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test_utils.py
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test_utils.py
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import os
import re
import sys
import tempfile
from io import BytesIO
import numpy as np
import pytest
import torch
import torchvision.transforms.functional as F
import torchvision.utils as utils
from common_utils import assert_equal, cpu_and_cuda
from PIL import __version__ as PILLOW_VERSION, Image, ImageColor
from torchvision.transforms.v2.functional import to_dtype
PILLOW_VERSION = tuple(int(x) for x in PILLOW_VERSION.split("."))
boxes = torch.tensor([[0, 0, 20, 20], [0, 0, 0, 0], [10, 15, 30, 35], [23, 35, 93, 95]], dtype=torch.float)
keypoints = torch.tensor([[[10, 10], [5, 5], [2, 2]], [[20, 20], [30, 30], [3, 3]]], dtype=torch.float)
def test_make_grid_not_inplace():
t = torch.rand(5, 3, 10, 10)
t_clone = t.clone()
utils.make_grid(t, normalize=False)
assert_equal(t, t_clone, msg="make_grid modified tensor in-place")
utils.make_grid(t, normalize=True, scale_each=False)
assert_equal(t, t_clone, msg="make_grid modified tensor in-place")
utils.make_grid(t, normalize=True, scale_each=True)
assert_equal(t, t_clone, msg="make_grid modified tensor in-place")
def test_normalize_in_make_grid():
t = torch.rand(5, 3, 10, 10) * 255
norm_max = torch.tensor(1.0)
norm_min = torch.tensor(0.0)
grid = utils.make_grid(t, normalize=True)
grid_max = torch.max(grid)
grid_min = torch.min(grid)
# Rounding the result to one decimal for comparison
n_digits = 1
rounded_grid_max = torch.round(grid_max * 10**n_digits) / (10**n_digits)
rounded_grid_min = torch.round(grid_min * 10**n_digits) / (10**n_digits)
assert_equal(norm_max, rounded_grid_max, msg="Normalized max is not equal to 1")
assert_equal(norm_min, rounded_grid_min, msg="Normalized min is not equal to 0")
@pytest.mark.skipif(sys.platform in ("win32", "cygwin"), reason="temporarily disabled on Windows")
def test_save_image():
with tempfile.NamedTemporaryFile(suffix=".png") as f:
t = torch.rand(2, 3, 64, 64)
utils.save_image(t, f.name)
assert os.path.exists(f.name), "The image is not present after save"
@pytest.mark.skipif(sys.platform in ("win32", "cygwin"), reason="temporarily disabled on Windows")
def test_save_image_single_pixel():
with tempfile.NamedTemporaryFile(suffix=".png") as f:
t = torch.rand(1, 3, 1, 1)
utils.save_image(t, f.name)
assert os.path.exists(f.name), "The pixel image is not present after save"
@pytest.mark.skipif(sys.platform in ("win32", "cygwin"), reason="temporarily disabled on Windows")
def test_save_image_file_object():
with tempfile.NamedTemporaryFile(suffix=".png") as f:
t = torch.rand(2, 3, 64, 64)
utils.save_image(t, f.name)
img_orig = Image.open(f.name)
fp = BytesIO()
utils.save_image(t, fp, format="png")
img_bytes = Image.open(fp)
assert_equal(F.pil_to_tensor(img_orig), F.pil_to_tensor(img_bytes), msg="Image not stored in file object")
@pytest.mark.skipif(sys.platform in ("win32", "cygwin"), reason="temporarily disabled on Windows")
def test_save_image_single_pixel_file_object():
with tempfile.NamedTemporaryFile(suffix=".png") as f:
t = torch.rand(1, 3, 1, 1)
utils.save_image(t, f.name)
img_orig = Image.open(f.name)
fp = BytesIO()
utils.save_image(t, fp, format="png")
img_bytes = Image.open(fp)
assert_equal(F.pil_to_tensor(img_orig), F.pil_to_tensor(img_bytes), msg="Image not stored in file object")
def test_draw_boxes():
img = torch.full((3, 100, 100), 255, dtype=torch.uint8)
