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Alexandre Chapin
Segment-Object-Centric
Commits
3bbd64ae
"examples/data/data.RData" did not exist on "97e0c26d12a276a1fdc88ae884d366bd363c23d5"
Commit
3bbd64ae
authored
2 years ago
by
Alexandre Chapin
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osrt/layers.py
+37
-39
37 additions, 39 deletions
osrt/layers.py
train_sa.py
+6
-6
6 additions, 6 deletions
train_sa.py
with
43 additions
and
45 deletions
osrt/layers.py
+
37
−
39
View file @
3bbd64ae
...
@@ -300,43 +300,43 @@ class TransformerSlotAttention(nn.Module):
...
@@ -300,43 +300,43 @@ class TransformerSlotAttention(nn.Module):
"""
"""
An extension of Slot Attention using self-attention
An extension of Slot Attention using self-attention
"""
"""
def
__init__
(
self
,
depth
,
heads
,
dim_head
,
mlp_dim
,
num_slots
,
input_dim
=
768
,
slot_dim
=
1536
,
hidden_dim
=
3072
,
eps
=
1e-8
,
"""
def __init__(self, num_slots, input_dim=768, slot_dim=1536, hidden_dim=3072, iters=3, eps=1e-8,
randomize_initial_slots=False):
"""
def
__init__
(
self
,
num_slots
=
10
,
depth
=
6
,
input_dim
=
768
,
slot_dim
=
1536
,
hidden_dim
=
3072
,
cross_heads
=
1
,
self_heads
=
6
,
randomize_initial_slots
=
False
):
randomize_initial_slots
=
False
):
super
().
__init__
()
super
().
__init__
()
self
.
num_slots
=
num_slots
self
.
num_slots
=
num_slots
self
.
input_dim
=
input_dim
self
.
batch_slots
=
[]
self
.
batch_slots
=
[]
self
.
scale
=
slot_dim
**
-
0.5
self
.
scale
=
slot_dim
**
-
0.5
self
.
slot_dim
=
slot_dim
self
.
slot_dim
=
slot_dim
# latent_dim
self
.
hidden_dim
=
hidden_dim
self
.
depth
=
depth
self
.
depth
=
depth
self
.
num_heads
=
8
self
.
self_head
=
self_heads
self
.
cross_heads
=
cross_heads
### Cross-attention layers
self
.
cs_layers
=
nn
.
ModuleList
([])
for
_
in
range
(
depth
):
# def __init__(self, dim, heads=8, dim_head=64, dropout=0., selfatt=True, kv_dim=None):
self
.
cs_layers
.
append
(
nn
.
ModuleList
([
PreNorm
(
self
.
slot_dim
,
Attention
(
self
.
slot_dim
,
heads
=
self
.
cross_heads
,
dim_head
=
self
.
hidden_dim
,
selfatt
=
False
)),
PreNorm
(
self
.
slot_dim
,
FeedForward
(
self
.
slot_dim
,
self
.
hidden_dim
))
]))
### Self-attention layers
self
.
sf_layers
=
nn
.
ModuleList
([])
for
_
in
range
(
depth
-
1
):
self
.
sf_layers
.
append
(
nn
.
ModuleList
([
PreNorm
(
self
.
input_dim
,
Attention
(
self
.
input_dim
,
heads
=
self
.
self_head
,
dim_head
=
self
.
hidden_dim
)),
PreNorm
(
self
.
input_dim
,
FeedForward
(
self
.
input_dim
,
self
.
hidden_dim
))
]))
### Initialize slots
self
.
randomize_initial_slots
=
randomize_initial_slots
self
.
randomize_initial_slots
=
randomize_initial_slots
self
.
initial_slots
=
nn
.
Parameter
(
torch
.
randn
(
num_slots
,
slot_dim
))
self
.
initial_slots
=
nn
.
Parameter
(
torch
.
randn
(
num_slots
,
slot_dim
))
#def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout=0., selfatt=True, kv_dim=None):
self
.
transformer_stage_1
=
Transformer
(
dim
=
384
,
depth
=
2
,
heads
=
8
)
self
.
transformer_stage_2
=
Transformer
(
dim
=
384
,
depth
=
2
,
heads
=
8
)
self
.
eps
=
eps
self
.
to_q
=
JaxLinear
(
slot_dim
,
slot_dim
,
bias
=
False
)
self
.
to_k
=
JaxLinear
(
input_dim
,
slot_dim
,
bias
=
False
)
self
.
to_v
=
JaxLinear
(
input_dim
,
slot_dim
,
bias
=
False
)
self
.
gru
=
nn
.
GRUCell
(
slot_dim
,
slot_dim
)
self
.
mlp
=
nn
.
Sequential
(
JaxLinear
(
slot_dim
,
hidden_dim
),
nn
.
ReLU
(
inplace
=
True
),
JaxLinear
(
hidden_dim
,
slot_dim
)
)
self
.
norm_input
=
nn
.
LayerNorm
(
input_dim
)
self
.
norm_slots
=
nn
.
LayerNorm
(
slot_dim
)
self
.
norm_pre_mlp
=
nn
.
LayerNorm
(
slot_dim
)
def
forward
(
self
,
inputs
):
def
forward
(
self
,
inputs
):
"""
"""
Args:
Args:
...
@@ -352,23 +352,21 @@ class TransformerSlotAttention(nn.Module):
...
