
[490.0600, 244.3900, 700.0000, 308.0000, 304.0000, 260.0000, 398.9014,
322.6663],
[494.5853, 237.9932, 661.0566, 183.8918, 282.0930, 249.6721, 495.3221,
317.4111],
[497.8200, 250.2600, 700.0000, 330.0000, 324.0000, 289.0000, 454.0000,
336.0000],
[494.0300, 245.4600, 629.0000, 168.0000, 291.0000, 215.0000, 495.0000,
326.0000]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 18
running loss: nan
Train Steps: 18/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[500.1200, 249.7500, 693.0000, 268.0000, 290.0000, 214.0000, 483.0000,
332.0000],
[501.7200, 242.6800, 683.0000, 354.0000, 300.0000, 265.0000, 479.1508,
278.9525],
[493.5900, 246.1400, 597.4271, 221.6781, 277.0000, 226.0000, 419.0314,
349.3644],
[490.3067, 235.8042, 701.4897, 306.3193, 331.7047, 338.5089, 479.9964,
304.8923],
[497.5100, 245.3300, 715.0000, 288.0000, 306.0000, 267.0000, 468.0000,
312.0000],
[496.2100, 244.5800, 688.8793, 172.7032, 324.0115, 153.2296, 472.5629,
329.7797],
[503.4279, 238.9313, 696.0000, 318.0000, 301.0000, 283.0000, 563.9941,
317.8241],
[501.9300, 247.0000, 648.0244, 348.0684, 320.0000, 275.0000, 446.5888,
367.1014]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 19
running loss: nan
Train Steps: 19/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[485.8600, 235.9000, 669.0000, 349.0000, 354.0000, 307.0000, 416.3159,
289.0111],
[494.3001, 240.4949, 600.4067, 160.6728, 349.4514, 113.4323, 466.4738,
315.7639],
[490.2800, 246.8100, 556.0000, 148.0000, 324.0000, 128.0000, 429.0000,
333.0000],
[498.8700, 237.9300, 708.0000, 298.0000, 291.0000, 241.0000, 468.0000,
311.0000],
[494.5853, 237.9932, 661.0566, 183.8918, 282.0930, 249.6721, 495.3221,
317.4111],
[501.4500, 243.6300, 668.0000, 146.0000, 366.0000, 137.0000, 508.0000,
318.0000],
[494.1500, 245.1300, 699.0000, 237.0000, 302.0000, 336.0000, 498.0000,
342.0000],
[496.1000, 242.9900, 620.5920, 134.0372, 356.6960, 107.8909, 478.0393,
325.5612]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 20
running loss: nan
Train Steps: 20/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[495.8500, 246.0500, 707.0000, 317.0000, 301.0000, 316.0000, 473.0000,
334.0000],
[494.8400, 247.7400, 712.0000, 274.0000, 315.0000, 325.0000, 458.6980,
306.5814],
[ nan, nan, 578.0000, 130.0000, 319.0000, 137.0000, 434.1235,
310.8764],
[497.4600, 248.2400, 581.0000, 134.0000, 326.0000, 159.0000, 497.0000,
347.0000],
[502.5721, 242.0584, 626.3995, 124.7889, 362.5892, 124.7976, 512.3327,
319.3755],
[488.7400, 242.4700, 558.0000, 190.0000, 281.0000, 203.0000, 412.2570,
319.1522],
[ nan, nan, 715.0000, 171.0000, 373.0000, 187.0000, 592.4862,
331.7068],
[501.0800, 241.7700, 720.0000, 286.0000, 304.0000, 310.0000, 513.1892,
286.2780]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 21
running loss: nan
Train Steps: 21/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[502.8574, 242.3712, 695.7136, 182.8364, 313.9077, 173.2258, 504.0367,
322.0233],
[501.4600, 245.6700, 723.0000, 258.0000, 296.0000, 209.0000, 501.0000,
310.0000],
[489.7362, 239.5567, 699.0000, 280.0000, 361.3809, 292.7355, 412.3982,
295.6488],
[ nan, nan, 559.3947, 167.4743, 316.0000, 143.0000, 438.6643,
349.1448],
[506.5656, 249.8763, 728.0000, 201.0000, 335.0000, 221.0000, 595.0188,
331.7068],
[500.8607, 239.2440, 695.0000, 295.0000, 344.0000, 320.0000, 560.8282,
318.5182],
[ nan, nan, 567.7618, 140.7894, 340.0000, 111.0000, 414.0000,
335.0000],
[490.1900, 247.0600, 692.0000, 305.0000, 327.0000, 322.0000, 424.0101,
327.3517]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 22
running loss: nan
Train Steps: 22/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[487.6300, 240.1400, 682.6197, 310.0718, 402.5010, 305.6570, 410.0000,
326.0000],
[498.4000, 246.7900, 577.0000, 119.0000, 346.0000, 142.0000, 501.0000,
324.0000],
[ nan, nan, 517.5590, 116.6062, 322.0000, 120.0000, 410.0000,
332.0000],
[507.9918, 248.6254, 740.0000, 246.0000, 330.0000, 225.0000, 570.3256,
356.6957],
[ nan, nan, 727.0000, 227.0000, 365.0000, 157.0000, 539.3008,
334.4833],
[502.2400, 255.1600, 710.0000, 301.0000, 329.0000, 165.0000, 433.0551,
371.7868],
[502.8574, 238.6186, 723.0000, 284.0000, 312.0000, 249.0000, 565.8936,
319.2123],
[508.8475, 249.8763, 723.0000, 301.0000, 300.0000, 227.0000, 515.8740,
318.5182]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 23
running loss: nan
Train Steps: 23/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[482.0700, 238.7200, 684.0000, 254.0000, 289.0000, 314.0000, 446.5888,
297.9915],
[481.7500, 239.6200, 642.0000, 201.0000, 268.0000, 264.0000, 445.1642,
301.5056],
[498.3100, 251.9000, 613.0000, 162.0000, 376.0000, 128.0000, 454.0000,
347.0000],
[509.7032, 245.4983, 667.0000, 351.0000, 316.0000, 307.0000, 524.7382,
315.7417],
[498.1100, 240.0800, 675.0000, 344.0000, 309.0000, 255.0000, 460.0000,
317.0000],
[489.8800, 245.0100, 556.3521, 184.1524, 292.0000, 165.0000, 413.0584,
329.1087],
[494.5853, 239.8694, 703.4152, 251.4380, 284.1584, 257.0997, 483.0015,
318.7289],
[496.0000, 245.8900, 659.0000, 175.0000, 321.0000, 178.0000, 480.0000,
341.0000]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 24
running loss: nan
Train Steps: 24/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[496.1000, 244.9000, 706.1387, 222.8866, 306.0016, 162.7361, 467.5804,
324.7273],
[495.9800, 246.0800, 707.0000, 319.0000, 306.0000, 228.0000, 433.0000,
341.0000],
[488.2000, 240.2900, 695.0000, 306.0000, 385.0000, 324.0000, 432.3427,
303.8483],
[495.7000, 245.4200, 676.0000, 234.0000, 286.0000, 236.0000, 478.0000,
335.0000],
[490.0600, 244.3900, 700.0000, 308.0000, 304.0000, 260.0000, 398.9014,
322.6663],
[487.7395, 241.4330, 679.0000, 223.0000, 310.0000, 331.0000, 466.8895,
335.4748],
[501.3200, 243.8900, 665.0000, 148.0000, 383.0000, 104.0000, 505.0000,
308.0000],
[ nan, nan, 586.7780, 154.1319, 303.0000, 160.0000, 405.3121,
334.6727]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 25
running loss: nan
Train Steps: 25/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[503.7131, 242.6839, 731.0000, 246.0000, 338.5284, 254.5401, 593.7525,
317.8241],
[494.7800, 244.0100, 707.0000, 267.0000, 323.0000, 284.0000, 417.0282,
308.5337],
[493.2300, 246.3500, 606.0000, 104.0000, 307.0000, 159.0000, 454.4241,
306.9719],
[490.3400, 244.1100, 700.0000, 304.0000, 310.0000, 254.0000, 418.8311,
352.8785],
[504.5688, 241.7457, 719.0000, 289.0000, 315.0000, 210.0000, 584.8883,
322.6830],
[486.5300, 242.5300, 558.0000, 115.0000, 328.0000, 119.0000, 440.1781,
334.6939],
[ nan, nan, 548.7455, 131.6165, 332.0000, 112.0000, 412.2570,
343.7506],
[498.2935, 243.9347, 681.0000, 343.0000, 360.0000, 303.0000, 482.0000,
321.0000]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 26
running loss: nan
Train Steps: 26/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[496.2200, 241.2300, 702.3867, 301.8638, 293.3280, 232.9381, 467.3902,
324.3103],
[495.0900, 242.9100, 711.0000, 265.0000, 337.0000, 312.0000, 479.0000,
338.0000],
[490.7800, 239.6200, 633.0000, 183.0000, 290.0000, 183.0000, 467.0000,
303.0000],
[495.6300, 246.0600, 692.0000, 235.0000, 286.0000, 242.0000, 464.0000,
339.0000],
[499.7197, 240.4949, 700.5271, 305.2639, 328.0690, 323.5423, 531.4897,
308.0097],
[490.0800, 243.9900, 691.0000, 323.0000, 335.0000, 291.0000, 401.3054,
