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How to use multiple_of method in Lemoncheesecake

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test_crop_size_multiple.py

Source:test_crop_size_multiple.py

1#  ------------------------------------------------------------------------------------------2#  Copyright (c) Microsoft Corporation. All rights reserved.3#  Licensed under the MIT License (MIT). See LICENSE in the repo root for license information.4#  ------------------------------------------------------------------------------------------5from typing import Any, Optional6import pytest7import torch8from InnerEye.Common.type_annotations import IntOrTuple3, TupleInt39from InnerEye.ML.config import ModelArchitectureConfig, SegmentationModelBase10from InnerEye.ML.models.architectures.base_model import BaseSegmentationModel, CropSizeConstraints11from InnerEye.ML.models.architectures.unet_3d import UNet3D12from InnerEye.ML.utils.model_util import build_net13class SimpleModel(BaseSegmentationModel):14    def __init__(self, input_channels: int, channels: list, n_classes: int, kernel_size: int,15                 crop_size_constraints: CropSizeConstraints = None):16        super().__init__(input_channels=input_channels, name="SimpleModel", crop_size_constraints=crop_size_constraints)17        self.channels = channels18        self.n_classes = n_classes19        self.kernel_size = kernel_size20        self.model = torch.nn.Sequential(21            torch.nn.Conv3d(input_channels, channels[0], kernel_size=self.kernel_size),22            torch.nn.ConvTranspose3d(channels[0], n_classes, kernel_size=self.kernel_size)23        )24    def forward(self, x: Any) -> Any:  # type: ignore25        x = self.model(x)26        return x27def test_crop_size_constraints() -> None:28    """29    Test the basic logic to validate a crop size inside of a CropSizeConstraints instance.30    """31    def check(crop_size: TupleInt3,32              multiple_of: Optional[IntOrTuple3] = None,33              minimum: Optional[IntOrTuple3] = None) -> CropSizeConstraints:34        constraints = CropSizeConstraints(minimum_size=minimum, multiple_of=multiple_of)35        constraints.validate(crop_size)36        return constraints37    # crop_size_multiple == 1: Any crop size is allowed.38    c = check((9, 10, 11), multiple_of=1)39    # If a scalar multiple_of is used, it should be stored expanded along dimensions40    assert c.multiple_of == (1, 1, 1)41    # Using a tuple multiple_of: Crop is twice as large as multiple_of in each dimension, this is hence valid.42    c = check((10, 12, 14), multiple_of=(5, 6, 7))43    assert c.multiple_of == (5, 6, 7)44    # Minimum size has not been provided, should default to multiple_of.45    assert c.minimum_size == (5, 6, 7)46    # Try with a couple more common crop sizes47    check((32, 64, 64), multiple_of=16)48    check((1, 64, 64), multiple_of=(1, 16, 16))49    # Crop size is at the allowed minimum: This is valid50    check((3, 4, 5), multiple_of=(3, 4, 5))51    # Provide a scalar minimum: Should be stored expanded into 3 dimensions52    c = check((9, 10, 11), minimum=2)53    assert c.minimum_size == (2, 2, 2)54    assert c.multiple_of is None55    # Provide a tuple minimum56    c = check((9, 10, 11), minimum=(5, 6, 7))57    assert c.minimum_size == (5, 6, 7)58    # A crop size at exactly the minimum is valid59    check((5, 6, 7), minimum=(5, 6, 7))60    # Checking for minimum and multiple at the same time61    check((9, 10, 11), minimum=1, multiple_of=1)62    check((10, 12, 14), minimum=(5, 6, 7), multiple_of=2)63    def assert_fails(crop_size: TupleInt3,64                     multiple_of: Optional[IntOrTuple3] = None,65                     minimum: Optional[IntOrTuple3] = None) -> None:66        with pytest.raises(ValueError) as err:67            check(crop_size, multiple_of, minimum)68        assert str(crop_size) in str(err)69        assert "Crop size is not valid" in str(err)70    # Crop size is not a multiple of 2 in dimensions 0 and 171    assert_fails((3, 4, 5), 2)72    # Crop size is not a multiple of 6 in dimension 273    assert_fails((3, 4, 5), (3, 4, 6))74    assert_fails((16, 16, 200), 16)75    # Crop size is too small76    assert_fails((1, 2, 3), (10, 10, 10))77    assert_fails((10, 20, 30), multiple_of=10, minimum=20)78    # Minimum size must be at least as large as multiple_of:79    with pytest.raises(ValueError) as err:80        CropSizeConstraints(minimum_size=10, multiple_of=20, num_dimensions=2)81    assert "minimum size must be at least as large as the multiple_of" in str(err)82    assert "(10, 10)" in str(err)83    assert "(20, 20)" in str(err)84    # Check that num_dimensions is working as expected (though not presently used in the codebase)85    c = CropSizeConstraints(minimum_size=30, multiple_of=10, num_dimensions=2)86    assert c.multiple_of == (10, 10)87    assert c.minimum_size == (30, 30)88def test_crop_size_constructor() -> None:89    """90    Test error handling in the constructor of CropSizeConstraints91    """92    # Minimum size is given as 3-tuple, but working with 2 dimensions:93    with pytest.raises(ValueError) as err:94        CropSizeConstraints(minimum_size=(1, 2, 3), num_dimensions=2)95    assert "must have length 2" in str(err)96    # Minimum size and multiple_of are not compatible:97    with pytest.raises(ValueError) as err:98        CropSizeConstraints(minimum_size=(1, 2, 3), multiple_of=16)99    assert "The minimum size must be at least as large" in str(err)100def test_crop_size_check_in_model() -> None:101    """102    Test that crop size checks are correctly integrated and called from within BaseModel103    """104    def create_model(crop_size: TupleInt3, multiple_of: IntOrTuple3) -> BaseSegmentationModel:105        model = SimpleModel(1, [1], 2, 2, crop_size_constraints=CropSizeConstraints(multiple_of=multiple_of))106        model.validate_crop_size(crop_size)107        return model108    # crop_size_multiple == 1: Any crop size is allowed.109    m = create_model((9, 10, 11), 1)110    assert m.crop_size_constraints.multiple_of == (1, 1, 1)111    m = create_model((10, 12, 14), (5, 6, 7))112    assert m.crop_size_constraints.multiple_of == (5, 6, 7)113    def assert_fails(crop_size: TupleInt3, crop_size_multiple: IntOrTuple3) -> None:114        with pytest.raises(ValueError) as err:115            create_model(crop_size, crop_size_multiple)116        assert "Crop size is not valid" in str(err)117    # Crop size is not a multiple of 2 in dimensions 0 and 1118    assert_fails((3, 4, 5), 2)119    # Crop size constraints field should be generated as not empty if not provided in the120    # constructor - this simplifies code in the inference pipeline.121    model = SimpleModel(1, [1], 2, 2)122    assert model.crop_size_constraints is not None123    assert model.crop_size_constraints.minimum_size == (1, 1, 1)124    assert model.crop_size_constraints.multiple_of == (1, 1, 1)125    model.validate_crop_size((1, 1, 1))126def test_crop_size_multiple_in_build_net() -> None:127    """128    Tests if the the crop_size validation is really called in the model creation code129    """130    config = SegmentationModelBase(architecture=ModelArchitectureConfig.UNet3D,131                                   image_channels=["ct"],132                                   feature_channels=[1],133                                   kernel_size=3,134                                   crop_size=(17, 16, 16),135                                   should_validate=False)136    # Crop size of 17 in dimension 0 is invalid for a UNet3D, should be multiple of 16.137    # This should raise a ValueError.138    with pytest.raises(ValueError) as ex:139        build_net(config)140    assert "Training crop size: Crop size is not valid" in str(ex)141    config.crop_size = (16, 16, 16)142    config.test_crop_size = (17, 18, 19)143    with pytest.raises(ValueError) as ex:144        build_net(config)145    assert "Test crop size: Crop size is not valid" in str(ex)146def crop_size_multiple_unet3d(crop_size: TupleInt3,147                              downsampling_factor: IntOrTuple3,148                              num_downsampling_paths: int,149                              expected_crop_size_multiple: TupleInt3) -> None:150    model = UNet3D(name="",151                   input_image_channels=1,152                   initial_feature_channels=32,153                   num_classes=2,154                   kernel_size=3,155                   downsampling_factor=downsampling_factor,156                   num_downsampling_paths=num_downsampling_paths)157    assert model.crop_size_constraints.multiple_of == expected_crop_size_multiple158    model.validate_crop_size(crop_size)159def test_crop_size_multiple_unet3d() -> None:160    # Standard UNet with downsampling 2, 4 downsampling stages: Minimum crop is 16161    crop_size_multiple_unet3d(crop_size=(16, 64, 64), downsampling_factor=2, num_downsampling_paths=4,162                              expected_crop_size_multiple=(16, 16, 