# 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. """Test code for bilinear scale """ import numpy as np import tvm import topi import topi.testing import math from common import get_all_backend def verify_resize(batch, in_channel, in_height, in_width, out_height, out_width, layout='NCHW', align_corners=True, method="bilinear"): if layout == 'NCHW': A = tvm.placeholder((batch, in_channel, in_height, in_width), name='A', dtype='float32') dtype = A.dtype out_shape = (batch, in_channel, out_height, out_width) a_np = np.random.uniform(size=(batch, in_channel, in_height, in_width)).astype(dtype) elif layout == 'NHWC': A = tvm.placeholder((batch, in_height, in_width, in_channel), name='A', dtype='float32') dtype = A.dtype out_shape = (batch, out_height, out_width, in_channel) a_np = np.random.uniform(size=(batch, in_height, in_width, in_channel)).astype(dtype) else: raise NotImplementedError( 'Layout not supported {} '.format(layout)) B = topi.image.resize(A, (out_height, out_width), layout=layout, align_corners=align_corners, method=method) if method == "bilinear": b_np = topi.testing.bilinear_resize_python(a_np, (out_height, out_width), layout, align_corners) else: scale_h = out_height / in_height scale_w = out_width / in_width b_np = topi.testing.upsampling_python(a_np, (scale_h, scale_w), layout) def check_device(device): ctx = tvm.context(device, 0) if not ctx.exist: print("Skip because %s is not enabled" % device) return print("Running on target: %s" % device) with tvm.target.create(device): s = topi.generic.schedule_injective(B) a = tvm.nd.array(a_np, ctx) b = tvm.nd.array(np.zeros(out_shape, dtype=dtype), ctx) f = tvm.build(s, [A, B], device) f(a, b) tvm.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-3, atol=1e-3) for device in get_all_backend(): check_device(device) def test_resize(): # Scale NCHW verify_resize(4, 16, 32, 32, 50, 50, 'NCHW') # Scale NCHW + Align Corners verify_resize(6, 32, 64, 64, 20, 20, 'NCHW', True) # Scale NHWC verify_resize(4, 16, 32, 32, 50, 50, "NHWC") # Scale NHWC + Align Corners verify_resize(6, 32, 64, 64, 20, 20, "NHWC", True) # Nearest + Fractional verify_resize(4, 16, 32, 32, 50, 50, 'NCHW', method="nearest_neighbor", align_corners=False) verify_resize(4, 16, 32, 32, 50, 50, 'NHWC', method="nearest_neighbor", align_corners=False) if __name__ == "__main__": test_resize()