python_code stringlengths 0 4.04M | repo_name stringlengths 7 58 | file_path stringlengths 5 147 |
|---|---|---|
"""Unittests for Datasets."""
from itertools import product
import numpy as np
import pytest
from meerkat import concat
from meerkat.columns.object.base import ObjectColumn
from meerkat.columns.tensor.numpy import NumPyTensorColumn
from meerkat.dataframe import DataFrame
from meerkat.errors import ConcatError, Concat... | meerkat-main | tests/meerkat/ops/test_concat.py |
import pytest
from meerkat import DeferredColumn
from meerkat.dataframe import DataFrame
from ...utils import product_parametrize
from ..columns.abstract import AbstractColumnTestBed, column_parametrize
# from ..columns.deferred.test_deferred import DeferredColumnTestBed
# from ..columns.deferred.test_image import I... | meerkat-main | tests/meerkat/ops/test_map.py |
import hashlib
import os
import numpy as np
import PIL
import pytest
import torch
from PIL import Image
import meerkat as mk
from meerkat import embed
from meerkat.ops.embed import encoders
from meerkat.ops.embed.encoder import Encoder
class ImageColumnTestBed:
def __init__(
self,
tmpdir: str,
... | meerkat-main | tests/meerkat/ops/embed/test__init__.py |
meerkat-main | tests/meerkat/ops/embed/__init__.py | |
meerkat-main | tests/meerkat/ops/sliceby/__init __.py | |
import numpy as np
from meerkat import NumPyTensorColumn, ObjectColumn
from meerkat.dataframe import DataFrame
from meerkat.ops.sliceby.groupby import GroupBy, groupby
# Comment for meeting 5/19: Testing group by multiple columns,
# single columns on list, on string.
# Different columns as by: including ListColumn, ... | meerkat-main | tests/meerkat/ops/sliceby/test_groupby.py |
from functools import wraps
from itertools import product
from typing import Any, Dict, List, Type, Union
import numpy as np
import pytest
def column_parametrize(
testbed_classes: List[Union[Type, Dict]],
config: dict = None,
single: bool = False,
):
params = [
c.get_params(config=config, sin... | meerkat-main | tests/meerkat/columns/abstract.py |
import os
import numpy as np
import pandas as pd
import pytest
from meerkat import DeferredColumn, NumPyTensorColumn, TorchTensorColumn
from meerkat.columns.deferred.base import DeferredCell
from meerkat.columns.scalar.pandas import PandasScalarColumn
from meerkat.errors import ConversionError, ImmutableError
from .... | meerkat-main | tests/meerkat/columns/test_common.py |
meerkat-main | tests/meerkat/columns/__init__.py | |
"""Unittests for NumpyColumn."""
from __future__ import annotations
import os
from typing import List, Union
import numpy as np
import pytest
import torch
import torchaudio
from meerkat import AudioColumn
from meerkat.columns.abstract import Column
from meerkat.columns.deferred.base import DeferredCell
from meerkat.... | meerkat-main | tests/meerkat/columns/deferred/test_audio.py |
"""Unittests for NumpyColumn."""
from __future__ import annotations
import os
from typing import List, Union
import numpy as np
import pandas as pd
import pytest
import torch
import torchvision.datasets.folder as folder
from PIL import Image
import meerkat
from meerkat import ImageColumn
from meerkat.block.deferred_... | meerkat-main | tests/meerkat/columns/deferred/test_image.py |
meerkat-main | tests/meerkat/columns/deferred/__init__.py | |
import json
import os
from typing import Union
import dill
import numpy as np
import pytest
from PIL import Image
import meerkat as mk
from meerkat.block.deferred_block import DeferredCellOp, DeferredOp
from meerkat.columns.deferred.base import DeferredCell
from meerkat.columns.deferred.file import FILE_TYPES, FileCe... | meerkat-main | tests/meerkat/columns/deferred/test_file_column.py |
import json
import os
from typing import Union
import dill
import numpy as np
import pytest
from PIL import Image
from meerkat.block.deferred_block import DeferredCellOp, DeferredOp
from meerkat.columns.deferred.base import DeferredCell
from meerkat.columns.deferred.file import FileCell, FileColumn, FileLoader
from m... | meerkat-main | tests/meerkat/columns/deferred/test_file.py |
"""Unittests for LambdaColumn."""