323.5448],
[501.3800, 245.6600, 697.0000, 185.0000, 352.0000, 136.0000, 500.0000,
312.0000],
[498.2935, 246.4364, 651.0000, 173.0000, 380.0000, 103.0000, 465.0000,
324.0000]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 27
running loss: nan
Train Steps: 27/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[489.3200, 241.0900, 525.0000, 118.0000, 299.0000, 153.0000, 422.3705,
306.1910],
[495.7400, 245.4600, 704.0000, 287.0000, 283.0000, 286.0000, 476.0000,
333.0000],
[501.0900, 244.0100, 724.0000, 251.0000, 302.0000, 276.0000, 504.6415,
291.7443],
[486.8400, 238.8300, 696.0000, 285.0000, 361.0000, 317.0000, 425.9320,
302.2865],
[490.9700, 242.1100, 708.0000, 265.0000, 312.0000, 257.0000, 435.9043,
337.0366],
[500.7400, 249.3600, 705.0000, 191.0000, 382.0000, 112.0000, 497.5185,
297.6010],
[500.1400, 249.4700, 719.0000, 245.0000, 303.0000, 287.0000, 498.0000,
338.0000],
[495.9800, 246.0800, 707.0000, 319.0000, 306.0000, 228.0000, 433.0000,
341.0000]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 28
running loss: nan
Train Steps: 28/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[496.8673, 236.1169, 690.9001, 290.4881, 353.3458, 307.5619, 506.4407,
316.0934],
[496.7200, 235.1800, 692.0000, 322.0000, 352.0000, 304.0000, 482.0000,
297.0000],
[491.4477, 243.3093, 652.0000, 166.0000, 306.0000, 154.0000, 413.8228,
294.0870],
[498.2935, 246.4364, 651.0000, 173.0000, 380.0000, 103.0000, 465.0000,
324.0000],
[504.1500, 240.4100, 708.0000, 330.0000, 289.0000, 271.0000, 506.7784,
300.7246],
[498.2935, 243.9347, 681.0000, 343.0000, 360.0000, 303.0000, 482.0000,
321.0000],
[494.7600, 244.7600, 707.0000, 277.0000, 387.0000, 339.0000, 494.0000,
351.0000],
[492.8600, 245.9100, 699.0000, 263.0000, 303.0000, 329.0000, 448.3696,
301.1151]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 29
running loss: nan
Train Steps: 29/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[492.7800, 245.5500, 665.1248, 265.0410, 294.0000, 275.0000, 420.0331,
348.4859],
[492.8739, 241.4330, 693.0000, 281.0000, 394.7994, 321.8399, 485.0000,
334.0000],
[501.9800, 249.4200, 667.0000, 348.0000, 301.0000, 252.0000, 443.7396,
367.4918],
[484.5200, 240.6700, 700.0000, 256.0000, 352.0000, 348.0000, 467.6018,
335.0844],
[496.2700, 244.6800, 704.0000, 305.0000, 312.0000, 300.0000, 488.0000,
335.0000],
[504.2836, 241.7457, 673.0000, 313.0000, 330.0000, 337.0000, 567.1599,
340.7306],
[490.2000, 245.0600, 699.0000, 281.0000, 289.0000, 222.0000, 396.7645,
323.8376],
[498.9000, 245.0400, 619.0000, 128.0000, 293.0000, 194.0000, 465.0000,
334.0000]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 30
running loss: nan
Train Steps: 30/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[494.0148, 240.8076, 618.6979, 167.0053, 357.9991, 107.1851, 463.1682,
321.0350],
[495.8500, 249.8600, 696.0000, 304.0000, 370.0000, 339.0000, 437.0986,
311.8313],
[502.0200, 242.8900, 679.0000, 173.0000, 357.0000, 122.0000, 505.7100,
309.3146],
[497.7500, 250.2900, 708.0000, 313.0000, 299.0000, 276.0000, 456.0000,
338.0000],
[508.5623, 245.8110, 723.0000, 233.0000, 337.0000, 177.0000, 534.8688,
323.3772],
[488.0700, 242.5300, 622.0000, 157.0000, 297.0000, 169.0000, 435.1920,
338.9889],
[487.2800, 239.8400, 665.1248, 260.0376, 303.0000, 273.0000, 417.0651,
339.3581],
[ nan, nan, 643.0000, 149.0000, 318.0000, 151.0000, 446.0000,
336.0000]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 31
running loss: nan
Train Steps: 31/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[495.8300, 246.2900, 636.0000, 196.0000, 294.0000, 226.0000, 483.0000,
370.0000],
[488.2000, 240.2900, 695.0000, 306.0000, 385.0000, 324.0000, 432.3427,
303.8483],
[499.7000, 245.3900, 557.1127, 121.6097, 314.0000, 161.0000, 487.0000,
335.0000],
[505.9951, 243.9347, 675.0000, 321.0000, 314.0000, 316.0000, 569.0593,
347.6719],
[500.8400, 246.3800, 642.0000, 155.0000, 364.0000, 112.0000, 502.5046,
292.1347],
[499.1492, 246.4364, 652.9544, 165.8065, 290.0000, 216.0000, 479.0000,
342.0000],
[494.1500, 245.1300, 699.0000, 237.0000, 302.0000, 336.0000, 498.0000,
342.0000],
[489.0500, 245.2700, 548.7455, 132.4504, 349.0000, 102.0000, 415.0000,
332.0000]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 32
running loss: nan
Train Steps: 32/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[495.4600, 246.4600, 595.0000, 162.0000, 292.0000, 221.0000, 499.0000,
343.0000],
[495.2000, 248.0900, 640.0000, 293.0000, 285.2831, 218.8381, 449.0000,
354.0000],
[489.9700, 238.4500, 618.0000, 151.0000, 283.0000, 199.0000, 471.0000,
330.0000],
[499.5800, 246.0100, 621.0000, 155.0000, 397.0000, 91.0000, 470.0000,
325.0000],
[490.2500, 241.3400, 699.0000, 304.0000, 398.6197, 313.8339, 429.1374,
303.8483],
[497.7300, 244.3100, 573.0863, 129.9487, 299.0000, 190.0000, 488.0000,
332.0000],
[502.0300, 244.2400, 646.0000, 126.0000, 376.0000, 92.0000, 491.8140,
290.0587],
[490.8772, 241.7457, 661.0000, 201.0000, 290.0000, 184.0000, 454.0000,
310.0000]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 33
running loss: nan
Train Steps: 33/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[503.6500, 239.3200, 720.0000, 249.0000, 289.0000, 232.0000, 512.0000,
306.0000],
[494.7600, 244.7600, 707.0000, 277.0000, 387.0000, 339.0000, 494.0000,
351.0000],
[489.9700, 238.4500, 618.0000, 151.0000, 283.0000, 199.0000, 471.0000,
330.0000],
[492.8739, 244.5602, 712.0000, 280.0000, 330.0000, 355.0000, 501.0000,
322.0000],
[495.9600, 234.7900, 703.0000, 313.0000, 326.0000, 306.0000, 484.8128,
294.7599],
[491.1624, 242.0584, 708.0000, 259.0000, 343.0000, 304.0000, 466.0000,
332.0000],
[495.0900, 241.7500, 670.0000, 346.0000, 379.6035, 289.7198, 444.0957,
335.8653],
[484.8000, 235.4200, 676.0000, 343.0000, 336.0000, 313.0000, 420.2336,
285.1066]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 34
running loss: nan
Train Steps: 34/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[484.6600, 239.1800, 665.8854, 277.5496, 307.0000, 299.0000, 411.7228,
327.9374],
[490.5919, 243.9347, 580.6928, 144.1250, 287.0000, 198.0000, 480.0000,
336.0000],
[495.8700, 247.7900, 701.0000, 247.0000, 292.0000, 294.0000, 456.5611,
306.1910],
[ nan, nan, 617.9645, 156.6336, 294.0000, 164.0000, 433.0000,
310.0000],
[503.4279, 241.4330, 700.0000, 300.0000, 321.0000, 344.0000, 569.6925,
337.9540],
[490.5200, 243.8100, 691.0000, 312.0000, 383.0000, 287.0000, 420.6341,
352.0000],
[496.1200, 243.3100, 617.3571, 115.1269, 340.0202, 124.7101, 469.6721,
302.6288],
[505.1393, 246.4364, 700.0000, 306.0000, 303.0000, 294.0000, 569.6925,
351.8367]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 35
running loss: nan
Train Steps: 35/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[496.2100, 244.5800, 688.8793, 172.7032, 324.0115, 153.2296, 472.5629,
329.7797],
[488.7600, 236.5500, 682.0000, 297.0000, 347.0000, 288.0000, 435.9043,
322.9804],
[490.7900, 246.9100, 707.0000, 280.0000, 343.0000, 363.0000, 462.2595,
305.8005],
[494.3001, 239.5567, 714.0048, 287.3219, 310.3356, 293.9974, 483.3020,
316.7523],
[493.1591, 237.3677, 700.5271, 305.2639, 343.9919, 319.1815, 481.7994,
312.1400],
[501.7164, 242.3712, 731.0000, 225.0000, 370.0000, 157.0000, 578.5567,
324.7654],
[495.7000, 245.4200, 676.0000, 234.0000, 286.0000, 236.0000, 478.0000,
335.0000],
