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Marius Laska
boxPrediction
Commits
4c700ad7
Commit
4c700ad7
authored
May 08, 2020
by
Marius Laska
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minor changes
parent
aed5c9bb
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6 changed files
with
250 additions
and
23 deletions
+250
-23
analysis/full_visual_cmp.py
analysis/full_visual_cmp.py
+13
-11
analysis/visualize_box_overlap.py
analysis/visualize_box_overlap.py
+20
-9
base/bbox_model_definition.py
base/bbox_model_definition.py
+37
-1
base/floor_plan_plot.py
base/floor_plan_plot.py
+7
-1
main.py
main.py
+1
-1
talos_script.py
talos_script.py
+172
-0
No files found.
analysis/full_visual_cmp.py
View file @
4c700ad7
...
...
@@ -19,15 +19,16 @@ PI = 3.14159265359
def
main
():
p_reg
:
Pipeline
=
Storable
.
load
(
"../evaluation/
gia
/evaluation/grid/output/REG
_2
"
)
p_reg
:
Pipeline
=
None
#
Storable.load("../evaluation/
lohan
/evaluation/grid/output/REG")
p_circle
:
Pipeline
=
None
#Storable.load("evaluation/lohan/full_cmp/output/CIRCLE")
p_quantile
:
Pipeline
=
None
#Storable.load("evaluation/lohan/full_cmp/output/QUANTILE")
p_box
:
Pipeline
=
Storable
.
load
(
"../evaluation/
gia
/evaluation/grid/output/BBOX_
2
"
)
#"evaluation/lohan/scaled_output_2/output/BBOX") # Storable.load("evaluation/gia/full_cmp/output/BBOX")
p_box
:
Pipeline
=
Storable
.
load
(
"../evaluation/
lohan
/evaluation/grid/output/BBOX_
NEW
"
)
#"evaluation/lohan/scaled_output_2/output/BBOX") # Storable.load("evaluation/gia/full_cmp/output/BBOX")
fp_dims
=
(
83.32
,
17.16
)
#convert_from_2dim_grid(p_box, grid_size=20)
convert_from_grid
(
p_box
)
# for fold_idx in range(p_box.data_provider.num_splits):
# p_box.summary.y_true_labels[fold_idx][:, 0] *= fp_dims[0]
# p_box.summary.y_true_labels[fold_idx][:, 1] *= fp_dims[1]
...
...
@@ -106,6 +107,7 @@ def main():
print
(
"
\n
--------REG---------
\n
"
)
y_true
=
np
.
concatenate
(
p_reg
.
summary
.
y_true_labels
,
axis
=
0
)
y_pred
=
np
.
concatenate
(
p_reg
.
summary
.
y_pred
,
axis
=
0
)
print
(
"center-dist: {}"
.
format
(
center_diff
(
y_true
,
y_pred
)))
# comparison to REG
#radius_c = find_radius_for_acc(p_reg, accuracy=success)
...
...
@@ -119,10 +121,10 @@ def main():
def
visualize
(
p_reg
:
Pipeline
=
None
,
p_circle
:
Pipeline
=
None
,
p_quantile
:
Pipeline
=
None
,
p_box
:
Pipeline
=
None
):
img
=
"../evaluation/gia/gia_floor_4.jpg"
fp_dims
=
(
83.32
,
17.16
)
#
img = resource_filename('data', 'lohan/CrowdsourcedDS1floor.png')
#
fp_dims = (200, 80)
#
img = "../evaluation/gia/gia_floor_4.jpg"
#
fp_dims = (83.32, 17.16)
img
=
resource_filename
(
'data'
,
'lohan/CrowdsourcedDS1floor.png'
)
fp_dims
=
(
200
,
80
)
#fp = FloorPlanPlotRec((83.32, 17.16), 20, floorplan_bg_img=img)
# plot_data_heatmap(pipe, floor_plotter=fp)
if
p_reg
is
not
None
:
...
...
