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InternVL

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torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
torch.Size([7, 3, 448, 448])
question: ['How many dogs are in the image?'], responses:['2']
question: ['How many insects are in the image?'], responses:['11']
[('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']]
[('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
question: ['Does the bottle on the right have a blue label?'], 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
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: 3399
tensor([1.0000e+00, 1.5317e-09, 3.3647e-10, 3.9643e-10, 2.0398e-10, 2.6718e-08,
7.1381e-09, 1.0351e-09], device='cuda:3', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 1.5317e-09, 3.3647e-10, 3.9643e-10, 2.0398e-10, 2.6718e-08,
7.1381e-09, 1.0351e-09], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), False: tensor(3.7359e-08, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='Does the image contain a person holding a fish?')
FINAL_ANSWER=RESULT(var=ANSWER0)
torch.Size([3, 3, 448, 448])
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400
question: ['Does the image contain a person holding a fish?'], 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
tensor([1.0000e+00, 1.1611e-07, 5.0028e-09, 8.9024e-08, 7.3494e-10, 8.2629e-10,
1.6823e-09, 4.5985e-10], device='cuda:2', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 1.1611e-07, 5.0028e-09, 8.9024e-08, 7.3494e-10, 8.2629e-10,
1.6823e-09, 4.5985e-10], 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(8.9024e-08, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:2', grad_fn=<DivBackward0>)}
tensor([9.4975e-01, 1.8500e-03, 1.2785e-02, 1.2054e-04, 1.2244e-06, 2.1057e-02,
5.2171e-05, 1.4385e-02], device='cuda:1', grad_fn=<SoftmaxBackward0>)
11 *************
['11', '10', '12', '9', '8', '13', '7', '14'] tensor([9.4975e-01, 1.8500e-03, 1.2785e-02, 1.2054e-04, 1.2244e-06, 2.1057e-02,
5.2171e-05, 1.4385e-02], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
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: 3399
tensor([1.0000e+00, 4.6252e-09, 4.6254e-11, 9.1144e-08, 8.1954e-10, 9.1465e-10,
1.6545e-10, 1.6062e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 4.6252e-09, 4.6254e-11, 9.1144e-08, 8.1954e-10, 9.1465e-10,
1.6545e-10, 1.6062e-08], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:3', grad_fn=<UnbindBackward0>), False: tensor(4.6254e-11, device='cuda:3', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(1.1916e-07, device='cuda:3', grad_fn=<SubBackward0>)}
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400
tensor([1.0000e+00, 8.6778e-09, 1.8245e-07, 1.6752e-11, 3.9069e-12, 1.7203e-09,
1.6693e-10, 2.4917e-07], device='cuda:0', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 8.6778e-09, 1.8245e-07, 1.6752e-11, 3.9069e-12, 1.7203e-09,
1.6693e-10, 2.4917e-07], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(8.6778e-09, device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(4.7684e-07, device='cuda:0', grad_fn=<SubBackward0>)}
[2024-10-24 09:43:03,533] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.43 | optimizer_gradients: 0.22 | optimizer_step: 0.30
[2024-10-24 09:43:03,533] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 7024.40 | backward_microstep: 6763.67 | backward_inner_microstep: 6758.66 | backward_allreduce_microstep: 4.93 | step_microstep: 7.27
[2024-10-24 09:43:03,534] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 7024.40 | backward: 6763.66 | backward_inner: 6758.67 | backward_allreduce: 4.92 | step: 7.28
94%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–| 4577/4844 [19:01:47<1:05:21, 14.69s/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 standing in the water?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Are the animals standing on their hind legs?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Is there a paper poking out of the dispenser?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='How many pairs of mittens are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([5, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['Is there a paper poking out of the dispenser?'], 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([5, 3, 448, 448]) knan debug pixel values shape
question: ['How many pairs of mittens are in the image?'], responses:['9']
[('9', 0.12801736482258133), ('8', 0.12565135970392036), ('11', 0.1254560343890198), ('10', 0.1248838582125673), ('7', 0.12420801006143238), ('12', 0.12408347303550306), ('5', 0.12385261492086817), ('14', 0.12384728485410773)]
[['9', '8', '11', '10', '7', '12', '5', '14']]
torch.Size([7, 3, 448, 448]) knan debug pixel values shape