[508.2770, 247.6873, 679.0000, 156.0000, 441.9766, 96.9323, 535.5019,
332.4009]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 36
running loss: nan
Train Steps: 36/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[488.6800, 240.5300, 698.0000, 282.0000, 284.0000, 250.0000, 452.0000,
307.0000],
[ nan, nan, 517.5590, 116.6062, 322.0000, 120.0000, 410.0000,
332.0000],
[487.6300, 240.1400, 682.6197, 310.0718, 402.5010, 305.6570, 410.0000,
326.0000],
[496.1200, 249.0500, 687.0000, 328.0000, 296.0000, 237.0000, 451.0000,
356.0000],
[502.0016, 242.9966, 723.0000, 226.0000, 307.0000, 212.0000, 565.8936,
334.4833],
[490.3900, 244.2900, 684.0000, 274.0000, 291.0000, 220.0000, 423.2385,
353.5374],
[504.5688, 242.6839, 642.0000, 350.0000, 302.0000, 292.0000, 551.3309,
327.5420],
[487.2000, 240.6200, 627.0000, 209.0000, 283.0000, 227.0000, 436.9727,
304.6292]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 37
running loss: nan
Train Steps: 37/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[ nan, nan, 593.0000, 132.0000, 356.0000, 91.0000, 425.0000,
299.0000],
[502.4100, 246.0400, 724.0000, 272.0000, 302.0000, 193.0000, 507.0098,
294.9176],
[491.6300, 240.4700, 700.0000, 323.0000, 318.0000, 279.0000, 445.0000,
332.0000],
[499.8300, 241.9100, 619.0000, 114.0000, 385.0000, 84.0000, 475.3155,
294.2393],
[483.3700, 239.4200, 546.4636, 172.4778, 280.0000, 188.0000, 411.4557,
330.5729],
[504.1500, 240.4100, 708.0000, 330.0000, 289.0000, 271.0000, 506.7784,
300.7246],
[489.7700, 242.9000, 600.4697, 180.8168, 278.0000, 200.0000, 438.9684,
344.9220],
[507.7065, 248.6254, 727.0000, 280.0000, 308.0000, 260.0000, 569.6925,
353.9191]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 38
running loss: nan
Train Steps: 38/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[487.6300, 240.1400, 682.6197, 310.0718, 402.5010, 305.6570, 410.0000,
326.0000],
[501.9800, 249.4200, 667.0000, 348.0000, 301.0000, 252.0000, 443.7396,
367.4918],
[506.2803, 251.7526, 739.0000, 275.0000, 341.0000, 176.0000, 587.4209,
344.8954],
[502.4100, 246.0400, 724.0000, 272.0000, 302.0000, 193.0000, 507.0098,
294.9176],
[493.1591, 244.5602, 707.0000, 247.0000, 297.0000, 333.0000, 499.0000,
321.0000],
[495.8300, 246.9000, 621.0000, 163.0000, 297.0000, 192.0000, 467.0000,
341.0000],
[497.0400, 240.0600, 617.0000, 127.0000, 347.0000, 108.0000, 468.0000,
311.0000],
[501.1459, 243.3093, 674.0000, 166.0000, 354.0000, 166.0000, 563.9941,
335.1775]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 39
running loss: nan
Train Steps: 39/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[490.5919, 247.0619, 656.0000, 218.0000, 285.0000, 324.0000, 462.9718,
308.1432],
[497.4400, 245.9000, 579.0000, 111.0000, 339.0000, 151.0000, 503.0000,
321.0000],
[507.7065, 245.4983, 617.0000, 355.0000, 323.0000, 286.0000, 587.4209,
343.5071],
[500.8000, 249.1900, 720.0000, 272.0000, 322.0000, 158.0000, 497.8747,
297.9915],
[488.0247, 244.2474, 608.0761, 206.6678, 272.0000, 247.0000, 450.0000,
337.0000],
[494.5853, 235.8042, 707.2659, 233.4960, 284.4970, 297.9904, 497.7261,
316.7523],
[490.9700, 242.1100, 708.0000, 265.0000, 312.0000, 257.0000, 435.9043,
337.0366],
[500.5754, 241.4330, 693.7882, 325.3166, 335.6755, 311.8676, 535.5466,
312.2101]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 40
running loss: nan
Train Steps: 40/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[494.5600, 245.9300, 625.0000, 180.0000, 315.0000, 142.0000, 426.0000,
345.0000],
[491.2700, 242.2900, 692.0000, 290.0000, 387.0979, 310.5822, 463.0000,
336.0000],
[498.6600, 245.4800, 648.0000, 177.0000, 285.0000, 233.0000, 481.0000,
312.0000],
[507.1360, 247.3746, 691.0000, 322.0000, 326.0000, 328.0000, 601.3504,
326.1537],
[500.0049, 240.4949, 716.0000, 251.0000, 284.0000, 263.0000, 508.9154,
295.6488],
[502.8574, 256.4433, 680.0000, 270.0000, 362.0000, 155.0000, 435.1920,
372.5677],
[500.9500, 245.1100, 675.0000, 189.0000, 322.0000, 158.0000, 507.1346,
288.6207],
[496.1200, 249.0500, 687.0000, 328.0000, 296.0000, 237.0000, 451.0000,
356.0000]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 41
running loss: nan
Train Steps: 41/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[487.5800, 238.2200, 695.0000, 286.0000, 388.7642, 292.7355, 415.2475,
296.4297],
[502.0016, 240.1822, 728.0000, 227.0000, 351.0000, 188.0000, 564.6272,
320.6006],
[496.4300, 240.2100, 715.0000, 293.0000, 293.0000, 300.0000, 508.5592,
296.8201],
[502.8574, 243.3093, 720.0000, 283.0000, 301.0000, 281.0000, 561.4614,
329.6244],
[490.1500, 245.0200, 696.0000, 268.0000, 319.0000, 259.0000, 401.0383,
328.2302],
[495.7500, 245.3800, 626.0000, 150.0000, 336.0000, 149.0000, 479.0000,
340.0000],
[501.0800, 241.7700, 720.0000, 286.0000, 304.0000, 310.0000, 513.1892,
286.2780],
[486.6400, 237.4500, 691.0000, 297.0000, 349.0000, 305.0000, 427.7128,
298.7724]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 42
running loss: nan
Train Steps: 42/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[500.6500, 242.2600, 700.0000, 293.0000, 312.0000, 330.0000, 510.0000,
313.0000],
[498.7100, 250.7200, 626.0000, 207.0000, 305.0000, 172.0000, 454.0000,
337.0000],
[509.1327, 245.4983, 682.0000, 338.0000, 310.0000, 297.0000, 527.9040,
317.1299],
[496.8673, 236.1169, 690.9001, 290.4881, 353.3458, 307.5619, 506.4407,
316.0934],
[503.4279, 238.9313, 696.0000, 318.0000, 301.0000, 283.0000, 563.9941,
317.8241],
[489.4300, 243.1300, 677.0000, 235.0000, 283.0000, 207.0000, 404.2437,
335.5512],
[496.0200, 244.2800, 587.0000, 115.0000, 336.0000, 147.0000, 492.0000,
331.0000],
[501.1800, 245.4400, 716.0000, 212.0000, 288.0000, 238.0000, 503.5731,
294.0870]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 43
running loss: nan
Train Steps: 43/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[502.0500, 240.1100, 704.0000, 332.0000, 286.0000, 264.0000, 490.5476,
280.3407],
[503.9400, 258.1900, 637.0000, 236.0000, 388.0000, 137.0000, 438.3973,
373.3486],
[500.9100, 247.8700, 715.0000, 213.0000, 320.0000, 161.0000, 495.0000,
317.0000],
[506.5656, 247.6873, 736.0000, 211.0000, 352.0000, 230.0000, 596.9183,
329.6244],
[492.5886, 237.3677, 665.8701, 248.2718, 278.2819, 265.8849, 473.0849,
307.8573],
[497.2500, 245.9600, 578.0000, 122.0000, 335.0000, 133.0000, 478.0000,
317.0000],
[490.3600, 243.8000, 699.0000, 315.0000, 345.0000, 284.0000, 418.4304,
352.4393],
[492.0800, 243.5100, 565.4799, 160.8031, 272.0000, 245.0000, 462.0000,
344.0000]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 44
running loss: nan
Train Steps: 44/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[ nan, nan, 594.0000, 127.0000, 315.0000, 146.0000, 435.0000,
334.0000],
[507.9918, 247.0619, 669.0000, 163.0000, 388.0000, 102.0000, 515.2408,
310.1886],
[488.6800, 240.5300, 698.0000, 282.0000, 284.0000, 250.0000, 452.0000,
307.0000],
[496.2400, 244.3600, 655.1108, 143.9094, 352.0268, 123.2475, 474.3377,
330.0576],
[495.9500, 244.2800, 608.0000, 127.0000, 323.0000, 166.0000, 491.0000,
333.0000],
[486.0900, 237.1500, 650.0000, 235.0000, 282.0000, 245.0000, 427.7128,
297.2106],
[484.4000, 240.8700, 594.0000, 122.0000, 329.0000, 113.0000, 417.3844,
289.4016],
[483.0400, 236.7800, 673.0000, 293.0000, 285.0000, 273.0000, 421.3020,
281.5925]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 45
