Datasets:
TMarcus
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InternVL

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tensor([0.3316, 0.3136, 0.1583, 0.0213, 0.1220, 0.0170, 0.0311, 0.0050],
device='cuda:3', grad_fn=<SoftmaxBackward0>)
4 *************
['4', '5', '3', '8', '6', '1', '2', '11'] tensor([0.3316, 0.3136, 0.1583, 0.0213, 0.1220, 0.0170, 0.0311, 0.0050],
device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.0311, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.9689, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1921e-07, device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many collies are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} > 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448])
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3394
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3393
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3393
tensor([5.1494e-01, 4.8392e-01, 4.7815e-05, 1.0644e-04, 2.5125e-04, 3.5751e-04,
2.6931e-04, 1.0226e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([5.1494e-01, 4.8392e-01, 4.7815e-05, 1.0644e-04, 2.5125e-04, 3.5751e-04,
2.6931e-04, 1.0226e-04], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.4839, device='cuda:2', grad_fn=<UnbindBackward0>), False: tensor(0.5149, device='cuda:2', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0011, device='cuda:2', grad_fn=<SubBackward0>)}
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3394
question: ['How many boxes are in the image?'], responses:['1']
question: ['How many collies are in the image?'], responses:['1']
[('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([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3394
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
tensor([7.1218e-01, 2.4222e-02, 2.6015e-01, 1.1970e-03, 1.4969e-04, 5.8878e-04,
1.4971e-04, 1.3623e-03], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([7.1218e-01, 2.4222e-02, 2.6015e-01, 1.1970e-03, 1.4969e-04, 5.8878e-04,
1.4971e-04, 1.3623e-03], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.7122, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.2601, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0277, device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many perfumes are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 10')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
question: ['How many perfumes are in the image?'], responses:['3']
[('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([1, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
tensor([0.2291, 0.1947, 0.1016, 0.1517, 0.0418, 0.1784, 0.0881, 0.0146],
device='cuda:0', grad_fn=<SoftmaxBackward0>)
3 *************
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([0.2291, 0.1947, 0.1016, 0.1517, 0.0418, 0.1784, 0.0881, 0.0146],
device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.0146, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.9854, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
Encountered ExecuteError: CUDA out of memory. Tried to allocate 1.17 GiB. GPU 3 has a total capacty of 44.34 GiB of which 250.94 MiB is free. Including non-PyTorch memory, this process has 44.08 GiB memory in use. Of the allocated memory 39.10 GiB is allocated by PyTorch, and 4.43 GiB is reserved by PyTorch but unalloc...
Encountered TypeError: unsupported operand type(s) for +: 'NoneType' and 'str'
tensor([6.5294e-01, 5.4007e-02, 2.0343e-02, 1.0145e-02, 1.2738e-02, 9.0281e-03,
2.4022e-01, 5.8073e-04], device='cuda:1', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([6.5294e-01, 5.4007e-02, 2.0343e-02, 1.0145e-02, 1.2738e-02, 9.0281e-03,
2.4022e-01, 5.8073e-04], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.6529, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.3471, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: 1e-09, False: 1e-09, 'Execute Error': 0.999999998}
[2024-10-22 17:23:22,575] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.36 | optimizer_step: 0.33
[2024-10-22 17:23:22,575] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 10247.81 | backward_microstep: 13588.27 | backward_inner_microstep: 9805.33 | backward_allreduce_microstep: 3782.84 | step_microstep: 10.13
[2024-10-22 17:23:22,575] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 10247.83 | backward: 13588.26 | backward_inner: 9805.35 | backward_allreduce: 3782.83 | step: 10.14
0%| | 12/2424 [04:54<16:06:38, 24.05s/it]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='Is there a stack of three books on the front-most corner of the shelf under the couch?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
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='Does the sea creature in the photo have white tentacles with pink tips?')
ANSWER1=RESULT(var=ANSWER0)
ANSWER0=VQA(image=LEFT,question='Does the image on the left have a man's leg bending to the right with his heel up?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
torch.Size([7, 3, 448, 448])
ANSWER0=VQA(image=LEFT,question='How many tusked animals are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['Does the sea creature in the photo have white tentacles with pink tips?'], 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)]