[Eye-9] Python API for Eye-9 (#11552)
This commit is contained in:
Katarzyna Mitrus
GitHub
parent
2d0ffd8fe5
commit
50287625d7
@ -17,170 +17,171 @@ from ngraph.impl import Node
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from ngraph.impl import PartialShape
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from ngraph.helpers import function_from_cnn
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from ngraph.helpers import function_to_cnn
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from ngraph.opset8 import absolute
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from ngraph.opset8 import absolute as abs
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from ngraph.opset8 import acos
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from ngraph.opset8 import acosh
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from ngraph.opset8 import adaptive_avg_pool
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from ngraph.opset8 import adaptive_max_pool
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from ngraph.opset8 import add
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from ngraph.opset8 import asin
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from ngraph.opset8 import asinh
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from ngraph.opset8 import assign
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from ngraph.opset8 import atan
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from ngraph.opset8 import atanh
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from ngraph.opset8 import avg_pool
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from ngraph.opset8 import batch_norm_inference
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from ngraph.opset8 import batch_to_space
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from ngraph.opset8 import binary_convolution
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from ngraph.opset8 import broadcast
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from ngraph.opset8 import bucketize
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from ngraph.opset8 import ceiling
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from ngraph.opset8 import ceiling as ceil
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from ngraph.opset8 import clamp
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from ngraph.opset8 import concat
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from ngraph.opset8 import constant
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from ngraph.opset8 import convert
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from ngraph.opset8 import convert_like
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from ngraph.opset8 import convolution
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from ngraph.opset8 import convolution_backprop_data
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from ngraph.opset8 import cos
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from ngraph.opset8 import cosh
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from ngraph.opset8 import ctc_greedy_decoder
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from ngraph.opset8 import ctc_greedy_decoder_seq_len
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from ngraph.opset8 import ctc_loss
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from ngraph.opset8 import cum_sum
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from ngraph.opset8 import cum_sum as cumsum
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from ngraph.opset8 import deformable_convolution
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from ngraph.opset8 import deformable_psroi_pooling
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from ngraph.opset8 import depth_to_space
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from ngraph.opset8 import detection_output
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from ngraph.opset8 import dft
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from ngraph.opset8 import divide
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from ngraph.opset8 import einsum
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from ngraph.opset8 import elu
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from ngraph.opset8 import embedding_bag_offsets_sum
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from ngraph.opset8 import embedding_bag_packed_sum
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from ngraph.opset8 import embedding_segments_sum
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from ngraph.opset8 import extract_image_patches
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from ngraph.opset8 import equal
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from ngraph.opset8 import erf
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from ngraph.opset8 import exp
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from ngraph.opset8 import fake_quantize
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from ngraph.opset8 import floor
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from ngraph.opset8 import floor_mod
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from ngraph.opset8 import gather
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from ngraph.opset8 import gather_elements
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from ngraph.opset8 import gather_nd
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from ngraph.opset8 import gather_tree
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from ngraph.opset8 import gelu
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from ngraph.opset8 import greater
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from ngraph.opset8 import greater_equal
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from ngraph.opset8 import grn
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from ngraph.opset8 import group_convolution
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from ngraph.opset8 import group_convolution_backprop_data
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from ngraph.opset8 import gru_cell
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from ngraph.opset8 import gru_sequence
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from ngraph.opset8 import hard_sigmoid
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from ngraph.opset8 import hsigmoid
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from ngraph.opset8 import hswish
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from ngraph.opset8 import idft
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from ngraph.opset8 import if_op
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from ngraph.opset8 import interpolate
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from ngraph.opset8 import i420_to_bgr
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from ngraph.opset8 import i420_to_rgb
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from ngraph.opset8 import less
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from ngraph.opset8 import less_equal
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from ngraph.opset8 import log
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from ngraph.opset8 import logical_and
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from ngraph.opset8 import logical_not
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from ngraph.opset8 import logical_or
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from ngraph.opset8 import logical_xor
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from ngraph.opset8 import log_softmax
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from ngraph.opset8 import loop
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from ngraph.opset8 import lrn
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from ngraph.opset8 import lstm_cell
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from ngraph.opset8 import lstm_sequence
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from ngraph.opset8 import matmul
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from ngraph.opset8 import matrix_nms
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from ngraph.opset8 import max_pool
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from ngraph.opset8 import maximum
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from ngraph.opset8 import minimum
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from ngraph.opset8 import mish
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from ngraph.opset8 import mod
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from ngraph.opset8 import multiclass_nms
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from ngraph.opset8 import multiply
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from ngraph.opset8 import mvn
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from ngraph.opset8 import negative
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from ngraph.opset8 import non_max_suppression
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from ngraph.opset8 import non_zero
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from ngraph.opset8 import normalize_l2
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from ngraph.opset8 import not_equal
