Datasets:
TMarcus
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InternVL

Dataset card Data Studio Files
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tensor([1.0000e+00, 2.1266e-09, 1.2068e-10, 1.3449e-10, 1.1881e-10, 7.1918e-09,
3.5009e-06, 8.6074e-11], device='cuda:0', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 2.1266e-09, 1.2068e-10, 1.3449e-10, 1.1881e-10, 7.1918e-09,
3.5009e-06, 8.6074e-11], 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(3.5106e-06, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 4.7450e-10, 2.6777e-07, 3.5262e-10, 2.3066e-11, 6.1634e-08,
4.0179e-09, 8.2496e-07], device='cuda:1', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 4.7450e-10, 2.6777e-07, 3.5262e-10, 2.3066e-11, 6.1634e-08,
4.0179e-09, 8.2496e-07], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(4.7450e-10, device='cuda:1', grad_fn=<UnbindBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(1.0729e-06, device='cuda:1', grad_fn=<SubBackward0>)}
tensor([1.0000e+00, 1.5360e-10, 6.4721e-11, 1.3725e-10, 1.0363e-10, 4.3628e-09,
2.1601e-09, 4.0460e-11], device='cuda:3', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 1.5360e-10, 6.4721e-11, 1.3725e-10, 1.0363e-10, 4.3628e-09,
2.1601e-09, 4.0460e-11], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), False: tensor(7.0225e-09, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
[2024-10-24 09:35:41,405] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.46 | optimizer_gradients: 0.22 | optimizer_step: 0.30
[2024-10-24 09:35:41,406] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 7067.70 | backward_microstep: 6946.40 | backward_inner_microstep: 6787.14 | backward_allreduce_microstep: 159.19 | step_microstep: 8.10
[2024-10-24 09:35:41,406] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 7067.72 | backward: 6946.40 | backward_inner: 6787.16 | backward_allreduce: 159.18 | step: 8.12
94%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–| 4546/4844 [18:54:25<1:18:38, 15.83s/it]Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='Does the image contain a baby and its mother?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering VQA_lavis step
Registering VQA_lavis step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering EVAL step
Registering RESULT step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='Is the warthog facing the camera?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='How many birds are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([7, 3, 448, 448])
question: ['Is the warthog facing the camera?'], responses:['yes']
question: ['Does the image contain a baby and its mother?'], responses:['no']
question: ['How many birds are in the image?'], responses:['5']
question: ['How many dogs are in the image?'], responses:['11']
[('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']]
[('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']]
[('5', 0.12793059870235002), ('8', 0.12539646467821697), ('4', 0.12509737486793587), ('6', 0.12470234839853608), ('3', 0.12467331676337925), ('7', 0.12441254825093238), ('11', 0.12401867309944531), ('9', 0.12376867523920407)]
[['5', '8', '4', '6', '3', '7', '11', '9']]
[('11', 0.12740768001087358), ('10', 0.12548679249075975), ('12', 0.12538137681693887), ('9', 0.12485855662563465), ('8', 0.12469919178932766), ('13', 0.12431757055023795), ('7', 0.12396146028399917), ('14', 0.1238873714322284)]
[['11', '10', '12', '9', '8', '13', '7', '14']]
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
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: 1862
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
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: 1862
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
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: 1863
tensor([9.3196e-01, 9.0651e-07, 6.3416e-02, 7.9932e-04, 8.5261e-07, 3.8143e-03,
2.4627e-06, 1.5923e-06], device='cuda:2', grad_fn=<SoftmaxBackward0>)
5 *************
['5', '8', '4', '6', '3', '7', '11', '9'] tensor([9.3196e-01, 9.0651e-07, 6.3416e-02, 7.9932e-04, 8.5261e-07, 3.8143e-03,
2.4627e-06, 1.5923e-06], device='cuda:2', grad_fn=<SelectBackward0>)
tensor([1.0000e+00, 1.5196e-08, 1.4166e-09, 2.7247e-08, 5.1037e-11, 1.0045e-09,
3.5961e-11, 3.2600e-09], device='cuda:1', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 1.5196e-08, 1.4166e-09, 2.7247e-08, 5.1037e-11, 1.0045e-09,
3.5961e-11, 3.2600e-09], device='cuda:1', grad_fn=<SelectBackward0>)
tensor([1.0000e+00, 2.0092e-10, 4.4501e-07, 1.0816e-11, 3.3667e-10, 2.1777e-08,
7.2850e-10, 8.7688e-07], device='cuda:0', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 2.0092e-10, 4.4501e-07, 1.0816e-11, 3.3667e-10, 2.1777e-08,
7.2850e-10, 8.7688e-07], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(8.5261e-07, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1921e-07, device='cuda:2', grad_fn=<DivBackward0>)}
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.4166e-09, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.4166e-09, device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many wolves are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(2.0092e-10, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.4305e-06, device='cuda:0', grad_fn=<DivBackward0>)}
tensor([9.9284e-01, 1.2374e-03, 1.9156e-03, 1.1623e-03, 6.9119e-06, 2.3115e-03,
1.8974e-04, 3.3304e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>)
11 *************
['11', '10', '12', '9', '8', '13', '7', '14'] tensor([9.9284e-01, 1.2374e-03, 1.9156e-03, 1.1623e-03, 6.9119e-06, 2.3115e-03,
1.8974e-04, 3.3304e-04], device='cuda:3', grad_fn=<SelectBackward0>)