def main():
args = parse_args()
cfg = Config.fromfile(args.config)
img = cv2.imread(args.input, -1)
img = cv2.resize(img, (224, 224))
img_tensor = img_to_tensor(img, squeeze=True, cuda=args.use_cuda)
model = build_retriever(cfg.model)
checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu')
if args.use_cuda:
model.cuda()
model.eval()
query_embed = model(img_tensor, landmark=None, return_loss=False)
query_embed = query_embed.data.cpu().numpy()
gallery_set = build_dataset(cfg.data.gallery)
gallery_embeds = _process_embeds(gallery_set, model, cfg)
retriever = ClothesRetriever(cfg.data.gallery.img_file, cfg.data_root,
cfg.data.gallery.img_path)
retriever.show_retrieved_images(query_embed, gallery_embeds)
def main():
seed = 0
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
args = parse_args()
cfg = Config.fromfile(args.config)
model = build_retriever(cfg.model)
load_checkpoint(model, args.checkpoint)
print('load checkpoint from {}'.format(args.checkpoint))
if args.use_cuda:
model.cuda()
model.eval()
img_tensor = get_img_tensor(args.input, args.use_cuda)
query_feat = model(img_tensor, landmark=None, return_loss=False)
query_feat = query_feat.data.cpu().numpy()
gallery_set = build_dataset(cfg.data.gallery)
gallery_embeds = _process_embeds(gallery_set, model, cfg)
retriever = ClothesRetriever(cfg.data.gallery.img_file, cfg.data_root,
cfg.data.gallery.img_path)
retriever.show_retrieved_images(query_feat, gallery_embeds)
def retrieve():
file = request.files.get('image')
img_tensor = get_img_tensor(file, True)
query_feat = model_ret(img_tensor, landmark=None, return_loss=False)
query_feat = query_feat.data.cpu().numpy()
gallery_set = build_dataset(cfg_ret.data.gallery)
gallery_embeds = _process_embeds(gallery_set, model_ret, cfg_ret)
retriever = ClothesRetriever(cfg_ret.data.gallery.img_file, cfg_ret.data_root,
cfg_ret.data.gallery.img_path)
result = retriever.show_retrieved_images(query_feat, gallery_embeds)
resultDict = {}
resultDict['paths'] = result
return jsonify(resultDict)
def _init_models():
args = parse_args()
# Build retrieval model and load checkpoint
cfg = mmcv.Config.fromfile(args.config_retrieval)
model_rt = build_retriever(cfg.model)
load_checkpoint(model_rt, args.checkpoint_retrieval)
print('load retriever checkpoint from {}'.format(
args.checkpoint_retrieval))
# Build landmark detection model and load checkpoint
cfg_lm = mmcv.Config.fromfile(args.config_landmark)
model_lm = build_landmark_detector(cfg_lm.model)
load_checkpoint(model_lm, args.checkpoint_landmark)
print('load landmark detector checkpoint from: {}'.format(
args.checkpoint_landmark))
if args.use_cuda:
model_rt.cuda()
model_lm.cuda()
model_rt.eval()
model_lm.eval()
# Build database for retrieval
gallery_list = np.load(args.image_list)
gallery_embeds = _process_embeds(args.image_embeddings)
retriever = ClothesRetriever(gallery_list, [args.topk])
print('build database for retrieval')
# Return retrieval, landmark, and detection model, database for retrieval and retriever
return model_rt, model_lm, gallery_embeds, retriever
def main():
seed = 0
torch.manual_seed(seed)
args = parse_args()
if args.use_cuda and torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
cfg = Config.fromfile(args.config)
model = build_retriever(cfg.model)
load_checkpoint(model, args.checkpoint, map_location=torch.device('cuda:0'))
print('load checkpoint from {}'.format(args.checkpoint))
if args.use_cuda:
model.cuda()
model.eval()
print('Model evaled')
img_tensor = get_img_tensor(args.input, args.use_cuda)
print('Image tensor got.')
query_feat = model(img_tensor, landmark=None, return_loss=False)
print('Query feat 1')
query_feat = query_feat.data.cpu().numpy()
print('Query feat 2')
gallery_set = build_dataset(cfg.data.gallery)
print('Gallery set')
gallery_embeds = _process_embeds(gallery_set, model, cfg)
print('Gallery embeds')
retriever = ClothesRetriever(cfg.data.gallery.img_file, cfg.data_root,
cfg.data.gallery.img_path)
print('Retriever')
results = retriever.show_retrieved_images(query_feat, gallery_embeds)
print('Show retriever')
for result in results:
print(result)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
cfg = Config.fromfile(args.config)
model = build_retriever(cfg.model)
load_checkpoint(model, args.checkpoint)
print('load checkpoint from {}'.format(args.checkpoint))
if args.use_cuda:
model = model.cuda()
model.eval()
gallery_set = build_dataset(cfg.data.gallery)
gallery_embeds = _process_embeds(gallery_set, model, cfg)
retriever = ClothesRetriever(cfg.data.gallery.img_file, cfg.data_root,
cfg.data.gallery.img_path)
model = model.cpu()
torch.cuda.empty_cache()
# Flask
#------------------------------------
print("MMFashion server started!")
app.debug = args.debug
if (args.enable_threaded):
app.run(host=args.host, port=args.port, threaded=False)
else:
app.run(host=args.host, port=args.port, threaded=True)