microsoft
/
seismic-deeplearning
зеркало из https://github.com/microsoft/seismic-deeplearning.git
1
0
Форкнуть 0
Код Задачи Пакеты Проекты Релизы Вики Активность

added ability to score test images to disk for dutch F3 (#232) 

* added ability to score test images to disk for dutch F3

* gitpython package fix from staging
This commit is contained in:
maxkazmsft 2020-03-19 13:49:09 -04:00 коммит произвёл GitHub
Родитель b75e6476c9
Коммит 4484155801
Не найден ключ, соответствующий данной подписи
Идентификатор ключа GPG: 4AEE18F83AFDEB23
4 изменённых файлов: 124 добавлений и 31 удалений
Показать статистику Скачать Patch файл Скачать Diff файл Показать все файлы Свернуть все файлы

15
cv_lib/cv_lib/event_handlers/tensorboard_handlers.py
Просмотреть файл

@ -6,10 +6,7 @@ import torchvision
import logging
import logging.config
try:
from tensorboardX import SummaryWriter
except ImportError:
raise RuntimeError("No tensorboardX package is found. Please install with the command: \npip install tensorboardX")
def create_summary_writer(log_dir):
@ -52,16 +49,22 @@ _DEFAULT_METRICS = {"accuracy": "Avg accuracy :", "nll": "Avg loss :"}
def log_metrics(summary_writer, train_engine, log_interval, engine, metrics_dict=_DEFAULT_METRICS):
metrics = engine.state.metrics
for m in metrics_dict:
summary_writer.add_scalar(metrics_dict[m], metrics[m], getattr(train_engine.state, log_interval))
summary_writer.add_scalar(
metrics_dict[m], metrics[m], getattr(train_engine.state, log_interval)
)
def create_image_writer(summary_writer, label, output_variable, normalize=False, transform_func=lambda x: x):
def create_image_writer(
summary_writer, label, output_variable, normalize=False, transform_func=lambda x: x
):
logger = logging.getLogger(__name__)
def write_to(engine):
try:
data_tensor = transform_func(engine.state.output[output_variable])
image_grid = torchvision.utils.make_grid(data_tensor, normalize=normalize, scale_each=True)
image_grid = torchvision.utils.make_grid(
data_tensor, normalize=normalize, scale_each=True
)
summary_writer.add_image(label, image_grid, engine.state.epoch)
except KeyError:
logger.warning("Predictions and or ground truth labels not available to report")

2
environment/anaconda/local/environment.yml
Просмотреть файл

@ -26,7 +26,7 @@ dependencies:
- toolz==0.10.0
- tabulate==0.8.2
- Jinja2==2.10.3
- gitpython==3.0.5
- gitpython==3.0.6
- tensorboard==2.0.1
- tensorboardx==1.9
- invoke==1.3.0

