python_code stringlengths 0 187k | repo_name stringlengths 8 46 | file_path stringlengths 6 135 |
|---|---|---|
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
import datetime
import os
import pathlib
from unittest.mock import MagicMock, patch
import pytest
import pytest_httpserver
from composer import loggers
from composer.core.time import Time, Timestamp, TimeUnit
from composer.utils import ... | composer-dev | tests/utils/test_file_helpers.py |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
import itertools
from typing import Mapping, Type, cast
from unittest.mock import Mock
import pytest
import torch
from torch import nn
from composer.algorithms.blurpool import BlurMaxPool2d
from composer.utils import module_surgery
from... | composer-dev | tests/utils/test_module_surgery.py |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
from functools import partial
from composer.utils import import_object
def test_dynamic_import_object():
assert import_object('functools:partial') is partial
| composer-dev | tests/utils/test_dynamic_import.py |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
import pytest
from composer.utils import retry
@pytest.mark.parametrize('with_args', [True, False])
def test_retry(with_args: bool):
num_tries = 0
return_after = 2
if with_args:
decorator = retry(RuntimeError, num_... | composer-dev | tests/utils/test_retrying.py |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
from torch.optim import Adam
from torch.utils.data import DataLoader
from composer.algorithms import EMA
from composer.callbacks import SpeedMonitor
from composer.loggers import InMemoryLogger
from composer.trainer import Trainer
from co... | composer-dev | tests/utils/test_autolog_hparams.py |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
| composer-dev | tests/utils/__init__.py |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
from collections import ChainMap, Counter, OrderedDict, defaultdict, deque
from typing import NamedTuple
import numpy as np
import pytest
import torch
from composer.utils.batch_helpers import batch_get, batch_set
my_list = [3, 4, 5, 6,... | composer-dev | tests/utils/test_batch_helpers.py |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
# disabling unused class checks in this test, as string enum checks happen during class construction
# pyright: reportUnusedClass=none
import pytest
from composer.utils.string_enum import StringEnum
def test_string_enum_invalid_name()... | composer-dev | tests/utils/test_string_enum.py |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
import io
import numpy as np
import pytest
import torch
from composer.utils import IteratorFileStream, ensure_tuple
def test_none_to_tuple():
assert ensure_tuple(None) == ()
@pytest.mark.parametrize('x', ['test', b'test', bytear... | composer-dev | tests/utils/test_iter_helpers.py |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
from unittest.mock import patch
import pytest
from composer.utils import dist
@pytest.mark.world_size(2)
def test_run_local_rank_first_context_raises_error():
# This mocking is necessary because there is a fixture called configure... | composer-dev | tests/utils/test_dist.py |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
import operator
import pytest
import torch
from torch import nn
from torch.fx import symbolic_trace
from torch.fx.graph_module import GraphModule
from torchvision import models
from composer.utils.fx_utils import apply_stochastic_residu... | composer-dev | tests/utils/test_fx_utils.py |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
import contextlib
import copy
import pathlib
from typing import Any, Dict, Tuple
from urllib.parse import urlparse
import pytest
from composer.utils.object_store import LibcloudObjectStore, ObjectStore, S3ObjectStore, SFTPObjectStore
fr... | composer-dev | tests/utils/object_store/test_object_store.py |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
| composer-dev | tests/utils/object_store/__init__.py |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
import contextlib
import os
import pathlib
from typing import Any, Dict, Type
import mockssh
import moto
import pytest
from cryptography.hazmat.primitives import serialization
from cryptography.hazmat.primitives.asymmetric import rsa
im... | composer-dev | tests/utils/object_store/object_store_settings.py |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
from pathlib import Path
from unittest.mock import MagicMock, Mock
import pytest
from composer.utils import OCIObjectStore
@pytest.fixture
def mock_bucket_name():
return 'my_bucket'
@pytest.fixture
def test_oci_obj_store(mock_bu... | composer-dev | tests/utils/object_store/test_oci_object_store.py |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
import os
import pathlib
import pytest
from composer.utils.object_store import LibcloudObjectStore
@pytest.fixture
def remote_dir(tmp_path: pathlib.Path):
remote_dir = tmp_path / 'remote_dir'
os.makedirs(remote_dir)
return... | composer-dev | tests/utils/object_store/test_libcloud_object_store.py |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
import os
import pathlib
import threading
import pytest
from composer.utils.object_store import S3ObjectStore
def _worker(bucket: str, tmp_path: pathlib.Path, tid: int):
object_store = S3ObjectStore(bucket=bucket)