from typing import Type
import numpy as np
import pytest
import meerkat as mk
from meerkat import DeferredColumn, NumPyTensorColumn, ObjectColumn
from meerkat.errors import ConcatWarning
from ....testbeds import MockColumn, MockDatapanel
from ..abstract import AbstractColumnTestBed,... | meerkat-main | tests/meerkat/columns/deferred/test_deferred.py |
meerkat-main | tests/meerkat/columns/object/__init__.py | |
import time
import numpy as np
from PIL import Image
from meerkat.columns.object.base import ObjectColumn
from meerkat.interactive.formatter.image import ImageFormatterGroup
def test_formatters_image():
"""Test formatters when the object column is full of images."""
images = [
Image.fromarray(np.ran... | meerkat-main | tests/meerkat/columns/object/test_base.py |
import numpy as np
import pandas as pd
import pytest
import torch
from meerkat import TensorColumn, TorchTensorColumn
from meerkat.block.numpy_block import NumPyBlock
from ..abstract import AbstractColumnTestBed, column_parametrize
class TensorColumnTestBed(AbstractColumnTestBed):
DEFAULT_CONFIG = {
"nu... | meerkat-main | tests/meerkat/columns/tensor/test_tensor_column.py |
import numpy as np
import pandas as pd
import pytest
import torch
from meerkat import NumPyTensorColumn, TorchTensorColumn
from meerkat.block.torch_block import TorchBlock
from ..abstract import AbstractColumnTestBed, column_parametrize
class TorchTensorColumnTestBed(AbstractColumnTestBed):
DEFAULT_CONFIG = {
... | meerkat-main | tests/meerkat/columns/tensor/test_torch.py |
meerkat-main | tests/meerkat/columns/tensor/__init__.py | |
import os
import numpy as np
import numpy.testing as np_test
import pandas as pd
import pytest
from numpy.lib.format import open_memmap
from meerkat import NumPyTensorColumn, TorchTensorColumn
from meerkat.block.numpy_block import NumPyBlock
from ....utils import product_parametrize
from ..abstract import AbstractCo... | meerkat-main | tests/meerkat/columns/tensor/test_numpy.py |
meerkat-main | tests/meerkat/columns/scalar/__init__.py | |
import itertools
from typing import Dict
import numpy as np
import pandas as pd
import pyarrow as pa
import pytest
import torch
from meerkat import ScalarColumn
from meerkat.dataframe import DataFrame
from tests.utils import product_parametrize
BACKENDS = ["arrow", "pandas"]
@pytest.mark.parametrize(
"data",
... | meerkat-main | tests/meerkat/columns/scalar/test_scalar_column.py |
"""Unittests for NumpyColumn."""
import numpy as np
import pandas as pd
import pytest
import torch
from meerkat import ScalarColumn
from meerkat.block.torch_block import TorchBlock
from ..abstract import AbstractColumnTestBed, column_parametrize
class PandasScalarColumnTestBed(AbstractColumnTestBed):
DEFAULT_... | meerkat-main | tests/meerkat/columns/scalar/test_pandas.py |
"""Unittests for NumpyColumn."""
from typing import Union
import numpy as np
import pandas as pd
import pyarrow as pa
import pytest
import torch
from meerkat import ArrowScalarColumn
from meerkat.block.torch_block import TorchBlock
from ..abstract import AbstractColumnTestBed, column_parametrize
def to_numpy(array... | meerkat-main | tests/meerkat/columns/scalar/test_arrow.py |
import glob
import os
import tempfile
from pathlib import Path
import pytest
from meerkat import initialize_logging
def test_initialize_logging():
initialize_logging()
@pytest.fixture
def unreadable_dir(tmpdir):
unread_dir = tmpdir / "unreadable"
os.makedirs(unread_dir)
unread_dir.chmod(0)
if ... | meerkat-main | tests/meerkat/logging/test_utils.py |
import pytest
import torch
from meerkat import TorchTensorColumn
from meerkat.block.abstract import BlockView
from meerkat.block.ref import BlockRef
from meerkat.block.torch_block import TorchBlock
from meerkat.errors import ConsolidationError
def test_signature_hash():
# check equal
block1 = TorchBlock(torc... | meerkat-main | tests/meerkat/block/test_tensor_block.py |
import numpy as np
import pyarrow as pa
import pytest
from meerkat.block.abstract import BlockView
from meerkat.block.arrow_block import ArrowBlock
from meerkat.block.ref import BlockRef
from meerkat.columns.scalar.arrow import ArrowScalarColumn
