def test_model_checkonly(model_file, model_name=''): model = cm.models.MLModel(model_file) sym, params = nnvm.frontend.from_coreml(model) x = model_zoo.get_cat_image() for target, ctx in ctx_list(): tvm_output = get_tvm_output(sym, x, params, target, ctx) print(target, ctx, model_name, 'prediction id: ', np.argmax(tvm_output.flat))
def run_model_checkonly(model_file, model_name=''): model = cm.models.MLModel(model_file) sym, params = nnvm.frontend.from_coreml(model) x = model_zoo.get_cat_image() for target, ctx in ctx_list(): tvm_output = get_tvm_output(sym, x, params, target, ctx) print(target, ctx, model_name, 'prediction id: ', np.argmax(tvm_output.flat))
def run_model_checkonly(model_file, model_name='', input_name='image'): model = cm.models.MLModel(model_file) x = model_zoo.get_cat_image() shape_dict = {input_name : x.shape} func, params = relay.frontend.from_coreml(model, shape_dict) for target, ctx in ctx_list(): tvm_output = get_tvm_output(func, x, params, target, ctx) print(target, ctx, model_name, 'prediction id: ', np.argmax(tvm_output.flat))
def run_model_checkonly(model_file, model_name="", input_name="image"): model = cm.models.MLModel(model_file) x = model_zoo.get_cat_image() shape_dict = {input_name: x.shape} # Some Relay passes change operators on the fly. Ensuring that we generate # new graph for each target. for target, dev in tvm.testing.enabled_targets(): mod, params = relay.frontend.from_coreml(model, shape_dict) tvm_output = get_tvm_output(mod["main"], x, params, target, dev) print(target, dev, model_name, "prediction id: ", np.argmax(tvm_output.flat))
def run_model_checkonly(model_file, model_name='', input_name='image'): model = cm.models.MLModel(model_file) x = model_zoo.get_cat_image() shape_dict = {input_name : x.shape} # Some Relay passes change operators on the fly. Ensuring that we generate # new graph for each target. for target, ctx in ctx_list(): mod, params = relay.frontend.from_coreml(model, shape_dict) tvm_output = get_tvm_output(mod["main"], x, params, target, ctx) print(target, ctx, model_name, 'prediction id: ', np.argmax(tvm_output.flat))