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not json serializable values corner case handled #196

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merged 5 commits into from
May 18, 2023
Merged

not json serializable values corner case handled #196

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4ec51ef
experiment
MaxTeselkin May 17, 2023
f3feb4f
send metrics bug fix
MaxTeselkin May 17, 2023
80593b5
added float transform
MaxTeselkin May 18, 2023
6fc4f0c
added print for debug
MaxTeselkin May 18, 2023
0cb96a1
removed prints for debug
MaxTeselkin May 18, 2023
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125 changes: 79 additions & 46 deletions supervisely/train/src/sly_metrics.py
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Original file line number Diff line number Diff line change
@@ -1,77 +1,110 @@
import supervisely as sly
import sly_train_globals as globals
import math


def init_chart(title, names, xs, ys, smoothing=None):
series = []
for name, x, y in zip(names, xs, ys):
series.append({
"name": name,
"data": [[px, py] for px, py in zip(x, y)]
})
series.append({"name": name, "data": [[px, py] for px, py in zip(x, y)]})
result = {
"options": {
"title": title,
#"groupKey": "my-synced-charts",
# "groupKey": "my-synced-charts",
},
"series": series
"series": series,
}
if smoothing is not None:
result["options"]["smoothingWeight"] = smoothing
return result


def init(data, state):
demo_x = [[], []] #[[1, 2, 3, 4], [2, 4, 6, 8]]
demo_y = [[], []] #[[10, 15, 13, 17], [16, 5, 11, 9]]
data["mGIoU"] = init_chart("GIoU",
names=["train", "val"],
xs=demo_x,
ys=demo_y,
smoothing=0.6)
demo_x = [[], []] # [[1, 2, 3, 4], [2, 4, 6, 8]]
demo_y = [[], []] # [[10, 15, 13, 17], [16, 5, 11, 9]]
data["mGIoU"] = init_chart("GIoU", names=["train", "val"], xs=demo_x, ys=demo_y, smoothing=0.6)

data["mObjectness"] = init_chart("Objectness",
names=["train", "val"],
xs=demo_x,
ys=demo_y,
smoothing=0.6)
data["mObjectness"] = init_chart(
"Objectness", names=["train", "val"], xs=demo_x, ys=demo_y, smoothing=0.6
)

data["mClassification"] = init_chart("Classification",
names=["train", "val"],
xs=demo_x,
ys=demo_y,
smoothing=0.6)
data["mClassification"] = init_chart(
"Classification", names=["train", "val"], xs=demo_x, ys=demo_y, smoothing=0.6
)

data["mPR"] = init_chart("Pr + Rec",
names=["precision", "recall"],
xs=demo_x,
ys=demo_y)
data["mPR"] = init_chart("Pr + Rec", names=["precision", "recall"], xs=demo_x, ys=demo_y)

data["mMAP"] = init_chart("mAP",
names=["mAP@0.5", "mAP@0.5:0.95"],
xs=demo_x,
ys=demo_y)
data["mMAP"] = init_chart("mAP", names=["mAP@0.5", "mAP@0.5:0.95"], xs=demo_x, ys=demo_y)
state["smoothing"] = 0.6


def send_metrics(epoch, epochs, metrics, log_period=1):
sly.logger.debug(f"Metrics: epoch {epoch + 1} / {epochs}", extra={"metrics": metrics})

if epoch % log_period == 0 or epoch + 1 == epochs:
# search for problem metric values and set their values to zero
for key, value in metrics.items():
if not math.isfinite(value): # if value is NaN, infinity or negative infinity
sly.logger.info(
f"{key} value is not serializable, trying to transform value to float..."
)
value = float(value)
if not math.isfinite(value): # if transforming to float did not help
sly.logger.info(
f"{key} value is NaN, infinity or negative infinity, setting this value to 0"
)
value = 0
metrics[key] = value
fields = [
{"field": "data.mGIoU.series[0].data", "payload": [[epoch, metrics["train/box_loss"]]], "append": True},
{"field": "data.mGIoU.series[1].data", "payload": [[epoch, metrics["val/box_loss"]]], "append": True},

{"field": "data.mObjectness.series[0].data", "payload": [[epoch, metrics["train/obj_loss"]]], "append": True},
{"field": "data.mObjectness.series[1].data", "payload": [[epoch, metrics["val/obj_loss"]]], "append": True},

{"field": "data.mClassification.series[0].data", "payload": [[epoch, metrics["train/cls_loss"]]], "append": True},
{"field": "data.mClassification.series[1].data", "payload": [[epoch, metrics["val/cls_loss"]]], "append": True},

{"field": "data.mPR.series[0].data", "payload": [[epoch, metrics["metrics/precision"]]], "append": True},
{"field": "data.mPR.series[1].data", "payload": [[epoch, metrics["metrics/recall"]]], "append": True},

{"field": "data.mMAP.series[0].data", "payload": [[epoch, metrics["metrics/mAP_0.5"]]], "append": True},
{"field": "data.mMAP.series[1].data", "payload": [[epoch, metrics["metrics/mAP_0.5:0.95"]]], "append": True},
{
"field": "data.mGIoU.series[0].data",
"payload": [[epoch, metrics["train/box_loss"]]],
"append": True,
},
{
"field": "data.mGIoU.series[1].data",
"payload": [[epoch, metrics["val/box_loss"]]],
"append": True,
},
{
"field": "data.mObjectness.series[0].data",
"payload": [[epoch, metrics["train/obj_loss"]]],
"append": True,
},
{
"field": "data.mObjectness.series[1].data",
"payload": [[epoch, metrics["val/obj_loss"]]],
"append": True,
},
{
"field": "data.mClassification.series[0].data",
"payload": [[epoch, metrics["train/cls_loss"]]],
"append": True,
},
{
"field": "data.mClassification.series[1].data",
"payload": [[epoch, metrics["val/cls_loss"]]],
"append": True,
},
{
"field": "data.mPR.series[0].data",
"payload": [[epoch, metrics["metrics/precision"]]],
"append": True,
},
{
"field": "data.mPR.series[1].data",
"payload": [[epoch, metrics["metrics/recall"]]],
"append": True,
},
{
"field": "data.mMAP.series[0].data",
"payload": [[epoch, metrics["metrics/mAP_0.5"]]],
"append": True,
},
{
"field": "data.mMAP.series[1].data",
"payload": [[epoch, metrics["metrics/mAP_0.5:0.95"]]],
"append": True,
},
]
globals.api.app.set_fields(globals.task_id, fields)
globals.api.app.set_fields(globals.task_id, fields)
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