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shensq
mi_counseling
Commits
b98e456d
Commit
b98e456d
authored
4 years ago
by
shensq
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collate_fn for conditional experiment
parent
89d4adaa
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code/gpt_loader/load_data.py
+38
-4
38 additions, 4 deletions
code/gpt_loader/load_data.py
with
38 additions
and
4 deletions
code/gpt_loader/load_data.py
+
38
−
4
View file @
b98e456d
...
...
@@ -211,6 +211,34 @@ def collate_fn(data):
attention_mask
=
attention_mask
.
cuda
()
return
Variable
(
LongTensor
(
src_seqs
)),
Variable
(
LongTensor
(
trg_seqs
)),
Variable
(
LongTensor
(
pos_seqs
)),
Variable
(
LongTensor
(
lm_seqs
)),
total_input_length
,
attention_mask
def
collate_fn_conditional
(
data
):
def
merge
(
sequences
):
lengths
=
[
len
(
seq
)
for
seq
in
sequences
]
padded_seqs
=
torch
.
zeros
(
len
(
sequences
),
max
(
lengths
)).
long
()
for
i
,
seq
in
enumerate
(
sequences
):
end
=
lengths
[
i
]
padded_seqs
[
i
,
:
end
]
=
seq
[:
end
]
return
padded_seqs
,
lengths
# sort a list by sequence length (descending order) to use pack_padded_sequence
data
.
sort
(
key
=
lambda
x
:
len
(
x
[
0
]),
reverse
=
True
)
# seperate source and target sequences
src_seqs
,
trg_seqs
,
pos_seqs
,
lm_seqs
,
total_input_length
,
meta
=
zip
(
*
data
)
# merge sequences (from tuple of 1D tensor to 2D tensor)
src_seqs
,
src_lengths
=
merge
(
src_seqs
)
trg_seqs
,
trg_lengths
=
merge
(
trg_seqs
)
pos_seqs
,
pos_lengths
=
merge
(
pos_seqs
)
lm_seqs
,
lm_lengths
=
merge
(
lm_seqs
)
if
USE_CUDA
:
src_seqs
=
src_seqs
.
cuda
()
trg_seqs
=
trg_seqs
.
cuda
()
pos_seqs
=
pos_seqs
.
cuda
()
lm_seqs
=
lm_seqs
.
cuda
()
return
Variable
(
LongTensor
(
src_seqs
)),
Variable
(
LongTensor
(
trg_seqs
)),
Variable
(
LongTensor
(
pos_seqs
)),
Variable
(
LongTensor
(
lm_seqs
)),
total_input_length
,
meta
def
collate_fn_nli
(
data
):
"""
Creates mini-batch tensors from the list of tuples (src_seq, trg_seq).
We should build a custom collate_fn rather than using default collate_fn,
...
...
@@ -857,10 +885,16 @@ def get_data(args, tokenizer, split_size):
if
'
train_batch_size
'
not
in
args
:
args
.
train_batch_size
=
1
data_loader
=
DataLoader
(
dataset
=
gpt_train
,
batch_size
=
args
.
train_batch_size
,
shuffle
=
True
,
drop_last
=
True
,
collate_fn
=
collate_fn
)
test_loader
=
DataLoader
(
dataset
=
gpt_test
,
batch_size
=
1
,
shuffle
=
False
,
drop_last
=
False
,
collate_fn
=
collate_fn
)
val_loader
=
DataLoader
(
dataset
=
gpt_val
,
batch_size
=
1
,
shuffle
=
False
,
drop_last
=
False
,
collate_fn
=
collate_fn
)
if
args
.
conditional
:
data_loader
=
DataLoader
(
dataset
=
gpt_train
,
batch_size
=
args
.
train_batch_size
,
shuffle
=
True
,
drop_last
=
True
,
collate_fn
=
collate_fn_conditional
)
test_loader
=
DataLoader
(
dataset
=
gpt_test
,
batch_size
=
1
,
shuffle
=
False
,
drop_last
=
False
,
collate_fn
=
collate_fn_conditional
)
val_loader
=
DataLoader
(
dataset
=
gpt_val
,
batch_size
=
1
,
shuffle
=
False
,
drop_last
=
False
,
collate_fn
=
collate_fn_conditional
)
else
:
data_loader
=
DataLoader
(
dataset
=
gpt_train
,
batch_size
=
args
.
train_batch_size
,
shuffle
=
True
,
drop_last
=
True
,
collate_fn
=
collate_fn
)
test_loader
=
DataLoader
(
dataset
=
gpt_test
,
batch_size
=
1
,
shuffle
=
False
,
drop_last
=
False
,
collate_fn
=
collate_fn
)
val_loader
=
DataLoader
(
dataset
=
gpt_val
,
batch_size
=
1
,
shuffle
=
False
,
drop_last
=
False
,
collate_fn
=
collate_fn
)
return
data_loader
,
test_loader
,
val_loader
def
prepare_mix_review
(
args
,
tokenizer
):
...
...
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