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from ngraph.opset8 import nv12_to_bgr
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from ngraph.opset8 import nv12_to_rgb
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from ngraph.opset8 import one_hot
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from ngraph.opset8 import pad
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from ngraph.opset8 import parameter
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from ngraph.opset8 import power
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from ngraph.opset8 import prelu
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from ngraph.opset8 import prior_box
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from ngraph.opset8 import prior_box_clustered
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from ngraph.opset8 import psroi_pooling
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from ngraph.opset8 import proposal
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from ngraph.opset8 import random_uniform
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from ngraph.opset8 import range
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from ngraph.opset8 import read_value
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from ngraph.opset8 import reduce_l1
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from ngraph.opset8 import reduce_l2
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from ngraph.opset8 import reduce_logical_and
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from ngraph.opset8 import reduce_logical_or
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from ngraph.opset8 import reduce_max
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from ngraph.opset8 import reduce_mean
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from ngraph.opset8 import reduce_min
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from ngraph.opset8 import reduce_prod
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from ngraph.opset8 import reduce_sum
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from ngraph.opset8 import region_yolo
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from ngraph.opset8 import reorg_yolo
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from ngraph.opset8 import relu
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from ngraph.opset8 import reshape
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from ngraph.opset8 import result
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from ngraph.opset8 import reverse_sequence
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from ngraph.opset8 import rnn_cell
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from ngraph.opset8 import rnn_sequence
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from ngraph.opset8 import roi_align
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from ngraph.opset8 import roi_pooling
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from ngraph.opset8 import roll
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from ngraph.opset8 import round
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from ngraph.opset8 import scatter_elements_update
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from ngraph.opset8 import scatter_update
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from ngraph.opset8 import select
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from ngraph.opset8 import selu
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from ngraph.opset8 import shape_of
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from ngraph.opset8 import shuffle_channels
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from ngraph.opset8 import sigmoid
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from ngraph.opset8 import sign
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from ngraph.opset8 import sin
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from ngraph.opset8 import sinh
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from ngraph.opset8 import slice
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from ngraph.opset8 import softmax
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from ngraph.opset8 import softplus
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from ngraph.opset8 import space_to_batch
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from ngraph.opset8 import space_to_depth
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from ngraph.opset8 import split
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from ngraph.opset8 import sqrt
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from ngraph.opset8 import squared_difference
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from ngraph.opset8 import squeeze
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from ngraph.opset8 import strided_slice
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from ngraph.opset8 import subtract
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from ngraph.opset8 import swish
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from ngraph.opset8 import tan
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from ngraph.opset8 import tanh
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from ngraph.opset8 import tensor_iterator
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from ngraph.opset8 import tile
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from ngraph.opset8 import topk
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from ngraph.opset8 import transpose
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from ngraph.opset8 import unsqueeze
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from ngraph.opset8 import variadic_split
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from ngraph.opset9 import absolute
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from ngraph.opset9 import absolute as abs
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from ngraph.opset9 import acos
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from ngraph.opset9 import acosh
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from ngraph.opset9 import adaptive_avg_pool
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from ngraph.opset9 import adaptive_max_pool
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from ngraph.opset9 import add
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from ngraph.opset9 import asin
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from ngraph.opset9 import asinh
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from ngraph.opset9 import assign
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from ngraph.opset9 import atan
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from ngraph.opset9 import atanh
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from ngraph.opset9 import avg_pool
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from ngraph.opset9 import batch_norm_inference
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from ngraph.opset9 import batch_to_space
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from ngraph.opset9 import binary_convolution
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from ngraph.opset9 import broadcast
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from ngraph.opset9 import bucketize
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from ngraph.opset9 import ceiling
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from ngraph.opset9 import ceiling as ceil
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from ngraph.opset9 import clamp
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from ngraph.opset9 import concat
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from ngraph.opset9 import constant
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from ngraph.opset9 import convert
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from ngraph.opset9 import convert_like
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from ngraph.opset9 import convolution
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from ngraph.opset9 import convolution_backprop_data
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from ngraph.opset9 import cos
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from ngraph.opset9 import cosh
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from ngraph.opset9 import ctc_greedy_decoder
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from ngraph.opset9 import ctc_greedy_decoder_seq_len
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from ngraph.opset9 import ctc_loss
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from ngraph.opset9 import cum_sum
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from ngraph.opset9 import cum_sum as cumsum
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from ngraph.opset9 import deformable_convolution
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from ngraph.opset9 import deformable_psroi_pooling
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from ngraph.opset9 import depth_to_space
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from ngraph.opset9 import detection_output
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from ngraph.opset9 import dft
