Datasets:
TMarcus
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InternVL

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[('1', 0.12829009354978346), ('3', 0.12529928082343206), ('4', 0.12464806219229535), ('8', 0.12460015878893425), ('6', 0.12451220062887247), ('12', 0.124338487048427), ('2', 0.12420459433498025), ('47', 0.12410712263327517)]
[['1', '3', '4', '8', '6', '12', '2', '47']]
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1866
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864
tensor([1.0000e+00, 6.9632e-09, 9.9312e-08, 1.3394e-09, 6.2566e-12, 1.8113e-11,
1.0836e-11, 1.1869e-09], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 6.9632e-09, 9.9312e-08, 1.3394e-09, 6.2566e-12, 1.8113e-11,
1.0836e-11, 1.1869e-09], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(9.9312e-08, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.9897e-08, device='cuda:0', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 8.3278e-10, 8.8118e-11, 5.6791e-10, 4.5991e-10, 4.1399e-08,
2.0377e-07, 1.8379e-10], device='cuda:2', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 8.3278e-10, 8.8118e-11, 5.6791e-10, 4.5991e-10, 4.1399e-08,
2.0377e-07, 1.8379e-10], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(2.4731e-07, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 2.7302e-10, 1.0321e-11, 3.4370e-11, 1.5223e-11, 6.2444e-09,
3.8070e-07, 5.8643e-11], device='cuda:3', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 2.7302e-10, 1.0321e-11, 3.4370e-11, 1.5223e-11, 6.2444e-09,
3.8070e-07, 5.8643e-11], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(3.8734e-07, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
[2024-10-24 09:40:43,321] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.27 | optimizer_step: 0.32
[2024-10-24 09:40:43,321] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 5035.50 | backward_microstep: 4966.15 | backward_inner_microstep: 4840.82 | backward_allreduce_microstep: 125.19 | step_microstep: 7.61
[2024-10-24 09:40:43,321] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 5035.52 | backward: 4966.14 | backward_inner: 4840.87 | backward_allreduce: 125.13 | step: 7.62
94%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–| 4567/4844 [18:59:27<56:06, 12.15s/it] Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='How many wild boars are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 5')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='How many sails does the boat in the image have?')
ANSWER1=EVAL(expr='{ANSWER0} == 4')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Are the golfballs in shadow?')
ANSWER1=EVAL(expr='not {ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
ANSWER0=VQA(image=RIGHT,question='How many pencil cases are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['How many wild boars are in the image?'], responses:['7']
question: ['How many pencil cases are in the image?'], responses:['3']
[('7', 0.12828776251745355), ('8', 0.1258361832781132), ('11', 0.12481772898325143), ('5', 0.124759881092759), ('9', 0.12447036165452931), ('10', 0.1239759375399529), ('6', 0.12393017600998846), ('12', 0.12392196892395223)]
[['7', '8', '11', '5', '9', '10', '6', '12']]
[('3', 0.12809209985493852), ('4', 0.12520382509374006), ('1', 0.1251059160028928), ('5', 0.12483070991268265), ('8', 0.12458076282181878), ('2', 0.12413212281858195), ('6', 0.1241125313968017), ('12', 0.12394203209854344)]
[['3', '4', '1', '5', '8', '2', '6', '12']]
torch.Size([1, 3, 448, 448]) knan debug pixel values shape
torch.Size([1, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
tensor([9.6471e-01, 9.9672e-04, 3.6670e-04, 1.5593e-02, 4.7074e-04, 1.9625e-04,
1.7662e-02, 5.9230e-06], device='cuda:2', grad_fn=<SoftmaxBackward0>)
7 *************
['7', '8', '11', '5', '9', '10', '6', '12'] tensor([9.6471e-01, 9.9672e-04, 3.6670e-04, 1.5593e-02, 4.7074e-04, 1.9625e-04,
1.7662e-02, 5.9230e-06], device='cuda:2', grad_fn=<SelectBackward0>)
tensor([9.8087e-01, 1.9125e-02, 1.3033e-06, 3.4334e-06, 3.5131e-10, 4.1419e-06,
2.5160e-09, 1.5842e-08], device='cuda:0', grad_fn=<SoftmaxBackward0>)
3 *************
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([9.8087e-01, 1.9125e-02, 1.3033e-06, 3.4334e-06, 3.5131e-10, 4.1419e-06,
2.5160e-09, 1.5842e-08], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.0156, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.9844, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many binders are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: torch.Size([1, 3, 448, 448])
{True: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(5.4452e-06, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='Is the dispenser round?')
FINAL_ANSWER=RESULT(var=ANSWER0)
torch.Size([1, 3, 448, 448])
question: ['Are the golfballs in shadow?'], responses:['no']
[('no', 0.1313955057270409), ('yes', 0.12592208734904367), ('no smoking', 0.12472972590078177), ('gone', 0.12376514658020793), ('man', 0.12367833016285167), ('meow', 0.1235796378467502), ('kia', 0.12347643720898455), ('no clock', 0.12345312922433942)]