add support for operations: reduceMean, add, mul, div, sigmoid
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NiccoloN
@@ -1,5 +1,5 @@
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#!/usr/bin/env python3
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"""Generate ONNX test models for validating GEMM, Conv, Pooling, and Relu implementations."""
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"""Generate ONNX test models for validating GEMM, Conv, Pooling, Relu, and ReduceMean implementations."""
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import numpy as np
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import onnx
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@@ -19,102 +19,8 @@ def save_model(model, directory, filename):
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print(f" {path.relative_to(OPERATIONS_DIR)}")
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# ---------------------------------------------------------------------------
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# GEMM tests
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# ---------------------------------------------------------------------------
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def gemm_non_square():
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"""GEMM with non-square weight matrix: [B, K] @ [K, N], K != N."""
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B, K, N = 4, 128, 64
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W = numpy_helper.from_array(np.random.default_rng(42).uniform(-1, 1, (K, N)).astype(np.float32), name="W")
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A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [B, K])
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Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [B, N])
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node = helper.make_node("Gemm", ["A", "W"], ["Y"])
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graph = helper.make_graph([node], "gemm_non_square", [A], [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, "gemm/non_square", "gemm_non_square.onnx")
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def gemm_with_bias():
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"""GEMM with bias: Y = A @ W + C."""
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B, K, N = 4, 128, 128
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rng = np.random.default_rng(43)
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W = numpy_helper.from_array(rng.uniform(-1, 1, (K, N)).astype(np.float32), name="W")
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C = numpy_helper.from_array(rng.uniform(-1, 1, (N,)).astype(np.float32), name="C")
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A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [B, K])
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Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [B, N])
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node = helper.make_node("Gemm", ["A", "W", "C"], ["Y"])
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graph = helper.make_graph([node], "gemm_with_bias", [A], [Y], initializer=[W, C])
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model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
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save_model(model, "gemm/with_bias", "gemm_with_bias.onnx")
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def gemm_transB():
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"""GEMM with transB=1: Y = A @ W^T."""
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B, K, N = 4, 128, 64
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rng = np.random.default_rng(44)
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# W stored as [N, K], transposed during computation
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W = numpy_helper.from_array(rng.uniform(-1, 1, (N, K)).astype(np.float32), name="W")
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A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [B, K])
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Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [B, N])
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node = helper.make_node("Gemm", ["A", "W"], ["Y"], transB=1)
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graph = helper.make_graph([node], "gemm_transB", [A], [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, "gemm/transB", "gemm_transB.onnx")
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def gemm_alpha_beta():
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"""GEMM with alpha and beta: Y = 0.5 * A @ W + 0.25 * C."""
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B, K, N = 4, 64, 64
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rng = np.random.default_rng(45)
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W = numpy_helper.from_array(rng.uniform(-1, 1, (K, N)).astype(np.float32), name="W")
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C = numpy_helper.from_array(rng.uniform(-1, 1, (N,)).astype(np.float32), name="C")
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A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [B, K])
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Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [B, N])
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node = helper.make_node("Gemm", ["A", "W", "C"], ["Y"], alpha=0.5, beta=0.25)
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graph = helper.make_graph([node], "gemm_alpha_beta", [A], [Y], initializer=[W, C])
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model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
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save_model(model, "gemm/alpha_beta", "gemm_alpha_beta.onnx")
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def gemm_small():
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"""Small GEMM: [2, 8] @ [8, 4]."""
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B, K, N = 2, 8, 4
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rng = np.random.default_rng(46)
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W = numpy_helper.from_array(rng.uniform(-1, 1, (K, N)).astype(np.float32), name="W")
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A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [B, K])
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Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [B, N])
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node = helper.make_node("Gemm", ["A", "W"], ["Y"])
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graph = helper.make_graph([node], "gemm_small", [A], [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, "gemm/small", "gemm_small.onnx")
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def gemm_large():
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"""Larger GEMM: [8, 256] @ [256, 128]."""
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B, K, N = 8, 256, 128
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rng = np.random.default_rng(47)
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W = numpy_helper.from_array(rng.uniform(-1, 1, (K, N)).astype(np.float32), name="W")
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A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [B, K])
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Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [B, N])
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node = helper.make_node("Gemm", ["A", "W"], ["Y"])
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graph = helper.make_graph([node], "gemm_large", [A], [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, "gemm/large", "gemm_large.onnx")
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def gemm_transB_with_bias():
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"""GEMM with transB and bias: Y = A @ W^T + C."""
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B, K, N = 4, 128, 64
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rng = np.random.default_rng(48)
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W = numpy_helper.from_array(rng.uniform(-1, 1, (N, K)).astype(np.float32), name="W")
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C = numpy_helper.from_array(rng.uniform(-1, 1, (N,)).astype(np.float32), name="C")
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A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [B, K])
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Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [B, N])
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node = helper.make_node("Gemm", ["A", "W", "C"], ["Y"], transB=1)
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graph = helper.make_graph([node], "gemm_transB_with_bias", [A], [Y], initializer=[W, C])
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model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
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save_model(model, "gemm/transB_with_bias", "gemm_transB_with_bias.onnx")
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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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@@ -248,6 +154,104 @@ def conv_large_spatial():
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save_model(model, "conv/large_spatial", "conv_large_spatial.onnx")
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# ---------------------------------------------------------------------------
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# GEMM tests
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# ---------------------------------------------------------------------------
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def gemm_non_square():
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"""GEMM with non-square weight matrix: [B, K] @ [K, N], K != N."""
