Datasets:
TMarcus
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InternVL

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tensor([1.0000e+00, 2.2547e-09, 2.3589e-08, 4.2261e-10, 7.6696e-12, 1.2256e-11,
3.3433e-12, 1.8713e-09], device='cuda:1', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 2.2547e-09, 2.3589e-08, 4.2261e-10, 7.6696e-12, 1.2256e-11,
3.3433e-12, 1.8713e-09], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(2.3589e-08, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-2.3589e-08, device='cuda:1', grad_fn=<DivBackward0>)}
question: ['How many glass bottles are in the image?'], responses:['11']
[('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([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
tensor([9.4191e-01, 1.3979e-04, 1.4299e-02, 2.0455e-05, 3.9336e-09, 3.1842e-02,
2.9362e-08, 1.1789e-02], device='cuda:0', grad_fn=<SoftmaxBackward0>)
11 *************
['11', '10', '12', '9', '8', '13', '7', '14'] tensor([9.4191e-01, 1.3979e-04, 1.4299e-02, 2.0455e-05, 3.9336e-09, 3.1842e-02,
2.9362e-08, 1.1789e-02], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), False: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
[2024-10-24 09:37:27,217] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.37 | optimizer_gradients: 0.28 | optimizer_step: 0.32
[2024-10-24 09:37:27,217] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 9040.51 | backward_microstep: 8753.93 | backward_inner_microstep: 8748.04 | backward_allreduce_microstep: 5.72 | step_microstep: 7.72
[2024-10-24 09:37:27,217] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 9040.51 | backward: 8753.92 | backward_inner: 8748.16 | backward_allreduce: 5.71 | step: 7.73
94%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–| 4553/4844 [18:56:11<1:18:55, 16.27s/it]Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='Does the laptop on the right have a slightly curved, concave screen?')
FINAL_ANSWER=RESULT(var=ANSWER0)
Registering VQA_lavis step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='Does the shoe in the image have orange laces?')
FINAL_ANSWER=RESULT(var=ANSWER0)
Registering VQA_lavis step
ANSWER0=VQA(image=RIGHT,question='How many all white dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='How many graduates wearing green robes are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
torch.Size([1, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([7, 3, 448, 448])
question: ['Does the laptop on the right have a slightly curved, concave screen?'], responses:['no']
question: ['How many graduates wearing green robes are in the image?'], responses:['0']
[('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']]
[('0', 0.13077743594303964), ('circles', 0.12449813349255197), ('maroon', 0.12428926693968681), ('large', 0.1242263466991631), ('rooster', 0.12409315512763705), ('nuts', 0.12408018414184876), ('beige', 0.1240288472550799), ('bottle', 0.12400663040099273)]
[['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle']]
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: 328
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 329
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 329
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 328
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 329
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 329
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 329
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 330
tensor([9.9409e-01, 5.9111e-03, 6.4015e-08, 9.4345e-11, 7.7510e-11, 6.4360e-10,
4.1356e-09, 2.9343e-08], device='cuda:1', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([9.9409e-01, 5.9111e-03, 6.4015e-08, 9.4345e-11, 7.7510e-11, 6.4360e-10,
4.1356e-09, 2.9343e-08], device='cuda:1', grad_fn=<SelectBackward0>)
tensor([1.0000e+00, 2.3597e-07, 2.2163e-08, 3.4747e-13, 9.1348e-08, 8.3639e-10,
4.0079e-08, 6.0956e-08], device='cuda:0', grad_fn=<SoftmaxBackward0>)
0 *************
['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle'] tensor([1.0000e+00, 2.3597e-07, 2.2163e-08, 3.4747e-13, 9.1348e-08, 8.3639e-10,
4.0079e-08, 6.0956e-08], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.0059, device='cuda:1', grad_fn=<UnbindBackward0>), False: tensor(0.9941, device='cuda:1', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(1.7881e-07, device='cuda:1', grad_fn=<SubBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many people are in the canoe?')
ANSWER1=EVAL(expr='{ANSWER0} == 3')
ANSWER2=EVAL(expr='{ANSWER1} and {ANSWER1}')
FINAL_ANSWER=RESULT(var=ANSWER2)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(4.7684e-07, device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many gorillas are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 4')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
question: ['How many all white dogs are in the image?'], responses:['1']
torch.Size([7, 3, 448, 448])
question: ['Does the shoe in the image have orange laces?'], responses:['yes']
[('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']]
[('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']]
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
question: ['How many people are in the canoe?'], responses:['2']