# Copyright (c) ONNX Project Contributors # # SPDX-License-Identifier: Apache-2.0 import numpy as np import onnx from onnx.backend.test.case.base import Base from onnx.backend.test.case.node import expect class GridSample(Base): @staticmethod def export_gridsample() -> None: node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], mode="linear", padding_mode="zeros", align_corners=0, ) # X shape, [N, C, H, W] - [1, 1, 4, 4] X = np.array( [ [ [ [0.0, 1.0, 2.0, 3.0], [4.0, 5.0, 6.0, 7.0], [8.0, 9.0, 10.0, 11.0], [12.0, 13.0, 14.0, 15.0], ] ] ], dtype=np.float32, ) # Grid shape, [N, H_out, W_out, 2] - [1, 6, 6, 2] Grid = np.array( [ [ [ [-1.0000, -1.0000], [-0.6000, -1.0000], [-0.2000, -1.0000], [0.2000, -1.0000], [0.6000, -1.0000], [1.0000, -1.0000], ], [ [-1.0000, -0.6000], [-0.6000, -0.6000], [-0.2000, -0.6000], [0.2000, -0.6000], [0.6000, -0.6000], [1.0000, -0.6000], ], [ [-1.0000, -0.2000], [-0.6000, -0.2000], [-0.2000, -0.2000], [0.2000, -0.2000], [0.6000, -0.2000], [1.0000, -0.2000], ], [ [-1.0000, 0.2000], [-0.6000, 0.2000], [-0.2000, 0.2000], [0.2000, 0.2000], [0.6000, 0.2000], [1.0000, 0.2000], ], [ [-1.0000, 0.6000], [-0.6000, 0.6000], [-0.2000, 0.6000], [0.2000, 0.6000], [0.6000, 0.6000], [1.0000, 0.6000], ], [ [-1.0000, 1.0000], [-0.6000, 1.0000], [-0.2000, 1.0000], [0.2000, 1.0000], [0.6000, 1.0000], [1.0000, 1.0000], ], ] ], dtype=np.float32, ) # Y shape, [N, C, H_out, W_out] - [1, 1, 6, 6] Y = np.array( [ [ [ [0.0000, 0.1500, 0.5500, 0.9500, 1.3500, 0.7500], [0.6000, 1.5000, 2.3000, 3.1000, 3.9000, 2.1000], [2.2000, 4.7000, 5.5000, 6.3000, 7.1000, 3.7000], [3.8000, 7.9000, 8.7000, 9.5000, 10.3000, 5.3000], [5.4000, 11.1000, 11.9000, 12.7000, 13.5000, 6.9000], [3.0000, 6.1500, 6.5500, 6.9500, 7.3500, 3.7500], ] ] ], dtype=np.float32, ) expect(node, inputs=[X, Grid], outputs=[Y], name="test_gridsample") @staticmethod def export_gridsample_paddingmode() -> None: # X shape, [N, C, H, W] - [1, 1, 3, 2] X = np.array( [[[[0.0, 1.0], [2.0, 3.0], [4.0, 5.0]]]], dtype=np.float32, ) # Grid shape, [N, H_out, W_out, 2] - [1, 2, 4, 2] Grid = np.array( [ [ [ [-10.0000, -10.0000], [-5.0000, -5.0000], [-0.2000, -0.2000], [10.0000, 10.0000], ], [ [10.0000, 10.0000], [-0.2000, -0.2000], [5.0000, 5.0000], [10.0000, 10.0000], ], ] ], dtype=np.float32, ) # setting padding_mode = 'zeros' node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], padding_mode="zeros", ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_zeros = np.array( [[[[0.0000, 0.0000, 1.7000, 0.0000], [0.0000, 1.7000, 0.0000, 0.0000]]]], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_zeros], name="test_gridsample_zeros_padding", ) # setting padding_mode = 'border' node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], padding_mode="border", ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_border = np.array( [[[[0.0000, 0.0000, 1.7000, 5.0000], [5.0000, 1.7000, 5.0000, 5.0000]]]], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_border], name="test_gridsample_border_padding", ) # setting padding_mode = 'reflection' node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], padding_mode="reflection", ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_reflection = np.array( [[[[2.5000, 0.0000, 1.7000, 2.5000], [2.5000, 1.7000, 5.0000, 2.5000]]]], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_reflection], name="test_gridsample_reflection_padding", ) @staticmethod def export_gridsample_mode_aligncorners() -> None: # X shape, [N, C, H, W] - [1, 1, 3, 2] X = np.array( [[[[0.0, 1.0], [2.0, 3.0], [4.0, 5.0]]]], dtype=np.float32, ) # Grid shape, [N, H_out, W_out, 2] - [1, 2, 4, 2] Grid = np.array( [ [ [ [-1.0000, -1.0000], [-0.5000, -0.5000], [-0.2000, -0.2000], [0.0000, 