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}
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))