img_cp = img.clone()
boxes_cp = boxes.clone()
labels = ["a", "b", "c", "d"]
colors = ["green", "#FF00FF", (0, 255, 0), "red"]
result = utils.draw_bounding_boxes(img, boxes, labels=labels, colors=colors, fill=True)
path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "assets", "fakedata", "draw_boxes_util.png")
if not os.path.exists(path):
res = Image.fromarray(result.permute(1, 2, 0).contiguous().numpy())
res.save(path)
if PILLOW_VERSION >= (10, 1):
# The reference image is only valid for new PIL versions
expected = torch.as_tensor(np.array(Image.open(path))).permute(2, 0, 1)
assert_equal(result, expected)
# Check if modification is not in place
assert_equal(boxes, boxes_cp)
assert_equal(img, img_cp)
@pytest.mark.skipif(PILLOW_VERSION < (10, 1), reason="The reference image is only valid for PIL >= 10.1")
def test_draw_boxes_with_coloured_labels():
img = torch.full((3, 100, 100), 255, dtype=torch.uint8)
labels = ["a", "b", "c", "d"]
colors = ["green", "#FF00FF", (0, 255, 0), "red"]
label_colors = ["green", "red", (0, 255, 0), "#FF00FF"]
result = utils.draw_bounding_boxes(img, boxes, labels=labels, colors=colors, fill=True, label_colors=label_colors)
path = os.path.join(
os.path.dirname(os.path.abspath(__file__)), "assets", "fakedata", "draw_boxes_different_label_colors.png"
)
expected = torch.as_tensor(np.array(Image.open(path))).permute(2, 0, 1)
assert_equal(result, expected)
@pytest.mark.parametrize("fill", [True, False])
def test_draw_boxes_dtypes(fill):
img_uint8 = torch.full((3, 100, 100), 255, dtype=torch.uint8)
out_uint8 = utils.draw_bounding_boxes(img_uint8, boxes, fill=fill)
assert img_uint8 is not out_uint8
assert out_uint8.dtype == torch.uint8
img_float = to_dtype(img_uint8, torch.float, scale=True)
out_float = utils.draw_bounding_boxes(img_float, boxes, fill=fill)
assert img_float is not out_float
assert out_float.is_floating_point()
torch.testing.assert_close(out_uint8, to_dtype(out_float, torch.uint8, scale=True), rtol=0, atol=1)
@pytest.mark.parametrize("colors", [None, ["red", "blue", "#FF00FF", (1, 34, 122)], "red", "#FF00FF", (1, 34, 122)])
def test_draw_boxes_colors(colors):
img = torch.full((3, 100, 100), 0, dtype=torch.uint8)
utils.draw_bounding_boxes(img, boxes, fill=False, width=7, colors=colors)
with pytest.raises(ValueError, match="Number of colors must be equal or larger than the number of objects"):
utils.draw_bounding_boxes(image=img, boxes=boxes, colors=[])
def test_draw_boxes_vanilla():
img = torch.full((3, 100, 100), 0, dtype=torch.uint8)
img_cp = img.clone()
boxes_cp = boxes.clone()
result = utils.draw_bounding_boxes(img, boxes, fill=False, width=7, colors="white")
path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "assets", "fakedata", "draw_boxes_vanilla.png")
if not os.path.exists(path):
res = Image.fromarray(result.permute(1, 2, 0).contiguous().numpy())
res.save(path)
expected = torch.as_tensor(np.array(Image.open(path))).permute(2, 0, 1)
assert_equal(result, expected)
# Check if modification is not in place
assert_equal(boxes, boxes_cp)
assert_equal(img, img_cp)
def test_draw_boxes_grayscale():
img = torch.full((1, 4, 4), fill_value=255, dtype=torch.uint8)
boxes = torch.tensor([[0, 0, 3, 3]], dtype=torch.int64)
bboxed_img = utils.draw_bounding_boxes(image=img, boxes=boxes, colors=["#1BBC9B"])
assert bboxed_img.size(0) == 3
def test_draw_invalid_boxes():
img_tp = ((1, 1, 1), (1, 2, 3))
img_wrong2 = torch.full((1, 3, 5, 5), 255, dtype=torch.uint8)