from meerkat.errors import ConsolidationError
def test_signature_hash()... | meerkat-main | tests/meerkat/block/test_arrow_block.py |
import numpy as np
import pytest
from meerkat import NumPyTensorColumn
from meerkat.block.abstract import BlockView
from meerkat.block.numpy_block import NumPyBlock
from meerkat.block.ref import BlockRef
from meerkat.errors import ConsolidationError
def test_signature_hash():
# check equal
block1 = NumPyBloc... | meerkat-main | tests/meerkat/block/test_numpy_block.py |
meerkat-main | tests/meerkat/block/__init__.py | |
import numpy as np
import pandas as pd
import pytest
from meerkat import ScalarColumn
from meerkat.block.abstract import BlockView
from meerkat.block.pandas_block import PandasBlock
from meerkat.block.ref import BlockRef
from meerkat.errors import ConsolidationError
def test_signature_hash():
# check equal
b... | meerkat-main | tests/meerkat/block/test_pandas_block.py |
import numpy as np
from meerkat import DeferredColumn, TensorColumn
from meerkat.block.deferred_block import DeferredBlock, DeferredOp
from meerkat.block.ref import BlockRef
from ...utils import product_parametrize
def fn(x: int) -> int:
return x + 1, x + 2, x + 3
@product_parametrize(params={"num_blocks": [1... | meerkat-main | tests/meerkat/block/test_deferred_block.py |
import os
from itertools import product
import numpy as np
import pytest
import torch
import meerkat as mk
from meerkat.block.manager import BlockManager
from meerkat.tools.utils import load_yaml
from ...utils import product_parametrize
def test_consolidate_no_op():
mgr = BlockManager()
col1 = mk.TensorCol... | meerkat-main | tests/meerkat/block/test_manager.py |
"""Functions to generate certain RST files."""
# import math
import inspect
import os
import pathlib
from collections import defaultdict
from typing import List, Union
# import numpy as np
import pandas as pd
import meerkat as mk
_DIR = pathlib.Path(os.path.dirname(os.path.abspath(__file__)))
def _replace_contents... | meerkat-main | docs/source/rst_gen.py |
# Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If ex... | meerkat-main | docs/source/conf.py |
import os
import meerkat as mk
def display_df(df: mk.DataFrame, name: str):
# need to get absolute paths so this works on readthedocs
base_dir = os.path.join(os.path.dirname(os.path.dirname(mk.__file__)), "docs")
body_html = df._repr_html_()
css = open(os.path.join(base_dir, "source/html/display/data... | meerkat-main | docs/source/display.py |
from typing import List
import meerkat as mk
def get_rst_class_ref(klass: type):
return f":class:`dcbench.{klass.__name__}`"
def get_link(text: str, url: str):
return f"`{text} <{url}>`_"
def create_tags_html(tags: List[str]):
tags = "".join([f"<div class='tag'>{tag.replace('_', ' ')}</div>" for tag ... | meerkat-main | docs/source/datasets/build_datasets_docs.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
# Evaluation script for object localization
import json
import argparse
import torch
import itertools
import numpy as np... | ActivityNet-Entities-main | scripts/eval_grd_anet_entities.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
# Script to print stats on the NP annotation file
import numpy as np
import json
import csv
import sys
src_file = sys.a... | ActivityNet-Entities-main | scripts/anet_entities_np_stats.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
# Based on
# https://github.com/jiasenlu/NeuralBabyTalk/blob/master/misc/bbox_transform.py
# Licensed under The MIT Licen... | ActivityNet-Entities-main | scripts/utils.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
# Script to preprocess the raw annotation output to NP/object annotation files
import os
import sys
import json
import a... | ActivityNet-Entities-main | scripts/attr_prep_tag_NP.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
# Script to print stats on the object annotation file
import numpy as np
import json
import csv
# import visdom
import s... | ActivityNet-Entities-main | scripts/anet_entities_object_stats.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import os
class Path(object):
"""
User-specific path configuration.
