233 lines
8.0 KiB
Python
233 lines
8.0 KiB
Python
# ******************************************************************************
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# Copyright 2017-2020 Intel Corporation
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ******************************************************************************
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import numpy as np
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import ngraph as ng
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from tests.runtime import get_runtime
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from tests.test_ngraph.test_ops import convolution2d
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from tests.test_ngraph.util import run_op_node
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def test_convolution_2d():
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# input_x should have shape N(batch) x C x H x W
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input_x = np.array(
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[
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[0.0, 0.0, 5.0, 5.0, 0.0, 0.0, 0.0, 0.0, 0.0],
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[0.0, 0.0, 5.0, 5.0, 0.0, 0.0, 0.0, 0.0, 0.0],
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[0.0, 0.0, 5.0, 5.0, 0.0, 0.0, 0.0, 0.0, 0.0],
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[0.0, 0.0, 5.0, 5.0, 0.0, 0.0, 0.0, 0.0, 0.0],
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[0.0, 0.0, 5.0, 5.0, 0.0, 0.0, 0.0, 0.0, 0.0],
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[0.0, 0.0, 5.0, 5.0, 0.0, 0.0, 0.0, 0.0, 0.0],
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[0.0, 0.0, 5.0, 5.0, 0.0, 0.0, 0.0, 0.0, 0.0],
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[0.0, 0.0, 5.0, 5.0, 0.0, 0.0, 0.0, 0.0, 0.0],
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[0.0, 0.0, 5.0, 5.0, 0.0, 0.0, 0.0, 0.0, 0.0],
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],
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dtype=np.float32,
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).reshape(1, 1, 9, 9)
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# filter weights should have shape M x C x kH x kW
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input_filter = np.array([[1.0, 0.0, -1.0], [2.0, 0.0, -2.0], [1.0, 0.0, -1.0]], dtype=np.float32).reshape(
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1, 1, 3, 3
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)
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strides = np.array([1, 1])
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pads_begin = np.array([1, 1])
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pads_end = np.array([1, 1])
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dilations = np.array([1, 1])
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# convolution with padding=1 should produce 9 x 9 output:
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result = run_op_node([input_x, input_filter], ng.convolution, strides, pads_begin, pads_end, dilations)
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assert np.allclose(
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result,
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np.array(
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[
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[
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[
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[0.0, -15.0, -15.0, 15.0, 15.0, 0.0, 0.0, 0.0, 0.0],
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[0.0, -20.0, -20.0, 20.0, 20.0, 0.0, 0.0, 0.0, 0.0],
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[0.0, -20.0, -20.0, 20.0, 20.0, 0.0, 0.0, 0.0, 0.0],
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[0.0, -20.0, -20.0, 20.0, 20.0, 0.0, 0.0, 0.0, 0.0],
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[0.0, -20.0, -20.0, 20.0, 20.0, 0.0, 0.0, 0.0, 0.0],
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[0.0, -20.0, -20.0, 20.0, 20.0, 0.0, 0.0, 0.0, 0.0],
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[0.0, -20.0, -20.0, 20.0, 20.0, 0.0, 0.0, 0.0, 0.0],
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[0.0, -20.0, -20.0, 20.0, 20.0, 0.0, 0.0, 0.0, 0.0],
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[0.0, -15.0, -15.0, 15.0, 15.0, 0.0, 0.0, 0.0, 0.0],
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]
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]
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],
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dtype=np.float32,
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),
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)
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# convolution with padding=0 should produce 7 x 7 output:
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strides = np.array([1, 1])
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pads_begin = np.array([0, 0])
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pads_end = np.array([0, 0])
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dilations = np.array([1, 1])
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result = run_op_node([input_x, input_filter], ng.convolution, strides, pads_begin, pads_end, dilations)
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assert np.allclose(
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result,
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np.array(
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[
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[
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[
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[-20, -20, 20, 20, 0, 0, 0],
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[-20, -20, 20, 20, 0, 0, 0],
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[-20, -20, 20, 20, 0, 0, 0],
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[-20, -20, 20, 20, 0, 0, 0],
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[-20, -20, 20, 20, 0, 0, 0],
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[-20, -20, 20, 20, 0, 0, 0],
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[-20, -20, 20, 20, 0, 0, 0],
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]
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]
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],
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dtype=np.float32,
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),
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)
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strides = np.array([2, 2])
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pads_begin = np.array([0, 0])
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pads_end = np.array([0, 0])
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dilations = np.array([1, 1])
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# convolution with strides=2 should produce 4 x 4 output:
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result = run_op_node([input_x, input_filter], ng.convolution, strides, pads_begin, pads_end, dilations)
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assert np.allclose(
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result,
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np.array(
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[
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[
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[
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[-20.0, 20.0, 0.0, 0.0],
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[-20.0, 20.0, 0.0, 0.0],