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from ngraph.opset9 import divide
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from ngraph.opset9 import einsum
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from ngraph.opset9 import elu
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from ngraph.opset9 import embedding_bag_offsets_sum
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from ngraph.opset9 import embedding_bag_packed_sum
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from ngraph.opset9 import embedding_segments_sum
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from ngraph.opset9 import extract_image_patches
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from ngraph.opset9 import equal
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from ngraph.opset9 import erf
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from ngraph.opset9 import exp
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from ngraph.opset9 import eye
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from ngraph.opset9 import fake_quantize
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from ngraph.opset9 import floor
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from ngraph.opset9 import floor_mod
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from ngraph.opset9 import gather
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from ngraph.opset9 import gather_elements
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from ngraph.opset9 import gather_nd
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from ngraph.opset9 import gather_tree
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from ngraph.opset9 import gelu
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from ngraph.opset9 import greater
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from ngraph.opset9 import greater_equal
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from ngraph.opset9 import grn
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from ngraph.opset9 import group_convolution
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from ngraph.opset9 import group_convolution_backprop_data
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from ngraph.opset9 import gru_cell
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from ngraph.opset9 import gru_sequence
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from ngraph.opset9 import hard_sigmoid
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from ngraph.opset9 import hsigmoid
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from ngraph.opset9 import hswish
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from ngraph.opset9 import idft
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from ngraph.opset9 import if_op
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from ngraph.opset9 import interpolate
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from ngraph.opset9 import i420_to_bgr
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from ngraph.opset9 import i420_to_rgb
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from ngraph.opset9 import less
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from ngraph.opset9 import less_equal
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from ngraph.opset9 import log
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from ngraph.opset9 import logical_and
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from ngraph.opset9 import logical_not
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from ngraph.opset9 import logical_or
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from ngraph.opset9 import logical_xor
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from ngraph.opset9 import log_softmax
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from ngraph.opset9 import loop
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from ngraph.opset9 import lrn
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from ngraph.opset9 import lstm_cell
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from ngraph.opset9 import lstm_sequence
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from ngraph.opset9 import matmul
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from ngraph.opset9 import matrix_nms
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from ngraph.opset9 import max_pool
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from ngraph.opset9 import maximum
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from ngraph.opset9 import minimum
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from ngraph.opset9 import mish
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from ngraph.opset9 import mod
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from ngraph.opset9 import multiclass_nms
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from ngraph.opset9 import multiply
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from ngraph.opset9 import mvn
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from ngraph.opset9 import negative
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from ngraph.opset9 import non_max_suppression
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from ngraph.opset9 import non_zero
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from ngraph.opset9 import normalize_l2
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from ngraph.opset9 import not_equal
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from ngraph.opset9 import nv12_to_bgr
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from ngraph.opset9 import nv12_to_rgb
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from ngraph.opset9 import one_hot
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from ngraph.opset9 import pad
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from ngraph.opset9 import parameter
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from ngraph.opset9 import power
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from ngraph.opset9 import prelu
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from ngraph.opset9 import prior_box
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from ngraph.opset9 import prior_box_clustered
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from ngraph.opset9 import psroi_pooling
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from ngraph.opset9 import proposal
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from ngraph.opset9 import random_uniform
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from ngraph.opset9 import range
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from ngraph.opset9 import read_value
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from ngraph.opset9 import reduce_l1
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from ngraph.opset9 import reduce_l2
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from ngraph.opset9 import reduce_logical_and
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from ngraph.opset9 import reduce_logical_or
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from ngraph.opset9 import reduce_max
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from ngraph.opset9 import reduce_mean
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from ngraph.opset9 import reduce_min
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from ngraph.opset9 import reduce_prod
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from ngraph.opset9 import reduce_sum
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from ngraph.opset9 import region_yolo
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from ngraph.opset9 import reorg_yolo
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from ngraph.opset9 import relu
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from ngraph.opset9 import reshape
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from ngraph.opset9 import result
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from ngraph.opset9 import reverse_sequence
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from ngraph.opset9 import rnn_cell
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from ngraph.opset9 import rnn_sequence
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from ngraph.opset9 import roi_align
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from ngraph.opset9 import roi_pooling
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from ngraph.opset9 import roll
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from ngraph.opset9 import round
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from ngraph.opset9 import scatter_elements_update
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from ngraph.opset9 import scatter_update
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from ngraph.opset9 import select
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from ngraph.opset9 import selu
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from ngraph.opset9 import shape_of
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from ngraph.opset9 import shuffle_channels
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from ngraph.opset9 import sigmoid
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from ngraph.opset9 import sign
