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# coding=utf-8
# Copyright 2024 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""The inference pipeline for the CaR model."""
import numpy as np
from PIL import Image
import torch
# pylint: disable=g-importing-member
# pylint: disable=g-bad-import-order
from modeling.post_process.post_process import generate_masks_from_sam
from modeling.post_process.post_process import match_masks
from utils.utils import process_sentence
from utils.metrics import IoU
IMAGE_WIDTH = 512
IMAGE_HEIGHT = 512
def get_sam_masks(
config, image_path, masks, matching_thresh=0.9, img_sam=None, pipeline=None
):
"""Generate SAM masks."""
print("generating sam masks online")
mask_tensor, mask_list = generate_masks_from_sam(
image_path,
save_path="./",
pipeline=pipeline,
img_sam=img_sam,
visualize=False,
)
mask_tensor = mask_tensor.to(masks.device)
# only conduct sam on masks that is not all zero
attn_map, mask_ids = [], []
for mask_id, mask in enumerate(masks):
if torch.sum(mask) > 0:
attn_map.append(mask.unsqueeze(0))
mask_ids.append(mask_id)
matched_masks = [
match_masks(
mask_tensor,
attn,
mask_list,
iom_thres=config.car.iom_thres,
min_pred_threshold=config.sam.min_pred_threshold,
)
for attn in attn_map
]
for matched_mask, mask_id in zip(matched_masks, mask_ids):
sam_masks = np.array([item["segmentation"] for item in matched_mask])
sam_mask = np.any(sam_masks, axis=0)
cur_mask = masks[mask_id]
iou = IoU(torch.from_numpy(sam_mask).to(cur_mask.device), cur_mask)
if iou > matching_thresh:
masks[mask_id] = torch.from_numpy(sam_mask).to(masks.device)
return masks
def inference_car(cfg, car_model, image_path, sentences, sam_pipeline=None):
sentences = [process_sentence(sen, cfg.test.ds_name) for sen in sentences]
img = Image.open(image_path).convert("RGB")
if cfg.test.use_pseudo:
masks, scores = car_model(img, sentences)
return masks, scores
masks, scores = car_model(img, sentences, cfg.car.num_iteration)
sam_masks = get_sam_masks(
cfg, image_path, masks, cfg.sam.matching_thresh, pipeline=sam_pipeline
)
return sam_masks, scores
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