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chore: import upstream snapshot with attribution
2026-07-13 12:49:27 +08:00

56 lines
1.9 KiB
Python

# LICENSE HEADER MANAGED BY add-license-header
#
# Copyright 2018 Kornia Team
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
"""ONNX export tests for SAM (image encoder subgraph)."""
import pytest
from kornia.models.sam.model import Sam, SamConfig
def test_sam_has_to_onnx():
"""Sam exposes a to_onnx() method via ONNXExportMixin."""
assert hasattr(Sam, "to_onnx")
def test_sam_encoder_to_onnx(tmp_path):
"""Sam.to_onnx() exports the image encoder subgraph with correct output name."""
onnx = pytest.importorskip("onnx")
pytest.importorskip("onnxscript")
# Create a tiny pseudo-SAM model to prevent CI timeouts during ONNX trace
config = SamConfig(
model_type="vit_b",
encoder_embed_dim=16,
encoder_depth=1,
encoder_num_heads=1,
encoder_global_attn_indexes=[0],
)
model = Sam.from_config(config)
model.eval()
onnx_path = tmp_path / "sam_encoder.onnx"
# Export only the image encoder (Sam.to_onnx overrides the full-model export).
# Use a small spatial size to keep the test fast; only batch dimension is dynamic.
op = model.to_onnx(save=True, onnx_name=str(onnx_path), pseudo_shape=[1, 3, 1024, 1024])
assert isinstance(op, onnx.ModelProto)
assert onnx_path.exists(), "ONNX file was not written to disk"
output_names = [o.name for o in op.graph.output]
assert "image_embeddings" in output_names, f"Expected 'image_embeddings', got {output_names}"