16))163    # Only 3 downsampling stages: each reduces by a factor 2, hence minimum crop size should be 8164    crop_size_multiple_unet3d(crop_size=(16, 64, 64), downsampling_factor=2, num_downsampling_paths=3,165                              expected_crop_size_multiple=(8, 8, 8))166    # UNet3D as used in UNet2D: No down-sampling in Z direction. Any crop size in Z is allowed, crop size167    # at least 16 in X and Y168    crop_size_multiple_unet3d(crop_size=(16, 64, 64), downsampling_factor=(1, 2, 2), num_downsampling_paths=4,169                              expected_crop_size_multiple=(1, 16, 16))170    # Non-standard downsampling factor of 3, 2 downsampling stages: Minimum crop is 3*3 = 9171    crop_size_multiple_unet3d(crop_size=(9, 9, 9), downsampling_factor=3, num_downsampling_paths=2,172                              expected_crop_size_multiple=(9, 9, 9))173def test_restrict_crop_size_too_small() -> None:174    """Test the modification of crop sizes when the image size is below the minimum."""175    shape = (10, 30, 40)176    crop_size = (20, 40, 20)177    stride = (10, 20, 20)178    constraint = CropSizeConstraints(multiple_of=16)179    with pytest.raises(ValueError) as e:180        constraint.restrict_crop_size_to_image(shape, crop_size, stride)181    # Error message should contain the actual image size182    assert str(shape) in e.value.args[0]183    assert "16" in e.value.args[0]184def check_restrict_crop(constraint: CropSizeConstraints,185                        shape: TupleInt3,186                        crop_size: TupleInt3,187                        stride: TupleInt3,188                        expected_crop: TupleInt3,189                        expected_stride: TupleInt3) -> None:190    (crop_new, stride_new) = constraint.restrict_crop_size_to_image(shape, crop_size, stride)191    assert crop_new == expected_crop192    assert stride_new == expected_stride193    # Stride and crop must be integer tuples194    assert isinstance(crop_new[0], int)195    assert isinstance(stride_new[0], int)196def test_restrict_crop_size() -> None:197    """Test the modification of crop sizes when the image size is smaller than the crop."""198    shape = (20, 35, 40)199    crop_size = (25, 40, 20)200    stride = (10, 20, 20)201    constraint = CropSizeConstraints(multiple_of=16)202    # Expected new crop size is the elementwise minimum of crop_size and shape,203    # rounded up to the nearest multiple of 16204    expected_crop = (32, 48, 32)205    # Stride should maintain (elementwise) the same ratio to crop as before206    expected_stride = (12, 24, 32)207    check_restrict_crop(constraint, shape, crop_size, stride, expected_crop, expected_stride)208def test_restrict_crop_size_tuple() -> None:209    """Test the modification of crop sizes when the image size is smaller than the crop,210    and the crop_multiple is a tuple with element-wise multiples."""211    shape = (20, 35, 40)212    crop_size = (25, 40, 20)213    stride = (10, 20, 20)214    constraint = CropSizeConstraints(multiple_of=(1, 16, 16))215    # Expected new crop size is the elementwise minimum of crop_size and shape,216    # rounded down to the nearest multiple of 16, apart from dimension 0217    expected_crop = (20, 48, 32)218    expected_stride = (8, 24, 32)219    check_restrict_crop(constraint, shape, crop_size, stride, expected_crop, expected_stride)220def test_restrict_crop_size_large_image() -> None:221    """Test the modification of crop sizes when the image size is larger than the crop:222    The crop and stride should be returned unchanged."""223    shape = (30, 50, 50)224    crop_size = (20, 40, 40)225    stride = crop_size226    constraint = CropSizeConstraints(multiple_of=1)227    expected_crop = crop_size228    expected_stride = stride229    check_restrict_crop(constraint, shape, crop_size, stride, expected_crop, expected_stride)230    constraint2 = CropSizeConstraints(multiple_of=1, minimum_size=999)231    with pytest.raises(ValueError) as err:232        check_restrict_crop(constraint2, shape, crop_size, stride, expected_crop, expected_stride)233    assert "at least a size of" in str(err)234    assert "999" in str(err)235def test_restrict_crop_size_uneven() -> None:236    """237    Test a case when the image is larger than the crop in Z, but not in X and Y.238    """239    shape = (20, 30, 30)240    crop_size = (10, 60, 60)241    stride = crop_size242    constraint = CropSizeConstraints(multiple_of=1)243    expected_crop = (10, 30, 30)244    expected_stride = (10, 30, 30)...

