-
Notifications
You must be signed in to change notification settings - Fork 0
/
create_torchscripted_model.py
40 lines (30 loc) · 1.15 KB
/
create_torchscripted_model.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
import torch
import segmentation_models_pytorch as smp
import numpy as np
from pathlib import Path
from utils.mobilenetv3 import mobilenetv3
ckpt_path = Path("/mnt/HDD/home/druzhinin/kaggle/kaggle_severstal/download/11resnet50-hard-severstal/best.pth")
input = torch.from_numpy(np.random.random([2, 3, 256, 1600])).float()
device = torch.device("cpu")
model = smp.Unet("resnet50", encoder_weights=None, classes=4, activation=None)
model.to(device)
model.eval()
model.load_state_dict(torch.load(ckpt_path)['model_state_dict'], strict=True)
def set_requires_grad(model, requires_grad: bool):
"""
Sets the ``requires_grad`` value for all model parameters.
Args:
model (torch.nn.Module): Model
requires_grad (bool): value
Examples:
>>> model = SimpleModel()
>>> set_requires_grad(model, requires_grad=True)
"""
requires_grad = bool(requires_grad)
for param in model.parameters():
param.requires_grad = requires_grad
set_requires_grad(model, False)
module = torch.jit.trace(model.forward, input)
res = module(input)
print(res.shape)
torch.jit.save(module, str(ckpt_path.parent.joinpath('torchscript.pth')))