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[-20.0, 20.0, 0.0, 0.0],
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[-20.0, 20.0, 0.0, 0.0],
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]
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]
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],
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dtype=np.float32,
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),
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)
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strides = np.array([1, 1])
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pads_begin = np.array([0, 0])
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pads_end = np.array([0, 0])
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dilations = np.array([2, 2])
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# convolution with dilation=2 should produce 5 x 5 output:
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result = run_op_node([input_x, input_filter], ng.convolution, strides, pads_begin, pads_end, dilations)
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assert np.allclose(
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result,
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np.array(
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[
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[
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[
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[0, 0, 20, 20, 0],
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[0, 0, 20, 20, 0],
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[0, 0, 20, 20, 0],
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[0, 0, 20, 20, 0],
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[0, 0, 20, 20, 0],
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]
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]
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],
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dtype=np.float32,
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),
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)
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def test_convolution_backprop_data():
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runtime = get_runtime()
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output_spatial_shape = [9, 9]
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filter_shape = [1, 1, 3, 3]
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data_shape = [1, 1, 7, 7]
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strides = [1, 1]
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data_node = ng.parameter(shape=data_shape)
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filter_node = ng.parameter(shape=filter_shape)
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output_shape_node = ng.constant(np.array(output_spatial_shape, dtype=np.int64))
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deconvolution = ng.convolution_backprop_data(data_node, filter_node, strides, output_shape_node)
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input_data = np.array(
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[
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[
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[
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[-20, -20, 20, 20, 0, 0, 0],
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[-20, -20, 20, 20, 0, 0, 0],
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[-20, -20, 20, 20, 0, 0, 0],
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[-20, -20, 20, 20, 0, 0, 0],
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[-20, -20, 20, 20, 0, 0, 0],
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[-20, -20, 20, 20, 0, 0, 0],
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[-20, -20, 20, 20, 0, 0, 0],
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]
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]
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],
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dtype=np.float32,
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)
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filter_data = np.array([[1.0, 0.0, -1.0], [2.0, 0.0, -2.0], [1.0, 0.0, -1.0]], dtype=np.float32).reshape(
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1, 1, 3, 3
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)
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model = runtime.computation(deconvolution, data_node, filter_node)
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result = model(input_data, filter_data)
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assert np.allclose(
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result,
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np.array(
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[
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[
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[
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[-20.0, -20.0, 40.0, 40.0, -20.0, -20.0, 0.0, 0.0, 0.0],
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[-60.0, -60.0, 120.0, 120.0, -60.0, -60.0, 0.0, 0.0, 0.0],
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[-80.0, -80.0, 160.0, 160.0, -80.0, -80.0, 0.0, 0.0, 0.0],
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[-80.0, -80.0, 160.0, 160.0, -80.0, -80.0, 0.0, 0.0, 0.0],
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[-80.0, -80.0, 160.0, 160.0, -80.0, -80.0, 0.0, 0.0, 0.0],
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[-80.0, -80.0, 160.0, 160.0, -80.0, -80.0, 0.0, 0.0, 0.0],
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[-80.0, -80.0, 160.0, 160.0, -80.0, -80.0, 0.0, 0.0, 0.0],
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[-60.0, -60.0, 120.0, 120.0, -60.0, -60.0, 0.0, 0.0, 0.0],
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[-20.0, -20.0, 40.0, 40.0, -20.0, -20.0, 0.0, 0.0, 0.0],
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]
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]
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],
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dtype=np.float32,
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),
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)
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def test_convolution_v1():
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input_tensor = np.arange(-128, 128, 1, dtype=np.float32).reshape(1, 1, 16, 16)
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filters = np.ones(9, dtype=np.float32).reshape(1, 1, 3, 3)
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filters[0, 0, 0, 0] = -1
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filters[0, 0, 1, 1] = -1
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filters[0, 0, 2, 2] = -1
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filters[0, 0, 0, 2] = -1
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filters[0, 0, 2, 0] = -1
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strides = np.array([1, 1])
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pads_begin = np.array([0, 0])
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pads_end = np.array([0, 0])
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dilations = np.array([1, 1])
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result = run_op_node([input_tensor, filters], ng.convolution, strides, pads_begin, pads_end, dilations)
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expected = convolution2d(input_tensor[0, 0], filters[0, 0]).reshape(1, 1, 14, 14)
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assert np.allclose(result, expected)
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