kice commented on issue #4531: Fix wrong NHWC shape parameters for cudnn conv2d URL: https://github.com/apache/incubator-tvm/pull/4531#issuecomment-566470339 Here is the code I used for testing. If the existing test case is correct, then there is something wrong with mxnet import. And I also used a pre-train model for testing, but the output is garbage. I assume the shape was mess up somewhere. ``` data = mx.sym.var('data') sym = mx.sym.Convolution(data, dilate=(1, 1), kernel=(4, 5), no_bias=False, num_filter=64, num_group=1, pad=[1, 1], stride=(1, 1), layout='NHWC') arg_params = {} aux_params = {} dtype = 'float16' input_shape = (1, 720, 1280, 3) shape_dict = {'data': input_shape} mod, params = relay.frontend.from_mxnet(sym, shape_dict, dtype, arg_params, aux_params) func = mod["main"] target = tvm.target.create('cuda -libs=cudnn') with relay.build_config(opt_level=3): graph, lib, params = relay.build(func, target, params=params) ctx = tvm.gpu(0) x = np.ones(input_shape).astype(dtype) m = graph_runtime.create(graph, lib, ctx) m.set_input(input_name, tvm.nd.array(x)) m.set_input(**params) m.run() tvm_output = m.get_output(0) print(tvm_output.shape) ```
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