@@ -155,17 +157,17 @@ def visualize(p_reg: Pipeline=None, p_circle: Pipeline=None, p_quantile: Pipelin
alpha
=
0.5
)
fp
.
show_plot
()
#
fp = FloorPlanPlotRec(fp_dims, 2, floorplan_bg_img=img)
for
idx
in
range
(
len
(
y_true
)
):
#len(y_true)):
fp
=
FloorPlanPlotRec
(
fp_dims
,
2
,
floorplan_bg_img
=
img
)
for
idx
in
range
(
50
):
#len(y_true)):
idx
+=
0
fp
=
FloorPlanPlotRec
(
fp_dims
,
2
,
floorplan_bg_img
=
img
)
#
fp = FloorPlanPlotRec(fp_dims, 2, floorplan_bg_img=img)
fp
.
draw_points
(
y_true
[
idx
,
0
],
y_true
[
idx
,
1
],
color
=
'g'
,
alpha
=
0.5
)
# box_size = y_pred[:]
if
p_circle
is
not
None
:
fp
.
draw_circles
(
centers
=
y_pred_circle
[
idx
,
:
2
],
radius
=
y_pred_circle
[
idx
,
2
],
color
=
'r'
)
if
p_reg
is
not
None
:
fp
.
draw_circles
(
centers
=
y_pred_reg
[
idx
,
:
2
],
radius
=
4.7243
,
color
=
'b'
)
fp
.
draw_circles
(
centers
=
y_pred_reg
[
idx
,
:
2
],
radius
=
12.506
,
color
=
'b'
)
if
p_quantile
is
not
None
:
fp
.
draw_points
(
y_pred_quantile
[
idx
,
0
],
y_pred_quantile
[
idx
,
1
],
color
=
"r"
)
fp
.
draw_points
(
y_pred_quantile
[
idx
,
2
],
y_pred_quantile
[
idx
,
3
],
...
...
@@ -175,7 +177,7 @@ def visualize(p_reg: Pipeline=None, p_circle: Pipeline=None, p_quantile: Pipelin
if
p_box
is
not
None
:
fp
.
draw_rectangles_new
(
anchors
=
y_pred_box
[
idx
,
:],
color
=
'black'
)
fp
.
show_plot
()
fp
.
show_plot
()
if
__name__
==
"__main__"
:
...
...
analysis/visualize_box_overlap.py
View file @
4c700ad7
...
...
@@ -55,6 +55,7 @@ def plot_mean_error_per_grid(pipe: Pipeline):
pred_size
=
np
.
full
((
num_g_y
,
num_g_x
),
np
.
nan
)
succ_rate
=
np
.
full
((
num_g_y
,
num_g_x
),
np
.
nan
)
num_grid
=
np
.
full
((
num_g_y
,
num_g_x
),
np
.
nan
)
std_size_grid
=
np
.
full
((
num_g_y
,
num_g_x
),
np
.
nan
)
for
y_idx
in
range
(
num_g_y
):
for
x_idx
in
range
(
num_g_x
):
...
...
@@ -62,12 +63,13 @@ def plot_mean_error_per_grid(pipe: Pipeline):
upper_right
=
np
.
array
([(
x_idx
+
1
)
*
g_size
,
(
y_idx
+
1
)
*
g_size
])
# get average error per grid
error
,
size
,
succ
=
error_grid
(
y_pred
,
y_true
,
y_pred_reg
,
lower_left
,
upper_right
)
error
,
size
,
size_std
,
succ
=
error_grid
(
y_pred
,
y_true
,
y_pred_reg
,
lower_left
,
upper_right
)
num
=
get_num_grid
(
y_pred
,
y_true
,
y_pred_reg
,
lower_left
,
upper_right
)
overlap_count
[
num_g_y
-
y_idx
-
1
,
x_idx
]
=
error
pred_size
[
num_g_y
-
y_idx
-
1
,
x_idx
]
=
size
#avg_box_size_grid(y_pred, lower_left, upper_right)
succ_rate
[
num_g_y
-
y_idx
-
1
,
x_idx
]
=
succ
num_grid
[
num_g_y
-
y_idx
-
1
,
x_idx
]
=
num
std_size_grid
[
num_g_y
-
y_idx
-
1
,
x_idx
]
=
size_std
overlap_count
=
(((
overlap_count
-
np
.
nanmin
(
overlap_count
))
*
(
255
-
1
))
/
(
np
.
nanmax
(
overlap_count
)
-
np
.
nanmin
(
overlap_count
)))
+
1
...
...
