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Unverified Commit d367a01a authored by Jirka Borovec's avatar Jirka Borovec Committed by GitHub
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Use f-strings almost everywhere, and other cleanups by applying pyupgrade (#4585) 


Co-authored-by: default avatarNicolas Hug <nicolashug@fb.com>
parent 50dfe207
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Showing with 106 additions and 117 deletions
references/similarity/loss.py
View file @ d367a01a
......@@ -8,7 +8,7 @@ import torch.nn as nn
class TripletMarginLoss(nn.Module):
def __init__(self, margin=1.0, p=2.0, mining="batch_all"):
super(TripletMarginLoss, self).__init__()
super().__init__()
self.margin = margin
self.p = p
self.mining = mining
......
references/similarity/model.py
View file @ d367a01a
......@@ -4,7 +4,7 @@ import torchvision.models as models
class EmbeddingNet(nn.Module):
def __init__(self, backbone=None):
super(EmbeddingNet, self).__init__()
super().__init__()
if backbone is None:
backbone = models.resnet50(num_classes=128)
......
references/similarity/train.py
View file @ d367a01a
......@@ -31,7 +31,7 @@ def train_epoch(model, optimizer, criterion, data_loader, device, epoch, print_f
i += 1
avg_loss = running_loss / print_freq
avg_trip = 100.0 * running_frac_pos_triplets / print_freq
print("[{:d}, {:d}] | loss: {:.4f} | % avg hard triplets: {:.2f}%".format(epoch, i, avg_loss, avg_trip))
print(f"[{epoch:d}, {i:d}] | loss: {avg_loss:.4f} | % avg hard triplets: {avg_trip:.2f}%")
running_loss = 0
running_frac_pos_triplets = 0
......@@ -77,7 +77,7 @@ def evaluate(model, loader, device):
threshold, accuracy = find_best_threshold(dists, targets, device)
print("accuracy: {:.3f}%, threshold: {:.2f}".format(accuracy, threshold))
print(f"accuracy: {accuracy:.3f}%, threshold: {threshold:.2f}")
def save(model, epoch, save_dir, file_name):
......
references/video_classification/train.py
View file @ d367a01a
......@@ -24,7 +24,7 @@ def train_one_epoch(model, criterion, optimizer, lr_scheduler, data_loader, devi
metric_logger.add_meter("lr", utils.SmoothedValue(window_size=1, fmt="{value}"))
metric_logger.add_meter("clips/s", utils.SmoothedValue(window_size=10, fmt="{value:.3f}"))
header = "Epoch: [{}]".format(epoch)
header = f"Epoch: [{epoch}]"
for video, target in metric_logger.log_every(data_loader, print_freq, header):
start_time = time.time()
video, target = video.to(device), target.to(device)
......@@ -122,12 +122,12 @@ def main(args):
transform_train = presets.VideoClassificationPresetTrain((128, 171), (112, 112))
if args.cache_dataset and os.path.exists(cache_path):
print("Loading dataset_train from {}".format(cache_path))
print(f"Loading dataset_train from {cache_path}")
dataset, _ = torch.load(cache_path)
dataset.transform = transform_train
else:
if args.distributed:
print("It is recommended to pre-compute the dataset cache " "on a single-gpu first, as it will be faster")
print("It is recommended to pre-compute the dataset cache on a single-gpu first, as it will be faster")
dataset = torchvision.datasets.Kinetics400(
traindir,
frames_per_clip=args.clip_len,
......@@ -140,7 +140,7 @@ def main(args):
),
)
if args.cache_dataset:
print("Saving dataset_train to {}".format(cache_path))
print(f"Saving dataset_train to {cache_path}")
utils.mkdir(os.path.dirname(cache_path))
utils.save_on_master((dataset, traindir), cache_path)
......@@ -152,12 +152,12 @@ def main(args):
transform_test = presets.VideoClassificationPresetEval((128, 171), (112, 112))
if args.cache_dataset and os.path.exists(cache_path):
print("Loading dataset_test from {}".format(cache_path))
print(f"Loading dataset_test from {cache_path}")
dataset_test, _ = torch.load(cache_path)
dataset_test.transform = transform_test
else:
if args.distributed:
print("It is recommended to pre-compute the dataset cache " "on a single-gpu first, as it will be faster")
print("It is recommended to pre-compute the dataset cache on a single-gpu first, as it will be faster")
dataset_test = torchvision.datasets.Kinetics400(
valdir,
frames_per_clip=args.clip_len,
......@@ -170,7 +170,7 @@ def main(args):
),
)
if args.cache_dataset:
print("Saving dataset_test to {}".format(cache_path))
print(f"Saving dataset_test to {cache_path}")
utils.mkdir(os.path.dirname(cache_path))
utils.save_on_master((dataset_test, valdir), cache_path)
......@@ -232,8 +232,7 @@ def main(args):
)
else:
raise RuntimeError(
"Invalid warmup lr method '{}'. Only linear and constant "
"are supported.".format(args.lr_warmup_method)
f"Invalid warmup lr method '{args.lr_warmup_method}'. Only linear and constant are supported."