os.makedirs(... | composer-dev | tests/utils/object_store/test_s3_object_store.py |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
import copy
import json
import os
import tempfile
from contextlib import nullcontext
from pathlib import Path
from typing import Any, Dict, List, Optional
from unittest.mock import patch
from urllib.parse import urlparse
import pytest
imp... | composer-dev | tests/models/test_hf_model.py |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
import pytest
from torch.utils.data import DataLoader
from composer.models.gpt2 import create_gpt2
from composer.trainer import Trainer
from tests.common.datasets import RandomTextLMDataset
def test_gpt2_hf_factory(tiny_gpt2_config, ti... | composer-dev | tests/models/test_gpt2.py |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
import pytest
import torch
from composer.models.efficientnetb0.efficientnets import EfficientNet
@pytest.mark.gpu
def test_efficientb0_activate_shape():
# Running this test on cuda as convolutions are slow on CPU
random_input =... | composer-dev | tests/models/test_efficientnet.py |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import pytest
import torch
@pytest.fixture
def mmdet_detection_batch():
batch_size = 2
num_labels_per_image = 20
image_size = 224
return {
'img_metas': [{
'filename': '../../data/co... | composer-dev | tests/models/test_mmdet_model.py |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
import copy
import pickle
from typing import Iterable
import pytest
import torch
from torch.utils.data import DataLoader
from composer.trainer import Trainer
from tests.common.datasets import RandomClassificationDataset
from tests.commo... | composer-dev | tests/models/test_composer_model.py |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
import pytest
from torch.utils.data import DataLoader
from composer.models.bert import create_bert_classification, create_bert_mlm
from composer.trainer import Trainer
from tests.common.datasets import RandomTextClassificationDataset, Ra... | composer-dev | tests/models/test_bert.py |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
| composer-dev | tests/cli/__init__.py |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
import subprocess
import sys
from typing import List
import pytest
import composer
@pytest.mark.parametrize('args', [
['composer', '--version'],
[sys.executable, '-m', 'composer', '--version'],
[sys.executable, '-m', 'comp... | composer-dev | tests/cli/test_cli.py |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
"""Contains commonly used models that are shared across the test suite."""
import copy
from functools import partial
from typing import Any, Dict, Optional, Tuple, Union
import pytest
import torch
from torchmetrics import Metric, MetricC... | composer-dev | tests/common/models.py |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
from typing import Any, Dict
from composer.core import Callback, Event, State
from composer.loggers import Logger
class EventCounterCallback(Callback):
def __init__(self) -> None:
self.event_to_num_calls: Dict[Event, int] ... | composer-dev | tests/common/events.py |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
from typing import Sequence
import pytest
import torch
from PIL import Image
from torch.utils.data import DataLoader, Dataset, IterableDataset
from torchvision.datasets import VisionDataset
from composer.utils import dist
from tests.comm... | composer-dev | tests/common/datasets.py |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
import types
from typing import List, Type
from tests.common.compare import deep_compare
from tests.common.datasets import (InfiniteClassificationDataset, RandomClassificationDataset, RandomImageDataset,
... | composer-dev | tests/common/__init__.py |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
"""Pytest marker helpers."""
from typing import Callable
import pytest
from composer.core import Precision
def device(*args, precision=False):
"""Decorator for device and optionally precision.
Input choices are ('cpu', 'gpu'... | composer-dev | tests/common/markers.py |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
import datetime
from typing import Any, Dict, List, Tuple, Union
import numpy as np
import torch
import torchmetrics
from composer import Time
from composer.core.time import TimeUnit
def deep_compare(item1: Any, item2: Any, atol: floa... | composer-dev | tests/common/compare.py |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
from typing import Any, Dict
from composer.core import State
from composer.utils import is_model_deepspeed
from tests.common.compare import deep_compare
def _del_wct_timestamp_fields(timestamp_state_dict: Dict[str, Any]):
del times... | composer-dev | tests/common/state.py |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
import logging
import os
import pathlib
import pytest
import tqdm.std
import composer
from composer.devices import DeviceCPU, DeviceGPU
from composer.utils import dist, reproducibility
@pytest.fixture(autouse=True)
def disable_tokeniz... | composer-dev | tests/fixtures/autouse_fixtures.py |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
| composer-dev | tests/fixtures/__init__.py |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
"""These fixtures are shared globally across the test suite."""