@@ -88,6 +90,10 @@ def plot_mean_error_per_grid(pipe: Pipeline):
255
-
0
))
/
(
np
.
nanmax
(
num_grid
)
-
np
.
nanmin
(
num_grid
)))
+
0
succ_rate
[
np
.
where
(
np
.
isnan
(
num_grid
))]
=
0
std_size_grid
=
(((
std_size_grid
-
np
.
nanmin
(
std_size_grid
))
*
(
255
-
0
))
/
(
np
.
nanmax
(
std_size_grid
)
-
np
.
nanmin
(
std_size_grid
)))
+
0
std_size_grid
[
np
.
where
(
np
.
isnan
(
std_size_grid
))]
=
0
# colormap
cmap
=
plt
.
cm
.
jet
# define the colormap
# extract all colors from the .jet map
...
...
@@ -107,10 +113,11 @@ def plot_mean_error_per_grid(pipe: Pipeline):
#fp_dims = (83.32, 17.16)
fp_dims
=
(
200
,
80
)
fp
=
FloorPlanPlotRec
(
fp_dims
,
20
,
floorplan_bg_img
=
img
)
fp_size
=
FloorPlanPlotRec
(
fp_dims
,
20
,
floorplan_bg_img
=
img
)
fp_succ
=
FloorPlanPlotRec
(
fp_dims
,
20
,
floorplan_bg_img
=
img
)
fp_num
=
FloorPlanPlotRec
(
fp_dims
,
20
,
floorplan_bg_img
=
img
)
fp
=
FloorPlanPlotRec
(
fp_dims
,
20
,
floorplan_bg_img
=
img
,
title
=
"Error"
)
fp_size
=
FloorPlanPlotRec
(
fp_dims
,
20
,
floorplan_bg_img
=
img
,
title
=
"BoxSize"
)
fp_succ
=
FloorPlanPlotRec
(
fp_dims
,
20
,
floorplan_bg_img
=
img
,
title
=
"ACC"
)
fp_num
=
FloorPlanPlotRec
(
fp_dims
,
20
,
floorplan_bg_img
=
img
,
title
=
"#Test"
)
fp_size_std
=
FloorPlanPlotRec
(
fp_dims
,
20
,
floorplan_bg_img
=
img
,
title
=
"BoxSizeSTD"
)
for
y_idx
in
range
(
num_g_y
):
for
x_idx
in
range
(
num_g_x
):
lower_left
=
np
.
array
([
x_idx
*
g_size
,
(
y_idx
)
*
g_size
])
...
...
@@ -120,6 +127,7 @@ def plot_mean_error_per_grid(pipe: Pipeline):
size
=
pred_size
[
num_g_y
-
y_idx
-
1
,
x_idx
]
succ
=
succ_rate
[
num_g_y
-
y_idx
-
1
,
x_idx
]
num
=
num_grid
[
num_g_y
-
y_idx
-
1
,
x_idx
]
s_std
=
std_size_grid
[
num_g_y
-
y_idx
-
1
,
x_idx
]
fp
.
draw_rectangles
(
np
.
concatenate
((
lower_left
,
upper_right
)),
color
=
my_cmap
[
int
(
count
)],
fill
=
True
)
...
...
@@ -129,6 +137,8 @@ def plot_mean_error_per_grid(pipe: Pipeline):
color
=
my_cmap
[
int
(
succ
)],
fill
=
True
)
fp_num
.
draw_rectangles
(
np
.
concatenate
((
lower_left
,
upper_right
)),
color
=
my_cmap
[
int
(
num
)],
fill
=
True
)
fp_size_std
.
draw_rectangles
(
np
.
concatenate
((
lower_left
,
upper_right
)),
color
=
my_cmap
[
int
(
s_std
)],
fill
=
True
)
fp
.
show_plot
()
...
...
@@ -217,6 +227,7 @@ def avg_box_size_grid(y_pred, lower_left, upper_right):
# size of prediction cells
return
np
.
mean
(
np
.
prod
(
y_pred
[
mask
,
2
:],
axis
=
1
))
def
get_num_grid
(
y_pred
,
y_true
,
y_pred_reg
,
lower_left
,
upper_right
):
min_x
=
lower_left
[
0
]
min_y
=
lower_left
[
1
]
...
...
@@ -247,8 +258,8 @@ def error_grid(y_pred, y_true, y_pred_reg, lower_left, upper_right):
mask
=
np
.
where
(
range
)[
0
]
if
len
(
mask
)
==
0
:
return
np
.
nan
,
np
.
nan
,
np
.
nan
if
len
(
mask
)
<
5
:
return
np
.
nan
,
np
.
nan
,
np
.
nan
,
np
.
nan
#size = np.abs(y_pred[mask, 0] - y_pred[mask, 2]) * np.abs(y_pred[mask, 1] - y_pred[mask, 3])
...
