fix typo and docs, add topi test

python/tvm/contrib/cudnn.py

@@ -391,14 +391,16 @@ 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
     ----------
     in_channel: int
         input channel
     out_channel: int
         output channel
+    filter_d: int
+        filter depth
     filter_h: int
         filter height
     filter_w: int

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<Conv3DAttrs>()

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:

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()