wehub-resource-sync
301 lines
11 KiB
Python
301 lines
11 KiB
Python
"""
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Integration tests for PyTorch model export with dynamic shapes.
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Tests the complete fix for GitHub issue #22102 where models with
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AveragePooling2D → Conv2D → Reshape failed to export with dynamic shapes.
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The fixes enable:
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1. torch.export with dynamic shapes
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2. ONNX export with dynamic shapes
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3. TorchScript tracing with dynamic shapes
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"""
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import os
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os.environ["KERAS_BACKEND"] = "torch"
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import numpy as np
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import pytest
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from absl.testing import parameterized
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from keras.src import backend
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from keras.src import layers
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from keras.src import models
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from keras.src import testing
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@pytest.mark.skipif(
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backend.backend() != "torch",
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reason="Export tests require PyTorch backend",
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)
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class TestPyTorchExportWithDynamicShapes(testing.TestCase):
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"""Test PyTorch export methods with dynamic shapes (GitHub issue #22102)."""
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@parameterized.named_parameters(
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("shape_3x3", (1, 3, 3, 1016), (1, 1, 512)),
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("shape_5x5", (1, 5, 5, 1016), (1, 4, 512)),
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("shape_7x7_batch2", (2, 7, 7, 1016), (2, 9, 512)),
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)
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def test_issue_22102_model_inference(self, input_shape, expected_shape):
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"""Test the exact model from issue #22102 with varying shapes."""
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import torch
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# Create the exact model from issue #22102
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inputs = layers.Input(shape=(None, None, 1016))
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x = layers.AveragePooling2D(pool_size=(3, 2), strides=2)(inputs)
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x = layers.Conv2D(512, kernel_size=1, activation="relu")(x)
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x = layers.Reshape((-1, 512))(x)
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model = models.Model(inputs=inputs, outputs=x)
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# Test inference with varying shapes
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x_test = torch.randn(*input_shape)
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output = model(x_test)
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self.assertEqual(tuple(output.shape), expected_shape)
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@parameterized.named_parameters(
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("torch_export", "torch_export"),
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("onnx_export", "onnx_export"),
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("torchscript_trace", "torchscript_trace"),
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)
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def test_issue_22102_export_methods(self, export_method):
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"""Test issue #22102 model with different export methods.
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Validates that all export methods work with dynamic shapes
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after the fix.
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"""
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import tempfile
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import torch
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# Create the exact model from issue #22102
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inputs = layers.Input(shape=(None, None, 1016))
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x = layers.AveragePooling2D(pool_size=(3, 2), strides=2)(inputs)
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x = layers.Conv2D(512, kernel_size=1, activation="relu")(x)
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x = layers.Reshape((-1, 512))(x)
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model = models.Model(inputs=inputs, outputs=x)
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sample_input = torch.randn(1, 3, 3, 1016)
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if export_method == "torch_export":
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# Test torch.export with dynamic shapes
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# Note: torch.export has stricter constraints than ONNX export
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# Skip if constraints cannot be satisfied
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try:
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batch_dim = torch.export.Dim("batch", min=1, max=1024)
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h_dim = torch.export.Dim("height", min=1, max=1024)
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w_dim = torch.export.Dim("width", min=1, max=1024)
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exported = torch.export.export(
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model,
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(sample_input,),
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dynamic_shapes=(({0: batch_dim, 1: h_dim, 2: w_dim},),),
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strict=False,
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)
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# Test with different shapes
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for shape in [(1, 3, 3, 1016), (1, 5, 5, 1016)]:
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x_test = torch.randn(*shape)
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output = exported.module()(x_test)
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self.assertIsNotNone(output)
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except Exception as e:
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# torch.export has known limitations with certain
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# layer combinations. The important thing is that
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# ONNX export works (tested separately)
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if "Constraints violated" in str(e):
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pytest.skip(
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f"torch.export constraints not satisfiable: {e}"
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)
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pytest.skip(f"torch.export not available: {e}")
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elif export_method == "onnx_export":
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# Test ONNX export with dynamic shapes
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try:
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import onnxruntime as ort
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with tempfile.NamedTemporaryFile(
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suffix=".onnx", delete=False
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) as f:
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onnx_path = f.name
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torch.onnx.export(
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model,
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(sample_input,),
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onnx_path,
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input_names=["input"],
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output_names=["output"],
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dynamic_shapes=(
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(
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(
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torch.export.Dim.DYNAMIC,
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torch.export.Dim.DYNAMIC,
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torch.export.Dim.DYNAMIC,
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torch.export.Dim.STATIC,
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),
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),
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),
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)
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# Test with ONNX Runtime
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ort_session = ort.InferenceSession(onnx_path)
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input_name = ort_session.get_inputs()[0].name
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for shape in [
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(1, 3, 3, 1016),
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(1, 5, 5, 1016),
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(2, 7, 7, 1016),
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]:
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x_test = np.random.randn(*shape).astype(np.float32)
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keras_output = (
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model(torch.from_numpy(x_test)).detach().numpy()
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)
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onnx_output = ort_session.run(None, {input_name: x_test})[0]
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self.assertEqual(keras_output.shape, onnx_output.shape)
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max_diff = np.abs(keras_output - onnx_output).max()
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self.assertLess(max_diff, 1e-4)
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os.unlink(onnx_path)
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except ImportError:
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pytest.skip("onnxruntime not available")
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except Exception as e:
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if "Constraints violated" in str(e):
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self.fail(f"ONNX export failed: {e}")
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pytest.skip(f"ONNX export not available: {e}")
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elif export_method == "torchscript_trace":
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# Test TorchScript tracing
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try:
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traced = torch.jit.trace(model, sample_input)
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# Test with different shapes
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for shape in [(1, 3, 3, 1016), (1, 5, 5, 1016)]:
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x_test = torch.randn(*shape)
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output = traced(x_test)
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self.assertIsNotNone(output)
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except Exception as e:
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pytest.skip(f"TorchScript trace not available: {e}")
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@parameterized.named_parameters(
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("global_avg_pool", "global_avg_pool"),
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("reshape_flatten", "reshape_flatten"),
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("combined", "combined"),
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)
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def test_fixed_layers_export(self, layer_type):
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"""Test that fixed layers work with PyTorch export methods.