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from ngraph.opset9 import sin
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from ngraph.opset9 import sinh
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from ngraph.opset9 import slice
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from ngraph.opset9 import softmax
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from ngraph.opset9 import softplus
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from ngraph.opset9 import space_to_batch
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from ngraph.opset9 import space_to_depth
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from ngraph.opset9 import split
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from ngraph.opset9 import sqrt
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from ngraph.opset9 import squared_difference
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from ngraph.opset9 import squeeze
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from ngraph.opset9 import strided_slice
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from ngraph.opset9 import subtract
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from ngraph.opset9 import swish
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from ngraph.opset9 import tan
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from ngraph.opset9 import tanh
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from ngraph.opset9 import tensor_iterator
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from ngraph.opset9 import tile
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from ngraph.opset9 import topk
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from ngraph.opset9 import transpose
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from ngraph.opset9 import unsqueeze
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from ngraph.opset9 import variadic_split
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# Extend Node class to support binary operators
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168
src/bindings/python/src/compatibility/ngraph/opset9/__init__.py
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168
src/bindings/python/src/compatibility/ngraph/opset9/__init__.py
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@ -0,0 +1,168 @@
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# Copyright (C) 2018-2022 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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from ngraph.opset1.ops import absolute
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from ngraph.opset1.ops import absolute as abs
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from ngraph.opset1.ops import acos
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from ngraph.opset4.ops import acosh
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from ngraph.opset8.ops import adaptive_avg_pool
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from ngraph.opset8.ops import adaptive_max_pool
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from ngraph.opset1.ops import add
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from ngraph.opset1.ops import asin
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from ngraph.opset4.ops import asinh
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from ngraph.opset3.ops import assign
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from ngraph.opset1.ops import atan
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from ngraph.opset4.ops import atanh
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from ngraph.opset1.ops import avg_pool
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from ngraph.opset5.ops import batch_norm_inference
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from ngraph.opset2.ops import batch_to_space
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from ngraph.opset1.ops import binary_convolution
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from ngraph.opset3.ops import broadcast
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from ngraph.opset3.ops import bucketize
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from ngraph.opset1.ops import ceiling
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from ngraph.opset1.ops import ceiling as ceil
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from ngraph.opset1.ops import clamp
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from ngraph.opset1.ops import concat
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from ngraph.opset1.ops import constant
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from ngraph.opset1.ops import convert
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from ngraph.opset1.ops import convert_like
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from ngraph.opset1.ops import convolution
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from ngraph.opset1.ops import convolution_backprop_data
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from ngraph.opset1.ops import cos
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from ngraph.opset1.ops import cosh
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from ngraph.opset1.ops import ctc_greedy_decoder
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from ngraph.opset6.ops import ctc_greedy_decoder_seq_len
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from ngraph.opset4.ops import ctc_loss
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from ngraph.opset3.ops import cum_sum
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from ngraph.opset3.ops import cum_sum as cumsum
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from ngraph.opset8.ops import deformable_convolution
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from ngraph.opset1.ops import deformable_psroi_pooling
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from ngraph.opset1.ops import depth_to_space
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from ngraph.opset8.ops import detection_output
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from ngraph.opset7.ops import dft
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from ngraph.opset1.ops import divide
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from ngraph.opset7.ops import einsum
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from ngraph.opset1.ops import elu
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from ngraph.opset3.ops import embedding_bag_offsets_sum
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from ngraph.opset3.ops import embedding_bag_packed_sum
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from ngraph.opset3.ops import embedding_segments_sum
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from ngraph.opset3.ops import extract_image_patches
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from ngraph.opset1.ops import equal
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from ngraph.opset1.ops import erf
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from ngraph.opset1.ops import exp
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from ngraph.opset9.ops import eye
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from ngraph.opset1.ops import fake_quantize
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from ngraph.opset1.ops import floor
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from ngraph.opset1.ops import floor_mod
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from ngraph.opset8.ops import gather
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from ngraph.opset6.ops import gather_elements
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from ngraph.opset8.ops import gather_nd
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from ngraph.opset1.ops import gather_tree
|
||||
from ngraph.opset7.ops import gelu
|
||||
from ngraph.opset1.ops import greater
|
||||
from ngraph.opset1.ops import greater_equal
|
||||
from ngraph.opset1.ops import grn
|
||||
from ngraph.opset1.ops import group_convolution
|
||||
from ngraph.opset1.ops import group_convolution_backprop_data
|
||||
from ngraph.opset3.ops import gru_cell
|
||||
from ngraph.opset5.ops import gru_sequence
|
||||
from ngraph.opset1.ops import hard_sigmoid
|
||||
from ngraph.opset5.ops import hsigmoid
|
||||
from ngraph.opset4.ops import hswish
|
||||
from ngraph.opset7.ops import idft
|
||||
from ngraph.opset8.ops import if_op
|
||||
from ngraph.opset1.ops import interpolate
|
||||
from ngraph.opset8.ops import i420_to_bgr
|
||||
from ngraph.opset8.ops import i420_to_rgb
|
||||
from ngraph.opset1.ops import less
|
||||
from ngraph.opset1.ops import less_equal
|
||||
from ngraph.opset1.ops import log
|
||||
from ngraph.opset1.ops import logical_and
|
||||
from ngraph.opset1.ops import logical_not
|
||||
from ngraph.opset1.ops import logical_or
|
||||
from ngraph.opset1.ops import logical_xor
|
||||
from ngraph.opset5.ops import log_softmax
|
||||
from ngraph.opset5.ops import loop
|
||||
from ngraph.opset1.ops import lrn
|
||||
from ngraph.opset4.ops import lstm_cell
|
||||
from ngraph.opset5.ops import lstm_sequence
|
||||
from ngraph.opset1.ops import matmul
|
||||
from ngraph.opset8.ops import matrix_nms
|
||||
from ngraph.opset8.ops import max_pool
|
