NiccoloN
1923 lines
100 KiB
Python
1923 lines
100 KiB
Python
#!/usr/bin/env python3
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"""Generate ONNX test models for validating supported ONNX operations."""
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import numpy as np
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import onnx
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from onnx import helper, TensorProto, numpy_helper
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from pathlib import Path
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OPERATIONS_DIR = Path(__file__).parent
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def save_model(model, directory, filename):
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"""Save an ONNX model, creating the directory if needed."""
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d = OPERATIONS_DIR / directory
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d.mkdir(parents=True, exist_ok=True)
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path = d / filename
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onnx.checker.check_model(model)
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onnx.save(model, str(path))
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print(f" {path.relative_to(OPERATIONS_DIR)}")
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def make_int64_initializer(name, values):
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return numpy_helper.from_array(np.asarray(values, dtype=np.int64), name=name)
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# ---------------------------------------------------------------------------
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# Conv tests
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# ---------------------------------------------------------------------------
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def conv_3x3_kernel():
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"""Conv with 3x3 kernel, no padding."""
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# Input: [1, 1, 5, 5], Kernel: [1, 1, 3, 3] -> Output: [1, 1, 3, 3]
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X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 1, 5, 5])
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Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 1, 3, 3])
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W = numpy_helper.from_array(
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np.random.default_rng(50).uniform(-1, 1, (1, 1, 3, 3)).astype(np.float32), name="W")
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node = helper.make_node("Conv", ["X", "W"], ["Y"],
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kernel_shape=[3, 3], strides=[1, 1], pads=[0, 0, 0, 0])
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graph = helper.make_graph([node], "conv_3x3", [X], [Y], initializer=[W])
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model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
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save_model(model, "conv/kernel_3x3", "conv_kernel_3x3.onnx")
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def conv_stride2():
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"""Conv with 3x3 kernel and stride 2."""
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# Input: [1, 1, 6, 6], Kernel: [1, 1, 3, 3], stride 2 -> Output: [1, 1, 2, 2]
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X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 1, 6, 6])
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Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 1, 2, 2])
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W = numpy_helper.from_array(
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np.random.default_rng(51).uniform(-1, 1, (1, 1, 3, 3)).astype(np.float32), name="W")
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node = helper.make_node("Conv", ["X", "W"], ["Y"],
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kernel_shape=[3, 3], strides=[2, 2], pads=[0, 0, 0, 0])
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graph = helper.make_graph([node], "conv_stride2", [X], [Y], initializer=[W])
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model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
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save_model(model, "conv/stride_2", "conv_stride_2.onnx")
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def conv_multi_channel():
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"""Conv with multiple input and output channels."""
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# Input: [1, 3, 5, 5], Kernel: [4, 3, 3, 3] -> Output: [1, 4, 3, 3]
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X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 3, 5, 5])
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Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 4, 3, 3])
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W = numpy_helper.from_array(
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np.random.default_rng(52).uniform(-1, 1, (4, 3, 3, 3)).astype(np.float32), name="W")
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node = helper.make_node("Conv", ["X", "W"], ["Y"],
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kernel_shape=[3, 3], strides=[1, 1], pads=[0, 0, 0, 0])
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graph = helper.make_graph([node], "conv_multi_channel", [X], [Y], initializer=[W])
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model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
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save_model(model, "conv/multi_channel", "conv_multi_channel.onnx")
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def conv_1x1():
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"""1x1 pointwise convolution (channel mixing)."""
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# Input: [1, 8, 4, 4], Kernel: [4, 8, 1, 1] -> Output: [1, 4, 4, 4]
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X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 8, 4, 4])
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Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 4, 4, 4])
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W = numpy_helper.from_array(
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np.random.default_rng(53).uniform(-1, 1, (4, 8, 1, 1)).astype(np.float32), name="W")
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node = helper.make_node("Conv", ["X", "W"], ["Y"],
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kernel_shape=[1, 1], strides=[1, 1], pads=[0, 0, 0, 0])
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graph = helper.make_graph([node], "conv_1x1", [X], [Y], initializer=[W])
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model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
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save_model(model, "conv/pointwise_1x1", "conv_1x1.onnx")
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def conv_same_padding_3x3():
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"""Conv 3x3 with SAME_UPPER padding, preserving spatial dimensions."""
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# Input: [1, 1, 5, 5], Kernel: [1, 1, 3, 3], SAME_UPPER -> Output: [1, 1, 5, 5]
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X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 1, 5, 5])
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Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 1, 5, 5])
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W = numpy_helper.from_array(
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np.random.default_rng(54).uniform(-1, 1, (1, 1, 3, 3)).astype(np.float32), name="W")
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node = helper.make_node("Conv", ["X", "W"], ["Y"],
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kernel_shape=[3, 3], strides=[1, 1], auto_pad="SAME_UPPER")
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graph = helper.make_graph([node], "conv_same_3x3", [X], [Y], initializer=[W])
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model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
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save_model(model, "conv/same_padding_3x3", "conv_same_padding_3x3.onnx")
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def conv_explicit_padding():
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"""Conv 3x3 with explicit symmetric padding."""
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# Input: [1, 1, 4, 4], Kernel: [1, 1, 3, 3], pads=[1,1,1,1] -> Output: [1, 1, 4, 4]
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X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 1, 4, 4])
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Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 1, 4, 4])
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W = numpy_helper.from_array(
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np.random.default_rng(55).uniform(-1, 1, (1, 1, 3, 3)).astype(np.float32), name="W")
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node = helper.make_node("Conv", ["X", "W"], ["Y"],
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kernel_shape=[3, 3], strides=[1, 1], pads=[1, 1, 1, 1])
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graph = helper.make_graph([node], "conv_explicit_pad", [X], [Y], initializer=[W])
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model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
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save_model(model, "conv/explicit_padding", "conv_explicit_padding.onnx")
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def conv_with_bias_3x3():
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"""Conv 3x3 with bias."""
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# Input: [1, 3, 5, 5], Kernel: [2, 3, 3, 3], Bias: [2] -> Output: [1, 2, 3, 3]
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rng = np.random.default_rng(56)
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X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 3, 5, 5])
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Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 2, 3, 3])
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W = numpy_helper.from_array(rng.uniform(-1, 1, (2, 3, 3, 3)).astype(np.float32), name="W")
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B = numpy_helper.from_array(rng.uniform(-1, 1, (2,)).astype(np.float32), name="B")
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node = helper.make_node("Conv", ["X", "W", "B"], ["Y"],
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kernel_shape=[3, 3], strides=[1, 1], pads=[0, 0, 0, 0])
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graph = helper.make_graph([node], "conv_with_bias_3x3", [X], [Y], initializer=[W, B])
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model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
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save_model(model, "conv/with_bias_3x3", "conv_with_bias_3x3.onnx")
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def conv_batch_2():
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"""Batched conv (batch=2) with SAME_UPPER padding and bias."""
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# Input: [2, 3, 3, 3], Kernel: [1, 3, 2, 2], Bias: [1] -> Output: [2, 1, 3, 3]
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rng = np.random.default_rng(57)
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X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [2, 3, 3, 3])
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Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [2, 1, 3, 3])
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W = numpy_helper.from_array(rng.uniform(-1, 1, (1, 3, 2, 2)).astype(np.float32), name="W")
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B = numpy_helper.from_array(rng.uniform(-1, 1, (1,)).astype(np.float32), name="B")
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node = helper.make_node("Conv", ["X", "W", "B"], ["Y"],
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kernel_shape=[2, 2], strides=[1, 1], auto_pad="SAME_UPPER")
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graph = helper.make_graph([node], "conv_batch_2", [X], [Y], initializer=[W, B])
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model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
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save_model(model, "conv/batch_2", "conv_batch_2.onnx")
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def conv_large_spatial():
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"""Conv on larger spatial input: [1, 1, 8, 8] with 3x3 kernel."""
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X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 1, 8, 8])
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Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 1, 6, 6])
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W = numpy_helper.from_array(
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np.random.default_rng(58).uniform(-1, 1, (1, 1, 3, 3)).astype(np.float32), name="W")
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node = helper.make_node("Conv", ["X", "W"], ["Y"],
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kernel_shape=[3, 3], strides=[1, 1], pads=[0, 0, 0, 0])
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graph = helper.make_graph([node], "conv_large_spatial", [X], [Y], initializer=[W])
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model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
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save_model(model, "conv/large_spatial", "conv_large_spatial.onnx")
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def conv_grouped_two_groups():
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"""Grouped Conv with two groups, pointwise kernels, and bias."""
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rng = np.random.default_rng(59)
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X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 4, 4, 4])
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Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 4, 4, 4])
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W = numpy_helper.from_array(rng.uniform(-1, 1, (4, 2, 1, 1)).astype(np.float32), name="W")
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B = numpy_helper.from_array(rng.uniform(-1, 1, (4,)).astype(np.float32), name="B")
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node = helper.make_node("Conv", ["X", "W", "B"], ["Y"],
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kernel_shape=[1, 1], strides=[1, 1], pads=[0, 0, 0, 0], group=2)
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graph = helper.make_graph([node], "conv_grouped_two_groups", [X], [Y], initializer=[W, B])
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model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
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save_model(model, "conv/grouped_two_groups", "conv_grouped_two_groups.onnx")
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def conv_depthwise_grouped():
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"""Depthwise-style grouped Conv with one input channel per group."""
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X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 3, 4, 4])
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Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 3, 2, 2])
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W = numpy_helper.from_array(
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np.random.default_rng(60).uniform(-1, 1, (3, 1, 3, 3)).astype(np.float32), name="W")
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node = helper.make_node("Conv", ["X", "W"], ["Y"],
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kernel_shape=[3, 3], strides=[1, 1], pads=[0, 0, 0, 0], group=3)
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graph = helper.make_graph([node], "conv_depthwise_grouped", [X], [Y], initializer=[W])
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model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
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save_model(model, "conv/depthwise_grouped", "conv_depthwise_grouped.onnx")
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def conv_dynamic():
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"""Conv with input and weight both provided at runtime."""
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X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 1, 4, 4])
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W = helper.make_tensor_value_info("W", TensorProto.FLOAT, [1, 1, 3, 3])
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Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 1, 2, 2])
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node = helper.make_node("Conv", ["X", "W"], ["Y"],
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kernel_shape=[3, 3], strides=[1, 1], pads=[0, 0, 0, 0])
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graph = helper.make_graph([node], "conv_dynamic", [X, W], [Y])
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model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
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save_model(model, "conv/dynamic", "conv_dynamic.onnx")
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def conv_huge_pointwise_1024():
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"""Huge 1x1 Conv with 1024 input and output channels."""
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X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 1024, 1, 1])
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Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 1024, 1, 1])
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W = numpy_helper.from_array(
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np.random.default_rng(73).uniform(-1, 1, (1024, 1024, 1, 1)).astype(np.float32), name="W")
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node = helper.make_node("Conv", ["X", "W"], ["Y"],
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kernel_shape=[1, 1], strides=[1, 1], pads=[0, 0, 0, 0])
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graph = helper.make_graph([node], "conv_huge_pointwise_1024", [X], [Y], initializer=[W])
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model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
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save_model(model, "conv/huge_pointwise_1024", "conv_huge_pointwise_1024.onnx")
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def conv_huge_pointwise_1024_dynamic():
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"""Huge 1x1 Conv with runtime weights and 1024 channels."""
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X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 1024, 1, 1])
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W = helper.make_tensor_value_info("W", TensorProto.FLOAT, [1024, 1024, 1, 1])
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Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 1024, 1, 1])
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node = helper.make_node("Conv", ["X", "W"], ["Y"],
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kernel_shape=[1, 1], strides=[1, 1], pads=[0, 0, 0, 0])
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graph = helper.make_graph([node], "conv_huge_pointwise_1024_dynamic", [X, W], [Y])
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model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
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save_model(model, "conv/huge_pointwise_1024_dynamic", "conv_huge_pointwise_1024_dynamic.onnx")
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def conv_large_output_channels_1x1():
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"""1x1 Conv with modest inputs and very large output channel count."""