...
@@ -263,7 +274,7 @@ def error_grid(y_pred, y_true, y_pred_reg, lower_left, upper_right):
size
=
np
.
prod
(
y_pred
[
mask
,
2
:],
axis
=
1
)
diff
=
np
.
linalg
.
norm
(
y_true
[
mask
]
-
y_pred_reg
[
mask
,
:
2
],
axis
=
1
)
return
np
.
me
di
an
(
diff
),
np
.
me
di
an
(
size
),
len
(
correct
)
/
len
(
mask
)
return
np
.
mean
(
diff
),
np
.
mean
(
size
),
np
.
std
(
size
),
len
(
correct
)
/
len
(
mask
)
def
overlap_reg
(
y_pred
,
radius
,
lower_left
,
upper_right
):
...
...
@@ -314,6 +325,6 @@ if __name__ == "__main__":
for
id
in
[
128
]:
pipe
:
Pipeline
=
Storable
.
load
(
"../evaluation/lohan/evaluation/grid/output/BBOX"
)
#{}".format(id))
"../evaluation/lohan/evaluation/grid/output/BBOX
_NEW
"
)
#{}".format(id))
plot_mean_error_per_grid
(
pipe
)
\ No newline at end of file
base/bbox_model_definition.py
View file @
4c700ad7
from
tensorflow.keras
import
regularizers
,
initializers
from
tensorflow.keras.callbacks
import
ModelCheckpoint
,
EarlyStopping
from
tensorflow.keras.layers
import
Dense
,
Dropout
,
Conv2D
,
MaxPooling2D
,
Flatten
from
tensorflow.keras.layers
import
Dense
,
Dropout
,
Conv2D
,
MaxPooling2D
,
Flatten
,
BatchNormalization
from
tensorflow.keras.models
import
Sequential
from
il_pipeline.models.layer_generator
import
hidden_layers
from
il_pipeline.models.lr_normalizer
import
lr_normalizer
def
bbox_model_for_talos
(
x_train
,
y_train
,
x_val
,
y_val
,
params
):
model
=
Sequential
()
model
.
add
(
Dense
(
params
[
'first_neuron'
],
input_dim
=
x_train
.
shape
[
1
],
activation
=
params
[
'activation'
],
kernel_regularizer
=
regularizers
.
l2
(
params
[
'regularization_penalty'
])
))
model
.
add
(
Dropout
(
params
[
'dropout'
]))
hidden_layers
(
model
,
params
,
x_train
.
shape
[
1
])
model
.
add
(
Dense
(
5
*
3
,
activation
=
params
[
'last_activation'
],
# kernel_initializer=params['kernel_initializer'],
kernel_regularizer
=
regularizers
.
l2
(
params
[
'regularization_penalty'
])))
# ,
# kernel_initializer=initializers.RandomUniform(minval=-10, maxval=15)))#,
# bias_initializer=initializers.RandomUniform(minval=0, maxval=0)))
# compile the model
model
.
compile
(
loss
=
params
[
'losses'
],
optimizer
=
params
[
'optimizer'
](
lr
=
lr_normalizer
(
params
[
'lr'
],
params
[
'optimizer'
])))
out
=
model
.
fit
(
x_train
,
y_train
,
batch_size
=
params
[
'batch_size'
],
epochs
=
params
[
'epochs'
],
validation_data
=
[
x_val
,
y_val
],
verbose
=
0
)
return
out
,
model
def
bbox_model_for_generator
(
metrics
):
def
define_classification_model_for_generator
(
X_cols
,
Y_cols
,
params
)
->
Sequential
:
...
...
@@ -17,6 +51,8 @@ def bbox_model_for_generator(metrics):
params
[
'regularization_penalty'
])
))
#model.add(BatchNormalization())
model
.
add
(
Dropout
(
params
[
'dropout'
]))
hidden_layers
(
model
,
params
,
X_cols
)
...
...
base/floor_plan_plot.py
View file @
4c700ad7
...
...