running loss: nan
Train Steps: 45/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[493.8100, 246.8200, 586.0000, 150.0000, 337.0000, 115.0000, 427.0000,
342.0000],
[488.9500, 241.7100, 691.0000, 288.0000, 390.0000, 305.0000, 461.0000,
334.0000],
[487.8500, 239.4600, 691.0000, 283.0000, 341.0000, 298.0000, 417.0000,
339.0000],
[495.3700, 247.6800, 681.0000, 337.0000, 336.0000, 316.0000, 468.0000,
338.0000],
[488.1700, 239.9200, 700.0000, 308.0000, 306.0000, 285.0000, 451.0000,
305.0000],
[495.9100, 243.6200, 711.0000, 280.0000, 304.0000, 303.0000, 495.0000,
326.0000],
[497.7600, 236.2000, 668.0000, 337.0000, 331.0000, 276.0000, 464.0000,
314.0000],
[500.9500, 245.1100, 675.0000, 189.0000, 322.0000, 158.0000, 507.1346,
288.6207]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 46
running loss: nan
Train Steps: 46/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[508.2770, 247.6873, 679.0000, 156.0000, 441.9766, 96.9323, 535.5019,
332.4009],
[497.5100, 245.3300, 715.0000, 288.0000, 306.0000, 267.0000, 468.0000,
312.0000],
[497.2900, 250.0000, 687.0000, 335.0000, 318.0000, 310.0000, 462.0000,
340.0000],
[495.9300, 246.6900, 678.0000, 223.0000, 284.0000, 261.0000, 485.0000,
365.0000],
[492.9800, 236.9300, 707.0000, 271.0000, 340.0000, 311.0000, 467.0000,
330.0000],
[496.3600, 243.7000, 667.0000, 161.0000, 294.0000, 257.0000, 507.0000,
315.0000],
[491.4000, 238.9100, 692.0000, 293.0000, 313.0000, 259.0000, 425.2197,
321.0281],
[507.1360, 248.9381, 704.0000, 300.0000, 312.0000, 317.0000, 603.2499,
325.4596]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 47
running loss: nan
Train Steps: 47/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[501.0900, 254.8600, 715.0000, 251.0000, 310.0000, 246.0000, 432.0000,
321.0000],
[ nan, nan, 617.9645, 156.6336, 294.0000, 164.0000, 433.0000,
310.0000],
[498.6600, 245.4800, 648.0000, 177.0000, 285.0000, 233.0000, 481.0000,
312.0000],
[492.5886, 237.3677, 665.8701, 248.2718, 278.2819, 265.8849, 473.0849,
307.8573],
[483.3700, 239.4200, 546.4636, 172.4778, 280.0000, 188.0000, 411.4557,
330.5729],
[494.0100, 239.8300, 539.0000, 150.0000, 345.0000, 116.0000, 441.0000,
345.0000],
[488.6800, 240.5300, 698.0000, 282.0000, 284.0000, 250.0000, 452.0000,
307.0000],
[492.4400, 247.4700, 708.0000, 290.0000, 364.0000, 349.0000, 461.1910,
305.0196]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 48
running loss: nan
Train Steps: 48/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[496.1000, 243.8800, 584.0000, 116.0000, 326.0000, 152.0000, 493.0000,
329.0000],
[502.5721, 245.4983, 640.0000, 131.0000, 360.0000, 143.0000, 542.9840,
321.8463],
[ nan, nan, 643.0000, 149.0000, 318.0000, 151.0000, 446.0000,
336.0000],
[507.4213, 245.8110, 743.0000, 262.0000, 345.0000, 216.0000, 579.8230,
350.4485],
[501.7700, 247.2200, 723.0000, 247.0000, 298.0000, 192.0000, 494.0000,
315.0000],
[502.5721, 242.0584, 626.3995, 124.7889, 362.5892, 124.7976, 512.3327,
319.3755],
[482.4700, 239.1800, 597.0000, 170.0000, 291.0000, 163.0000, 420.2336,
283.5448],
[495.9400, 237.1000, 685.8777, 322.4308, 326.0126, 281.2020, 475.3770,
322.6425]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 49
running loss: nan
Train Steps: 49/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[501.4300, 243.1200, 654.0000, 135.0000, 375.0000, 115.0000, 510.0000,
316.0000],
[496.2000, 243.9500, 671.8087, 158.8574, 314.0060, 157.6172, 467.5804,
307.4238],
[489.4300, 243.1300, 677.0000, 235.0000, 283.0000, 207.0000, 404.2437,
335.5512],
[503.1100, 241.8900, 673.4919, 326.7499, 326.0000, 301.0000, 505.0000,
307.0000],
[501.0300, 253.9500, 633.9382, 277.5496, 303.0000, 173.0000, 445.8765,
362.8064],
[499.9400, 248.5100, 668.0000, 197.0000, 289.0000, 222.0000, 495.0000,
324.0000],
[495.3700, 247.6800, 681.0000, 337.0000, 336.0000, 316.0000, 468.0000,
338.0000],
[495.4411, 242.9966, 585.0000, 146.0000, 326.0000, 127.0000, 451.9311,
339.3793]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 50
running loss: nan
Train Steps: 50/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[490.5919, 243.9347, 580.6928, 144.1250, 287.0000, 198.0000, 480.0000,
336.0000],
[495.9600, 238.6900, 712.0000, 273.0000, 302.0000, 312.0000, 492.0000,
322.0000],
[496.0400, 243.3100, 640.8530, 179.2846, 307.3357, 167.8550, 475.9475,
310.9679],
[493.7296, 243.3093, 654.0000, 159.0000, 284.0000, 221.0000, 463.0000,
333.0000],
[495.9100, 243.6200, 711.0000, 280.0000, 304.0000, 303.0000, 495.0000,
326.0000],
[497.0000, 249.5400, 694.0000, 347.0000, 327.0000, 259.0000, 449.0000,
355.0000],
[496.1000, 243.8800, 584.0000, 116.0000, 326.0000, 152.0000, 493.0000,
329.0000],
[508.5623, 249.5636, 703.0000, 335.0000, 291.0000, 266.0000, 519.0398,
317.8241]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 51
running loss: nan
Train Steps: 51/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[486.9100, 241.1400, 622.5284, 256.7020, 290.0000, 261.0000, 411.9899,
317.1024],
[493.9800, 237.4300, 696.0000, 289.0000, 373.0000, 308.0000, 464.0000,
331.0000],
[491.1100, 241.1500, 571.0000, 129.0000, 350.0000, 92.0000, 423.4389,
300.3342],
[492.0181, 245.8110, 597.4271, 191.6575, 306.0000, 158.0000, 437.0000,
348.0000],
[498.1200, 244.9800, 715.0000, 288.0000, 304.0000, 177.0000, 459.0000,
321.0000],
[484.6600, 239.1800, 665.8854, 277.5496, 307.0000, 299.0000, 411.7228,
327.9374],
[490.0000, 242.0700, 626.0000, 186.0000, 277.0000, 294.0000, 466.5333,
338.2080],
[500.6700, 248.5800, 682.0000, 157.0000, 396.0000, 100.0000, 497.5185,
297.9915]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 52
running loss: nan
Train Steps: 52/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[502.0400, 245.7100, 659.0000, 135.0000, 373.0000, 107.0000, 493.7134,
292.1411],
[495.7300, 239.1300, 704.0000, 277.0000, 335.0000, 287.0000, 455.0000,
333.0000],
[504.8541, 240.4949, 634.8398, 344.7328, 312.0000, 302.0000, 556.3961,
321.9889],
[501.7700, 247.2200, 723.0000, 247.0000, 298.0000, 192.0000, 494.0000,
315.0000],
[486.9100, 239.8900, 703.0000, 267.0000, 322.0000, 279.0000, 424.5074,
306.1910],
[ nan, nan, 517.5590, 116.6062, 322.0000, 120.0000, 410.0000,
332.0000],
[495.4411, 242.9966, 585.0000, 146.0000, 326.0000, 127.0000, 451.9311,
339.3793],
[502.8574, 245.1856, 672.6089, 168.0607, 333.0000, 168.0000, 538.0257,
323.5759]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 53
running loss: nan
Train Steps: 53/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[497.9500, 245.8000, 595.0000, 136.0000, 308.0000, 171.0000, 479.0000,
315.0000],
[490.9100, 237.3000, 672.0000, 196.0000, 280.0000, 252.0000, 469.0000,
328.0000],
[500.7800, 249.6400, 720.0000, 230.0000, 356.0000, 128.0000, 498.9431,
299.1628],
[491.8400, 239.7800, 679.0000, 232.0000, 279.0000, 244.0000, 469.0000,
300.0000],
[493.8100, 240.7600, 697.0720, 278.3835, 384.0000, 342.0000, 482.0000,
336.0000],
[504.5688, 241.7457, 719.0000, 289.0000, 315.0000, 210.0000, 584.8883,
322.6830],
[499.9700, 254.1700, 691.0000, 226.0000, 324.0000, 189.0000, 451.9311,
347.9693],
[ nan, nan, 543.4210, 126.6131, 321.0000, 130.0000, 409.0000,
335.0000]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 54
running loss: nan
Train Steps: 54/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[491.1624, 242.0584, 708.0000, 259.0000, 343.0000, 304.0000, 466.0000,