||||
from ngraph.opset1.ops import maximum
|
||||
from ngraph.opset1.ops import minimum
|
||||
from ngraph.opset4.ops import mish
|
||||
from ngraph.opset1.ops import mod
|
||||
from ngraph.opset8.ops import multiclass_nms
|
||||
from ngraph.opset1.ops import multiply
|
||||
from ngraph.opset6.ops import mvn
|
||||
from ngraph.opset1.ops import negative
|
||||
from ngraph.opset5.ops import non_max_suppression
|
||||
from ngraph.opset3.ops import non_zero
|
||||
from ngraph.opset1.ops import normalize_l2
|
||||
from ngraph.opset1.ops import not_equal
|
||||
from ngraph.opset8.ops import nv12_to_bgr
|
||||
from ngraph.opset8.ops import nv12_to_rgb
|
||||
from ngraph.opset1.ops import one_hot
|
||||
from ngraph.opset1.ops import pad
|
||||
from ngraph.opset1.ops import parameter
|
||||
from ngraph.opset1.ops import power
|
||||
from ngraph.opset1.ops import prelu
|
||||
from ngraph.opset8.ops import prior_box
|
||||
from ngraph.opset1.ops import prior_box_clustered
|
||||
from ngraph.opset1.ops import psroi_pooling
|
||||
from ngraph.opset4.ops import proposal
|
||||
from ngraph.opset8.ops import random_uniform
|
||||
from ngraph.opset1.ops import range
|
||||
from ngraph.opset3.ops import read_value
|
||||
from ngraph.opset4.ops import reduce_l1
|
||||
from ngraph.opset4.ops import reduce_l2
|
||||
from ngraph.opset1.ops import reduce_logical_and
|
||||
from ngraph.opset1.ops import reduce_logical_or
|
||||
from ngraph.opset1.ops import reduce_max
|
||||
from ngraph.opset1.ops import reduce_mean
|
||||
from ngraph.opset1.ops import reduce_min
|
||||
from ngraph.opset1.ops import reduce_prod
|
||||
from ngraph.opset1.ops import reduce_sum
|
||||
from ngraph.opset1.ops import region_yolo
|
||||
from ngraph.opset2.ops import reorg_yolo
|
||||
from ngraph.opset1.ops import relu
|
||||
from ngraph.opset1.ops import reshape
|
||||
from ngraph.opset1.ops import result
|
||||
from ngraph.opset1.ops import reverse_sequence
|
||||
from ngraph.opset3.ops import rnn_cell
|
||||
from ngraph.opset5.ops import rnn_sequence
|
||||
from ngraph.opset3.ops import roi_align
|
||||
from ngraph.opset2.ops import roi_pooling
|
||||
from ngraph.opset7.ops import roll
|
||||
from ngraph.opset5.ops import round
|
||||
from ngraph.opset3.ops import scatter_elements_update
|
||||
from ngraph.opset3.ops import scatter_update
|
||||
from ngraph.opset1.ops import select
|
||||
from ngraph.opset1.ops import selu
|
||||
from ngraph.opset3.ops import shape_of
|
||||
from ngraph.opset3.ops import shuffle_channels
|
||||
from ngraph.opset1.ops import sigmoid
|
||||
from ngraph.opset1.ops import sign
|
||||
from ngraph.opset1.ops import sin
|
||||
from ngraph.opset1.ops import sinh
|
||||
from ngraph.opset8.ops import slice
|
||||
from ngraph.opset8.ops import softmax
|
||||
from ngraph.opset4.ops import softplus
|
||||
from ngraph.opset2.ops import space_to_batch
|
||||
from ngraph.opset1.ops import space_to_depth
|
||||
from ngraph.opset1.ops import split
|
||||
from ngraph.opset1.ops import sqrt
|
||||
from ngraph.opset1.ops import squared_difference
|
||||
from ngraph.opset1.ops import squeeze
|
||||
from ngraph.opset1.ops import strided_slice
|
||||
from ngraph.opset1.ops import subtract
|
||||
from ngraph.opset4.ops import swish
|
||||
from ngraph.opset1.ops import tan
|
||||
from ngraph.opset1.ops import tanh
|
||||
from ngraph.opset1.ops import tensor_iterator
|
||||
from ngraph.opset1.ops import tile
|
||||
from ngraph.opset3.ops import topk
|
||||
from ngraph.opset1.ops import transpose
|
||||
from ngraph.opset1.ops import unsqueeze
|
||||
from ngraph.opset1.ops import variadic_split
|
||||
48
src/bindings/python/src/compatibility/ngraph/opset9/ops.py
Normal file
48
src/bindings/python/src/compatibility/ngraph/opset9/ops.py
Normal file
@ -0,0 +1,48 @@
|
||||
# Copyright (C) 2018-2022 Intel Corporation
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
"""Factory functions for all ngraph ops."""
|
||||
from functools import partial
|
||||
from typing import Optional
|
||||
|
||||
import numpy as np
|
||||
from ngraph.impl import Node
|
||||
from ngraph.opset_utils import _get_node_factory
|
||||
from ngraph.utils.decorators import nameable_op
|
||||
from ngraph.utils.types import (
|
||||
NodeInput,
|
||||
as_nodes,
|
||||
as_node
|
||||
)
|
||||
|
||||
_get_node_factory_opset9 = partial(_get_node_factory, "opset9")
|
||||
|
||||
|
||||
# -------------------------------------------- ops ------------------------------------------------
|
||||
|
||||
|
||||
@nameable_op
|
||||
def eye(
|
||||
num_rows: NodeInput,
|
||||
num_columns: NodeInput,
|
||||
diagonal_index: NodeInput,
|
||||
output_type: str,
|
||||
batch_shape: Optional[NodeInput] = None,
|
||||
name: Optional[str] = None,
|
||||
) -> Node:
|
||||
"""Return a node which performs eye operation.
|
||||
|
||||
:param num_rows: The node providing row number tensor.
|
||||
:param num_columns: The node providing column number tensor.
|
||||
:param diagonal_index: The node providing the index of the diagonal to be populated.
|
||||
:param output_type: Specifies the output tensor type, supports any numeric types.
|
||||
:param batch_shape: The node providing the leading batch dimensions of output shape. Optionally.
|
||||
:param name: The optional new name for output node.
|
||||
:return: New node performing deformable convolution operation.
|
||||
"""
|
||||
if batch_shape is not None:
|
||||
inputs = as_nodes(num_rows, num_columns, diagonal_index, batch_shape)
|
||||
else:
|
||||
inputs = as_nodes(num_rows, num_columns, diagonal_index)
|
||||
|
||||
return _get_node_factory_opset9().create("Eye", inputs, {"output_type": output_type})
|
||||
@ -12,7 +12,7 @@ from ngraph.impl import Node, Output
|
||||
|
||||
from ngraph.exceptions import UserInputError
|
||||
|
||||
DEFAULT_OPSET = "opset8"
|
||||
DEFAULT_OPSET = "opset9"
|
||||
|
||||
|
||||
class NodeFactory(object):
|
||||
|
||||
@ -83,6 +83,7 @@ private:
|
||||
{"opset6", OpsetFunction(ngraph::get_opset6)},
|
||||
{"opset7", OpsetFunction(ngraph::get_opset7)},
|
||||
{"opset8", OpsetFunction(ngraph::get_opset8)},
|
||||
{"opset9", OpsetFunction(ngraph::get_opset9)},
|
||||
};
|
||||
|
||||
auto it = s_opsets.find(opset_ver);
|
||||
@ -92,7 +93,7 @@ private:
|
||||
return it->second();
|
||||
}
|
||||
|
||||
const ngraph::OpSet& m_opset = ngraph::get_opset8();
|
||||
const ngraph::OpSet& m_opset = ngraph::get_opset9();
|
||||
std::unordered_map<std::string, std::shared_ptr<ngraph::Variable>> m_variables;
|
||||
};
|
||||
} // namespace
|
||||
|
||||
@ -56,6 +56,7 @@ from openvino.runtime import opset5
|
||||
from openvino.runtime import opset6
|
||||
from openvino.runtime import opset7
|
||||
from openvino.runtime import opset8
|
||||
from openvino.runtime import opset9
|
||||
|
||||
# Import properties API
|
||||
from openvino.pyopenvino import properties
|
||||
@ -65,19 +66,19 @@ from openvino.runtime.ie_api import tensor_from_file
|
||||
from openvino.runtime.ie_api import compile_model
|
||||
|
||||
# Extend Node class to support binary operators
|
||||
Node.__add__ = opset8.add
|
||||
Node.__sub__ = opset8.subtract
|
||||
Node.__mul__ = opset8.multiply
|
||||
Node.__div__ = opset8.divide
|
||||
Node.__truediv__ = opset8.divide
|
||||
Node.__radd__ = lambda left, right: opset8.add(right, left)
|
||||
Node.__rsub__ = lambda left, right: opset8.subtract(right, left)
|
||||
Node.__rmul__ = lambda left, right: opset8.multiply(right, left)
|
||||
Node.__rdiv__ = lambda left, right: opset8.divide(right, left)
|
||||
Node.__rtruediv__ = lambda left, right: opset8.divide(right, left)
|
||||
Node.__eq__ = opset8.equal
|
||||
Node.__ne__ = opset8.not_equal
|
||||
Node.__lt__ = opset8.less
|
||||
Node.__le__ = opset8.less_equal
|
||||
Node.__gt__ = opset8.greater
|
||||
Node.__ge__ = opset8.greater_equal
|
||||
Node.__add__ = opset9.add
|
||||
Node.__sub__ = opset9.subtract
|
||||
Node.__mul__ = opset9.multiply
|
||||
Node.__div__ = opset9.divide
|
||||
Node.__truediv__ = opset9.divide
|
||||
Node.__radd__ = lambda left, right: opset9.add(right, left)
|
||||
Node.__rsub__ = lambda left, right: opset9.subtract(right, left)
|
||||
Node.__rmul__ = lambda left, right: opset9.multiply(right, left)
|
||||
Node.__rdiv__ = lambda left, right: opset9.divide(right, left)
|
||||
Node.__rtruediv__ = lambda left, right: opset9.divide(right, left)
|
||||
Node.__eq__ = opset9.equal
|
||||
Node.__ne__ = opset9.not_equal
|
||||
Node.__lt__ = opset9.less
|
||||
Node.__le__ = opset9.less_equal
|
||||
Node.__gt__ = opset9.greater
|
||||
Node.__ge__ = opset9.greater_equal
|
||||
|
||||
168
src/bindings/python/src/openvino/runtime/opset9/__init__.py
Normal file
168
src/bindings/python/src/openvino/runtime/opset9/__init__.py
Normal file
@ -0,0 +1,168 @@
|
||||
# Copyright (C) 2018-2022 Intel Corporation
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
from openvino.runtime.opset1.ops import absolute
|
||||
from openvino.runtime.opset1.ops import absolute as abs
|
||||
from openvino.runtime.opset1.ops import acos
|
||||
from openvino.runtime.opset4.ops import acosh
|
||||
from openvino.runtime.opset8.ops import adaptive_avg_pool
|
||||
from openvino.runtime.opset8.ops import adaptive_max_pool
|
||||
from openvino.runtime.opset1.ops import add
|
||||
from openvino.runtime.opset1.ops import asin
|
||||