game_list.py

Source:game_list.py

1import collections2import functools3import gspread_asyncio4from oauth2client.service_account import ServiceAccountCredentials5import re6import common7## Range & co8@functools.total_ordering9class _Least:10    # Default __eq__ is fine assuming there is only one instance11    def __gt__(self, other):12        return False13least = _Least()14@functools.total_ordering15class _Greatest:16    # Default __eq__ is fine assuming there is only one instance17    def __lt__(self, other):18        return False19greatest = _Greatest()20def _round_low_to_multiple(low, multiple_of):21    return low if low is least else low + (-low % multiple_of)22def _round_high_to_multiple(high, multiple_of):23    return high if high is greatest else high - (high % multiple_of)24class Range:25    def __init__(self, low, high, multiple_of=1):26        assert (low is least) or isinstance(low, int)27        assert (high is greatest) or isinstance(high, int)28        assert isinstance(multiple_of, int)29        self.low = _round_low_to_multiple(low, multiple_of)30        self.high = _round_high_to_multiple(high, multiple_of)31        self.multiple_of = multiple_of32    def __bool__(self):33        return self.low <= self.high34    def __contains__(self, x):35        return (self.low <= x <= self.high) and ((x % self.multiple_of) == 0)36    def __str__(self):37        if not self:38            return 'none'39        if self.low is least:40            if self.high is greatest:41                s = 'any'42            else:43                s = f'up to {self.high}'44        elif self.high is greatest:45            s = f'{self.low}+'46        elif self.low == self.high:47            return f'{self.low}'48        else:49            s = f'{self.low}..{self.high}'50        if self.multiple_of == 1:51            return s52        if self.multiple_of == 2:53            return f'{s} even'54        return f'{s} multiple of {self.multiple_of}'55    def simplified(self, implicit_low, implicit_high):56        assert (implicit_low is least) or isinstance(implicit_low, int)57        assert (implicit_high is greatest) or isinstance(implicit_high, int)58        implicit_low = _round_low_to_multiple(implicit_low, self.multiple_of)59        implicit_high = _round_high_to_multiple(implicit_high, self.multiple_of)60        low = max(self.low, implicit_low)61        high = min(self.high, implicit_high)62        if low >= high:63            # Either empty or one number in range64            return Range(low, high)65        return Range(66            least if low == implicit_low else low,67            greatest if high == implicit_high else high,68            self.multiple_of)69## Parsing70_num_re = re.compile('[0-9]+')71def _nums(s):72    for m in _num_re.finditer(s):73        yield int(m.group())74def _max_num(s):75    return max(_nums(s), default=None)76_num_plus_re = re.compile(r'([0-9]+)\+')77def _num_range(s):78    multiple_of = 2 if 'even' in s.lower() else 179    if m := _num_plus_re.search(s):80        return Range(int(m.group(1)), greatest, multiple_of)81    if nums := list(_nums(s)):82        return Range(min(nums), max(nums), multiple_of)83    return None84_Field = collections.namedtuple('_Field',85    'name heading_re parse sub_heading_row required',86    defaults=(lambda s: s, None, False))87_fields = [88    _Field('title', re.compile('title', re.IGNORECASE), required=True),89    _Field('platform', re.compile('platform', re.IGNORECASE)),90    _Field('max_players', re.compile('max.+player', re.IGNORECASE), _max_num),91    _Field('good_players', re.compile('good.+player', re.IGNORECASE), _num_range),92    _Field('owns', re.compile('who.+owns', re.IGNORECASE), bool, sub_heading_row=2)]93_heading_row = 094_data_begin_row = 395Game = collections.namedtuple('Game', (field.name for field in _fields))96def _fixup(game):97    if game.good_players is None:98        return game99    if good_players := game.good_players.simplified(1,100            greatest if game.max_players is None else game.max_players):101        return game._replace(good_players=good_players)102    # Assume we didn't parse the good players field properly...103    return game._replace(good_players=None)104class _Loc:105    def __init__(self, field):106        self.field = field107        self.begin_col = None108        # self.end_col set once found109        # self.sub_headings set at end of _get_locs110def _get_locs(rows):111    locs = {field.name: _Loc(field) for field in _fields}112    cont_loc = None113    for col, heading in enumerate(rows[_heading_row]):114        if heading:115            cont_loc = None116            for loc in locs.values():117                heading_re = loc.field.heading_re118                if heading_re.search(heading):119                    if loc.begin_col is not None:120                        raise common.Error(121                            f'I found multiple headings matching "{heading_re.pattern}" in the GL spreadsheet.')122                    loc.begin_col = col123                    loc.end_col = col + 1124                    if loc.field.sub_heading_row is not None:125                        cont_loc = loc126                    break127        elif cont_loc is not None:128            if rows[cont_loc.field.sub_heading_row][col]:129                cont_loc.end_col = col + 1130            else:131                cont_loc = None132    for loc in locs.values():133        if loc.begin_col is None:134            raise common.Error(135                f'I couldn\'t find a heading matching "{loc.field.heading_re.pattern}" in the GL spreadsheet.')136        if loc.field.sub_heading_row is not None:137            loc.sub_headings = rows[loc.field.sub_heading_row][loc.begin_col:loc.end_col]138            seen = set()139            for sub_heading in loc.sub_headings:140                if sub_heading in seen:141                    raise common.Error(142                        'There are multiple columns in the GL spreadsheet under '143                        f'{rows[_heading_row][loc.begin_col]} with the same '144                        f'sub-heading ({sub_heading}).')145                seen.add(sub_heading)146    return locs147class _MissingRequiredField(Exception):148    pass149def _parse_field(loc, row):150    values = []151    for s in row[loc.begin_col:loc.end_col]:152        if loc.field.required and not s:153            raise _MissingRequiredField()154        values.append(loc.field.parse(s))155    return values[0] if loc.field.sub_heading_row is None else dict(zip(loc.sub_headings, values))156GameList = collections.namedtuple('GameList', 'names games')157def _parse(rows):158    rows = [[s.strip() for s in row] for row in rows]159    locs = _get_locs(rows)160    names = set(locs['owns'].sub_headings)161    games = []162    for row in rows[_data_begin_row:]:163        try:164            values = {loc.field.name: _parse_field(loc, row) for loc in locs.values()}165        except _MissingRequiredField:166            continue167        games.append(_fixup(Game(**values)))168    return GameList(names, games)169## Google Sheets stuff170def _make_gsheets_client_manager(gsheets_creds):171    def get_creds():172        return ServiceAccountCredentials.from_json_keyfile_name(173            gsheets_creds, ['https://www.googleapis.com/auth/spreadsheets.readonly'])174    return gspread_asyncio.AsyncioGspreadClientManager(get_creds)175async def _fetch_gsheets_rows(cm, id):176    c = await cm.authorize()177    ss = await c.open_by_key(id)178    ws = await ss.get_worksheet(0)179    return await ws.get_all_values()180## Top level181class Session:182    def __init__(self, gsheets_creds):183        self.__cm = _make_gsheets_client_manager(gsheets_creds)184    async def fetch(self, id):185        rows = await _fetch_gsheets_rows(self.__cm, id)...

