/
LSQ_torch_quantizer.py
142 lines (118 loc) · 4.75 KB
/
LSQ_torch_quantizer.py
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
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
import torch
import torch.nn.functional as F
from torchvision import datasets, transforms
from nni.algorithms.compression.pytorch.quantization import LsqQuantizer
from nni.compression.pytorch.quantization_speedup import ModelSpeedupTensorRT
class Mnist(torch.nn.Module):
def __init__(self):
super().__init__()
self.conv1 = torch.nn.Conv2d(1, 20, 5, 1)
self.conv2 = torch.nn.Conv2d(20, 50, 5, 1)
self.fc1 = torch.nn.Linear(4 * 4 * 50, 500)
self.fc2 = torch.nn.Linear(500, 10)
self.relu1 = torch.nn.ReLU6()
self.relu2 = torch.nn.ReLU6()
self.relu3 = torch.nn.ReLU6()
self.max_pool1 = torch.nn.MaxPool2d(2, 2)
self.max_pool2 = torch.nn.MaxPool2d(2, 2)
def forward(self, x):
x = self.relu1(self.conv1(x))
x = self.max_pool1(x)
x = self.relu2(self.conv2(x))
x = self.max_pool2(x)
x = x.view(-1, 4 * 4 * 50)
x = self.relu3(self.fc1(x))
x = self.fc2(x)
return F.log_softmax(x, dim=1)
def train(model, quantizer, device, train_loader, optimizer):
model.train()
for batch_idx, (data, target) in enumerate(train_loader):
data, target = data.to(device), target.to(device)
optimizer.zero_grad()
output = model(data)
loss = F.nll_loss(output, target)
loss.backward()
optimizer.step()
if batch_idx % 100 == 0:
print('{:2.0f}% Loss {}'.format(100 * batch_idx / len(train_loader), loss.item()))
def test(model, device, test_loader):
model.eval()
test_loss = 0
correct = 0
with torch.no_grad():
for data, target in test_loader:
data, target = data.to(device), target.to(device)
output = model(data)
test_loss += F.nll_loss(output, target, reduction='sum').item()
pred = output.argmax(dim=1, keepdim=True)
correct += pred.eq(target.view_as(pred)).sum().item()
test_loss /= len(test_loader.dataset)
print('Loss: {} Accuracy: {}%)\n'.format(
test_loss, 100 * correct / len(test_loader.dataset)))
def test_trt(engine, test_loader):
test_loss = 0
correct = 0
time_elasped = 0
for data, target in test_loader:
output, time = engine.inference(data)
test_loss += F.nll_loss(output, target, reduction='sum').item()
pred = output.argmax(dim=1, keepdim=True)
correct += pred.eq(target.view_as(pred)).sum().item()
time_elasped += time
test_loss /= len(test_loader.dataset)
print('Loss: {} Accuracy: {}%'.format(
test_loss, 100 * correct / len(test_loader.dataset)))
print("Inference elapsed_time (whole dataset): {}s".format(time_elasped))
def main():
torch.manual_seed(0)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
trans = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
train_loader = torch.utils.data.DataLoader(
datasets.MNIST('data', train=True, download=True, transform=trans),
batch_size=64, shuffle=True)
test_loader = torch.utils.data.DataLoader(
datasets.MNIST('data', train=False, transform=trans),
batch_size=1000, shuffle=True)
model = Mnist()
configure_list = [{
'quant_types': ['weight', 'input'],
'quant_bits': {'weight': 8, 'input': 8},
'op_names': ['conv1']
}, {
'quant_types': ['output'],
'quant_bits': {'output': 8, },
'op_names': ['relu1']
}, {
'quant_types': ['weight', 'input'],
'quant_bits': {'weight': 8, 'input': 8},
'op_names': ['conv2']
}, {
'quant_types': ['output'],
'quant_bits': {'output': 8},
'op_names': ['relu2']
}, {
'quant_types': ['output'],
'quant_bits': {'output': 8},
'op_names': ['max_pool2']
}
]
optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.5)
quantizer = LsqQuantizer(model, configure_list, optimizer)
quantizer.compress()
model.to(device)
for epoch in range(40):
print('# Epoch {} #'.format(epoch))
train(model, quantizer, device, train_loader, optimizer)
test(model, device, test_loader)
model_path = "mnist_model.pth"
calibration_path = "mnist_calibration.pth"
calibration_config = quantizer.export_model(model_path, calibration_path)
test(model, device, test_loader)
print("calibration_config: ", calibration_config)
batch_size = 32
input_shape = (batch_size, 1, 28, 28)
engine = ModelSpeedupTensorRT(model, input_shape, config=calibration_config, batchsize=batch_size)
engine.compress()
test_trt(engine, test_loader)
if __name__ == '__main__':
main()