import copy
import time
import coolname
import pytest
import torch
from torch.utils.data import DataLoader
from composer.core import State
from composer.devices import Devi... | composer-dev | tests/fixtures/fixtures.py |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
| composer-dev | tests/profiler/__init__.py |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
from unittest.mock import MagicMock
import pytest
from composer.core import State
from composer.profiler import Profiler, ProfilerAction, SystemProfiler, TorchProfiler, cyclic_schedule
@pytest.mark.parametrize('repeat', [1, 0])
def te... | composer-dev | tests/profiler/test_profiler.py |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
import json
import os
import pathlib
import pytest
from torch.utils.data import DataLoader
from composer.profiler import Profiler
from composer.profiler.json_trace_handler import JSONTraceHandler
from composer.profiler.profiler_schedule... | composer-dev | tests/profiler/test_json_trace_handler.py |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
from typing import List
import numpy as np
import pytest
import torch
from torch.optim import Optimizer
from torch.optim.lr_scheduler import ExponentialLR
from torch.utils.data import DataLoader
from composer import Trainer
from compose... | composer-dev | tests/trainer/test_scale_schedule.py |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
import pytest
import torch
from packaging import version
from torch.utils.data import DataLoader
from composer.models import ComposerClassifier
from composer.trainer.trainer import Trainer
from composer.utils import dist
from tests.commo... | composer-dev | tests/trainer/test_fsdp.py |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
import os
import pathlib
import pytest
import torch
import torch.distributed
from packaging import version
from torch.utils.data import DataLoader
import composer.core.types as types
from composer import Callback, Event
from composer.co... | composer-dev | tests/trainer/test_ddp.py |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
import contextlib
from typing import Callable, Optional, Union
import pytest
from torch.utils.data import DataLoader
from composer.core import Algorithm, Event
from composer.core.evaluator import Evaluator, evaluate_periodically
from co... | composer-dev | tests/trainer/test_trainer_eval.py |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
import os
import pathlib
import textwrap
import numpy as np
import pytest
import torch
from packaging import version
from torch.utils.data import DataLoader
from composer.trainer.trainer import Trainer
from composer.utils import dist
fr... | composer-dev | tests/trainer/test_sharded_checkpoint.py |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
| composer-dev | tests/trainer/__init__.py |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
from typing import List, Optional
import pytest
import torch
import torch.nn as nn
from torch import Tensor
from torch.utils.data import DataLoader
from composer.core import State
from composer.devices import DeviceCPU, DeviceGPU
from c... | composer-dev | tests/trainer/test_ddp_sync_strategy.py |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
import os
import pathlib
import pytest
import torch
from torch.utils.data import DataLoader
from composer.core import Callback, Event, State
from composer.loggers import Logger
from composer.trainer.trainer import Trainer
from tests.com... | composer-dev | tests/trainer/test_predict.py |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
from typing import Any
import pytest
import torch
from torch.utils.data import DataLoader
from composer import DataSpec, Trainer
from tests.common import RandomClassificationDataset, SimpleModel
N = 128
class TestDefaultGetNumSamples... | composer-dev | tests/trainer/test_dataspec.py |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
import collections.abc
import contextlib
import copy
import datetime
import os
import pathlib
import time
from typing import Any, Dict, List, Optional, Union
import pytest
import torch
from packaging import version
from torch.nn.parallel... | composer-dev | tests/trainer/test_trainer.py |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
import contextlib
import copy
import os
import pathlib
import shutil
import tarfile
import tempfile
import time
from glob import glob
from typing import Any, Dict, List, Optional, Union
from unittest.mock import MagicMock
import pytest
i... | composer-dev | tests/trainer/test_checkpoint.py |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
# disabling general type issues because of monkeypatching
#yright: reportGeneralTypeIssues=none
"""Fixtures available in doctests.
