You are viewing a plain text version of this content. The canonical link for it is here.
Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2022/04/29 16:11:31 UTC
[GitHub] [tvm] mehrdadh commented on a diff in pull request #11175: [Hexagon] Add schedule and test for conv2d_transpose_nchw
mehrdadh commented on code in PR #11175:
URL: https://github.com/apache/tvm/pull/11175#discussion_r861946901
##########
tests/python/contrib/test_hexagon/topi/test_conv2d_transpose.py:
##########
@@ -0,0 +1,177 @@
+# 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 tvm.testing
+from tvm import te
+from tvm import topi
+import tvm.topi.testing
+from tvm.contrib.pickle_memoize import memoize
+from tvm.topi.utils import get_const_tuple
+from ..conftest import requires_hexagon_toolchain
+
+
+# TODO Should add kernal to tvm.testing.fixture
+
+random_seed = tvm.testing.parameter(0)
+
+
+@tvm.testing.fixture
+def shift_shape(batch):
+ return batch
+
+
+@tvm.testing.fixture
+def shift_shape(in_channel):
+ return in_channel
+
+
+@tvm.testing.fixture
+def shift_shape(in_size):
+ return in_size
+
+
+@tvm.testing.fixture
+def shift_shape(num_filter):
+ return num_filter
+
+
+@tvm.testing.fixture
+def shift_shape(stride):
+ return stride
+
+
+@tvm.testing.fixture
+def shift_shape(padding):
+ return padding
+
+
+@tvm.testing.fixture
+def shift_shape(output_padding):
+ return output_padding
+
+
+class BaseConv2DTransposeTests:
+ @requires_hexagon_toolchain
+ def test_conv2d(
+ self,
+ hexagon_session,
+ batch,
+ in_channel,
+ in_size,
+ num_filter,
+ stride,
+ padding,
+ output_padding,
+ random_seed,
+ ):
+
+ target_hexagon = tvm.target.hexagon("v68")
+
+ in_height, in_width = in_size
+ kernel_height, kernel_width = (1, 1)
+ stride_height, stride_width = stride
+ pad_top, pad_left, pad_bottom, pad_right = padding
+
+ A = te.placeholder((batch, in_channel, in_height, in_width), name="A")
+ W = te.placeholder((in_channel, num_filter, kernel_height, kernel_width), name="W")
+
+ a_shape = get_const_tuple(A.shape)
+ w_shape = get_const_tuple(W.shape)
+ dtype = A.dtype
+
+ def get_ref_data():
+
+ np.random.seed(random_seed)
+ a_np = np.random.uniform(size=a_shape).astype(dtype)
+ w_np = np.random.uniform(size=w_shape).astype(dtype)
+ b_np = tvm.topi.testing.conv2d_transpose_nchw_python(
+ a_np, w_np, stride, padding, output_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()
+
+ fcompute_args = (
+ A,
+ W,
+ [stride_height, stride_width],
+ [pad_top, pad_left, pad_bottom, pad_right],
+ A.dtype,
+ output_padding,
+ )
+
+ with tvm.target.Target(target_hexagon):
+ fcompute = topi.nn.conv2d_transpose_nchw
+ fschedule = topi.hexagon.schedule_conv2d_transpose_nchw
+ B = fcompute(*fcompute_args)
+ C = topi.nn.relu(B)
+ s1 = fschedule([B])
+ s2 = fschedule([C])
+
+ dev = hexagon_session.device
+
+ a = tvm.nd.array(a_np, dev)
+ w = tvm.nd.array(w_np, dev)
+ b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=B.dtype), dev)
+ c = tvm.nd.array(np.zeros(get_const_tuple(C.shape), dtype=C.dtype), dev)
+
+ func1 = tvm.build(s1, [A, W, B], tvm.target.Target(target_hexagon, host=target_hexagon))
+ func2 = tvm.build(s2, [A, W, C], tvm.target.Target(target_hexagon, host=target_hexagon))
+
+ mod1 = hexagon_session.load_module(func1)
+ mod2 = hexagon_session.load_module(func2)
+
+ mod1(a, w, b)
+ mod2(a, w, c)
+ tvm.testing.assert_allclose(b.numpy(), b_np, rtol=1e-5)
+ tvm.testing.assert_allclose(c.numpy(), c_np, rtol=1e-5)
+
+
+class TestConv2DTranspose_1(BaseConv2DTransposeTests):
