800 lines
22 KiB
Python
800 lines
22 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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# flake8: noqa
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import numpy as np
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import ngraph as ng
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from ngraph.impl import AxisSet, Function, Shape, Type
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from ngraph.impl.op import Constant, Parameter
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from tests.runtime import get_runtime
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def binary_op(op_str, a, b):
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if op_str == "+":
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return a + b
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elif op_str == "Add":
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return ng.add(a, b)
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elif op_str == "-":
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return a - b
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elif op_str == "Sub":
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return ng.subtract(a, b)
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elif op_str == "*":
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return a * b
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elif op_str == "Mul":
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return ng.multiply(a, b)
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elif op_str == "/":
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return a / b
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elif op_str == "Div":
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return ng.divide(a, b)
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elif op_str == "Equal":
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return ng.equal(a, b)
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elif op_str == "Greater":
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return ng.greater(a, b)
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elif op_str == "GreaterEq":
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return ng.greater_equal(a, b)
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elif op_str == "Less":
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return ng.less(a, b)
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elif op_str == "LessEq":
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return ng.less_equal(a, b)
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elif op_str == "Maximum":
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return ng.maximum(a, b)
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elif op_str == "Minimum":
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return ng.minimum(a, b)
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elif op_str == "NotEqual":
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return ng.not_equal(a, b)
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elif op_str == "Power":
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return ng.power(a, b)
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def binary_op_ref(op_str, a, b):
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if op_str == "+" or op_str == "Add":
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return a + b
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elif op_str == "-" or op_str == "Sub":
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return a - b
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elif op_str == "*" or op_str == "Mul":
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return a * b
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elif op_str == "/" or op_str == "Div":
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return a / b
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elif op_str == "Dot":
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return np.dot(a, b)
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elif op_str == "Equal":
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return np.equal(a, b)
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elif op_str == "Greater":
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return np.greater(a, b)
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elif op_str == "GreaterEq":
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return np.greater_equal(a, b)
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elif op_str == "Less":
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return np.less(a, b)
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elif op_str == "LessEq":
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return np.less_equal(a, b)
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elif op_str == "Maximum":
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return np.maximum(a, b)
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elif op_str == "Minimum":
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return np.minimum(a, b)
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elif op_str == "NotEqual":
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return np.not_equal(a, b)
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elif op_str == "Power":
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return np.power(a, b)
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def binary_op_exec(op_str):
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element_type = Type.f32
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shape = Shape([2, 2])
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A = Parameter(element_type, shape)
