support dilation in conv2d (#439)

nnvm/python/nnvm/top/nn.py

@@ -85,11 +85,18 @@ def compute_conv2d(attrs, inputs, _):
     channels = attrs.get_int("channels")
     layout = attrs["layout"]
     assert layout == "NCHW" or layout == "NHWC"
-    assert dilation == (1, 1), "not support dilate now"
+    (dilation_h, dilation_w) = dilation
+    if dilation_h < 1 or dilation_w < 1:
+        raise ValueError("dilation should be positive value")
+    elif layout == "NCHW":
+        kernel = topi.nn.dilate(inputs[1], [1, 1, dilation_h, dilation_w])
+    else: #layout == NHWC
+        kernel = topi.nn.dilate(inputs[1], [1, dilation_h, dilation_w, 1])
+
     if groups == 1:
-        out = topi.nn.conv2d(inputs[0], inputs[1], strides, padding, layout)
+        out = topi.nn.conv2d(inputs[0], kernel, strides, padding, layout)
     elif groups == get_const_int(inputs[0].shape[1]) and groups == channels:
-        out = topi.nn.depthwise_conv2d_nchw(inputs[0], inputs[1], strides, padding)
+        out = topi.nn.depthwise_conv2d_nchw(inputs[0], kernel, strides, padding)
     else:
         raise ValueError("not support arbitrary group number for now")
     if attrs.get_bool("use_bias"):

nnvm/tests/python/compiler/test_top_level2.py

@@ -32,6 +32,32 @@ def test_conv2d():
         np.testing.assert_allclose(out.asnumpy(), c_np, rtol=1e-5)
 
 
+def test_dilated_conv2d():
+    dilation = 3
+    x = sym.Variable("x")
+    y = sym.conv2d(x, channels=10, kernel_size=(3, 3), dilation=(dilation, dilation),
+                   name="y", padding=(1, 1))
+    dtype = "float32"
+    dshape = (1, 3, 18, 18)
+    kshape = (10, 3, 3, 3)
+    oshape = (1, 10, 14, 14)
+    shape_dict = {"x": dshape}
+    for target, ctx in ctx_list():
+        graph, lib, _ = nnvm.compiler.build(y, target, shape_dict)
+        m = graph_runtime.create(graph, lib, ctx)
+        data = tvm.nd.array(np.random.uniform(size=dshape).astype(dtype))
+        bias = tvm.nd.array(np.random.uniform(size=kshape[0]).astype(dtype))
+        kernel_np = np.random.uniform(size=kshape).astype(dtype)
+        kernel = tvm.nd.array(kernel_np)
+        dkernel_np = topi.testing.dilate_python(kernel_np, (1, 1, dilation, dilation))
+        m.run(x=data, y_weight=kernel, y_bias=bias)
+        out = m.get_output(0, tvm.nd.empty(oshape, dtype))
+        c_np = topi.testing.conv2d_nchw_python(
+            data.asnumpy(), dkernel_np, 1, 1)
+        c_np = c_np + bias.asnumpy().reshape(kshape[0], 1, 1)
+        np.testing.assert_allclose(out.asnumpy(), c_np, rtol=1e-5)
+
+
 def test_grouped_conv2d():
     x = sym.Variable("x")
     y = sym.conv2d(x, channels=32, kernel_size=(3,3), groups=32,
@@ -170,6 +196,7 @@ def test_upsampling():
 
 if __name__ == "__main__":
     test_conv2d()
+    test_dilated_conv2d()
     test_grouped_conv2d()
     test_conv2d_transpose()
     test_max_pool2d()