Datasets:
TMarcus
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InternVL

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Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='Are some of the crab's legs red?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='How many console televisions are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
ANSWER0=VQA(image=RIGHT,question='Does the image show broccoli on a wooden surface?')
FINAL_ANSWER=RESULT(var=ANSWER0)
ANSWER0=VQA(image=LEFT,question='How many small bags are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 4')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([5, 3, 448, 448])
torch.Size([5, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['Are some of the crab'], 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([3, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 833
question: ['How many console televisions are in the image?'], responses:['0']
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 833
question: ['How many small bags are in the image?'], responses:['four']
[('0', 0.13077743594303964), ('circles', 0.12449813349255197), ('maroon', 0.12428926693968681), ('large', 0.1242263466991631), ('rooster', 0.12409315512763705), ('nuts', 0.12408018414184876), ('beige', 0.1240288472550799), ('bottle', 0.12400663040099273)]
[['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle']]
[('7 eleven', 0.12650899275575006), ('4', 0.125210025275264), ('first', 0.12483048280083887), ('3', 0.12473532336671392), ('5', 0.1247268629491862), ('dark', 0.12470563072493092), ('forward', 0.12466964370422237), ('bag', 0.12461303842309367)]
[['7 eleven', '4', 'first', '3', '5', 'dark', 'forward', 'bag']]
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 834
torch.Size([5, 3, 448, 448]) knan debug pixel values shape
torch.Size([5, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 833
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 833
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 834
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 834
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 834
tensor([9.9982e-01, 1.7953e-04, 1.1183e-07, 5.1005e-09, 4.1572e-10, 2.6855e-08,
7.9441e-09, 1.2550e-07], device='cuda:0', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([9.9982e-01, 1.7953e-04, 1.1183e-07, 5.1005e-09, 4.1572e-10, 2.6855e-08,
7.9441e-09, 1.2550e-07], device='cuda:0', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.0002, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.9998, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(2.9802e-07, device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='Are the bottles in the image unopened?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
question: ['Does the image show broccoli on a wooden surface?'], 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
tensor([1.0000e+00, 7.3670e-07, 3.7957e-08, 1.8659e-10, 4.0951e-08, 2.6636e-09,
1.8567e-07, 5.1721e-07], device='cuda:1', grad_fn=<SoftmaxBackward0>)
0 *************
['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle'] tensor([1.0000e+00, 7.3670e-07, 3.7957e-08, 1.8659e-10, 4.0951e-08, 2.6636e-09,
1.8567e-07, 5.1721e-07], device='cuda:1', grad_fn=<SelectBackward0>)
tensor([2.8966e-14, 8.1648e-01, 4.9278e-07, 1.8351e-01, 7.3383e-06, 3.8503e-06,
5.2576e-06, 1.5244e-06], device='cuda:2', grad_fn=<SoftmaxBackward0>)
4 *************
['7 eleven', '4', 'first', '3', '5', 'dark', 'forward', 'bag'] tensor([2.8966e-14, 8.1648e-01, 4.9278e-07, 1.8351e-01, 7.3383e-06, 3.8503e-06,
5.2576e-06, 1.5244e-06], device='cuda:2', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.5497e-06, device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many pandas are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.8165, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.1835, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1146e-05, device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many computers are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['Are the bottles in the image unopened?'], 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']]
question: ['How many pandas 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
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
question: ['How many computers are in the image?'], responses:['1']
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400
[('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: 3397
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
tensor([1.0000e+00, 2.6165e-07, 2.5110e-08, 2.0817e-08, 6.5878e-10, 1.5528e-09,
1.8259e-09, 4.2878e-10], device='cuda:1', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 2.6165e-07, 2.5110e-08, 2.0817e-08, 6.5878e-10, 1.5528e-09,
1.8259e-09, 4.2878e-10], device='cuda:1', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(3.1204e-07, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 4.3884e-10, 3.9302e-07, 4.9236e-11, 2.4088e-10, 3.0705e-08,
3.8821e-09, 4.3812e-07], device='cuda:3', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 4.3884e-10, 3.9302e-07, 4.9236e-11, 2.4088e-10, 3.0705e-08,
3.8821e-09, 4.3812e-07], device='cuda:3', grad_fn=<SelectBackward0>)