0.0000], ], [ [0.0000, 0.0000], [-0.2000, -0.2000], [0.5000, 0.5000], [1.0000, 1.0000], ], ] ], dtype=np.float32, ) # setting mode = 'bilinear', default align_corners = 0 node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], mode="linear", ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_bilinear = np.array( [[[[0.0000, 0.5000, 1.7000, 2.5000], [2.5000, 1.7000, 4.5000, 1.2500]]]], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_bilinear], name="test_gridsample_bilinear", ) # setting mode = 'bilinear', align_corners = 1 node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], mode="linear", align_corners=1, ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_align_corners = np.array( [[[[0.0000, 1.2500, 2.0000, 2.5000], [2.5000, 2.0000, 3.7500, 5.0000]]]], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_align_corners], name="test_gridsample_aligncorners_true", ) # setting mode = 'nearest' node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], mode="nearest", ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_nearest = np.array( [[[[0.0, 0.0, 2.0, 2.0], [2.0, 2.0, 5.0, 0.0]]]], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_nearest], name="test_gridsample_nearest" ) # setting mode = 'bicubic' node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], mode="cubic", ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_bicubic = np.array( [[[[-0.1406, 0.3828, 1.7556, 2.9688], [2.9688, 1.7556, 5.1445, 1.3906]]]], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_bicubic], name="test_gridsample_bicubic" ) # ============================================================================ # Additional tests # The reference output tensors were generated using PyTorch 2.0. Grid = np.array( [ [ [[-1.0, -0.8], [-0.6, -0.5], [-0.1, -0.2], [0.7, 0.0]], [[0.0, 0.4], [0.2, -0.2], [-0.3, 0.5], [-1.0, 1.0]], ] ], dtype=np.float32, ) node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], mode="nearest", align_corners=0, ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_nearest = np.array( [[[[0.0, 0.0, 2.0, 3.0], [4.0, 3.0, 4.0, 4.0]]]], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_nearest], name="test_gridsample_nearest_align_corners_0_additional_1", ) # setting mode = 'nearest' node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], mode="nearest", align_corners=1, ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_nearest = np.array( [[[[0.0, 0.0, 2.0, 3.0], [2.0, 3.0, 4.0, 4.0]]]], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_nearest], name="test_gridsample_nearest_align_corners_1_additional_1", ) node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], mode="linear", align_corners=0, ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_bilinear = np.array( [[[[0.0000, 0.4500, 1.8000, 2.4000], [3.7000, 2.1000, 3.7000, 1.0000]]]], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_bilinear], name="test_gridsample_bilinear_align_corners_0_additional_1", ) node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], mode="linear", align_corners=1, ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_bilinear = np.array( [[[[0.4000, 1.2000, 2.0500, 2.8500], [3.3000, 2.2000, 3.3500, 4.0000]]]], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_bilinear], name="test_gridsample_bilinear_align_corners_1_additional_1", ) # These two new bicubic tests produces slightly higher error ~5e-5 node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], mode="cubic", align_corners=0, ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_bicubic = np.array( [ [ [ [-0.173250, 0.284265, 1.923106, 2.568000], [5.170375, 2.284414, 4.744844, 