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B = Parameter(element_type, shape)
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parameter_list = [A, B]
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function = Function([binary_op(op_str, A, B)], parameter_list, "test")
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a_arr = np.array([[1, 6], [7, 4]], dtype=np.float32)
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b_arr = np.array([[5, 2], [3, 8]], dtype=np.float32)
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runtime = get_runtime()
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computation = runtime.computation(function, A, B)
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result = computation(a_arr, b_arr)[0]
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expected = binary_op_ref(op_str, a_arr, b_arr)
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assert np.allclose(result, expected)
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def binary_op_comparison(op_str):
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element_type = Type.f32
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shape = Shape([2, 2])
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A = Parameter(element_type, shape)
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B = Parameter(element_type, shape)
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parameter_list = [A, B]
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function = Function([binary_op(op_str, A, B)], parameter_list, "test")
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a_arr = np.array([[1, 5], [3, 2]], dtype=np.float32)
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b_arr = np.array([[2, 4], [3, 1]], dtype=np.float32)
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runtime = get_runtime()
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computation = runtime.computation(function, A, B)
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result = computation(a_arr, b_arr)[0]
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expected = binary_op_ref(op_str, a_arr, b_arr)
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assert np.allclose(result, expected)
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def test_add():
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binary_op_exec("+")
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def test_add_op():
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binary_op_exec("Add")
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def test_sub():
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binary_op_exec("-")
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def test_sub_op():
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binary_op_exec("Sub")
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def test_mul():
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binary_op_exec("*")
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def test_mul_op():
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binary_op_exec("Mul")
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def test_div():
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binary_op_exec("/")
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def test_div_op():
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binary_op_exec("Div")
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def test_maximum():
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binary_op_exec("Maximum")
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def test_minimum():
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binary_op_exec("Minimum")
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def test_power():
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binary_op_exec("Power")
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def test_greater():
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binary_op_comparison("Greater")
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def test_greater_eq():
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binary_op_comparison("GreaterEq")
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def test_less():
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binary_op_comparison("Less")
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def test_less_eq():
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binary_op_comparison("LessEq")
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def test_not_equal():
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binary_op_comparison("NotEqual")
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def test_add_with_mul():
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element_type = Type.f32
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shape = Shape([4])
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A = Parameter(element_type, shape)
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B = Parameter(element_type, shape)
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C = Parameter(element_type, shape)
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parameter_list = [A, B, C]
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function = Function([ng.multiply(ng.add(A, B), C)], parameter_list, "test")
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runtime = get_runtime()
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computation = runtime.computation(function, A, B, C)
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result = computation(
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np.array([1, 2, 3, 4], dtype=np.float32),
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np.array([5, 6, 7, 8], dtype=np.float32),
