Datasets:
TMarcus
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InternVL

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tensor([1.0000e+00, 1.8947e-08, 3.5817e-10, 1.1366e-07, 9.4951e-10, 1.2117e-09,
9.7605e-11, 2.4463e-08], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 1.8947e-08, 3.5817e-10, 1.1366e-07, 9.4951e-10, 1.2117e-09,
9.7605e-11, 2.4463e-08], 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(3.5817e-10, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1885e-07, device='cuda:0', grad_fn=<DivBackward0>)}
tensor([9.9683e-01, 1.9884e-06, 1.6161e-08, 5.4132e-09, 7.7215e-11, 3.1727e-03,
5.3807e-10, 2.9412e-10], device='cuda:1', grad_fn=<SoftmaxBackward0>)
3 *************
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([9.9683e-01, 1.9884e-06, 1.6161e-08, 5.4132e-09, 7.7215e-11, 3.1727e-03,
5.3807e-10, 2.9412e-10], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.0032, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.9968, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
[2024-10-24 10:16:30,428] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.49 | optimizer_gradients: 0.25 | optimizer_step: 0.31
[2024-10-24 10:16:30,428] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 7110.17 | backward_microstep: 10707.96 | backward_inner_microstep: 6799.83 | backward_allreduce_microstep: 3908.04 | step_microstep: 7.82
[2024-10-24 10:16:30,428] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 7110.19 | backward: 10707.95 | backward_inner: 6799.85 | backward_allreduce: 3908.03 | step: 7.83
97%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹| 4708/4844 [19:35:14<34:18, 15.14s/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
ANSWER0=VQA(image=LEFT,question='What color are the vases in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == "silver"')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many open laptops can be seen in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='Are the ducks in the left image facing towards the right?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='How many cans of soda are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['How many open laptops can be seen in the image?'], responses:['2']
[('2', 0.12961991198727602), ('3', 0.12561270547489775), ('4', 0.12556127085987287), ('1', 0.1254920833223361), ('5', 0.12407835939022728), ('8', 0.124024076973589), ('7', 0.12288810153923228), ('29', 0.12272349045256851)]
[['2', '3', '4', '1', '5', '8', '7', '29']]
torch.Size([3, 3, 448, 448]) knan debug pixel values shape
question: ['What color are the vases in the image?'], responses:['sil']
question: ['How many cans of soda are in the image?'], responses:['3']
[('jal', 0.12711127546139203), ('asics', 0.1250181807174628), ('pug', 0.12498902974083527), ('camo', 0.12476128011675007), ('ge', 0.1245824295519601), ('can', 0.12453509855707018), ('kia', 0.12453205050659558), ('vent', 0.12447065534793383)]
[['jal', 'asics', 'pug', 'camo', 'ge', 'can', 'kia', 'vent']]
[('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]) knan debug pixel values shape
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
tensor([1.0000e+00, 3.2495e-08, 1.4594e-06, 4.2714e-08, 5.2114e-10, 1.8299e-08,
1.1206e-09, 1.6690e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 3.2495e-08, 1.4594e-06, 4.2714e-08, 5.2114e-10, 1.8299e-08,
1.1206e-09, 1.6690e-09], 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(1.5562e-06, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many zebras are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
question: ['Are the ducks in the left image facing towards the right?'], 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)]
[['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock']]
question: ['How many zebras are in the image?'], responses:['2']
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
[('2', 0.12961991198727602), ('3', 0.12561270547489775), ('4', 0.12556127085987287), ('1', 0.1254920833223361), ('5', 0.12407835939022728), ('8', 0.124024076973589), ('7', 0.12288810153923228), ('29', 0.12272349045256851)]
[['2', '3', '4', '1', '5', '8', '7', '29']]
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3401
torch.Size([3, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3401
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3402
tensor([1.0000e+00, 1.0407e-07, 7.1382e-09, 7.8707e-09, 8.8649e-10, 1.6051e-09,
2.5745e-09, 1.3141e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 1.0407e-07, 7.1382e-09, 7.8707e-09, 8.8649e-10, 1.6051e-09,
2.5745e-09, 1.3141e-09], 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(1.2546e-07, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
tensor([9.9997e-01, 1.8445e-05, 5.5634e-06, 1.0117e-08, 7.1602e-10, 4.7073e-08,
1.4001e-09, 1.0784e-06], device='cuda:1', grad_fn=<SoftmaxBackward0>)
3 *************
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([9.9997e-01, 1.8445e-05, 5.5634e-06, 1.0117e-08, 7.1602e-10, 4.7073e-08,
1.4001e-09, 1.0784e-06], device='cuda:1', grad_fn=<SelectBackward0>)
tensor([0.0047, 0.0892, 0.0212, 0.7830, 0.0046, 0.0012, 0.0898, 0.0064],
device='cuda:3', grad_fn=<SoftmaxBackward0>)
camo *************
['jal', 'asics', 'pug', 'camo', 'ge', 'can', 'kia', 'vent'] tensor([0.0047, 0.0892, 0.0212, 0.7830, 0.0046, 0.0012, 0.0898, 0.0064],
device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(5.6105e-06, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:1', grad_fn=<DivBackward0>)}
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many boars are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='Does the puppy on the left have its tongue visible?')
ANSWER1=EVAL(expr='{ANSWER0}')