)
lr_scheduler = torch.optim.lr_scheduler.SequentialLR(
......@@ -275,12 +274,12 @@ def main(args):
"epoch": epoch,
"args": args,
}
utils.save_on_master(checkpoint, os.path.join(args.output_dir, "model_{}.pth".format(epoch)))
utils.save_on_master(checkpoint, os.path.join(args.output_dir, f"model_{epoch}.pth"))
utils.save_on_master(checkpoint, os.path.join(args.output_dir, "checkpoint.pth"))
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
print("Training time {}".format(total_time_str))
print(f"Training time {total_time_str}")
def parse_args():
......
references/video_classification/utils.py
View file @ d367a01a
......@@ -8,7 +8,7 @@ import torch
import torch.distributed as dist
class SmoothedValue(object):
class SmoothedValue:
"""Track a series of values and provide access to smoothed values over a
window or the global series average.
"""
......@@ -67,7 +67,7 @@ class SmoothedValue(object):
)
class MetricLogger(object):
class MetricLogger:
def __init__(self, delimiter="\t"):
self.meters = defaultdict(SmoothedValue)
self.delimiter = delimiter
......@@ -84,12 +84,12 @@ class MetricLogger(object):
return self.meters[attr]
if attr in self.__dict__:
return self.__dict__[attr]
raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, attr))
raise AttributeError(f"'{type(self).__name__}' object has no attribute '{attr}'")
def __str__(self):
loss_str = []
for name, meter in self.meters.items():
loss_str.append("{}: {}".format(name, str(meter)))
loss_str.append(f"{name}: {str(meter)}")
return self.delimiter.join(loss_str)
def synchronize_between_processes(self):
......@@ -154,7 +154,7 @@ class MetricLogger(object):
end = time.time()
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
print("{} Total time: {}".format(header, total_time_str))
print(f"{header} Total time: {total_time_str}")
def accuracy(output, target, topk=(1,)):
......@@ -246,7 +246,7 @@ def init_distributed_mode(args):
torch.cuda.set_device(args.gpu)
args.dist_backend = "nccl"
print("| distributed init (rank {}): {}".format(args.rank, args.dist_url), flush=True)
print(f"| distributed init (rank {args.rank}): {args.dist_url}", flush=True)
torch.distributed.init_process_group(
backend=args.dist_backend, init_method=args.dist_url, world_size=args.world_size, rank=args.rank
)
......
scripts/release_notes/classify_prs.py
View file @ d367a01a
......@@ -14,7 +14,7 @@ df.tail()
# In[3]:
all_labels = set(lbl for labels in df["labels"] for lbl in labels)
all_labels = {lbl for labels in df["labels"] for lbl in labels}
all_labels
......
scripts/release_notes/retrieve_prs_data.py
View file @ d367a01a
......@@ -96,7 +96,7 @@ def run_query(query):
if request.status_code == 200:
return request.json()
else:
raise Exception("Query failed to run by returning code of {}. {}".format(request.status_code, query))
raise Exception(f"Query failed to run by returning code of {request.status_code}. {query}")
def gh_labels(pr_number):
......@@ -151,7 +151,7 @@ class CommitDataCache:
return self.data[commit]
def read_from_disk(self):
with open(self.path, "r") as f:
with open(self.path) as f:
data = json.load(f)
data = {commit: dict_to_features(dct) for commit, dct in data.items()}
return data
......
setup.py
View file @ d367a01a
import distutils.command.clean
import distutils.spawn
import glob
import io
import os
import shutil
import subprocess
......@@ -14,7 +13,7 @@ from torch.utils.cpp_extension import BuildExtension, CppExtension, CUDAExtensio
def read(*names, **kwargs):
with io.open(os.path.join(os.path.dirname(__file__), *names), encoding=kwargs.get("encoding", "utf8")) as fp:
with open(os.path.join(os.path.dirname(__file__), *names), encoding=kwargs.get("encoding", "utf8")) as fp:
return fp.read()
......@@ -28,7 +27,7 @@ def get_dist(pkgname):
cwd = os.path.dirname(os.path.abspath(__file__))
version_txt = os.path.join(cwd, "version.txt")
with open(version_txt, "r") as f:
with open(version_txt) as f:
version = f.readline().strip()
sha = "Unknown"
package_name = "torchvision"
......@@ -47,8 +46,8 @@ elif sha != "Unknown":
def write_version_file():
version_path = os.path.join(cwd, "torchvision", "version.py")
with open(version_path, "w") as f:
f.write("__version__ = '{}'\n".format(version))
f.write("git_version = {}\n".format(repr(sha)))
f.write(f"__version__ = '{version}'\n")
f.write(f"git_version = {repr(sha)}\n")
f.write("from torchvision.extension import _check_cuda_version\n")
f.write("if _check_cuda_version() > 0:\n")
f.write(" cuda = _check_cuda_version()\n")
......@@ -78,7 +77,7 @@ def find_library(name, vision_include):
conda_installed = False
lib_folder = None
include_folder = None
library_header = "{0}.h".format(name)
library_header = f"{name}.h"