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np.array([9, 10, 11, 12], dtype=np.float32),
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)[0]
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a_arr = np.array([1, 2, 3, 4], dtype=np.float32)
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b_arr = np.array([5, 6, 7, 8], dtype=np.float32)
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c_arr = np.array([9, 10, 11, 12], dtype=np.float32)
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result_arr_ref = (a_arr + b_arr) * c_arr
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assert np.allclose(result, result_arr_ref)
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def unary_op(op_str, a):
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if op_str == "Abs":
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return ng.abs(a)
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elif op_str == "Acos":
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return ng.acos(a)
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elif op_str == "Asin":
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return ng.asin(a)
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elif op_str == "Atan":
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return ng.atan(a)
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elif op_str == "Ceiling":
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return ng.ceiling(a)
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elif op_str == "Cos":
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return ng.cos(a)
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elif op_str == "Cosh":
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return ng.cosh(a)
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elif op_str == "Floor":
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return ng.floor(a)
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elif op_str == "log":
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return ng.log(a)
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elif op_str == "exp":
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return ng.exp(a)
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elif op_str == "negative":
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return ng.negative(a)
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elif op_str == "Reverse":
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return ng.reverse(a, np.array([1]), "index")
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elif op_str == "Sign":
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return ng.sign(a)
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elif op_str == "Sin":
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return ng.sin(a)
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elif op_str == "Sinh":
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return ng.sinh(a)
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elif op_str == "Sqrt":
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return ng.sqrt(a)
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elif op_str == "Tan":
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return ng.tan(a)
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elif op_str == "Tanh":
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return ng.tanh(a)
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def unary_op_ref(op_str, a):
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if op_str == "Abs":
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return np.abs(a)
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elif op_str == "Acos":
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return np.arccos(a)
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elif op_str == "Asin":
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return np.arcsin(a)
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elif op_str == "Atan":
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return np.arctan(a)
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elif op_str == "Ceiling":
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return np.ceil(a)
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elif op_str == "Cos":
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return np.cos(a)
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elif op_str == "Cosh":
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return np.cosh(a)
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elif op_str == "Floor":
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return np.floor(a)
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elif op_str == "log":
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return np.log(a)
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elif op_str == "exp":
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return np.exp(a)
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elif op_str == "negative":
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return np.negative(a)
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elif op_str == "Reverse":
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return np.fliplr(a)
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elif op_str == "Sign":
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return np.sign(a)
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elif op_str == "Sin":
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return np.sin(a)
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elif op_str == "Sinh":
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return np.sinh(a)
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elif op_str == "Sqrt":
