mirror of
https://github.com/lordmathis/CUDANet.git
synced 2025-11-05 17:34:21 +00:00
78 lines
2.1 KiB
Python
78 lines
2.1 KiB
Python
import torch
|
|
import struct
|
|
|
|
from PIL import Image
|
|
|
|
from torchvision import transforms
|
|
|
|
|
|
def print_cpp_vector(vector, name="expected"):
|
|
print("std::vector<float> " + name + " = {", end="")
|
|
for i in range(len(vector)):
|
|
if i != 0:
|
|
print(", ", end="")
|
|
print(str(round(vector[i].item(), 5)) + "f", end="")
|
|
print("};")
|
|
|
|
|
|
def export_model_weights(model: torch.nn.Module, filename):
|
|
with open(filename, "wb") as f:
|
|
|
|
version = 1
|
|
header = ""
|
|
offset = 0
|
|
tensor_data = b""
|
|
|
|
for name, param in model.named_parameters():
|
|
if "weight" not in name and "bias" not in name:
|
|
continue
|
|
|
|
tensor_bytes = param.type(torch.float32).detach().numpy().tobytes()
|
|
tensor_size = param.numel()
|
|
|
|
header += f"{name},{tensor_size},{offset}\n"
|
|
offset += len(tensor_bytes)
|
|
|
|
tensor_data += tensor_bytes
|
|
|
|
f.seek(0)
|
|
f.write(struct.pack("H", version))
|
|
f.write(struct.pack("Q", len(header)))
|
|
f.write(header.encode("utf-8"))
|
|
f.write(tensor_data)
|
|
|
|
|
|
def print_model_parameters(model: torch.nn.Module):
|
|
for name, param in model.named_parameters():
|
|
print(name, param.numel())
|
|
|
|
|
|
def predict(model, image_path, preprocess=None):
|
|
input_image = Image.open(image_path)
|
|
|
|
if preprocess is None:
|
|
preprocess = transforms.Compose(
|
|
[
|
|
transforms.Resize(299),
|
|
transforms.CenterCrop(299),
|
|
transforms.ToTensor(),
|
|
transforms.Normalize(
|
|
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
|
|
),
|
|
]
|
|
)
|
|
|
|
input_tensor = preprocess(input_image)
|
|
input_batch = input_tensor.unsqueeze(
|
|
0
|
|
) # create a mini-batch as expected by the model
|
|
|
|
# move the input and model to GPU for speed if available
|
|
if torch.cuda.is_available():
|
|
input_batch = input_batch.to("cuda")
|
|
model.to("cuda")
|
|
|
|
with torch.no_grad():
|
|
output = model(input_batch)
|
|
return torch.argmax(output)
|