from openvino.runtime.opset4.ops import asinh
|
||||
from openvino.runtime.opset3.ops import assign
|
||||
from openvino.runtime.opset1.ops import atan
|
||||
from openvino.runtime.opset4.ops import atanh
|
||||
from openvino.runtime.opset1.ops import avg_pool
|
||||
from openvino.runtime.opset5.ops import batch_norm_inference
|
||||
from openvino.runtime.opset2.ops import batch_to_space
|
||||
from openvino.runtime.opset1.ops import binary_convolution
|
||||
from openvino.runtime.opset3.ops import broadcast
|
||||
from openvino.runtime.opset3.ops import bucketize
|
||||
from openvino.runtime.opset1.ops import ceiling
|
||||
from openvino.runtime.opset1.ops import ceiling as ceil
|
||||
from openvino.runtime.opset1.ops import clamp
|
||||
from openvino.runtime.opset1.ops import concat
|
||||
from openvino.runtime.opset1.ops import constant
|
||||
from openvino.runtime.opset1.ops import convert
|
||||
from openvino.runtime.opset1.ops import convert_like
|
||||
from openvino.runtime.opset1.ops import convolution
|
||||
from openvino.runtime.opset1.ops import convolution_backprop_data
|
||||
from openvino.runtime.opset1.ops import cos
|
||||
from openvino.runtime.opset1.ops import cosh
|
||||
from openvino.runtime.opset1.ops import ctc_greedy_decoder
|
||||
from openvino.runtime.opset6.ops import ctc_greedy_decoder_seq_len
|
||||
from openvino.runtime.opset4.ops import ctc_loss
|
||||
from openvino.runtime.opset3.ops import cum_sum
|
||||
from openvino.runtime.opset3.ops import cum_sum as cumsum
|
||||
from openvino.runtime.opset8.ops import deformable_convolution
|
||||
from openvino.runtime.opset1.ops import deformable_psroi_pooling
|
||||
from openvino.runtime.opset1.ops import depth_to_space
|
||||
from openvino.runtime.opset8.ops import detection_output
|
||||
from openvino.runtime.opset7.ops import dft
|
||||
from openvino.runtime.opset1.ops import divide
|
||||
from openvino.runtime.opset7.ops import einsum
|
||||
from openvino.runtime.opset1.ops import elu
|
||||
from openvino.runtime.opset3.ops import embedding_bag_offsets_sum
|
||||
from openvino.runtime.opset3.ops import embedding_bag_packed_sum
|
||||
from openvino.runtime.opset3.ops import embedding_segments_sum
|
||||
from openvino.runtime.opset3.ops import extract_image_patches
|
||||
from openvino.runtime.opset1.ops import equal
|
||||
from openvino.runtime.opset1.ops import erf
|
||||
from openvino.runtime.opset1.ops import exp
|
||||
from openvino.runtime.opset9.ops import eye
|
||||
from openvino.runtime.opset1.ops import fake_quantize
|
||||
from openvino.runtime.opset1.ops import floor
|
||||
from openvino.runtime.opset1.ops import floor_mod
|
||||
from openvino.runtime.opset8.ops import gather
|
||||
from openvino.runtime.opset6.ops import gather_elements
|
||||
from openvino.runtime.opset8.ops import gather_nd
|
||||
from openvino.runtime.opset1.ops import gather_tree
|
||||
from openvino.runtime.opset7.ops import gelu
|
||||
from openvino.runtime.opset1.ops import greater
|
||||
from openvino.runtime.opset1.ops import greater_equal
|
||||
from openvino.runtime.opset1.ops import grn
|
||||
from openvino.runtime.opset1.ops import group_convolution
|
||||
from openvino.runtime.opset1.ops import group_convolution_backprop_data
|
||||
from openvino.runtime.opset3.ops import gru_cell
|
||||
from openvino.runtime.opset5.ops import gru_sequence
|
||||
from openvino.runtime.opset1.ops import hard_sigmoid
|
||||
from openvino.runtime.opset5.ops import hsigmoid
|
||||
from openvino.runtime.opset4.ops import hswish
|
||||
from openvino.runtime.opset7.ops import idft
|
||||
from openvino.runtime.opset8.ops import if_op
|
||||
from openvino.runtime.opset1.ops import interpolate
|
||||
from openvino.runtime.opset8.ops import i420_to_bgr
|
||||
from openvino.runtime.opset8.ops import i420_to_rgb
|
||||
from openvino.runtime.opset1.ops import less
|
||||
from openvino.runtime.opset1.ops import less_equal
|
||||
from openvino.runtime.opset1.ops import log
|
||||
from openvino.runtime.opset1.ops import logical_and
|
||||
from openvino.runtime.opset1.ops import logical_not
|
||||
from openvino.runtime.opset1.ops import logical_or
|
||||
from openvino.runtime.opset1.ops import logical_xor
|
||||
from openvino.runtime.opset5.ops import log_softmax
|
||||
from openvino.runtime.opset5.ops import loop
|
||||
from openvino.runtime.opset1.ops import lrn
|
||||
from openvino.runtime.opset4.ops import lstm_cell
|
||||
from openvino.runtime.opset5.ops import lstm_sequence
|
||||
from openvino.runtime.opset1.ops import matmul
|
||||
from openvino.runtime.opset8.ops import matrix_nms
|
||||
from openvino.runtime.opset8.ops import max_pool
|
||||
from openvino.runtime.opset1.ops import maximum
|
||||
from openvino.runtime.opset1.ops import minimum
|
||||
from openvino.runtime.opset4.ops import mish
|
||||
from openvino.runtime.opset1.ops import mod
|
||||
from openvino.runtime.opset8.ops import multiclass_nms
|
||||
from openvino.runtime.opset1.ops import multiply
|
||||
from openvino.runtime.opset6.ops import mvn
|
||||
from openvino.runtime.opset1.ops import negative
|
||||
from openvino.runtime.opset5.ops import non_max_suppression
|
||||
from openvino.runtime.opset3.ops import non_zero
|
||||
from openvino.runtime.opset1.ops import normalize_l2
|
||||
from openvino.runtime.opset1.ops import not_equal
|
||||
from openvino.runtime.opset8.ops import nv12_to_bgr
|
||||
from openvino.runtime.opset8.ops import nv12_to_rgb
|
||||
from openvino.runtime.opset1.ops import one_hot
|
||||
from openvino.runtime.opset1.ops import pad
|
||||
from openvino.runtime.opset1.ops import parameter
|
||||
from openvino.runtime.opset1.ops import power
|
||||
from openvino.runtime.opset1.ops import prelu
|
||||
from openvino.runtime.opset8.ops import prior_box
|
||||
from openvino.runtime.opset1.ops import prior_box_clustered
|
||||
from openvino.runtime.opset1.ops import psroi_pooling
|
||||
from openvino.runtime.opset4.ops import proposal
|
||||
from openvino.runtime.opset1.ops import range
|
||||
from openvino.runtime.opset8.ops import random_uniform
|
||||
from openvino.runtime.opset3.ops import read_value
|
||||
from openvino.runtime.opset4.ops import reduce_l1
|
||||
from openvino.runtime.opset4.ops import reduce_l2
|
||||
from openvino.runtime.opset1.ops import reduce_logical_and
|
||||
from openvino.runtime.opset1.ops import reduce_logical_or
|
||||
from openvino.runtime.opset1.ops import reduce_max
|
||||
from openvino.runtime.opset1.ops import reduce_mean
|
||||
from openvino.runtime.opset1.ops import reduce_min
|
||||
from openvino.runtime.opset1.ops import reduce_prod
|
||||
from openvino.runtime.opset1.ops import reduce_sum
|
||||
from openvino.runtime.opset1.ops import region_yolo
|
||||
from openvino.runtime.opset2.ops import reorg_yolo
|
||||
from openvino.runtime.opset1.ops import relu
|
||||
from openvino.runtime.opset1.ops import reshape
|
||||
from openvino.runtime.opset1.ops import result
|
||||
from openvino.runtime.opset1.ops import reverse_sequence
|
||||
from openvino.runtime.opset3.ops import rnn_cell
|
||||
from openvino.runtime.opset5.ops import rnn_sequence
|
||||
from openvino.runtime.opset3.ops import roi_align
|
||||
from openvino.runtime.opset2.ops import roi_pooling
|
||||
from openvino.runtime.opset7.ops import roll
|
||||
from openvino.runtime.opset5.ops import round
|
||||
from openvino.runtime.opset3.ops import scatter_elements_update
|
||||
from openvino.runtime.opset3.ops import scatter_update
|
||||
from openvino.runtime.opset1.ops import select
|
||||
from openvino.runtime.opset1.ops import selu
|
||||
from openvino.runtime.opset3.ops import shape_of
|
||||
from openvino.runtime.opset3.ops import shuffle_channels
|
||||
from openvino.runtime.opset1.ops import sigmoid
|
||||
from openvino.runtime.opset1.ops import sign
|
||||
from openvino.runtime.opset1.ops import sin
|
||||
from openvino.runtime.opset1.ops import sinh
|
||||
from openvino.runtime.opset8.ops import slice
|
||||
from openvino.runtime.opset8.ops import softmax
|
||||
from openvino.runtime.opset4.ops import softplus
|
||||
from openvino.runtime.opset2.ops import space_to_batch
|
||||
from openvino.runtime.opset1.ops import space_to_depth
|
||||
from openvino.runtime.opset1.ops import split
|
||||
from openvino.runtime.opset1.ops import sqrt
|
||||
from openvino.runtime.opset1.ops import squared_difference
|
||||
from openvino.runtime.opset1.ops import squeeze
|
||||
from openvino.runtime.opset1.ops import strided_slice
|
||||
from openvino.runtime.opset1.ops import subtract
|
||||
from openvino.runtime.opset4.ops import swish
|
||||
from openvino.runtime.opset1.ops import tan
|
||||
from openvino.runtime.opset1.ops import tanh
|
||||
from openvino.runtime.opset1.ops import tensor_iterator
|
||||
from openvino.runtime.opset1.ops import tile
|
||||
from openvino.runtime.opset3.ops import topk
|
||||
from openvino.runtime.opset1.ops import transpose
|
||||
from openvino.runtime.opset1.ops import unsqueeze
|
||||
from openvino.runtime.opset1.ops import variadic_split
|
||||
48
src/bindings/python/src/openvino/runtime/opset9/ops.py
Normal file
48
src/bindings/python/src/openvino/runtime/opset9/ops.py
Normal file
@ -0,0 +1,48 @@
|
||||
# Copyright (C) 2018-2022 Intel Corporation
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
"""Factory functions for all ngraph ops."""