332.0000],
[491.1800, 244.2000, 696.0000, 300.0000, 369.0000, 294.0000, 420.8344,
351.1215],
[490.0100, 240.6100, 692.0000, 274.0000, 293.0000, 234.0000, 448.0000,
334.0000],
[496.2100, 244.5800, 688.8793, 172.7032, 324.0115, 153.2296, 472.5629,
329.7797],
[493.5900, 246.1400, 597.4271, 221.6781, 277.0000, 226.0000, 419.0314,
349.3644],
[490.8772, 243.6220, 642.0000, 193.0000, 292.0000, 180.0000, 404.7779,
338.7724],
[501.9300, 247.0000, 648.0244, 348.0684, 320.0000, 275.0000, 446.5888,
367.1014],
[495.8300, 246.2900, 636.0000, 196.0000, 294.0000, 226.0000, 483.0000,
370.0000]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 55
running loss: nan
Train Steps: 55/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[498.8640, 238.6186, 718.8183, 288.3773, 306.7708, 312.9795, 529.0106,
309.9863],
[497.9500, 245.8000, 595.0000, 136.0000, 308.0000, 171.0000, 479.0000,
315.0000],
[501.4300, 246.1100, 715.0000, 220.0000, 322.0000, 170.0000, 502.0000,
311.0000],
[ nan, nan, 724.0000, 210.0000, 411.0000, 138.0000, 588.6873,
342.1188],
[503.1426, 241.4330, 727.0000, 274.0000, 315.0000, 338.0000, 564.6272,
336.5657],
[495.0900, 241.9900, 692.0000, 316.0000, 359.0000, 309.0000, 474.0000,
303.0000],
[509.1327, 248.6254, 690.0000, 185.0000, 393.0000, 120.0000, 515.8740,
316.4358],
[493.5600, 243.1200, 699.3539, 286.7225, 343.0000, 295.0000, 461.0000,
337.0000]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 56
running loss: nan
Train Steps: 56/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[504.5688, 243.6220, 716.0000, 311.0000, 300.0000, 231.0000, 507.0098,
296.3059],
[499.7000, 245.3900, 557.1127, 121.6097, 314.0000, 161.0000, 487.0000,
335.0000],
[500.8000, 249.1900, 720.0000, 272.0000, 322.0000, 158.0000, 497.8747,
297.9915],
[499.1492, 252.3780, 700.0000, 324.0000, 295.0000, 291.0000, 459.0000,
342.0000],
[504.3800, 238.9900, 716.0000, 290.0000, 295.0000, 281.0000, 510.0000,
307.0000],
[485.6100, 238.7500, 686.0000, 305.0000, 348.0000, 324.0000, 414.3940,
327.9374],
[ nan, nan, 679.0000, 138.0000, 445.0000, 126.0000, 591.2198,
340.7306],
[501.7164, 242.3712, 731.0000, 225.0000, 370.0000, 157.0000, 578.5567,
324.7654]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 57
running loss: nan
Train Steps: 57/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[493.3600, 242.8800, 546.4636, 218.3425, 301.0000, 181.0000, 456.0000,
353.0000],
[495.8900, 246.1100, 716.0000, 265.0000, 321.0000, 322.0000, 489.0000,
358.0000],
[488.7100, 240.8900, 613.4007, 218.3425, 281.0000, 220.0000, 415.9966,
338.4796],
[489.9400, 241.8100, 692.0000, 292.0000, 399.9338, 306.3606, 410.9215,
346.3862],
[488.0247, 244.2474, 608.0761, 206.6678, 272.0000, 247.0000, 450.0000,
337.0000],
[495.3700, 238.8200, 566.2405, 164.9726, 340.0000, 126.0000, 436.0000,
347.0000],
[499.2800, 248.5500, 715.0000, 279.0000, 326.0000, 321.0000, 500.0000,
333.0000],
[484.2800, 242.0800, 551.7881, 114.9384, 320.0000, 127.0000, 435.1920,
310.0955]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 58
running loss: nan
Train Steps: 58/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[495.8300, 246.9000, 621.0000, 163.0000, 297.0000, 192.0000, 467.0000,
341.0000],
[497.2500, 245.9600, 578.0000, 122.0000, 335.0000, 133.0000, 478.0000,
317.0000],
[485.9900, 240.3900, 662.0000, 295.0000, 324.0000, 306.0000, 413.3255,
316.8095],
[495.7400, 243.0700, 625.0000, 159.0000, 281.0000, 243.0000, 489.0000,
330.0000],
[502.0016, 240.1822, 728.0000, 227.0000, 351.0000, 188.0000, 564.6272,
320.6006],
[486.9100, 239.8900, 703.0000, 267.0000, 322.0000, 279.0000, 424.5074,
306.1910],
[498.2500, 240.2200, 700.0000, 315.0000, 306.0000, 314.0000, 509.0000,
300.0000],
[508.8475, 244.2474, 728.0000, 287.0000, 299.0000, 238.0000, 533.6024,
319.9065]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 59
running loss: nan
Train Steps: 59/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[501.4312, 241.7457, 680.0000, 161.0000, 315.0000, 210.0000, 548.7982,
317.8241],
[494.1500, 245.1300, 699.0000, 237.0000, 302.0000, 336.0000, 498.0000,
342.0000],
[494.6500, 244.5700, 707.0000, 271.0000, 305.0000, 269.0000, 462.0000,
338.0000],
[497.5500, 246.8200, 654.0000, 169.0000, 314.0000, 167.0000, 472.0000,
321.0000],
[502.0016, 241.4330, 617.7352, 124.7889, 351.3434, 134.0229, 514.8118,
317.3988],
[502.6900, 241.7500, 707.0000, 227.0000, 318.0000, 171.0000, 506.7784,
305.4101],
[500.7600, 247.1900, 641.0000, 141.0000, 391.0000, 92.0000, 502.5046,
293.6965],
[501.8700, 246.5800, 712.0000, 229.0000, 335.0000, 130.0000, 468.6673,
290.0746]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 60
running loss: nan
Train Steps: 60/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[495.6700, 245.2700, 711.0000, 275.0000, 360.0000, 341.0000, 491.0000,
353.0000],
[496.3500, 242.9600, 638.0000, 162.0000, 315.0000, 154.0000, 456.0000,
311.0000],
[496.1100, 241.6200, 642.3539, 163.6537, 323.3445, 138.6042, 478.4196,
323.4764],
[501.7164, 244.8729, 726.5198, 293.6544, 296.0000, 257.0000, 532.8420,
316.4105],
[503.7131, 242.6839, 731.0000, 246.0000, 338.5284, 254.5401, 593.7525,
317.8241],
[490.7800, 239.6200, 633.0000, 183.0000, 290.0000, 183.0000, 467.0000,
303.0000],
[503.7131, 243.6220, 728.0000, 196.0000, 378.0822, 202.0042, 595.6520,
321.2947],
[501.7164, 242.3712, 731.0000, 225.0000, 370.0000, 157.0000, 578.5567,
324.7654]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 61
running loss: nan
Train Steps: 61/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[500.7400, 249.3600, 705.0000, 191.0000, 382.0000, 112.0000, 497.5185,
297.6010],
[490.0800, 243.9900, 691.0000, 323.0000, 335.0000, 291.0000, 401.3054,
323.5448],
[501.6600, 240.3000, 676.0000, 364.0000, 308.0000, 299.0000, 485.4824,
278.9525],
[499.1300, 250.4500, 674.0000, 344.0000, 386.0000, 270.0000, 450.0000,
356.0000],
[487.8500, 239.4600, 691.0000, 283.0000, 341.0000, 298.0000, 417.0000,
339.0000],
[497.4400, 245.9000, 579.0000, 111.0000, 339.0000, 151.0000, 503.0000,
321.0000],
[501.0700, 243.4400, 704.0000, 230.0000, 292.0000, 223.0000, 509.9838,
288.2302],
[496.3500, 242.9600, 638.0000, 162.0000, 315.0000, 154.0000, 456.0000,
311.0000]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 62
running loss: nan
Train Steps: 62/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[490.9200, 242.2600, 685.0000, 243.0000, 305.0000, 153.0000, 408.4806,
297.2106],
[ nan, nan, 548.7455, 131.6165, 332.0000, 112.0000, 412.2570,
343.7506],
[ nan, nan, 682.0000, 133.0000, 433.0000, 142.0000, 589.3204,
328.9302],
[498.0082, 251.4398, 680.0000, 250.0000, 295.0000, 194.0000, 452.0000,
339.0000],
[494.8000, 245.8500, 707.0000, 294.0000, 363.0000, 348.0000, 503.0000,
324.0000],
[486.4500, 236.9900, 683.0000, 280.0000, 308.0000, 295.0000, 427.3566,
297.2106],
[497.9000, 243.6700, 719.0000, 258.0000, 307.0000, 285.0000, 489.0000,
329.0000],
[496.1000, 243.8400, 695.0000, 303.0000, 338.0000, 306.0000, 491.0000,
330.0000]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 63
running loss: nan
Train Steps: 63/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[491.6300, 240.4200, 702.0000, 272.0000, 365.0000, 332.0000, 487.0000,
332.0000],
[488.5952, 242.9966, 696.0000, 291.0000, 357.5752, 290.8813, 403.4423,
