Hugging Face's logo

HyperbeeAI
/
vision-bnn-benchmarks-hf

Image Classification
vision
Model card Files Community
vision-bnn-benchmarks-hf
File size: 3,044 Bytes 
5096607
 
4e45d68
5096607
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
 
###########################################################################
# Computer vision - Binary neural networks demo software by HyperbeeAI.   #
# Copyrights © 2023 Hyperbee.AI Inc. All rights reserved. hello@hyperbee.ai #
###########################################################################
import torch, sys, time
import torch.nn as nn
import torch.optim as optim

# bizden
import layers, models, dataloader
from library.utils import compute_batch_accuracy, compute_set_accuracy

bs = 100;
train_loader, test_loader = dataloader.load_cifar100(batch_size=bs, num_workers=6, shuffle=False, act_8b_mode=False);

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model  = models.maxim_nas()
model  = model.to(device) 

# first, ftp2qat
weight_dictionary = {}
weight_dictionary['conv1_1' ] = 8;
weight_dictionary['conv1_2' ] = 8;
weight_dictionary['conv1_3' ] = 2;
weight_dictionary['conv2_1' ] = 2;
weight_dictionary['conv2_2' ] = 2;
weight_dictionary['conv3_1' ] = 2;
weight_dictionary['conv3_2' ] = 2;
weight_dictionary['conv4_1' ] = 2;
weight_dictionary['conv4_2' ] = 2;
weight_dictionary['conv5_1' ] = 8;
weight_dictionary['fc']       = 8;

layer_attributes = []
for layer_string in dir(model):
    if(layer_string in weight_dictionary):
        layer_attribute = getattr(model, layer_string)
        print('Folding BN for:', layer_string)
        layer_attribute.configure_layer_base(weight_bits=weight_dictionary[layer_string], bias_bits=8, shift_quantile=0.985)
        layer_attribute.mode_fpt2qat('qat');
        setattr(model, layer_string, layer_attribute)
model.to(device) # somehow new parameters are left out, so they need a reload to the GPU

# then, load trained checkpoint
checkpoint = torch.load('training_checkpoint.pth.tar');
model.load_state_dict(checkpoint['state_dict'])

print('')
print('Computing test set accuracy, training checkpoint')
test_acc = compute_set_accuracy(model, test_loader)

print('')
print('Test accuracy:', test_acc*100.0)
print('')

train_loader, test_loader = dataloader.load_cifar100(batch_size=bs, num_workers=6, shuffle=False, act_8b_mode=True);

# then, qat2hw
model  = model.to(device) 
for layer_string in dir(model):
	layer_attribute = getattr(model, layer_string)
	if isinstance(layer_attribute, layers.shallow_base_layer):
		print('Generating HW parameters for:', layer_string)
		layer_attribute.mode_qat2hw('eval');
		setattr(model, layer_string, layer_attribute)
model.to(device) # somehow new parameters are left out, so they need a reload

print('')
print('Computing test set accuracy, hardware checkpoint')
test_acc = compute_set_accuracy(model, test_loader)

torch.save({
            'epoch': 123456789,
            'extras': {'best epoch':123456789, 'best_top1':100*test_acc.cpu().numpy(), 'clipping_method':'MAX_BIT_SHIFT', 'current_top1':100*test_acc.cpu().numpy()},
            'state_dict': model.state_dict(),
            'arch': 'ai85nascifarnet'
            }, 'hardware_checkpoint.pth.tar')

print('')
print('Test accuracy:', test_acc*100.0)