img_correct = torch.zeros((3, 10, 10), dtype=torch.uint8)
boxes = torch.tensor([[0, 0, 20, 20], [0, 0, 0, 0], [10, 15, 30, 35], [23, 35, 93, 95]], dtype=torch.float)
boxes_wrong = torch.tensor([[10, 10, 4, 5], [30, 20, 10, 5]], dtype=torch.float)
labels_wrong = ["one", "two"]
colors_wrong = ["pink", "blue"]
with pytest.raises(TypeError, match="Tensor expected"):
utils.draw_bounding_boxes(img_tp, boxes)
with pytest.raises(ValueError, match="Pass individual images, not batches"):
utils.draw_bounding_boxes(img_wrong2, boxes)
with pytest.raises(ValueError, match="Only grayscale and RGB images are supported"):
utils.draw_bounding_boxes(img_wrong2[0][:2], boxes)
with pytest.raises(ValueError, match="Number of boxes"):
utils.draw_bounding_boxes(img_correct, boxes, labels_wrong)
with pytest.raises(ValueError, match="Number of colors"):
utils.draw_bounding_boxes(img_correct, boxes, colors=colors_wrong)
with pytest.raises(ValueError, match="Boxes need to be in"):
utils.draw_bounding_boxes(img_correct, boxes_wrong)
def test_draw_boxes_warning():
img = torch.full((3, 100, 100), 255, dtype=torch.uint8)
with pytest.warns(UserWarning, match=re.escape("Argument 'font_size' will be ignored since 'font' is not set.")):
utils.draw_bounding_boxes(img, boxes, font_size=11)
def test_draw_no_boxes():
img = torch.full((3, 100, 100), 0, dtype=torch.uint8)
boxes = torch.full((0, 4), 0, dtype=torch.float)
with pytest.warns(UserWarning, match=re.escape("boxes doesn't contain any box. No box was drawn")):
res = utils.draw_bounding_boxes(img, boxes)
# Check that the function didn't change the image
assert res.eq(img).all()
@pytest.mark.parametrize(
"colors",
[
None,
"blue",
"#FF00FF",
(1, 34, 122),
["red", "blue"],
["#FF00FF", (1, 34, 122)],
],
)
@pytest.mark.parametrize("alpha", (0, 0.5, 0.7, 1))
@pytest.mark.parametrize("device", cpu_and_cuda())
def test_draw_segmentation_masks(colors, alpha, device):
"""This test makes sure that masks draw their corresponding color where they should"""
num_masks, h, w = 2, 100, 100
dtype = torch.uint8
img = torch.randint(0, 256, size=(3, h, w), dtype=dtype, device=device)
masks = torch.zeros((num_masks, h, w), dtype=torch.bool, device=device)
masks[0, 10:20, 10:20] = True
masks[1, 15:25, 15:25] = True
overlap = masks[0] & masks[1]
out = utils.draw_segmentation_masks(img, masks, colors=colors, alpha=alpha)
assert out.dtype == dtype
assert out is not img
# Make sure the image didn't change where there's no mask
masked_pixels = masks[0] | masks[1]
assert_equal(img[:, ~masked_pixels], out[:, ~masked_pixels])
if colors is None:
colors = utils._generate_color_palette(num_masks)
elif isinstance(colors, str) or isinstance(colors, tuple):
colors = [colors]
# Make sure each mask draws with its own color
for mask, color in zip(masks, colors):
if isinstance(color, str):
color = ImageColor.getrgb(color)
color = torch.tensor(color, dtype=dtype, device=device)
if alpha == 1:
assert (out[:, mask & ~overlap] == color[:, None]).all()
elif alpha == 0:
assert (out[:, mask & ~overlap] == img[:, mask & ~overlap]).all()
interpolated_color = (img[:, mask & ~overlap] * (1 - alpha) + color[:, None] * alpha).to(dtype)
torch.testing.assert_close(out[:, mask & ~overlap], interpolated_color, rtol=0.0, atol=1.0)
interpolated_overlap = (img[:, overlap] * (1 - alpha)).to(dtype)
torch.testing.assert_close(out[:, overlap], interpolated_overlap, rtol=0.0, atol=1.0)
def test_draw_segmentation_masks_dtypes():
num_masks, h, w = 2, 100, 100
masks = torch.randint(0, 2, (num_masks, h, w), dtype=torch.bool)