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X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 64, 1, 1])
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Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 1024, 1, 1])
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W = numpy_helper.from_array(
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np.random.default_rng(74).uniform(-1, 1, (1024, 64, 1, 1)).astype(np.float32), name="W")
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node = helper.make_node("Conv", ["X", "W"], ["Y"],
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kernel_shape=[1, 1], strides=[1, 1], pads=[0, 0, 0, 0])
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graph = helper.make_graph([node], "conv_large_output_channels_1x1", [X], [Y], initializer=[W])
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model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
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save_model(model, "conv/large_output_channels_1x1", "conv_large_output_channels_1x1.onnx")
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def conv_large_input_channels_1x1():
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"""1x1 Conv with very large input channel count and modest outputs."""
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X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 1024, 1, 1])
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Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 64, 1, 1])
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W = numpy_helper.from_array(
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np.random.default_rng(75).uniform(-1, 1, (64, 1024, 1, 1)).astype(np.float32), name="W")
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node = helper.make_node("Conv", ["X", "W"], ["Y"],
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kernel_shape=[1, 1], strides=[1, 1], pads=[0, 0, 0, 0])
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graph = helper.make_graph([node], "conv_large_input_channels_1x1", [X], [Y], initializer=[W])
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model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
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save_model(model, "conv/large_input_channels_1x1", "conv_large_input_channels_1x1.onnx")
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def conv_depthwise_1024_channels():
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"""Depthwise 1x1 Conv with 1024 groups and preserved spatial shape."""
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X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 1024, 4, 4])
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Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 1024, 4, 4])
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W = numpy_helper.from_array(
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np.random.default_rng(76).uniform(-1, 1, (1024, 1, 1, 1)).astype(np.float32), name="W")
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node = helper.make_node("Conv", ["X", "W"], ["Y"],
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kernel_shape=[1, 1], strides=[1, 1], pads=[0, 0, 0, 0], group=1024)
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graph = helper.make_graph([node], "conv_depthwise_1024_channels", [X], [Y], initializer=[W])
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model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
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save_model(model, "conv/depthwise_1024_channels", "conv_depthwise_1024_channels.onnx")
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def conv_grouped_many_groups():
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"""Grouped 1x1 Conv with many groups and high channel counts."""
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X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 1024, 2, 2])
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Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 1024, 2, 2])
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W = numpy_helper.from_array(
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np.random.default_rng(77).uniform(-1, 1, (1024, 16, 1, 1)).astype(np.float32), name="W")
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node = helper.make_node("Conv", ["X", "W"], ["Y"],
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kernel_shape=[1, 1], strides=[1, 1], pads=[0, 0, 0, 0], group=64)
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graph = helper.make_graph([node], "conv_grouped_many_groups", [X], [Y], initializer=[W])
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model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
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save_model(model, "conv/grouped_many_groups", "conv_grouped_many_groups.onnx")
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def conv_non_square_kernel_1x3():
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"""Conv with a non-square 1x3 kernel."""
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X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 3, 5, 5])
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Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 4, 5, 3])
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W = numpy_helper.from_array(
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np.random.default_rng(78).uniform(-1, 1, (4, 3, 1, 3)).astype(np.float32), name="W")
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node = helper.make_node("Conv", ["X", "W"], ["Y"],
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kernel_shape=[1, 3], strides=[1, 1], pads=[0, 0, 0, 0])
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graph = helper.make_graph([node], "conv_non_square_kernel_1x3", [X], [Y], initializer=[W])
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model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
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save_model(model, "conv/non_square_kernel_1x3", "conv_non_square_kernel_1x3.onnx")
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def conv_non_square_kernel_3x1():
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"""Conv with a non-square 3x1 kernel."""
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X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 3, 5, 5])
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Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 4, 3, 5])
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W = numpy_helper.from_array(
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np.random.default_rng(79).uniform(-1, 1, (4, 3, 3, 1)).astype(np.float32), name="W")
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node = helper.make_node("Conv", ["X", "W"], ["Y"],
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kernel_shape=[3, 1], strides=[1, 1], pads=[0, 0, 0, 0])
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graph = helper.make_graph([node], "conv_non_square_kernel_3x1", [X], [Y], initializer=[W])
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model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
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save_model(model, "conv/non_square_kernel_3x1", "conv_non_square_kernel_3x1.onnx")
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def conv_non_uniform_stride():
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"""Conv with non-uniform stride across spatial dimensions."""
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X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 3, 6, 7])
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Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 4, 4, 3])
|
|
W = numpy_helper.from_array(
|
|
np.random.default_rng(80).uniform(-1, 1, (4, 3, 3, 3)).astype(np.float32), name="W")
|
|
node = helper.make_node("Conv", ["X", "W"], ["Y"],
|
|
kernel_shape=[3, 3], strides=[1, 2], pads=[0, 0, 0, 0])
|
|
graph = helper.make_graph([node], "conv_non_uniform_stride", [X], [Y], initializer=[W])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "conv/non_uniform_stride", "conv_non_uniform_stride.onnx")
|
|
|
|
|
|
def conv_dilated_3x3():
|
|
"""Conv with a dilated 3x3 kernel."""
|
|
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 1, 7, 7])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 1, 3, 3])
|
|
W = numpy_helper.from_array(
|
|
np.random.default_rng(81).uniform(-1, 1, (1, 1, 3, 3)).astype(np.float32), name="W")
|
|
node = helper.make_node("Conv", ["X", "W"], ["Y"],
|
|
kernel_shape=[3, 3], strides=[1, 1], pads=[0, 0, 0, 0], dilations=[2, 2])
|
|
graph = helper.make_graph([node], "conv_dilated_3x3", [X], [Y], initializer=[W])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "conv/dilated_3x3", "conv_dilated_3x3.onnx")
|
|
|
|
|
|
def conv_real_asymmetric_padding():
|
|
"""Conv with truly asymmetric explicit padding."""
|
|
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 1, 4, 5])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 2, 4, 7])
|
|
W = numpy_helper.from_array(
|
|
np.random.default_rng(82).uniform(-1, 1, (2, 1, 3, 2)).astype(np.float32), name="W")
|
|
node = helper.make_node("Conv", ["X", "W"], ["Y"],
|
|
kernel_shape=[3, 2], strides=[1, 1], pads=[0, 1, 2, 3])
|
|
graph = helper.make_graph([node], "conv_real_asymmetric_padding", [X], [Y], initializer=[W])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "conv/real_asymmetric_padding", "conv_real_asymmetric_padding.onnx")
|
|
|
|
|
|
def conv_same_lower_3x3():
|
|
"""Conv 3x3 with SAME_LOWER padding, preserving spatial dimensions."""
|
|
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 1, 5, 5])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 1, 5, 5])
|
|
W = numpy_helper.from_array(
|
|
np.random.default_rng(83).uniform(-1, 1, (1, 1, 3, 3)).astype(np.float32), name="W")
|
|
node = helper.make_node("Conv", ["X", "W"], ["Y"],
|
|
kernel_shape=[3, 3], strides=[1, 1], auto_pad="SAME_LOWER")
|
|
graph = helper.make_graph([node], "conv_same_lower_3x3", [X], [Y], initializer=[W])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "conv/same_lower_3x3", "conv_same_lower_3x3.onnx")
|
|
|
|
|
|
def conv_kernel_equals_input_spatial():
|
|
"""Conv where the kernel spans the full input spatial extent."""
|
|
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 3, 7, 7])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 8, 1, 1])
|
|
W = numpy_helper.from_array(
|
|
np.random.default_rng(84).uniform(-1, 1, (8, 3, 7, 7)).astype(np.float32), name="W")
|
|
node = helper.make_node("Conv", ["X", "W"], ["Y"],
|
|
kernel_shape=[7, 7], strides=[1, 1], pads=[0, 0, 0, 0])
|
|
graph = helper.make_graph([node], "conv_kernel_equals_input_spatial", [X], [Y], initializer=[W])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "conv/kernel_equals_input_spatial", "conv_kernel_equals_input_spatial.onnx")
|
|
|
|
|
|
def conv_batch_4_pointwise():
|
|
"""Batched 1x1 Conv with batch size 4."""
|
|
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [4, 128, 1, 1])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [4, 64, 1, 1])
|
|
W = numpy_helper.from_array(
|
|
np.random.default_rng(85).uniform(-1, 1, (64, 128, 1, 1)).astype(np.float32), name="W")
|
|
node = helper.make_node("Conv", ["X", "W"], ["Y"],
|
|
kernel_shape=[1, 1], strides=[1, 1], pads=[0, 0, 0, 0])
|
|
graph = helper.make_graph([node], "conv_batch_4_pointwise", [X], [Y], initializer=[W])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "conv/batch_4_pointwise", "conv_batch_4_pointwise.onnx")
|
|
|
|
|
|
def conv_without_kernel_shape_attr():
|
|
"""Conv where kernel_shape is inferred from the weight tensor."""
|
|
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 1, 5, 5])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 1, 3, 3])
|
|
W = numpy_helper.from_array(
|
|
np.random.default_rng(86).uniform(-1, 1, (1, 1, 3, 3)).astype(np.float32), name="W")
|
|
node = helper.make_node("Conv", ["X", "W"], ["Y"], strides=[1, 1], pads=[0, 0, 0, 0])
|
|
graph = helper.make_graph([node], "conv_without_kernel_shape_attr", [X], [Y], initializer=[W])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "conv/without_kernel_shape_attr", "conv_without_kernel_shape_attr.onnx")
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# GEMM tests
|
|
# ---------------------------------------------------------------------------
|
|
|
|
def gemm_simple():
|
|
"""Simple GEMM with square weights: [10, 132] @ [132, 132]."""
|
|
B, K, N = 10, 132, 132
|
|
W = numpy_helper.from_array(np.random.default_rng(41).uniform(-1, 1, (K, N)).astype(np.float32), name="W")
|
|
A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [B, K])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [B, N])
|
|
node = helper.make_node("Gemm", ["A", "W"], ["Y"])
|
|
graph = helper.make_graph([node], "gemm_simple", [A], [Y], initializer=[W])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "gemm/simple", "gemm_simple.onnx")
|
|
|
|
|
|
def gemm_non_square():
|
|
"""GEMM with non-square weight matrix: [B, K] @ [K, N], K != N."""
|
|
B, K, N = 4, 128, 64
|
|
W = numpy_helper.from_array(np.random.default_rng(42).uniform(-1, 1, (K, N)).astype(np.float32), name="W")
|
|
A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [B, K])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [B, N])
|
|
node = helper.make_node("Gemm", ["A", "W"], ["Y"])
|
|
graph = helper.make_graph([node], "gemm_non_square", [A], [Y], initializer=[W])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "gemm/non_square", "gemm_non_square.onnx")
|
|
|
|
|
|
def gemm_with_bias():
|
|
"""GEMM with bias: Y = A @ W + C."""
|
|
B, K, N = 4, 128, 128
|
|
rng = np.random.default_rng(43)
|
|
W = numpy_helper.from_array(rng.uniform(-1, 1, (K, N)).astype(np.float32), name="W")
|
|
C = numpy_helper.from_array(rng.uniform(-1, 1, (N,)).astype(np.float32), name="C")
|
|
A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [B, K])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [B, N])
|
|
node = helper.make_node("Gemm", ["A", "W", "C"], ["Y"])
|
|
graph = helper.make_graph([node], "gemm_with_bias", [A], [Y], initializer=[W, C])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "gemm/with_bias", "gemm_with_bias.onnx")
|
|
|
|
|
|
def gemm_transB():
|
|
"""GEMM with transB=1: Y = A @ W^T."""
|
|
B, K, N = 4, 128, 64
|
|
rng = np.random.default_rng(44)
|
|
# W stored as [N, K], transposed during computation
|
|
W = numpy_helper.from_array(rng.uniform(-1, 1, (N, K)).astype(np.float32), name="W")
|
|
A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [B, K])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [B, N])
|
|
node = helper.make_node("Gemm", ["A", "W"], ["Y"], transB=1)
|
|
graph = helper.make_graph([node], "gemm_transB", [A], [Y], initializer=[W])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "gemm/transB", "gemm_transB.onnx")
|
|
|
|
|
|
def gemm_alpha_beta():
|
|
"""GEMM with alpha and beta: Y = 0.5 * A @ W + 0.25 * C."""