# Lookup in TORCHVISION_INCLUDE or in the package file
package_path = [os.path.join(this_dir, "torchvision")]
......@@ -89,7 +88,7 @@ def find_library(name, vision_include):
break
if not library_found:
print("Running build on conda-build: {0}".format(is_conda_build))
print(f"Running build on conda-build: {is_conda_build}")
if is_conda_build:
# Add conda headers/libraries
if os.name == "nt":
......@@ -103,7 +102,7 @@ def find_library(name, vision_include):
# Check if using Anaconda to produce wheels
conda = distutils.spawn.find_executable("conda")
is_conda = conda is not None
print("Running build on conda: {0}".format(is_conda))
print(f"Running build on conda: {is_conda}")
if is_conda:
python_executable = sys.executable
py_folder = os.path.dirname(python_executable)
......@@ -119,8 +118,8 @@ def find_library(name, vision_include):
if not library_found:
if sys.platform == "linux":
library_found = os.path.exists("/usr/include/{0}".format(library_header))
library_found = library_found or os.path.exists("/usr/local/include/{0}".format(library_header))
library_found = os.path.exists(f"/usr/include/{library_header}")
library_found = library_found or os.path.exists(f"/usr/local/include/{library_header}")
return library_found, conda_installed, include_folder, lib_folder
......@@ -258,13 +257,13 @@ def get_extensions():
libpng = distutils.spawn.find_executable("libpng-config")
pngfix = distutils.spawn.find_executable("pngfix")
png_found = libpng is not None or pngfix is not None
print("PNG found: {0}".format(png_found))
print(f"PNG found: {png_found}")
if png_found:
if libpng is not None:
# Linux / Mac
png_version = subprocess.run([libpng, "--version"], stdout=subprocess.PIPE)
png_version = png_version.stdout.strip().decode("utf-8")
print("libpng version: {0}".format(png_version))
print(f"libpng version: {png_version}")
png_version = parse_version(png_version)
if png_version >= parse_version("1.6.0"):
print("Building torchvision with PNG image support")
......@@ -275,11 +274,11 @@ def get_extensions():
png_include = subprocess.run([libpng, "--I_opts"], stdout=subprocess.PIPE)
png_include = png_include.stdout.strip().decode("utf-8")
_, png_include = png_include.split("-I")
print("libpng include path: {0}".format(png_include))
print(f"libpng include path: {png_include}")
image_include += [png_include]
image_link_flags.append("png")
else:
print("libpng installed version is less than 1.6.0, " "disabling PNG support")
print("libpng installed version is less than 1.6.0, disabling PNG support")
png_found = False
else:
# Windows
......@@ -292,7 +291,7 @@ def get_extensions():
# Locating libjpeg
(jpeg_found, jpeg_conda, jpeg_include, jpeg_lib) = find_library("jpeglib", vision_include)
print("JPEG found: {0}".format(jpeg_found))
print(f"JPEG found: {jpeg_found}")
image_macros += [("PNG_FOUND", str(int(png_found)))]
image_macros += [("JPEG_FOUND", str(int(jpeg_found)))]
if jpeg_found:
......@@ -310,7 +309,7 @@ def get_extensions():
and os.path.exists(os.path.join(CUDA_HOME, "include", "nvjpeg.h"))
)
print("NVJPEG found: {0}".format(nvjpeg_found))
print(f"NVJPEG found: {nvjpeg_found}")
image_macros += [("NVJPEG_FOUND", str(int(nvjpeg_found)))]
if nvjpeg_found:
print("Building torchvision with NVJPEG image support")
......@@ -352,7 +351,7 @@ def get_extensions():
print("Error fetching ffmpeg version, ignoring ffmpeg.")
has_ffmpeg = False
print("FFmpeg found: {}".format(has_ffmpeg))
print(f"FFmpeg found: {has_ffmpeg}")
if has_ffmpeg:
ffmpeg_libraries = {"libavcodec", "libavformat", "libavutil", "libswresample", "libswscale"}
......@@ -386,8 +385,8 @@ def get_extensions():
has_ffmpeg = False
if has_ffmpeg:
print("ffmpeg include path: {}".format(ffmpeg_include_dir))
print("ffmpeg library_dir: {}".format(ffmpeg_library_dir))
print(f"ffmpeg include path: {ffmpeg_include_dir}")
print(f"ffmpeg library_dir: {ffmpeg_library_dir}")
# TorchVision base decoder + video reader
video_reader_src_dir = os.path.join(this_dir, "torchvision", "csrc", "io", "video_reader")
......@@ -432,7 +431,7 @@ def get_extensions():
class clean(distutils.command.clean.clean):
def run(self):
with open(".gitignore", "r") as f:
with open(".gitignore") as f:
ignores = f.read()
for wildcard in filter(None, ignores.split("\n")):
for filename in glob.glob(wildcard):
......@@ -446,7 +445,7 @@ class clean(distutils.command.clean.clean):
if __name__ == "__main__":
print("Building wheel {}-{}".format(package_name, version))
print(f"Building wheel {package_name}-{version}")
write_version_file()
......@@ -472,6 +471,7 @@ if __name__ == "__main__":
"scipy": ["scipy"],
},
ext_modules=get_extensions(),
python_requires=">=3.6",
cmdclass={
"build_ext": BuildExtension.with_options(no_python_abi_suffix=True),
"clean": clean,
......
