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import os |
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import sys |
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import logging |
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pth = '/'.join(sys.path[0].split('/')[:-1]) |
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sys.path.insert(0, pth) |
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from PIL import Image |
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import numpy as np |
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np.random.seed(1) |
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import torch |
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from torchvision import transforms |
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from utils.arguments import load_opt_command |
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from detectron2.data import MetadataCatalog |
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from detectron2.utils.colormap import random_color |
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from modeling.BaseModel import BaseModel |
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from modeling import build_model |
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from utils.visualizer import Visualizer |
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from utils.distributed import init_distributed |
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logger = logging.getLogger(__name__) |
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def main(args=None): |
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''' |
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Main execution point for PyLearn. |
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''' |
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opt, cmdline_args = load_opt_command(args) |
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if cmdline_args.user_dir: |
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absolute_user_dir = os.path.abspath(cmdline_args.user_dir) |
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opt['base_path'] = absolute_user_dir |
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opt = init_distributed(opt) |
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pretrained_pth = os.path.join(opt['RESUME_FROM']) |
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output_root = './output' |
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image_pth = 'inference/images/animals.png' |
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model = BaseModel(opt, build_model(opt)).from_pretrained(pretrained_pth).eval().cuda() |
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t = [] |
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t.append(transforms.Resize(512, interpolation=Image.BICUBIC)) |
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transform = transforms.Compose(t) |
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stuff_classes = ['zebra','antelope','giraffe','ostrich','sky','water','grass','sand','tree'] |
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stuff_colors = [random_color(rgb=True, maximum=255).astype(np.int).tolist() for _ in range(len(stuff_classes))] |
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stuff_dataset_id_to_contiguous_id = {x:x for x in range(len(stuff_classes))} |
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MetadataCatalog.get("demo").set( |
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stuff_colors=stuff_colors, |
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stuff_classes=stuff_classes, |
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stuff_dataset_id_to_contiguous_id=stuff_dataset_id_to_contiguous_id, |
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) |
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model.model.sem_seg_head.predictor.lang_encoder.get_text_embeddings(stuff_classes + ["background"], is_eval=True) |
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metadata = MetadataCatalog.get('demo') |
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model.model.metadata = metadata |
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model.model.sem_seg_head.num_classes = len(stuff_classes) |
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with torch.no_grad(): |
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image_ori = Image.open(image_pth).convert("RGB") |
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width = image_ori.size[0] |
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height = image_ori.size[1] |
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image = transform(image_ori) |
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image = np.asarray(image) |
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image_ori = np.asarray(image_ori) |
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images = torch.from_numpy(image.copy()).permute(2,0,1).cuda() |
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batch_inputs = [{'image': images, 'height': height, 'width': width}] |
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outputs = model.forward(batch_inputs) |
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visual = Visualizer(image_ori, metadata=metadata) |
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sem_seg = outputs[-1]['sem_seg'].max(0)[1] |
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demo = visual.draw_sem_seg(sem_seg.cpu(), alpha=0.5) |
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if not os.path.exists(output_root): |
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os.makedirs(output_root) |
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demo.save(os.path.join(output_root, 'sem.png')) |
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if __name__ == "__main__": |
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main() |
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sys.exit(0) |