The script is run before any doctests are executed,
so all imports and variables are available in any doc... | composer-dev | docs/source/doctest_fixtures.py |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
"""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 set... | composer-dev | docs/source/conf.py |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
"""Cleanup script that is executed at the end of each doctest."""
import os
import shutil
# variables are defined in doctest_fixtures.py
# pyright: reportUndefinedVariable=none
# tmpdir and cwd were defined in doctest_fixtures.py
os.c... | composer-dev | docs/source/doctest_cleanup.py |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
"""Helper function to generate the README table."""
import os
from pathlib import Path
from . import utils
HEADER = ['Task', 'Dataset', 'Name', 'Quality', 'Metric', 'TTT', 'Hparams']
ATTRIBUTES = ['_task', '_dataset', '_name', '_quality... | composer-dev | docs/source/tables/update_model_tables.py |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
"""Table helpers for composer docs."""
| composer-dev | docs/source/tables/__init__.py |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
"""Helper functions for auto-generating tables from metadata."""
import importlib
import os
import shutil
import tempfile
def list_dirs(folder):
"""Lists all dirs for a given folder.
Args:
folder (str): The folder to li... | composer-dev | docs/source/tables/utils.py |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
"""Script to generate graphs for repo README.md.
After generating images, upload to public hosting and update the README URLs.
"""
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
models = [{
'name': 'GPT... | composer-dev | docs/source/tables/generate_cost_graphs.py |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
"""Helper function to generate the method overview rst."""
import json
import os
from pathlib import Path
import utils
import composer
EXCLUDE_METHODS = ['no_op_model', 'utils']
folder_path = os.path.join(os.path.dirname(composer.__fi... | composer-dev | docs/source/tables/update_methods_overview.py |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
"""Helper function to generate the README table."""
import json
import os
from pathlib import Path
import utils
import composer
from composer import functional as CF
EXCLUDE_METHODS = ['no_op_model', 'utils']
HEADER = ['Name', 'Functi... | composer-dev | docs/source/tables/update_alg_tables.py |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
"""Profiling Example.
For a walk-through of this example, please see the `profiling guide</trainer/performance_tutorials/profiling>`_.
"""
# [imports-start]
import torch
from torch.utils.data import DataLoader
from torchvision import da... | composer-dev | examples/profiler_demo.py |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
"""Save and Load Checkpoints with `Weights and Biases <https://wandb.ai/>`."""
import shutil
import torch.utils.data
from torch.optim import SGD
from torchvision.datasets import MNIST
from torchvision.transforms import ToTensor
from co... | composer-dev | examples/checkpoint_with_wandb.py |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
# Written by Gihyun Park, Junyeol Lee, and Jiwon Seo
"""Example for training with an algorithm on a custom model."""
import torch
import torch.nn as nn
import torch.utils.data
from torchvision import datasets, transforms
import compose... | composer-dev | examples/gyro_dropout_example.py |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
"""Example for training with an algorithm on a custom model."""
import torch
import torch.utils.data
from torchvision import datasets, transforms
import composer.models
from composer import Trainer
# Example algorithms to train with
fro... | composer-dev | examples/custom_models.py |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
"""Example script to train a DeepLabv3+ model on ADE20k for semantic segmentation."""
import argparse
import logging
import os
import torch
import torchvision
from torch.utils.data import DataLoader
from torchmetrics import MetricCollec... | composer-dev | examples/segmentation/train_deeplabv3_ade20k.py |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
"""Example script to train a ResNet model on ImageNet."""
import argparse
import logging
import os
import torch
from torch.utils.data import DataLoader
from torchmetrics import MetricCollection
from torchmetrics.classification import Mu... | composer-dev | examples/imagenet/train_resnet_imagenet1k.py |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
"""Helper utilities to create FFCV datasets."""
import logging
import os
import sys
import textwrap
from argparse import ArgumentParser
from io import BytesIO
from typing import Tuple
import numpy as np
import torch
from PIL import Imag... | composer-dev | scripts/ffcv/create_ffcv_datasets.py |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
"""Reads in the Docker build matrix and generates a GHA job matrix."""
import json
from argparse import ArgumentParser, FileType, Namespace
from uuid import uuid4
import yaml
def _parse_args() -> Namespace:
"""Parse command-line a... | composer-dev | .github/bin/gen_docker_matrix.py |
# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0
"""Run pytest using MCP."""
import argparse
import time
from mcli.sdk import RunConfig, RunStatus, create_run, follow_run_logs, stop_run, wait_for_run_status
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser... | composer-dev | .github/mcp/mcp_pytest.py |
#!/usr/bin/python
import os
import sys
import argparse
import numpy as np
from skimage import color, io
import scipy.ndimage.interpolation as sni
import caffe
def parse_args(argv):
parser = argparse.ArgumentParser(description=__doc__,
formatter_class=argparse.ArgumentDefault... | colorization-master | demo/batch_process.py |
#!/usr/bin/env python
import datetime
import os
import sys
def extract_datetime_from_line(line, year):
# Expected format: I0210 13:39:22.381027 25210 solver.cpp:204] Iteration 100, lr = 0.00992565
line = line.strip().split()
month = int(line[0][1:3])
day = int(line[0][3:])
timestamp = line[1]
p... | colorization-master | caffe-colorization/tools/extra/extract_seconds.py |
#!/usr/bin/env python
"""
Parse training log
Evolved from parse_log.sh
"""
import os
import re
import extract_seconds
import argparse
import csv
from collections import OrderedDict
def parse_log(path_to_log):
"""Parse log file
Returns (train_dict_list, train_dict_names, test_dict_list, test_dict_names)
... | colorization-master | caffe-colorization/tools/extra/parse_log.py |
#!/usr/bin/env python
"""Net summarization tool.
This tool summarizes the structure of a net in a concise but comprehensive
tabular listing, taking a prototxt file as input.
Use this tool to check at a glance that the computation you've specified is the
computation you expect.
"""
from caffe.proto import caffe_pb2
... | colorization-master | caffe-colorization/tools/extra/summarize.py |
#!/usr/bin/env python
from mincepie import mapreducer, launcher
import gflags
import os
import cv2
from PIL import Image
# gflags
gflags.DEFINE_string('image_lib', 'opencv',
'OpenCV or PIL, case insensitive. The default value is the faster OpenCV.')
gflags.DEFINE_string('input_folder', '',
... | colorization-master | caffe-colorization/tools/extra/resize_and_crop_images.py |
#!/usr/bin/env python
"""
classify.py is an out-of-the-box image classifer callable from the command line.
By default it configures and runs the Caffe reference ImageNet model.
"""
import numpy as np
import os
import sys
import argparse
import glob
import time
import caffe
def main(argv):
pycaffe_dir = os.path.... | colorization-master | caffe-colorization/python/classify.py |
import numpy as np
import caffe
import sys
def f(a, b):
sm = 0
for i in range(a.shape[0]):
sm += ((a[0]-b[0]) / (a[0]+b[0])).mean()
return sm
A = np.random.random((16, 500, 500, 3))
B = np.random.random((16, 500, 500, 3))
a, b = caffe.Array(A.shape), caffe.Array(B.shape)
a[...] = A
b[...] = B
from time import ti... | colorization-master | caffe-colorization/python/test_array.py |
#!/usr/bin/env python
"""
Draw a graph of the net architecture.
"""
from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter
from google.protobuf import text_format
import caffe
import caffe.draw
from caffe.proto import caffe_pb2
def parse_args():
"""Parse input arguments
"""
parser = Argument... | colorization-master | caffe-colorization/python/draw_net.py |
#!/usr/bin/env python
"""
detector.py is an out-of-the-box windowed detector
callable from the command line.
By default it configures and runs the Caffe reference ImageNet model.
Note that this model was trained for image classification and not detection,
and finetuning for detection can be expected to improve results... | colorization-master | caffe-colorization/python/detect.py |
#!/usr/bin/env python
"""
Do windowed detection by classifying a number of images/crops at once,
optionally using the selective search window proposal method.
This implementation follows ideas in
Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik.
Rich feature hierarchies for accurate object detection... | colorization-master | caffe-colorization/python/caffe/detector.py |
from __future__ import division
import caffe
import numpy as np
def transplant(new_net, net):
for p in net.params:
if p not in new_net.params:
print 'dropping', p
continue
for i in range(len(net.params[p])):
if net.params[p][i].data.shape != new_net.params[p][i].... | colorization-master | caffe-colorization/python/caffe/surgery.py |
#!/usr/bin/env python
"""
Classifier is an image classifier specialization of Net.