test/common_utils.py
View file @ d367a01a
......@@ -37,7 +37,7 @@ def set_rng_seed(seed):
random.seed(seed)
class MapNestedTensorObjectImpl(object):
class MapNestedTensorObjectImpl:
def __init__(self, tensor_map_fn):
self.tensor_map_fn = tensor_map_fn
......@@ -152,7 +152,7 @@ def get_list_of_videos(tmpdir, num_videos=5, sizes=None, fps=None):
else:
f = fps[i]
data = torch.randint(0, 256, (size, 300, 400, 3), dtype=torch.uint8)
name = os.path.join(tmpdir, "{}.mp4".format(i))
name = os.path.join(tmpdir, f"{i}.mp4")
names.append(name)
io.write_video(name, data, fps=f)
......@@ -165,7 +165,7 @@ def _assert_equal_tensor_to_pil(tensor, pil_image, msg=None):
np_pil_image = np_pil_image[:, :, None]
pil_tensor = torch.as_tensor(np_pil_image.transpose((2, 0, 1)))
if msg is None:
msg = "tensor:\n{} \ndid not equal PIL tensor:\n{}".format(tensor, pil_tensor)
msg = f"tensor:\n{tensor} \ndid not equal PIL tensor:\n{pil_tensor}"
assert_equal(tensor.cpu(), pil_tensor, msg=msg)
......
test/datasets_utils.py
View file @ d367a01a
......@@ -133,7 +133,7 @@ def test_all_configs(test):
def maybe_remove_duplicates(configs):
try:
return [dict(config_) for config_ in set(tuple(sorted(config.items())) for config in configs)]
return [dict(config_) for config_ in {tuple(sorted(config.items())) for config in configs}]
except TypeError:
# A TypeError will be raised if a value of any config is not hashable, e.g. a list. In that case duplicate
# removal would be a lot more elaborate and we simply bail out.
......
test/preprocess-bench.py
View file @ d367a01a
......@@ -26,7 +26,7 @@ if __name__ == "__main__":
if args.accimage:
torchvision.set_image_backend("accimage")
print("Using {}".format(torchvision.get_image_backend()))
print(f"Using {torchvision.get_image_backend()}")
# Data loading code
transform = transforms.Compose(
......
test/test_backbone_utils.py
View file @ d367a01a
......@@ -144,7 +144,7 @@ class TestFxFeatureExtraction:
model, train_return_nodes=train_return_nodes, eval_return_nodes=eval_return_nodes
)
out = model(self.inp)
sum([o.mean() for o in out.values()]).backward()
sum(o.mean() for o in out.values()).backward()
def test_feature_extraction_methods_equivalence(self):
model = models.resnet18(**self.model_defaults).eval()
......@@ -176,7 +176,7 @@ class TestFxFeatureExtraction:
)
model = torch.jit.script(model)
fgn_out = model(self.inp)
sum([o.mean() for o in fgn_out.values()]).backward()
sum(o.mean() for o in fgn_out.values()).backward()
def test_train_eval(self):
class TestModel(torch.nn.Module):
......
test/test_datasets.py
View file @ d367a01a
......@@ -654,7 +654,7 @@ class VOCSegmentationTestCase(datasets_utils.ImageDatasetTestCase):
shutil.copytree(src, root / "Segmentation")
num_images = max(itertools.chain(*idcs.values())) + 1
num_images_per_image_set = dict([(image_set, len(idcs_)) for image_set, idcs_ in idcs.items()])
num_images_per_image_set = {image_set: len(idcs_) for image_set, idcs_ in idcs.items()}
return num_images, num_images_per_image_set
def _create_image_set_file(self, root, image_set, idcs):
......@@ -1174,7 +1174,7 @@ class SBDatasetTestCase(datasets_utils.ImageDatasetTestCase):
self._create_split_file(root, split, idcs)
num_images = max(itertools.chain(*splits.values())) + 1
num_images_per_split = dict([(split, len(idcs)) for split, idcs in splits.items()])
num_images_per_split = {split: len(idcs) for split, idcs in splits.items()}
return num_images, num_images_per_split
def _create_split_file(self, root, name, idcs):
......
test/test_datasets_utils.py
View file @ d367a01a
......@@ -129,7 +129,7 @@ class TestDatasetsUtils:
assert os.path.exists(file)
with open(file, "r") as fh:
with open(file) as fh:
assert fh.read() == content
def test_decompress_no_compression(self):
......@@ -179,7 +179,7 @@ class TestDatasetsUtils:
assert os.path.exists(file)
with open(file, "r") as fh:
with open(file) as fh:
assert fh.read() == content
@pytest.mark.parametrize(
......@@ -205,7 +205,7 @@ class TestDatasetsUtils:
assert os.path.exists(file)
with open(file, "r") as fh:
with open(file) as fh:
assert fh.read() == content
def test_verify_str_arg(self):
......
