def main(args): start_t = time.time() dla_module = DLA( num_classes=10, levels=[1, 1, 1, 2, 2, 1], channels=[16, 32, 64, 128, 256, 512] ) end_t = time.time() print("init time : {}".format(end_t - start_t)) start_t = time.time() pretrain_models = flow.load(args.model_path) dla_module.load_state_dict(pretrain_models) end_t = time.time() print("load params time : {}".format(end_t - start_t)) dla_module.eval() dla_module.to("cuda") start_t = time.time() image = load_image(args.image_path) image = flow.Tensor(image, device=flow.device("cuda")) logits = dla_module(image) predictions = logits.softmax() predictions = predictions.numpy() end_t = time.time() print("infer time : {}".format(end_t - start_t)) clsidx = np.argmax(predictions) print( "predict prob: %f, class name: %s" % (np.max(predictions), clsidx_2_labels[clsidx]) )
def main(args): start_t = time.time() model = build_model(args) end_t = time.time() print("init time : {}".format(end_t - start_t)) start_t = time.time() pretrain_models = flow.load(args.model_path) model.load_state_dict(pretrain_models) end_t = time.time() print("load params time : {}".format(end_t - start_t)) model.eval() model.to("cuda") start_t = time.time() image = load_image(args.image_path, image_size=(args.image_size, args.image_size)) image = flow.Tensor(image, device=flow.device("cuda")) predictions = model(image).softmax() predictions = predictions.numpy() end_t = time.time() print("infer time : {}".format(end_t - start_t)) clsidx = np.argmax(predictions) print("predict prob: %f, class name: %s" % (np.max(predictions), clsidx_2_labels[clsidx]))
def main(args): assert args.model in model_dict print("Predicting using", args.model, "...") start_t = time.time() net_module = model_dict[args.model]() end_t = time.time() print("init time : {}".format(end_t - start_t)) start_t = time.time() pretrain_models = flow.load(args.model_path) net_module.load_state_dict(pretrain_models) end_t = time.time() print("load params time : {}".format(end_t - start_t)) net_module.eval() net_module.to("cuda") start_t = time.time() image = load_image(args.image_path) image = flow.Tensor(image, device=flow.device("cuda")) predictions = net_module(image).softmax() predictions = predictions.numpy() end_t = time.time() print("infer time : {}".format(end_t - start_t)) clsidx = np.argmax(predictions) print("predict prob: %f, class name: %s" % (np.max(predictions), clsidx_2_labels[clsidx]))
def main(args): start_t = time.time() repVGGA0 = create_RepVGG_A0() end_t = time.time() print("init time : {}".format(end_t - start_t)) start_t = time.time() pretrain_models = flow.load(args.model_path) repVGGA0.load_state_dict(pretrain_models) end_t = time.time() print("load params time : {}".format(end_t - start_t)) repVGGA0.eval() repVGGA0.to("cuda") start_t = time.time() image = load_image(args.image_path) image = flow.Tensor(image, device=flow.device("cuda")) predictions = repVGGA0(image).softmax() predictions = predictions.numpy() end_t = time.time() print("infer time : {}".format(end_t - start_t)) clsidx = np.argmax(predictions) print("predict prob: %f, class name: %s" % (np.max(predictions), clsidx_2_labels[clsidx]))
def main(args): flow.env.init() flow.enable_eager_execution() start_t = time.time() posenet_module = PoseNet() end_t = time.time() print("init time : {}".format(end_t - start_t)) start_t = time.time() pretrain_models = flow.load(args.model_path) posenet_module.load_state_dict(pretrain_models) end_t = time.time() print("load params time : {}".format(end_t - start_t)) posenet_module.eval() posenet_module.to("cuda") start_t = time.time() image = load_image(args.image_path) image = flow.Tensor(image, device=flow.device("cuda")) logits = posenet_module(image) predictions = logits.softmax() predictions = predictions.numpy() end_t = time.time() print("infer time : {}".format(end_t - start_t)) clsidx = np.argmax(predictions) print("predict prob: %f, class name: %s" % (np.max(predictions), clsidx_2_labels[clsidx]))