img_uint8 = torch.randint(0, 256, size=(3, h, w), dtype=torch.uint8)
out_uint8 = utils.draw_segmentation_masks(img_uint8, masks)
assert img_uint8 is not out_uint8
assert out_uint8.dtype == torch.uint8
img_float = to_dtype(img_uint8, torch.float, scale=True)
out_float = utils.draw_segmentation_masks(img_float, masks)
assert img_float is not out_float
assert out_float.is_floating_point()
torch.testing.assert_close(out_uint8, to_dtype(out_float, torch.uint8, scale=True), rtol=0, atol=1)
@pytest.mark.parametrize("device", cpu_and_cuda())
def test_draw_segmentation_masks_errors(device):
h, w = 10, 10
masks = torch.randint(0, 2, size=(h, w), dtype=torch.bool, device=device)
img = torch.randint(0, 256, size=(3, h, w), dtype=torch.uint8, device=device)
with pytest.raises(TypeError, match="The image must be a tensor"):
utils.draw_segmentation_masks(image="Not A Tensor Image", masks=masks)
with pytest.raises(ValueError, match="The image dtype must be"):
img_bad_dtype = torch.randint(0, 256, size=(3, h, w), dtype=torch.int64)
utils.draw_segmentation_masks(image=img_bad_dtype, masks=masks)
with pytest.raises(ValueError, match="Pass individual images, not batches"):
batch = torch.randint(0, 256, size=(10, 3, h, w), dtype=torch.uint8)
utils.draw_segmentation_masks(image=batch, masks=masks)
with pytest.raises(ValueError, match="Pass an RGB image"):
one_channel = torch.randint(0, 256, size=(1, h, w), dtype=torch.uint8)
utils.draw_segmentation_masks(image=one_channel, masks=masks)
with pytest.raises(ValueError, match="The masks must be of dtype bool"):
masks_bad_dtype = torch.randint(0, 2, size=(h, w), dtype=torch.float)
utils.draw_segmentation_masks(image=img, masks=masks_bad_dtype)
with pytest.raises(ValueError, match="masks must be of shape"):
masks_bad_shape = torch.randint(0, 2, size=(3, 2, h, w), dtype=torch.bool)
utils.draw_segmentation_masks(image=img, masks=masks_bad_shape)
with pytest.raises(ValueError, match="must have the same height and width"):
masks_bad_shape = torch.randint(0, 2, size=(h + 4, w), dtype=torch.bool)
utils.draw_segmentation_masks(image=img, masks=masks_bad_shape)
with pytest.raises(ValueError, match="Number of colors must be equal or larger than the number of objects"):
utils.draw_segmentation_masks(image=img, masks=masks, colors=[])
with pytest.raises(ValueError, match="`colors` must be a tuple or a string, or a list thereof"):
bad_colors = np.array(["red", "blue"]) # should be a list
utils.draw_segmentation_masks(image=img, masks=masks, colors=bad_colors)
with pytest.raises(ValueError, match="If passed as tuple, colors should be an RGB triplet"):
bad_colors = ("red", "blue") # should be a list
utils.draw_segmentation_masks(image=img, masks=masks, colors=bad_colors)
@pytest.mark.parametrize("device", cpu_and_cuda())
def test_draw_no_segmention_mask(device):
img = torch.full((3, 100, 100), 0, dtype=torch.uint8, device=device)
masks = torch.full((0, 100, 100), 0, dtype=torch.bool, device=device)
with pytest.warns(UserWarning, match=re.escape("masks doesn't contain any mask. No mask was drawn")):
res = utils.draw_segmentation_masks(img, masks)
# Check that the function didn't change the image
assert res.eq(img).all()
def test_draw_keypoints_vanilla():
# Keypoints is declared on top as global variable
keypoints_cp = keypoints.clone()
img = torch.full((3, 100, 100), 0, dtype=torch.uint8)
img_cp = img.clone()
result = utils.draw_keypoints(
img,
keypoints,
colors="red",
connectivity=[
(0, 1),