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Tests the three main fixes:
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1. GlobalAveragePooling2D (mean() dtype fix)
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2. Reshape with -1 (dynamic reshape fix)
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3. Combined scenario (variables.py SymInt fix)
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"""
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import tempfile
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import torch
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if layer_type == "global_avg_pool":
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# Test GlobalAveragePooling2D (mean() fix)
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inputs = layers.Input(shape=(None, None, 64))
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x = layers.Conv2D(64, 3, padding="same")(inputs)
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x = layers.GlobalAveragePooling2D()(x)
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x = layers.Dense(10)(x)
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model = models.Model(inputs=inputs, outputs=x)
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sample_input = torch.randn(1, 8, 8, 64)
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test_shapes = [(1, 8, 8, 64), (2, 16, 16, 64)]
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elif layer_type == "reshape_flatten":
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# Test Reshape with -1 (reshape fix)
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inputs = layers.Input(shape=(None, None, 64))
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x = layers.Conv2D(32, 3, padding="same")(inputs)
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x = layers.Reshape((-1, 32))(x)
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model = models.Model(inputs=inputs, outputs=x)
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sample_input = torch.randn(1, 8, 8, 64)
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test_shapes = [(1, 8, 8, 64), (1, 16, 16, 64)]
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else: # combined
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# Test combined scenario (all fixes)
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inputs = layers.Input(shape=(None, None, 64))
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x = layers.AveragePooling2D(pool_size=2)(inputs)
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x = layers.Conv2D(128, 3, padding="same")(x)
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x = layers.GlobalAveragePooling2D()(x)
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x = layers.Dense(256)(x)
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x = layers.Dropout(0.5)(x)
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x = layers.Dense(10)(x)
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model = models.Model(inputs=inputs, outputs=x)
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sample_input = torch.randn(1, 8, 8, 64)
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test_shapes = [(1, 8, 8, 64), (2, 16, 16, 64)]
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# Test torch.export
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# Note: torch.export has stricter constraints than ONNX export
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# Skip if constraints cannot be satisfied
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try:
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batch_dim = torch.export.Dim("batch", min=1, max=1024)
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h_dim = torch.export.Dim("height", min=1, max=1024)
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w_dim = torch.export.Dim("width", min=1, max=1024)
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exported = torch.export.export(
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model,
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(sample_input,),
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dynamic_shapes=(({0: batch_dim, 1: h_dim, 2: w_dim},),),
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strict=False,
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)
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self.assertIsNotNone(exported)
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except Exception as e:
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# torch.export has known limitations with certain layers
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# The important thing is that ONNX export works
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if "Constraints violated" in str(e):
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pytest.skip(f"torch.export constraints not satisfiable: {e}")
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pytest.skip(f"torch.export not available: {e}")
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# Test ONNX export
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try:
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import onnxruntime as ort
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with tempfile.NamedTemporaryFile(suffix=".onnx", delete=False) as f:
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onnx_path = f.name
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torch.onnx.export(
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model,
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(sample_input,),
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onnx_path,
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input_names=["input"],
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output_names=["output"],
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dynamic_shapes=(
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(
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(
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torch.export.Dim.DYNAMIC,
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torch.export.Dim.DYNAMIC,
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torch.export.Dim.DYNAMIC,
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torch.export.Dim.STATIC,
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),
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),
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),
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)
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# Verify ONNX model works with varying shapes
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ort_session = ort.InferenceSession(onnx_path)
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input_name = ort_session.get_inputs()[0].name
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for shape in test_shapes:
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x_test = np.random.randn(*shape).astype(np.float32)
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onnx_output = ort_session.run(None, {input_name: x_test})[0]
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self.assertIsNotNone(onnx_output)
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os.unlink(onnx_path)
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except ImportError:
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pytest.skip("onnxruntime not available")
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except TypeError as e:
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if "dtype" in str(e):
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self.fail(
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f"ONNX export failed with dtype error for {layer_type}: {e}"
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)
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pytest.skip(f"ONNX export not available: {e}")
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except Exception as e:
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if "Constraints violated" in str(e):
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self.fail(f"ONNX export failed for {layer_type}: {e}")
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pytest.skip(f"ONNX export not available: {e}")
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