Datasets:
TMarcus
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InternVL

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torch.Size([4, 3, 448, 448]) knan debug pixel values shape
question: ['Does the image in the right television display portray a person?'], 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']]
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
tensor([9.9768e-01, 1.4646e-07, 2.1399e-05, 1.5001e-03, 2.0128e-11, 8.0159e-04,
2.2970e-08, 4.6808e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>)
5 *************
['5', '8', '4', '6', '3', '7', '11', '9'] tensor([9.9768e-01, 1.4646e-07, 2.1399e-05, 1.5001e-03, 2.0128e-11, 8.0159e-04,
2.2970e-08, 4.6808e-08], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.9977, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.0023, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:3', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 3.2612e-10, 3.3581e-07, 5.9731e-12, 7.0074e-11, 1.7060e-09,
1.1750e-10, 4.0596e-07], device='cuda:1', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 3.2612e-10, 3.3581e-07, 5.9731e-12, 7.0074e-11, 1.7060e-09,
1.1750e-10, 4.0596e-07], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(3.2612e-10, device='cuda:1', grad_fn=<UnbindBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(7.1526e-07, device='cuda:1', grad_fn=<SubBackward0>)}
[2024-10-24 09:33:51,917] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.38 | optimizer_gradients: 0.28 | optimizer_step: 0.32
[2024-10-24 09:33:51,917] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 5086.49 | backward_microstep: 12412.58 | backward_inner_microstep: 4954.09 | backward_allreduce_microstep: 7458.37 | step_microstep: 7.57
[2024-10-24 09:33:51,917] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 5086.50 | backward: 12412.57 | backward_inner: 4954.12 | backward_allreduce: 7458.34 | step: 7.58
94%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž| 4539/4844 [18:52:35<1:21:30, 16.03s/it]Registering VQA_lavis step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT 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='Is the dog mostly black?')
ANSWER1=RESULT(var=ANSWER0)
ANSWER0=VQA(image=LEFT,question='How many seals are laying on the ground in the image?')
ANSWER1=EVAL(expr='{ANSWER0} < 4')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many people are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
ANSWER0=VQA(image=RIGHT,question='Does the image have a beetle crawling on a person's hand?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([11, 3, 448, 448])
torch.Size([7, 3, 448, 448])
question: ['Is the dog mostly black?'], responses:['yes']
[('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([3, 3, 448, 448]) knan debug pixel values shape
question: ['How many seals are laying on the ground in the image?'], responses:['11']
question: ['Does the image have a beetle crawling on a person'], responses:['yes']
[('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']]
[('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
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
question: ['How many people are in the image?'], responses:['11']
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864
[('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']]
tensor([1.0000e+00, 2.2054e-09, 5.6349e-10, 1.5668e-08, 5.7213e-10, 1.5166e-10,
1.6781e-11, 1.2723e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 2.2054e-09, 5.6349e-10, 1.5668e-08, 5.7213e-10, 1.5166e-10,
1.6781e-11, 1.2723e-08], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<UnbindBackward0>), False: tensor(5.6349e-10, device='cuda:3', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(-5.6349e-10, device='cuda:3', grad_fn=<SubBackward0>)}
ANSWER0=VQA(image=LEFT,question='Does a rug sit on the floor in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864
torch.Size([13, 3, 448, 448])
torch.Size([11, 3, 448, 448]) knan debug pixel values shape
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
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
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864
tensor([9.6363e-01, 1.2128e-02, 8.3368e-03, 1.2009e-03, 1.1061e-05, 9.4470e-03,
2.7066e-03, 2.5424e-03], device='cuda:0', grad_fn=<SoftmaxBackward0>)
11 *************
['11', '10', '12', '9', '8', '13', '7', '14'] tensor([9.6363e-01, 1.2128e-02, 8.3368e-03, 1.2009e-03, 1.1061e-05, 9.4470e-03,
2.7066e-03, 2.5424e-03], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:0', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 7.4209e-09, 4.2202e-11, 9.4487e-08, 3.3764e-10, 4.1231e-10,
2.8454e-10, 1.8063e-08], device='cuda:2', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 7.4209e-09, 4.2202e-11, 9.4487e-08, 3.3764e-10, 4.1231e-10,
2.8454e-10, 1.8063e-08], device='cuda:2', grad_fn=<SelectBackward0>)
ANSWER0=VQA(image=RIGHT,question='Does the dispenser on the right have a black base?')
FINAL_ANSWER=RESULT(var=ANSWER0)
question: ['Does a rug sit on the floor in the image?'], responses:['yes']
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(4.2202e-11, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1917e-07, device='cuda:2', grad_fn=<DivBackward0>)}
torch.Size([5, 3, 448, 448])
ANSWER0=VQA(image=RIGHT,question='How many dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 3')