1283 lines
48 KiB
Python
1283 lines
48 KiB
Python
import colorsys
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import itertools
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import math
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import os
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from collections.abc import Sequence
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from functools import partial
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import numpy as np
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import PIL.Image
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import pytest
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import torch
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import torchvision.transforms as T
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import torchvision.transforms._functional_pil as F_pil
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import torchvision.transforms._functional_tensor as F_t
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import torchvision.transforms.functional as F
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from common_utils import (
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_assert_approx_equal_tensor_to_pil,
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_assert_equal_tensor_to_pil,
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_create_data,
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_create_data_batch,
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_test_fn_on_batch,
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assert_equal,
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cpu_and_cuda,
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needs_cuda,
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)
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from torchvision.transforms import InterpolationMode
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NEAREST, NEAREST_EXACT, BILINEAR, BICUBIC = (
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InterpolationMode.NEAREST,
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InterpolationMode.NEAREST_EXACT,
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InterpolationMode.BILINEAR,
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InterpolationMode.BICUBIC,
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)
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@pytest.mark.parametrize("device", cpu_and_cuda())
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@pytest.mark.parametrize("fn", [F.get_image_size, F.get_image_num_channels, F.get_dimensions])
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def test_image_sizes(device, fn):
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script_F = torch.jit.script(fn)
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img_tensor, pil_img = _create_data(16, 18, 3, device=device)
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value_img = fn(img_tensor)
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value_pil_img = fn(pil_img)
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assert value_img == value_pil_img
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value_img_script = script_F(img_tensor)
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assert value_img == value_img_script
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batch_tensors = _create_data_batch(16, 18, 3, num_samples=4, device=device)
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value_img_batch = fn(batch_tensors)
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assert value_img == value_img_batch
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@needs_cuda
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def test_scale_channel():
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"""Make sure that _scale_channel gives the same results on CPU and GPU as
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histc or bincount are used depending on the device.
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"""
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# TODO: when # https://github.com/pytorch/pytorch/issues/53194 is fixed,
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# only use bincount and remove that test.
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size = (1_000,)
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img_chan = torch.randint(0, 256, size=size).to("cpu")
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scaled_cpu = F_t._scale_channel(img_chan)
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scaled_cuda = F_t._scale_channel(img_chan.to("cuda"))
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assert_equal(scaled_cpu, scaled_cuda.to("cpu"))
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class TestRotate:
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ALL_DTYPES = [None, torch.float32, torch.float64, torch.float16]
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scripted_rotate = torch.jit.script(F.rotate)
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IMG_W = 26
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@pytest.mark.parametrize("device", cpu_and_cuda())
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@pytest.mark.parametrize("height, width", [(7, 33), (26, IMG_W), (32, IMG_W)])
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@pytest.mark.parametrize(
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"center",
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[
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None,
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(int(IMG_W * 0.3), int(IMG_W * 0.4)),
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[int(IMG_W * 0.5), int(IMG_W * 0.6)],
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],
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)
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@pytest.mark.parametrize("dt", ALL_DTYPES)
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@pytest.mark.parametrize("angle", range(-180, 180, 34))
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@pytest.mark.parametrize("expand", [True, False])
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@pytest.mark.parametrize(
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"fill",
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[
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None,
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[0, 0, 0],
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(1, 2, 3),
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[255, 255, 255],
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[
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1,
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],
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(2.0,),
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],
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)
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@pytest.mark.parametrize("fn", [F.rotate, scripted_rotate])
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def test_rotate(self, device, height, width, center, dt, angle, expand, fill, fn):
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tensor, pil_img = _create_data(height, width, device=device)
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if dt == torch.float16 and torch.device(device).type == "cpu":
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# skip float16 on CPU case
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return
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if dt is not None:
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tensor = tensor.to(dtype=dt)
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f_pil = int(fill[0]) if fill is not None and len(fill) == 1 else fill
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out_pil_img = F.rotate(pil_img, angle=angle, interpolation=NEAREST, expand=expand, center=center, fill=f_pil)
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out_pil_tensor = torch.from_numpy(np.array(out_pil_img).transpose((2, 0, 1)))
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out_tensor = fn(tensor, angle=angle, interpolation=NEAREST, expand=expand, center=center, fill=fill).cpu()
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if out_tensor.dtype != torch.uint8:
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out_tensor = out_tensor.to(torch.uint8)
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assert (
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out_tensor.shape == out_pil_tensor.shape
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), f"{(height, width, NEAREST, dt, angle, expand, center)}: {out_tensor.shape} vs {out_pil_tensor.shape}"
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num_diff_pixels = (out_tensor != out_pil_tensor).sum().item() / 3.0
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ratio_diff_pixels = num_diff_pixels / out_tensor.shape[-1] / out_tensor.shape[-2]
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# Tolerance : less than 3% of different pixels
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assert ratio_diff_pixels < 0.03, (
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f"{(height, width, NEAREST, dt, angle, expand, center, fill)}: "
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f"{ratio_diff_pixels}\n{out_tensor[0, :7, :7]} vs \n"
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f"{out_pil_tensor[0, :7, :7]}"
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)
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@pytest.mark.parametrize("device", cpu_and_cuda())
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@pytest.mark.parametrize("dt", ALL_DTYPES)
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def test_rotate_batch(self, device, dt):
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if dt == torch.float16 and device == "cpu":
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# skip float16 on CPU case
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return
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batch_tensors = _create_data_batch(26, 36, num_samples=4, device=device)
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if dt is not None:
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batch_tensors = batch_tensors.to(dtype=dt)
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center = (20, 22)
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_test_fn_on_batch(batch_tensors, F.rotate, angle=32, interpolation=NEAREST, expand=True, center=center)
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def test_rotate_interpolation_type(self):
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tensor, _ = _create_data(26, 26)
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res1 = F.rotate(tensor, 45, interpolation=PIL.Image.BILINEAR)
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res2 = F.rotate(tensor, 45, interpolation=BILINEAR)
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assert_equal(res1, res2)
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class TestAffine:
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ALL_DTYPES = [None, torch.float32, torch.float64, torch.float16]
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scripted_affine = torch.jit.script(F.affine)
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@pytest.mark.parametrize("device", cpu_and_cuda())
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@pytest.mark.parametrize("height, width", [(26, 26), (32, 26)])
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@pytest.mark.parametrize("dt", ALL_DTYPES)
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def test_identity_map(self, device, height, width, dt):
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# Tests on square and rectangular images
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tensor, pil_img = _create_data(height, width, device=device)
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if dt == torch.float16 and device == "cpu":
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# skip float16 on CPU case
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return
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if dt is not None:
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tensor = tensor.to(dtype=dt)
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# 1) identity map
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out_tensor = F.affine(tensor, angle=0, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], interpolation=NEAREST)
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assert_equal(tensor, out_tensor, msg=f"{out_tensor[0, :5, :5]} vs {tensor[0, :5, :5]}")
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out_tensor = self.scripted_affine(
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tensor, angle=0, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], interpolation=NEAREST
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)
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assert_equal(tensor, out_tensor, msg=f"{out_tensor[0, :5, :5]} vs {tensor[0, :5, :5]}")
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@pytest.mark.parametrize("device", cpu_and_cuda())
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@pytest.mark.parametrize("height, width", [(26, 26)])
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@pytest.mark.parametrize("dt", ALL_DTYPES)
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@pytest.mark.parametrize(
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"angle, config",
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[
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(90, {"k": 1, "dims": (-1, -2)}),
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(45, None),