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B, K, N = 4, 128, 64
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W = numpy_helper.from_array(np.random.default_rng(42).uniform(-1, 1, (K, N)).astype(np.float32), name="W")
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A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [B, K])
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Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [B, N])
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node = helper.make_node("Gemm", ["A", "W"], ["Y"])
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graph = helper.make_graph([node], "gemm_non_square", [A], [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, "gemm/non_square", "gemm_non_square.onnx")
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def gemm_with_bias():
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"""GEMM with bias: Y = A @ W + C."""
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B, K, N = 4, 128, 128
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rng = np.random.default_rng(43)
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W = numpy_helper.from_array(rng.uniform(-1, 1, (K, N)).astype(np.float32), name="W")
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C = numpy_helper.from_array(rng.uniform(-1, 1, (N,)).astype(np.float32), name="C")
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A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [B, K])
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Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [B, N])
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node = helper.make_node("Gemm", ["A", "W", "C"], ["Y"])
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graph = helper.make_graph([node], "gemm_with_bias", [A], [Y], initializer=[W, C])
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model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
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save_model(model, "gemm/with_bias", "gemm_with_bias.onnx")
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def gemm_transB():
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"""GEMM with transB=1: Y = A @ W^T."""
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B, K, N = 4, 128, 64
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rng = np.random.default_rng(44)
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# W stored as [N, K], transposed during computation
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W = numpy_helper.from_array(rng.uniform(-1, 1, (N, K)).astype(np.float32), name="W")
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A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [B, K])
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Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [B, N])
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node = helper.make_node("Gemm", ["A", "W"], ["Y"], transB=1)
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graph = helper.make_graph([node], "gemm_transB", [A], [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, "gemm/transB", "gemm_transB.onnx")
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def gemm_alpha_beta():
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"""GEMM with alpha and beta: Y = 0.5 * A @ W + 0.25 * C."""
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B, K, N = 4, 64, 64
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rng = np.random.default_rng(45)
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W = numpy_helper.from_array(rng.uniform(-1, 1, (K, N)).astype(np.float32), name="W")
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C = numpy_helper.from_array(rng.uniform(-1, 1, (N,)).astype(np.float32), name="C")
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A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [B, K])
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Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [B, N])
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node = helper.make_node("Gemm", ["A", "W", "C"], ["Y"], alpha=0.5, beta=0.25)
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graph = helper.make_graph([node], "gemm_alpha_beta", [A], [Y], initializer=[W, C])
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model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
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save_model(model, "gemm/alpha_beta", "gemm_alpha_beta.onnx")
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def gemm_small():
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"""Small GEMM: [2, 8] @ [8, 4]."""
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B, K, N = 2, 8, 4
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rng = np.random.default_rng(46)
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W = numpy_helper.from_array(rng.uniform(-1, 1, (K, N)).astype(np.float32), name="W")
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A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [B, K])
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Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [B, N])
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node = helper.make_node("Gemm", ["A", "W"], ["Y"])
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graph = helper.make_graph([node], "gemm_small", [A], [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, "gemm/small", "gemm_small.onnx")
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def gemm_large():
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"""Larger GEMM: [8, 256] @ [256, 128]."""
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B, K, N = 8, 256, 128
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rng = np.random.default_rng(47)
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W = numpy_helper.from_array(rng.uniform(-1, 1, (K, N)).astype(np.float32), name="W")
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A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [B, K])
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Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [B, N])
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node = helper.make_node("Gemm", ["A", "W"], ["Y"])
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graph = helper.make_graph([node], "gemm_large", [A], [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, "gemm/large", "gemm_large.onnx")
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def gemm_transB_with_bias():
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"""GEMM with transB and bias: Y = A @ W^T + C."""
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B, K, N = 4, 128, 64
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rng = np.random.default_rng(48)
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W = numpy_helper.from_array(rng.uniform(-1, 1, (N, K)).astype(np.float32), name="W")
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C = numpy_helper.from_array(rng.uniform(-1, 1, (N,)).astype(np.float32), name="C")
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A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [B, K])
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Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [B, N])
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node = helper.make_node("Gemm", ["A", "W", "C"], ["Y"], transB=1)
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graph = helper.make_graph([node], "gemm_transB_with_bias", [A], [Y], initializer=[W, C])
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model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
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save_model(model, "gemm/transB_with_bias", "gemm_transB_with_bias.onnx")
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# ---------------------------------------------------------------------------
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# Pooling tests
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# ---------------------------------------------------------------------------
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@@ -327,6 +331,55 @@ def maxpool_after_conv():
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save_model(model, "pool/max_after_conv", "maxpool_after_conv.onnx")
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# ---------------------------------------------------------------------------
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# ReduceMean tests
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# ---------------------------------------------------------------------------
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def reducemean_basic():
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"""ReduceMean over the feature dimension, preserving rank."""
