はじめに
がちもとさんアドベントカレンダー24日目の記事です。
今日は、なんでもセグメンテーションできるSAM（Segment Anything Model）をやっていきます
開発環境
- Windows 11 PC
- Python 3.11
導入
1．segment-anythingをクローン
2．モデルをダウンロード
3．プログラムを作成（notebooks/onnx_model_example.ipynbを参考）
onnx_model_example.py
import torch
import numpy as np
import cv2
import matplotlib.pyplot as plt
from segment_anything import sam_model_registry, SamPredictor
from segment_anything.utils.onnx import SamOnnxModel
import onnxruntime
from onnxruntime.quantization import QuantType
from onnxruntime.quantization.quantize import quantize_dynamic
def show_mask(mask, ax):
color = np.array([30/255, 144/255, 255/255, 0.6])
h, w = mask.shape[-2:]
mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)
ax.imshow(mask_image)
def show_points(coords, labels, ax, marker_size=375):
pos_points = coords[labels==1]
neg_points = coords[labels==0]
ax.scatter(pos_points[:, 0], pos_points[:, 1], color='green', marker='*', s=marker_size, edgecolor='white', linewidth=1.25)
ax.scatter(neg_points[:, 0], neg_points[:, 1], color='red', marker='*', s=marker_size, edgecolor='white', linewidth=1.25)
def show_box(box, ax):
x0, y0 = box[0], box[1]
w, h = box[2] - box[0], box[3] - box[1]
ax.add_patch(plt.Rectangle((x0, y0), w, h, edgecolor='green', facecolor=(0,0,0,0), lw=2))
checkpoint = "sam_vit_h_4b8939.pth"
model_type = "vit_h"
sam = sam_model_registry[model_type](checkpoint=checkpoint)
onnx_model_path = None # Set to use an already exported model, then skip to the next section.
import warnings
onnx_model_path = "sam_onnx_example.onnx"
onnx_model = SamOnnxModel(sam, return_single_mask=True)
dynamic_axes = {
"point_coords": {1: "num_points"},
"point_labels": {1: "num_points"},
}
embed_dim = sam.prompt_encoder.embed_dim
embed_size = sam.prompt_encoder.image_embedding_size
mask_input_size = [4 * x for x in embed_size]
dummy_inputs = {
"image_embeddings": torch.randn(1, embed_dim, *embed_size, dtype=torch.float),
"point_coords": torch.randint(low=0, high=1024, size=(1, 5, 2), dtype=torch.float),
"point_labels": torch.randint(low=0, high=4, size=(1, 5), dtype=torch.float),
"mask_input": torch.randn(1, 1, *mask_input_size, dtype=torch.float),
"has_mask_input": torch.tensor([1], dtype=torch.float),
"orig_im_size": torch.tensor([1500, 2250], dtype=torch.float),
}
output_names = ["masks", "iou_predictions", "low_res_masks"]
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=torch.jit.TracerWarning)
warnings.filterwarnings("ignore", category=UserWarning)
with open(onnx_model_path, "wb") as f:
torch.onnx.export(
onnx_model,
tuple(dummy_inputs.values()),
f,
export_params=True,
verbose=False,
opset_version=17,
do_constant_folding=True,
input_names=list(dummy_inputs.keys()),
output_names=output_names,
dynamic_axes=dynamic_axes,
)
onnx_model_quantized_path = "sam_onnx_quantized_example.onnx"
quantize_dynamic(
model_input=onnx_model_path,
model_output=onnx_model_quantized_path,
# optimize_model=True,
per_channel=False,
reduce_range=False,
weight_type=QuantType.QUInt8,
)
onnx_model_path = onnx_model_quantized_path
image = cv2.imread('notebooks/images/truck.jpg')
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
plt.figure(figsize=(10,10))
plt.imshow(image)
plt.axis('on')
plt.show()
ort_session = onnxruntime.InferenceSession(onnx_model_path)
sam.to(device='cuda')
predictor = SamPredictor(sam)
predictor.set_image(image)
image_embedding = predictor.get_image_embedding().cpu().numpy()
image_embedding.shape
input_point = np.array([[500, 375]])