Please complete the /path/to/* paths to ... | astmt-master | mypath.py |
astmt-master | experiments/__init__.py | |
astmt-master | experiments/classification/__init__.py | |
astmt-master | experiments/classification/imagenet/__init__.py | |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import argparse
import os
import copy
import shutil
import time
import torch
import torch.nn as nn
import torch.nn.parall... | astmt-master | experiments/classification/imagenet/train.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import torch
from torchvision import transforms
from torch.utils.data import DataLoader
from fblib.util.helpers import wo... | astmt-master | experiments/dense_predict/common_configs.py |
MAX_N_IMAGES_PER_GPU = {
'res26-8': 8,
'res26-16': 12,
'res50-8': 8,
'res50-16': 10,
'res101-8': 4,
'res101-16': 10,
'x50-8': 4,
'x50-16': 10,
'x101-8': 2,
'x101-16': 6,
}
| astmt-master | experiments/dense_predict/__init__.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import os
import sys
import cv2
import argparse
import torch
import tarfile
from six.moves import urllib
from easydict imp... | astmt-master | experiments/dense_predict/pascal_resnet/config.py |
MAX_N_IMAGES_PER_GPU = {
'se_res26-8': 10,
'se_res26-16': 16,
'se_res50-8': 8,
'se_res50-16': 10,
'se_res101-8': 2,
'se_res101-16': 8,
}
| astmt-master | experiments/dense_predict/pascal_resnet/__init__.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import os
import socket
import timeit
import cv2
from datetime import datetime
import imageio
import numpy as np
# PyTorc... | astmt-master | experiments/dense_predict/pascal_resnet/main.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import os
import sys
import cv2
import argparse
import torch
import tarfile
from six.moves import urllib
from easydict imp... | astmt-master | experiments/dense_predict/pascal_mnet/config.py |
MAX_N_IMAGES_PER_GPU = {
'mnetv2-8': 10,
'mnetv2-16': 16,
}
| astmt-master | experiments/dense_predict/pascal_mnet/__init__.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import os
import socket
import timeit
import cv2
from datetime import datetime
import imageio
import numpy as np
# PyTorc... | astmt-master | experiments/dense_predict/pascal_mnet/main.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import os
import sys
import cv2
import argparse
import torch
import tarfile
from six.moves import urllib
from easydict imp... | astmt-master | experiments/dense_predict/nyud_resnet/config.py |
MAX_N_IMAGES_PER_GPU = {
'se_res26-8': 10,
'se_res26-16': 16,
'se_res50-8': 8,
'se_res50-16': 16,
'se_res101-8': 2,
'se_res101-16': 10,
}
| astmt-master | experiments/dense_predict/nyud_resnet/__init__.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import os
import socket
import timeit
import cv2
from datetime import datetime
import imageio
import scipy.io as sio
impor... | astmt-master | experiments/dense_predict/nyud_resnet/main.py |
import os
PROJECT_ROOT_DIR = os.path.dirname(os.path.abspath(__file__)) | astmt-master | fblib/__init__.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import torch
import torch.nn as nn
import torch.nn.functional as F
class AttentionModuleFree(nn.Module):
"""
Att... | astmt-master | fblib/layers/attention.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import torch.nn as nn
import torch.nn.functional as F
class Normalize(object):
"""Given mean: (R, G, B) and std: (R,... | astmt-master | fblib/layers/image_features.py |
astmt-master | fblib/layers/__init__.py | |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
from torch.autograd import Function
class ReverseLayerF(Function):
@staticmethod
def forward(ctx, x, alpha):
... | astmt-master | fblib/layers/reverse_grad.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.modules.module import Module
import numpy... | astmt-master | fblib/layers/loss.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import numpy as np
import torch
from torch.nn import functional as F
def logit(x):
return np.log(x/(1-x+1e-08)+1e-08... | astmt-master | fblib/layers/misc_layers.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
from torch import nn
from fblib.util.custom_container import SequentialMultiTask
class SELayer(nn.Module):
"""
S... | astmt-master | fblib/layers/squeeze.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import torch
from torch.autograd import Variable
from torchvision import models
from graphviz import Digraph
def make_do... | astmt-master | fblib/util/pdf_visualizer.py |
astmt-master | fblib/util/__init__.py | |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import torch
import collections
import re
from torch._six import string_classes, int_classes
_use_shared_memory = False
r... | astmt-master | fblib/util/custom_collate.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import torch
import cv2
import numpy as np
# set random seed in each worker
worker_seed = lambda x: np.random.seed((torch... | astmt-master | fblib/util/helpers.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
from collections import OrderedDict
from torch.nn.modules.container import Sequential
class SequentialMultiTask(Sequenti... | astmt-master | fblib/util/custom_container.py |
astmt-master | fblib/util/classification/__init__.py | |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import time
import random