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return np.sqrt(a)
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elif op_str == "Tan":
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return np.tan(a)
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elif op_str == "Tanh":
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return np.tanh(a)
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def unary_op_exec(op_str, input_list):
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"""
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input_list needs to have deep length of 4
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"""
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element_type = Type.f32
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shape = Shape(np.array(input_list).shape)
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A = Parameter(element_type, shape)
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parameter_list = [A]
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function = Function([unary_op(op_str, A)], parameter_list, "test")
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runtime = get_runtime()
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computation = runtime.computation(function, *parameter_list)
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result = computation(np.array(input_list, dtype=np.float32))[0]
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expected = unary_op_ref(op_str, np.array(input_list, dtype=np.float32))
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assert np.allclose(result, expected)
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def test_abs():
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input_list = [-1, 0, 1, 2]
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op_str = "Abs"
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unary_op_exec(op_str, input_list)
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def test_acos():
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input_list = [-1, 0, 0.5, 1]
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op_str = "Acos"
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unary_op_exec(op_str, input_list)
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def test_asin():
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input_list = [-1, 0, 0.5, 1]
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op_str = "Asin"
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unary_op_exec(op_str, input_list)
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def test_atan():
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input_list = [-1, 0, 0.5, 1]
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op_str = "Atan"
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unary_op_exec(op_str, input_list)
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def test_ceiling():
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input_list = [0.5, 0, 0.4, 0.5]
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op_str = "Ceiling"
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unary_op_exec(op_str, input_list)
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def test_cos():
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input_list = [0, 0.7, 1.7, 3.4]
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op_str = "Cos"
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unary_op_exec(op_str, input_list)
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def test_cosh():
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input_list = [-1, 0.0, 0.5, 1]
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op_str = "Cosh"
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unary_op_exec(op_str, input_list)
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def test_floor():
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input_list = [-0.5, 0, 0.4, 0.5]
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op_str = "Floor"
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unary_op_exec(op_str, input_list)
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def test_log():
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input_list = [1, 2, 3, 4]
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op_str = "log"
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unary_op_exec(op_str, input_list)
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def test_exp():
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input_list = [-1, 0, 1, 2]
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op_str = "exp"
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unary_op_exec(op_str, input_list)
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def test_negative():
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input_list = [-1, 0, 1, 2]
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op_str = "negative"
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unary_op_exec(op_str, input_list)
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def test_sign():
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input_list = [-1, 0, 0.5, 1]
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op_str = "Sign"
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unary_op_exec(op_str, input_list)
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def test_sin():
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input_list = [0, 0.7, 1.7, 3.4]
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op_str = "Sin"
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unary_op_exec(op_str, input_list)
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def test_sinh():
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input_list = [-1, 0.0, 0.5, 1]