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X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [4, 8])
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Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [4, 1])
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node = helper.make_node("ReduceMean", ["X"], ["Y"], axes=[1], keepdims=1)
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graph = helper.make_graph([node], "reducemean_basic", [X], [Y])
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model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
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save_model(model, "reduce_mean/basic", "reduce_mean_basic.onnx")
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def reducemean_keepdims_0():
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"""ReduceMean over the feature dimension, dropping the reduced axis."""
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X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [4, 8])
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Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [4])
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node = helper.make_node("ReduceMean", ["X"], ["Y"], axes=[1], keepdims=0)
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graph = helper.make_graph([node], "reducemean_keepdims_0", [X], [Y])
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model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
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save_model(model, "reduce_mean/keepdims_0", "reduce_mean_keepdims_0.onnx")
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def reducemean_4d_spatial():
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"""ReduceMean over H and W on an NCHW tensor."""
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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, 1, 1])
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node = helper.make_node("ReduceMean", ["X"], ["Y"], axes=[2, 3], keepdims=1)
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graph = helper.make_graph([node], "reducemean_4d_spatial", [X], [Y])
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model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
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save_model(model, "reduce_mean/4d_spatial", "reduce_mean_4d_spatial.onnx")
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def reducemean_after_conv():
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"""Conv followed by ReduceMean over the spatial dimensions."""
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rng = np.random.default_rng(62)
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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, 1, 1])
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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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conv = helper.make_node("Conv", ["X", "W", "B"], ["C"],
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kernel_shape=[3, 3], strides=[1, 1], pads=[0, 0, 0, 0])
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reduce = helper.make_node("ReduceMean", ["C"], ["Y"], axes=[2, 3], keepdims=1)
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graph = helper.make_graph([conv, reduce], "reducemean_after_conv", [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, "reduce_mean/after_conv", "reduce_mean_after_conv.onnx")
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# ---------------------------------------------------------------------------
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# Relu tests
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# ---------------------------------------------------------------------------
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@@ -381,6 +434,220 @@ def relu_after_gemm():
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save_model(model, "relu/after_gemm", "relu_after_gemm.onnx")
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# ---------------------------------------------------------------------------
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# Sigmoid tests
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# ---------------------------------------------------------------------------
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def sigmoid_basic():
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"""Standalone Sigmoid on a simple 2D tensor."""
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X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [4, 8])
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Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [4, 8])
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node = helper.make_node("Sigmoid", ["X"], ["Y"])
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graph = helper.make_graph([node], "sigmoid_basic", [X], [Y])
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model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
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save_model(model, "sigmoid/basic", "sigmoid_basic.onnx")
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def sigmoid_4d():
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"""Standalone Sigmoid on an NCHW tensor."""
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X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [2, 3, 4, 4])
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Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [2, 3, 4, 4])
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node = helper.make_node("Sigmoid", ["X"], ["Y"])
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graph = helper.make_graph([node], "sigmoid_4d", [X], [Y])
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model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
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save_model(model, "sigmoid/4d", "sigmoid_4d.onnx")
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def sigmoid_after_gemm():
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"""Gemm followed by Sigmoid."""
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B, K, N = 4, 64, 32
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rng = np.random.default_rng(63)
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W = numpy_helper.from_array(rng.uniform(-1, 1, (K, N)).astype(np.float32), name="W")
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C = numpy_helper.from_array(rng.uniform(-1, 1, (N,)).astype(np.float32), name="C")
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A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [B, K])
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Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [B, N])
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gemm = helper.make_node("Gemm", ["A", "W", "C"], ["G"])
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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")
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# 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")
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# 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")
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# 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")
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# 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")
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Main
|
||||
# ---------------------------------------------------------------------------
|
||||
@@ -415,10 +682,36 @@ if __name__ == "__main__":
|
||||
avgpool_include_pad()
|
||||
maxpool_after_conv()
|
||||
|
||||
print("\nGenerating ReduceMean tests:")
|
||||
reducemean_basic()
|
||||
reducemean_keepdims_0()
|
||||
reducemean_4d_spatial()
|
||||
reducemean_after_conv()
|
||||
|
||||
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 Add tests:")
|
||||
add_basic()
|
||||
add_broadcast_row()
|
||||
add_after_gemm()
|
||||
|
||||
print("\nGenerating Mul tests:")
|
||||
mul_basic()
|
||||
mul_scalar_constant()
|
||||
mul_after_conv()
|
||||
|
||||
print("\nGenerating Div tests:")
|
||||
div_basic()
|
||||
div_scalar_constant()
|
||||
div_after_gemm()
|
||||
|
||||
print("\nDone.")
|
||||
|
||||
Reference in New Issue
Block a user