|
|
B, K, N = 4, 64, 64
|
|
rng = np.random.default_rng(45)
|
|
W = numpy_helper.from_array(rng.uniform(-1, 1, (K, N)).astype(np.float32), name="W")
|
|
C = numpy_helper.from_array(rng.uniform(-1, 1, (N,)).astype(np.float32), name="C")
|
|
A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [B, K])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [B, N])
|
|
node = helper.make_node("Gemm", ["A", "W", "C"], ["Y"], alpha=0.5, beta=0.25)
|
|
graph = helper.make_graph([node], "gemm_alpha_beta", [A], [Y], initializer=[W, C])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "gemm/alpha_beta", "gemm_alpha_beta.onnx")
|
|
|
|
|
|
def gemm_small():
|
|
"""Small GEMM: [2, 8] @ [8, 4]."""
|
|
B, K, N = 2, 8, 4
|
|
rng = np.random.default_rng(46)
|
|
W = numpy_helper.from_array(rng.uniform(-1, 1, (K, N)).astype(np.float32), name="W")
|
|
A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [B, K])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [B, N])
|
|
node = helper.make_node("Gemm", ["A", "W"], ["Y"])
|
|
graph = helper.make_graph([node], "gemm_small", [A], [Y], initializer=[W])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "gemm/small", "gemm_small.onnx")
|
|
|
|
|
|
def gemm_large():
|
|
"""Larger GEMM: [8, 256] @ [256, 128]."""
|
|
B, K, N = 8, 256, 128
|
|
rng = np.random.default_rng(47)
|
|
W = numpy_helper.from_array(rng.uniform(-1, 1, (K, N)).astype(np.float32), name="W")
|
|
A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [B, K])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [B, N])
|
|
node = helper.make_node("Gemm", ["A", "W"], ["Y"])
|
|
graph = helper.make_graph([node], "gemm_large", [A], [Y], initializer=[W])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "gemm/large", "gemm_large.onnx")
|
|
|
|
|
|
def gemm_transB_with_bias():
|
|
"""GEMM with transB and bias: Y = A @ W^T + C."""
|
|
B, K, N = 4, 128, 64
|
|
rng = np.random.default_rng(48)
|
|
W = numpy_helper.from_array(rng.uniform(-1, 1, (N, K)).astype(np.float32), name="W")
|
|
C = numpy_helper.from_array(rng.uniform(-1, 1, (N,)).astype(np.float32), name="C")
|
|
A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [B, K])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [B, N])
|
|
node = helper.make_node("Gemm", ["A", "W", "C"], ["Y"], transB=1)
|
|
graph = helper.make_graph([node], "gemm_transB_with_bias", [A], [Y], initializer=[W, C])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "gemm/transB_with_bias", "gemm_transB_with_bias.onnx")
|
|
|
|
|
|
def gemm_dynamic():
|
|
"""GEMM with both matrix operands provided at runtime."""
|
|
A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [2, 8])
|
|
B = helper.make_tensor_value_info("B", TensorProto.FLOAT, [8, 4])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [2, 4])
|
|
node = helper.make_node("Gemm", ["A", "B"], ["Y"])
|
|
graph = helper.make_graph([node], "gemm_dynamic", [A, B], [Y])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "gemm/dynamic", "gemm_dynamic.onnx")
|
|
|
|
|
|
def gemm_dynamic_transB():
|
|
"""GEMM with runtime matrix operands and transposed runtime B."""
|
|
A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [2, 8])
|
|
B = helper.make_tensor_value_info("B", TensorProto.FLOAT, [4, 8])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [2, 4])
|
|
node = helper.make_node("Gemm", ["A", "B"], ["Y"], transB=1)
|
|
graph = helper.make_graph([node], "gemm_dynamic_transB", [A, B], [Y])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "gemm/dynamic_transB", "gemm_dynamic_transB.onnx")
|
|
|
|
|
|
def gemm_dynamic_bias():
|
|
"""GEMM with runtime matrix operands and runtime bias."""
|
|
A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [2, 8])
|
|
B = helper.make_tensor_value_info("B", TensorProto.FLOAT, [8, 4])
|
|
C = helper.make_tensor_value_info("C", TensorProto.FLOAT, [4])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [2, 4])
|
|
node = helper.make_node("Gemm", ["A", "B", "C"], ["Y"])
|
|
graph = helper.make_graph([node], "gemm_dynamic_bias", [A, B, C], [Y])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "gemm/dynamic_bias", "gemm_dynamic_bias.onnx")
|
|
|
|
|
|
def gemm_dynamic_alpha():
|
|
"""GEMM with runtime matrix operands and runtime alpha scaling."""
|
|
A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [2, 8])
|
|
B = helper.make_tensor_value_info("B", TensorProto.FLOAT, [8, 4])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [2, 4])
|
|
node = helper.make_node("Gemm", ["A", "B"], ["Y"], alpha=0.5)
|
|
graph = helper.make_graph([node], "gemm_dynamic_alpha", [A, B], [Y])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "gemm/dynamic_alpha", "gemm_dynamic_alpha.onnx")
|
|
|
|
|
|
def gemm_dynamic_beta():
|
|
"""GEMM with runtime matrix operands, runtime bias, and runtime beta scaling."""
|
|
A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [2, 8])
|
|
B = helper.make_tensor_value_info("B", TensorProto.FLOAT, [8, 4])
|
|
C = helper.make_tensor_value_info("C", TensorProto.FLOAT, [4])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [2, 4])
|
|
node = helper.make_node("Gemm", ["A", "B", "C"], ["Y"], beta=2.0)
|
|
graph = helper.make_graph([node], "gemm_dynamic_beta", [A, B, C], [Y])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "gemm/dynamic_beta", "gemm_dynamic_beta.onnx")
|
|
|
|
|
|
def gemm_dynamic_bias_alpha_beta():
|
|
"""GEMM with runtime matrix operands, runtime bias, alpha, and beta."""
|
|
A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [2, 8])
|
|
B = helper.make_tensor_value_info("B", TensorProto.FLOAT, [8, 4])
|
|
C = helper.make_tensor_value_info("C", TensorProto.FLOAT, [4])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [2, 4])
|
|
node = helper.make_node("Gemm", ["A", "B", "C"], ["Y"], alpha=0.5, beta=2.0)
|
|
graph = helper.make_graph([node], "gemm_dynamic_bias_alpha_beta", [A, B, C], [Y])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "gemm/dynamic_bias_alpha_beta", "gemm_dynamic_bias_alpha_beta.onnx")
|
|
|
|
|
|
def gemm_huge_1024():
|
|
"""Huge GEMM with 1024-wide inner and output dimensions."""
|
|
A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [1, 1024])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 1024])
|
|
W = numpy_helper.from_array(
|
|
np.random.default_rng(87).uniform(-1, 1, (1024, 1024)).astype(np.float32), name="W")
|
|
node = helper.make_node("Gemm", ["A", "W"], ["Y"])
|
|
graph = helper.make_graph([node], "gemm_huge_1024", [A], [Y], initializer=[W])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "gemm/huge_1024", "gemm_huge_1024.onnx")
|
|
|
|
|
|
def gemm_large_k_small_n():
|
|
"""GEMM with large K and smaller output width."""
|
|
A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [1, 1024])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 64])
|
|
W = numpy_helper.from_array(
|
|
np.random.default_rng(88).uniform(-1, 1, (1024, 64)).astype(np.float32), name="W")
|
|
node = helper.make_node("Gemm", ["A", "W"], ["Y"])
|
|
graph = helper.make_graph([node], "gemm_large_k_small_n", [A], [Y], initializer=[W])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "gemm/large_k_small_n", "gemm_large_k_small_n.onnx")
|
|
|
|
|
|
def gemm_small_k_large_n():
|
|
"""GEMM with modest K and very large output width."""
|
|
A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [1, 64])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 1024])
|
|
W = numpy_helper.from_array(
|
|
np.random.default_rng(89).uniform(-1, 1, (64, 1024)).astype(np.float32), name="W")
|
|
node = helper.make_node("Gemm", ["A", "W"], ["Y"])
|
|
graph = helper.make_graph([node], "gemm_small_k_large_n", [A], [Y], initializer=[W])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "gemm/small_k_large_n", "gemm_small_k_large_n.onnx")
|
|
|
|
|
|
def gemm_transA():
|
|
"""GEMM with transA=1: A is stored as [K, M] and used as [M, K]."""
|
|
A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [8, 4])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [4, 6])
|
|
W = numpy_helper.from_array(np.random.default_rng(90).uniform(-1, 1, (8, 6)).astype(np.float32), name="W")
|
|
node = helper.make_node("Gemm", ["A", "W"], ["Y"], transA=1)
|
|
graph = helper.make_graph([node], "gemm_transA", [A], [Y], initializer=[W])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "gemm/transA", "gemm_transA.onnx")
|
|
|
|
|
|
def gemm_transA_transB():
|
|
"""GEMM with transA=1 and transB=1."""
|
|
A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [8, 4])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [4, 6])
|
|
W = numpy_helper.from_array(np.random.default_rng(91).uniform(-1, 1, (6, 8)).astype(np.float32), name="W")
|
|
node = helper.make_node("Gemm", ["A", "W"], ["Y"], transA=1, transB=1)
|
|
graph = helper.make_graph([node], "gemm_transA_transB", [A], [Y], initializer=[W])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "gemm/transA_transB", "gemm_transA_transB.onnx")
|
|
|
|
|
|
def gemm_bias_rank2_broadcast():
|
|
"""GEMM with rank-2 bias broadcasting across rows."""
|
|
rng = np.random.default_rng(92)
|
|
A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [4, 8])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [4, 6])
|
|
W = numpy_helper.from_array(rng.uniform(-1, 1, (8, 6)).astype(np.float32), name="W")
|
|
C = numpy_helper.from_array(rng.uniform(-1, 1, (1, 6)).astype(np.float32), name="C")
|
|
node = helper.make_node("Gemm", ["A", "W", "C"], ["Y"])
|
|
graph = helper.make_graph([node], "gemm_bias_rank2_broadcast", [A], [Y], initializer=[W, C])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "gemm/bias_rank2_broadcast", "gemm_bias_rank2_broadcast.onnx")
|
|
|
|
|
|
def gemm_scalar_bias():
|
|
"""GEMM with scalar bias broadcasting to the full output."""
|
|
rng = np.random.default_rng(93)
|
|
A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [4, 8])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [4, 6])
|
|
W = numpy_helper.from_array(rng.uniform(-1, 1, (8, 6)).astype(np.float32), name="W")
|
|
C = numpy_helper.from_array(np.asarray(0.25, dtype=np.float32), name="C")
|
|
node = helper.make_node("Gemm", ["A", "W", "C"], ["Y"])
|
|
graph = helper.make_graph([node], "gemm_scalar_bias", [A], [Y], initializer=[W, C])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "gemm/scalar_bias", "gemm_scalar_bias.onnx")
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# MatMul tests
|
|
# ---------------------------------------------------------------------------
|
|
|
|
def matmul_basic():
|
|
"""Direct 2D MatMul with constant RHS."""
|
|
A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [2, 3])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [2, 4])
|
|
B = numpy_helper.from_array(np.random.default_rng(49).uniform(-1, 1, (3, 4)).astype(np.float32), name="B")
|
|
node = helper.make_node("MatMul", ["A", "B"], ["Y"])
|
|
graph = helper.make_graph([node], "matmul_basic", [A], [Y], initializer=[B])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "matmul/basic", "matmul_basic.onnx")
|
|
|
|
|
|
def matmul_left_constant():
|
|
"""Direct 2D MatMul with constant LHS."""
|
|
A = numpy_helper.from_array(np.random.default_rng(69).uniform(-1, 1, (2, 3)).astype(np.float32), name="A")
|
|
B = helper.make_tensor_value_info("B", TensorProto.FLOAT, [3, 4])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [2, 4])
|
|
node = helper.make_node("MatMul", ["A", "B"], ["Y"])
|
|
graph = helper.make_graph([node], "matmul_left_constant", [B], [Y], initializer=[A])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "matmul/left_constant", "matmul_left_constant.onnx")
|
|
|
|
|
|
def matmul_dynamic():
|
|
"""Direct 2D MatMul with both operands provided at runtime."""
|
|
A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [2, 3])
|
|
B = helper.make_tensor_value_info("B", TensorProto.FLOAT, [3, 4])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [2, 4])
|
|
node = helper.make_node("MatMul", ["A", "B"], ["Y"])
|
|
graph = helper.make_graph([node], "matmul_dynamic", [A, B], [Y])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "matmul/dynamic", "matmul_dynamic.onnx")
|
|
|
|
|
|
def matmul_batched_3d():
|
|
"""Batched 3D MatMul with matching batch dimensions."""
|
|
rng = np.random.default_rng(50)
|
|
A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [2, 2, 3])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [2, 2, 4])
|
|
B = numpy_helper.from_array(rng.uniform(-1, 1, (2, 3, 4)).astype(np.float32), name="B")
|
|
node = helper.make_node("MatMul", ["A", "B"], ["Y"])
|
|
graph = helper.make_graph([node], "matmul_batched_3d", [A], [Y], initializer=[B])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "matmul/batched_3d", "matmul_batched_3d.onnx")
|
|
|
|
|
|
def matmul_batched_3d_dynamic():
|
|
"""Batched 3D MatMul with both operands provided at runtime."""
|
|
A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [2, 2, 3])
|
|
B = helper.make_tensor_value_info("B", TensorProto.FLOAT, [2, 3, 4])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [2, 2, 4])
|
|
node = helper.make_node("MatMul", ["A", "B"], ["Y"])
|
|
graph = helper.make_graph([node], "matmul_batched_3d_dynamic", [A, B], [Y])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "matmul/batched_3d_dynamic", "matmul_batched_3d_dynamic.onnx")
|
|
|
|
|
|
def matmul_batched_left_constant():
|
|
"""Batched 3D MatMul with constant LHS and runtime RHS."""
|
|
rng = np.random.default_rng(70)
|
|
A = numpy_helper.from_array(rng.uniform(-1, 1, (2, 2, 3)).astype(np.float32), name="A")
|
|
B = helper.make_tensor_value_info("B", TensorProto.FLOAT, [2, 3, 4])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [2, 2, 4])
|
|
node = helper.make_node("MatMul", ["A", "B"], ["Y"])
|
|
graph = helper.make_graph([node], "matmul_batched_left_constant", [B], [Y], initializer=[A])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "matmul/batched_left_constant", "matmul_batched_left_constant.onnx")
|
|
|
|
|
|
def matmul_batched_rhs_broadcast():
|
|
"""Batched 3D MatMul with 2D constant RHS broadcast across batch."""
|
|
rng = np.random.default_rng(71)
|
|
A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [2, 2, 3])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [2, 2, 4])
|
|
B = numpy_helper.from_array(rng.uniform(-1, 1, (3, 4)).astype(np.float32), name="B")
|
|
node = helper.make_node("MatMul", ["A", "B"], ["Y"])
|
|
graph = helper.make_graph([node], "matmul_batched_rhs_broadcast", [A], [Y], initializer=[B])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "matmul/batched_rhs_broadcast", "matmul_batched_rhs_broadcast.onnx")
|
|
|
|
|
|
def matmul_batched_lhs_broadcast():
|
|
"""Batched 3D MatMul with 2D runtime LHS broadcast across batched RHS."""
|
|
rng = np.random.default_rng(72)
|
|
A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [2, 3])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [2, 2, 4])
|
|
B = numpy_helper.from_array(rng.uniform(-1, 1, (2, 3, 4)).astype(np.float32), name="B")
|
|
node = helper.make_node("MatMul", ["A", "B"], ["Y"])
|
|
graph = helper.make_graph([node], "matmul_batched_lhs_broadcast", [A], [Y], initializer=[B])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "matmul/batched_lhs_broadcast", "matmul_batched_lhs_broadcast.onnx")
|
|
|
|
|
|
def matmul_huge_1024():
|
|
"""Huge MatMul with 1024-wide inner and output dimensions."""
|
|
A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [1, 1024])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 1024])
|
|
B = numpy_helper.from_array(
|
|
np.random.default_rng(94).uniform(-1, 1, (1024, 1024)).astype(np.float32), name="B")
|
|
node = helper.make_node("MatMul", ["A", "B"], ["Y"])
|
|
graph = helper.make_graph([node], "matmul_huge_1024", [A], [Y], initializer=[B])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "matmul/huge_1024", "matmul_huge_1024.onnx")
|
|
|
|
|
|
def matmul_vector_matrix():
|
|
"""Vector-matrix MatMul producing a 1D output."""
|
|
A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [1024])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [64])
|
|
B = numpy_helper.from_array(np.random.default_rng(95).uniform(-1, 1, (1024, 64)).astype(np.float32), name="B")
|
|
node = helper.make_node("MatMul", ["A", "B"], ["Y"])
|
|
graph = helper.make_graph([node], "matmul_vector_matrix", [A], [Y], initializer=[B])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "matmul/vector_matrix", "matmul_vector_matrix.onnx")
|
|
|
|
|
|
def matmul_matrix_vector():
|
|
"""Matrix-vector MatMul producing a 1D output."""
|
|
A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [64, 1024])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [64])
|
|
B = numpy_helper.from_array(np.random.default_rng(96).uniform(-1, 1, (1024,)).astype(np.float32), name="B")
|
|
node = helper.make_node("MatMul", ["A", "B"], ["Y"])
|
|
graph = helper.make_graph([node], "matmul_matrix_vector", [A], [Y], initializer=[B])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "matmul/matrix_vector", "matmul_matrix_vector.onnx")
|
|
|
|
|
|
def matmul_vector_vector_dot():
|
|
"""Vector-vector MatMul producing a scalar output."""
|
|
A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [1024])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [])
|
|
B = numpy_helper.from_array(np.random.default_rng(97).uniform(-1, 1, (1024,)).astype(np.float32), name="B")
|
|
node = helper.make_node("MatMul", ["A", "B"], ["Y"])
|
|
graph = helper.make_graph([node], "matmul_vector_vector_dot", [A], [Y], initializer=[B])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "matmul/vector_vector_dot", "matmul_vector_vector_dot.onnx")
|
|
|
|
|
|
def matmul_batched_4d_broadcast():
|
|
"""Batched 4D MatMul with broadcast across leading dimensions."""
|
|
A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [2, 1, 3, 4])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [2, 5, 3, 6])
|
|
B = numpy_helper.from_array(np.random.default_rng(98).uniform(-1, 1, (1, 5, 4, 6)).astype(np.float32), name="B")
|
|
node = helper.make_node("MatMul", ["A", "B"], ["Y"])
|
|
graph = helper.make_graph([node], "matmul_batched_4d_broadcast", [A], [Y], initializer=[B])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "matmul/batched_4d_broadcast", "matmul_batched_4d_broadcast.onnx")
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Pooling tests
|
|
# ---------------------------------------------------------------------------
|
|
|
|
def maxpool_basic():
|
|
"""MaxPool 2x2 with stride 1."""
|
|
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 1, 4, 4])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 1, 3, 3])
|
|
node = helper.make_node("MaxPool", ["X"], ["Y"], kernel_shape=[2, 2], strides=[1, 1], pads=[0, 0, 0, 0])
|
|
graph = helper.make_graph([node], "maxpool_basic", [X], [Y])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "pool/max_basic", "maxpool_basic.onnx")
|
|
|
|
|
|
def maxpool_stride2_multichannel():
|
|
"""MaxPool 2x2 with stride 2 on multiple channels."""
|
|
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 5, 6, 6])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 5, 3, 3])
|
|
node = helper.make_node("MaxPool", ["X"], ["Y"], kernel_shape=[2, 2], strides=[2, 2], pads=[0, 0, 0, 0])
|
|
graph = helper.make_graph([node], "maxpool_stride2_multichannel", [X], [Y])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "pool/max_stride2_multichannel", "maxpool_stride2_multichannel.onnx")
|
|
|
|
|
|
def maxpool_same_upper():
|
|
"""MaxPool 3x3 with SAME_UPPER padding."""
|
|
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 1, 5, 5])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 1, 3, 3])
|
|
node = helper.make_node("MaxPool", ["X"], ["Y"], kernel_shape=[3, 3], strides=[2, 2], auto_pad="SAME_UPPER")
|
|
graph = helper.make_graph([node], "maxpool_same_upper", [X], [Y])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "pool/max_same_upper", "maxpool_same_upper.onnx")
|
|
|
|
|
|
def avgpool_basic():
|
|
"""AveragePool 2x2 with stride 1."""
|
|
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 3, 4, 4])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 3, 3, 3])
|
|
node = helper.make_node("AveragePool", ["X"], ["Y"], kernel_shape=[2, 2], strides=[1, 1], pads=[0, 0, 0, 0])
|
|
graph = helper.make_graph([node], "avgpool_basic", [X], [Y])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "pool/avg_basic", "avgpool_basic.onnx")
|
|
|
|
|
|
def avgpool_explicit_padding():
|
|
"""AveragePool 3x3 with explicit padding, excluding pad from the divisor."""
|
|
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 2, 4, 4])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 2, 2, 2])
|
|
node = helper.make_node("AveragePool", ["X"], ["Y"],
|
|
kernel_shape=[3, 3], strides=[2, 2], pads=[1, 1, 1, 1], count_include_pad=0)
|
|
graph = helper.make_graph([node], "avgpool_explicit_padding", [X], [Y])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "pool/avg_explicit_padding", "avgpool_explicit_padding.onnx")
|
|
|
|
|
|
def avgpool_include_pad():
|
|
"""AveragePool 3x3 with explicit padding, including pad in the divisor."""
|
|
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 2, 4, 4])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 2, 2, 2])
|
|
node = helper.make_node("AveragePool", ["X"], ["Y"],
|
|
kernel_shape=[3, 3], strides=[2, 2], pads=[1, 1, 1, 1], count_include_pad=1)
|
|
graph = helper.make_graph([node], "avgpool_include_pad", [X], [Y])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "pool/avg_include_pad", "avgpool_include_pad.onnx")
|
|
|
|
|
|
def maxpool_after_conv():
|
|
"""Conv followed by MaxPool to validate pooling on lowered conv results."""
|
|
rng = np.random.default_rng(59)
|
|
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 3, 6, 6])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 4, 2, 2])
|
|
W = numpy_helper.from_array(rng.uniform(-1, 1, (4, 3, 3, 3)).astype(np.float32), name="W")
|
|
conv = helper.make_node("Conv", ["X", "W"], ["C"], kernel_shape=[3, 3], strides=[1, 1], pads=[0, 0, 0, 0])
|
|
pool = helper.make_node("MaxPool", ["C"], ["Y"], kernel_shape=[2, 2], strides=[2, 2], pads=[0, 0, 0, 0])
|
|
graph = helper.make_graph([conv, pool], "maxpool_after_conv", [X], [Y], initializer=[W])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "pool/max_after_conv", "maxpool_after_conv.onnx")
|
|
|
|
|
|
def maxpool_ceil_mode():
|
|
"""MaxPool with ceil_mode enabled."""
|
|
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 1, 5, 5])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 1, 2, 2])
|
|
node = helper.make_node("MaxPool", ["X"], ["Y"],
|
|
kernel_shape=[2, 2], strides=[2, 2], pads=[0, 0, 0, 0], ceil_mode=1)
|
|
graph = helper.make_graph([node], "maxpool_ceil_mode", [X], [Y])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "pool/max_ceil_mode", "maxpool_ceil_mode.onnx")
|
|
|
|
|
|
def avgpool_ceil_mode():
|
|
"""AveragePool with ceil_mode enabled."""
|
|
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 2, 5, 5])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 2, 2, 2])
|
|
node = helper.make_node("AveragePool", ["X"], ["Y"],
|
|
kernel_shape=[2, 2], strides=[2, 2], pads=[0, 0, 0, 0], ceil_mode=1)
|
|
graph = helper.make_graph([node], "avgpool_ceil_mode", [X], [Y])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "pool/avg_ceil_mode", "avgpool_ceil_mode.onnx")
|
|
|
|
|
|
def maxpool_real_asymmetric_padding():
|
|
"""MaxPool with truly asymmetric explicit padding."""
