Datasets:
TMarcus
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InternVL

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ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.5156, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.4844, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
[2024-10-22 17:24:10,763] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.46 | optimizer_gradients: 0.21 | optimizer_step: 0.31
[2024-10-22 17:24:10,763] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 12165.81 | backward_microstep: 11851.90 | backward_inner_microstep: 11613.96 | backward_allreduce_microstep: 237.87 | step_microstep: 7.68
[2024-10-22 17:24:10,763] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 12165.83 | backward: 11851.89 | backward_inner: 11613.97 | backward_allreduce: 237.86 | step: 7.69
1%| | 14/2424 [05:43<16:06:37, 24.07s/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 VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='Is there a hound standing on a hard surface in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many dogs are standing in the snow?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Does the image contain a black dispenser with a chrome top?')
FINAL_ANSWER=RESULT(var=ANSWER0)
ANSWER0=VQA(image=LEFT,question='How many cats are lying down?')
ANSWER1=EVAL(expr='{ANSWER0} > 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([7, 3, 448, 448])
question: ['Does the image contain a black dispenser with a chrome top?'], 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([1, 3, 448, 448]) knan debug pixel values shape
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: 332
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
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
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 330
tensor([6.2108e-01, 2.0852e-02, 3.5389e-01, 2.0140e-03, 1.6324e-04, 5.2042e-04,
1.8383e-04, 1.2971e-03], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([6.2108e-01, 2.0852e-02, 3.5389e-01, 2.0140e-03, 1.6324e-04, 5.2042e-04,
1.8383e-04, 1.2971e-03], device='cuda:0', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.6211, device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(0.3539, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0250, device='cuda:0', grad_fn=<SubBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many wild pigs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
question: ['How many dogs are standing in the snow?'], responses:['1']
question: ['Is there a hound standing on a hard surface in the image?'], responses:['yes']
torch.Size([7, 3, 448, 448])
question: ['How many cats are lying down?'], responses:['1']
[('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']]
[('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']]
[('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']]
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
question: ['How many wild pigs are in the image?'], responses:['10']
[('10', 0.1277249466426885), ('11', 0.12579928416580372), ('12', 0.12560051978633632), ('8', 0.1247991444010043), ('9', 0.12459861387933152), ('26', 0.12389435171102943), ('13', 0.12388731669200545), ('6', 0.12369582272180085)]
[['10', '11', '12', '8', '9', '26', '13', '6']]
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
tensor([0.3805, 0.1729, 0.0703, 0.0138, 0.0218, 0.0089, 0.3312, 0.0005],
device='cuda:1', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([0.3805, 0.1729, 0.0703, 0.0138, 0.0218, 0.0089, 0.3312, 0.0005],
device='cuda:1', grad_fn=<SelectBackward0>)
tensor([5.8832e-01, 2.1358e-02, 3.8723e-01, 1.0763e-03, 2.0330e-04, 5.2008e-04,
9.4461e-05, 1.1979e-03], device='cuda:3', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([5.8832e-01, 2.1358e-02, 3.8723e-01, 1.0763e-03, 2.0330e-04, 5.2008e-04,
9.4461e-05, 1.1979e-03], device='cuda:3', grad_fn=<SelectBackward0>)
tensor([5.5798e-01, 7.6720e-02, 1.9076e-02, 1.9203e-03, 4.6742e-03, 1.4045e-03,
3.3813e-01, 9.6675e-05], device='cuda:2', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([5.5798e-01, 7.6720e-02, 1.9076e-02, 1.9203e-03, 4.6742e-03, 1.4045e-03,
3.3813e-01, 9.6675e-05], device='cuda:2', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.3312, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.6688, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:1', grad_fn=<DivBackward0>)}
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.5883, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.3872, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0245, device='cuda:3', grad_fn=<DivBackward0>)}
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.4420, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.5580, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
ANSWER0=VQA(image=LEFT,question='How many Samoyed puppies are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many animals are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)