Review Comment:
also rename the class to `TestConv2DTranspose`
##########
tests/python/contrib/test_hexagon/topi/test_conv2d_transpose.py:
##########
@@ -0,0 +1,177 @@
+# 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 tvm.testing
+from tvm import te
+from tvm import topi
+import tvm.topi.testing
+from tvm.contrib.pickle_memoize import memoize
+from tvm.topi.utils import get_const_tuple
+from ..conftest import requires_hexagon_toolchain
+
+
+# TODO Should add kernal to tvm.testing.fixture
+
+random_seed = tvm.testing.parameter(0)
+
+
+@tvm.testing.fixture
+def shift_shape(batch):
+ return batch
+
+
+@tvm.testing.fixture
+def shift_shape(in_channel):
+ return in_channel
+
+
+@tvm.testing.fixture
+def shift_shape(in_size):
+ return in_size
+
+
+@tvm.testing.fixture
+def shift_shape(num_filter):
+ return num_filter
+
+
+@tvm.testing.fixture
+def shift_shape(stride):
+ return stride
+
+
+@tvm.testing.fixture
+def shift_shape(padding):
+ return padding
+
+
+@tvm.testing.fixture
+def shift_shape(output_padding):
+ return output_padding
+
+
+class BaseConv2DTransposeTests:
+ @requires_hexagon_toolchain
+ def test_conv2d(
+ self,
+ hexagon_session,
+ batch,
+ in_channel,
+ in_size,
+ num_filter,
+ stride,
+ padding,
+ output_padding,
+ random_seed,
+ ):
+
+ target_hexagon = tvm.target.hexagon("v68")
+
+ in_height, in_width = in_size
+ kernel_height, kernel_width = (1, 1)
+ stride_height, stride_width = stride
+ pad_top, pad_left, pad_bottom, pad_right = padding
+
+ A = te.placeholder((batch, in_channel, in_height, in_width), name="A")
+ W = te.placeholder((in_channel, num_filter, kernel_height, kernel_width), name="W")
+
+ a_shape = get_const_tuple(A.shape)
+ w_shape = get_const_tuple(W.shape)
+ dtype = A.dtype
+
+ def get_ref_data():
+
+ np.random.seed(random_seed)
+ a_np = np.random.uniform(size=a_shape).astype(dtype)
+ w_np = np.random.uniform(size=w_shape).astype(dtype)
+ b_np = tvm.topi.testing.conv2d_transpose_nchw_python(
+ a_np, w_np, stride, padding, output_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()
+
+ fcompute_args = (
+ A,
+ W,
+ [stride_height, stride_width],
+ [pad_top, pad_left, pad_bottom, pad_right],
+ A.dtype,
+ output_padding,
+ )
+
+ with tvm.target.Target(target_hexagon):
+ fcompute = topi.nn.conv2d_transpose_nchw
+ fschedule = topi.hexagon.schedule_conv2d_transpose_nchw
+ B = fcompute(*fcompute_args)
+ C = topi.nn.relu(B)
+ s1 = fschedule([B])
+ s2 = fschedule([C])
+
+ dev = hexagon_session.device
+
+ a = tvm.nd.array(a_np, dev)
+ w = tvm.nd.array(w_np, dev)
+ b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=B.dtype), dev)
+ c = tvm.nd.array(np.zeros(get_const_tuple(C.shape), dtype=C.dtype), dev)
+
+ func1 = tvm.build(s1, [A, W, B], tvm.target.Target(target_hexagon, host=target_hexagon))
+ func2 = tvm.build(s2, [A, W, C], tvm.target.Target(target_hexagon, host=target_hexagon))
+
+ mod1 = hexagon_session.load_module(func1)
+ mod2 = hexagon_session.load_module(func2)
+
+ mod1(a, w, b)
+ mod2(a, w, c)
+ tvm.testing.assert_allclose(b.numpy(), b_np, rtol=1e-5)
+ tvm.testing.assert_allclose(c.numpy(), c_np, rtol=1e-5)
+
+
+class TestConv2DTranspose_1(BaseConv2DTransposeTests):
Review Comment:
can you combine these three classes using this patter:
batch, in_channel, ... = tvm.testing.parameters((1, (3, 8), ...)
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
To unsubscribe, e-mail: commits-unsubscribe@tvm.apache.org
For queries about this service, please contact Infrastructure at:
users@infra.apache.org