|
|
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 1, 4, 5])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 1, 4, 3])
|
|
node = helper.make_node("MaxPool", ["X"], ["Y"],
|
|
kernel_shape=[3, 3], strides=[1, 2], pads=[0, 1, 2, 1])
|
|
graph = helper.make_graph([node], "maxpool_real_asymmetric_padding", [X], [Y])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "pool/max_real_asymmetric_padding", "maxpool_real_asymmetric_padding.onnx")
|
|
|
|
|
|
def avgpool_real_asymmetric_padding():
|
|
"""AveragePool with truly asymmetric explicit padding."""
|
|
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 2, 4, 5])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 2, 4, 3])
|
|
node = helper.make_node("AveragePool", ["X"], ["Y"],
|
|
kernel_shape=[3, 3], strides=[1, 2], pads=[0, 1, 2, 1], count_include_pad=0)
|
|
graph = helper.make_graph([node], "avgpool_real_asymmetric_padding", [X], [Y])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "pool/avg_real_asymmetric_padding", "avgpool_real_asymmetric_padding.onnx")
|
|
|
|
|
|
def maxpool_non_square_kernel():
|
|
"""MaxPool with a non-square kernel."""
|
|
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 3, 5, 6])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 3, 4, 2])
|
|
node = helper.make_node("MaxPool", ["X"], ["Y"],
|
|
kernel_shape=[2, 3], strides=[1, 2], pads=[0, 0, 0, 0])
|
|
graph = helper.make_graph([node], "maxpool_non_square_kernel", [X], [Y])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "pool/max_non_square_kernel", "maxpool_non_square_kernel.onnx")
|
|
|
|
|
|
def avgpool_non_uniform_stride():
|
|
"""AveragePool with non-uniform stride."""
|
|
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 3, 5, 6])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 3, 4, 2])
|
|
node = helper.make_node("AveragePool", ["X"], ["Y"],
|
|
kernel_shape=[2, 3], strides=[1, 2], pads=[0, 0, 0, 0])
|
|
graph = helper.make_graph([node], "avgpool_non_uniform_stride", [X], [Y])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "pool/avg_non_uniform_stride", "avgpool_non_uniform_stride.onnx")
|
|
|
|
|
|
def maxpool_global_style_kernel_equals_input():
|
|
"""MaxPool where the kernel covers the full spatial input."""
|
|
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 8, 4, 4])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 8, 1, 1])
|
|
node = helper.make_node("MaxPool", ["X"], ["Y"], kernel_shape=[4, 4], strides=[1, 1], pads=[0, 0, 0, 0])
|
|
graph = helper.make_graph([node], "maxpool_global_style_kernel_equals_input", [X], [Y])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "pool/max_global_style_kernel_equals_input", "maxpool_global_style_kernel_equals_input.onnx")
|
|
|
|
|
|
def avgpool_large_channels():
|
|
"""AveragePool with a large channel count and small spatial dimensions."""
|
|
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 1024, 2, 2])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 1024, 1, 1])
|
|
node = helper.make_node("AveragePool", ["X"], ["Y"], kernel_shape=[2, 2], strides=[1, 1], pads=[0, 0, 0, 0])
|
|
graph = helper.make_graph([node], "avgpool_large_channels", [X], [Y])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "pool/avg_large_channels", "avgpool_large_channels.onnx")
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# ReduceMean tests
|
|
# ---------------------------------------------------------------------------
|
|
|
|
def reducemean_basic():
|
|
"""ReduceMean over the feature dimension, preserving rank."""
|
|
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [4, 8])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [4, 1])
|
|
node = helper.make_node("ReduceMean", ["X"], ["Y"], axes=[1], keepdims=1)
|
|
graph = helper.make_graph([node], "reducemean_basic", [X], [Y])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "reduce_mean/basic", "reduce_mean_basic.onnx")
|
|
|
|
|
|
def reducemean_keepdims_0():
|
|
"""ReduceMean over the feature dimension, dropping the reduced axis."""
|
|
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [4, 8])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [4])
|
|
node = helper.make_node("ReduceMean", ["X"], ["Y"], axes=[1], keepdims=0)
|
|
graph = helper.make_graph([node], "reducemean_keepdims_0", [X], [Y])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "reduce_mean/keepdims_0", "reduce_mean_keepdims_0.onnx")
|
|
|
|
|
|
def reducemean_4d_spatial():
|
|
"""ReduceMean over H and W on an NCHW tensor."""
|
|
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 3, 4, 4])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 3, 1, 1])
|
|
node = helper.make_node("ReduceMean", ["X"], ["Y"], axes=[2, 3], keepdims=1)
|
|
graph = helper.make_graph([node], "reducemean_4d_spatial", [X], [Y])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "reduce_mean/4d_spatial", "reduce_mean_4d_spatial.onnx")
|
|
|
|
|
|
def reducemean_after_conv():
|
|
"""Conv followed by ReduceMean over the spatial dimensions."""
|
|
rng = np.random.default_rng(62)
|
|
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 3, 5, 5])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 2, 1, 1])
|
|
W = numpy_helper.from_array(rng.uniform(-1, 1, (2, 3, 3, 3)).astype(np.float32), name="W")
|
|
B = numpy_helper.from_array(rng.uniform(-1, 1, (2,)).astype(np.float32), name="B")
|
|
conv = helper.make_node("Conv", ["X", "W", "B"], ["C"],
|
|
kernel_shape=[3, 3], strides=[1, 1], pads=[0, 0, 0, 0])
|
|
reduce = helper.make_node("ReduceMean", ["C"], ["Y"], axes=[2, 3], keepdims=1)
|
|
graph = helper.make_graph([conv, reduce], "reducemean_after_conv", [X], [Y], initializer=[W, B])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "reduce_mean/after_conv", "reduce_mean_after_conv.onnx")
|
|
|
|
|
|
def reducemean_negative_axis():
|
|
"""ReduceMean using a negative axis."""
|
|
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [2, 3, 4])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [2, 3, 1])
|
|
node = helper.make_node("ReduceMean", ["X"], ["Y"], axes=[-1], keepdims=1)
|
|
graph = helper.make_graph([node], "reducemean_negative_axis", [X], [Y])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "reduce_mean/negative_axis", "reduce_mean_negative_axis.onnx")
|
|
|
|
|
|
def reducemean_all_axes_keepdims_1():
|
|
"""ReduceMean across all axes while preserving rank."""
|
|
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [2, 3, 4])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 1, 1])
|
|
node = helper.make_node("ReduceMean", ["X"], ["Y"], axes=[0, 1, 2], keepdims=1)
|
|
graph = helper.make_graph([node], "reducemean_all_axes_keepdims_1", [X], [Y])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "reduce_mean/all_axes_keepdims_1", "reduce_mean_all_axes_keepdims_1.onnx")
|
|
|
|
|
|
def reducemean_all_axes_keepdims_0():
|
|
"""ReduceMean across all axes producing a scalar."""
|
|
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [2, 3, 4])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [])
|
|
node = helper.make_node("ReduceMean", ["X"], ["Y"], axes=[0, 1, 2], keepdims=0)
|
|
graph = helper.make_graph([node], "reducemean_all_axes_keepdims_0", [X], [Y])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "reduce_mean/all_axes_keepdims_0", "reduce_mean_all_axes_keepdims_0.onnx")
|
|
|
|
|
|
def reducemean_4d_spatial_keepdims_0():
|
|
"""ReduceMean over H and W on an NCHW tensor, dropping reduced axes."""
|
|
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 3, 4, 4])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 3])
|
|
node = helper.make_node("ReduceMean", ["X"], ["Y"], axes=[2, 3], keepdims=0)
|
|
graph = helper.make_graph([node], "reducemean_4d_spatial_keepdims_0", [X], [Y])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "reduce_mean/4d_spatial_keepdims_0", "reduce_mean_4d_spatial_keepdims_0.onnx")
|
|
|
|
|
|
def reducemean_channel_axis_nchw():
|
|
"""ReduceMean over the channel axis of an NCHW tensor."""
|
|
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 1024, 2, 2])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 1, 2, 2])
|
|
node = helper.make_node("ReduceMean", ["X"], ["Y"], axes=[1], keepdims=1)
|
|
graph = helper.make_graph([node], "reducemean_channel_axis_nchw", [X], [Y])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "reduce_mean/channel_axis_nchw", "reduce_mean_channel_axis_nchw.onnx")
|
|
|
|
|
|
def reducemean_large_dimension_1024():
|
|
"""ReduceMean over a large 1024-length dimension."""
|
|
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 1024])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 1])
|
|
node = helper.make_node("ReduceMean", ["X"], ["Y"], axes=[1], keepdims=1)
|
|
graph = helper.make_graph([node], "reducemean_large_dimension_1024", [X], [Y])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "reduce_mean/large_dimension_1024", "reduce_mean_large_dimension_1024.onnx")
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Relu tests
|
|
# ---------------------------------------------------------------------------
|
|
|
|
def relu_basic():
|
|
"""Standalone Relu on a simple 2D tensor."""
|
|
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [4, 8])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [4, 8])
|
|
node = helper.make_node("Relu", ["X"], ["Y"])
|
|
graph = helper.make_graph([node], "relu_basic", [X], [Y])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "relu/basic", "relu_basic.onnx")
|
|
|
|
|
|
def relu_4d():
|
|
"""Standalone Relu on an NCHW tensor."""
|
|
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [2, 3, 4, 4])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [2, 3, 4, 4])
|
|
node = helper.make_node("Relu", ["X"], ["Y"])
|
|
graph = helper.make_graph([node], "relu_4d", [X], [Y])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "relu/4d", "relu_4d.onnx")
|
|
|
|
|
|
def relu_after_conv():
|
|
"""Conv followed by Relu."""
|
|
rng = np.random.default_rng(60)
|
|
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 3, 5, 5])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 2, 3, 3])
|
|
W = numpy_helper.from_array(rng.uniform(-1, 1, (2, 3, 3, 3)).astype(np.float32), name="W")
|
|
B = numpy_helper.from_array(rng.uniform(-1, 1, (2,)).astype(np.float32), name="B")
|
|
conv = helper.make_node("Conv", ["X", "W", "B"], ["C"],
|
|
kernel_shape=[3, 3], strides=[1, 1], pads=[0, 0, 0, 0])
|
|
relu = helper.make_node("Relu", ["C"], ["Y"])
|
|
graph = helper.make_graph([conv, relu], "relu_after_conv", [X], [Y], initializer=[W, B])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "relu/after_conv", "relu_after_conv.onnx")
|
|
|
|
|
|
def relu_after_gemm():
|
|
"""Gemm followed by Relu."""
|
|
B, K, N = 4, 64, 32
|
|
rng = np.random.default_rng(61)
|
|
W = numpy_helper.from_array(rng.uniform(-1, 1, (K, N)).astype(np.float32), name="W")
|
|
C = numpy_helper.from_array(rng.uniform(-1, 1, (N,)).astype(np.float32), name="C")
|
|
A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [B, K])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [B, N])
|
|
gemm = helper.make_node("Gemm", ["A", "W", "C"], ["G"])
|
|
relu = helper.make_node("Relu", ["G"], ["Y"])
|
|
graph = helper.make_graph([gemm, relu], "relu_after_gemm", [A], [Y], initializer=[W, C])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "relu/after_gemm", "relu_after_gemm.onnx")
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Sigmoid tests
|
|
# ---------------------------------------------------------------------------
|
|
|
|
def sigmoid_basic():
|
|
"""Standalone Sigmoid on a simple 2D tensor."""
|
|
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [4, 8])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [4, 8])
|
|
node = helper.make_node("Sigmoid", ["X"], ["Y"])
|
|
graph = helper.make_graph([node], "sigmoid_basic", [X], [Y])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "sigmoid/basic", "sigmoid_basic.onnx")
|
|
|
|
|
|
def sigmoid_4d():
|
|
"""Standalone Sigmoid on an NCHW tensor."""
|
|
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [2, 3, 4, 4])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [2, 3, 4, 4])
|
|
node = helper.make_node("Sigmoid", ["X"], ["Y"])
|
|
graph = helper.make_graph([node], "sigmoid_4d", [X], [Y])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "sigmoid/4d", "sigmoid_4d.onnx")
|
|
|
|
|
|
def sigmoid_after_gemm():
|
|
"""Gemm followed by Sigmoid."""