@@ -352,23 +352,21 @@ class TransformerSlotAttention(nn.Module):
else
:
else
:
slots
=
self
.
initial_slots
.
unsqueeze
(
0
).
expand
(
batch_size
,
-
1
,
-
1
).
to
(
inputs
.
device
)
slots
=
self
.
initial_slots
.
unsqueeze
(
0
).
expand
(
batch_size
,
-
1
,
-
1
).
to
(
inputs
.
device
)
k
,
v
=
self
.
to_k
(
inputs
),
self
.
to_v
(
inputs
)
############### TODO : adapt this part of code
# data = torch.cat((data, enc_pos.reshape(b,-1,enc_pos.shape[-1])), dim = -1) TODO : add a positional encoding here
for
_
in
range
(
self
.
iters
):
x0
=
repeat
(
self
.
latents
,
'
n d -> b n d
'
,
b
=
b
)
slots_prev
=
slots
for
i
in
range
(
self
.
depth
):
norm_slots
=
self
.
norm_slots
(
slots
)
cross_attn
,
cross_ff
=
self
.
cs_layers
[
i
]
q
=
self
.
to_q
(
norm_slots
)
# cross attention only happens once for Perceiver IO
dots
=
torch
.
einsum
(
'
bid,bjd->bij
'
,
q
,
k
)
*
self
.
scale
x
=
cross_attn
(
x0
,
context
=
data
,
mask
=
mask
)
+
x0
x0
=
cross_ff
(
x
)
+
x
# shape: [batch_size, num_slots, num_inputs]
if
i
!=
self
.
depth
-
1
:
attn
=
dots
.
softmax
(
dim
=
1
)
+
self
.
eps
self_attn
,
self_ff
=
self
.
layers
[
i
]
attn
=
attn
/
attn
.
sum
(
dim
=-
1
,
keepdim
=
True
)
x_d
=
self_attn
(
data
)
+
data
updates
=
torch
.
einsum
(
'
bjd,bij->bid
'
,
v
,
attn
)
# shape: [batch_size, num_inputs, slot_dim]
data
=
self_ff
(
x_d
)
+
x_d
slots
=
self
.
gru
(
updates
.
flatten
(
0
,
1
),
slots_prev
.
flatten
(
0
,
1
))
slots
=
slots
.
reshape
(
batch_size
,
self
.
num_slots
,
self
.
slot_dim
)
slots
=
slots
+
self
.
mlp
(
self
.
norm_pre_mlp
(
slots
))
return
slots
# [batch_size, num_slots, dim]
return
slots
# [batch_size, num_slots, dim]
This diff is collapsed.
Click to expand it.
train_sa.py
+
6
−
6
View file @
3bbd64ae
...
@@ -68,13 +68,13 @@ def main():
...
@@ -68,13 +68,13 @@ def main():
train_dataset
=
data
.
get_dataset
(
'
train
'
,
cfg
[
'
data
'
])
train_dataset
=
data
.
get_dataset
(
'
train
'
,
cfg
[
'
data
'
])
train_loader
=
DataLoader
(
train_loader
=
DataLoader
(
train_dataset
,
batch_size
=
batch_size
,
num_workers
=
cfg
[
"
training
"
][
"
num_workers
"
],
pin_memory
=
True
,
train_dataset
,
batch_size
=
batch_size
,
num_workers
=
cfg
[
"
training
"
][
"
num_workers
"
],
shuffle
=
True
,
worker_init_fn
=
data
.
worker_init_fn
,
persistent_workers
=
True
)
shuffle
=
True
,
worker_init_fn
=
data
.
worker_init_fn
)
vis_dataset
=
data
.
get_dataset
(
'
test
'
,
cfg
[
'
data
'
])
vis_dataset
=
data
.
get_dataset
(
'
test
'
,
cfg
[
'
data
'
])
vis_loader
=
DataLoader
(
vis_loader
=
DataLoader
(
vis_dataset
,
batch_size
=
batch_size
,
num_workers
=
cfg
[
"
training
"
][
"
num_workers
"
],
pin_memory
=
True
,
vis_dataset
,
batch_size
=
batch_size
,
num_workers
=
cfg
[
"
training
"
][
"
num_workers
"
],
shuffle
=
True
,
worker_init_fn
=
data
.
worker_init_fn
,
persistent_workers
=
True
)
shuffle
=
True
,
worker_init_fn
=
data
.
worker_init_fn
)
model
=
SlotAttentionAutoEncoder
(
resolution
,
num_slots
,
num_iterations
).
to
(
device
)
model
=
SlotAttentionAutoEncoder
(
resolution
,
num_slots
,
num_iterations
).
to
(
device
)
num_params
=
sum
(
p
.
numel
()
for
p
in
model
.
parameters
())
num_params
=
sum
(
p
.
numel
()
for
p
in
model
.
parameters
())
...
@@ -98,8 +98,8 @@ def main():
...
@@ -98,8 +98,8 @@ def main():
global_step
=
ckpt
[
'
global_step
'
]
global_step
=
ckpt
[
'
global_step
'
]
start
=
time
.
time
()
start
=
time
.
time
()
for
_
in
range
(
num_train_steps
)
:
for
batch
in
train_loader
:
batch
=
next
(
iter
(
train_loader
))
#
batch = next(iter(train_loader))
# Learning rate warm-up.
# Learning rate warm-up.
if
global_step
<
warmup_steps
:
if
global_step
<
warmup_steps
:
...
...
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