100
experiments/interpretation/dutchf3_patch/local/test.py
Просмотреть файл

@ -10,8 +10,6 @@
"""
Modified version of the Alaudah testing script
Runs only on single GPU
Estimated time to run on single V100: 5 hours
"""
import itertools
@ -25,9 +23,16 @@ import fire
import numpy as np
import torch
import torch.nn.functional as F
from PIL import Image
from albumentations import Compose, Normalize, PadIfNeeded, Resize
from cv_lib.utils import load_log_configuration
from cv_lib.segmentation import models
from cv_lib.segmentation.dutchf3.utils import (
current_datetime,
generate_path,
git_branch,
git_hash,
)
from deepseismic_interpretation.dutchf3.data import (
add_patch_depth_channels,
get_seismic_labels,
@ -39,6 +44,8 @@ from toolz import compose, curry, itertoolz, pipe
from torch.utils import data
from toolz import take
from matplotlib import cm
_CLASS_NAMES = [
"upper_ns",
@ -57,9 +64,9 @@ class runningScore(object):
def _fast_hist(self, label_true, label_pred, n_class):
mask = (label_true >= 0) & (label_true < n_class)
hist = np.bincount(n_class * label_true[mask].astype(int) + label_pred[mask], minlength=n_class ** 2,).reshape(
n_class, n_class
)
hist = np.bincount(
n_class * label_true[mask].astype(int) + label_pred[mask], minlength=n_class ** 2,
).reshape(n_class, n_class)
return hist
def update(self, label_trues, label_preds):
@ -99,6 +106,21 @@ class runningScore(object):
self.confusion_matrix = np.zeros((self.n_classes, self.n_classes))
def normalize(array):
"""
Normalizes a segmentation mask array to be in [0,1] range
"""
min = array.min()
return (array - min) / (array.max() - min)
def mask_to_disk(mask, fname):
"""
write segmentation mask to disk using a particular colormap
"""
Image.fromarray(cm.gist_earth(normalize(mask), bytes=True)).save(fname)
def _transform_CHW_to_HWC(numpy_array):
return np.moveaxis(numpy_array, 0, -1)
@ -180,7 +202,9 @@ def _compose_processing_pipeline(depth, aug=None):
def _generate_batches(h, w, ps, patch_size, stride, batch_size=64):
hdc_wdx_generator = itertools.product(range(0, h - patch_size + ps, stride), range(0, w - patch_size + ps, stride),)
hdc_wdx_generator = itertools.product(
range(0, h - patch_size + ps, stride), range(0, w - patch_size + ps, stride),
)
for batch_indexes in itertoolz.partition_all(batch_size, hdc_wdx_generator):
yield batch_indexes
@ -191,7 +215,9 @@ def _output_processing_pipeline(config, output):
_, _, h, w = output.shape
if config.TEST.POST_PROCESSING.SIZE != h or config.TEST.POST_PROCESSING.SIZE != w:
output = F.interpolate(
output, size=(config.TEST.POST_PROCESSING.SIZE, config.TEST.POST_PROCESSING.SIZE,), mode="bilinear",
output,
size=(config.TEST.POST_PROCESSING.SIZE, config.TEST.POST_PROCESSING.SIZE,),
mode="bilinear",
)
if config.TEST.POST_PROCESSING.CROP_PIXELS > 0:
@ -206,7 +232,15 @@ def _output_processing_pipeline(config, output):
def _patch_label_2d(
model, img, pre_processing, output_processing, patch_size, stride, batch_size, device, num_classes,
model,
img,
pre_processing,
output_processing,
patch_size,
stride,
batch_size,
device,
num_classes,
):
"""Processes a whole section
"""
@ -221,14 +255,19 @@ def _patch_label_2d(
# generate output:
for batch_indexes in _generate_batches(h, w, ps, patch_size, stride, batch_size=batch_size):
batch = torch.stack(
[pipe(img_p, _extract_patch(hdx, wdx, ps, patch_size), pre_processing,) for hdx, wdx in batch_indexes],
[
pipe(img_p, _extract_patch(hdx, wdx, ps, patch_size), pre_processing,)
for hdx, wdx in batch_indexes
],
dim=0,
)
model_output = model(batch.to(device))
for (hdx, wdx), output in zip(batch_indexes, model_output.detach().cpu()):
output = output_processing(output)
output_p[:, :, hdx + ps : hdx + ps + patch_size, wdx + ps : wdx + ps + patch_size,] += output
output_p[
:, :, hdx + ps : hdx + ps + patch_size, wdx + ps : wdx + ps + patch_size,
] += output
# crop the output_p in the middle
output = output_p[:, :, ps:-ps, ps:-ps]
@ -253,12 +292,22 @@ def to_image(label_mask, n_classes=6):
def _evaluate_split(
split, section_aug, model, pre_processing, output_processing, device, running_metrics_overall, config, debug=False
split,
section_aug,
model,
pre_processing,
output_processing,
device,
running_metrics_overall,
config,
debug=False,
):
logger = logging.getLogger(__name__)
TestSectionLoader = get_test_loader(config)
test_set = TestSectionLoader(config.DATASET.ROOT, split=split, is_transform=True, augmentations=section_aug,)
test_set = TestSectionLoader(
config.DATASET.ROOT, split=split, is_transform=True, augmentations=section_aug,
)
n_classes = test_set.n_classes
@ -268,6 +317,21 @@ def _evaluate_split(
logger.info("Running in Debug/Test mode")
test_loader = take(1, test_loader)
try:
output_dir = generate_path(
config.OUTPUT_DIR + "_test",
git_branch(),
git_hash(),
config.MODEL.NAME,
current_datetime(),
)
except TypeError:
output_dir = generate_path(
config.OUTPUT_DIR + "_test",
config.MODEL.NAME,
current_datetime(),
)
running_metrics_split = runningScore(n_classes)
# testing mode:
@ -295,6 +359,10 @@ def _evaluate_split(
running_metrics_split.update(gt, pred)
running_metrics_overall.update(gt, pred)
# dump images to disk for review
mask_to_disk(pred.squeeze(), os.path.join(output_dir, f"{i}_pred.png"))
mask_to_disk(gt.squeeze(), os.path.join(output_dir, f"{i}_gt.png"))
# get scores
score, class_iou = running_metrics_split.get_scores()
@ -350,12 +418,16 @@ def test(*options, cfg=None, debug=False):
running_metrics_overall = runningScore(n_classes)
# Augmentation
section_aug = Compose([Normalize(mean=(config.TRAIN.MEAN,), std=(config.TRAIN.STD,), max_pixel_value=1,)])
section_aug = Compose(
[Normalize(mean=(config.TRAIN.MEAN,), std=(config.TRAIN.STD,), max_pixel_value=1,)]
)
patch_aug = Compose(
[
Resize(
config.TRAIN.AUGMENTATIONS.RESIZE.HEIGHT, config.TRAIN.AUGMENTATIONS.RESIZE.WIDTH, always_apply=True,
config.TRAIN.AUGMENTATIONS.RESIZE.HEIGHT,
config.TRAIN.AUGMENTATIONS.RESIZE.WIDTH,
always_apply=True,
),
PadIfNeeded(
min_height=config.TRAIN.AUGMENTATIONS.PAD.HEIGHT,