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(30, None),
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(-30, None),
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(-45, None),
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(-90, {"k": -1, "dims": (-1, -2)}),
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(180, {"k": 2, "dims": (-1, -2)}),
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],
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)
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@pytest.mark.parametrize("fn", [F.affine, scripted_affine])
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def test_square_rotations(self, device, height, width, dt, angle, config, fn):
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# 2) Test rotation
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tensor, pil_img = _create_data(height, width, device=device)
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if dt == torch.float16 and device == "cpu":
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# skip float16 on CPU case
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return
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if dt is not None:
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tensor = tensor.to(dtype=dt)
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out_pil_img = F.affine(
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pil_img, angle=angle, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], interpolation=NEAREST
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)
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out_pil_tensor = torch.from_numpy(np.array(out_pil_img).transpose((2, 0, 1))).to(device)
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out_tensor = fn(tensor, angle=angle, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], interpolation=NEAREST)
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if config is not None:
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assert_equal(torch.rot90(tensor, **config), out_tensor)
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if out_tensor.dtype != torch.uint8:
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out_tensor = out_tensor.to(torch.uint8)
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num_diff_pixels = (out_tensor != out_pil_tensor).sum().item() / 3.0
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ratio_diff_pixels = num_diff_pixels / out_tensor.shape[-1] / out_tensor.shape[-2]
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# Tolerance : less than 6% of different pixels
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assert ratio_diff_pixels < 0.06
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@pytest.mark.parametrize("device", cpu_and_cuda())
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@pytest.mark.parametrize("height, width", [(32, 26)])
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@pytest.mark.parametrize("dt", ALL_DTYPES)
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@pytest.mark.parametrize("angle", [90, 45, 15, -30, -60, -120])
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@pytest.mark.parametrize("fn", [F.affine, scripted_affine])
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@pytest.mark.parametrize("center", [None, [0, 0]])
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def test_rect_rotations(self, device, height, width, dt, angle, fn, center):
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# Tests on rectangular images
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tensor, pil_img = _create_data(height, width, device=device)
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if dt == torch.float16 and device == "cpu":
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# skip float16 on CPU case
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return
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if dt is not None:
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tensor = tensor.to(dtype=dt)
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out_pil_img = F.affine(
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pil_img, angle=angle, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], interpolation=NEAREST, center=center
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)
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out_pil_tensor = torch.from_numpy(np.array(out_pil_img).transpose((2, 0, 1)))
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out_tensor = fn(
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tensor, angle=angle, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], interpolation=NEAREST, center=center
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).cpu()
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if out_tensor.dtype != torch.uint8:
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out_tensor = out_tensor.to(torch.uint8)
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num_diff_pixels = (out_tensor != out_pil_tensor).sum().item() / 3.0
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ratio_diff_pixels = num_diff_pixels / out_tensor.shape[-1] / out_tensor.shape[-2]
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# Tolerance : less than 3% of different pixels
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assert ratio_diff_pixels < 0.03
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@pytest.mark.parametrize("device", cpu_and_cuda())
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@pytest.mark.parametrize("height, width", [(26, 26), (32, 26)])
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@pytest.mark.parametrize("dt", ALL_DTYPES)
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@pytest.mark.parametrize("t", [[10, 12], (-12, -13)])
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@pytest.mark.parametrize("fn", [F.affine, scripted_affine])
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def test_translations(self, device, height, width, dt, t, fn):
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# 3) Test translation
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tensor, pil_img = _create_data(height, width, device=device)
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if dt == torch.float16 and device == "cpu":
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# skip float16 on CPU case
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return
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if dt is not None:
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tensor = tensor.to(dtype=dt)
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out_pil_img = F.affine(pil_img, angle=0, translate=t, scale=1.0, shear=[0.0, 0.0], interpolation=NEAREST)
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out_tensor = fn(tensor, angle=0, translate=t, scale=1.0, shear=[0.0, 0.0], interpolation=NEAREST)
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if out_tensor.dtype != torch.uint8:
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out_tensor = out_tensor.to(torch.uint8)
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_assert_equal_tensor_to_pil(out_tensor, out_pil_img)
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@pytest.mark.parametrize("device", cpu_and_cuda())
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@pytest.mark.parametrize("height, width", [(26, 26), (32, 26)])
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@pytest.mark.parametrize("dt", ALL_DTYPES)
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@pytest.mark.parametrize(
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"a, t, s, sh, f",
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[
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(45.5, [5, 6], 1.0, [0.0, 0.0], None),
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(33, (5, -4), 1.0, [0.0, 0.0], [0, 0, 0]),
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(45, [-5, 4], 1.2, [0.0, 0.0], (1, 2, 3)),
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(33, (-4, -8), 2.0, [0.0, 0.0], [255, 255, 255]),
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(85, (10, -10), 0.7, [0.0, 0.0], [1]),
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(0, [0, 0], 1.0, [35.0], (2.0,)),
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(-25, [0, 0], 1.2, [0.0, 15.0], None),
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(-45, [-10, 0], 0.7, [2.0, 5.0], None),
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(-45, [-10, -10], 1.2, [4.0, 5.0], None),
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(-90, [0, 0], 1.0, [0.0, 0.0], None),
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],
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)
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@pytest.mark.parametrize("fn", [F.affine, scripted_affine])
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def test_all_ops(self, device, height, width, dt, a, t, s, sh, f, fn):
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# 4) Test rotation + translation + scale + shear
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tensor, pil_img = _create_data(height, width, device=device)
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if dt == torch.float16 and device == "cpu":
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# skip float16 on CPU case
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return
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if dt is not None:
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tensor = tensor.to(dtype=dt)
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f_pil = int(f[0]) if f is not None and len(f) == 1 else f
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out_pil_img = F.affine(pil_img, angle=a, translate=t, scale=s, shear=sh, interpolation=NEAREST, fill=f_pil)
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out_pil_tensor = torch.from_numpy(np.array(out_pil_img).transpose((2, 0, 1)))
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out_tensor = fn(tensor, angle=a, translate=t, scale=s, shear=sh, interpolation=NEAREST, fill=f).cpu()
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if out_tensor.dtype != torch.uint8:
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out_tensor = out_tensor.to(torch.uint8)
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num_diff_pixels = (out_tensor != out_pil_tensor).sum().item() / 3.0
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ratio_diff_pixels = num_diff_pixels / out_tensor.shape[-1] / out_tensor.shape[-2]
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# Tolerance : less than 5% (cpu), 6% (cuda) of different pixels
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tol = 0.06 if device == "cuda" else 0.05
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assert ratio_diff_pixels < tol
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@pytest.mark.parametrize("device", cpu_and_cuda())
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@pytest.mark.parametrize("dt", ALL_DTYPES)
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def test_batches(self, device, dt):
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if dt == torch.float16 and device == "cpu":
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# skip float16 on CPU case
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return
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batch_tensors = _create_data_batch(26, 36, num_samples=4, device=device)
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if dt is not None:
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batch_tensors = batch_tensors.to(dtype=dt)
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_test_fn_on_batch(batch_tensors, F.affine, angle=-43, translate=[-3, 4], scale=1.2, shear=[4.0, 5.0])
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@pytest.mark.parametrize("device", cpu_and_cuda())
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def test_interpolation_type(self, device):
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tensor, pil_img = _create_data(26, 26, device=device)
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res1 = F.affine(tensor, 45, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], interpolation=PIL.Image.BILINEAR)
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res2 = F.affine(tensor, 45, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], interpolation=BILINEAR)
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assert_equal(res1, res2)
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|
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def _get_data_dims_and_points_for_perspective():
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# Ideally we would parametrize independently over data dims and points, but
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# we want to tests on some points that also depend on the data dims.
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# Pytest doesn't support covariant parametrization, so we do it somewhat manually here.
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data_dims = [(26, 34), (26, 26)]
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points = [
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[[[0, 0], [33, 0], [33, 25], [0, 25]], [[3, 2], [32, 3], [30, 24], [2, 25]]],
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[[[3, 2], [32, 3], [30, 24], [2, 25]], [[0, 0], [33, 0], [33, 25], [0, 25]]],
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[[[3, 2], [32, 3], [30, 24], [2, 25]], [[5, 5], [30, 3], [33, 19], [4, 25]]],
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]
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dims_and_points = list(itertools.product(data_dims, points))
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# up to here, we could just have used 2 @parametrized.