class AverageMeter(object):
"""Computes and stores the average and current value"""
d... | astmt-master | fblib/util/classification/utils.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import torch
from torchvision import utils as vutils
import fblib.util.pdf_visualizer as viz
from fblib.util.mypath impor... | astmt-master | fblib/util/mtl_tools/multitask_visualizer.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
def imagenet_categ_names():
return { 0: 'tench, Tinca tinca',
1: 'goldfish, Carassius auratus',
... | astmt-master | fblib/util/db_info/imagenet_categ.py |
astmt-master | fblib/util/db_info/__init__.py | |
astmt-master | fblib/util/model_resources/__init__.py | |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import torch
# ---- Public functions
def compute_gflops(net, in_shape=(1, 3, 224, 224), tasks=None):
net = add_flop... | astmt-master | fblib/util/model_resources/flops.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
def count_parameters(model):
return sum(p.numel() for p in model.parameters() if p.requires_grad)
| astmt-master | fblib/util/model_resources/num_parameters.py |
astmt-master | fblib/util/dense_predict/__init__.py | |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
def lr_poly(base_lr, iter_, max_iter=100, power=0.9):
return base_lr * ((1 - float(iter_) / max_iter) ** power)
cl... | astmt-master | fblib/util/dense_predict/utils.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import torch
import torch.nn as nn
def traverse_graph(var):
"""
Args:
var: output Variable
"""
... | astmt-master | fblib/util/optimizer_mtl/select_used_modules.py |
astmt-master | fblib/util/optimizer_mtl/__init__.py | |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import os
from fblib.util.mypath import Path
import numpy as np
import torch.utils.data as data
import cv2
class FSVGTA... | astmt-master | fblib/dataloaders/fsv.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import os
import os.path
from pycocotools.coco import COCO
import torch.utils.data as data
from PIL import Image
import n... | astmt-master | fblib/dataloaders/coco.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import torch.utils.data as data
class CombineIMDBs(data.Dataset):
"""
Combine two datasets, for example to creat... | astmt-master | fblib/dataloaders/combine_im_dbs.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import os
import sys
import tarfile
import cv2
import numpy as np
import torch.utils.data as data
from six.moves import u... | astmt-master | fblib/dataloaders/msra10k.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import os
import sys
import tarfile
import json
import cv2
import numpy as np
import scipy.io as sio
import torch.utils.d... | astmt-master | fblib/dataloaders/pascal_context.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import os
import sys
import tarfile
import cv2
from PIL import Image
import numpy as np
import torch.utils.data as data
i... | astmt-master | fblib/dataloaders/nyud.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import numpy.random as random
import numpy as np
import torch
import cv2
import math
import fblib.util.helpers as helpers
... | astmt-master | fblib/dataloaders/custom_transforms.py |
from .bsds import BSDS500
from .coco import COCOSegmentation
from .fsv import FSVGTA
from .nyud import NYUD_MT, NYUDRaw
from .pascal_context import PASCALContext
from .pascal_voc import VOC12
from .sbd import SBD
from .msra10k import MSRA
from .pascal_sal import PASCALS
__all__ = ['BSDS500', 'COCOSegmentation', 'FSVGT... | astmt-master | fblib/dataloaders/__init__.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import os
import sys
import errno
import cv2
import hashlib
import tarfile
import numpy as np
import scipy.io as sio
impo... | astmt-master | fblib/dataloaders/sbd.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
from __future__ import print_function
import torch.utils.data as data
from PIL import Image
import os
import os.path
impor... | astmt-master | fblib/dataloaders/mnist_multitask.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import os
import sys
import tarfile
from PIL import Image
import numpy as np
from glob import glob
import scipy.io as sio... | astmt-master | fblib/dataloaders/bsds.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import os
import sys
import errno
import cv2
import hashlib
import tarfile
import numpy as np
import torch.utils.data as ... | astmt-master | fblib/dataloaders/pascal_voc.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import os
import sys
import tarfile
import cv2
from PIL import Image
import numpy as np
import torch.utils.data as data
f... | astmt-master | fblib/dataloaders/pascal_sal.py |
astmt-master | fblib/networks/__init__.py | |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import os
import math
import torch
import torch.nn as nn
from fblib.util.mypath import Path
try:
from torch.hub imp... | astmt-master | fblib/networks/classification/mobilenet_v2.py |
astmt-master | fblib/networks/classification/__init__.py | |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import os
import math
from collections import OrderedDict
import torch
import torch.nn as nn
from fblib.util.mypath imp... | astmt-master | fblib/networks/classification/se_mobilenet_v2.py |
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