|
||||
from functools import partial
|
||||
from typing import Optional
|
||||
|
||||
import numpy as np
|
||||
from openvino.runtime import Node
|
||||
from openvino.runtime.opset_utils import _get_node_factory
|
||||
from openvino.runtime.utils.decorators import nameable_op
|
||||
from openvino.runtime.utils.types import (
|
||||
NodeInput,
|
||||
as_nodes,
|
||||
as_node
|
||||
)
|
||||
|
||||
_get_node_factory_opset9 = partial(_get_node_factory, "opset9")
|
||||
|
||||
|
||||
# -------------------------------------------- ops ------------------------------------------------
|
||||
|
||||
|
||||
@nameable_op
|
||||
def eye(
|
||||
num_rows: NodeInput,
|
||||
num_columns: NodeInput,
|
||||
diagonal_index: NodeInput,
|
||||
output_type: str,
|
||||
batch_shape: Optional[NodeInput] = None,
|
||||
name: Optional[str] = None,
|
||||
) -> Node:
|
||||
"""Return a node which performs eye operation.
|
||||
|
||||
:param num_rows: The node providing row number tensor.
|
||||
:param num_columns: The node providing column number tensor.
|
||||
:param diagonal_index: The node providing the index of the diagonal to be populated.
|
||||
:param output_type: Specifies the output tensor type, supports any numeric types.
|
||||
:param batch_shape: The node providing the leading batch dimensions of output shape. Optionally.
|
||||
:param name: The optional new name for output node.
|
||||
:return: New node performing deformable convolution operation.
|
||||
"""
|
||||
if batch_shape is not None:
|
||||
inputs = as_nodes(num_rows, num_columns, diagonal_index, batch_shape)
|
||||
else:
|
||||
inputs = as_nodes(num_rows, num_columns, diagonal_index)
|
||||
|
||||
return _get_node_factory_opset9().create("Eye", inputs, {"output_type": output_type})
|
||||
@ -83,6 +83,7 @@ private:
|
||||
{"opset6", OpsetFunction(ov::get_opset6)},
|
||||
{"opset7", OpsetFunction(ov::get_opset7)},
|
||||
{"opset8", OpsetFunction(ov::get_opset8)},
|
||||
{"opset9", OpsetFunction(ov::get_opset9)},
|
||||
};
|
||||
|
||||
auto it = s_opsets.find(opset_ver);
|
||||
@ -92,7 +93,7 @@ private:
|
||||
return it->second();
|
||||
}
|
||||
|
||||
const ov::OpSet& m_opset = ov::get_opset8();
|
||||
const ov::OpSet& m_opset = ov::get_opset9();
|
||||
std::unordered_map<std::string, std::shared_ptr<ov::op::util::Variable>> m_variables;
|
||||
};
|
||||
} // namespace
|
||||
|
||||
@ -45,6 +45,7 @@ ov::NodeTypeInfo get_type(const std::string& type_name) {
|
||||
{"opset6", ngraph::get_opset6},
|
||||
{"opset7", ngraph::get_opset7},
|
||||
{"opset8", ngraph::get_opset8},
|
||||
{"opset9", ngraph::get_opset9},
|
||||
};
|
||||
|
||||
if (!get_opset.count(opset_type)) {
|
||||
|
||||
102
src/bindings/python/tests/test_ngraph/test_eye.py
Normal file
102
src/bindings/python/tests/test_ngraph/test_eye.py
Normal file
@ -0,0 +1,102 @@
|
||||
# Copyright (C) 2018-2022 Intel Corporation
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
import openvino.runtime.opset9 as ov
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
from tests.runtime import get_runtime
|
||||
from openvino.runtime.utils.types import get_element_type_str
|
||||
from openvino.runtime.utils.types import get_element_type
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"num_rows, num_columns, diagonal_index, out_type",
|
||||
[
|
||||
pytest.param(2, 5, 0, np.float32),
|
||||
pytest.param(5, 3, 2, np.int64),
|
||||
pytest.param(3, 3, -1, np.float16),
|
||||
pytest.param(5, 5, -10, np.float32),
|
||||
],
|
||||
)
|
||||
def test_eye_rectangle(num_rows, num_columns, diagonal_index, out_type):
|
||||
num_rows_array = np.array([num_rows], np.int32)
|
||||
num_columns_array = np.array([num_columns], np.int32)
|
||||
diagonal_index_array = np.array([diagonal_index], np.int32)
|
||||
num_rows_tensor = ov.constant(num_rows_array)
|
||||
num_columns_tensor = ov.constant(num_columns_array)
|
||||
diagonal_index_tensor = ov.constant(diagonal_index_array)
|
||||
|
||||
# Create with param names
|
||||
eye_node = ov.eye(num_rows=num_rows_tensor,
|
||||
num_columns=num_columns_tensor,
|
||||
diagonal_index=diagonal_index_tensor,
|
||||
output_type=get_element_type_str(out_type))
|
||||
|
||||
# Create with default orded
|
||||
eye_node = ov.eye(num_rows_tensor,
|
||||
num_columns_tensor,
|
||||
diagonal_index_tensor,
|
||||
get_element_type_str(out_type))
|
||||
|
||||
expected_results = np.eye(num_rows, M=num_columns, k=diagonal_index, dtype=np.float32)
|
||||
|
||||
assert eye_node.get_type_name() == "Eye"
|
||||
assert eye_node.get_output_size() == 1
|
||||
assert eye_node.get_output_element_type(0) == get_element_type(out_type)
|
||||
assert tuple(eye_node.get_output_shape(0)) == expected_results.shape
|
||||
|
||||
# TODO: Enable with Eye reference implementation
|
||||
# runtime = get_runtime()
|
||||
# computation = runtime.computation(eye_node)
|
||||
# eye_results = computation()
|
||||
# assert np.allclose(eye_results, expected_results)
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"num_rows, num_columns, diagonal_index, batch_shape, out_type",
|
||||
[
|
||||
pytest.param(2, 5, 0, [1], np.float32),
|
||||
pytest.param(5, 3, 2, [2, 2], np.int64),
|
||||
pytest.param(3, 3, -1, [1, 3, 2], np.float16),
|
||||
pytest.param(5, 5, -10, [1, 1], np.float32),
|
||||
],
|
||||
)
|
||||
def test_eye_batch_shape(num_rows, num_columns, diagonal_index, batch_shape, out_type):
|
||||
num_rows_array = np.array([num_rows], np.int32)
|
||||
num_columns_array = np.array([num_columns], np.int32)
|
||||
diagonal_index_array = np.array([diagonal_index], np.int32)
|
||||
batch_shape_array = np.array(batch_shape, np.int32)
|
||||
num_rows_tensor = ov.constant(num_rows_array)
|
||||
num_columns_tensor = ov.constant(num_columns_array)
|
||||
diagonal_index_tensor = ov.constant(diagonal_index_array)
|
||||
batch_shape_tensor = ov.constant(batch_shape_array)
|
||||
|
||||
# Create with param names
|
||||
eye_node = ov.eye(num_rows=num_rows_tensor,
|
||||
num_columns=num_columns_tensor,
|
||||
diagonal_index=diagonal_index_tensor,
|
||||
batch_shape=batch_shape_tensor,
|
||||
output_type=get_element_type_str(out_type))