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# Down below is the covarariant part as the points depend on the data dims.
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n = 10
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for dim in data_dims:
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points += [(dim, T.RandomPerspective.get_params(dim[1], dim[0], i / n)) for i in range(n)]
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return dims_and_points
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@pytest.mark.parametrize("device", cpu_and_cuda())
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@pytest.mark.parametrize("dims_and_points", _get_data_dims_and_points_for_perspective())
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@pytest.mark.parametrize("dt", [None, torch.float32, torch.float64, torch.float16])
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@pytest.mark.parametrize("fill", (None, [0, 0, 0], [1, 2, 3], [255, 255, 255], [1], (2.0,)))
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@pytest.mark.parametrize("fn", [F.perspective, torch.jit.script(F.perspective)])
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def test_perspective_pil_vs_tensor(device, dims_and_points, dt, fill, fn):
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if dt == torch.float16 and device == "cpu":
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# skip float16 on CPU case
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return
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data_dims, (spoints, epoints) = dims_and_points
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tensor, pil_img = _create_data(*data_dims, device=device)
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if dt is not None:
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tensor = tensor.to(dtype=dt)
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interpolation = NEAREST
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fill_pil = int(fill[0]) if fill is not None and len(fill) == 1 else fill
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out_pil_img = F.perspective(
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pil_img, startpoints=spoints, endpoints=epoints, interpolation=interpolation, fill=fill_pil
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)
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out_pil_tensor = torch.from_numpy(np.array(out_pil_img).transpose((2, 0, 1)))
|
|
out_tensor = fn(tensor, startpoints=spoints, endpoints=epoints, interpolation=interpolation, fill=fill).cpu()
|
|
|
|
if out_tensor.dtype != torch.uint8:
|
|
out_tensor = out_tensor.to(torch.uint8)
|
|
|
|
num_diff_pixels = (out_tensor != out_pil_tensor).sum().item() / 3.0
|
|
ratio_diff_pixels = num_diff_pixels / out_tensor.shape[-1] / out_tensor.shape[-2]
|
|
# Tolerance : less than 5% of different pixels
|
|
assert ratio_diff_pixels < 0.05
|
|
|
|
|
|
@pytest.mark.parametrize("device", cpu_and_cuda())
|
|
@pytest.mark.parametrize("dims_and_points", _get_data_dims_and_points_for_perspective())
|
|
@pytest.mark.parametrize("dt", [None, torch.float32, torch.float64, torch.float16])
|
|
def test_perspective_batch(device, dims_and_points, dt):
|
|
|
|
if dt == torch.float16 and device == "cpu":
|
|
# skip float16 on CPU case
|
|
return
|
|
|
|
data_dims, (spoints, epoints) = dims_and_points
|
|
|
|
batch_tensors = _create_data_batch(*data_dims, num_samples=4, device=device)
|
|
if dt is not None:
|
|
batch_tensors = batch_tensors.to(dtype=dt)
|
|
|
|
# Ignore the equivalence between scripted and regular function on float16 cuda. The pixels at
|
|
# the border may be entirely different due to small rounding errors.
|
|
scripted_fn_atol = -1 if (dt == torch.float16 and device == "cuda") else 1e-8
|
|
_test_fn_on_batch(
|
|
batch_tensors,
|
|
F.perspective,
|
|
scripted_fn_atol=scripted_fn_atol,
|
|
startpoints=spoints,
|
|
endpoints=epoints,
|
|
interpolation=NEAREST,
|
|
)
|
|
|
|
|
|
def test_perspective_interpolation_type():
|
|
spoints = [[0, 0], [33, 0], [33, 25], [0, 25]]
|
|
epoints = [[3, 2], [32, 3], [30, 24], [2, 25]]
|
|
tensor = torch.randint(0, 256, (3, 26, 26))
|
|
|
|
res1 = F.perspective(tensor, startpoints=spoints, endpoints=epoints, interpolation=PIL.Image.BILINEAR)
|
|
res2 = F.perspective(tensor, startpoints=spoints, endpoints=epoints, interpolation=BILINEAR)
|
|
assert_equal(res1, res2)
|
|
|
|
|
|
@pytest.mark.parametrize("device", cpu_and_cuda())
|
|
@pytest.mark.parametrize("dt", [None, torch.float32, torch.float64, torch.float16])
|
|
@pytest.mark.parametrize("size", [32, 26, [32], [32, 32], (32, 32), [26, 35]])
|
|
@pytest.mark.parametrize("max_size", [None, 34, 40, 1000])
|
|
@pytest.mark.parametrize("interpolation", [BILINEAR, BICUBIC, NEAREST, NEAREST_EXACT])
|
|
def test_resize(device, dt, size, max_size, interpolation):
|
|
|
|
if dt == torch.float16 and device == "cpu":
|
|
# skip float16 on CPU case
|
|
return
|
|
|
|
if max_size is not None and isinstance(size, Sequence) and len(size) != 1:
|
|
return # unsupported
|
|
|
|
torch.manual_seed(12)
|
|
script_fn = torch.jit.script(F.resize)
|
|
tensor, pil_img = _create_data(26, 36, device=device)
|
|
batch_tensors = _create_data_batch(16, 18, num_samples=4, device=device)
|
|
|
|
if dt is not None:
|
|
# This is a trivial cast to float of uint8 data to test all cases
|
|
tensor = tensor.to(dt)
|
|
batch_tensors = batch_tensors.to(dt)
|
|
|
|
resized_tensor = F.resize(tensor, size=size, interpolation=interpolation, max_size=max_size, antialias=True)
|
|
resized_pil_img = F.resize(pil_img, size=size, interpolation=interpolation, max_size=max_size, antialias=True)
|
|
|
|
assert resized_tensor.size()[1:] == resized_pil_img.size[::-1]
|
|
|
|
if interpolation != NEAREST:
|
|
# We can not check values if mode = NEAREST, as results are different
|
|
# E.g. resized_tensor = [[a, a, b, c, d, d, e, ...]]
|
|
# E.g. resized_pil_img = [[a, b, c, c, d, e, f, ...]]
|
|
resized_tensor_f = resized_tensor
|
|
# we need to cast to uint8 to compare with PIL image
|
|
if resized_tensor_f.dtype == torch.uint8:
|
|
resized_tensor_f = resized_tensor_f.to(torch.float)
|
|
|
|
# Pay attention to high tolerance for MAE
|
|
_assert_approx_equal_tensor_to_pil(resized_tensor_f, resized_pil_img, tol=3.0)
|
|
|
|
if isinstance(size, int):
|
|
script_size = [size]
|
|
else:
|
|
script_size = size
|
|
|
|
resize_result = script_fn(tensor, size=script_size, interpolation=interpolation, max_size=max_size, antialias=True)
|
|
assert_equal(resized_tensor, resize_result)
|
|
|
|
_test_fn_on_batch(
|
|
batch_tensors, F.resize, size=script_size, interpolation=interpolation, max_size=max_size, antialias=True
|
|
)
|
|
|
|
|
|
@pytest.mark.parametrize("device", cpu_and_cuda())
|
|
def test_resize_asserts(device):
|
|
|
|
tensor, pil_img = _create_data(26, 36, device=device)
|
|
|
|
res1 = F.resize(tensor, size=32, interpolation=PIL.Image.BILINEAR)
|
|
res2 = F.resize(tensor, size=32, interpolation=BILINEAR)
|
|
assert_equal(res1, res2)
|
|
|
|
for img in (tensor, pil_img):
|
|
exp_msg = "max_size should only be passed if size specifies the length of the smaller edge"
|
|
with pytest.raises(ValueError, match=exp_msg):
|
|
F.resize(img, size=(32, 34), max_size=35)
|
|
with pytest.raises(ValueError, match="max_size = 32 must be strictly greater"):
|
|
F.resize(img, size=32, max_size=32)
|
|
|
|
|
|
@pytest.mark.parametrize("device", cpu_and_cuda())
|
|
@pytest.mark.parametrize("dt", [None, torch.float32, torch.float64, torch.float16])
|
|
@pytest.mark.parametrize("size", [[96, 72], [96, 420], [420, 72]])
|
|
@pytest.mark.parametrize("interpolation", [BILINEAR, BICUBIC])
|
|
def test_resize_antialias(device, dt, size, interpolation):
|
|
|
|
if dt == torch.float16 and device == "cpu":
|
|
# skip float16 on CPU case
|
|
return
|
|
|
|
torch.manual_seed(12)
|
|
script_fn = torch.jit.script(F.resize)
|
|
tensor, pil_img = _create_data(320, 290, device=device)
|
|
|
|
if dt is not None:
|
|
# This is a trivial cast to float of uint8 data to test all cases
|
|
tensor = tensor.to(dt)
|
|
|
|
resized_tensor = F.resize(tensor, size=size, interpolation=interpolation, antialias=True)
|
|
resized_pil_img = F.resize(pil_img, size=size, interpolation=interpolation, antialias=True)
|
|
|
|
assert resized_tensor.size()[1:] == resized_pil_img.size[::-1]
|
|
|
|
resized_tensor_f = resized_tensor
|
|
# we need to cast to uint8 to compare with PIL image
|
|
if resized_tensor_f.dtype == torch.uint8:
|
|
resized_tensor_f = resized_tensor_f.to(torch.float)
|
|
|
|
_assert_approx_equal_tensor_to_pil(resized_tensor_f, resized_pil_img, tol=0.5, msg=f"{size}, {interpolation}, {dt}")
|
|
|
|
accepted_tol = 1.0 + 1e-5
|
|
if interpolation == BICUBIC:
|
|
# this overall mean value to make the tests pass
|
|
# High value is mostly required for test cases with
|
|
# downsampling and upsampling where we can not exactly
|
|
# match PIL implementation.
|
|
accepted_tol = 15.0
|
|
|
|
_assert_approx_equal_tensor_to_pil(
|
|
resized_tensor_f, resized_pil_img, tol=accepted_tol, agg_method="max", msg=f"{size}, {interpolation}, {dt}"
|
|
)
|
|
|
|
if isinstance(size, int):
|
|
script_size = [
|
|
size,
|
|
]
|
|
else:
|
|
script_size = size
|
|
|
|
resize_result = script_fn(tensor, size=script_size, interpolation=interpolation, antialias=True)
|
|
assert_equal(resized_tensor, resize_result)
|
|
|
|
|
|
def check_functional_vs_PIL_vs_scripted(
|
|
fn, fn_pil, fn_t, config, device, dtype, channels=3, tol=2.0 + 1e-10, agg_method="max"
|
|
):
|
|
|
|
script_fn = torch.jit.script(fn)
|
|
torch.manual_seed(15)
|
|
tensor, pil_img = _create_data(26, 34, channels=channels, device=device)
|
|
batch_tensors = _create_data_batch(16, 18, num_samples=4, channels=channels, device=device)
|
|
|
|
if dtype is not None:
|
|
tensor = F.convert_image_dtype(tensor, dtype)
|
|
batch_tensors = F.convert_image_dtype(batch_tensors, dtype)
|
|
|
|
out_fn_t = fn_t(tensor, **config)
|
|
out_pil = fn_pil(pil_img, **config)
|
|
out_scripted = script_fn(tensor, **config)
|
|
assert out_fn_t.dtype == out_scripted.dtype
|
|
assert out_fn_t.size()[1:] == out_pil.size[::-1]
|
|
|
|
rbg_tensor = out_fn_t
|
|
|
|
if out_fn_t.dtype != torch.uint8:
|
|
rbg_tensor = F.convert_image_dtype(out_fn_t, torch.uint8)
|
|
|
|
# Check that max difference does not exceed 2 in [0, 255] range
|
|
# Exact matching is not possible due to incompatibility convert_image_dtype and PIL results
|
|
_assert_approx_equal_tensor_to_pil(rbg_tensor.float(), out_pil, tol=tol, agg_method=agg_method)
|
|
|
|
atol = 1e-6
|
|
if out_fn_t.dtype == torch.uint8 and "cuda" in torch.device(device).type:
|
|
atol = 1.0
|
|
assert out_fn_t.allclose(out_scripted, atol=atol)
|
|
|
|
# FIXME: fn will be scripted again in _test_fn_on_batch. We could avoid that.
|
|
_test_fn_on_batch(batch_tensors, fn, scripted_fn_atol=atol, **config)
|
|
|
|
|
|
@pytest.mark.parametrize("device", cpu_and_cuda())
|
|
@pytest.mark.parametrize("dtype", (None, torch.float32, torch.float64))
|
|
@pytest.mark.parametrize("config", [{"brightness_factor": f} for f in (0.1, 0.5, 1.0, 1.34, 2.5)])
|
|
@pytest.mark.parametrize("channels", [1, 3])
|
|
def test_adjust_brightness(device, dtype, config, channels):
|
|
check_functional_vs_PIL_vs_scripted(
|
|
F.adjust_brightness,
|
|
F_pil.adjust_brightness,
|
|
F_t.adjust_brightness,
|
|
config,
|
|
device,
|
|
dtype,
|
|
channels,
|
|
)
|
|
|
|
|
|
@pytest.mark.parametrize("device", cpu_and_cuda())
|
|
@pytest.mark.parametrize("dtype", (None, torch.float32, torch.float64))
|
|
@pytest.mark.parametrize("channels", [1, 3])
|
|
def test_invert(device, dtype, channels):
|
|
check_functional_vs_PIL_vs_scripted(
|
|
F.invert, F_pil.invert, F_t.invert, {}, device, dtype, channels, tol=1.0, agg_method="max"
|
|
)
|
|
|
|
|
|
@pytest.mark.parametrize("device", cpu_and_cuda())
|
|
@pytest.mark.parametrize("config", [{"bits": bits} for bits in range(0, 8)])
|
|
@pytest.mark.parametrize("channels", [1, 3])
|
|
def test_posterize(device, config, channels):
|
|
check_functional_vs_PIL_vs_scripted(
|
|
F.posterize,
|
|
F_pil.posterize,
|
|
F_t.posterize,
|
|
config,
|
|
device,
|
|
dtype=None,
|
|
channels=channels,
|
|
tol=1.0,
|
|
agg_method="max",
|
|
)
|
|
|
|
|
|
@pytest.mark.parametrize("device", cpu_and_cuda())