input_label = np.array([1])
onnx_coord = np.concatenate([input_point, np.array([[0.0, 0.0]])], axis=0)[None, :, :]
onnx_label = np.concatenate([input_label, np.array([-1])], axis=0)[None, :].astype(np.float32)
onnx_coord = predictor.transform.apply_coords(onnx_coord, image.shape[:2]).astype(np.float32)
onnx_mask_input = np.zeros((1, 1, 256, 256), dtype=np.float32)
onnx_has_mask_input = np.zeros(1, dtype=np.float32)
ort_inputs = {
"image_embeddings": image_embedding,
"point_coords": onnx_coord,
"point_labels": onnx_label,
"mask_input": onnx_mask_input,
"has_mask_input": onnx_has_mask_input,
"orig_im_size": np.array(image.shape[:2], dtype=np.float32)
}
masks, _, low_res_logits = ort_session.run(None, ort_inputs)
masks = masks > predictor.model.mask_threshold
masks.shape
plt.figure(figsize=(10,10))
plt.imshow(image)
show_mask(masks, plt.gca())
show_points(input_point, input_label, plt.gca())
plt.axis('off')
plt.show()
input_point = np.array([[500, 375], [1125, 625]])
input_label = np.array([1, 1])
# Use the mask output from the previous run. It is already in the correct form for input to the ONNX model.
onnx_mask_input = low_res_logits
onnx_coord = np.concatenate([input_point, np.array([[0.0, 0.0]])], axis=0)[None, :, :]
onnx_label = np.concatenate([input_label, np.array([-1])], axis=0)[None, :].astype(np.float32)
onnx_coord = predictor.transform.apply_coords(onnx_coord, image.shape[:2]).astype(np.float32)
onnx_has_mask_input = np.ones(1, dtype=np.float32)
ort_inputs = {
"image_embeddings": image_embedding,
"point_coords": onnx_coord,
"point_labels": onnx_label,
"mask_input": onnx_mask_input,
"has_mask_input": onnx_has_mask_input,
"orig_im_size": np.array(image.shape[:2], dtype=np.float32)
}
masks, _, _ = ort_session.run(None, ort_inputs)
masks = masks > predictor.model.mask_threshold
plt.figure(figsize=(10,10))
plt.imshow(image)
show_mask(masks, plt.gca())
show_points(input_point, input_label, plt.gca())
plt.axis('off')
plt.show()
input_box = np.array([425, 600, 700, 875])
input_point = np.array([[575, 750]])
input_label = np.array([0])
onnx_box_coords = input_box.reshape(2, 2)
onnx_box_labels = np.array([2,3])
onnx_coord = np.concatenate([input_point, onnx_box_coords], axis=0)[None, :, :]
onnx_label = np.concatenate([input_label, onnx_box_labels], axis=0)[None, :].astype(np.float32)
onnx_coord = predictor.transform.apply_coords(onnx_coord, image.shape[:2]).astype(np.float32)
onnx_mask_input = np.zeros((1, 1, 256, 256), dtype=np.float32)
onnx_has_mask_input = np.zeros(1, dtype=np.float32)
ort_inputs = {
"image_embeddings": image_embedding,
"point_coords": onnx_coord,
"point_labels": onnx_label,
"mask_input": onnx_mask_input,
"has_mask_input": onnx_has_mask_input,
"orig_im_size": np.array(image.shape[:2], dtype=np.float32)
}
masks, _, _ = ort_session.run(None, ort_inputs)
masks = masks > predictor.model.mask_threshold
plt.figure(figsize=(10, 10))
plt.imshow(image)
show_mask(masks[0], plt.gca())
show_box(input_box, plt.gca())
show_points(input_point, input_label, plt.gca())
plt.axis('off')
plt.show()
torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB. GPU 0 has a total capacty of 6.00 GiB of which 276.38 MiB is free. Of the allocated memory 4.60 GiB is allocated by PyTorch, and 129.79 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
GPUが足りませんでした！
4．'cuda'から'cpu'に変更
sam.to(device='cpu') # sam.to(device='cuda')
実行結果
指定した点を含むようにセグメンテーションする
指定した2点を含むようにセグメンテーションする
指定したバウンディングボックス内で、指定した点を除き、セグメンテーションする
お疲れさまでした。