dtype = "float32" width = 224 height = 224 image_data = load_test_image(dtype, width, height) input_tensor = "input" input_shape = (1, 224, 224, 3) input_dtype = dtype # Parse TFLite model and convert it to a Relay module mod, params = relay.frontend.from_tflite( tflite_model, shape_dict={input_tensor: input_shape}, dtype_dict={input_tensor: input_dtype}) tvm_target = get_tvm_target(device, get_device_type(), get_device_arch(), get_device_attributes()) tvm_targets = tvm.target.Target(tvm_target) cpu_target = "llvm" target_host = cpu_target cpudevice = tvm.runtime.cpu() ctx = tvm.runtime.context("cpu") enable_acl = True tvm_ops = 80 acl_partitions = 38 atol = 0.002 rtol = 0.01
image_data = load_test_image(dtype, width, height) input_tensor = "Placeholder" input_shape = (1, 224, 224, 3) input_dtype = dtype # Parse TFLite model and convert it to a Relay module mod, params = relay.frontend.from_tflite(tflite_model, shape_dict={input_tensor: input_shape}, dtype_dict={input_tensor: input_dtype}) desired_layouts = {'nn.conv2d': ['NCHW', 'default']} seq = tvm.transform.Sequential([relay.transform.RemoveUnusedFunctions(),relay.transform.ConvertLayout(desired_layouts)]) with tvm.transform.PassContext(opt_level=3): mod = seq(mod) tvm_target = get_tvm_target(device, get_device_type(), get_device_arch(), get_device_attributes()) tvm_targets = tvm.target.Target(tvm_target) cpu_target = "llvm" target_host=cpu_target cpudevice = tvm.runtime.cpu() ctx = tvm.runtime.context("cpu") enable_acl=True tvm_ops=171 acl_partitions=1 atol=0.002 rtol=0.01 try: lib = build_module(mod, tvm_target, params, enable_acl, tvm_ops, acl_partitions)