"""
import numpy as np
import caffe
class Classifier(caffe.Net):
"""
Classifier extends Net for image class prediction
by scaling, center cropping, or oversampling.
Parameters
----------
image_dims : dimensio... | colorization-master | caffe-colorization/python/caffe/classifier.py |
from __future__ import print_function
import caffe
class bcolors:
HEADER = '\033[95m'
OKBLUE = '\033[94m'
OKGREEN = '\033[92m'
WARNING = '\033[93m'
FAIL = '\033[91m'
ENDC = '\033[0m'
BOLD = '\033[1m'
UNDERLINE = '\033[4m'
class Solver:
def __init__(self, prototxt, final_file=None, snap_file=None, solver='Ada... | colorization-master | caffe-colorization/python/caffe/solver.py |
from __future__ import print_function
from time import time
class bcolors:
HEADER = '\033[95m'
OKBLUE = '\033[94m'
OKGREEN = '\033[92m'
WARNING = '\033[93m'
FAIL = '\033[91m'
ENDC = '\033[0m'
BOLD = '\033[1m'
UNDERLINE = '\033[4m'
def time_net(net, NIT=100, top=5):
import numpy as np
import caffe
if hasatt... | colorization-master | caffe-colorization/python/caffe/timer.py |
"""Python net specification.
This module provides a way to write nets directly in Python, using a natural,
functional style. See examples/pycaffe/caffenet.py for an example.
Currently this works as a thin wrapper around the Python protobuf interface,
with layers and parameters automatically generated for the "layers"... | colorization-master | caffe-colorization/python/caffe/net_spec.py |
import numpy as np
import skimage.io
from scipy.ndimage import zoom
from skimage.transform import resize
try:
# Python3 will most likely not be able to load protobuf
from caffe.proto import caffe_pb2
except:
import sys
if sys.version_info >= (3, 0):
print("Failed to include caffe_pb2, things mi... | colorization-master | caffe-colorization/python/caffe/io.py |
"""
Wrap the internal caffe C++ module (_caffe.so) with a clean, Pythonic
interface.
"""
from collections import OrderedDict
try:
from itertools import izip_longest
except:
from itertools import zip_longest as izip_longest
import numpy as np
from ._caffe import Net, SGDSolver, NesterovSolver, AdaGradSolver, \... | colorization-master | caffe-colorization/python/caffe/pycaffe.py |
from ._caffe import *
from .pycaffe import Net, SGDSolver, NesterovSolver, AdaGradSolver, RMSPropSolver, AdaDeltaSolver, AdamSolver
from .proto.caffe_pb2 import TRAIN, TEST
from .classifier import Classifier
from .detector import Detector
from . import io
from .net_spec import layers, params, NetSpec, to_proto
| colorization-master | caffe-colorization/python/caffe/__init__.py |
"""
Determine spatial relationships between layers to relate their coordinates.
Coordinates are mapped from input-to-output (forward), but can
be mapped output-to-input (backward) by the inverse mapping too.
This helps crop and align feature maps among other uses.
"""
from __future__ import division
import numpy as np... | colorization-master | caffe-colorization/python/caffe/coord_map.py |
"""
Caffe network visualization: draw the NetParameter protobuffer.
.. note::
This requires pydot>=1.0.2, which is not included in requirements.txt since
it requires graphviz and other prerequisites outside the scope of the
Caffe.
"""
from caffe.proto import caffe_pb2
"""
pydot is not supported under p... | colorization-master | caffe-colorization/python/caffe/draw.py |
from __future__ import division
import caffe
import numpy as np
import os
import sys
from datetime import datetime
from PIL import Image
def fast_hist(a, b, n):
k = (a >= 0) & (a < n)
return np.bincount(n * a[k].astype(int) + b[k], minlength=n**2).reshape(n, n)
def compute_hist(net, save_dir, dataset, layer='... | colorization-master | caffe-colorization/python/caffe/score.py |
import unittest
import tempfile
import os
import numpy as np
import six
import caffe
def simple_net_file(num_output):
"""Make a simple net prototxt, based on test_net.cpp, returning the name
of the (temporary) file."""