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op_str = "Sinh"
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unary_op_exec(op_str, input_list)
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def test_sqrt():
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input_list = [0.0, 0.5, 1, 2]
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op_str = "Sqrt"
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unary_op_exec(op_str, input_list)
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def test_tan():
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input_list = [-np.pi / 4, 0, np.pi / 8, np.pi / 8]
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op_str = "Tan"
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unary_op_exec(op_str, input_list)
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def test_tanh():
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input_list = [-1, 0, 0.5, 1]
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op_str = "Tanh"
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unary_op_exec(op_str, input_list)
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def test_reverse():
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input_list = [[-1, 0], [0.5, 1]]
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op_str = "Reverse"
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unary_op_exec(op_str, input_list)
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def test_reshape():
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element_type = Type.f32
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shape = Shape([2, 3])
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A = Parameter(element_type, shape)
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parameter_list = [A]
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function = Function([ng.reshape(A, Shape([3, 2]), special_zero=False)], parameter_list, "test")
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runtime = get_runtime()
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computation = runtime.computation(function, *parameter_list)
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result = computation(np.array(np.array([[1, 2, 3], [4, 5, 6]], dtype=np.float32), dtype=np.float32))[0]
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expected = np.reshape(np.array([[1, 2, 3], [4, 5, 6]], dtype=np.float32), (3, 2))
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assert np.allclose(result, expected)
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def test_broadcast():
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element_type = Type.f32
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A = Parameter(element_type, Shape([3]))
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parameter_list = [A]
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function = Function([ng.broadcast(A, [3, 3])], parameter_list, "test")
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runtime = get_runtime()
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computation = runtime.computation(function, *parameter_list)
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result = computation(np.array([1, 2, 3], dtype=np.float32))[0]
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a_arr = np.array([[0], [0], [0]], dtype=np.float32)
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b_arr = np.array([[1, 2, 3]], dtype=np.float32)
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expected = np.add(a_arr, b_arr)
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assert np.allclose(result, expected)
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def test_constant():
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element_type = Type.f32
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parameter_list = []
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function = Function([Constant(element_type, Shape([3, 3]), list(range(9)))], parameter_list, "test")
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runtime = get_runtime()
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computation = runtime.computation(function, *parameter_list)
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result = computation()[0]
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expected = np.arange(9).reshape(3, 3)
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assert np.allclose(result, expected)
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def test_concat():
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element_type = Type.f32
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A = Parameter(element_type, Shape([1, 2]))
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B = Parameter(element_type, Shape([1, 2]))
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C = Parameter(element_type, Shape([1, 2]))
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parameter_list = [A, B, C]
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axis = 0
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function = Function([ng.concat([A, B, C], axis)], parameter_list, "test")
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a_arr = np.array([[1, 2]], dtype=np.float32)
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b_arr = np.array([[5, 6]], dtype=np.float32)
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c_arr = np.array([[7, 8]], dtype=np.float32)
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runtime = get_runtime()
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computation = runtime.computation(function, *parameter_list)
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result = computation(a_arr, b_arr, c_arr)[0]