325.8875],
[492.5886, 246.1237, 681.0000, 229.0000, 289.0000, 220.0000, 407.7161,
353.4143],
[489.6200, 240.8100, 549.0000, 169.0000, 296.0000, 167.0000, 441.0000,
340.0000],
[492.3300, 242.9000, 568.0000, 124.0000, 347.0000, 100.0000, 433.0551,
313.2191],
[498.8640, 244.8729, 686.0000, 180.0000, 297.0000, 182.0000, 444.0000,
338.0000],
[502.0200, 242.8900, 679.0000, 173.0000, 357.0000, 122.0000, 505.7100,
309.3146],
[490.7200, 245.4200, 554.0701, 169.1422, 283.0000, 194.0000, 445.0000,
340.0000]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 64
running loss: nan
Train Steps: 64/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[497.1300, 238.9300, 715.0000, 271.0000, 293.0000, 245.0000, 469.0000,
303.0000],
[497.5400, 245.8000, 699.0000, 204.0000, 285.0000, 247.0000, 478.0000,
341.0000],
[491.7329, 244.8729, 683.0000, 204.0000, 293.0000, 189.0000, 411.3298,
292.5252],
[490.0100, 240.6100, 692.0000, 274.0000, 293.0000, 234.0000, 448.0000,
334.0000],
[496.6100, 244.9400, 683.0000, 184.0000, 287.0000, 223.0000, 489.0000,
331.0000],
[496.5300, 244.8400, 613.0000, 124.0000, 317.0000, 192.0000, 505.0000,
318.0000],
[494.0900, 241.0100, 703.0000, 306.0000, 326.0000, 315.0000, 473.0000,
302.0000],
[494.0100, 245.5700, 704.0000, 266.0000, 326.0000, 262.0000, 410.2613,
294.0870]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 65
running loss: nan
Train Steps: 65/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[504.1500, 240.4100, 708.0000, 330.0000, 289.0000, 271.0000, 506.7784,
300.7246],
[492.8600, 245.9100, 699.0000, 263.0000, 303.0000, 329.0000, 448.3696,
301.1151],
[495.4411, 249.8763, 707.0000, 282.0000, 332.0000, 292.0000, 434.1604,
315.6382],
[495.4411, 242.0584, 620.5229, 140.8099, 298.6643, 175.1677, 474.4262,
295.5407],
[498.4000, 246.7900, 577.0000, 119.0000, 346.0000, 142.0000, 501.0000,
324.0000],
[501.9300, 256.1800, 715.0000, 298.0000, 284.0000, 257.0000, 456.0000,
344.0000],
[ nan, nan, 601.2303, 162.4709, 319.0000, 136.0000, 413.0000,
334.0000],
[491.5200, 241.9000, 685.0000, 197.0000, 282.0000, 252.0000, 462.0000,
335.0000]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 66
running loss: nan
Train Steps: 66/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[488.8600, 241.9700, 664.0000, 196.0000, 287.0000, 200.0000, 435.5481,
338.2080],
[496.2200, 243.3300, 715.3384, 245.1700, 291.4701, 217.0026, 473.3853,
328.9417],
[492.3034, 246.7491, 563.0000, 139.0000, 339.0000, 110.0000, 428.0000,
336.0000],
[501.0800, 242.3100, 720.0000, 264.0000, 290.0000, 280.0000, 513.9015,
288.2302],
[492.2300, 247.1400, 677.0000, 230.0000, 288.0000, 192.0000, 408.5175,
333.7941],
[489.7400, 240.3700, 708.0000, 253.0000, 327.0000, 331.0000, 485.0000,
331.0000],
[500.0100, 246.6100, 579.0000, 124.0000, 341.0000, 113.0000, 450.0000,
338.0000],
[495.0900, 241.9900, 692.0000, 316.0000, 359.0000, 309.0000, 474.0000,
303.0000]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 67
running loss: nan
Train Steps: 67/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[492.3300, 242.9000, 568.0000, 124.0000, 347.0000, 100.0000, 433.0551,
313.2191],
[483.0400, 236.7800, 673.0000, 293.0000, 285.0000, 273.0000, 421.3020,
281.5925],
[501.7164, 244.8729, 726.5198, 293.6544, 296.0000, 257.0000, 532.8420,
316.4105],
[496.7200, 235.1800, 692.0000, 322.0000, 352.0000, 304.0000, 482.0000,
297.0000],
[486.3200, 237.8300, 593.6238, 177.4812, 285.0000, 175.0000, 428.0689,
298.7724],
[498.6900, 241.4000, 711.0000, 278.0000, 318.0000, 346.0000, 512.0000,
311.0000],
[492.9800, 236.9300, 707.0000, 271.0000, 340.0000, 311.0000, 467.0000,
330.0000],
[494.3001, 240.4949, 688.0120, 240.8839, 314.5486, 150.9156, 456.8577,
326.3061]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 68
running loss: nan
Train Steps: 68/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[488.1900, 240.0300, 619.0000, 215.0000, 277.0000, 228.0000, 441.9588,
341.3316],
[501.1200, 242.0800, 711.0000, 293.0000, 324.0000, 313.0000, 508.9154,
287.4493],
[492.8600, 245.9100, 699.0000, 263.0000, 303.0000, 329.0000, 448.3696,
301.1151],
[489.3200, 241.1600, 683.0000, 244.0000, 281.0000, 215.0000, 453.0000,
308.0000],
[498.0082, 251.7526, 703.0000, 208.0000, 300.0000, 204.0000, 433.0000,
326.0000],
[507.9918, 247.0619, 669.0000, 163.0000, 388.0000, 102.0000, 515.2408,
310.1886],
[489.7400, 240.3700, 708.0000, 253.0000, 327.0000, 331.0000, 485.0000,
331.0000],
[486.6400, 237.4500, 691.0000, 297.0000, 349.0000, 305.0000, 427.7128,
298.7724]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 69
running loss: nan
Train Steps: 69/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[ nan, nan, 677.0000, 153.0000, 468.0000, 128.0000, 570.3256,
364.3312],
[491.1100, 241.1500, 571.0000, 129.0000, 350.0000, 92.0000, 423.4389,
300.3342],
[501.0800, 241.7300, 712.0000, 296.0000, 326.0000, 311.0000, 512.4769,
285.4970],
[503.9984, 240.8076, 715.0000, 321.0000, 294.0000, 276.0000, 516.5071,
298.3883],
[496.1000, 242.9900, 620.5920, 134.0372, 356.6960, 107.8909, 478.0393,
325.5612],
[501.7164, 244.8729, 726.5198, 293.6544, 296.0000, 257.0000, 532.8420,
316.4105],
[489.3200, 241.0900, 525.0000, 118.0000, 299.0000, 153.0000, 422.3705,
306.1910],
[496.8600, 244.1200, 700.0000, 307.0000, 332.0000, 294.0000, 470.0000,
310.0000]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 70
running loss: nan
Train Steps: 70/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[ nan, nan, 617.9645, 156.6336, 294.0000, 164.0000, 433.0000,
310.0000],
[498.8640, 238.6186, 718.8183, 288.3773, 306.7708, 312.9795, 529.0106,
309.9863],
[496.8600, 244.1200, 700.0000, 307.0000, 332.0000, 294.0000, 470.0000,
310.0000],
[497.0900, 250.5100, 584.0000, 173.0000, 347.0000, 130.0000, 455.8488,
346.4075],
[496.0800, 242.1700, 687.7038, 230.9800, 279.3204, 227.0880, 478.2295,
307.8407],
[495.4600, 246.4600, 595.0000, 162.0000, 292.0000, 221.0000, 499.0000,
343.0000],
[502.0016, 240.1822, 728.0000, 227.0000, 351.0000, 188.0000, 564.6272,
320.6006],
[492.9600, 240.8300, 704.0000, 320.0000, 300.0000, 289.0000, 479.0000,
317.0000]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 71
running loss: nan
Train Steps: 71/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[504.8541, 239.8694, 638.0000, 346.0000, 307.0000, 291.0000, 586.1546,
320.6006],
[497.8700, 247.7900, 648.0000, 179.0000, 282.0000, 234.0000, 455.4926,
306.1910],
[501.0800, 241.7700, 720.0000, 286.0000, 304.0000, 310.0000, 513.1892,
286.2780],
[484.2800, 242.0800, 551.7881, 114.9384, 320.0000, 127.0000, 435.1920,
310.0955],
[499.1492, 246.4364, 652.9544, 165.8065, 290.0000, 216.0000, 479.0000,
342.0000],
[491.4477, 242.3712, 659.0000, 200.0000, 326.0000, 127.0000, 410.9736,
298.3820],
[487.7395, 241.4330, 679.0000, 223.0000, 310.0000, 331.0000, 466.8895,
335.4748],
[507.1360, 248.9381, 704.0000, 300.0000, 312.0000, 317.0000, 603.2499,
325.4596]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 72
running loss: nan
Train Steps: 72/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[501.8900, 245.2000, 670.0000, 342.0000, 322.0000, 288.0000, 456.0000,
367.0000],
[498.7100, 250.7200, 626.0000, 207.0000, 305.0000, 172.0000, 454.0000,
337.0000],
[507.7065, 249.8763, 731.0000, 239.0000, 310.0000, 259.0000, 597.5515,
328.2361],