@@ -8,11 +8,17 @@ class FloorPlanPlotRec(FloorplanPlot):
def
__init__
(
self
,
floorplan_dimensions
,
grid_size
,
draw_grid
=
False
,
floorplan_bg_img
=
""
,
filename
=
None
,
sample_points_file
=
None
,
add_points
=
False
,
walls_file
=
None
,
add_walls
=
False
,
xtick_freq
=
None
,
artificial_labels
=
False
,
correct_walls
=
False
):
correct_walls
=
False
,
title
=
None
):
super
().
__init__
(
floorplan_dimensions
,
grid_size
,
draw_grid
,
floorplan_bg_img
,
filename
,
sample_points_file
,
add_points
,
walls_file
,
add_walls
,
xtick_freq
,
artificial_labels
,
correct_walls
)
if
title
is
not
None
:
self
.
set_title
(
title
)
def
set_title
(
self
,
title
=
"title"
):
self
.
axis
.
set_title
(
title
)
def
draw_circles
(
self
,
centers
,
radius
,
color
=
'r'
):
if
centers
.
ndim
==
1
:
centers
=
np
.
array
([
centers
])
...
...
main.py
View file @
4c700ad7
...
...
@@ -197,7 +197,7 @@ def main():
file
=
"gia_evaluation.yml"
file
=
"ujiindoor_circle.yml"
#file = "config_lohan.yml"
file
=
"config/
gia_grid
.yml"
file
=
"config/
lohan_deep
.yml"
execute
(
file
)
#point_uncertainty_estimation(file, "evaluation/lohan/old_loss/progress/output/BBOX_256_base.hdf5")
...
...
talos_script.py
0 → 100644
View file @
4c700ad7
import
copy
import
logging
import
numpy
as
np
from
debug_tools.logger
import
getLogger
from
il_pipeline.utility.config_reader
import
ConfigReader
from
ldce.base
import
ClusterBase
from
ldce.plotting.floorplan_plot
import
FloorplanPlot
from
base.BboxModel
import
BboxModel
from
base.bbox_model_definition
import
bbox_model_for_talos
log
=
getLogger
(
level
=
logging
.
INFO
)
from
base.bbox_pipeline
import
BboxPipeline
import
talos
as
ta
def
execute
(
conf_file
):
area_classification
=
True
logging
.
basicConfig
(
level
=
"INFO"
)
conf
=
ConfigReader
(
conf_file
)
conf
.
setup_directories
()
# only PD
conf
.
download_floor_plan
()
# sequentially execute all training pipelines
for
p_idx
,
pipeline_params
in
enumerate
(
conf
.
pipelines
):
num_iterations
=
conf
.
get_params
(
'repetitions'
,
pipeline_params
)
log
.
info
(
'Train and evaluate il_pipeline "{}"... ({}/{}))'
.
format
(
pipeline_params
[
'name'
],
p_idx
+
1
,
len
(
conf
.
pipelines
)))
log
.
info
(
'Repeat {} time(s) and build average...'
.
format
(
num_iterations
))
# read pipeline parameters
pre_params
=
conf
.
get_params
(
'preprocessing'
,
pipeline_params
,
merge_level
=
1
)
model_params
=
conf
.
get_params
(
'model_params'
,
pipeline_params
,
merge_level
=
1
)
fp_params
=
conf
.
get_params
(
'floor_plan'
,
pipeline_params
,
merge_level
=
1
)
assign_closest
=
conf
.
get_params
(
'assign_closest'
,
pre_params
,
'preprocessing'
)
area_mode
=
conf
.
get_params
(
'area_assignment'
,
pre_params
,
'preprocessing'
)
# get data provider
base_data_provider
=
BboxPipeline
.
get_data_provider
(
conf
.
data_params
,
pre_params
)
# for storing pipeline iteration names
p_names
=
[]
for
run
in
range
(
num_iterations
):
pipe_params
=
copy
.
deepcopy
(
pipeline_params
)
p_name
=
pipe_params
[
'name'
]
# + "[{}]".format(run + 1)
pipe_params
[
'name'
]
=
p_name
p_names
.
append
(
p_name
)
data_provider
=
copy
.
deepcopy
(
base_data_provider
)
clusterer
:
ClusterBase
=
BboxPipeline
.
get_floor_plan_segmentation
(
data_provider
,
fp_params
)
# either use segmented floor plan or apply regression
if
fp_params
[
'type'
]
==
'segmentation'
:
area_labels
,
_
,
coverage
,
mask
=
clusterer
.
