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InternVL

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[2024-10-24 10:15:16,876] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 3795.69 | backward: 10122.45 | backward_inner: 3510.13 | backward_allreduce: 6612.27 | step: 7.67
97%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹| 4703/4844 [19:34:00<34:47, 14.80s/it]Registering VQA_lavis step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT 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
ANSWER0=VQA(image=LEFT,question='How many birds are flying in the air?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Does the vanity in the image feature a pair of squarish white basins sitting on top?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
ANSWER0=VQA(image=RIGHT,question='What color are the caps on the bottles in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == "white"')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['Does the vanity in the image feature a pair of squarish white basins sitting on top?'], 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([1, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 336
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 336
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 337
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 336
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 336
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 337
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 337
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 337
tensor([9.9984e-01, 1.5844e-04, 3.4118e-07, 2.8655e-12, 7.2649e-13, 2.1138e-10,
6.1876e-11, 2.7257e-07], device='cuda:0', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([9.9984e-01, 1.5844e-04, 3.4118e-07, 2.8655e-12, 7.2649e-13, 2.1138e-10,
6.1876e-11, 2.7257e-07], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.0002, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.9998, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(7.1526e-07, device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many pigs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
question: ['How many dogs are in the image?'], responses:['2']
[('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']]
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
question: ['How many pigs are in the image?'], responses:['2']
[('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']]
torch.Size([3, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836
question: ['How many birds are flying in the air?'], responses:['five']
question: ['What color are the caps on the bottles in the image?'], responses:['blue']
[('7 eleven', 0.1264466744091217), ('babies', 0.124977990347662), ('sunrise', 0.12490143984830117), ('eating', 0.1247676656843781), ('feet', 0.12475702323703439), ('candle', 0.12473210928138137), ('light', 0.12472650705175181), ('floating', 0.12469059014036947)]
[['7 eleven', 'babies', 'sunrise', 'eating', 'feet', 'candle', 'light', 'floating']]
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836
[('blue', 0.12610723189030773), ('kitten', 0.12505925935446505), ('iris', 0.12496487399785434), ('lemon', 0.12480860793572608), ('cherry', 0.12478264542061647), ('bright', 0.12478001416316817), ('peach', 0.12475640037922975), ('cookie', 0.12474096685863247)]
[['blue', 'kitten', 'iris', 'lemon', 'cherry', 'bright', 'peach', 'cookie']]
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836
tensor([1.0000e+00, 1.7603e-06, 4.4669e-09, 2.8453e-08, 6.2861e-10, 3.9337e-10,
1.1228e-09, 6.7708e-10], device='cuda:0', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 1.7603e-06, 4.4669e-09, 2.8453e-08, 6.2861e-10, 3.9337e-10,
1.1228e-09, 6.7708e-10], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.7961e-06, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 2.5907e-08, 2.7150e-08, 1.4616e-09, 1.8294e-10, 2.8169e-09,
5.8204e-10, 2.4804e-10], device='cuda:3', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 2.5907e-08, 2.7150e-08, 1.4616e-09, 1.8294e-10, 2.8169e-09,
5.8204e-10, 2.4804e-10], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.4616e-09, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many laptops are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
question: ['How many laptops 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([3, 3, 448, 448]) knan debug pixel values shape
tensor([9.9987e-01, 1.3172e-04, 7.0030e-07, 4.7877e-09, 4.9961e-11, 5.4703e-07,
1.0879e-10, 2.7848e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>)
3 *************
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([9.9987e-01, 1.3172e-04, 7.0030e-07, 4.7877e-09, 4.9961e-11, 5.4703e-07,