1.046875], ] ] ], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_bicubic], name="test_gridsample_bicubic_align_corners_0_additional_1", ) node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], mode="cubic", align_corners=1, ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_bicubic = np.array( [ [ [ [0.304001, 1.128750, 2.266270, 3.144844], [4.531500, 2.455360, 4.599819, 4.000000], ] ] ], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_bicubic], name="test_gridsample_bicubic_align_corners_1_additional_1", ) @staticmethod def export_volumeetric_gridsample_mode_aligncorners() -> None: X = np.array( [ [ [ [[1.0, 2.0], [3.0, 4.0]], [[5.0, 6.0], [7.0, 8.0]], [[9.0, 10.0], [11.0, 12.0]], ] ] ], dtype=np.float32, ) Grid = np.array( [ [ [ [[-1.0, -1.0, -1.0], [-1.0, -0.5, 0.3]], [[-0.5, -0.5, -0.5], [1.0, -0.6, -1.0]], [[-0.2, -0.2, -0.2], [0.4, 0.2, 0.6]], [[0.0, 0.0, 0.0], [-1.0, 0.0, 0.0]], ], [ [[0.0, 0.0, 0.0], [-1.0, 1.0, 0.0]], [[-0.2, -0.2, -0.2], [1.0, 0.4, -0.2]], [[0.5, 0.5, 0.5], [-1.0, -0.8, 0.8]], [[1.0, 1.0, 1.0], [0.4, 0.6, -0.3]], ], ] ], dtype=np.float32, ) node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], mode="nearest", align_corners=0, ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_nearest = np.array( [ [ [ [[1.0, 5.0], [1.0, 0.0], [5.0, 12.0], [5.0, 5.0]], [[5.0, 0.0], [5.0, 0.0], [12.0, 9.0], [0.0, 8.0]], ] ] ], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_nearest], name="test_gridsample_volumetric_nearest_align_corners_0", ) node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], mode="nearest", align_corners=1, ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_nearest = np.array( [ [ [ [[1.0, 5.0], [1.0, 2.0], [5.0, 12.0], [5.0, 5.0]], [[5.0, 7.0], [5.0, 8.0], [12.0, 9.0], [12.0, 8.0]], ] ] ], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_nearest], name="test_gridsample_volumetric_nearest_align_corners_1", ) node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], mode="linear", align_corners=0, ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_bilinear = np.array( [ [ [ [ [0.1250, 3.4000], [2.0000, 0.4500], [4.7000, 10.9000], [6.5000, 3.0000], ], [ [6.5000, 1.7500], [4.7000, 3.3000], [11.0000, 2.5200], [1.5000, 5.4900], ], ] ] ], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_bilinear], name="test_gridsample_volumetric_bilinear_align_corners_0", ) node = onnx.helper.make_node( "GridSample", inputs=["X", "Grid"], outputs=["Y"], mode="linear", align_corners=1, ) # Y shape, [N, C, H_out, W_out] - [1, 1, 2, 4] Y_bilinear = np.array( [ [ [ [ [1.0000, 6.7000], [3.7500, 2.4000], [5.4000, 9.3000], [6.5000, 6.0000], ], [ [6.5000, 7.0000], [5.4000, 6.6000], [9.2500, 8.4000], [12.0000, 6.1000], ], ] ] ], dtype=np.float32, ) expect( node, inputs=[X, Grid], outputs=[Y_bilinear], name="test_gridsample_volumetric_bilinear_align_corners_1", ) """ For someone who want to test by script. Comment it cause github ONNX CI do not have the torch python package. @staticmethod def export_gridsample_torch(): # type: () -> None node = onnx.helper.make_node( 'GridSample', inputs=['X', 'Grid'], outputs=['Y'], mode='bilinear', padding_mode='zeros', align_corners=0, ) # X shape, [N, C, H, W] - [1, 1, 4, 4] # Grid shape, [N, H_out, W_out, 2] - [1, 6, 6, 2] # Y shape, [N, C, H_out, W_out] - [1, 1, 6, 6] import torch X = torch.arange(3 * 3).view(1, 1, 3, 3).float() d = torch.linspace(-1, 1, 6) meshx, meshy = torch.meshgrid((d, d)) grid = torch.stack((meshy, meshx), 2) Grid = grid.unsqueeze(0) Y = torch.nn.functional.grid_sample(X, Grid, mode='bilinear', padding_mode='zeros', align_corners=False) expect(node, inputs=[X.numpy(), Grid.numpy()], outputs=[Y.numpy()], name='test_gridsample_torch') """