get_cluster_labels_for_areas
(
data_provider
.
labels
,
area_mode
=
area_mode
,
assign_closest
=
assign_closest
)
data_provider
.
set_area_labels
(
area_labels
,
delete_uncovered
=
True
,
pre_params
=
pre_params
)
data_provider
.
remove_APs_with_low_correlation_to_areas
(
pre_params
)
if
data_provider
.
get_data_dims
(
model_type
=
"classification"
)[
1
]
<
2
:
log
.
info
(
"Skipping segmentation (only single class)"
)
continue
elif
fp_params
[
'type'
]
==
'regression'
:
data_provider
.
generate_random_splits_from_clusters
(
clusterer
.
base_cluster_mappings
)
elif
fp_params
[
'type'
]
==
'floor_classification'
:
data_provider
.
area_labels
=
data_provider
.
labels
# compute grid encoding
data_provider
.
transform_to_grid_encoding
()
#data_provider.transform_to_2dim_grid_encoding(grid_size=20)
pipeline
=
BboxPipeline
(
data_provider
,
clusterer
,
conf
,
model_params
,
pipe_params
[
'name'
])
if
model_params
is
not
None
:
train_model
(
model_params
,
pipeline
)
pipeline
.
store
()
if
model_params
is
not
None
:
pipe_files
=
[
conf
.
output_dir
+
p_name
for
p_name
in
p_names
]
BboxPipeline
.
merge_summaries
(
pipe_files
,
pipeline_params
[
'name'
])
def
train_model
(
params
,
pipe
:
BboxPipeline
):
dp
=
pipe
.
data_provider
x_train
,
y_train
=
dp
.
get_train_data
(
labels
=
dp
.
grid_labels
,
split_idx
=
0
,
area_labels
=
False
)
x_test
,
y_test
=
dp
.
get_test_data
(
labels
=
dp
.
grid_labels
,
split_idx
=
0
,
area_labels
=
False
)
x_val
,
y_val
=
pipe
.
data_provider
.
get_val_data
(
labels
=
dp
.
grid_labels
,
split_idx
=
0
,
area_labels
=
False
)
x_train_val
=
np
.
concatenate
((
x_train
,
x_val
),
axis
=
0
)
y_train_val
=
np
.
concatenate
((
y_train
,
y_val
),
axis
=
0
)
m_type
=
params
[
'type'
]
model
=
BboxModel
(
params
[
'type'
],
pipe
.
summary
,
pipe
.
data_provider
,
params
,
pipe
.
config
.
output_dir
,
pipe
.
filename
)
model
.
setup_params
()
num_epochs
=
params
[
'epochs'
]
if
'pretrain'
in
params
:
model
.
pre_train_model
(
params
[
'pretrain'
])
model
.
type
=
m_type
model
.
params
.
update
({
'epochs'
:
num_epochs
})
m_params
=
model
.
params
del
m_params
[
'loss'
]
del
m_params
[
'type'
]
#del m_params['pred']
del
m_params
[
'augmentation'
]
# put every element in list
m_params
=
{
k
:
[
v
]
if
type
(
v
)
not
in
[
list
,
tuple
]
else
v
for
(
k
,
v
)
in
m_params
.
items
()}
t
=
ta
.
Scan
(
x
=
x_train_val
,
y
=
y_train_val
,
x_val
=
x_test
,
y_val
=
y_test
,
model
=
bbox_model_for_talos
,
grid_downsample
=
0.1
,
params
=
m_params
,
dataset_name
=
'indoor_loc_box'
,
experiment_no
=
'1'
)
print
(
"test"
)
def
report
():
r
=
ta
.
Reporting
(
"indoor_loc_2.csv"
)
data
=
r
.
data
test
=
data
.
sort_values
(
by
=
[
'val_loss'
],
ascending
=
True
).
head
(
5
)
test
=
r
.
best_params
(
metric
=
"val_loss"
)
r
.
plot_corr
(
metric
=
"val_loss"
)
print
(
test
)
def
main
():
file
=
"config/lohan_talos.yml"
execute
(
file
)
#point_uncertainty_estimation(file, "evaluation/lohan/old_loss/progress/output/BBOX_256_base.hdf5")
if
__name__
==
"__main__"
:
#report()
main
()
\ No newline at end of file
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