zhiics commented on a change in pull request #6273: URL: https://github.com/apache/incubator-tvm/pull/6273#discussion_r473167167
########## File path: tests/python/relay/dyn/test_dynamic_op_level2.py ########## @@ -0,0 +1,81 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +""" Support level2 dynamic operator test cases. +""" + +import numpy as np +import tvm +from tvm import relay +from tvm import te +from tvm.relay.testing import ctx_list +import random +from test_dynamic_op_level3 import verify_func +import tvm.topi.testing +from tvm.relay.testing import run_infer_type + +def test_dyn_upsampling_run(): + def verify_upsampling(dshape, scale_h, scale_w, layout, method, align_corners=False): + + if layout == "NCHW": + (n, c, h, w) = dshape + x_data = np.random.uniform(size=(n, c, h, w)).astype("float32") + + elif layout == "NHWC": + (n, h, w, c) = dshape + x_data = np.random.uniform(size=(n, h, w, c)).astype("float32") + + if method == "nearest_neighbor": + ref_res = tvm.topi.testing.upsampling_python(x_data, (scale_h, scale_w), layout) + else: + ref_res = tvm.topi.testing.bilinear_resize_python(x_data, (int(round(h*scale_h)), + int(round(w*scale_w))), layout) + x = relay.Var("x", relay.TensorType(dshape, "float32")) + scale_h_var = relay.var("scale_h", relay.TensorType((), "float32")) + scale_w_var = relay.var("scale_h", relay.TensorType((), "float32")) + + z = relay.nn.upsampling(x, scale_h_var, scale_w_var, method=method, layout=layout, align_corners=align_corners) + zz = run_infer_type(z) + func = relay.Function([x, scale_h_var, scale_w_var], z) + + for target, ctx in ctx_list(): + if "llvm" not in target: continue + for kind in ["vm", "debug"]: + mod = tvm.ir.IRModule.from_expr(func) + intrp = relay.create_executor(kind, mod=mod, ctx=ctx, target=target) + op_res = intrp.evaluate()(x_data, np.array(scale_h).astype("float32"), np.array(scale_w).astype("float32")) + tvm.testing.assert_allclose(op_res.asnumpy(), ref_res, rtol=1e-4, atol=1e-6) + + verify_upsampling((1, 16, 32, 32), 2.0, 2.0,"NCHW", "nearest_neighbor") + verify_upsampling((1, 16, 32, 32), 2.0, 2.0,"NCHW", "bilinear", True) + verify_upsampling((1, 16, 32, 32), 2.0, 2.0, "NHWC", "nearest_neighbor") + verify_upsampling((1, 16, 32, 32), 2.0, 2.0,"NHWC", "bilinear", True) Review comment: s/2.0,"NCHW"/2.0, "NCHW", so does the others (add a comma after 2.0) just a QQ, it looks all the inputs are static, will we test dynamic upsampling? ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: [email protected]
