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Delete three deletion-eligible omnivore forward tests #553

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87dc6d5
Delete four deletion-eligible ALBEF embedding tests (#551)
twmoveon Aug 3, 2026
4674d73
Delete deletion-eligible ALBEF test_vision_transformer (#552)
twmoveon Aug 3, 2026
4ba4463
Delete three deletion-eligible omnivore forward tests
twmoveon Aug 3, 2026
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44 changes: 0 additions & 44 deletions tests/models/albef/test_albef.py
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Original file line number Diff line number Diff line change
Expand Up @@ -79,26 +79,6 @@ def albef_model_output(albef_model):
return albef_model(image, text, text_atts)


def test_albef_image_embeddings(albef_model_output):
expected = Tensor(
[
[[1.364883, -1.003092, -0.361791], [-0.634884, 1.411830, -0.776947]],
[[1.401580, -0.537510, -0.864071], [1.378901, -0.417473, -0.961429]],
]
)
assert_expected(albef_model_output.image_embeddings, expected, rtol=0, atol=1e-4)


def test_albef_image_embeddings_momentum(albef_model_output):
expected = Tensor(
[
[[1.364883, -1.003092, -0.361791], [-0.634884, 1.411830, -0.776947]],
[[1.401580, -0.537510, -0.864070], [1.378902, -0.417473, -0.961429]],
]
)
assert_expected(albef_model_output.image_embeddings_m, expected, rtol=0, atol=1e-4)


def test_albef_text_embeddings(albef_model_output):
expected = Tensor(
[
Expand All @@ -119,30 +99,6 @@ def test_albef_text_embeddings_momentum(albef_model_output):
assert_expected(albef_model_output.text_embeddings_m, expected, rtol=0, atol=1e-4)


def test_albef_multimodal_embeddings(albef_model_output):
expected = Tensor(
[
[[-0.068738, 1.257666, -1.188928], [1.409873, -0.609056, -0.800817]],
[[-1.402520, 0.544084, 0.858435], [1.202279, -1.246038, 0.043760]],
]
)
assert_expected(
albef_model_output.multimodal_embeddings, expected, rtol=0, atol=1e-4
)


def test_albef_multimodal_embeddings_momentum(albef_model_output):
expected = Tensor(
[
[[-0.068738, 1.257666, -1.188928], [1.409873, -0.609056, -0.800817]],
[[-1.402520, 0.544084, 0.858435], [1.202279, -1.246038, 0.043760]],
]
)
assert_expected(
albef_model_output.multimodal_embeddings_m, expected, rtol=0, atol=1e-4
)


def test_copy_params_momentum_models():
model = nn.Linear(3, 2)
model_m = copy.deepcopy(model)
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16 changes: 1 addition & 15 deletions tests/models/albef/test_image_encoder.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,8 +7,7 @@

import pytest
import torch
from tests.test_utils import assert_expected, set_rng_seed
from torch import Tensor
from tests.test_utils import set_rng_seed
from torchmultimodal.models.albef.image_encoder import ALBEFVisionEncoder


Expand All @@ -24,19 +23,6 @@ class TestALBEFVisionEncoder:
mlp_dim=6,
)

def test_vision_transformer(self):
set_rng_seed(0)
vit = self.vision_encoder
input = torch.randn(1, 3, 4, 4)
output = vit(input)
expected = Tensor(
[
[1.399478, -0.875986, -0.523492],
[-0.869867, 1.400589, -0.530722],
]
).unsqueeze(0)
assert_expected(output, expected, rtol=0, atol=1e-4)

def test_invalid_input_length(self):
input = torch.randn(3, 4, 4)
with pytest.raises(IndexError, match="index out of range"):
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84 changes: 1 addition & 83 deletions tests/models/test_omnivore.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,7 +7,7 @@
import pytest
import torch
import torchmultimodal.models.omnivore as omnivore
from tests.test_utils import assert_expected, set_rng_seed
from tests.test_utils import set_rng_seed
from torchmultimodal.utils.common import get_current_device


Expand All @@ -22,88 +22,6 @@ def omnivore_swin_t_model(device):
return omnivore.omnivore_swin_t().to(device)


@pytest.fixture()
def omnivore_swin_s_model(device):
return omnivore.omnivore_swin_s().to(device)


@pytest.fixture()
def omnivore_swin_b_model(device):
return omnivore.omnivore_swin_b().to(device)


def test_omnivore_swin_t_forward(omnivore_swin_t_model, device):
model = omnivore_swin_t_model

image = torch.randn((1, 3, 1, 112, 112), device=device) # B C D H W
image_score = model(image, input_type="image")

assert_expected(image_score.size(), torch.Size((1, 1000)))
assert_expected(
image_score.abs().sum(), torch.tensor(184.01417), rtol=1e-3, atol=1e-3
)

rgbd = torch.randn((1, 4, 1, 112, 112), device=device)
rgbd_score = model(rgbd, input_type="rgbd")
assert_expected(rgbd_score.size(), torch.Size((1, 19)))
assert_expected(rgbd_score.abs().sum(), torch.tensor(3.60813), rtol=1e-3, atol=1e-3)

video = torch.randn((1, 3, 4, 112, 112), device=device)
video_score = model(video, input_type="video")
assert_expected(video_score.size(), torch.Size((1, 400)))
assert_expected(
video_score.abs().sum(), torch.tensor(110.70048), rtol=1e-3, atol=1e-3
)


def test_omnivore_swin_s_forward(omnivore_swin_s_model, device):
model = omnivore_swin_s_model

image = torch.randn((1, 3, 1, 112, 112), device=device) # B C D H W
image_score = model(image, input_type="image")

assert_expected(image_score.size(), torch.Size((1, 1000)))
assert_expected(
image_score.abs().sum(), torch.tensor(239.73104), rtol=1e-3, atol=1e-3
)

rgbd = torch.randn((1, 4, 1, 112, 112), device=device)
rgbd_score = model(rgbd, input_type="rgbd")
assert_expected(rgbd_score.size(), torch.Size((1, 19)))
assert_expected(rgbd_score.abs().sum(), torch.tensor(5.80919), rtol=1e-3, atol=1e-3)

video = torch.randn((1, 3, 4, 112, 112), device=device)
video_score = model(video, input_type="video")
assert_expected(video_score.size(), torch.Size((1, 400)))
assert_expected(
video_score.abs().sum(), torch.tensor(136.49894), rtol=1e-3, atol=1e-3
)


def test_omnivore_swin_b_forward(omnivore_swin_b_model, device):
model = omnivore_swin_b_model

image = torch.randn((1, 3, 1, 112, 112), device=device) # B C D H W
image_score = model(image, input_type="image")

assert_expected(image_score.size(), torch.Size((1, 1000)))
assert_expected(
image_score.abs().sum(), torch.tensor(278.06488), rtol=1e-3, atol=1e-3
)

rgbd = torch.randn((1, 4, 1, 112, 112), device=device)
rgbd_score = model(rgbd, input_type="rgbd")
assert_expected(rgbd_score.size(), torch.Size((1, 19)))
assert_expected(rgbd_score.abs().sum(), torch.tensor(4.52186), rtol=1e-3, atol=1e-3)

video = torch.randn((1, 3, 4, 112, 112), device=device)
video_score = model(video, input_type="video")
assert_expected(video_score.size(), torch.Size((1, 400)))
assert_expected(
video_score.abs().sum(), torch.tensor(138.22859), rtol=1e-3, atol=1e-3
)


def test_omnivore_forward_wrong_input_type(omnivore_swin_t_model, device):
model = omnivore_swin_t_model

Expand Down
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