],
)
path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "assets", "fakedata", "draw_keypoint_vanilla.png")
if not os.path.exists(path):
res = Image.fromarray(result.permute(1, 2, 0).contiguous().numpy())
res.save(path)
expected = torch.as_tensor(np.array(Image.open(path))).permute(2, 0, 1)
assert_equal(result, expected)
# Check that keypoints are not modified inplace
assert_equal(keypoints, keypoints_cp)
# Check that image is not modified in place
assert_equal(img, img_cp)
def test_draw_keypoins_K_equals_one():
# Non-regression test for https://github.com/pytorch/vision/pull/8439
img = torch.full((3, 100, 100), 0, dtype=torch.uint8)
keypoints = torch.tensor([[[10, 10]]], dtype=torch.float)
utils.draw_keypoints(img, keypoints)
@pytest.mark.parametrize("colors", ["red", "#FF00FF", (1, 34, 122)])
def test_draw_keypoints_colored(colors):
# Keypoints is declared on top as global variable
keypoints_cp = keypoints.clone()
img = torch.full((3, 100, 100), 0, dtype=torch.uint8)
img_cp = img.clone()
result = utils.draw_keypoints(
img,
keypoints,
colors=colors,
connectivity=[
(0, 1),
],
)
assert result.size(0) == 3
assert_equal(keypoints, keypoints_cp)
assert_equal(img, img_cp)
@pytest.mark.parametrize("connectivity", [[(0, 1)], [(0, 1), (1, 2)]])
@pytest.mark.parametrize(
"vis",
[
torch.tensor([[1, 1, 0], [1, 1, 0]], dtype=torch.bool),
torch.tensor([[1, 1, 0], [1, 1, 0]], dtype=torch.float).unsqueeze_(-1),
],
)
def test_draw_keypoints_visibility(connectivity, vis):
# Keypoints is declared on top as global variable
keypoints_cp = keypoints.clone()
img = torch.full((3, 100, 100), 0, dtype=torch.uint8)
img_cp = img.clone()
vis_cp = vis if vis is None else vis.clone()
result = utils.draw_keypoints(
image=img,
keypoints=keypoints,
connectivity=connectivity,
colors="red",
visibility=vis,
)
assert result.size(0) == 3
assert_equal(keypoints, keypoints_cp)
assert_equal(img, img_cp)
# compare with a fakedata image
# connect the key points 0 to 1 for both skeletons and do not show the other key points
path = os.path.join(
os.path.dirname(os.path.abspath(__file__)), "assets", "fakedata", "draw_keypoints_visibility.png"
)
if not os.path.exists(path):
res = Image.fromarray(result.permute(1, 2, 0).contiguous().numpy())
res.save(path)
expected = torch.as_tensor(np.array(Image.open(path))).permute(2, 0, 1)
assert_equal(result, expected)
if vis_cp is None:
assert vis is None
else:
assert_equal(vis, vis_cp)
assert vis.dtype == vis_cp.dtype
def test_draw_keypoints_visibility_default():
# Keypoints is declared on top as global variable
keypoints_cp = keypoints.clone()
img = torch.full((3, 100, 100), 0, dtype=torch.uint8)
img_cp = img.clone()
result = utils.draw_keypoints(
image=img,
keypoints=keypoints,
connectivity=[(0, 1)],
colors="red",
visibility=None,
)
assert result.size(0) == 3
assert_equal(keypoints, keypoints_cp)
assert_equal(img, img_cp)
# compare against fakedata image, which connects 0->1 for both key-point skeletons
path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "assets", "fakedata", "draw_keypoint_vanilla.png")
expected = torch.as_tensor(np.array(Image.open(path))).permute(2, 0, 1)
assert_equal(result, expected)
def test_draw_keypoints_dtypes():
image_uint8 = torch.randint(0, 256, size=(3, 100, 100), dtype=torch.uint8)
image_float = to_dtype(image_uint8, torch.float, scale=True)
out_uint8 = utils.draw_keypoints(image_uint8, keypoints)
out_float = utils.draw_keypoints(image_float, keypoints)
assert out_uint8.dtype == torch.uint8
