Datasets:
TMarcus
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InternVL

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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
torch.Size([13, 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
tensor([9.9972e-01, 2.7802e-04, 2.2153e-08, 9.6027e-09, 2.0201e-11, 7.8092e-07,
9.6373e-11, 6.9019e-10], device='cuda:2', grad_fn=<SoftmaxBackward0>)
3 *************
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([9.9972e-01, 2.7802e-04, 2.2153e-08, 9.6027e-09, 2.0201e-11, 7.8092e-07,
9.6373e-11, 6.9019e-10], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.9997, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.0003, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1921e-07, device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='Is the base of the anemone red?')
FINAL_ANSWER=RESULT(var=ANSWER0)
torch.Size([1, 3, 448, 448])
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
question: ['Is the base of the anemone red?'], 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: 7, images per sample: 7.0, dynamic token length: 1861
tensor([1.0000e+00, 1.3574e-07, 8.6778e-09, 2.1478e-08, 4.9728e-10, 6.7042e-10,
1.5587e-09, 1.7191e-10], device='cuda:0', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 1.3574e-07, 8.6778e-09, 2.1478e-08, 4.9728e-10, 6.7042e-10,
1.5587e-09, 1.7191e-10], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), False: tensor(2.1478e-08, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 5.9061e-09, 1.7630e-09, 8.9050e-09, 2.7003e-10, 1.0262e-10,
1.2652e-11, 4.8576e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 5.9061e-09, 1.7630e-09, 8.9050e-09, 2.7003e-10, 1.0262e-10,
1.2652e-11, 4.8576e-09], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:2', grad_fn=<UnbindBackward0>), False: tensor(1.7630e-09, device='cuda:2', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(-1.7630e-09, device='cuda:2', grad_fn=<SubBackward0>)}
tensor([9.7646e-01, 3.0771e-04, 5.8064e-03, 1.5320e-05, 1.2522e-08, 1.2292e-02,
9.6974e-08, 5.1231e-03], device='cuda:1', grad_fn=<SoftmaxBackward0>)
11 *************
['11', '10', '12', '9', '8', '13', '7', '14'] tensor([9.7646e-01, 3.0771e-04, 5.8064e-03, 1.5320e-05, 1.2522e-08, 1.2292e-02,
9.6974e-08, 5.1231e-03], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), False: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
[2024-10-24 10:00:34,557] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.33 | optimizer_step: 0.32
[2024-10-24 10:00:34,558] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 5068.22 | backward_microstep: 8754.55 | backward_inner_microstep: 4814.98 | backward_allreduce_microstep: 3939.47 | step_microstep: 7.67
[2024-10-24 10:00:34,558] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 5068.24 | backward: 8754.54 | backward_inner: 4815.02 | backward_allreduce: 3939.45 | step: 7.68
96%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ| 4646/4844 [19:19:18<48:05, 14.58s/it]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='Is there at least one cherry with a stem in the image?')
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
torch.Size([1, 3, 448, 448])
ANSWER0=VQA(image=LEFT,question='Is the animal in the image turned directly toward the camera?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Does the sail boat in the image have three sails engaged?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='Is the dog wearing a leash?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['Is there at least one cherry with a stem in the image?'], 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([1.0000e+00, 1.4165e-08, 1.6788e-10, 1.7966e-08, 6.4327e-09, 5.3960e-10,
1.1765e-10, 1.5304e-08], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 1.4165e-08, 1.6788e-10, 1.7966e-08, 6.4327e-09, 5.3960e-10,
1.1765e-10, 1.5304e-08], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.6788e-10, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.6788e-10, device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
question: ['Does the sail boat in the image have three sails engaged?'], 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([7, 3, 448, 448]) knan debug pixel values shape
question: ['Is the animal in the image turned directly toward the camera?'], responses:['yes']
question: ['Is the dog wearing a leash?'], responses:['no']
question: ['How many dogs are in the image?'], 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']]