test/test_functional_tensor.py
View file @ d367a01a
......@@ -111,9 +111,9 @@ class TestRotate:
if out_tensor.dtype != torch.uint8:
out_tensor = out_tensor.to(torch.uint8)
assert out_tensor.shape == out_pil_tensor.shape, (
f"{(height, width, NEAREST, dt, angle, expand, center)}: " f"{out_tensor.shape} vs {out_pil_tensor.shape}"
)
assert (
out_tensor.shape == out_pil_tensor.shape
), f"{(height, width, NEAREST, dt, angle, expand, center)}: {out_tensor.shape} vs {out_pil_tensor.shape}"
num_diff_pixels = (out_tensor != out_pil_tensor).sum().item() / 3.0
ratio_diff_pixels = num_diff_pixels / out_tensor.shape[-1] / out_tensor.shape[-2]
......@@ -177,11 +177,11 @@ class TestAffine:
# 1) identity map
out_tensor = F.affine(tensor, angle=0, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], interpolation=NEAREST)
assert_equal(tensor, out_tensor, msg="{} vs {}".format(out_tensor[0, :5, :5], tensor[0, :5, :5]))
assert_equal(tensor, out_tensor, msg=f"{out_tensor[0, :5, :5]} vs {tensor[0, :5, :5]}")
out_tensor = self.scripted_affine(
tensor, angle=0, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], interpolation=NEAREST
)
assert_equal(tensor, out_tensor, msg="{} vs {}".format(out_tensor[0, :5, :5], tensor[0, :5, :5]))
assert_equal(tensor, out_tensor, msg=f"{out_tensor[0, :5, :5]} vs {tensor[0, :5, :5]}")
@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize("height, width", [(26, 26)])
......@@ -225,9 +225,7 @@ class TestAffine:
num_diff_pixels = (out_tensor != out_pil_tensor).sum().item() / 3.0
ratio_diff_pixels = num_diff_pixels / out_tensor.shape[-1] / out_tensor.shape[-2]
# Tolerance : less than 6% of different pixels
assert ratio_diff_pixels < 0.06, "{}\n{} vs \n{}".format(
ratio_diff_pixels, out_tensor[0, :7, :7], out_pil_tensor[0, :7, :7]
)
assert ratio_diff_pixels < 0.06
@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize("height, width", [(32, 26)])
......@@ -258,9 +256,7 @@ class TestAffine:
num_diff_pixels = (out_tensor != out_pil_tensor).sum().item() / 3.0
ratio_diff_pixels = num_diff_pixels / out_tensor.shape[-1] / out_tensor.shape[-2]
# Tolerance : less than 3% of different pixels
assert ratio_diff_pixels < 0.03, "{}: {}\n{} vs \n{}".format(
angle, ratio_diff_pixels, out_tensor[0, :7, :7], out_pil_tensor[0, :7, :7]
)
assert ratio_diff_pixels < 0.03
@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize("height, width", [(26, 26), (32, 26)])
......@@ -346,9 +342,7 @@ class TestAffine:
ratio_diff_pixels = num_diff_pixels / out_tensor.shape[-1] / out_tensor.shape[-2]
# Tolerance : less than 5% (cpu), 6% (cuda) of different pixels
tol = 0.06 if device == "cuda" else 0.05
assert ratio_diff_pixels < tol, "{}: {}\n{} vs \n{}".format(
(NEAREST, a, t, s, sh, f), ratio_diff_pixels, out_tensor[0, :7, :7], out_pil_tensor[0, :7, :7]
)
assert ratio_diff_pixels < tol
@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize("dt", ALL_DTYPES)
......@@ -936,7 +930,7 @@ def test_pad(device, dt, pad, config):
if pad_tensor_8b.dtype != torch.uint8:
pad_tensor_8b = pad_tensor_8b.to(torch.uint8)
_assert_equal_tensor_to_pil(pad_tensor_8b, pad_pil_img, msg="{}, {}".format(pad, config))
_assert_equal_tensor_to_pil(pad_tensor_8b, pad_pil_img, msg=f"{pad}, {config}")
if isinstance(pad, int):
script_pad = [
......@@ -945,7 +939,7 @@ def test_pad(device, dt, pad, config):
else:
script_pad = pad
pad_tensor_script = script_fn(tensor, script_pad, **config)
assert_equal(pad_tensor, pad_tensor_script, msg="{}, {}".format(pad, config))
assert_equal(pad_tensor, pad_tensor_script, msg=f"{pad}, {config}")
_test_fn_on_batch(batch_tensors, F.pad, padding=script_pad, **config)
......@@ -958,7 +952,7 @@ def test_resized_crop(device, mode):
tensor, _ = _create_data(26, 36, device=device)
