class SegmentationReportBuilder:
def __init__(self,
session: Session,
task: Task,
layout: str,
part: str = 'valid',
name: str = 'img_segment',
max_img_size: Tuple[int, int] = None,
stack_type: str = 'vertical',
main_metric: str = 'dice',
plot_count: int = 0,
colors: List[Tuple] = None):
self.session = session
self.task = task
self.layout = layout
self.part = part
self.name = name or 'img_segment'
self.max_img_size = max_img_size
self.stack_type = stack_type
self.main_metric = main_metric
self.colors = colors
self.plot_count = plot_count
self.dag_provider = DagProvider(session)
self.report_provider = ReportProvider(session)
self.layout_provider = ReportLayoutProvider(session)
self.task_provider = TaskProvider(session)
self.report_img_provider = ReportImgProvider(session)
self.report_task_provider = ReportTasksProvider(session)
self.report_series_provider = ReportSeriesProvider(session)
self.project = self.task_provider.project(task.id).id
self.layout = self.layout_provider.by_name(layout)
self.layout_dict = yaml_load(self.layout.content)
self.create_base()
def create_base(self):
report = Report(config=yaml_dump(self.layout_dict),
time=now(),
layout=self.layout.name,
project=self.project,
name=self.name)
self.report_provider.add(report)
self.report_task_provider.add(
ReportTasks(report=report.id, task=self.task.id))
self.task.report = report.id
self.task_provider.update()
def encode_pred(self, mask: np.array):
res = np.zeros((*mask.shape[1:], 3), dtype=np.uint8)
for i, c in enumerate(mask):
c = np.repeat(c[:, :, None], 3, axis=2)
color = self.colors[i] if self.colors is not None else (255, 255,
255)
res += (c * color).astype(np.uint8)
return res
def plot_mask(self, img: np.array, mask: np.array):
if len(img.shape) == 2:
img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
img = img.astype(np.uint8)
mask = mask.astype(np.uint8)
for i, c in enumerate(mask):
contours, _ = cv2.findContours(c, cv2.RETR_LIST,
cv2.CHAIN_APPROX_NONE)
color = self.colors[i] if self.colors else (0, 255, 0)
for i in range(0, len(contours)):
cv2.polylines(img, contours[i], True, color, 2)
return img
def process_scores(self, scores):
for key, item in self.layout_dict['items'].items():
item['name'] = key
if item['type'] == 'series' and item['key'] in scores:
series = ReportSeries(name=item['name'],
value=scores[item['key']],
epoch=0,
time=now(),
task=self.task.id,
part='valid',
stage='stage1')
self.report_series_provider.add(series)
def process_pred(self,
imgs: np.array,
preds: dict,
targets: np.array = None,
attrs=None,
scores=None):
for key, item in self.layout_dict['items'].items():
item['name'] = key
if item['type'] != 'img_segment':
continue
report_imgs = []
dag = self.dag_provider.by_id(self.task.dag)
for i in range(len(imgs)):
if self.plot_count <= 0:
break
if targets is not None:
img = self.plot_mask(imgs[i], targets[i])
else:
img = imgs[i]
imgs_add = [img]
for key, value in preds.items():
imgs_add.append(self.encode_pred(value[i]))
for j in range(len(imgs_add)):
imgs_add[j] = resize_saving_ratio(imgs_add[j],
self.max_img_size)
if self.stack_type == 'horizontal':
img = np.hstack(imgs_add)
else:
img = np.vstack(imgs_add)
attr = attrs[i] if attrs else {}
score = None
if targets is not None:
score = scores[self.main_metric][i]
retval, buffer = cv2.imencode('.jpg', img)
report_img = ReportImg(group=item['name'],
epoch=0,
task=self.task.id,
img=buffer,
dag=self.task.dag,
part=self.part,
project=self.project,
score=score,
**attr)
self.plot_count -= 1
report_imgs.append(report_img)
dag.img_size += report_img.size
self.dag_provider.commit()
self.report_img_provider.bulk_save_objects(report_imgs)
class ClassificationReportBuilder:
def __init__(self,
session: Session,
task: Task,
layout: str,
part: str = 'valid',
name: str = 'img_classify',
max_img_size: Tuple[int, int] = None,
main_metric: str = 'accuracy',
plot_count: int = 0):
self.session = session
self.task = task
self.layout = layout
self.part = part
self.name = name or 'img_classify'
self.max_img_size = max_img_size
self.main_metric = main_metric
self.plot_count = plot_count
self.dag_provider = DagProvider(session)
self.report_provider = ReportProvider(session)
self.layout_provider = ReportLayoutProvider(session)
self.task_provider = TaskProvider(session)
self.report_img_provider = ReportImgProvider(session)
self.report_task_provider = ReportTasksProvider(session)
self.report_series_provider = ReportSeriesProvider(session)
self.project = self.task_provider.project(task.id).id
self.layout = self.layout_provider.by_name(layout)
self.layout_dict = yaml_load(self.layout.content)
def create_base(self):
report = Report(config=yaml_dump(self.layout_dict),
time=now(),
layout=self.layout.name,
project=self.project,
name=self.name)
self.report_provider.add(report)
self.report_task_provider.add(
ReportTasks(report=report.id, task=self.task.id))
self.task.report = report.id
self.task_provider.update()
def process_scores(self, scores):
for key, item in self.layout_dict['items'].items():
item['name'] = key
if item['type'] == 'series' and item['key'] in scores:
series = ReportSeries(name=item['name'],
value=float(scores[item['key']]),
epoch=0,
time=now(),
task=self.task.id,
part='valid',
stage='stage1')
self.report_series_provider.add(series)
def process_pred(self,
imgs: np.array,
preds: np.array,
targets: np.array = None,
attrs=None,
scores=None):
for key, item in self.layout_dict['items'].items():
item['name'] = key
if item['type'] != 'img_classify':
continue
report_imgs = []
dag = self.dag_provider.by_id(self.task.dag)
for i in range(len(imgs)):
if self.plot_count <= 0:
break
img = resize_saving_ratio(imgs[i], self.max_img_size)
pred = preds[i]
attr = attrs[i] if attrs else {}
y = None
score = None
if targets is not None:
y = targets[i]
score = float(scores[self.main_metric][i])
y_pred = pred.argmax()
retval, buffer = cv2.imencode('.jpg', img)
report_img = ReportImg(group=item['name'],
epoch=0,
task=self.task.id,
img=buffer,
dag=self.task.dag,
part=self.part,
project=self.project,
y_pred=y_pred,
y=y,
score=score,
**attr)
report_imgs.append(report_img)
dag.img_size += report_img.size
self.dag_provider.commit()
self.report_img_provider.bulk_save_objects(report_imgs)
if targets is not None and item.get('confusion_matrix'):
matrix = confusion_matrix(targets,
preds.argmax(axis=1),
labels=np.arange(preds.shape[1]))
matrix = np.array(matrix)
c = {'data': matrix}
obj = ReportImg(group=item['name'] + '_confusion',
epoch=0,
task=self.task.id,
img=pickle.dumps(c),
project=self.project,
dag=self.task.dag,
part=self.part)
self.report_img_provider.add(obj)
self.plot_count -= 1