def main(args): start_t = time.time() inceptionv3_module = inception_v3() end_t = time.time() print("init time : {}".format(end_t - start_t)) start_t = time.time() pretrain_models = flow.load(args.model_path) inceptionv3_module.load_state_dict(pretrain_models) end_t = time.time() print("load params time : {}".format(end_t - start_t)) inceptionv3_module.eval() inceptionv3_module.to("cuda") start_t = time.time() image = load_image(args.image_path) image = flow.Tensor(image, device=flow.device("cuda")) predictions, aux_predictions = inceptionv3_module(image) predictions = predictions.softmax() predictions = predictions.numpy() end_t = time.time() print("infer time : {}".format(end_t - start_t)) clsidx = np.argmax(predictions) print("predict prob: %f, class name: %s" % (np.max(predictions), clsidx_2_labels[clsidx]))
def main(args): start_t = time.time() quantization_module = QuantizationAlexNet() quantization_module.quantize( quantization_bit=args.quantization_bit, quantization_scheme=args.quantization_scheme, quantization_formula=args.quantization_formula, per_layer_quantization=args.per_layer_quantization, ) end_t = time.time() print("init time : {}".format(end_t - start_t)) start_t = time.time() pretrain_models = flow.load(args.model_path) quantization_module.load_state_dict(pretrain_models) end_t = time.time() print("load params time : {}".format(end_t - start_t)) quantization_module.eval() quantization_module.to("cuda") start_t = time.time() image = load_image(args.image_path) image = flow.Tensor(image, device=flow.device("cuda")) predictions = quantization_module(image).softmax() predictions = predictions.numpy() end_t = time.time() print("infer time : {}".format(end_t - start_t)) clsidx = np.argmax(predictions) print("predict prob: %f, class name: %s" % (np.max(predictions), clsidx_2_labels[clsidx]))
def main(args): start_t = time.perf_counter() print("***** Model Init *****") model = resnet50() model.load_state_dict(flow.load(args.model_path)) model = model.to("cuda") model.eval() end_t = time.perf_counter() print(f"***** Model Init Finish, time escapled {end_t - start_t:.6f} s *****") if args.graph: model_graph = InferGraph(model) start_t = end_t image = load_image(args.image_path) image = flow.Tensor(image, device=flow.device("cuda")) if args.graph: pred = model_graph(image) else: pred = model(image).softmax() pred = pred.numpy() prob = np.max(pred) clsidx = np.argmax(pred) cls = clsidx_2_labels[clsidx] end_t = time.perf_counter() print( "predict image ({}) prob: {:.5f}, class name: {}, time escapled: {:.6f} s".format( os.path.basename(args.image_path), prob, cls, end_t - start_t ) )
def main(args): start_t = time.time() model = inception_v3() end_t = time.time() print("init time : {}".format(end_t - start_t)) start_t = time.time() pretrain_models = flow.load(args.model_path) model.load_state_dict(pretrain_models) end_t = time.time() print("load params time : {}".format(end_t - start_t)) model.eval() model.to("cuda") class EvalGraph(flow.nn.Graph): def __init__(self): super().__init__() self.model = model def build(self, image): with flow.no_grad(): predictions, aux = self.model(image) return predictions inception_eval_graph = EvalGraph() start_t = time.time() image = load_image(args.image_path) image = flow.Tensor(image, device=flow.device("cuda")) predictions = inception_eval_graph(image).softmax() predictions = predictions.numpy() end_t = time.time() print("infer time : {}".format(end_t - start_t)) clsidx = np.argmax(predictions) print( "predict prob: %f, class name: %s" % (np.max(predictions), clsidx_2_labels[clsidx]) )