out_tensor = F.resized_crop(tensor, top=0, left=0, height=26, width=36, size=[26, 36], interpolation=mode)
assert_equal(tensor, out_tensor, msg="{} vs {}".format(out_tensor[0, :5, :5], tensor[0, :5, :5]))
assert_equal(tensor, out_tensor, msg=f"{out_tensor[0, :5, :5]} vs {tensor[0, :5, :5]}")
# 2) resize by half and crop a TL corner
tensor, _ = _create_data(26, 36, device=device)
......@@ -967,7 +961,7 @@ def test_resized_crop(device, mode):
assert_equal(
expected_out_tensor,
out_tensor,
msg="{} vs {}".format(expected_out_tensor[0, :10, :10], out_tensor[0, :10, :10]),
msg=f"{expected_out_tensor[0, :10, :10]} vs {out_tensor[0, :10, :10]}",
)
batch_tensors = _create_data_batch(26, 36, num_samples=4, device=device)
......@@ -1126,7 +1120,7 @@ def test_gaussian_blur(device, image_size, dt, ksize, sigma, fn):
_ksize = (ksize, ksize) if isinstance(ksize, int) else ksize
_sigma = sigma[0] if sigma is not None else None
shape = tensor.shape
gt_key = "{}_{}_{}__{}_{}_{}".format(shape[-2], shape[-1], shape[-3], _ksize[0], _ksize[1], _sigma)
gt_key = f"{shape[-2]}_{shape[-1]}_{shape[-3]}__{_ksize[0]}_{_ksize[1]}_{_sigma}"
if gt_key not in true_cv2_results:
return
......@@ -1135,7 +1129,7 @@ def test_gaussian_blur(device, image_size, dt, ksize, sigma, fn):
)
out = fn(tensor, kernel_size=ksize, sigma=sigma)
torch.testing.assert_close(out, true_out, rtol=0.0, atol=1.0, msg="{}, {}".format(ksize, sigma))
torch.testing.assert_close(out, true_out, rtol=0.0, atol=1.0, msg=f"{ksize}, {sigma}")
@pytest.mark.parametrize("device", cpu_and_gpu())
......
test/test_image.py
View file @ d367a01a
......@@ -233,7 +233,7 @@ def test_write_png(img_path, tmpdir):
img_pil = img_pil.permute(2, 0, 1)
filename, _ = os.path.splitext(os.path.basename(img_path))
torch_png = os.path.join(tmpdir, "{0}_torch.png".format(filename))
torch_png = os.path.join(tmpdir, f"{filename}_torch.png")
write_png(img_pil, torch_png, compression_level=6)
saved_image = torch.from_numpy(np.array(Image.open(torch_png)))
saved_image = saved_image.permute(2, 0, 1)
......@@ -393,10 +393,10 @@ def test_encode_jpeg_errors():
with pytest.raises(RuntimeError, match="Input tensor dtype should be uint8"):
encode_jpeg(torch.empty((3, 100, 100), dtype=torch.float32))
with pytest.raises(ValueError, match="Image quality should be a positive number " "between 1 and 100"):
with pytest.raises(ValueError, match="Image quality should be a positive number between 1 and 100"):
encode_jpeg(torch.empty((3, 100, 100), dtype=torch.uint8), quality=-1)
with pytest.raises(ValueError, match="Image quality should be a positive number " "between 1 and 100"):
with pytest.raises(ValueError, match="Image quality should be a positive number between 1 and 100"):
encode_jpeg(torch.empty((3, 100, 100), dtype=torch.uint8), quality=101)
with pytest.raises(RuntimeError, match="The number of channels should be 1 or 3, got: 5"):
......@@ -440,7 +440,7 @@ def test_encode_jpeg_reference(img_path):
dirname = os.path.dirname(img_path)
filename, _ = os.path.splitext(os.path.basename(img_path))
write_folder = os.path.join(dirname, "jpeg_write")
expected_file = os.path.join(write_folder, "{0}_pil.jpg".format(filename))
expected_file = os.path.join(write_folder, f"{filename}_pil.jpg")
img = decode_jpeg(read_file(img_path))
with open(expected_file, "rb") as f:
......@@ -464,8 +464,8 @@ def test_write_jpeg_reference(img_path, tmpdir):
basedir = os.path.dirname(img_path)
filename, _ = os.path.splitext(os.path.basename(img_path))
torch_jpeg = os.path.join(tmpdir, "{0}_torch.jpg".format(filename))
pil_jpeg = os.path.join(basedir, "jpeg_write", "{0}_pil.jpg".format(filename))
torch_jpeg = os.path.join(tmpdir, f"{filename}_torch.jpg")
pil_jpeg = os.path.join(basedir, "jpeg_write", f"{filename}_pil.jpg")
write_jpeg(img, torch_jpeg, quality=75)
......