[497.9900, 246.9700, 693.0000, 211.0000, 293.0000, 194.0000, 467.0000,
319.0000],
[504.8541, 240.4949, 634.8398, 344.7328, 312.0000, 302.0000, 556.3961,
321.9889],
[482.4700, 239.1800, 597.0000, 170.0000, 291.0000, 163.0000, 420.2336,
283.5448],
[493.1591, 237.3677, 700.5271, 305.2639, 343.9919, 319.1815, 481.7994,
312.1400],
[494.7600, 244.7600, 707.0000, 277.0000, 387.0000, 339.0000, 494.0000,
351.0000]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 73
running loss: nan
Train Steps: 73/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[493.2300, 246.3500, 606.0000, 104.0000, 307.0000, 159.0000, 454.4241,
306.9719],
[496.3600, 244.8300, 716.0000, 241.0000, 303.0000, 282.0000, 477.0000,
340.0000],
[ nan, nan, 601.0000, 127.0000, 343.0000, 120.0000, 448.0000,
337.0000],
[493.4200, 246.6600, 521.3622, 172.4778, 295.0000, 169.0000, 418.2301,
350.2430],
[ nan, nan, 669.0000, 199.0000, 285.0000, 202.0000, 426.2881,
308.5337],
[490.3400, 244.1100, 700.0000, 304.0000, 310.0000, 254.0000, 418.8311,
352.8785],
[491.6200, 238.9700, 696.0000, 301.0000, 352.0000, 288.0000, 430.0000,
345.0000],
[494.0900, 241.0100, 703.0000, 306.0000, 326.0000, 315.0000, 473.0000,
302.0000]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 74
running loss: nan
Train Steps: 74/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[495.9500, 244.2800, 608.0000, 127.0000, 323.0000, 166.0000, 491.0000,
333.0000],
[501.3800, 245.6600, 697.0000, 185.0000, 352.0000, 136.0000, 500.0000,
312.0000],
[507.1360, 246.1237, 687.0000, 188.0000, 354.0000, 127.0000, 513.3414,
305.3296],
[495.8900, 246.1100, 716.0000, 265.0000, 321.0000, 322.0000, 489.0000,
358.0000],
[496.4300, 240.2100, 715.0000, 293.0000, 293.0000, 300.0000, 508.5592,
296.8201],
[494.3001, 240.4949, 688.0120, 240.8839, 314.5486, 150.9156, 456.8577,
326.3061],
[485.1400, 241.1400, 692.0000, 271.0000, 323.0000, 322.0000, 456.2567,
336.5189],
[491.8400, 239.7800, 679.0000, 232.0000, 279.0000, 244.0000, 469.0000,
300.0000]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 75
running loss: nan
Train Steps: 75/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[501.8400, 245.9700, 571.0000, 128.0000, 320.0000, 159.0000, 486.0000,
338.0000],
[503.7131, 244.5602, 672.0000, 335.0000, 296.0000, 262.0000, 550.0646,
329.6244],
[492.0700, 247.4900, 699.0000, 265.0000, 286.0000, 227.0000, 411.0000,
329.0000],
[500.8000, 252.8600, 683.0000, 330.0000, 293.0000, 226.0000, 447.0000,
359.0000],
[502.4100, 243.2900, 688.0000, 319.0000, 304.0000, 282.0000, 502.0000,
308.0000],
[506.8508, 245.1856, 712.0000, 237.0000, 312.0000, 177.0000, 520.3061,
303.9413],
[495.8300, 246.2900, 636.0000, 196.0000, 294.0000, 226.0000, 483.0000,
370.0000],
[502.0016, 242.9966, 723.0000, 226.0000, 307.0000, 212.0000, 565.8936,
334.4833]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 76
running loss: nan
Train Steps: 76/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[ nan, nan, 593.0000, 132.0000, 356.0000, 91.0000, 425.0000,
299.0000],
[496.1400, 243.1500, 691.0000, 317.0000, 363.6628, 306.4949, 472.0000,
308.0000],
[ nan, nan, 567.7618, 140.7894, 340.0000, 111.0000, 414.0000,
335.0000],
[498.7100, 250.7200, 626.0000, 207.0000, 305.0000, 172.0000, 454.0000,
337.0000],
[494.2245, 240.7296, 712.0364, 269.7266, 311.2985, 337.0000, 477.7910,
292.7526],
[492.8600, 245.9100, 699.0000, 263.0000, 303.0000, 329.0000, 448.3696,
301.1151],
[503.9400, 258.1900, 637.0000, 236.0000, 388.0000, 137.0000, 438.3973,
373.3486],
[504.3800, 238.9900, 716.0000, 290.0000, 295.0000, 281.0000, 510.0000,
307.0000]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 77
running loss: nan
Train Steps: 77/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[488.8600, 241.9700, 664.0000, 196.0000, 287.0000, 200.0000, 435.5481,
338.2080],
[490.7800, 239.6200, 633.0000, 183.0000, 290.0000, 183.0000, 467.0000,
303.0000],
[500.5754, 241.4330, 693.7882, 325.3166, 335.6755, 311.8676, 535.5466,
312.2101],
[500.0049, 237.6805, 693.7882, 295.7652, 335.9166, 298.3375, 504.9382,
318.0700],
[499.1500, 243.6600, 708.0000, 276.0000, 338.0000, 312.0000, 491.0000,
327.0000],
[488.7100, 240.8900, 613.4007, 218.3425, 281.0000, 220.0000, 415.9966,
338.4796],
[502.8574, 243.3093, 720.0000, 283.0000, 301.0000, 281.0000, 561.4614,
329.6244],
[493.0100, 246.8500, 612.0000, 121.0000, 301.0000, 172.0000, 450.5065,
304.2387]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 78
running loss: nan
Train Steps: 78/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[498.0700, 246.1700, 686.0000, 207.0000, 351.0000, 124.0000, 463.0000,
323.0000],
[494.0148, 240.8076, 618.6979, 167.0053, 357.9991, 107.1851, 463.1682,
321.0350],
[491.4477, 242.0584, 704.0000, 290.0000, 361.0000, 322.0000, 423.0828,
305.8005],
[495.0900, 242.9100, 711.0000, 265.0000, 337.0000, 312.0000, 479.0000,
338.0000],
[496.0300, 237.6400, 676.8727, 329.8349, 331.3488, 274.6205, 471.3836,
324.1019],
[494.5853, 238.3059, 684.1613, 354.8681, 294.6041, 250.8712, 455.0547,
322.6822],
[494.5853, 237.9932, 661.0566, 183.8918, 282.0930, 249.6721, 495.3221,
317.4111],
[490.3600, 243.8000, 699.0000, 315.0000, 345.0000, 284.0000, 418.4304,
352.4393]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 79
running loss: nan
Train Steps: 79/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[496.3600, 244.8300, 716.0000, 241.0000, 303.0000, 282.0000, 477.0000,
340.0000],
[494.3001, 237.6805, 667.7954, 361.2005, 310.9871, 277.4219, 457.1582,
322.3528],
[487.5800, 238.2200, 695.0000, 286.0000, 388.7642, 292.7355, 415.2475,
296.4297],
[501.7164, 244.8729, 726.5198, 293.6544, 296.0000, 257.0000, 532.8420,
316.4105],
[493.4444, 243.3093, 606.0000, 177.0000, 324.0000, 163.0000, 475.0000,
370.0000],
[494.7800, 244.0100, 707.0000, 267.0000, 323.0000, 284.0000, 417.0282,
308.5337],
[500.0100, 246.6100, 579.0000, 124.0000, 341.0000, 113.0000, 450.0000,
338.0000],
[ nan, nan, 677.0000, 153.0000, 468.0000, 128.0000, 570.3256,
364.3312]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 80
running loss: nan
Train Steps: 80/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[ nan, nan, 513.0000, 114.0000, 339.0000, 113.0000, 439.0000,
342.0000],
[490.1500, 245.0200, 696.0000, 268.0000, 319.0000, 259.0000, 401.0383,
328.2302],
[486.6900, 238.8800, 687.0000, 314.0000, 368.0000, 322.0000, 454.4241,
300.3342],
[492.8739, 241.4330, 707.0000, 275.0000, 295.0000, 224.0000, 424.8636,
320.6377],
[496.9300, 241.9800, 715.0000, 250.0000, 305.0000, 256.0000, 449.0000,
335.0000],
[496.1400, 243.1500, 691.0000, 317.0000, 363.6628, 306.4949, 472.0000,
308.0000],
[501.8300, 248.5600, 700.0000, 342.0000, 319.0000, 283.0000, 481.0000,
328.0000],
[489.7400, 240.3700, 708.0000, 253.0000, 327.0000, 331.0000, 485.0000,
331.0000]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 81
running loss: nan
Train Steps: 81/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[501.2500, 244.2100, 697.0000, 336.0000, 297.0000, 287.0000, 462.0000,
366.0000],
[496.1500, 243.9800, 699.6677, 202.5880, 297.3302, 185.4055, 467.3902,
309.9255],
[502.0016, 241.4330, 617.7352, 124.7889, 351.3434, 134.0229, 514.8118,
317.3988],
[501.6600, 240.3000, 676.0000, 364.0000, 308.0000, 299.0000, 485.4824,
278.9525],
[499.7000, 245.3900, 557.1127, 121.6097, 314.0000, 161.0000, 487.0000,