f = tempfile.NamedTemporaryFile(mode='w+', delete=False)
f.write("""name: 'testne... | colorization-master | caffe-colorization/python/caffe/test/test_net.py |
import unittest
import tempfile
import caffe
from caffe import layers as L
from caffe import params as P
def lenet(batch_size):
n = caffe.NetSpec()
n.data, n.label = L.DummyData(shape=[dict(dim=[batch_size, 1, 28, 28]),
dict(dim=[batch_size, 1, 1, 1])],
... | colorization-master | caffe-colorization/python/caffe/test/test_net_spec.py |
import unittest
import caffe
class TestLayerTypeList(unittest.TestCase):
def test_standard_types(self):
#removing 'Data' from list
for type_name in ['Data', 'Convolution', 'InnerProduct']:
self.assertIn(type_name, caffe.layer_type_list(),
'%s not in layer_type_lis... | colorization-master | caffe-colorization/python/caffe/test/test_layer_type_list.py |
import unittest
import tempfile
import os
import six
import caffe
class SimpleLayer(caffe.Layer):
"""A layer that just multiplies by ten"""
def setup(self, bottom, top):
pass
def reshape(self, bottom, top):
top[0].reshape(*bottom[0].data.shape)
def forward(self, bottom, top):
... | colorization-master | caffe-colorization/python/caffe/test/test_python_layer.py |
import numpy as np
import unittest
import caffe
class TestBlobProtoToArray(unittest.TestCase):
def test_old_format(self):
data = np.zeros((10,10))
blob = caffe.proto.caffe_pb2.BlobProto()
blob.data.extend(list(data.flatten()))
shape = (1,1,10,10)
blob.num, blob.channels, b... | colorization-master | caffe-colorization/python/caffe/test/test_io.py |
import unittest
import tempfile
import os
import six
import caffe
class SimpleParamLayer(caffe.Layer):
"""A layer that just multiplies by the numeric value of its param string"""
def setup(self, bottom, top):
try:
self.value = float(self.param_str)
except ValueError:
... | colorization-master | caffe-colorization/python/caffe/test/test_python_layer_with_param_str.py |
import unittest
import tempfile
import os
import numpy as np
import six
import caffe
from test_net import simple_net_file
class TestSolver(unittest.TestCase):
def setUp(self):
self.num_output = 13
net_f = simple_net_file(self.num_output)
f = tempfile.NamedTemporaryFile(mode='w+', delete=F... | colorization-master | caffe-colorization/python/caffe/test/test_solver.py |
import unittest
import numpy as np
import random
import caffe
from caffe import layers as L
from caffe import params as P
from caffe.coord_map import coord_map_from_to, crop
def coord_net_spec(ks=3, stride=1, pad=0, pool=2, dstride=2, dpad=0):
"""
Define net spec for simple conv-pool-deconv pattern common t... | colorization-master | caffe-colorization/python/caffe/test/test_coord_map.py |
from __future__ import print_function
from caffe import layers as L, params as P, to_proto
from caffe.proto import caffe_pb2
# helper function for common structures
def conv_relu(bottom, ks, nout, stride=1, pad=0, group=1):
conv = L.Convolution(bottom, kernel_size=ks, stride=stride,
... | colorization-master | caffe-colorization/examples/pycaffe/caffenet.py |
import numpy as np
class SimpleTransformer:
"""
SimpleTransformer is a simple class for preprocessing and deprocessing
images for caffe.
"""
def __init__(self, mean=[128, 128, 128]):
self.mean = np.array(mean, dtype=np.float32)
self.scale = 1.0
def set_mean(self, mean):
... | colorization-master | caffe-colorization/examples/pycaffe/tools.py |
# imports
import json
import time
import pickle
import scipy.misc
import skimage.io
import caffe
import numpy as np
import os.path as osp
from xml.dom import minidom
from random import shuffle
from threading import Thread
from PIL import Image
from tools import SimpleTransformer
class PascalMultilabelDataLayerSync... | colorization-master | caffe-colorization/examples/pycaffe/layers/pascal_multilabel_datalayers.py |
import caffe
import numpy as np
class EuclideanLossLayer(caffe.Layer):
"""
Compute the Euclidean Loss in the same manner as the C++ EuclideanLossLayer
to demonstrate the class interface for developing layers in Python.
"""
def setup(self, bottom, top):
# check input pair
if len(bo... | colorization-master | caffe-colorization/examples/pycaffe/layers/pyloss.py |
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