test/test_models.py
View file @ d367a01a
......@@ -54,12 +54,12 @@ def _assert_expected(output, name, prec):
if ACCEPT:
filename = {os.path.basename(expected_file)}
print("Accepting updated output for {}:\n\n{}".format(filename, output))
print(f"Accepting updated output for {filename}:\n\n{output}")
torch.save(output, expected_file)
MAX_PICKLE_SIZE = 50 * 1000 # 50 KB
binary_size = os.path.getsize(expected_file)
if binary_size > MAX_PICKLE_SIZE:
raise RuntimeError("The output for {}, is larger than 50kb".format(filename))
raise RuntimeError(f"The output for {filename}, is larger than 50kb")
else:
expected = torch.load(expected_file)
rtol = atol = prec
......@@ -99,12 +99,12 @@ def _check_jit_scriptable(nn_module, args, unwrapper=None, skip=False):
if not TEST_WITH_SLOW or skip:
# TorchScript is not enabled, skip these tests
msg = (
"The check_jit_scriptable test for {} was skipped. "
f"The check_jit_scriptable test for {nn_module.__class__.__name__} was skipped. "
"This test checks if the module's results in TorchScript "
"match eager and that it can be exported. To run these "
"tests make sure you set the environment variable "
"PYTORCH_TEST_WITH_SLOW=1 and that the test is not "
"manually skipped.".format(nn_module.__class__.__name__)
"manually skipped."
)
warnings.warn(msg, RuntimeWarning)
return None
......@@ -300,11 +300,11 @@ def test_memory_efficient_densenet(model_fn):
model1 = model_fn(num_classes=50, memory_efficient=True)
params = model1.state_dict()
num_params = sum([x.numel() for x in model1.parameters()])
num_params = sum(x.numel() for x in model1.parameters())
model1.eval()
out1 = model1(x)
out1.sum().backward()
num_grad = sum([x.grad.numel() for x in model1.parameters() if x.grad is not None])
num_grad = sum(x.grad.numel() for x in model1.parameters() if x.grad is not None)
model2 = model_fn(num_classes=50, memory_efficient=False)
model2.load_state_dict(params)
......@@ -451,8 +451,8 @@ def test_generalizedrcnn_transform_repr():
# Check integrity of object __repr__ attribute
expected_string = "GeneralizedRCNNTransform("
_indent = "\n "
expected_string += "{0}Normalize(mean={1}, std={2})".format(_indent, image_mean, image_std)
expected_string += "{0}Resize(min_size=({1},), max_size={2}, ".format(_indent, min_size, max_size)
expected_string += f"{_indent}Normalize(mean={image_mean}, std={image_std})"
expected_string += f"{_indent}Resize(min_size=({min_size},), max_size={max_size}, "
expected_string += "mode='bilinear')\n)"
assert t.__repr__() == expected_string
......@@ -541,10 +541,10 @@ def test_segmentation_model(model_fn, dev):
if not full_validation:
msg = (
"The output of {} could only be partially validated. "
f"The output of {test_segmentation_model.__name__} could only be partially validated. "
"This is likely due to unit-test flakiness, but you may "
"want to do additional manual checks if you made "
"significant changes to the codebase.".format(test_segmentation_model.__name__)
"significant changes to the codebase."
)
warnings.warn(msg, RuntimeWarning)
pytest.skip(msg)
......@@ -638,10 +638,10 @@ def test_detection_model(model_fn, dev):
if not full_validation:
msg = (
"The output of {} could only be partially validated. "
f"The output of {test_detection_model.__name__} could only be partially validated. "
"This is likely due to unit-test flakiness, but you may "
"want to do additional manual checks if you made "
"significant changes to the codebase.".format(test_detection_model.__name__)
"significant changes to the codebase."
)
warnings.warn(msg, RuntimeWarning)
pytest.skip(msg)
......
test/test_onnx.py
View file @ d367a01a
......@@ -78,7 +78,7 @@ class TestONNXExporter:
ort_session = onnxruntime.InferenceSession(onnx_io.getvalue())
# compute onnxruntime output prediction
ort_inputs = dict((ort_session.get_inputs()[i].name, inpt) for i, inpt in enumerate(inputs))
ort_inputs = {ort_session.get_inputs()[i].name: inpt for i, inpt in enumerate(inputs)}
ort_outs = ort_session.run(None, ort_inputs)
for i in range(0, len(outputs)):
......@@ -185,7 +185,7 @@ class TestONNXExporter:
def test_resize_images(self):
class TransformModule(torch.nn.Module):
def __init__(self_module):
super(TransformModule, self_module).__init__()
super().__init__()
self_module.transform = self._init_test_generalized_rcnn_transform()
def forward(self_module, images):
......@@ -200,7 +200,7 @@ class TestONNXExporter:
def test_transform_images(self):
class TransformModule(torch.nn.Module):
def __init__(self_module):
super(TransformModule, self_module).__init__()
super().__init__()
self_module.transform = self._init_test_generalized_rcnn_transform()
def forward(self_module, images):
......@@ -301,7 +301,7 @@ class TestONNXExporter:
class RPNModule(torch.nn.Module):
def __init__(self_module):
super(RPNModule, self_module).__init__()
super().__init__()
self_module.rpn = self._init_test_rpn()
def forward(self_module, images, features):
......@@ -335,7 +335,7 @@ class TestONNXExporter:
def test_multi_scale_roi_align(self):
class TransformModule(torch.nn.Module):
def __init__(self):
super(TransformModule, self).__init__()
super().__init__()
self.model = ops.MultiScaleRoIAlign(["feat1", "feat2"], 3, 2)
self.image_sizes = [(512, 512)]
......@@ -371,7 +371,7 @@ class TestONNXExporter:
def test_roi_heads(self):
class RoiHeadsModule(torch.nn.Module):
def __init__(self_module):
super(RoiHeadsModule, self_module).__init__()
super().__init__()
self_module.transform = self._init_test_generalized_rcnn_transform()
self_module.rpn = self._init_test_rpn()
self_module.roi_heads = self._init_test_roi_heads_faster_rcnn()
......
