[GitHub] [incubator-tvm] alexgl-github commented on a change in pull request #4476: Implement 1d deconvolution

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

alexgl-github commented on a change in pull request #4476: Implement 1d deconvolution
URL: https://github.com/apache/incubator-tvm/pull/4476#discussion_r359001149
 
 

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 File path: topi/tests/python/test_topi_conv1d_transpose_ncw.py
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 @@ -0,0 +1,84 @@
+# 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.
+"""Test code for transposed convolution."""
+import numpy as np
+import tvm
+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_conv1d_transpose_ncw(batch, in_channel, in_size, num_filter, kernel, stride, padding):
+    in_width = in_size
+    A = tvm.placeholder((batch, in_channel, in_width), name='A')
+    W = tvm.placeholder((in_channel, num_filter, kernel), name='W')
+
+    a_shape = get_const_tuple(A.shape)
+    w_shape = get_const_tuple(W.shape)
+    dtype = A.dtype
+
+    @memoize("topi.tests.test_topi_conv1d_transpose.verify_conv1d_transpose_ncw")
+    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 = topi.testing.conv1d_transpose_ncw_python(a_np, w_np, stride, padding)
+        c_np = np.maximum(b_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
+        with tvm.target.create(device):
+            B = topi.nn.conv1d_transpose_ncw(A, W, stride, padding, A.dtype)
+            C = topi.nn.relu(B)
+            s1 = topi.generic.schedule_conv1d_transpose_ncw([B])
+            s2 = topi.generic.schedule_conv1d_transpose_ncw([C])
+        a = tvm.nd.array(a_np, ctx)
+        w = tvm.nd.array(w_np, ctx)
+        b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=B.dtype), ctx)
+        c = tvm.nd.array(np.zeros(get_const_tuple(C.shape), dtype=C.dtype), ctx)
+
+        func1 = tvm.build(s1, [A, W, B], device)
+        func2 = tvm.build(s2, [A, W, C], device)
+        func1(a, w, b)
+        func2(a, w, c)
+        tvm.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5)
+        tvm.testing.assert_allclose(c.asnumpy(), c_np, rtol=1e-5)
+
+    for device in get_all_backend():
+        if device != "nvptx":
+            check_device(device)
+
+
+def test_conv1d_transpose_ncw():
+    verify_conv1d_transpose_ncw(1, 1, 1024, 1, 512, 5, 256)
+    verify_conv1d_transpose_ncw(1, 3, 224, 32, 5, 1, 0)
+    verify_conv1d_transpose_ncw(1, 3, 224, 32, 7, 1, 2)
+    verify_conv1d_transpose_ncw(1, 3, 224, 32, 5, 2, 1)
+    verify_conv1d_transpose_ncw(1, 3, 224, 32, 5, 2, 0)
+    verify_conv1d_transpose_ncw(1, 32, 32, 128, 5, 1, 0)
+    verify_conv1d_transpose_ncw(1, 32, 32, 128, 5, 2, 1)
+
 
 Review comment:
   @optima2005 Would you mind reviewing again the next revision? Thanks.

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