|
|
@pytest.mark.parametrize("config", [{"threshold": threshold} for threshold in [0, 64, 128, 192, 255]])
|
|
@pytest.mark.parametrize("channels", [1, 3])
|
|
def test_solarize1(device, config, channels):
|
|
check_functional_vs_PIL_vs_scripted(
|
|
F.solarize,
|
|
F_pil.solarize,
|
|
F_t.solarize,
|
|
config,
|
|
device,
|
|
dtype=None,
|
|
channels=channels,
|
|
tol=1.0,
|
|
agg_method="max",
|
|
)
|
|
|
|
|
|
@pytest.mark.parametrize("device", cpu_and_cuda())
|
|
@pytest.mark.parametrize("dtype", (torch.float32, torch.float64))
|
|
@pytest.mark.parametrize("config", [{"threshold": threshold} for threshold in [0.0, 0.25, 0.5, 0.75, 1.0]])
|
|
@pytest.mark.parametrize("channels", [1, 3])
|
|
def test_solarize2(device, dtype, config, channels):
|
|
check_functional_vs_PIL_vs_scripted(
|
|
F.solarize,
|
|
lambda img, threshold: F_pil.solarize(img, 255 * threshold),
|
|
F_t.solarize,
|
|
config,
|
|
device,
|
|
dtype,
|
|
channels,
|
|
tol=1.0,
|
|
agg_method="max",
|
|
)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
("dtype", "threshold"),
|
|
[
|
|
*[
|
|
(dtype, threshold)
|
|
for dtype, threshold in itertools.product(
|
|
[torch.float32, torch.float16],
|
|
[0.0, 0.25, 0.5, 0.75, 1.0],
|
|
)
|
|
],
|
|
*[(torch.uint8, threshold) for threshold in [0, 64, 128, 192, 255]],
|
|
*[(torch.int64, threshold) for threshold in [0, 2**32, 2**63 - 1]],
|
|
],
|
|
)
|
|
@pytest.mark.parametrize("device", cpu_and_cuda())
|
|
def test_solarize_threshold_within_bound(threshold, dtype, device):
|
|
make_img = torch.rand if dtype.is_floating_point else partial(torch.randint, 0, torch.iinfo(dtype).max)
|
|
img = make_img((3, 12, 23), dtype=dtype, device=device)
|
|
F_t.solarize(img, threshold)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
("dtype", "threshold"),
|
|
[
|
|
(torch.float32, 1.5),
|
|
(torch.float16, 1.5),
|
|
(torch.uint8, 260),
|
|
(torch.int64, 2**64),
|
|
],
|
|
)
|
|
@pytest.mark.parametrize("device", cpu_and_cuda())
|
|
def test_solarize_threshold_above_bound(threshold, dtype, device):
|
|
make_img = torch.rand if dtype.is_floating_point else partial(torch.randint, 0, torch.iinfo(dtype).max)
|
|
img = make_img((3, 12, 23), dtype=dtype, device=device)
|
|
with pytest.raises(TypeError, match="Threshold should be less than bound of img."):
|
|
F_t.solarize(img, threshold)
|
|
|
|
|
|
@pytest.mark.parametrize("device", cpu_and_cuda())
|
|
@pytest.mark.parametrize("dtype", (None, torch.float32, torch.float64))
|
|
@pytest.mark.parametrize("config", [{"sharpness_factor": f} for f in [0.2, 0.5, 1.0, 1.5, 2.0]])
|
|
@pytest.mark.parametrize("channels", [1, 3])
|
|
def test_adjust_sharpness(device, dtype, config, channels):
|
|
check_functional_vs_PIL_vs_scripted(
|
|
F.adjust_sharpness,
|
|
F_pil.adjust_sharpness,
|
|
F_t.adjust_sharpness,
|
|
config,
|
|
device,
|
|
dtype,
|
|
channels,
|
|
)
|
|
|
|
|
|
@pytest.mark.parametrize("device", cpu_and_cuda())
|
|
@pytest.mark.parametrize("dtype", (None, torch.float32, torch.float64))
|
|
@pytest.mark.parametrize("channels", [1, 3])
|
|
def test_autocontrast(device, dtype, channels):
|
|
check_functional_vs_PIL_vs_scripted(
|
|
F.autocontrast, F_pil.autocontrast, F_t.autocontrast, {}, device, dtype, channels, tol=1.0, agg_method="max"
|
|
)
|
|
|
|
|
|
@pytest.mark.parametrize("device", cpu_and_cuda())
|
|
@pytest.mark.parametrize("dtype", (None, torch.float32, torch.float64))
|
|
@pytest.mark.parametrize("channels", [1, 3])
|
|
def test_autocontrast_equal_minmax(device, dtype, channels):
|
|
a = _create_data_batch(32, 32, num_samples=1, channels=channels, device=device)
|
|
a = a / 2.0 + 0.3
|
|
assert (F.autocontrast(a)[0] == F.autocontrast(a[0])).all()
|
|
|
|
a[0, 0] = 0.7
|
|
assert (F.autocontrast(a)[0] == F.autocontrast(a[0])).all()
|
|
|
|
|
|
@pytest.mark.parametrize("device", cpu_and_cuda())
|
|
@pytest.mark.parametrize("channels", [1, 3])
|
|
def test_equalize(device, channels):
|
|
torch.use_deterministic_algorithms(False)
|
|
check_functional_vs_PIL_vs_scripted(
|
|
F.equalize,
|
|
F_pil.equalize,
|
|
F_t.equalize,
|
|
{},
|
|
device,
|
|
dtype=None,
|
|
channels=channels,
|
|
tol=1.0,
|
|
agg_method="max",
|
|
)
|
|
|
|
|
|
@pytest.mark.parametrize("device", cpu_and_cuda())
|
|
@pytest.mark.parametrize("dtype", (None, torch.float32, torch.float64))
|
|
@pytest.mark.parametrize("config", [{"contrast_factor": f} for f in [0.2, 0.5, 1.0, 1.5, 2.0]])
|
|
@pytest.mark.parametrize("channels", [1, 3])
|
|
def test_adjust_contrast(device, dtype, config, channels):
|
|
check_functional_vs_PIL_vs_scripted(
|
|
F.adjust_contrast, F_pil.adjust_contrast, F_t.adjust_contrast, config, device, dtype, channels
|
|
)
|
|
|
|
|
|
@pytest.mark.parametrize("device", cpu_and_cuda())
|
|
@pytest.mark.parametrize("dtype", (None, torch.float32, torch.float64))
|
|
@pytest.mark.parametrize("config", [{"saturation_factor": f} for f in [0.5, 0.75, 1.0, 1.5, 2.0]])
|
|
@pytest.mark.parametrize("channels", [1, 3])
|
|
def test_adjust_saturation(device, dtype, config, channels):
|
|
check_functional_vs_PIL_vs_scripted(
|
|
F.adjust_saturation, F_pil.adjust_saturation, F_t.adjust_saturation, config, device, dtype, channels
|
|
)
|
|
|
|
|
|
@pytest.mark.parametrize("device", cpu_and_cuda())
|
|
@pytest.mark.parametrize("dtype", (None, torch.float32, torch.float64))
|
|
@pytest.mark.parametrize("config", [{"hue_factor": f} for f in [-0.45, -0.25, 0.0, 0.25, 0.45]])
|
|
@pytest.mark.parametrize("channels", [1, 3])
|
|
def test_adjust_hue(device, dtype, config, channels):
|
|
check_functional_vs_PIL_vs_scripted(
|
|
F.adjust_hue, F_pil.adjust_hue, F_t.adjust_hue, config, device, dtype, channels, tol=16.1, agg_method="max"
|
|
)
|
|
|
|
|
|
@pytest.mark.parametrize("device", cpu_and_cuda())
|
|
@pytest.mark.parametrize("dtype", (None, torch.float32, torch.float64))
|
|
@pytest.mark.parametrize("config", [{"gamma": g1, "gain": g2} for g1, g2 in zip([0.8, 1.0, 1.2], [0.7, 1.0, 1.3])])
|
|
@pytest.mark.parametrize("channels", [1, 3])
|
|
def test_adjust_gamma(device, dtype, config, channels):
|
|
check_functional_vs_PIL_vs_scripted(
|
|
F.adjust_gamma,
|
|
F_pil.adjust_gamma,
|
|
F_t.adjust_gamma,
|
|
config,
|
|
device,
|
|
dtype,
|
|
channels,
|
|
)
|
|
|
|
|
|
@pytest.mark.parametrize("device", cpu_and_cuda())
|
|
@pytest.mark.parametrize("dt", [None, torch.float32, torch.float64, torch.float16])
|
|
@pytest.mark.parametrize("pad", [2, [3], [0, 3], (3, 3), [4, 2, 4, 3]])
|
|
@pytest.mark.parametrize(
|
|
"config",
|
|
[
|
|
{"padding_mode": "constant", "fill": 0},