Ostroumov_DZ_6-1.py

Source:Ostroumov_DZ_6-1.py

1"""21. ÐÐ¾Ð´ÑÑÐ¸ÑÐ°ÑÑ, ÑÐºÐ¾Ð»ÑÐºÐ¾ Ð±ÑÐ»Ð¾ Ð²ÑÐ´ÐµÐ»ÐµÐ½Ð¾ Ð¿Ð°Ð¼ÑÑÐ¸ Ð¿Ð¾Ð´ Ð¿ÐµÑÐµÐ¼ÐµÐ½Ð½ÑÐµ Ð² ÑÐ°Ð½ÐµÐµ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ð½ÑÑ3Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð°Ñ Ð² ÑÐ°Ð¼ÐºÐ°Ñ Ð¿ÐµÑÐ²ÑÑ ÑÑÐµÑ ÑÑÐ¾ÐºÐ¾Ð².4ÐÑÐ¾Ð°Ð½Ð°Ð»Ð¸Ð·Ð¸ÑÐ¾Ð²Ð°ÑÑ ÑÐµÐ·ÑÐ»ÑÑÐ°Ñ Ð¸ Ð¾Ð¿ÑÐµÐ´ÐµÐ»Ð¸ÑÑ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ñ Ñ Ð½Ð°Ð¸Ð±Ð¾Ð»ÐµÐµ ÑÑÑÐµÐºÑÐ¸Ð²Ð½ÑÐ¼ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð¿Ð°Ð¼ÑÑÐ¸.5ÐÑÐ¸Ð¼ÐµÑÐ°Ð½Ð¸Ðµ: ÐÐ»Ñ Ð°Ð½Ð°Ð»Ð¸Ð·Ð° Ð²Ð¾Ð·ÑÐ¼Ð¸ÑÐµ Ð»ÑÐ±ÑÐµ 1-3 Ð²Ð°ÑÐ¸Ñ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ñ Ð¸Ð»Ð¸ Ð½ÐµÑÐºÐ¾Ð»ÑÐºÐ¾ Ð²Ð°ÑÐ¸Ð°Ð½ÑÐ¾Ð² ÐºÐ¾Ð´Ð° Ð´Ð»Ñ Ð¾Ð´Ð½Ð¾Ð¹ Ð¸ ÑÐ¾Ð¹ Ð¶Ðµ Ð·Ð°Ð´Ð°ÑÐ¸.6Ð ÐµÐ·ÑÐ»ÑÑÐ°ÑÑ Ð°Ð½Ð°Ð»Ð¸Ð·Ð° Ð²ÑÑÐ°Ð²ÑÑÐµ Ð² Ð²Ð¸Ð´Ðµ ÐºÐ¾Ð¼Ð¼ÐµÐ½ÑÐ°ÑÐ¸ÐµÐ² Ðº ÐºÐ¾Ð´Ñ.7Ð¢Ð°ÐºÐ¶Ðµ ÑÐºÐ°Ð¶Ð¸ÑÐµ Ð² ÐºÐ¾Ð¼Ð¼ÐµÐ½ÑÐ°ÑÐ¸ÑÑ Ð²ÐµÑÑÐ¸Ñ Python Ð¸ ÑÐ°Ð·ÑÑÐ´Ð½Ð¾ÑÑÑ Ð²Ð°ÑÐµÐ¹ ÐÐ¡.8/// Ð²ÐµÑÑÐ¸Ñ Python 3.99/// Windows 10 pro x6410/// 3-8. ÐÐ°ÑÑÐ¸ÑÐ° 5x4 Ð·Ð°Ð¿Ð¾Ð»Ð½ÑÐµÑÑÑ Ð²Ð²Ð¾Ð´Ð¾Ð¼ Ñ ÐºÐ»Ð°Ð²Ð¸Ð°ÑÑÑÑ ÐºÑÐ¾Ð¼Ðµ Ð¿Ð¾ÑÐ»ÐµÐ´Ð½Ð¸Ñ ÑÐ»ÐµÐ¼ÐµÐ½ÑÐ¾Ð² ÑÑÑÐ¾Ðº.11ÐÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð° Ð´Ð¾Ð»Ð¶Ð½Ð° Ð²ÑÑÐ¸ÑÐ»ÑÑÑ ÑÑÐ¼Ð¼Ñ Ð²Ð²ÐµÐ´ÐµÐ½Ð½ÑÑ ÑÐ»ÐµÐ¼ÐµÐ½ÑÐ¾Ð² ÐºÐ°Ð¶Ð´Ð¾Ð¹ ÑÑÑÐ¾ÐºÐ¸ Ð¸ Ð·Ð°Ð¿Ð¸ÑÑÐ²Ð°ÑÑ ÐµÐµ Ð² Ð¿Ð¾ÑÐ»ÐµÐ´Ð½ÑÑ ÑÑÐµÐ¹ÐºÑ ÑÑÑÐ¾ÐºÐ¸.12Ð ÐºÐ¾Ð½ÑÐµ ÑÐ»ÐµÐ´ÑÐµÑ Ð²ÑÐ²ÐµÑÑÐ¸ Ð¿Ð¾Ð»ÑÑÐµÐ½Ð½ÑÑ Ð¼Ð°ÑÑÐ¸ÑÑ.13"""14import random15import sys16def m_string(a):17    """18    ÑÐ¾ÑÐ¼Ð¸ÑÑÐµÑ Ð¼Ð°ÑÑÐ¸Ð² Ð½Ð° Ð·Ð°Ð´Ð°Ð½Ð½Ð¾Ðµ ÐºÐ¾Ð»Ð¸ÑÐµÑÑÐ²Ð¾ ÑÐ»ÐµÐ¼ÐµÐ½ÑÐ¾Ð²19    :param a: Ð´Ð»Ð¸Ð½Ð½Ð° ÑÑÐµÐ±ÑÐµÐ¼Ð¾Ð³Ð¾ Ð¼Ð°ÑÑÐ¸Ð²Ð°20    :return: Ð²Ð¾Ð·ÑÐ°ÑÐ°ÐµÑ Ð¼Ð°ÑÑÐ¸Ð²21    """22    array = [0] * a23    for i in range(a):24        array[i] = int(random.randint(1, 100))25    return array26def counter(a):27    """28    ÑÑÐ¸ÑÐ°ÐµÑ ÑÑÐ¼Ð¼Ñ ÑÐ»ÐµÐ¼ÐµÐ½ÑÐ¾Ð² Ð¼Ð°ÑÑÐ¸Ð²Ð° Ð¸ Ð·Ð°Ð¿Ð¸ÑÑÐ²Ð°ÐµÑ ÑÐµÐ·ÑÐ»ÑÑÐ°Ñ Ð² ÐºÐ¾Ð½ÐµÑ Ð¼Ð°ÑÑÐ¸Ð²Ð°29    :param a: Ð¿ÐµÑÐµÐ´Ð°ÐµÑÑÑ Ð¼Ð°ÑÑÐ¸Ð²30    :return: Ð²Ð¾Ð·ÑÐ°ÑÐ°ÐµÑÑÑ Ð¼Ð°ÑÑÐ¸Ð² Ñ Ð¿Ð¾ÑÑÐ¸ÑÐ°Ð½Ð½Ð¾Ð¹ ÑÑÐ¼Ð¼Ð¾Ð¹ ÑÐ»ÐµÐ¼ÐµÐ½ÑÐ¾Ð² Ð¼Ð°ÑÑÐ¸Ð²Ð°31    """32    summ = 033    for item in a:34        summ += int(item)35    return a.append(summ)36m_string_1 = m_string(3)37counter(m_string_1)38result = [m_string_1]39result_3 = sys.getsizeof(result) + sys.getsizeof(m_string_1)40print(f'{sys.getsizeof(result) = }, {sys.getsizeof(m_string_1) = },\n'41      f'Ð¾Ð±ÑÐµÐµ ÐºÐ¾Ð»Ð¸ÑÐµÑÑÐ²Ð¾ Ð·Ð°ÑÑÐ°ÑÐµÐ½Ð½Ð¾Ð¹ Ð¿Ð°Ð¼ÑÑÐ¸: {result_3}\n \n \n')42"""43sys.getsizeof(result) = 64, sys.getsizeof(m_string_1) = 120,44Ð¾Ð±ÑÐµÐµ ÐºÐ¾Ð»Ð¸ÑÐµÑÑÐ²Ð¾ Ð·Ð°ÑÑÐ°ÑÐµÐ½Ð½Ð¾Ð¹ Ð¿Ð°Ð¼ÑÑÐ¸: 18445/// 3 - 1. Ð Ð´Ð¸Ð°Ð¿Ð°Ð·Ð¾Ð½Ðµ Ð½Ð°ÑÑÑÐ°Ð»ÑÐ½ÑÑ ÑÐ¸ÑÐµÐ» Ð¾Ñ 2 Ð´Ð¾ 99 Ð¾Ð¿ÑÐµÐ´ÐµÐ»Ð¸ÑÑ,46ÑÐºÐ¾Ð»ÑÐºÐ¾ Ð¸Ð· Ð½Ð¸Ñ ÐºÑÐ°ÑÐ½Ñ ÐºÐ°Ð¶Ð´Ð¾Ð¼Ñ Ð¸Ð· ÑÐ¸ÑÐµÐ» Ð² Ð´Ð¸Ð°Ð¿Ð°Ð·Ð¾Ð½Ðµ Ð¾Ñ 2 Ð´Ð¾ 9.47"""48nums = [n for n in range(2, 100)]49multiple_of = {"Ð´Ð²Ð°": 0, "ÑÑÐ¸": 0, "ÑÐµÑÑÑÐµ": 0, "Ð¿ÑÑÑ": 0, "ÑÐµÑÑÑ": 0, "ÑÐµÐ¼Ñ": 0, "Ð²Ð¾ÑÐµÐ¼Ñ": 0, "Ð´ÐµÐ²ÑÑÑ": 0}50# Ð´Ð»Ñ Ð°Ð½Ð°Ð»Ð¸Ð·Ð° Ð²Ð²Ð¾Ð¶Ñ Ð´Ð¾Ð¿Ð¾Ð»Ð½Ð¸ÐµÑÐµÐ»ÑÐ½ÑÐµ Ð¿ÐµÑÐµÐ¼ÐµÐ½Ð½ÑÐµ:51two = 052three = 053four = 054five = 055six = 056seven = 057eight = 058nine = 059for item in nums:60    if item % 2 == 0:61        multiple_of["Ð´Ð²Ð°"] += 162        two += 163    if item % 3 == 0:64        multiple_of["ÑÑÐ¸"] += 165        three += 166    if item % 4 == 0:67        multiple_of["ÑÐµÑÑÑÐµ"] += 168        four += 169    if item % 5 == 0:70        multiple_of["Ð¿ÑÑÑ"] += 171        five += 172    if item % 6 == 0:73        multiple_of["ÑÐµÑÑÑ"] += 174        six += 175    if item % 7 == 0:76        multiple_of["ÑÐµÐ¼Ñ"] += 177        seven += 178    if item % 8 == 0:79        multiple_of["Ð²Ð¾ÑÐµÐ¼Ñ"] += 180        eight += 181    if item % 9 == 0:82        multiple_of["Ð´ÐµÐ²ÑÑÑ"] += 183        nine += 184result_1 = sys.getsizeof(nums) + sys.getsizeof(multiple_of)85result_2 = two + three + four + five + six + seven + eight + nine + sys.getsizeof(nums)86print(f'{sys.getsizeof(nums) = }, {sys.getsizeof(multiple_of) = }, Ð²ÑÐµÐ³Ð¾ {result_1}')87print(f'ÐºÐ°Ð¶Ð´Ð°Ñ "Ð¸Ð½Ð´Ð¸Ð²Ð´ÑÐ°Ð»ÑÐ½Ð°Ñ" Ð¿ÐµÑÐµÐ¼ÐµÐ½Ð½Ð°Ñ Ð±ÑÐ´ÐµÑ Ð·Ð°Ð½Ð¸Ð¼Ð°ÑÑ  {two} Ð¿Ð°Ð¼ÑÑÐ¸')88print(f'Ð±ÐµÐ· ÑÐ»Ð¾Ð²Ð°ÑÑ, Ñ Ð¸Ð½Ð´Ð¸Ð²Ð¸Ð´ÑÐ°Ð»ÑÐ½ÑÐ¼Ð¸ Ð¿ÐµÑÐµÐ¼ÐµÐ½Ð½ÑÐ¼Ð¸: {result_2}')89'''90sys.getsizeof(nums) = 920, sys.getsizeof(multiple_of) = 360, Ð²ÑÐµÐ³Ð¾ 128091ÐºÐ°Ð¶Ð´Ð°Ñ "Ð¸Ð½Ð´Ð¸Ð²Ð´ÑÐ°Ð»ÑÐ½Ð°Ñ" Ð¿ÐµÑÐµÐ¼ÐµÐ½Ð½Ð°Ñ Ð±ÑÐ´ÐµÑ Ð·Ð°Ð½Ð¸Ð¼Ð°ÑÑ  49 Ð¿Ð°Ð¼ÑÑÐ¸92Ð±ÐµÐ· ÑÐ»Ð¾Ð²Ð°ÑÑ, Ñ Ð¸Ð½Ð´Ð¸Ð²Ð¸Ð´ÑÐ°Ð»ÑÐ½ÑÐ¼Ð¸ Ð¿ÐµÑÐµÐ¼ÐµÐ½Ð½ÑÐ¼Ð¸: 1098...

