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Teachable AI Lab
sparse_coding_torch
Commits
d00c9911
Commit
d00c9911
authored
3 years ago
by
hannandarryl
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import
torch
import
os
from
sparse_coding_torch.conv_sparse_model
import
ConvSparseLayer
from
sparse_coding_torch.small_data_classifier
import
SmallDataClassifierConv3d
import
time
import
numpy
as
np
import
torchvision
from
sparse_coding_torch.video_loader
import
VideoGrayScaler
,
MinMaxScaler
from
torchvision.datasets.video_utils
import
VideoClips
import
csv
from
datetime
import
datetime
from
yolov4.get_bounding_boxes
import
YoloModel
import
argparse
if
__name__
==
"
__main__
"
:
parser
=
argparse
.
ArgumentParser
(
description
=
'
Process some integers.
'
)
parser
.
add_argument
(
'
--fast
'
,
action
=
'
store_true
'
,
help
=
'
optimized for runtime
'
)
parser
.
add_argument
(
'
--accurate
'
,
action
=
'
store_true
'
,
help
=
'
optimized for accuracy
'
)
parser
.
add_argument
(
'
--verbose
'
,
action
=
'
store_true
'
,
help
=
'
output verbose
'
)
args
=
parser
.
parse_args
()
#print(args.accumulate(args.integers))
device
=
torch
.
device
(
"
cuda:0
"
if
torch
.
cuda
.
is_available
()
else
"
cpu
"
)
batch_size
=
1
frozen_sparse
=
ConvSparseLayer
(
in_channels
=
1
,
out_channels
=
64
,
kernel_size
=
(
5
,
15
,
15
),
stride
=
1
,
padding
=
(
0
,
7
,
7
),
convo_dim
=
3
,
rectifier
=
True
,
lam
=
0.05
,
max_activation_iter
=
150
,
activation_lr
=
1e-2
)
sparse_param
=
torch
.
load
(
'
sparse.pt
'
,
map_location
=
device
)
frozen_sparse
.
load_state_dict
(
sparse_param
[
'
model_state_dict
'
])
frozen_sparse
.
to
(
device
)
predictive_model
=
SmallDataClassifierConv3d
()
predictive_model
.
to
(
device
)
checkpoint
=
{
k
.
replace
(
'
module.
'
,
''
):
v
for
k
,
v
in
torch
.
load
(
'
classifier.pt
'
,
map_location
=
device
)[
'
model_state_dict
'
].
items
()}
predictive_model
.
load_state_dict
(
checkpoint
)
yolo_model
=
YoloModel
()
transform
=
torchvision
.
transforms
.
Compose
(
[
VideoGrayScaler
(),
MinMaxScaler
(
0
,
255
),
torchvision
.
transforms
.
Normalize
((
0.2592
,),
(
0.1251
,)),
torchvision
.
transforms
.
CenterCrop
((
100
,
200
))
])
frozen_sparse
.
eval
()
predictive_model
.
eval
()
all_predictions
=
[]
all_files
=
list
(
os
.
listdir
(
'
input_videos
'
))
for
f
in
all_files
:
print
(
'
Processing
'
,
f
)
#start_time = time.time()
clipstride
=
15
if
args
.
fast
:
clipstride
=
20
if
args
.
accurate
:
clipstride
=
10
vc
=
VideoClips
([
os
.
path
.
join
(
'
input_videos
'
,
f
)],
clip_length_in_frames
=
5
,
frame_rate
=
20
,
frames_between_clips
=
clipstride
)
### START time after loading video ###
start_time
=
time
.
time
()
clip_predictions
=
[]
i
=
0
cliplist
=
[]
countclips
=
0
for
i
in
range
(
vc
.
num_clips
()):
clip
,
_
,
_
,
_
=
vc
.
get_clip
(
i
)
clip
=
clip
.
swapaxes
(
1
,
3
).
swapaxes
(
0
,
1
).
swapaxes
(
2
,
3
).
numpy
()
bounding_boxes
=
yolo_model
.
get_bounding_boxes
(
clip
[:,
2
,
:,
:].
swapaxes
(
0
,
2
).
swapaxes
(
0
,
1
)).
squeeze
(
0
)
if
bounding_boxes
.
size
==
0
:
continue
#widths = []
countclips
=
countclips
+
len
(
bounding_boxes
)
widths
=
[(
bounding_boxes
[
i
][
3
]
-
bounding_boxes
[
i
][
1
])
for
i
in
range
(
len
(
bounding_boxes
))]
#for i in range(len(bounding_boxes)):
# widths.append(bounding_boxes[i][3] - bounding_boxes[i][1])
ind
=
np
.
argmax
(
np
.
array
(
widths
))
#for bb in bounding_boxes:
bb
=
bounding_boxes
[
ind
]
center_x
=
(
bb
[
3
]
+
bb
[
1
])
/
2
*
1920
center_y
=
(
bb
[
2
]
+
bb
[
0
])
/
2
*
1080
width
=
400
height
=
400
lower_y
=
round
(
center_y
-
height
/
2
)
upper_y
=
round
(
center_y
+
height
/
2
)
lower_x
=
round
(
center_x
-
width
/
2
)
upper_x
=
round
(
center_x
+
width
/
2
)
trimmed_clip
=
clip
[:,
:,
lower_y
:
upper_y
,
lower_x
:
upper_x
]
trimmed_clip
=
torch
.
tensor
(
trimmed_clip
).
to
(
torch
.
float
)
trimmed_clip
=
transform
(
trimmed_clip
)
trimmed_clip
.
pin_memory
()
cliplist
.
append
(
trimmed_clip
)
if
len
(
cliplist
)
>
0
:
with
torch
.
no_grad
():
trimmed_clip
=
torch
.
stack
(
cliplist
)
trimmed_clip
=
trimmed_clip
.
to
(
device
,
non_blocking
=
True
)
activations
=
frozen_sparse
(
trimmed_clip
)
pred
,
activations
=
predictive_model
(
activations
)
#print(torch.nn.Sigmoid()(pred))
clip_predictions
=
(
torch
.
nn
.
Sigmoid
()(
pred
).
round
().
detach
().
cpu
().
flatten
().
to
(
torch
.
long
))
if
args
.
verbose
:
print
(
clip_predictions
)
print
(
"
num of clips:
"
,
countclips
)
final_pred
=
torch
.
mode
(
clip_predictions
)[
0
].
item
()
if
len
(
clip_predictions
)
%
2
==
0
and
torch
.
sum
(
clip_predictions
).
item
()
==
len
(
clip_predictions
)
//
2
:
#print("I'm here")
final_pred
=
(
torch
.
nn
.
Sigmoid
()(
pred
)).
mean
().
round
().
detach
().
cpu
().
to
(
torch
.
long
).
item
()
if
final_pred
==
1
:
str_pred
=
'
No Sliding
'
else
:
str_pred
=
'
Sliding
'
else
:
str_pred
=
"
No Sliding
"
end_time
=
time
.
time
()
all_predictions
.
append
({
'
FileName
'
:
f
,
'
Prediction
'
:
str_pred
,
'
TotalTimeSec
'
:
end_time
-
start_time
})
with
open
(
'
output_
'
+
datetime
.
now
().
strftime
(
"
%Y%m%d-%H%M%S
"
)
+
'
.csv
'
,
'
w+
'
,
newline
=
''
)
as
csv_out
:
writer
=
csv
.
DictWriter
(
csv_out
,
fieldnames
=
all_predictions
[
0
].
keys
())
writer
.
writeheader
()
writer
.
writerows
(
all_predictions
)
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