|
||||
|
||||
# Create with default orded
|
||||
eye_node = ov.eye(num_rows_tensor,
|
||||
num_columns_tensor,
|
||||
diagonal_index_tensor,
|
||||
get_element_type_str(out_type),
|
||||
batch_shape_tensor)
|
||||
|
||||
output_shape = [*batch_shape, 1, 1]
|
||||
one_matrix = np.eye(num_rows, M=num_columns, k=diagonal_index, dtype=np.float32)
|
||||
expected_results = np.tile(one_matrix, output_shape)
|
||||
|
||||
assert eye_node.get_type_name() == "Eye"
|
||||
assert eye_node.get_output_size() == 1
|
||||
assert eye_node.get_output_element_type(0) == get_element_type(out_type)
|
||||
assert tuple(eye_node.get_output_shape(0)) == expected_results.shape
|
||||
|
||||
# TODO: Enable with Eye reference implementation
|
||||
# runtime = get_runtime()
|
||||
# computation = runtime.computation(eye_node)
|
||||
# eye_results = computation()
|
||||
# assert np.allclose(eye_results, expected_results)
|
||||
@ -12,12 +12,13 @@ from utils.utils import expect_exception
|
||||
|
||||
|
||||
def test_wrap_type_pattern_type():
|
||||
for i in range(1, 9):
|
||||
last_opstet_number = 9
|
||||
for i in range(1, last_opstet_number + 1):
|
||||
WrapType("opset{}.Parameter".format(i))
|
||||
WrapType("opset{}::Parameter".format(i))
|
||||
|
||||
# Negative check not to forget to update opset map in get_type function
|
||||
expect_exception(lambda: WrapType("opset9.Parameter"), "Unsupported opset type: opset9")
|
||||
expect_exception(lambda: WrapType("opset10.Parameter"), "Unsupported opset type: opset10")
|
||||
|
||||
# Generic negative test cases
|
||||
expect_exception(lambda: WrapType(""))
|
||||
|
||||
103
src/bindings/python/tests_compatibility/test_ngraph/test_eye.py
Normal file
103
src/bindings/python/tests_compatibility/test_ngraph/test_eye.py
Normal file
@ -0,0 +1,103 @@
|
||||
# Copyright (C) 2018-2022 Intel Corporation
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
import ngraph as ng
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
from ngraph.utils.types import get_element_type
|
||||
from ngraph.utils.types import get_element_type_str
|
||||
from tests_compatibility.runtime import get_runtime
|
||||
from tests_compatibility.test_ngraph.util import run_op_node
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"num_rows, num_columns, diagonal_index, out_type",
|
||||
[
|
||||
pytest.param(2, 5, 0, np.float32),
|
||||
pytest.param(5, 3, 2, np.int64),
|
||||
pytest.param(3, 3, -1, np.float16),
|
||||
pytest.param(5, 5, -10, np.float32),
|
||||
],
|
||||
)
|
||||
def test_eye_rectangle(num_rows, num_columns, diagonal_index, out_type):
|
||||
num_rows_array = np.array([num_rows], np.int32)
|
||||
num_columns_array = np.array([num_columns], np.int32)
|
||||
diagonal_index_array = np.array([diagonal_index], np.int32)
|
||||
num_rows_tensor = ng.constant(num_rows_array)
|
||||
num_columns_tensor = ng.constant(num_columns_array)
|
||||
diagonal_index_tensor = ng.constant(diagonal_index_array)
|
||||
|
||||
# Create with param names
|
||||
eye_node = ng.eye(num_rows=num_rows_tensor,
|
||||
num_columns=num_columns_tensor,
|
||||
diagonal_index=diagonal_index_tensor,
|
||||
output_type=get_element_type_str(out_type))
|
||||
|
||||
# Create with default orded
|
||||
eye_node = ng.eye(num_rows_tensor,
|
||||
num_columns_tensor,
|
||||
diagonal_index_tensor,
|
||||
get_element_type_str(out_type))
|
||||
|
||||
expected_results = np.eye(num_rows, M=num_columns, k=diagonal_index, dtype=np.float32)
|
||||
|
||||
assert eye_node.get_type_name() == "Eye"
|
||||
assert eye_node.get_output_size() == 1
|
||||
assert eye_node.get_output_element_type(0) == get_element_type(out_type)
|
||||
assert tuple(eye_node.get_output_shape(0)) == expected_results.shape
|
||||
|
||||
# TODO: Enable with Eye reference implementation
|
||||
# runtime = get_runtime()
|
||||
# computation = runtime.computation(eye_node)
|
||||
# eye_results = computation()
|
||||
# assert np.allclose(eye_results, expected_results)
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"num_rows, num_columns, diagonal_index, batch_shape, out_type",
|
||||
[
|
||||
pytest.param(2, 5, 0, [1], np.float32),
|
||||
pytest.param(5, 3, 2, [2, 2], np.int64),
|
||||
pytest.param(3, 3, -1, [1, 3, 2], np.float16),
|
||||
pytest.param(5, 5, -10, [1, 1], np.float32),
|
||||
],
|
||||
)
|
||||
def test_eye_batch_shape(num_rows, num_columns, diagonal_index, batch_shape, out_type):
|
||||
num_rows_array = np.array([num_rows], np.int32)
|
||||
num_columns_array = np.array([num_columns], np.int32)
|
||||
diagonal_index_array = np.array([diagonal_index], np.int32)
|
||||
batch_shape_array = np.array(batch_shape, np.int32)
|
||||
num_rows_tensor = ng.constant(num_rows_array)
|
||||
num_columns_tensor = ng.constant(num_columns_array)
|
||||
diagonal_index_tensor = ng.constant(diagonal_index_array)
|
||||
batch_shape_tensor = ng.constant(batch_shape_array)
|
||||
|
||||
# Create with param names
|
||||
eye_node = ng.eye(num_rows=num_rows_tensor,
|
||||
num_columns=num_columns_tensor,
|
||||
diagonal_index=diagonal_index_tensor,
|
||||
batch_shape=batch_shape_tensor,
|
||||
output_type=get_element_type_str(out_type))
|
||||
|
||||
# Create with default orded
|
||||
eye_node = ng.eye(num_rows_tensor,
|
||||
num_columns_tensor,
|
||||
diagonal_index_tensor,
|
||||
get_element_type_str(out_type),
|
||||
batch_shape_tensor)
|
||||
|
||||
output_shape = [*batch_shape, 1, 1]
|
||||
one_matrix = np.eye(num_rows, M=num_columns, k=diagonal_index, dtype=np.float32)
|
||||
expected_results = np.tile(one_matrix, output_shape)
|
||||
|
||||
assert eye_node.get_type_name() == "Eye"
|
||||
assert eye_node.get_output_size() == 1
|
||||
assert eye_node.get_output_element_type(0) == get_element_type(out_type)
|
||||
assert tuple(eye_node.get_output_shape(0)) == expected_results.shape
|
||||
|
||||
# TODO: Enable with Eye reference implementation
|
||||
# runtime = get_runtime()
|
||||
# computation = runtime.computation(eye_node)
|
||||
# eye_results = computation()
|
||||
# assert np.allclose(eye_results, expected_results)
|
||||
Loading…
Reference in New Issue
Block a user