assert out_uint8 is not image_uint8
assert out_float.is_floating_point()
assert out_float is not image_float
torch.testing.assert_close(out_uint8, to_dtype(out_float, torch.uint8, scale=True), rtol=0, atol=1)
def test_draw_keypoints_errors():
h, w = 10, 10
img = torch.full((3, 100, 100), 0, dtype=torch.uint8)
with pytest.raises(TypeError, match="The image must be a tensor"):
utils.draw_keypoints(image="Not A Tensor Image", keypoints=keypoints)
with pytest.raises(ValueError, match="The image dtype must be"):
img_bad_dtype = torch.full((3, h, w), 0, dtype=torch.int64)
utils.draw_keypoints(image=img_bad_dtype, keypoints=keypoints)
with pytest.raises(ValueError, match="Pass individual images, not batches"):
batch = torch.randint(0, 256, size=(10, 3, h, w), dtype=torch.uint8)
utils.draw_keypoints(image=batch, keypoints=keypoints)
with pytest.raises(ValueError, match="Pass an RGB image"):
one_channel = torch.randint(0, 256, size=(1, h, w), dtype=torch.uint8)
utils.draw_keypoints(image=one_channel, keypoints=keypoints)
with pytest.raises(ValueError, match="keypoints must be of shape"):
invalid_keypoints = torch.tensor([[10, 10, 10, 10], [5, 6, 7, 8]], dtype=torch.float)
utils.draw_keypoints(image=img, keypoints=invalid_keypoints)
with pytest.raises(ValueError, match=re.escape("visibility must be of shape (num_instances, K)")):
one_dim_visibility = torch.tensor([True, True, True], dtype=torch.bool)
utils.draw_keypoints(image=img, keypoints=keypoints, visibility=one_dim_visibility)
with pytest.raises(ValueError, match=re.escape("visibility must be of shape (num_instances, K)")):
three_dim_visibility = torch.ones((2, 3, 4), dtype=torch.bool)
utils.draw_keypoints(image=img, keypoints=keypoints, visibility=three_dim_visibility)
with pytest.raises(ValueError, match="keypoints and visibility must have the same dimensionality"):
vis_wrong_n = torch.ones((3, 3), dtype=torch.bool)
utils.draw_keypoints(image=img, keypoints=keypoints, visibility=vis_wrong_n)
with pytest.raises(ValueError, match="keypoints and visibility must have the same dimensionality"):
vis_wrong_k = torch.ones((2, 4), dtype=torch.bool)
utils.draw_keypoints(image=img, keypoints=keypoints, visibility=vis_wrong_k)
@pytest.mark.parametrize("batch", (True, False))
def test_flow_to_image(batch):
h, w = 100, 100
flow = torch.meshgrid(torch.arange(h), torch.arange(w), indexing="ij")
flow = torch.stack(flow[::-1], dim=0).float()
flow[0] -= h / 2
flow[1] -= w / 2
if batch:
flow = torch.stack([flow, flow])
img = utils.flow_to_image(flow)
assert img.shape == (2, 3, h, w) if batch else (3, h, w)
path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "assets", "expected_flow.pt")
expected_img = torch.load(path, map_location="cpu", weights_only=True)
if batch:
expected_img = torch.stack([expected_img, expected_img])
assert_equal(expected_img, img)
@pytest.mark.parametrize(
"input_flow, match",
(
(torch.full((3, 10, 10), 0, dtype=torch.float), "Input flow should have shape"),
(torch.full((5, 3, 10, 10), 0, dtype=torch.float), "Input flow should have shape"),
(torch.full((2, 10), 0, dtype=torch.float), "Input flow should have shape"),
(torch.full((5, 2, 10), 0, dtype=torch.float), "Input flow should have shape"),
(torch.full((2, 10, 30), 0, dtype=torch.int), "Flow should be of dtype torch.float"),
),
)
def test_flow_to_image_errors(input_flow, match):
with pytest.raises(ValueError, match=match):
utils.flow_to_image(flow=input_flow)
if __name__ == "__main__":
pytest.main([__file__])