Datasets:
TMarcus
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InternVL

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[['4', '5', '3', '8', '6', '1', '2', '11']]
ANSWER0=VQA(image=RIGHT,question='Is the lipstick in the image uncapped?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
torch.Size([5, 3, 448, 448]) knan debug pixel values shape
tensor([9.9029e-01, 8.6293e-09, 9.7080e-03, 9.7174e-09, 2.8394e-11, 4.7257e-11,
1.1803e-09, 7.6488e-09], device='cuda:1', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([9.9029e-01, 8.6293e-09, 9.7080e-03, 9.7174e-09, 2.8394e-11, 4.7257e-11,
1.1803e-09, 7.6488e-09], device='cuda:1', grad_fn=<SelectBackward0>)
tensor([1.0000e+00, 3.0831e-09, 9.0559e-11, 3.9601e-08, 9.7956e-10, 2.6617e-10,
8.3281e-11, 1.1835e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 3.0831e-09, 9.0559e-11, 3.9601e-08, 9.7956e-10, 2.6617e-10,
8.3281e-11, 1.1835e-08], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.9903, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.0097, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-8.3819e-09, device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many syringes are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<UnbindBackward0>), False: tensor(9.0559e-11, device='cuda:3', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(-9.0559e-11, device='cuda:3', grad_fn=<SubBackward0>)}
ANSWER0=VQA(image=LEFT,question='Is the dog sitting?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([7, 3, 448, 448])
question: ['Is the lipstick in the image uncapped?'], responses:['yes']
tensor([9.9891e-01, 1.0305e-03, 6.0007e-05, 2.7686e-09, 1.0716e-07, 1.3921e-07,
1.1474e-08, 5.6224e-08], device='cuda:2', grad_fn=<SoftmaxBackward0>)
4 *************
['4', '5', '3', '8', '6', '1', '2', '11'] tensor([9.9891e-01, 1.0305e-03, 6.0007e-05, 2.7686e-09, 1.0716e-07, 1.3921e-07,
1.1474e-08, 5.6224e-08], device='cuda:2', grad_fn=<SelectBackward0>)
[('yes', 0.1298617250866936), ('congratulations', 0.12464161604141298), ('no', 0.12445222599225532), ('honey', 0.12437056445881921), ('solid', 0.12422595371654564), ('right', 0.12419889376311324), ('candle', 0.12414264780165109), ('chocolate', 0.12410637313950891)]
[['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate']]
question: ['How many syringes are in the image?'], responses:['3']
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.5069e-07, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
question: ['Is the dog sitting?'], responses:['no']
ANSWER0=VQA(image=RIGHT,question='How many elephants are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
[('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([7, 3, 448, 448])
[('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)]
[['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock']]
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3401
question: ['How many elephants are in the image?'], responses:['1']
[('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: 13, images per sample: 13.0, dynamic token length: 3398
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
tensor([9.9999e-01, 1.1161e-05, 1.0460e-07, 1.5983e-08, 2.4637e-10, 9.0936e-08,
3.2297e-09, 1.2715e-07], device='cuda:1', grad_fn=<SoftmaxBackward0>)
3 *************
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([9.9999e-01, 1.1161e-05, 1.0460e-07, 1.5983e-08, 2.4637e-10, 9.0936e-08,
3.2297e-09, 1.2715e-07], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.1503e-05, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:1', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 7.9281e-10, 1.9431e-06, 5.7684e-10, 1.5507e-10, 1.4854e-07,
1.5878e-09, 2.6355e-06], device='cuda:3', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 7.9281e-10, 1.9431e-06, 5.7684e-10, 1.5507e-10, 1.4854e-07,
1.5878e-09, 2.6355e-06], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(7.9281e-10, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(4.6492e-06, device='cuda:3', grad_fn=<DivBackward0>)}
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
tensor([1.0000e+00, 3.5958e-10, 2.0207e-11, 9.3369e-11, 3.9504e-11, 6.8773e-09,
2.4721e-08, 1.0489e-10], device='cuda:2', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 3.5958e-10, 2.0207e-11, 9.3369e-11, 3.9504e-11, 6.8773e-09,
2.4721e-08, 1.0489e-10], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), False: tensor(3.2216e-08, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
tensor([9.9970e-01, 1.7873e-09, 2.9596e-04, 1.5782e-09, 6.5821e-11, 1.8977e-11,
5.5286e-11, 8.3535e-10], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([9.9970e-01, 1.7873e-09, 2.9596e-04, 1.5782e-09, 6.5821e-11, 1.8977e-11,
5.5286e-11, 8.3535e-10], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.9997, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.0003, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.8073e-08, device='cuda:0', grad_fn=<DivBackward0>)}
[2024-10-24 10:15:44,668] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.40 | optimizer_gradients: 0.26 | optimizer_step: 0.32
[2024-10-24 10:15:44,668] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 5096.41 | backward_microstep: 4956.20 | backward_inner_microstep: 4950.21 | backward_allreduce_microstep: 5.88 | step_microstep: 7.40
[2024-10-24 10:15:44,668] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 5096.42 | backward: 4956.19 | backward_inner: 4950.27 | backward_allreduce: 5.75 | step: 7.41
97%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹| 4705/4844 [19:34:28<32:25, 14.00s/it]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
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step