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04f866d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 | # SPDX-FileCopyrightText: Copyright (c) 2025 Centre for Research and Technology Hellas
# and University of Amsterdam. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# 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.
import unittest
import torch
from spai.models import backbones
class TestCLIPBackbone(unittest.TestCase):
def test_forward(self) -> None:
model = backbones.CLIPBackbone().cpu()
image: torch.Tensor = torch.randn(4, 3, 224, 224).cpu()
output: torch.Tensor = model(image)
self.assertEqual(output.shape, (4, 12, 196, 768))
self.assertEqual(image.dtype, output.dtype)
class TestDINOv2Backbone(unittest.TestCase):
def test_forward(self) -> None:
model = backbones.DINOv2Backbone().cpu()
image: torch.Tensor = torch.randn(4, 3, 224, 224).cpu()
output: torch.Tensor = model(image)
self.assertEqual(output.shape, (4, 12, 256, 768))
self.assertEqual(image.dtype, output.dtype)
def test_forward_dinov2_large(self) -> None:
model = backbones.DINOv2Backbone(dinov2_model="dinov2_vitl14").cpu()
image: torch.Tensor = torch.randn(4, 3, 224, 224).cpu()
output: torch.Tensor = model(image)
self.assertEqual(output.shape, (4, 12, 256, 1024))
self.assertEqual(image.dtype, output.dtype)
def test_forward_dinov2_large_custom_intermediate_layers(self) -> None:
intermediate_layers: tuple[int, ...] = (0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 23)
model = backbones.DINOv2Backbone(dinov2_model="dinov2_vitl14",
intermediate_layers=intermediate_layers).cpu()
image: torch.Tensor = torch.randn(4, 3, 224, 224).cpu()
output: torch.Tensor = model(image)
self.assertEqual(output.shape, (4, 12, 256, 1024))
self.assertEqual(image.dtype, output.dtype)
def test_forward_dinov2_grande(self) -> None:
model = backbones.DINOv2Backbone(dinov2_model="dinov2_vitg14").cpu()
image: torch.Tensor = torch.randn(4, 3, 224, 224).cpu()
output: torch.Tensor = model(image)
self.assertEqual(output.shape, (4, 12, 256, 1536))
self.assertEqual(image.dtype, output.dtype)
def test_forward_dinov2_grande_custom_intermediate_layers(self) -> None:
intermediate_layers: tuple[int, ...] = (0, 3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 39)
model = backbones.DINOv2Backbone(dinov2_model="dinov2_vitg14",
intermediate_layers=intermediate_layers).cpu()
image: torch.Tensor = torch.randn(4, 3, 224, 224).cpu()
output: torch.Tensor = model(image)
self.assertEqual(output.shape, (4, 12, 256, 1536))
self.assertEqual(image.dtype, output.dtype)
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