|
|
B, K, N = 4, 64, 32
|
|
rng = np.random.default_rng(63)
|
|
W = numpy_helper.from_array(rng.uniform(-1, 1, (K, N)).astype(np.float32), name="W")
|
|
C = numpy_helper.from_array(rng.uniform(-1, 1, (N,)).astype(np.float32), name="C")
|
|
A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [B, K])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [B, N])
|
|
gemm = helper.make_node("Gemm", ["A", "W", "C"], ["G"])
|
|
sigmoid = helper.make_node("Sigmoid", ["G"], ["Y"])
|
|
graph = helper.make_graph([gemm, sigmoid], "sigmoid_after_gemm", [A], [Y], initializer=[W, C])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "sigmoid/after_gemm", "sigmoid_after_gemm.onnx")
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Softmax tests
|
|
# ---------------------------------------------------------------------------
|
|
|
|
def softmax_basic():
|
|
"""Softmax over the last dimension of a 2D tensor."""
|
|
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [3, 5])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [3, 5])
|
|
node = helper.make_node("Softmax", ["X"], ["Y"], axis=1)
|
|
graph = helper.make_graph([node], "softmax_basic", [X], [Y])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "softmax/basic", "softmax_basic.onnx")
|
|
|
|
|
|
def softmax_3d_last_axis():
|
|
"""Softmax over the last axis of a 3D tensor."""
|
|
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [2, 3, 4])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [2, 3, 4])
|
|
node = helper.make_node("Softmax", ["X"], ["Y"], axis=2)
|
|
graph = helper.make_graph([node], "softmax_3d_last_axis", [X], [Y])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "softmax/3d_last_axis", "softmax_3d_last_axis.onnx")
|
|
|
|
|
|
def softmax_channel_axis():
|
|
"""Softmax over the channel axis of an NCHW tensor."""
|
|
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 3, 2, 2])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 3, 2, 2])
|
|
node = helper.make_node("Softmax", ["X"], ["Y"], axis=1)
|
|
graph = helper.make_graph([node], "softmax_channel_axis", [X], [Y])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "softmax/channel_axis", "softmax_channel_axis.onnx")
|
|
|
|
|
|
def softmax_negative_axis():
|
|
"""Softmax using a negative axis."""
|
|
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [2, 3, 4])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [2, 3, 4])
|
|
node = helper.make_node("Softmax", ["X"], ["Y"], axis=-1)
|
|
graph = helper.make_graph([node], "softmax_negative_axis", [X], [Y])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "softmax/negative_axis", "softmax_negative_axis.onnx")
|
|
|
|
|
|
def softmax_large_dimension_1024():
|
|
"""Softmax across a large last dimension."""
|
|
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 1024])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 1024])
|
|
node = helper.make_node("Softmax", ["X"], ["Y"], axis=1)
|
|
graph = helper.make_graph([node], "softmax_large_dimension_1024", [X], [Y])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "softmax/large_dimension_1024", "softmax_large_dimension_1024.onnx")
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Resize tests
|
|
# ---------------------------------------------------------------------------
|
|
|
|
def resize_nearest_2x():
|
|
"""Resize an NCHW tensor with nearest-neighbor upsampling by a factor of 2."""
|
|
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 1, 2, 3])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 1, 4, 6])
|
|
roi = numpy_helper.from_array(np.asarray([], dtype=np.float32), name="roi")
|
|
scales = numpy_helper.from_array(np.asarray([1.0, 1.0, 2.0, 2.0], dtype=np.float32), name="scales")
|
|
node = helper.make_node(
|
|
"Resize", ["X", "roi", "scales"], ["Y"],
|
|
mode="nearest", coordinate_transformation_mode="asymmetric", nearest_mode="floor")
|
|
graph = helper.make_graph([node], "resize_nearest_2x", [X], [Y], initializer=[roi, scales])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "resize/nearest_2x", "resize_nearest_2x.onnx")
|
|
|
|
|
|
def resize_nearest_non_uniform():
|
|
"""Resize an NCHW tensor with non-uniform nearest-neighbor scales."""
|
|
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 1, 2, 3])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 1, 6, 6])
|
|
roi = numpy_helper.from_array(np.asarray([], dtype=np.float32), name="roi")
|
|
scales = numpy_helper.from_array(np.asarray([1.0, 1.0, 3.0, 2.0], dtype=np.float32), name="scales")
|
|
node = helper.make_node(
|
|
"Resize", ["X", "roi", "scales"], ["Y"],
|
|
mode="nearest", coordinate_transformation_mode="asymmetric", nearest_mode="floor")
|
|
graph = helper.make_graph([node], "resize_nearest_non_uniform", [X], [Y], initializer=[roi, scales])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "resize/non_uniform", "resize_non_uniform.onnx")
|
|
|
|
|
|
def resize_with_sizes():
|
|
"""Resize an NCHW tensor to explicit output sizes."""
|
|
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 1, 2, 3])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 1, 3, 5])
|
|
roi = numpy_helper.from_array(np.asarray([], dtype=np.float32), name="roi")
|
|
sizes = make_int64_initializer("sizes", [1, 1, 3, 5])
|
|
node = helper.make_node(
|
|
"Resize", ["X", "roi", "", "sizes"], ["Y"],
|
|
mode="nearest", coordinate_transformation_mode="asymmetric", nearest_mode="floor")
|
|
graph = helper.make_graph([node], "resize_with_sizes", [X], [Y], initializer=[roi, sizes])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "resize/with_sizes", "resize_with_sizes.onnx")
|
|
|
|
|
|
def resize_nearest_downsample():
|
|
"""Resize an NCHW tensor with nearest-neighbor downsampling."""
|
|
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 1, 4, 6])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 1, 2, 3])
|
|
roi = numpy_helper.from_array(np.asarray([], dtype=np.float32), name="roi")
|
|
scales = numpy_helper.from_array(np.asarray([1.0, 1.0, 0.5, 0.5], dtype=np.float32), name="scales")
|
|
node = helper.make_node(
|
|
"Resize", ["X", "roi", "scales"], ["Y"],
|
|
mode="nearest", coordinate_transformation_mode="asymmetric", nearest_mode="floor")
|
|
graph = helper.make_graph([node], "resize_nearest_downsample", [X], [Y], initializer=[roi, scales])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "resize/nearest_downsample", "resize_nearest_downsample.onnx")
|
|
|
|
|
|
def resize_height_only():
|
|
"""Resize only the height dimension of an NCHW tensor."""
|
|
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 1, 2, 3])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 1, 4, 3])
|
|
roi = numpy_helper.from_array(np.asarray([], dtype=np.float32), name="roi")
|
|
scales = numpy_helper.from_array(np.asarray([1.0, 1.0, 2.0, 1.0], dtype=np.float32), name="scales")
|
|
node = helper.make_node(
|
|
"Resize", ["X", "roi", "scales"], ["Y"],
|
|
mode="nearest", coordinate_transformation_mode="asymmetric", nearest_mode="floor")
|
|
graph = helper.make_graph([node], "resize_height_only", [X], [Y], initializer=[roi, scales])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "resize/height_only", "resize_height_only.onnx")
|
|
|
|
|
|
def resize_width_only():
|
|
"""Resize only the width dimension of an NCHW tensor."""
|
|
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 1, 2, 3])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 1, 2, 6])
|
|
roi = numpy_helper.from_array(np.asarray([], dtype=np.float32), name="roi")
|
|
scales = numpy_helper.from_array(np.asarray([1.0, 1.0, 1.0, 2.0], dtype=np.float32), name="scales")
|
|
node = helper.make_node(
|
|
"Resize", ["X", "roi", "scales"], ["Y"],
|
|
mode="nearest", coordinate_transformation_mode="asymmetric", nearest_mode="floor")
|
|
graph = helper.make_graph([node], "resize_width_only", [X], [Y], initializer=[roi, scales])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "resize/width_only", "resize_width_only.onnx")
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Split tests
|
|
# ---------------------------------------------------------------------------
|
|
|
|
def split_basic():
|
|
"""Split a 2D tensor into two outputs along the feature axis."""
|
|
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [2, 6])
|
|
Y0 = helper.make_tensor_value_info("Y0", TensorProto.FLOAT, [2, 2])
|
|
Y1 = helper.make_tensor_value_info("Y1", TensorProto.FLOAT, [2, 4])
|
|
split = make_int64_initializer("split", [2, 4])
|
|
node = helper.make_node("Split", ["X", "split"], ["Y0", "Y1"], axis=1)
|
|
graph = helper.make_graph([node], "split_basic", [X], [Y0, Y1], initializer=[split])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "split/basic", "split_basic.onnx")
|
|
|
|
|
|
def split_equal_three_way():
|
|
"""Split a 2D tensor evenly into three outputs."""
|
|
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [2, 6])
|
|
Y0 = helper.make_tensor_value_info("Y0", TensorProto.FLOAT, [2, 2])
|
|
Y1 = helper.make_tensor_value_info("Y1", TensorProto.FLOAT, [2, 2])
|
|
Y2 = helper.make_tensor_value_info("Y2", TensorProto.FLOAT, [2, 2])
|
|
node = helper.make_node("Split", ["X"], ["Y0", "Y1", "Y2"], axis=1)
|
|
graph = helper.make_graph([node], "split_equal_three_way", [X], [Y0, Y1, Y2])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "split/equal_three_way", "split_equal_three_way.onnx")
|
|
|
|
|
|
def split_negative_axis():
|
|
"""Split a tensor using a negative axis."""
|
|
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [2, 3, 6])
|
|
Y0 = helper.make_tensor_value_info("Y0", TensorProto.FLOAT, [2, 3, 2])
|
|
Y1 = helper.make_tensor_value_info("Y1", TensorProto.FLOAT, [2, 3, 4])
|
|
split = make_int64_initializer("split", [2, 4])
|
|
node = helper.make_node("Split", ["X", "split"], ["Y0", "Y1"], axis=-1)
|
|
graph = helper.make_graph([node], "split_negative_axis", [X], [Y0, Y1], initializer=[split])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "split/negative_axis", "split_negative_axis.onnx")
|
|
|
|
|
|
def split_uneven_channel_axis_4d():
|
|
"""Split an NCHW tensor unevenly along the channel axis."""
|
|
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 7, 2, 2])
|
|
Y0 = helper.make_tensor_value_info("Y0", TensorProto.FLOAT, [1, 2, 2, 2])
|
|
Y1 = helper.make_tensor_value_info("Y1", TensorProto.FLOAT, [1, 5, 2, 2])
|
|
split = make_int64_initializer("split", [2, 5])
|
|
node = helper.make_node("Split", ["X", "split"], ["Y0", "Y1"], axis=1)
|
|
graph = helper.make_graph([node], "split_uneven_channel_axis_4d", [X], [Y0, Y1], initializer=[split])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "split/uneven_channel_axis_4d", "split_uneven_channel_axis_4d.onnx")
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Gather tests
|
|
# ---------------------------------------------------------------------------
|
|
|
|
def gather_axis1():
|
|
"""Gather selected columns from a 2D tensor."""
|
|
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [3, 4])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [3, 2])
|
|
indices = make_int64_initializer("indices", [0, 2])
|
|
node = helper.make_node("Gather", ["X", "indices"], ["Y"], axis=1)
|
|
graph = helper.make_graph([node], "gather_axis1", [X], [Y], initializer=[indices])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "gather/axis1", "gather_axis1.onnx")
|
|
|
|
|
|
def gather_axis0_matrix_indices():
|
|
"""Gather rows using a 2D indices tensor."""
|
|
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [4, 3])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [2, 2, 3])
|
|
indices = make_int64_initializer("indices", [[0, 2], [3, 1]])
|
|
node = helper.make_node("Gather", ["X", "indices"], ["Y"], axis=0)
|
|
graph = helper.make_graph([node], "gather_axis0_matrix_indices", [X], [Y], initializer=[indices])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "gather/axis0_matrix_indices", "gather_axis0_matrix_indices.onnx")
|
|
|
|
|
|
def gather_negative_indices():
|
|
"""Gather with negative indices along axis 0."""