335.0000],
[500.0400, 246.9800, 696.0000, 291.0000, 372.0000, 334.0000, 487.0000,
311.0000],
[501.4300, 246.1100, 715.0000, 220.0000, 322.0000, 170.0000, 502.0000,
311.0000],
[502.6300, 256.9900, 598.1877, 212.5051, 410.0000, 115.0000, 440.0000,
370.0000]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 82
running loss: nan
Train Steps: 82/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[490.8700, 247.0400, 696.0000, 310.0000, 335.0000, 305.0000, 411.7228,
329.6944],
[490.0100, 240.6100, 692.0000, 274.0000, 293.0000, 234.0000, 448.0000,
334.0000],
[496.2100, 244.5800, 688.8793, 172.7032, 324.0115, 153.2296, 472.5629,
329.7797],
[ nan, nan, 727.0000, 227.0000, 365.0000, 157.0000, 539.3008,
334.4833],
[493.1591, 237.3677, 700.5271, 305.2639, 343.9919, 319.1815, 481.7994,
312.1400],
[ nan, nan, 601.0000, 127.0000, 343.0000, 120.0000, 448.0000,
337.0000],
[490.9100, 237.3000, 672.0000, 196.0000, 280.0000, 252.0000, 469.0000,
328.0000],
[503.1100, 241.8900, 673.4919, 326.7499, 326.0000, 301.0000, 505.0000,
307.0000]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 83
running loss: nan
Train Steps: 83/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[490.0100, 240.6100, 692.0000, 274.0000, 293.0000, 234.0000, 448.0000,
334.0000],
[501.0800, 241.7300, 712.0000, 296.0000, 326.0000, 311.0000, 512.4769,
285.4970],
[490.1500, 245.0200, 696.0000, 268.0000, 319.0000, 259.0000, 401.0383,
328.2302],
[490.1700, 246.8700, 573.0000, 173.0000, 290.0000, 177.0000, 426.1470,
329.6944],
[501.7164, 242.3712, 720.0000, 195.0000, 395.0000, 138.0000, 575.3909,
324.7654],
[495.8100, 239.8400, 686.5782, 321.6804, 329.3477, 300.9463, 475.3770,
308.0492],
[502.8574, 242.3712, 695.7136, 182.8364, 313.9077, 173.2258, 504.0367,
322.0233],
[490.1900, 243.9500, 684.0000, 334.0000, 372.9783, 308.4714, 405.5792,
324.7162]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 84
running loss: nan
Train Steps: 84/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[494.0148, 240.8076, 618.6979, 167.0053, 357.9991, 107.1851, 463.1682,
321.0350],
[492.0181, 236.1169, 695.7136, 309.4855, 371.7718, 319.7672, 483.3020,
309.1750],
[501.0600, 243.8700, 723.0000, 259.0000, 287.0000, 273.0000, 506.0000,
315.0000],
[494.0148, 240.8076, 712.0000, 270.0000, 350.0000, 351.0000, 494.0000,
323.0000],
[493.0700, 240.3800, 703.0000, 281.0000, 293.0000, 293.0000, 471.0000,
301.0000],
[496.0800, 240.2600, 702.8997, 302.5531, 306.6687, 308.2590, 479.7508,
305.5475],
[495.6700, 245.2700, 711.0000, 275.0000, 360.0000, 341.0000, 491.0000,
353.0000],
[501.6600, 241.1700, 670.0000, 365.0000, 314.0000, 292.0000, 482.9497,
277.5642]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 85
running loss: nan
Train Steps: 85/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[483.8400, 239.2300, 609.5974, 231.6849, 278.0000, 250.0000, 410.9215,
327.9374],
[501.7700, 247.2200, 723.0000, 247.0000, 298.0000, 192.0000, 494.0000,
315.0000],
[488.0600, 238.6400, 684.0000, 340.0000, 309.0000, 265.0000, 410.2613,
292.1347],
[501.0800, 241.7700, 720.0000, 286.0000, 304.0000, 310.0000, 513.1892,
286.2780],
[492.2300, 247.1400, 677.0000, 230.0000, 288.0000, 192.0000, 408.5175,
333.7941],
[502.6300, 256.9900, 598.1877, 212.5051, 410.0000, 115.0000, 440.0000,
370.0000],
[491.7329, 244.8729, 683.0000, 204.0000, 293.0000, 189.0000, 411.3298,
292.5252],
[502.1600, 246.1500, 647.0000, 343.0000, 335.0000, 285.0000, 453.0000,
365.0000]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 86
running loss: nan
Train Steps: 86/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[507.1360, 249.5636, 672.0000, 337.0000, 306.0000, 249.0000, 587.4209,
344.8954],
[488.6600, 240.6300, 643.0000, 204.0000, 296.0000, 176.0000, 452.6434,
337.0366],
[491.8600, 244.5800, 693.2687, 289.2242, 331.0000, 304.0000, 420.0000,
346.0000],
[502.8574, 256.4433, 680.0000, 270.0000, 362.0000, 155.0000, 435.1920,
372.5677],
[ nan, nan, 593.0000, 132.0000, 356.0000, 91.0000, 425.0000,
299.0000],
[503.4279, 245.8110, 704.0000, 151.0000, 421.0000, 156.0000, 594.3857,
322.6830],
[495.7300, 239.1300, 704.0000, 277.0000, 335.0000, 287.0000, 455.0000,
333.0000],
[508.8475, 246.1237, 692.0000, 179.0000, 391.0000, 120.0000, 536.1351,
327.5420]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 87
running loss: nan
Train Steps: 87/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[486.9100, 241.1400, 622.5284, 256.7020, 290.0000, 261.0000, 411.9899,
317.1024],
[ nan, nan, 712.0000, 193.0000, 403.0000, 134.0000, 535.5019,
336.5657],
[493.1591, 244.5602, 707.0000, 247.0000, 297.0000, 333.0000, 499.0000,
321.0000],
[504.5688, 241.7457, 719.0000, 289.0000, 315.0000, 210.0000, 584.8883,
322.6830],
[502.0600, 247.2100, 699.0000, 188.0000, 338.0000, 133.0000, 496.2461,
293.5293],
[503.7131, 244.5602, 672.0000, 335.0000, 296.0000, 262.0000, 550.0646,
329.6244],
[490.2700, 247.0800, 691.0000, 320.0000, 370.0000, 316.0000, 415.4624,
328.5230],
[495.9800, 239.5800, 691.1306, 221.2412, 292.6610, 188.3306, 480.1311,
322.4341]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 88
running loss: nan
Train Steps: 88/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[491.1800, 244.2000, 696.0000, 300.0000, 369.0000, 294.0000, 420.8344,
351.1215],
[491.6300, 240.4700, 700.0000, 323.0000, 318.0000, 279.0000, 445.0000,
332.0000],
[488.0700, 242.5300, 622.0000, 157.0000, 297.0000, 169.0000, 435.1920,
338.9889],
[488.5952, 240.8076, 696.0000, 279.0000, 403.9442, 310.5674, 468.0000,
333.0000],
[506.5656, 247.0619, 739.0000, 256.0000, 321.0000, 284.0000, 601.9836,
326.1537],
[489.3200, 241.1600, 683.0000, 244.0000, 281.0000, 215.0000, 453.0000,
308.0000],
[504.7900, 241.0400, 685.0000, 348.0000, 295.0000, 285.0000, 506.0662,
300.3342],
[499.9900, 243.2300, 701.6359, 283.3869, 373.0000, 322.0000, 493.0000,
326.0000]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 89
running loss: nan
Train Steps: 89/90 Loss: nan predictions are: tensor([[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan],
[nan, nan, nan, nan, nan, nan, nan, nan]], device='cuda:0',
grad_fn=<AddmmBackward>)
landmarks are: tensor([[486.4500, 236.9900, 683.0000, 280.0000, 308.0000, 295.0000, 427.3566,
297.2106],
[505.9951, 249.8763, 720.0000, 277.0000, 305.0000, 294.0000, 598.8177,
326.8478],
[497.4600, 248.2400, 581.0000, 134.0000, 326.0000, 159.0000, 497.0000,
347.0000],
[500.0400, 246.9800, 696.0000, 291.0000, 372.0000, 334.0000, 487.0000,
311.0000],
[500.5700, 242.0600, 663.0000, 140.0000, 314.0000, 163.0000, 506.4223,
294.0870],
[494.3001, 239.5567, 714.0048, 287.3219, 310.3356, 293.9974, 483.3020,
316.7523],
[ nan, nan, 682.0000, 133.0000, 433.0000, 142.0000, 589.3204,
328.9302],
[495.9400, 237.1000, 685.8777, 322.4308, 326.0126, 281.2020, 475.3770,
322.6425]], device='cuda:0')
loss_train_step before backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train_step after backward: tensor(nan, device='cuda:0', grad_fn=<MseLossBackward>)
loss_train: nan
step: 90
running loss: nan
Valid Steps: 10/10 Loss: nan
--------------------------------------------------
Epoch: 1 Train Loss: nan Valid Loss: nan
--------------------------------------------------
Training Complete
Total Elapsed Time : 95.45278215408325 s
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