36
experiments/interpretation/dutchf3_patch/local/train.py
Просмотреть файл

@ -111,7 +111,9 @@ def run(*options, cfg=None, debug=False):
[
Normalize(mean=(config.TRAIN.MEAN,), std=(config.TRAIN.STD,), max_pixel_value=1),
Resize(
config.TRAIN.AUGMENTATIONS.RESIZE.HEIGHT, config.TRAIN.AUGMENTATIONS.RESIZE.WIDTH, always_apply=True,
config.TRAIN.AUGMENTATIONS.RESIZE.HEIGHT,
config.TRAIN.AUGMENTATIONS.RESIZE.WIDTH,
always_apply=True,
),
PadIfNeeded(
min_height=config.TRAIN.AUGMENTATIONS.PAD.HEIGHT,
@ -151,9 +153,14 @@ def run(*options, cfg=None, debug=False):
n_classes = train_set.n_classes
train_loader = data.DataLoader(
train_set, batch_size=config.TRAIN.BATCH_SIZE_PER_GPU, num_workers=config.WORKERS, shuffle=True,
train_set,
batch_size=config.TRAIN.BATCH_SIZE_PER_GPU,
num_workers=config.WORKERS,
shuffle=True,
)
val_loader = data.DataLoader(
val_set, batch_size=config.VALIDATION.BATCH_SIZE_PER_GPU, num_workers=config.WORKERS,
)
val_loader = data.DataLoader(val_set, batch_size=config.VALIDATION.BATCH_SIZE_PER_GPU, num_workers=config.WORKERS,)
model = getattr(models, config.MODEL.NAME).get_seg_model(config)
@ -170,14 +177,18 @@ def run(*options, cfg=None, debug=False):
)
try:
output_dir = generate_path(config.OUTPUT_DIR, git_branch(), git_hash(), config.MODEL.NAME, current_datetime(),)
output_dir = generate_path(
config.OUTPUT_DIR, git_branch(), git_hash(), config.MODEL.NAME, current_datetime(),
)
except TypeError:
output_dir = generate_path(config.OUTPUT_DIR, config.MODEL.NAME, current_datetime(),)
summary_writer = create_summary_writer(log_dir=path.join(output_dir, config.LOG_DIR))
snapshot_duration = scheduler_step * len(train_loader)
scheduler = CosineAnnealingScheduler(optimizer, "lr", config.TRAIN.MAX_LR, config.TRAIN.MIN_LR, snapshot_duration)
scheduler = CosineAnnealingScheduler(
optimizer, "lr", config.TRAIN.MAX_LR, config.TRAIN.MIN_LR, snapshot_duration
)
# weights are inversely proportional to the frequency of the classes in the
# training set
@ -190,7 +201,8 @@ def run(*options, cfg=None, debug=False):
trainer.add_event_handler(Events.ITERATION_STARTED, scheduler)
trainer.add_event_handler(
Events.ITERATION_COMPLETED, logging_handlers.log_training_output(log_interval=config.PRINT_FREQ),
Events.ITERATION_COMPLETED,
logging_handlers.log_training_output(log_interval=config.PRINT_FREQ),
)
trainer.add_event_handler(Events.EPOCH_STARTED, logging_handlers.log_lr(optimizer))
trainer.add_event_handler(
@ -208,7 +220,9 @@ def run(*options, cfg=None, debug=False):
prepare_batch,
metrics={
"nll": Loss(criterion, output_transform=_select_pred_and_mask),
"pixacc": pixelwise_accuracy(n_classes, output_transform=_select_pred_and_mask, device=device),
"pixacc": pixelwise_accuracy(
n_classes, output_transform=_select_pred_and_mask, device=device
),
"cacc": class_accuracy(n_classes, output_transform=_select_pred_and_mask),
"mca": mean_class_accuracy(n_classes, output_transform=_select_pred_and_mask),
"ciou": class_iou(n_classes, output_transform=_select_pred_and_mask),
@ -267,11 +281,15 @@ def run(*options, cfg=None, debug=False):
)
evaluator.add_event_handler(
Events.EPOCH_COMPLETED,
create_image_writer(summary_writer, "Validation/Mask", "mask", transform_func=transform_func),
create_image_writer(
summary_writer, "Validation/Mask", "mask", transform_func=transform_func
),
)
evaluator.add_event_handler(
Events.EPOCH_COMPLETED,
create_image_writer(summary_writer, "Validation/Pred", "y_pred", transform_func=transform_pred),
create_image_writer(
summary_writer, "Validation/Pred", "y_pred", transform_func=transform_pred
),
)
def snapshot_function():