diff --git a/python/tvm/contrib/cudnn.py b/python/tvm/contrib/cudnn.py index f3bc53a19d66d..609fbdea5802d 100644 --- a/python/tvm/contrib/cudnn.py +++ b/python/tvm/contrib/cudnn.py @@ -391,7 +391,7 @@ def conv3d_w_shape(in_channel, filter_d, filter_h, filter_w): - """Get weight shape for a 2D convolution + """Get weight shape for a 3D convolution Parameters ---------- @@ -399,6 +399,8 @@ def conv3d_w_shape(in_channel, input channel out_channel: int output channel + filter_d: int + filter depth filter_h: int filter height filter_w: int diff --git a/src/relay/op/nn/convolution.cc b/src/relay/op/nn/convolution.cc index 87bebbc35c227..cfb0f4f0d9d49 100644 --- a/src/relay/op/nn/convolution.cc +++ b/src/relay/op/nn/convolution.cc @@ -145,16 +145,17 @@ TVM_REGISTER_API("relay.op.nn._make.conv3d") RELAY_REGISTER_OP("nn.conv3d") -.describe(R"code(3D convolution layer (e.g. spatial convolution over images). +.describe(R"code(3D convolution layer (e.g. convolution over 3D image data, +like Magnetic Resonance Imaging (MRI) data in medicine). This layer creates a convolution kernel that is convolved with the layer input to produce a tensor of outputs. -- **data**: This depends on the `layout` parameter. Input is 4D array of shape - (batch_size, in_channels, height, width) if `layout` is `NCHW`. -- **weight**: (channels, in_channels, kernel_size[0], kernel_size[1]) -- **out**: This depends on the `layout` parameter. Output is 4D array of shape - (batch_size, channels, out_height, out_width) if `layout` is `NCHW`. +- **data**: This depends on the `layout` parameter. Input is 5D array of shape + (batch_size, in_channels, depth, height, width) if `layout` is `NCDHW`. +- **weight**: (channels, in_channels, kernel_size[0], kernel_size[1], kernel_size[2]) +- **out**: This depends on the `layout` parameter. Output is 5D array of shape + (batch_size, channels, out_depth, out_height, out_width) if `layout` is `NCDHW`. )code" TVM_ADD_FILELINE) .set_attrs_type() diff --git a/topi/python/topi/testing/conv3d_ncdhw_python.py b/topi/python/topi/testing/conv3d_ncdhw_python.py index 14e42c2cbf727..32c670e4dad18 100644 --- a/topi/python/topi/testing/conv3d_ncdhw_python.py +++ b/topi/python/topi/testing/conv3d_ncdhw_python.py @@ -49,7 +49,7 @@ def _conv3d_ncdhw_python(a_np, w_np, stride, padding): else: stride_d, stride_h, stride_w = stride if isinstance(padding, int): - pad_d, pad_h = pad_w = padding * 2 + pad_d = pad_h = pad_w = padding * 2 elif isinstance(padding, (list, tuple)): pad_d, pad_h, pad_w = padding[0] * 2, padding[1] * 2, padding[2] * 2 else: diff --git a/topi/tests/python/test_topi_conv3d_ncdhw.py b/topi/tests/python/test_topi_conv3d_ncdhw.py new file mode 100644 index 0000000000000..78827e4ca9d11 --- /dev/null +++ b/topi/tests/python/test_topi_conv3d_ncdhw.py @@ -0,0 +1,115 @@ +# 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. +"""Example code to do convolution.""" + +import numpy as np +import tvm +from tvm import autotvm +import topi +import topi.testing +from tvm.contrib.pickle_memoize import memoize +from topi.util import get_const_tuple + +from common import get_all_backend + +def verify_conv3d_ncdhw(batch, in_channel, in_size, num_filter, kernel, stride, padding, dilation=1, add_bias=False, add_relu=False): + print("Workload: (%d, %d, %d, %d, %d, %d, %d, %d)" % (batch, in_channel, in_size, num_filter, kernel, stride, padding, dilation)) + + in_depth = in_height = in_width = in_size + + A = tvm.placeholder((batch, in_channel, in_depth, in_height, in_width), name='A') + W = tvm.placeholder((num_filter, in_channel, kernel, kernel, kernel), name='W') + bias = tvm.placeholder((num_filter, 1, 1, 1), name='bias') + + a_shape = get_const_tuple(A.shape) + w_shape = get_const_tuple(W.shape) + bias_shape = get_const_tuple(bias.shape) + dtype = A.dtype + + @memoize("topi.tests.test_topi_conv3d_ncdhw.verify_conv3d_ncdhw") + def get_ref_data(): + a_np = np.random.uniform(size=a_shape).astype(dtype) + w_np = np.random.uniform(size=w_shape).astype(dtype) + b_np = np.random.uniform(size=bias_shape).astype(dtype) + dw_np = topi.testing.dilate_python(w_np, (1, 1, dilation, dilation, dilation)) + c_np = topi.testing.conv3d_ncdhw_python(a_np, dw_np, stride, padding) + if add_bias: + c_np += b_np + if add_relu: + c_np = np.maximum(c_np, 0) + return a_np, w_np, b_np, c_np + + a_np, w_np, b_np, c_np = get_ref_data() + + 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): + C = topi.nn.conv3d(A, W, (stride, stride, stride), (padding, padding, padding), + (dilation, dilation, dilation), layout='NCDHW', out_dtype=dtype) + if add_bias: + C = topi.add(C, bias) + if add_relu: + C = topi.nn.relu(C) + s = topi.generic.schedule_conv3d_ncdhw([C]) + + a = tvm.nd.array(a_np, ctx) + w = tvm.nd.array(w_np, ctx) + b = tvm.nd.array(b_np, ctx) + c = tvm.nd.array(np.zeros(get_const_tuple(C.shape), dtype=C.dtype), ctx) + if add_bias: + func = tvm.build(s, [A, W, bias, C], device, name="relu_%d_%d_%d_%d_%d_%d_%d_%d" % (batch, in_channel, in_size, num_filter, kernel, stride, padding, dilation)) + func(a, w, b, c) + else: + func = tvm.build(s, [A, W, C], device, name="relu_%d_%d_%d_%d_%d_%d_%d_%d" % (batch, in_channel, in_size, num_filter, kernel, stride, padding, dilation)) + func(a, w, c) + tvm.testing.assert_allclose(c.asnumpy(), c_np, rtol=1e-4) + + for device in get_all_backend(): + with autotvm.tophub.context(device): # load tophub pre-tuned parameters + check_device(device) + + +def test_conv3d_ncdhw(): + #3DCNN workloads + verify_conv3d_ncdhw(1, 32, 32, 5, 1, 1, 0) + verify_conv3d_ncdhw(1, 32, 32, 1, 1, 1, 0) + verify_conv3d_ncdhw(1, 32, 32, 5, 1, 1, 1) + verify_conv3d_ncdhw(1, 32, 32, 1, 1, 1, 1) + + # bias, relu + verify_conv3d_ncdhw(1, 64, 56, 3, 1, 1, 1, add_relu=True) + verify_conv3d_ncdhw(1, 64, 56, 3, 1, 1, 1, add_bias=True) + verify_conv3d_ncdhw(1, 64, 56, 3, 1, 1, 1, add_bias=True, add_relu=True) + + # dilation = 2 + verify_conv3d_ncdhw(1, 64, 56, 3, 3, 1, 1, dilation=2) + + # batch size + verify_conv3d_ncdhw(4, 64, 56, 5, 3, 1, 1) + + # weird workloads + verify_conv3d_ncdhw(2, 2, 2, 2, 2, 2, 2) + verify_conv3d_ncdhw(3, 3, 3, 3, 3, 3, 3) + + + +if __name__ == "__main__": + test_conv3d_ncdhw()