[GitHub] [tvm] comaniac commented on a change in pull request #6999: [TOPI] deformable_conv2d in NHWC

[GitBox <gi...@apache.org>]

comaniac commented on a change in pull request #6999:
URL: https://github.com/apache/tvm/pull/6999#discussion_r532784444



##########
File path: python/tvm/topi/testing/deformable_conv2d_python.py
##########
@@ -119,3 +119,51 @@ def _bilinear(n, c, h, w):
         b_np[n, f, h, w] += np.tensordot(a_deform[n, c, h, w], w_np[f, c])
 
     return b_np
+
+
+def deformable_conv2d_nhwc_python(
+    a_np, offset_np, w_np, stride, padding, dilation, deformable_groups, groups
+):
+    """Deformable convolution operator in NCHW layout.

Review comment:
       ```suggestion
       """Deformable convolution operator in NHWC layout.
   ```

##########
File path: python/tvm/topi/generic/nn.py
##########
@@ -462,6 +462,23 @@ def schedule_deformable_conv2d_nchw(outs):
     return _default_schedule(outs, False)
 
 
+def schedule_deformable_conv2d_nhwc(outs):
+    """Schedule for deformable_conv2d_nhwc

Review comment:
       ```suggestion
       """Schedule for deformable_conv2d_nhwc.
       We only use the default schedule here and rely on auto_scheduler.
   ```

##########
File path: tests/python/topi/python/test_topi_deformable_conv2d.py
##########
@@ -112,11 +115,94 @@ def check_device(device):
         check_device(device)
 
 
+def verify_deformable_conv2d_nhwc(
+    batch,
+    in_channel,
+    in_size,
+    num_filter,
+    kernel,
+    stride,
+    padding,
+    dilation=1,
+    deformable_groups=1,
+    groups=1,
+):
+    print(
+        "Workload: (%d, %d, %d, %d, %d, %d, %d, %d, %d, %d)"
+        % (
+            batch,
+            in_channel,
+            in_size,
+            num_filter,
+            kernel,
+            stride,
+            padding,
+            dilation,
+            deformable_groups,
+            groups,
+        )
+    )
+
+    A = te.placeholder((batch, in_size, in_size, in_channel), name="A")
+    out_size = (in_size - (kernel - 1) * dilation - 1 + 2 * padding) // stride + 1
+    Offset = te.placeholder(
+        (batch, out_size, out_size, deformable_groups * kernel * kernel * 2), name="offset"
+    )
+    W = te.placeholder((kernel, kernel, in_channel, num_filter), name="W")
+    bias = te.placeholder((num_filter,), name="bias")
+
+    a_shape = get_const_tuple(A.shape)
+    offset_shape = get_const_tuple(Offset.shape)
+    w_shape = get_const_tuple(W.shape)
+    bias_shape = get_const_tuple(bias.shape)
+    dtype = A.dtype
+
+    @memoize("topi.tests.test_topi_deformable_conv2d_nchw.verify_deformable_conv2d_nhwc")
+    def get_ref_data():
+        a_np = np.random.uniform(size=a_shape).astype(dtype)
+        offset_np = np.random.randn(*offset_shape).astype(dtype)
+        w_np = np.random.uniform(size=w_shape).astype(dtype)
+        b_np = np.random.uniform(size=bias_shape).astype(dtype)
+        c_np = tvm.topi.testing.deformable_conv2d_nhwc_python(
+            a_np, offset_np, w_np, stride, padding, dilation, deformable_groups, groups
+        )
+
+        return a_np, offset_np, w_np, c_np
+
+    a_np, offset_np, w_np, c_np = get_ref_data()
+
+    def check_device(device):
+        ctx = tvm.context(device, 0)
+        if not tvm.testing.device_enabled(device):
+            print("Skip because %s is not enabled" % device)
+            return
+        print("Running on target: %s" % device)
+        fcompute, fschedule = tvm.topi.testing.dispatch(device, _deformable_conv2d_nhwc_implement)
+        with tvm.target.Target(device):
+            C = fcompute(A, Offset, W, stride, padding, dilation, deformable_groups, groups, dtype)
+            s = fschedule([C])
+
+            a = tvm.nd.array(a_np, ctx)
+            offset = tvm.nd.array(offset_np, ctx)
+            w = tvm.nd.array(w_np, ctx)
+            c = tvm.nd.empty(c_np.shape, dtype=c_np.dtype, ctx=ctx)
+
+            func = tvm.build(s, [A, Offset, W, C], device)
+            func(a, offset, w, c)
+            tvm.testing.assert_allclose(c.asnumpy(), c_np, rtol=1e-5)
+
+    for device in ["llvm"]:

Review comment:
       Could we also check CUDA?




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