Exemplos de load_trained_model em Python

Exemplo n.º 1

Arquivo: run_eval.py Projeto: linhduongtuan/mobile-semantic-segmentation

# %%
if __name__ == '__main__':
    # %%
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

    ckpt_path = EXP_DIR / 'mobile_seg/1607075632/checkpoints/last.ckpt'

    loader = get_loader(
        DataParams(
            batch_size=1,
            fold=0,
            n_splits=5,
            img_size=224,
            seed=1,
        ))
    model = load_trained_model(ckpt_path).to(device).eval()

    # %%
    with torch.no_grad():
        inputs, labels = next(iter(loader))

        outputs = model(inputs.to(device)).cpu()

        inputs = inputs.squeeze()
        labels = labels.squeeze()
        outputs = outputs.squeeze()

        inputs = (inputs * 255).numpy().transpose((1, 2, 0)).astype(np.uint8)

        plt.subplot(131)
        plt.imshow(inputs)

Exemplo n.º 2

import coremltools as ct
import torch

from mobile_seg.const import TMP_DIR, EXP_DIR
from mobile_seg.modules.net import load_trained_model
from mobile_seg.modules.wrapper import Wrapper

# %%
IMG_SIZE = 224

TMP_ONNX = TMP_DIR / 'tmp.onnx'
CKPT_PATH = EXP_DIR / 'mobile_seg/1596704750/checkpoints/epoch=194.ckpt'

# %%
unet = load_trained_model(CKPT_PATH)
model = Wrapper(unet=unet).eval()

# %%
inputs = torch.randn(1, 3, IMG_SIZE, IMG_SIZE)
traced_model = torch.jit.trace(model, inputs)

# %%
# Convert to Core ML using the Unified Conversion API
# TODO 2020/08/06, It does not work, because coremltools does not support PyTorch 1.6 yet.
# See https://github.com/apple/coremltools/issues/827
model = ct.convert(
    traced_model,
    inputs=[ct.ImageType(name="input_1", shape=inputs.shape)],
)