|
|
{"padding_mode": "constant", "fill": 10},
|
|
{"padding_mode": "constant", "fill": 20.2},
|
|
{"padding_mode": "edge"},
|
|
{"padding_mode": "reflect"},
|
|
{"padding_mode": "symmetric"},
|
|
],
|
|
)
|
|
def test_pad(device, dt, pad, config):
|
|
script_fn = torch.jit.script(F.pad)
|
|
tensor, pil_img = _create_data(7, 8, device=device)
|
|
batch_tensors = _create_data_batch(16, 18, num_samples=4, device=device)
|
|
|
|
if dt == torch.float16 and device == "cpu":
|
|
# skip float16 on CPU case
|
|
return
|
|
|
|
if dt is not None:
|
|
# This is a trivial cast to float of uint8 data to test all cases
|
|
tensor = tensor.to(dt)
|
|
batch_tensors = batch_tensors.to(dt)
|
|
|
|
pad_tensor = F_t.pad(tensor, pad, **config)
|
|
pad_pil_img = F_pil.pad(pil_img, pad, **config)
|
|
|
|
pad_tensor_8b = pad_tensor
|
|
# we need to cast to uint8 to compare with PIL image
|
|
if pad_tensor_8b.dtype != torch.uint8:
|
|
pad_tensor_8b = pad_tensor_8b.to(torch.uint8)
|
|
|
|
_assert_equal_tensor_to_pil(pad_tensor_8b, pad_pil_img, msg=f"{pad}, {config}")
|
|
|
|
if isinstance(pad, int):
|
|
script_pad = [
|
|
pad,
|
|
]
|
|
else:
|
|
script_pad = pad
|
|
pad_tensor_script = script_fn(tensor, script_pad, **config)
|
|
assert_equal(pad_tensor, pad_tensor_script, msg=f"{pad}, {config}")
|
|
|
|
_test_fn_on_batch(batch_tensors, F.pad, padding=script_pad, **config)
|
|
|
|
|
|
@pytest.mark.parametrize("device", cpu_and_cuda())
|
|
@pytest.mark.parametrize("mode", [NEAREST, NEAREST_EXACT, BILINEAR, BICUBIC])
|
|
def test_resized_crop(device, mode):
|
|
# test values of F.resized_crop in several cases:
|
|
# 1) resize to the same size, crop to the same size => should be identity
|
|
tensor, _ = _create_data(26, 36, device=device)
|
|
|
|
out_tensor = F.resized_crop(
|
|
tensor, top=0, left=0, height=26, width=36, size=[26, 36], interpolation=mode, antialias=True
|
|
)
|
|
assert_equal(tensor, out_tensor, msg=f"{out_tensor[0, :5, :5]} vs {tensor[0, :5, :5]}")
|
|
|
|
# 2) resize by half and crop a TL corner
|
|
tensor, _ = _create_data(26, 36, device=device)
|
|
out_tensor = F.resized_crop(tensor, top=0, left=0, height=20, width=30, size=[10, 15], interpolation=NEAREST)
|
|
expected_out_tensor = tensor[:, :20:2, :30:2]
|
|
assert_equal(
|
|
expected_out_tensor,
|
|
out_tensor,
|
|
msg=f"{expected_out_tensor[0, :10, :10]} vs {out_tensor[0, :10, :10]}",
|
|
)
|
|
|
|
batch_tensors = _create_data_batch(26, 36, num_samples=4, device=device)
|
|
_test_fn_on_batch(
|
|
batch_tensors,
|
|
F.resized_crop,
|
|
top=1,
|
|
left=2,
|
|
height=20,
|
|
width=30,
|
|
size=[10, 15],
|
|
interpolation=NEAREST,
|
|
)
|
|
|
|
|
|
@pytest.mark.parametrize("device", cpu_and_cuda())
|
|
@pytest.mark.parametrize(
|
|
"func, args",
|
|
[
|
|
(F_t.get_dimensions, ()),
|
|
(F_t.get_image_size, ()),
|
|
(F_t.get_image_num_channels, ()),
|
|
(F_t.vflip, ()),
|
|
(F_t.hflip, ()),
|
|
(F_t.crop, (1, 2, 4, 5)),
|
|
(F_t.adjust_brightness, (0.0,)),
|
|
(F_t.adjust_contrast, (1.0,)),
|
|
(F_t.adjust_hue, (-0.5,)),
|
|
(F_t.adjust_saturation, (2.0,)),
|
|
(F_t.pad, ([2], 2, "constant")),
|
|
(F_t.resize, ([10, 11],)),
|
|
(F_t.perspective, ([0.2])),
|
|
(F_t.gaussian_blur, ((2, 2), (0.7, 0.5))),
|
|
(F_t.invert, ()),
|
|
(F_t.posterize, (0,)),
|
|
(F_t.solarize, (0.3,)),
|
|
(F_t.adjust_sharpness, (0.3,)),
|
|
(F_t.autocontrast, ()),
|
|
(F_t.equalize, ()),
|
|
],
|
|
)
|
|
def test_assert_image_tensor(device, func, args):
|
|
shape = (100,)
|
|
tensor = torch.rand(*shape, dtype=torch.float, device=device)
|
|
with pytest.raises(Exception, match=r"Tensor is not a torch image."):
|
|
func(tensor, *args)
|
|
|
|
|
|
@pytest.mark.parametrize("device", cpu_and_cuda())
|
|
def test_vflip(device):
|
|
script_vflip = torch.jit.script(F.vflip)
|
|
|
|
img_tensor, pil_img = _create_data(16, 18, device=device)
|
|
vflipped_img = F.vflip(img_tensor)
|
|
vflipped_pil_img = F.vflip(pil_img)
|
|
_assert_equal_tensor_to_pil(vflipped_img, vflipped_pil_img)
|
|
|
|
# scriptable function test
|
|
vflipped_img_script = script_vflip(img_tensor)
|
|
assert_equal(vflipped_img, vflipped_img_script)
|
|
|
|
batch_tensors = _create_data_batch(16, 18, num_samples=4, device=device)
|
|
_test_fn_on_batch(batch_tensors, F.vflip)
|
|
|
|
|
|
@pytest.mark.parametrize("device", cpu_and_cuda())
|
|
def test_hflip(device):
|
|
script_hflip = torch.jit.script(F.hflip)
|
|
|
|
img_tensor, pil_img = _create_data(16, 18, device=device)
|
|
hflipped_img = F.hflip(img_tensor)
|
|
hflipped_pil_img = F.hflip(pil_img)
|
|
_assert_equal_tensor_to_pil(hflipped_img, hflipped_pil_img)
|
|
|
|
# scriptable function test
|
|
hflipped_img_script = script_hflip(img_tensor)
|
|
assert_equal(hflipped_img, hflipped_img_script)
|
|
|
|
batch_tensors = _create_data_batch(16, 18, num_samples=4, device=device)
|
|
_test_fn_on_batch(batch_tensors, F.hflip)
|
|
|
|
|
|
@pytest.mark.parametrize("device", cpu_and_cuda())
|
|
@pytest.mark.parametrize(
|
|
"top, left, height, width",
|
|
[
|
|
(1, 2, 4, 5), # crop inside top-left corner
|
|
(2, 12, 3, 4), # crop inside top-right corner
|
|
(8, 3, 5, 6), # crop inside bottom-left corner
|
|
(8, 11, 4, 3), # crop inside bottom-right corner
|
|
(50, 50, 10, 10), # crop outside the image
|
|
(-50, -50, 10, 10), # crop outside the image
|
|
],
|
|
)
|
|
def test_crop(device, top, left, height, width):
|
|
script_crop = torch.jit.script(F.crop)
|
|
|
|
img_tensor, pil_img = _create_data(16, 18, device=device)
|
|
|
|
pil_img_cropped = F.crop(pil_img, top, left, height, width)
|
|
|
|
img_tensor_cropped = F.crop(img_tensor, top, left, height, width)
|
|
_assert_equal_tensor_to_pil(img_tensor_cropped, pil_img_cropped)
|
|
|
|
img_tensor_cropped = script_crop(img_tensor, top, left, height, width)
|
|
_assert_equal_tensor_to_pil(img_tensor_cropped, pil_img_cropped)
|
|
|
|
batch_tensors = _create_data_batch(16, 18, num_samples=4, device=device)
|
|
_test_fn_on_batch(batch_tensors, F.crop, top=top, left=left, height=height, width=width)
|
|
|
|
|
|
@pytest.mark.parametrize("device", cpu_and_cuda())
|
|
@pytest.mark.parametrize("image_size", ("small", "large"))
|
|
@pytest.mark.parametrize("dt", [None, torch.float32, torch.float64, torch.float16])
|
|
@pytest.mark.parametrize("ksize", [(3, 3), [3, 5], (23, 23)])
|
|
@pytest.mark.parametrize("sigma", [[0.5, 0.5], (0.5, 0.5), (0.8, 0.8), (1.7, 1.7)])
|
|
@pytest.mark.parametrize("fn", [F.gaussian_blur, torch.jit.script(F.gaussian_blur)])
|
|
def test_gaussian_blur(device, image_size, dt, ksize, sigma, fn):
