refiners/tests/foundationals/segment_anything/test_utils.py

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import pytest
import torch
from PIL import Image
from refiners.foundationals.segment_anything.utils import (
compute_scaled_size,
image_to_scaled_tensor,
pad_image_tensor,
preprocess_image,
)
@pytest.fixture
def image_encoder_resolution() -> int:
return 1024
def test_compute_scaled_size(image_encoder_resolution: int) -> None:
w, h = (1536, 768)
scaled_size = compute_scaled_size((h, w), image_encoder_resolution)
assert scaled_size == (512, 1024)
def test_rgb_image_to_scaled_tensor() -> None:
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image = Image.new("RGB", (1536, 768))
tensor = image_to_scaled_tensor(image, (512, 1024))
assert tensor.shape == (1, 3, 512, 1024)
def test_grayscale_image_to_scaled_tensor() -> None:
image = Image.new("L", (1536, 768))
tensor = image_to_scaled_tensor(image, (512, 1024))
assert tensor.shape == (1, 1, 512, 1024)
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def test_preprocess_image(image_encoder_resolution: int) -> None:
image = Image.new("RGB", (1536, 768))
preprocessed = preprocess_image(image, image_encoder_resolution)
assert preprocessed.shape == (1, 3, 1024, 1024)
def test_pad_image_tensor(image_encoder_resolution: int) -> None:
w, h = (1536, 768)
image = Image.new("RGB", (w, h), color="white")
scaled_size = compute_scaled_size((h, w), image_encoder_resolution)
scaled_image_tensor = image_to_scaled_tensor(image, scaled_size)
padded_image_tensor = pad_image_tensor(scaled_image_tensor, scaled_size, image_encoder_resolution)
assert padded_image_tensor.shape == (1, 3, 1024, 1024)
assert torch.all(padded_image_tensor[:, :, 512:, :] == 0)