transforms_resize_modulo_pad_crop.py

Source:transforms_resize_modulo_pad_crop.py

1import collections2import functools3from numbers import Number4from typing import Union, Optional5import numpy as np6from .transforms import CriteriaFn7from ..transforms import transforms8from ..transforms import crop9from ..basic_typing import ShapeX10from ..utils import batch_pad_minmax_joint11from typing_extensions import Literal12def _transform_resize_modulo_crop_pad(13        features_names,14        batch,15        multiple_of,16        mode,17        padding_mode,18        padding_constant_value):19    resized_arrays = []20    arrays = [batch[name] for name in features_names]21    for a in arrays[1:]:22        assert len(a.shape) == len(arrays[0].shape)23        assert a.shape[2:] == arrays[0].shape[2:], f'selected tensors MUST have the same size. ' \24                                                   f'Expected={a.shape}, got={arrays[0].shape}'25    if len(features_names) > 0:26        shape = arrays[0].shape[2:]27        if isinstance(multiple_of, Number):28            multiple_of = [multiple_of] * (len(shape))29        multiple_of = np.asarray(multiple_of)30        assert len(shape) == len(multiple_of), 'expected multiple_of expressed as [D]HW shape!'31        shape_extra = arrays[0].shape[2:] % multiple_of32        if mode == 'pad':33            # padding is centered34            padding = multiple_of - shape_extra35            padding_left = padding // 236            padding_right = padding - padding_left37            padding_left = [0] + list(padding_left)38            padding_right = [0] + list(padding_right)39            resized_arrays = batch_pad_minmax_joint(40                arrays,41                padding_left,42                padding_right,43                padding_mode,44                padding_constant_value45            )46        elif mode == 'crop':47            s = list(shape - shape_extra)48            assert min(s) > 0, f'shape is too small! Use a different mode. ' \49                               f'Got={arrays[0].shape[2:]}, multiple_of={multiple_of}'50            resized_arrays = crop.transform_batch_random_crop_joint(arrays, [None] + s)51        else:52            raise NotImplementedError(f'mode={mode} is not handled!')53    new_batch = collections.OrderedDict(zip(features_names, resized_arrays))54    for feature_name, feature_value in batch.items():55        if feature_name not in features_names:56            # not in the transformed features, so copy the original value57            new_batch[feature_name] = feature_value58    return new_batch59class TransformResizeModuloCropPad(transforms.TransformBatchWithCriteria):60    """61    Resize tensors by padding or cropping the tensor so its shape is a multiple of a ``multiple_of``.62    This can be particularly helpful in encoder-decoder architecture with skip connection which can impose63    constraints on the input shape (e.g., the input must be a multiple of 32 pixels).64    Args:65        multiple_of: a sequence of size `len(array.shape)-2` such that shape % multiple_of == 0. To achieve this,66            the tensors will be padded or cropped.67        criteria_fn: function applied on each feature. If satisfied, the feature will be transformed, if not68            the original feature is returned69        padding_mode: `numpy.pad` mode. Currently supported are ('constant', 'edge', 'symmetric')70        mode: one of `crop`, `pad`. If `pad`, the selected tensors will be padded to achieve the71            size tensor.shape % multiple_of == 0. If `crop`, the selected tensors will be cropped72            instead with a randomly selected cropping position.73    Returns:74        dictionary with the selected tensors cropped or padded to the appropriate size75    """76    def __init__(77            self,78            multiple_of: Union[int, ShapeX],79            criteria_fn: Optional[CriteriaFn] = None,80            mode: Literal['crop', 'pad'] = 'crop',81            padding_mode: Literal['edge', 'constant', 'symmetric'] = 'constant',82            padding_constant_value: int = 0):83        if criteria_fn is None:84            criteria_fn = transforms.criteria_is_array_4_or_above85        super().__init__(86            criteria_fn=criteria_fn,87            transform_fn=functools.partial(88                _transform_resize_modulo_crop_pad,89                padding_mode=padding_mode,90                mode=mode,91                padding_constant_value=padding_constant_value,92                multiple_of=multiple_of)93         )...

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