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expected = np.concatenate((a_arr, b_arr, c_arr), axis)
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assert np.allclose(result, expected)
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def test_axisset():
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set_axisset = AxisSet({1, 2, 3})
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list_axisset = AxisSet([1, 2, 3])
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tuple_axisset = AxisSet((1, 2, 3))
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assert len(set_axisset) == 3
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assert set(set_axisset) == {1, 2, 3}
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assert len(list_axisset) == 3
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assert set(list_axisset) == set(set_axisset)
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assert len(tuple_axisset) == 3
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assert set(tuple_axisset) == set(set_axisset)
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def test_select():
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element_type = Type.f32
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A = Parameter(Type.boolean, Shape([1, 2]))
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B = Parameter(element_type, Shape([1, 2]))
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C = Parameter(element_type, Shape([1, 2]))
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parameter_list = [A, B, C]
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function = Function([ng.select(A, B, C)], parameter_list, "test")
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runtime = get_runtime()
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computation = runtime.computation(function, *parameter_list)
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result = computation(
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np.array([[True, False]], dtype=np.bool),
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np.array([[5, 6]], dtype=np.float32),
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np.array([[7, 8]], dtype=np.float32),
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)[0]
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expected = np.array([[5, 8]])
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assert np.allclose(result, expected)
|
|
|
|
|
|
def test_max_pool():
|
|
# test 1d
|
|
element_type = Type.f32
|
|
shape = Shape([1, 1, 10])
|
|
A = Parameter(element_type, shape)
|
|
parameter_list = [A]
|
|
|
|
input_arr = np.arange(10, dtype=np.float32).reshape([1, 1, 10])
|
|
window_shape = [3]
|
|
|
|
strides = [1] * len(window_shape)
|
|
pads_begin = [0] * len(window_shape)
|
|
pads_end = [0] * len(window_shape)
|
|
|
|
model = ng.max_pool(A, strides, pads_begin, pads_end, window_shape)
|
|
function = Function([model], parameter_list, "test")
|
|
|
|
runtime = get_runtime()
|
|
computation = runtime.computation(function, *parameter_list)
|
|
result = computation(input_arr)[0]
|
|
|
|
expected = (np.arange(8) + 2).reshape(1, 1, 8)
|
|
assert np.allclose(result, expected)
|
|
|
|
# test 1d with strides
|
|
strides = [2]
|
|
pads_begin = [0] * len(window_shape)
|
|
pads_end = [0] * len(window_shape)
|
|
|
|
model = ng.max_pool(A, strides, pads_begin, pads_end, window_shape)
|
|
function = Function([model], parameter_list, "test")
|
|
|
|
size = 4
|
|
computation = runtime.computation(function, *parameter_list)
|
|
result = computation(input_arr)[0]
|
|
|
|
expected = ((np.arange(size) + 1) * 2).reshape(1, 1, size)
|
|
assert np.allclose(result, expected)
|
|
|
|
# test 2d
|
|
element_type = Type.f32
|
|
shape = Shape([1, 1, 10, 10])
|
|
A = Parameter(element_type, shape)
|
|
parameter_list = [A]
|
|
|
|
input_arr = np.arange(100, dtype=np.float32).reshape(1, 1, 10, 10)
|
|
window_shape = [3, 3]
|
|
|
|
strides = [1, 1]
|
|
pads_begin = [0, 0]
|
|
pads_end = [0, 0]
|
|
|
|
model = ng.max_pool(A, strides, pads_begin, pads_end, window_shape)
|
|
function = Function([model], parameter_list, "test")
|
|
|
|
computation = runtime.computation(function, *parameter_list)
|
|
result = computation(input_arr)[0]
|
|
|
|
expected = ((np.arange(100).reshape(10, 10))[2:, 2:]).reshape(1, 1, 8, 8)
|
|
assert np.allclose(result, expected)
|
|
|
|
# test 2d with strides
|
|
strides = [2, 2]
|
|
pads_begin = [0, 0]
|
|
pads_end = [0, 0]
|
|
|
|
model = ng.max_pool(A, strides, pads_begin, pads_end, window_shape)
|
|
function = Function([model], parameter_list, "test")
|
|
computation = runtime.computation(function, *parameter_list)
|
|
result = computation(input_arr)[0]
|
|
|
|
size = 4
|
|
expected = ((np.arange(100).reshape(10, 10))[2::2, 2::2]).reshape(1, 1, size, size)
|
|
assert np.allclose(result, expected)
|
|
|
|
|
|
def convolution2d(
|
|
image,
|
|
filterit,
|
|
strides=(1, 1),
|
|
dilation=(1, 1),
|
|
padding_below=(0, 0),
|
|
padding_above=(0, 0),
|
|
data_dilation=(1, 1),
|
|
):
|
|
def dilate(arr, dil=(1, 1)):
|
|
m, n = arr.shape
|
|
new_m, new_n = (m - 1) * dil[0] + 1, (n - 1) * dil[1] + 1
|
|
new_arr = np.zeros(new_m * new_n, dtype=np.float32).reshape(new_m, new_n)
|
|
for i in range(m):
|
|
for j in range(n):
|
|
new_arr[dil[0] * i][dil[1] * j] = arr[i][j]
|
|
return new_arr
|
|
|
|
i_m, i_n = image.shape
|
|
new_image = np.zeros(
|
|
(i_m + padding_below[0] + padding_above[0]) * (i_n + padding_below[1] + padding_above[1]),
|
|
dtype=np.float32,
|
|
).reshape(i_m + padding_below[0] + padding_above[0], i_n + padding_below[1] + padding_above[1])
|
|
new_image[padding_below[0] : padding_below[0] + i_m, padding_below[1] : padding_below[1] + i_n] = image
|
|
image = new_image
|
|