|
|
|
|
# true_cv2_results = {
|
|
# # np_img = np.arange(3 * 10 * 12, dtype="uint8").reshape((10, 12, 3))
|
|
# # cv2.GaussianBlur(np_img, ksize=(3, 3), sigmaX=0.8)
|
|
# "3_3_0.8": ...
|
|
# # cv2.GaussianBlur(np_img, ksize=(3, 3), sigmaX=0.5)
|
|
# "3_3_0.5": ...
|
|
# # cv2.GaussianBlur(np_img, ksize=(3, 5), sigmaX=0.8)
|
|
# "3_5_0.8": ...
|
|
# # cv2.GaussianBlur(np_img, ksize=(3, 5), sigmaX=0.5)
|
|
# "3_5_0.5": ...
|
|
# # np_img2 = np.arange(26 * 28, dtype="uint8").reshape((26, 28))
|
|
# # cv2.GaussianBlur(np_img2, ksize=(23, 23), sigmaX=1.7)
|
|
# "23_23_1.7": ...
|
|
# }
|
|
p = os.path.join(os.path.dirname(os.path.abspath(__file__)), "assets", "gaussian_blur_opencv_results.pt")
|
|
|
|
true_cv2_results = torch.load(p, weights_only=False)
|
|
|
|
if image_size == "small":
|
|
tensor = (
|
|
torch.from_numpy(np.arange(3 * 10 * 12, dtype="uint8").reshape((10, 12, 3))).permute(2, 0, 1).to(device)
|
|
)
|
|
else:
|
|
tensor = torch.from_numpy(np.arange(26 * 28, dtype="uint8").reshape((1, 26, 28))).to(device)
|
|
|
|
if dt == torch.float16 and device == "cpu":
|
|
# skip float16 on CPU case
|
|
return
|
|
|
|
if dt is not None:
|
|
tensor = tensor.to(dtype=dt)
|
|
|
|
_ksize = (ksize, ksize) if isinstance(ksize, int) else ksize
|
|
_sigma = sigma[0] if sigma is not None else None
|
|
shape = tensor.shape
|
|
gt_key = f"{shape[-2]}_{shape[-1]}_{shape[-3]}__{_ksize[0]}_{_ksize[1]}_{_sigma}"
|
|
if gt_key not in true_cv2_results:
|
|
return
|
|
|
|
true_out = (
|
|
torch.tensor(true_cv2_results[gt_key]).reshape(shape[-2], shape[-1], shape[-3]).permute(2, 0, 1).to(tensor)
|
|
)
|
|
|
|
out = fn(tensor, kernel_size=ksize, sigma=sigma)
|
|
torch.testing.assert_close(out, true_out, rtol=0.0, atol=1.0, msg=f"{ksize}, {sigma}")
|
|
|
|
|
|
@pytest.mark.parametrize("device", cpu_and_cuda())
|
|
def test_hsv2rgb(device):
|
|
scripted_fn = torch.jit.script(F_t._hsv2rgb)
|
|
shape = (3, 100, 150)
|
|
for _ in range(10):
|
|
hsv_img = torch.rand(*shape, dtype=torch.float, device=device)
|
|
rgb_img = F_t._hsv2rgb(hsv_img)
|
|
ft_img = rgb_img.permute(1, 2, 0).flatten(0, 1)
|
|
|
|
(
|
|
h,
|
|
s,
|
|
v,
|
|
) = hsv_img.unbind(0)
|
|
h = h.flatten().cpu().numpy()
|
|
s = s.flatten().cpu().numpy()
|
|
v = v.flatten().cpu().numpy()
|
|
|
|
rgb = []
|
|
for h1, s1, v1 in zip(h, s, v):
|
|
rgb.append(colorsys.hsv_to_rgb(h1, s1, v1))
|
|
colorsys_img = torch.tensor(rgb, dtype=torch.float32, device=device)
|
|
torch.testing.assert_close(ft_img, colorsys_img, rtol=0.0, atol=1e-5)
|
|
|
|
s_rgb_img = scripted_fn(hsv_img)
|
|
torch.testing.assert_close(rgb_img, s_rgb_img)
|
|
|
|
batch_tensors = _create_data_batch(120, 100, num_samples=4, device=device).float()
|
|
_test_fn_on_batch(batch_tensors, F_t._hsv2rgb)
|
|
|
|
|
|
@pytest.mark.parametrize("device", cpu_and_cuda())
|
|
def test_rgb2hsv(device):
|
|
scripted_fn = torch.jit.script(F_t._rgb2hsv)
|
|
shape = (3, 150, 100)
|
|
for _ in range(10):
|
|
rgb_img = torch.rand(*shape, dtype=torch.float, device=device)
|
|
hsv_img = F_t._rgb2hsv(rgb_img)
|
|
ft_hsv_img = hsv_img.permute(1, 2, 0).flatten(0, 1)
|
|
|
|
(
|
|
r,
|
|
g,
|
|
b,
|
|
) = rgb_img.unbind(dim=-3)
|
|
r = r.flatten().cpu().numpy()
|
|
g = g.flatten().cpu().numpy()
|
|
b = b.flatten().cpu().numpy()
|
|
|
|
hsv = []
|
|
for r1, g1, b1 in zip(r, g, b):
|
|
hsv.append(colorsys.rgb_to_hsv(r1, g1, b1))
|
|
|
|
colorsys_img = torch.tensor(hsv, dtype=torch.float32, device=device)
|
|
|
|
ft_hsv_img_h, ft_hsv_img_sv = torch.split(ft_hsv_img, [1, 2], dim=1)
|
|
colorsys_img_h, colorsys_img_sv = torch.split(colorsys_img, [1, 2], dim=1)
|
|
|
|
max_diff_h = ((colorsys_img_h * 2 * math.pi).sin() - (ft_hsv_img_h * 2 * math.pi).sin()).abs().max()
|
|
max_diff_sv = (colorsys_img_sv - ft_hsv_img_sv).abs().max()
|
|
max_diff = max(max_diff_h, max_diff_sv)
|
|
assert max_diff < 1e-5
|
|
|
|
s_hsv_img = scripted_fn(rgb_img)
|
|
torch.testing.assert_close(hsv_img, s_hsv_img, rtol=1e-5, atol=1e-7)
|
|
|
|
batch_tensors = _create_data_batch(120, 100, num_samples=4, device=device).float()
|
|
_test_fn_on_batch(batch_tensors, F_t._rgb2hsv)
|
|
|
|
|
|
@pytest.mark.parametrize("device", cpu_and_cuda())
|
|
@pytest.mark.parametrize("num_output_channels", (3, 1))
|
|
def test_rgb_to_grayscale(device, num_output_channels):
|
|
script_rgb_to_grayscale = torch.jit.script(F.rgb_to_grayscale)
|
|
|
|
img_tensor, pil_img = _create_data(32, 34, device=device)
|
|
|
|
gray_pil_image = F.rgb_to_grayscale(pil_img, num_output_channels=num_output_channels)
|
|
gray_tensor = F.rgb_to_grayscale(img_tensor, num_output_channels=num_output_channels)
|
|
|
|
_assert_approx_equal_tensor_to_pil(gray_tensor.float(), gray_pil_image, tol=1.0 + 1e-10, agg_method="max")
|
|
|
|
s_gray_tensor = script_rgb_to_grayscale(img_tensor, num_output_channels=num_output_channels)
|
|
assert_equal(s_gray_tensor, gray_tensor)
|
|
|
|
batch_tensors = _create_data_batch(16, 18, num_samples=4, device=device)
|
|
_test_fn_on_batch(batch_tensors, F.rgb_to_grayscale, num_output_channels=num_output_channels)
|
|
|
|
|
|
@pytest.mark.parametrize("device", cpu_and_cuda())
|
|
def test_center_crop(device):
|
|
script_center_crop = torch.jit.script(F.center_crop)
|
|
|
|
img_tensor, pil_img = _create_data(32, 34, device=device)
|
|
|
|
cropped_pil_image = F.center_crop(pil_img, [10, 11])
|
|
|
|
cropped_tensor = F.center_crop(img_tensor, [10, 11])
|
|
_assert_equal_tensor_to_pil(cropped_tensor, cropped_pil_image)
|
|
|
|
cropped_tensor = script_center_crop(img_tensor, [10, 11])
|
|
_assert_equal_tensor_to_pil(cropped_tensor, cropped_pil_image)
|
|
|
|
batch_tensors = _create_data_batch(16, 18, num_samples=4, device=device)
|
|
