# 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. import tvm from tvm import te import topi import numpy as np from tvm.contrib.pickle_memoize import memoize from scipy import signal from topi.util import get_const_tuple from topi.nn.util import get_pad_tuple import topi.testing from topi.cuda.depthwise_conv2d import schedule_depthwise_conv2d_backward_input_nhwc def verify_depthwise_conv2d_back_input(batch, in_channel, in_h, channel_multiplier, filter_h, stride_h, padding_h): in_w = in_h filter_channel = in_channel filter_w = filter_h stride_w = stride_h padding_w = padding_h out_h = np.int((in_h+2*padding_h-filter_h)/stride_h+1) out_w = np.int((in_w+2*padding_w-filter_w)/stride_w+1) out_channel = in_channel * channel_multiplier ishape = [batch, in_h, in_w, in_channel] oshape = [batch, out_h, out_w, out_channel] # placeholder Out_grad = te.placeholder(oshape, name='Out_grad') Filter = te.placeholder((filter_h, filter_w, filter_channel, channel_multiplier)) # declare In_grad = topi.nn.depthwise_conv2d_backward_input_nhwc(Filter, Out_grad, oshape, ishape, stride=[stride_h, stride_w], padding=[padding_h, padding_w]) # schedule schedule = schedule_depthwise_conv2d_backward_input_nhwc(In_grad) 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) # build the kernel f = tvm.build(schedule, [Filter, Out_grad, In_grad], device) # prepare pod type for test data closure dtype = Out_grad.dtype out_grad_shape = get_const_tuple(Out_grad.shape) filter_shape = get_const_tuple(Filter.shape) # use memoize to pickle the test data for next time use @memoize("topi.tests.test_topi_depthwise_conv2d_backward_input.nhwc") def get_ref_data(): out_grad_np = np.random.uniform(size=out_grad_shape).astype(dtype) filter_np = np.random.uniform(size=filter_shape).astype(dtype) dilated_out_grad_np = topi.testing.dilate_python(out_grad_np, [1, stride_h, stride_w, 1]) # padding params in forward propagation fpad_top, fpad_left, fpad_bottom, fpad_right = get_pad_tuple([padding_h, padding_w], (filter_h, filter_w)) # padding params in backward propagation bpad_top = filter_h - 1 - fpad_top bpad_bottom = (filter_h - 1 - fpad_bottom) + (stride_h - 1) bpad_left = filter_w - 1 - fpad_left bpad_right = (filter_w - 1 - fpad_right) + (stride_w - 1) padded_out_grad = np.zeros((batch, dilated_out_grad_np.shape[1]+bpad_top+bpad_bottom, dilated_out_grad_np.shape[2]+bpad_left+bpad_right, out_channel)) padded_out_grad[:, bpad_top:dilated_out_grad_np.shape[1]+bpad_top, bpad_left:dilated_out_grad_np.shape[2]+bpad_left, :] = dilated_out_grad_np in_grad_np = np.zeros((batch, in_h, in_w, in_channel)) for b in range(batch): for c in range(in_channel): for m in range(channel_multiplier): in_grad_np[b, :, :, c] += signal.convolve2d(padded_out_grad[b, :, :, c*channel_multiplier+m], \ filter_np[:, :, c, m], mode='valid')[0:in_h, 0:in_w] return (out_grad_np, filter_np, in_grad_np) (out_grad_np, filter_np, in_grad_np) = get_ref_data() out_grad_tvm = tvm.nd.array(out_grad_np, ctx) filter_tvm = tvm.nd.array(filter_np, ctx) in_grad_tvm = tvm.nd.array(np.zeros(shape=ishape, dtype=dtype), ctx) # launch the kernel timer = f.time_evaluator(f.entry_name, ctx, number=1) tcost = timer(filter_tvm, out_grad_tvm, in_grad_tvm).mean tvm.testing.assert_allclose(in_grad_np, in_grad_tvm.asnumpy(), rtol=1e-5) check_device("opencl") check_device("cuda") check_device("metal") check_device("rocm") check_device("vulkan") check_device("nvptx") def test_topi_depthwise_conv2d_backward_input_nhwc(): verify_depthwise_conv2d_back_input(16, 256, 56, 1, 3, 1, 1) verify_depthwise_conv2d_back_input(16, 256, 56, 2, 3, 1, 1) verify_depthwise_conv2d_back_input(16, 256, 56, 1, 5, 1, 2) verify_depthwise_conv2d_back_input(16, 256, 56, 2, 5, 1, 2) verify_depthwise_conv2d_back_input(16, 256, 56, 1, 3, 2, 1) verify_depthwise_conv2d_back_input(16, 256, 56, 2, 3, 2, 1) verify_depthwise_conv2d_back_input(16, 256, 56, 1, 5, 2, 2) verify_depthwise_conv2d_back_input(16, 256, 56, 2, 5, 2, 2) verify_depthwise_conv2d_back_input(16, 256, 56, 1, 3, 1, 0) verify_depthwise_conv2d_back_input(16, 256, 56, 2, 3, 1, 0) verify_depthwise_conv2d_back_input(16, 256, 56, 1, 5, 1, 0) verify_depthwise_conv2d_back_input(16, 256, 56, 2, 5, 1, 0) verify_depthwise_conv2d_back_input(16, 256, 56, 1, 3, 2, 0) verify_depthwise_conv2d_back_input(16, 256, 56, 2, 3, 2, 0) verify_depthwise_conv2d_back_input(16, 256, 56, 1, 5, 2, 0) verify_depthwise_conv2d_back_input(16, 256, 56, 2, 5, 2, 0) if __name__ == "__main__": test_topi_depthwise_conv2d_backward_input_nhwc()