image = image if data_dilation[0] == data_dilation[1] == 1 else dilate(image, data_dilation)
|
|
i_m, i_n = image.shape
|
|
|
|
filterit = filterit if dilation[0] == dilation[1] == 1 else dilate(filterit, dilation)
|
|
f_m, f_n = filterit.shape
|
|
|
|
# result_shape
|
|
r_m = i_m - f_m + 1
|
|
r_n = i_n - f_n + 1
|
|
r_m //= strides[0]
|
|
r_n //= strides[1]
|
|
|
|
result = np.zeros(r_m * r_n, dtype=np.float32).reshape(r_m, r_n)
|
|
|
|
for i in range(r_m):
|
|
for j in range(r_n):
|
|
sub_m = image[i * strides[0] : i * strides[0] + f_m, j * strides[1] : j * strides[1] + f_n]
|
|
result[i][j] = np.sum(sub_m * filterit)
|
|
return result
|
|
|
|
|
|
def test_convolution_simple():
|
|
|
|
element_type = Type.f32
|
|
image_shape = Shape([1, 1, 16, 16])
|
|
filter_shape = Shape([1, 1, 3, 3])
|
|
data = Parameter(element_type, image_shape)
|
|
filters = Parameter(element_type, filter_shape)
|
|
parameter_list = [data, filters]
|
|
|
|
image_arr = np.arange(-128, 128, 1, dtype=np.float32).reshape(1, 1, 16, 16)
|
|
filter_arr = np.ones(9, dtype=np.float32).reshape(1, 1, 3, 3)
|
|
filter_arr[0][0][0][0] = -1
|
|
filter_arr[0][0][1][1] = -1
|
|
filter_arr[0][0][2][2] = -1
|
|
filter_arr[0][0][0][2] = -1
|
|
filter_arr[0][0][2][0] = -1
|
|
|
|
strides = [1, 1]
|
|
pads_begin = [0, 0]
|
|
pads_end = [0, 0]
|
|
dilations = [1, 1]
|
|
|
|
model = ng.convolution(data, filters, strides, pads_begin, pads_end, dilations)
|
|
function = Function([model], parameter_list, "test")
|
|
|
|
runtime = get_runtime()
|
|
computation = runtime.computation(function, *parameter_list)
|
|
result = computation(image_arr, filter_arr)[0]
|
|
|
|
expected = convolution2d(image_arr[0][0], filter_arr[0][0]).reshape(1, 1, 14, 14)
|
|
assert np.allclose(result, expected)
|
|
|
|
|
|
def test_convolution_with_strides():
|
|
|
|
element_type = Type.f32
|
|
image_shape = Shape([1, 1, 10, 10])
|
|
filter_shape = Shape([1, 1, 3, 3])
|
|
data = Parameter(element_type, image_shape)
|
|
filters = Parameter(element_type, filter_shape)
|
|
parameter_list = [data, filters]
|
|
|
|
image_arr = np.arange(100, dtype=np.float32).reshape([1, 1, 10, 10])
|
|
filter_arr = np.zeros(9, dtype=np.float32).reshape([1, 1, 3, 3])
|
|
filter_arr[0][0][1][1] = 1
|
|
strides = [2, 2]
|
|
pads_begin = [0, 0]
|
|
pads_end = [0, 0]
|
|
dilations = [1, 1]
|
|
|
|
model = ng.convolution(data, filters, strides, pads_begin, pads_end, dilations)
|
|
function = Function([model], parameter_list, "test")
|
|
|
|
runtime = get_runtime()
|
|
computation = runtime.computation(function, *parameter_list)
|
|
result = computation(image_arr, filter_arr)[0]
|
|
|
|
expected = convolution2d(image_arr[0][0], filter_arr[0][0], strides).reshape(1, 1, 4, 4)
|
|
assert np.allclose(result, expected)
|
|
|
|
|
|
def test_convolution_with_filter_dilation():
|
|
|
|
element_type = Type.f32
|
|
image_shape = Shape([1, 1, 10, 10])
|
|
filter_shape = Shape([1, 1, 3, 3])
|
|
data = Parameter(element_type, image_shape)
|
|
filters = Parameter(element_type, filter_shape)
|
|
parameter_list = [data, filters]
|
|
|
|
image_arr = np.arange(100, dtype=np.float32).reshape([1, 1, 10, 10])
|
|
filter_arr = np.ones(9, dtype=np.float32).reshape([1, 1, 3, 3])
|
|
strides = [1, 1]
|
|
pads_begin = [0, 0]
|
|
pads_end = [0, 0]
|
|
dilations = [2, 2]
|
|
|
|
model = ng.convolution(data, filters, strides, pads_begin, pads_end, dilations)
|
|
function = Function([model], parameter_list, "test")
|
|
|
|
runtime = get_runtime()
|
|
computation = runtime.computation(function, *parameter_list)
|
|
result = computation(image_arr, filter_arr)[0]
|
|
|
|
expected = convolution2d(image_arr[0][0], filter_arr[0][0], strides, dilations).reshape([1, 1, 6, 6])
|
|
assert np.allclose(result, expected)
|
|
|
|
|
|
def test_convolution_with_padding():
|
|
|
|
element_type = Type.f32
|
|
image_shape = Shape([1, 1, 10, 10])
|
|
filter_shape = Shape([1, 1, 3, 3])
|
|
data = Parameter(element_type, image_shape)
|
|
filters = Parameter(element_type, filter_shape)
|
|
parameter_list = [data, filters]
|
|
|
|
image_arr = np.arange(100, dtype=np.float32).reshape(1, 1, 10, 10)
|
|
filter_arr = np.zeros(9, dtype=np.float32).reshape(1, 1, 3, 3)
|
|
filter_arr[0][0][1][1] = 1
|
|
strides = [1, 1]
|
|
dilations = [2, 2]
|
|
pads_begin = [0, 0]
|
|
pads_end = [0, 0]
|
|
|
|
model = ng.convolution(data, filters, strides, pads_begin, pads_end, dilations)
|
|
function = Function([model], parameter_list, "test")
|
|
|
|
runtime = get_runtime()
|
|
computation = runtime.computation(function, *parameter_list)
|
|
result = computation(image_arr, filter_arr)[0]
|
|
|
|
expected = convolution2d(
|
|
image_arr[0][0], filter_arr[0][0], strides, dilations, pads_begin, pads_end
|
|
).reshape([1, 1, 6, 6])
|
|
assert np.allclose(result, expected)
|
|
|
|
|
|
def test_convolution_with_non_zero_padding():
|
|
element_type = Type.f32
|
|
image_shape = Shape([1, 1, 10, 10])
|
|
filter_shape = Shape([1, 1, 3, 3])
|
|
data = Parameter(element_type, image_shape)
|
|
filters = Parameter(element_type, filter_shape)
|
|
parameter_list = [data, filters]
|
|
|
|
image_arr = np.arange(100, dtype=np.float32).reshape(1, 1, 10, 10)
|
|
filter_arr = (np.ones(9, dtype=np.float32).reshape(1, 1, 3, 3)) * -1
|
|
filter_arr[0][0][1][1] = 1
|
|
strides = [1, 1]
|
|
dilations = [2, 2]
|
|
pads_begin = [2, 1]
|
|
pads_end = [1, 2]
|
|
|
|
model = ng.convolution(data, filters, strides, pads_begin, pads_end, dilations)
|
|
function = Function([model], parameter_list, "test")
|
|
|
|
runtime = get_runtime()
|
|
computation = runtime.computation(function, *parameter_list)
|
|
result = computation(image_arr, filter_arr)[0]
|
|
|
|
expected = convolution2d(
|
|
image_arr[0][0], filter_arr[0][0], strides, dilations, pads_begin, pads_end
|
|
).reshape([1, 1, 9, 9])
|
|
assert np.allclose(result, expected)
|