test/test_ops.py
View file @ d367a01a
......@@ -711,7 +711,7 @@ class TestDeformConv:
expected = self.expected_fn(x, weight, offset, mask, bias, stride=stride, padding=padding, dilation=dilation)
torch.testing.assert_close(
res.to(expected), expected, rtol=tol, atol=tol, msg="\nres:\n{}\nexpected:\n{}".format(res, expected)
res.to(expected), expected, rtol=tol, atol=tol, msg=f"\nres:\n{res}\nexpected:\n{expected}"
)
# no modulation test
......@@ -719,7 +719,7 @@ class TestDeformConv:
expected = self.expected_fn(x, weight, offset, None, bias, stride=stride, padding=padding, dilation=dilation)
torch.testing.assert_close(
res.to(expected), expected, rtol=tol, atol=tol, msg="\nres:\n{}\nexpected:\n{}".format(res, expected)
res.to(expected), expected, rtol=tol, atol=tol, msg=f"\nres:\n{res}\nexpected:\n{expected}"
)
def test_wrong_sizes(self):
......
test/test_transforms.py
View file @ d367a01a
......@@ -115,7 +115,7 @@ class TestConvertImageDtype:
output_image,
rtol=0.0,
atol=1e-6,
msg="{} vs {}".format(output_image_script, output_image),
msg=f"{output_image_script} vs {output_image}",
)
actual_min, actual_max = output_image.tolist()
......@@ -369,7 +369,7 @@ def test_resize(height, width, osize, max_size):
t = transforms.Resize(osize, max_size=max_size)
result = t(img)
msg = "{}, {} - {} - {}".format(height, width, osize, max_size)
msg = f"{height}, {width} - {osize} - {max_size}"
osize = osize[0] if isinstance(osize, (list, tuple)) else osize
# If size is an int, smaller edge of the image will be matched to this number.
# i.e, if height > width, then image will be rescaled to (size * height / width, size).
......@@ -470,11 +470,11 @@ class TestPad:
width = random.randint(10, 32) * 2
img = transforms.ToPILImage()(torch.ones(3, height, width))
padding = tuple([random.randint(1, 20) for _ in range(2)])
padding = tuple(random.randint(1, 20) for _ in range(2))
output = transforms.Pad(padding)(img)
assert output.size == (width + padding[0] * 2, height + padding[1] * 2)
padding = tuple([random.randint(1, 20) for _ in range(4)])
padding = tuple(random.randint(1, 20) for _ in range(4))
output = transforms.Pad(padding)(img)
assert output.size[0] == width + padding[0] + padding[2]
assert output.size[1] == height + padding[1] + padding[3]
......@@ -1703,7 +1703,7 @@ def test_center_crop_2(odd_image_size, delta, delta_width, delta_height):
assert_equal(
output_tensor,
output_pil,
msg="image_size: {} crop_size: {}".format(input_image_size, crop_size),
msg=f"image_size: {input_image_size} crop_size: {crop_size}",
)
# Check if content in center of both image and cropped output is same.
......@@ -2029,9 +2029,9 @@ class TestAffine:
np_result = np.array(result)
n_diff_pixels = np.sum(np_result != true_result) / 3
# Accept 3 wrong pixels
error_msg = "angle={}, translate={}, scale={}, shear={}\n".format(
angle, translate, scale, shear
) + "n diff pixels={}\n".format(n_diff_pixels)
error_msg = (
f"angle={angle}, translate={translate}, scale={scale}, shear={shear}\nn diff pixels={n_diff_pixels}\n"
)
assert n_diff_pixels < 3, error_msg
def test_transformation_discrete(self, pil_image, input_img):
......@@ -2121,12 +2121,8 @@ def test_random_affine():
for _ in range(100):
angle, translations, scale, shear = t.get_params(t.degrees, t.translate, t.scale, t.shear, img_size=img.size)
assert -10 < angle < 10
assert -img.size[0] * 0.5 <= translations[0] <= img.size[0] * 0.5, "{} vs {}".format(
translations[0], img.size[0] * 0.5
)
assert -img.size[1] * 0.5 <= translations[1] <= img.size[1] * 0.5, "{} vs {}".format(
translations[1], img.size[1] * 0.5
)
assert -img.size[0] * 0.5 <= translations[0] <= img.size[0] * 0.5
assert -img.size[1] * 0.5 <= translations[1] <= img.size[1] * 0.5
assert 0.7 < scale < 1.3
assert -10 < shear[0] < 10
assert -20 < shear[1] < 40
......
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