|
|
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [4, 3])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [2, 3])
|
|
indices = make_int64_initializer("indices", [-1, -3])
|
|
node = helper.make_node("Gather", ["X", "indices"], ["Y"], axis=0)
|
|
graph = helper.make_graph([node], "gather_negative_indices", [X], [Y], initializer=[indices])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "gather/negative_indices", "gather_negative_indices.onnx")
|
|
|
|
|
|
def gather_negative_axis():
|
|
"""Gather using a negative axis."""
|
|
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [2, 3, 4])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [2, 3, 2])
|
|
indices = make_int64_initializer("indices", [0, 2])
|
|
node = helper.make_node("Gather", ["X", "indices"], ["Y"], axis=-1)
|
|
graph = helper.make_graph([node], "gather_negative_axis", [X], [Y], initializer=[indices])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "gather/negative_axis", "gather_negative_axis.onnx")
|
|
|
|
|
|
def gather_3d_input_axis1():
|
|
"""Gather along axis 1 of a 3D input tensor."""
|
|
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [2, 4, 3])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [2, 2, 3])
|
|
indices = make_int64_initializer("indices", [1, 3])
|
|
node = helper.make_node("Gather", ["X", "indices"], ["Y"], axis=1)
|
|
graph = helper.make_graph([node], "gather_3d_input_axis1", [X], [Y], initializer=[indices])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "gather/3d_input_axis1", "gather_3d_input_axis1.onnx")
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Concat tests
|
|
# ---------------------------------------------------------------------------
|
|
|
|
def concat_channel_axis():
|
|
"""Concat two runtime NCHW tensors along the channel axis."""
|
|
A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [1, 1, 2, 2])
|
|
B = helper.make_tensor_value_info("B", TensorProto.FLOAT, [1, 2, 2, 2])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 3, 2, 2])
|
|
node = helper.make_node("Concat", ["A", "B"], ["Y"], axis=1)
|
|
graph = helper.make_graph([node], "concat_channel_axis", [A, B], [Y])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "concat/channel_axis", "concat_channel_axis.onnx")
|
|
|
|
|
|
def concat_negative_axis():
|
|
"""Concat along a negative axis."""
|
|
A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [2, 3, 2])
|
|
B = helper.make_tensor_value_info("B", TensorProto.FLOAT, [2, 3, 4])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [2, 3, 6])
|
|
node = helper.make_node("Concat", ["A", "B"], ["Y"], axis=-1)
|
|
graph = helper.make_graph([node], "concat_negative_axis", [A, B], [Y])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "concat/negative_axis", "concat_negative_axis.onnx")
|
|
|
|
|
|
def concat_three_inputs_channel_axis():
|
|
"""Concat three runtime NCHW tensors along the channel axis."""
|
|
A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [1, 1, 2, 2])
|
|
B = helper.make_tensor_value_info("B", TensorProto.FLOAT, [1, 2, 2, 2])
|
|
C = helper.make_tensor_value_info("C", TensorProto.FLOAT, [1, 3, 2, 2])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 6, 2, 2])
|
|
node = helper.make_node("Concat", ["A", "B", "C"], ["Y"], axis=1)
|
|
graph = helper.make_graph([node], "concat_three_inputs_channel_axis", [A, B, C], [Y])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "concat/three_inputs_channel_axis", "concat_three_inputs_channel_axis.onnx")
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Reshape tests
|
|
# ---------------------------------------------------------------------------
|
|
|
|
def reshape_same_rank():
|
|
"""Runtime tensor Reshape with a static shape initializer and unchanged rank."""
|
|
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [2, 3])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [3, 2])
|
|
shape = make_int64_initializer("shape", [3, 2])
|
|
node = helper.make_node("Reshape", ["X", "shape"], ["Y"])
|
|
graph = helper.make_graph([node], "reshape_same_rank", [X], [Y], initializer=[shape])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "reshape/same_rank", "reshape_same_rank.onnx")
|
|
|
|
|
|
def reshape_infer_dim_minus_one():
|
|
"""Reshape using -1 to infer one output dimension."""
|
|
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [2, 3, 4])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [2, 12])
|
|
shape = make_int64_initializer("shape", [2, -1])
|
|
node = helper.make_node("Reshape", ["X", "shape"], ["Y"])
|
|
graph = helper.make_graph([node], "reshape_infer_dim_minus_one", [X], [Y], initializer=[shape])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "reshape/infer_dim_minus_one", "reshape_infer_dim_minus_one.onnx")
|
|
|
|
|
|
def reshape_zero_copies_input_dim():
|
|
"""Reshape using 0 to copy an input dimension."""
|
|
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [2, 3, 4])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [2, 12])
|
|
shape = make_int64_initializer("shape", [0, -1])
|
|
node = helper.make_node("Reshape", ["X", "shape"], ["Y"])
|
|
graph = helper.make_graph([node], "reshape_zero_copies_input_dim", [X], [Y], initializer=[shape])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "reshape/zero_copies_input_dim", "reshape_zero_copies_input_dim.onnx")
|
|
|
|
|
|
def reshape_4d_to_2d_flatten():
|
|
"""Reshape a 4D tensor to a 2D flattened view."""
|
|
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 3, 4, 5])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 60])
|
|
shape = make_int64_initializer("shape", [1, 60])
|
|
node = helper.make_node("Reshape", ["X", "shape"], ["Y"])
|
|
graph = helper.make_graph([node], "reshape_4d_to_2d_flatten", [X], [Y], initializer=[shape])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "reshape/4d_to_2d_flatten", "reshape_4d_to_2d_flatten.onnx")
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Add tests
|
|
# ---------------------------------------------------------------------------
|
|
|
|
def add_basic():
|
|
"""Elementwise Add on two inputs with identical shapes."""
|
|
A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [4, 8])
|
|
B = helper.make_tensor_value_info("B", TensorProto.FLOAT, [4, 8])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [4, 8])
|
|
node = helper.make_node("Add", ["A", "B"], ["Y"])
|
|
graph = helper.make_graph([node], "add_basic", [A, B], [Y])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "add/basic", "add_basic.onnx")
|
|
|
|
|
|
def add_broadcast_row():
|
|
"""Elementwise Add with row-vector broadcasting."""
|
|
A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [4, 8])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [4, 8])
|
|
B = numpy_helper.from_array(np.random.default_rng(64).uniform(-1, 1, (8,)).astype(np.float32), name="B")
|
|
node = helper.make_node("Add", ["A", "B"], ["Y"])
|
|
graph = helper.make_graph([node], "add_broadcast_row", [A], [Y], initializer=[B])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "add/broadcast_row", "add_broadcast_row.onnx")
|
|
|
|
|
|
def add_after_gemm():
|
|
"""Gemm followed by Add with a broadcast bias vector."""
|
|
B, K, N = 4, 64, 32
|
|
rng = np.random.default_rng(65)
|
|
W = numpy_helper.from_array(rng.uniform(-1, 1, (K, N)).astype(np.float32), name="W")
|
|
C = numpy_helper.from_array(rng.uniform(-1, 1, (N,)).astype(np.float32), name="C")
|
|
D = numpy_helper.from_array(rng.uniform(-1, 1, (N,)).astype(np.float32), name="D")
|
|
A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [B, K])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [B, N])
|
|
gemm = helper.make_node("Gemm", ["A", "W", "C"], ["G"])
|
|
add = helper.make_node("Add", ["G", "D"], ["Y"])
|
|
graph = helper.make_graph([gemm, add], "add_after_gemm", [A], [Y], initializer=[W, C, D])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "add/after_gemm", "add_after_gemm.onnx")
|
|
|
|
|
|
def add_channel_broadcast_1024():
|
|
"""Elementwise Add with NCHW per-channel broadcasting over 1024 channels."""
|
|
A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [1, 1024, 1, 1])
|
|
B = helper.make_tensor_value_info("B", TensorProto.FLOAT, [1, 1024, 1, 1])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 1024, 1, 1])
|
|
node = helper.make_node("Add", ["A", "B"], ["Y"])
|
|
graph = helper.make_graph([node], "add_channel_broadcast_1024", [A, B], [Y])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "add/channel_broadcast_1024", "add_channel_broadcast_1024.onnx")
|
|
|
|
|
|
def add_scalar_runtime():
|
|
"""Elementwise Add with a runtime scalar RHS."""
|
|
A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [1, 1024, 1, 1])
|
|
B = helper.make_tensor_value_info("B", TensorProto.FLOAT, [1, 1, 1, 1])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 1024, 1, 1])
|
|
node = helper.make_node("Add", ["A", "B"], ["Y"])
|
|
graph = helper.make_graph([node], "add_scalar_runtime", [A, B], [Y])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "add/scalar_runtime", "add_scalar_runtime.onnx")
|
|
|
|
|
|
def add_leading_dimension_broadcast():
|
|
"""Elementwise Add with trailing-dimension broadcasting."""
|
|
A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [2, 3, 4])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [2, 3, 4])
|
|
B = numpy_helper.from_array(np.random.default_rng(99).uniform(-1, 1, (4,)).astype(np.float32), name="B")
|
|
node = helper.make_node("Add", ["A", "B"], ["Y"])
|
|
graph = helper.make_graph([node], "add_leading_dimension_broadcast", [A], [Y], initializer=[B])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "add/leading_dimension_broadcast", "add_leading_dimension_broadcast.onnx")
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Mul tests
|
|
# ---------------------------------------------------------------------------
|
|
|
|
def mul_basic():
|
|
"""Elementwise Mul on two inputs with identical shapes."""
|
|
A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [4, 8])
|
|
B = helper.make_tensor_value_info("B", TensorProto.FLOAT, [4, 8])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [4, 8])
|
|
node = helper.make_node("Mul", ["A", "B"], ["Y"])
|
|
graph = helper.make_graph([node], "mul_basic", [A, B], [Y])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "mul/basic", "mul_basic.onnx")
|
|
|
|
|
|
def mul_scalar_constant():
|
|
"""Elementwise Mul with scalar broadcasting."""
|
|
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [4, 8])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [4, 8])
|
|
S = numpy_helper.from_array(np.asarray([1.5], dtype=np.float32), name="S")
|
|
node = helper.make_node("Mul", ["X", "S"], ["Y"])
|
|
graph = helper.make_graph([node], "mul_scalar_constant", [X], [Y], initializer=[S])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "mul/scalar_constant", "mul_scalar_constant.onnx")
|
|
|
|
|
|
def mul_after_conv():
|
|
"""Conv followed by Mul with per-channel scaling."""
|
|
rng = np.random.default_rng(66)
|
|
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 3, 5, 5])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 2, 3, 3])
|
|
W = numpy_helper.from_array(rng.uniform(-1, 1, (2, 3, 3, 3)).astype(np.float32), name="W")
|
|
B = numpy_helper.from_array(rng.uniform(-1, 1, (2,)).astype(np.float32), name="B")
|
|
S = numpy_helper.from_array(rng.uniform(0.5, 1.5, (1, 2, 1, 1)).astype(np.float32), name="S")
|
|
conv = helper.make_node("Conv", ["X", "W", "B"], ["C"],
|
|
kernel_shape=[3, 3], strides=[1, 1], pads=[0, 0, 0, 0])
|
|
mul = helper.make_node("Mul", ["C", "S"], ["Y"])
|
|
graph = helper.make_graph([conv, mul], "mul_after_conv", [X], [Y], initializer=[W, B, S])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "mul/after_conv", "mul_after_conv.onnx")
|
|
|
|
|
|
def mul_channel_broadcast_1024():
|
|
"""Elementwise Mul with NCHW per-channel broadcasting over 1024 channels."""
|
|
A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [1, 1024, 1, 1])
|
|
B = helper.make_tensor_value_info("B", TensorProto.FLOAT, [1, 1024, 1, 1])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 1024, 1, 1])
|
|
node = helper.make_node("Mul", ["A", "B"], ["Y"])
|
|
graph = helper.make_graph([node], "mul_channel_broadcast_1024", [A, B], [Y])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "mul/channel_broadcast_1024", "mul_channel_broadcast_1024.onnx")
|
|
|
|
|
|
def mul_scalar_runtime():
|
|
"""Elementwise Mul with a runtime scalar RHS."""