_test_fn_on_batch(batch_tensors, F.center_crop, output_size=[10, 11])
|
|
|
|
|
|
@pytest.mark.parametrize("device", cpu_and_cuda())
|
|
def test_five_crop(device):
|
|
script_five_crop = torch.jit.script(F.five_crop)
|
|
|
|
img_tensor, pil_img = _create_data(32, 34, device=device)
|
|
|
|
cropped_pil_images = F.five_crop(pil_img, [10, 11])
|
|
|
|
cropped_tensors = F.five_crop(img_tensor, [10, 11])
|
|
for i in range(5):
|
|
_assert_equal_tensor_to_pil(cropped_tensors[i], cropped_pil_images[i])
|
|
|
|
cropped_tensors = script_five_crop(img_tensor, [10, 11])
|
|
for i in range(5):
|
|
_assert_equal_tensor_to_pil(cropped_tensors[i], cropped_pil_images[i])
|
|
|
|
batch_tensors = _create_data_batch(16, 18, num_samples=4, device=device)
|
|
tuple_transformed_batches = F.five_crop(batch_tensors, [10, 11])
|
|
for i in range(len(batch_tensors)):
|
|
img_tensor = batch_tensors[i, ...]
|
|
tuple_transformed_imgs = F.five_crop(img_tensor, [10, 11])
|
|
assert len(tuple_transformed_imgs) == len(tuple_transformed_batches)
|
|
|
|
for j in range(len(tuple_transformed_imgs)):
|
|
true_transformed_img = tuple_transformed_imgs[j]
|
|
transformed_img = tuple_transformed_batches[j][i, ...]
|
|
assert_equal(true_transformed_img, transformed_img)
|
|
|
|
# scriptable function test
|
|
s_tuple_transformed_batches = script_five_crop(batch_tensors, [10, 11])
|
|
for transformed_batch, s_transformed_batch in zip(tuple_transformed_batches, s_tuple_transformed_batches):
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assert_equal(transformed_batch, s_transformed_batch)
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|
|
|
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@pytest.mark.parametrize("device", cpu_and_cuda())
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def test_ten_crop(device):
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script_ten_crop = torch.jit.script(F.ten_crop)
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|
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img_tensor, pil_img = _create_data(32, 34, device=device)
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|
|
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cropped_pil_images = F.ten_crop(pil_img, [10, 11])
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|
|
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cropped_tensors = F.ten_crop(img_tensor, [10, 11])
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|
for i in range(10):
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_assert_equal_tensor_to_pil(cropped_tensors[i], cropped_pil_images[i])
|
|
|
|
cropped_tensors = script_ten_crop(img_tensor, [10, 11])
|
|
for i in range(10):
|
|
_assert_equal_tensor_to_pil(cropped_tensors[i], cropped_pil_images[i])
|
|
|
|
batch_tensors = _create_data_batch(16, 18, num_samples=4, device=device)
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|
tuple_transformed_batches = F.ten_crop(batch_tensors, [10, 11])
|
|
for i in range(len(batch_tensors)):
|
|
img_tensor = batch_tensors[i, ...]
|
|
tuple_transformed_imgs = F.ten_crop(img_tensor, [10, 11])
|
|
assert len(tuple_transformed_imgs) == len(tuple_transformed_batches)
|
|
|
|
for j in range(len(tuple_transformed_imgs)):
|
|
true_transformed_img = tuple_transformed_imgs[j]
|
|
transformed_img = tuple_transformed_batches[j][i, ...]
|
|
assert_equal(true_transformed_img, transformed_img)
|
|
|
|
# scriptable function test
|
|
s_tuple_transformed_batches = script_ten_crop(batch_tensors, [10, 11])
|
|
for transformed_batch, s_transformed_batch in zip(tuple_transformed_batches, s_tuple_transformed_batches):
|
|
assert_equal(transformed_batch, s_transformed_batch)
|
|
|
|
|
|
def test_elastic_transform_asserts():
|
|
with pytest.raises(TypeError, match="Argument displacement should be a Tensor"):
|
|
_ = F.elastic_transform("abc", displacement=None)
|
|
|
|
with pytest.raises(TypeError, match="img should be PIL Image or Tensor"):
|
|
_ = F.elastic_transform("abc", displacement=torch.rand(1))
|
|
|
|
img_tensor = torch.rand(1, 3, 32, 24)
|
|
with pytest.raises(ValueError, match="Argument displacement shape should"):
|
|
_ = F.elastic_transform(img_tensor, displacement=torch.rand(1, 2))
|
|
|
|
|
|
@pytest.mark.parametrize("device", cpu_and_cuda())
|
|
@pytest.mark.parametrize("interpolation", [NEAREST, BILINEAR, BICUBIC])
|
|
@pytest.mark.parametrize("dt", [None, torch.float32, torch.float64, torch.float16])
|
|
@pytest.mark.parametrize(
|
|
"fill",
|
|
[None, [255, 255, 255], (2.0,)],
|
|
)
|
|
def test_elastic_transform_consistency(device, interpolation, dt, fill):
|
|
script_elastic_transform = torch.jit.script(F.elastic_transform)
|
|
img_tensor, _ = _create_data(32, 34, device=device)
|
|
# As there is no PIL implementation for elastic_transform,
|
|
# thus we do not run tests tensor vs pillow
|
|
|
|
if dt is not None:
|
|
img_tensor = img_tensor.to(dt)
|
|
|
|
displacement = T.ElasticTransform.get_params([1.5, 1.5], [2.0, 2.0], [32, 34])
|
|
kwargs = dict(
|
|
displacement=displacement,
|
|
interpolation=interpolation,
|
|
fill=fill,
|
|
)
|
|
|
|
out_tensor1 = F.elastic_transform(img_tensor, **kwargs)
|
|
out_tensor2 = script_elastic_transform(img_tensor, **kwargs)
|
|
assert_equal(out_tensor1, out_tensor2)
|
|
|
|
batch_tensors = _create_data_batch(16, 18, num_samples=4, device=device)
|
|
displacement = T.ElasticTransform.get_params([1.5, 1.5], [2.0, 2.0], [16, 18])
|
|
kwargs["displacement"] = displacement
|
|
if dt is not None:
|
|
batch_tensors = batch_tensors.to(dt)
|
|
_test_fn_on_batch(batch_tensors, F.elastic_transform, **kwargs)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
pytest.main([__file__])
|