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Solve the problem of fetching prediction and make data dim configurable #772

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Mar 27, 2018
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Solve the problem of fetching prediction and make data dim configurable #772

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8268ddc
Solve the problem of fetching prediction
Mar 26, 2018
b6baf32
Format the indent of args list
Mar 27, 2018
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14 changes: 12 additions & 2 deletions fluid/DeepASR/infer_by_ckpt.py
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Original file line number Diff line number Diff line change
Expand Up @@ -32,6 +32,11 @@ def parse_args():
default=1,
help='The minimum sequence number of a batch data. '
'(default: %(default)d)')
parser.add_argument(
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@zhxfl Seems this value should be calculated by data reader.

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There is a contradiction: After the data reader really begins to read data, it knows the actual dimensions of frame and label data. But before that, the network has been parsed and the right dimensions of feeding data are needed in advance. So the better way is to set this dimension manually.

'--frame_dim',
type=int,
default=120 * 11,
help='Frame dimension of feature data. (default: %(default)d)')
parser.add_argument(
'--stacked_num',
type=int,
Expand All @@ -47,6 +52,11 @@ def parse_args():
type=int,
default=1024,
help='Hidden size of lstmp unit. (default: %(default)d)')
parser.add_argument(
'--class_num',
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Same as above.

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Explained in last comment.

type=int,
default=1749,
help='Number of classes in label. (default: %(default)d)')
parser.add_argument(
'--learning_rate',
type=float,
Expand Down Expand Up @@ -99,10 +109,11 @@ def infer_from_ckpt(args):
raise IOError("Invalid checkpoint!")

prediction, avg_cost, accuracy = stacked_lstmp_model(
frame_dim=args.frame_dim,
hidden_dim=args.hidden_dim,
proj_dim=args.proj_dim,
stacked_num=args.stacked_num,
class_num=1749,
class_num=args.class_num,
parallel=args.parallel)

infer_program = fluid.default_main_program().clone()
Expand Down Expand Up @@ -156,7 +167,6 @@ def infer_from_ckpt(args):
for index, sample in enumerate(infer_batch):
print("Decoding %d: " % (batch_id * args.batch_size + index),
decoder.decode(sample))

print(np.mean(infer_costs), np.mean(infer_accs))


Expand Down
23 changes: 13 additions & 10 deletions fluid/DeepASR/model_utils/model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,7 +6,8 @@
import paddle.fluid as fluid


def stacked_lstmp_model(hidden_dim,
def stacked_lstmp_model(frame_dim,
hidden_dim,
proj_dim,
stacked_num,
class_num,
Expand All @@ -20,12 +21,13 @@ def stacked_lstmp_model(hidden_dim,
label data respectively. And in inference, only `feature` is needed.

Args:
hidden_dim(int): The hidden state's dimension of the LSTMP layer.
proj_dim(int): The projection size of the LSTMP layer.
stacked_num(int): The number of stacked LSTMP layers.
parallel(bool): Run in parallel or not, default `False`.
is_train(bool): Run in training phase or not, default `True`.
class_dim(int): The number of output classes.
frame_dim(int): The frame dimension of feature data.
hidden_dim(int): The hidden state's dimension of the LSTMP layer.
proj_dim(int): The projection size of the LSTMP layer.
stacked_num(int): The number of stacked LSTMP layers.
parallel(bool): Run in parallel or not, default `False`.
is_train(bool): Run in training phase or not, default `True`.
class_dim(int): The number of output classes.
"""

# network configuration
Expand Down Expand Up @@ -78,7 +80,7 @@ def _net_conf(feature, label):

# data feeder
feature = fluid.layers.data(
name="feature", shape=[-1, 120 * 11], dtype="float32", lod_level=1)
name="feature", shape=[-1, frame_dim], dtype="float32", lod_level=1)
label = fluid.layers.data(
name="label", shape=[-1, 1], dtype="int64", lod_level=1)

Expand All @@ -92,11 +94,12 @@ def _net_conf(feature, label):
feat_ = pd.read_input(feature)
label_ = pd.read_input(label)
prediction, avg_cost, acc = _net_conf(feat_, label_)
for out in [avg_cost, acc]:
for out in [prediction, avg_cost, acc]:
pd.write_output(out)

# get mean loss and acc through every devices.
avg_cost, acc = pd()
prediction, avg_cost, acc = pd()
prediction.stop_gradient = True
avg_cost = fluid.layers.mean(x=avg_cost)
acc = fluid.layers.mean(x=acc)
else:
Expand Down
13 changes: 12 additions & 1 deletion fluid/DeepASR/tools/profile.py
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Original file line number Diff line number Diff line change
Expand Up @@ -31,6 +31,11 @@ def parse_args():
default=1,
help='The minimum sequence number of a batch data. '
'(default: %(default)d)')
parser.add_argument(
'--frame_dim',
type=int,
default=120 * 11,
help='Frame dimension of feature data. (default: %(default)d)')
parser.add_argument(
'--stacked_num',
type=int,
Expand All @@ -46,6 +51,11 @@ def parse_args():
type=int,
default=1024,
help='Hidden size of lstmp unit. (default: %(default)d)')
parser.add_argument(
'--class_num',
type=int,
default=1749,
help='Number of classes in label. (default: %(default)d)')
parser.add_argument(
'--learning_rate',
type=float,
Expand Down Expand Up @@ -119,10 +129,11 @@ def profile(args):
"arg 'first_batches_to_skip' must not be smaller than 0.")

_, avg_cost, accuracy = stacked_lstmp_model(
frame_dim=args.frame_dim,
hidden_dim=args.hidden_dim,
proj_dim=args.proj_dim,
stacked_num=args.stacked_num,
class_num=1749,
class_num=args.class_num,
parallel=args.parallel)

optimizer = fluid.optimizer.Adam(learning_rate=args.learning_rate)
Expand Down
13 changes: 12 additions & 1 deletion fluid/DeepASR/train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -30,6 +30,11 @@ def parse_args():
default=1,
help='The minimum sequence number of a batch data. '
'(default: %(default)d)')
parser.add_argument(
'--frame_dim',
type=int,
default=120 * 11,
help='Frame dimension of feature data. (default: %(default)d)')
parser.add_argument(
'--stacked_num',
type=int,
Expand All @@ -45,6 +50,11 @@ def parse_args():
type=int,
default=1024,
help='Hidden size of lstmp unit. (default: %(default)d)')
parser.add_argument(
'--class_num',
type=int,
default=1749,
help='Number of classes in label. (default: %(default)d)')
parser.add_argument(
'--pass_num',
type=int,
Expand Down Expand Up @@ -137,10 +147,11 @@ def train(args):
os.mkdir(args.infer_models)

prediction, avg_cost, accuracy = stacked_lstmp_model(
frame_dim=args.frame_dim,
hidden_dim=args.hidden_dim,
proj_dim=args.proj_dim,
stacked_num=args.stacked_num,
class_num=1749,
class_num=args.class_num,
parallel=args.parallel)

# program for test
Expand Down