|
|
A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [1, 1024, 1, 1])
|
|
B = helper.make_tensor_value_info("B", TensorProto.FLOAT, [1, 1, 1, 1])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 1024, 1, 1])
|
|
node = helper.make_node("Mul", ["A", "B"], ["Y"])
|
|
graph = helper.make_graph([node], "mul_scalar_runtime", [A, B], [Y])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "mul/scalar_runtime", "mul_scalar_runtime.onnx")
|
|
|
|
|
|
def mul_leading_dimension_broadcast():
|
|
"""Elementwise Mul with trailing-dimension broadcasting."""
|
|
A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [2, 3, 4])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [2, 3, 4])
|
|
B = numpy_helper.from_array(np.random.default_rng(100).uniform(-1, 1, (4,)).astype(np.float32), name="B")
|
|
node = helper.make_node("Mul", ["A", "B"], ["Y"])
|
|
graph = helper.make_graph([node], "mul_leading_dimension_broadcast", [A], [Y], initializer=[B])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "mul/leading_dimension_broadcast", "mul_leading_dimension_broadcast.onnx")
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Div tests
|
|
# ---------------------------------------------------------------------------
|
|
|
|
def div_basic():
|
|
"""Elementwise Div by a same-shape constant tensor."""
|
|
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [4, 8])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [4, 8])
|
|
D = numpy_helper.from_array(np.random.default_rng(67).uniform(0.5, 2.0, (4, 8)).astype(np.float32), name="D")
|
|
node = helper.make_node("Div", ["X", "D"], ["Y"])
|
|
graph = helper.make_graph([node], "div_basic", [X], [Y], initializer=[D])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "div/basic", "div_basic.onnx")
|
|
|
|
|
|
def div_scalar_constant():
|
|
"""Elementwise Div with scalar broadcasting."""
|
|
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [4, 8])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [4, 8])
|
|
S = numpy_helper.from_array(np.asarray([2.0], dtype=np.float32), name="S")
|
|
node = helper.make_node("Div", ["X", "S"], ["Y"])
|
|
graph = helper.make_graph([node], "div_scalar_constant", [X], [Y], initializer=[S])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "div/scalar_constant", "div_scalar_constant.onnx")
|
|
|
|
|
|
def div_after_gemm():
|
|
"""Gemm followed by Div with a broadcast divisor vector."""
|
|
B, K, N = 4, 64, 32
|
|
rng = np.random.default_rng(68)
|
|
W = numpy_helper.from_array(rng.uniform(-1, 1, (K, N)).astype(np.float32), name="W")
|
|
C = numpy_helper.from_array(rng.uniform(-1, 1, (N,)).astype(np.float32), name="C")
|
|
D = numpy_helper.from_array(rng.uniform(0.5, 2.0, (N,)).astype(np.float32), name="D")
|
|
A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [B, K])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [B, N])
|
|
gemm = helper.make_node("Gemm", ["A", "W", "C"], ["G"])
|
|
div = helper.make_node("Div", ["G", "D"], ["Y"])
|
|
graph = helper.make_graph([gemm, div], "div_after_gemm", [A], [Y], initializer=[W, C, D])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "div/after_gemm", "div_after_gemm.onnx")
|
|
|
|
|
|
def div_channel_broadcast_1024():
|
|
"""Elementwise Div with NCHW per-channel broadcasting over 1024 channels."""
|
|
A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [1, 1024, 1, 1])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 1024, 1, 1])
|
|
B = numpy_helper.from_array(np.random.default_rng(102).uniform(0.5, 2.0, (1, 1024, 1, 1)).astype(np.float32), name="B")
|
|
node = helper.make_node("Div", ["A", "B"], ["Y"])
|
|
graph = helper.make_graph([node], "div_channel_broadcast_1024", [A], [Y], initializer=[B])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "div/channel_broadcast_1024", "div_channel_broadcast_1024.onnx")
|
|
|
|
|
|
def div_runtime_scalar_rhs():
|
|
"""Elementwise Div by a scalar constant."""
|
|
A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [1, 1024, 1, 1])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 1024, 1, 1])
|
|
B = numpy_helper.from_array(np.asarray([2.0], dtype=np.float32), name="B")
|
|
node = helper.make_node("Div", ["A", "B"], ["Y"])
|
|
graph = helper.make_graph([node], "div_runtime_scalar_rhs", [A], [Y], initializer=[B])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "div/runtime_scalar_rhs", "div_runtime_scalar_rhs.onnx")
|
|
|
|
|
|
def div_runtime_scalar_lhs():
|
|
"""Elementwise Div with a scalar constant numerator."""
|
|
B = helper.make_tensor_value_info("B", TensorProto.FLOAT, [1, 1024, 1, 1])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 1024, 1, 1])
|
|
A = numpy_helper.from_array(np.asarray([[[[2.0]]]], dtype=np.float32), name="A")
|
|
node = helper.make_node("Div", ["A", "B"], ["Y"])
|
|
graph = helper.make_graph([node], "div_runtime_scalar_lhs", [B], [Y], initializer=[A])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "div/runtime_scalar_lhs", "div_runtime_scalar_lhs.onnx")
|
|
|
|
|
|
def div_leading_dimension_broadcast():
|
|
"""Elementwise Div with trailing-dimension broadcasting."""
|
|
A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [2, 3, 4])
|
|
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [2, 3, 4])
|
|
B = numpy_helper.from_array(np.random.default_rng(101).uniform(0.5, 2.0, (4,)).astype(np.float32), name="B")
|
|
node = helper.make_node("Div", ["A", "B"], ["Y"])
|
|
graph = helper.make_graph([node], "div_leading_dimension_broadcast", [A], [Y], initializer=[B])
|
|
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
|
|
save_model(model, "div/leading_dimension_broadcast", "div_leading_dimension_broadcast.onnx")
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Main
|
|
# ---------------------------------------------------------------------------
|
|
|
|
if __name__ == "__main__":
|
|
print("Generating GEMM tests:")
|
|
gemm_simple()
|
|
gemm_non_square()
|
|
gemm_with_bias()
|
|
gemm_transB()
|
|
gemm_alpha_beta()
|
|
gemm_small()
|
|
gemm_large()
|
|
gemm_transB_with_bias()
|
|
gemm_dynamic()
|
|
gemm_dynamic_transB()
|
|
gemm_dynamic_bias()
|
|
gemm_dynamic_alpha()
|
|
gemm_dynamic_beta()
|
|
gemm_dynamic_bias_alpha_beta()
|
|
gemm_huge_1024()
|
|
gemm_large_k_small_n()
|
|
gemm_small_k_large_n()
|
|
gemm_transA()
|
|
gemm_transA_transB()
|
|
gemm_bias_rank2_broadcast()
|
|
gemm_scalar_bias()
|
|
|
|
print("\nGenerating Conv tests:")
|
|
conv_3x3_kernel()
|
|
conv_stride2()
|
|
conv_multi_channel()
|
|
conv_1x1()
|
|
conv_same_padding_3x3()
|
|
conv_explicit_padding()
|
|
conv_with_bias_3x3()
|
|
conv_batch_2()
|
|
conv_large_spatial()
|
|
conv_grouped_two_groups()
|
|
conv_depthwise_grouped()
|
|
conv_dynamic()
|
|
conv_huge_pointwise_1024()
|
|
conv_huge_pointwise_1024_dynamic()
|
|
conv_large_output_channels_1x1()
|
|
conv_large_input_channels_1x1()
|
|
conv_depthwise_1024_channels()
|
|
conv_grouped_many_groups()
|
|
conv_non_square_kernel_1x3()
|
|
conv_non_square_kernel_3x1()
|
|
conv_non_uniform_stride()
|
|
conv_dilated_3x3()
|
|
conv_real_asymmetric_padding()
|
|
conv_same_lower_3x3()
|
|
conv_kernel_equals_input_spatial()
|
|
conv_batch_4_pointwise()
|
|
conv_without_kernel_shape_attr()
|
|
|
|
print("\nGenerating MatMul tests:")
|
|
matmul_basic()
|
|
matmul_left_constant()
|
|
matmul_dynamic()
|
|
matmul_batched_3d()
|
|
matmul_batched_3d_dynamic()
|
|
matmul_batched_left_constant()
|
|
matmul_batched_rhs_broadcast()
|
|
matmul_batched_lhs_broadcast()
|
|
matmul_huge_1024()
|
|
matmul_vector_matrix()
|
|
matmul_matrix_vector()
|
|
matmul_vector_vector_dot()
|
|
matmul_batched_4d_broadcast()
|
|
|
|
print("\nGenerating Pooling tests:")
|
|
maxpool_basic()
|
|
maxpool_stride2_multichannel()
|
|
maxpool_same_upper()
|
|
avgpool_basic()
|
|
avgpool_explicit_padding()
|
|
avgpool_include_pad()
|
|
maxpool_after_conv()
|
|
maxpool_ceil_mode()
|
|
avgpool_ceil_mode()
|
|
maxpool_real_asymmetric_padding()
|
|
avgpool_real_asymmetric_padding()
|
|
maxpool_non_square_kernel()
|
|
avgpool_non_uniform_stride()
|
|
maxpool_global_style_kernel_equals_input()
|
|
avgpool_large_channels()
|
|
|
|
print("\nGenerating ReduceMean tests:")
|
|
reducemean_basic()
|
|
reducemean_keepdims_0()
|
|
reducemean_4d_spatial()
|
|
reducemean_after_conv()
|
|
reducemean_negative_axis()
|
|
reducemean_all_axes_keepdims_1()
|
|
reducemean_all_axes_keepdims_0()
|
|
reducemean_4d_spatial_keepdims_0()
|
|
reducemean_channel_axis_nchw()
|
|
reducemean_large_dimension_1024()
|
|
|
|
print("\nGenerating Relu tests:")
|
|
relu_basic()
|
|
relu_4d()
|
|
relu_after_conv()
|
|
relu_after_gemm()
|
|
|
|
print("\nGenerating Sigmoid tests:")
|
|
sigmoid_basic()
|
|
sigmoid_4d()
|
|
sigmoid_after_gemm()
|
|
|
|
print("\nGenerating Split tests:")
|
|
split_basic()
|
|
split_equal_three_way()
|
|
split_negative_axis()
|
|
split_uneven_channel_axis_4d()
|
|
|
|
print("\nGenerating Softmax tests:")
|
|
softmax_basic()
|
|
softmax_3d_last_axis()
|
|
softmax_channel_axis()
|
|
softmax_negative_axis()
|
|
softmax_large_dimension_1024()
|
|
|
|
print("\nGenerating Resize tests:")
|
|
resize_nearest_2x()
|
|
resize_nearest_non_uniform()
|
|
resize_with_sizes()
|
|
resize_nearest_downsample()
|
|
resize_height_only()
|
|
resize_width_only()
|
|
|
|
print("\nGenerating Gather tests:")
|
|
gather_axis1()
|
|
gather_axis0_matrix_indices()
|
|
gather_negative_indices()
|
|
gather_negative_axis()
|
|
gather_3d_input_axis1()
|
|
|
|
print("\nGenerating Concat tests:")
|
|
concat_channel_axis()
|
|
concat_negative_axis()
|
|
concat_three_inputs_channel_axis()
|
|
|
|
print("\nGenerating Reshape tests:")
|
|
reshape_same_rank()
|
|
reshape_infer_dim_minus_one()
|
|
reshape_zero_copies_input_dim()
|
|
reshape_4d_to_2d_flatten()
|
|
|
|
print("\nGenerating Add tests:")
|
|
add_basic()
|
|
add_broadcast_row()
|
|
add_after_gemm()
|
|
add_channel_broadcast_1024()
|
|
add_scalar_runtime()
|
|
add_leading_dimension_broadcast()
|
|
|
|
print("\nGenerating Mul tests:")
|
|
mul_basic()
|
|
mul_scalar_constant()
|
|
mul_after_conv()
|
|
mul_channel_broadcast_1024()
|
|
mul_scalar_runtime()
|
|
mul_leading_dimension_broadcast()
|
|
|
|
print("\nGenerating Div tests:")
|
|
div_basic()
|
|
div_scalar_constant()
|
|
div_after_gemm()
|
|
div_channel_broadcast_1024()
|
|
div_runtime_scalar_rhs()
|
|
div_runtime_scalar_lhs()
|
|
div_leading_dimension_broadcast()
|
|
|
|
print("\nDone.")
|