forked from BenSaunders27/ProgressiveTransformersSLP
-
Notifications
You must be signed in to change notification settings - Fork 0
/
text_to_sign.py
283 lines (238 loc) · 8.63 KB
/
text_to_sign.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
#!/usr/bin/env python3
import re
import sys
import os
import dill as pickle
import torch
from .model import build_model
from .helpers import (
load_config,
load_checkpoint,
get_latest_checkpoint,
)
from .prediction import validate_on_data
from .training import TrainManager
from torchtext import data
from torchtext.data import Dataset
from .constants import UNK_TOKEN, PAD_TOKEN, TARGET_PAD
from .vocabulary import build_vocab, Vocabulary
from .data import SignProdDataset
from flask import Flask, request
# shortened version of load_data
def load_data(
cfg: dict,
) -> (Dataset, Vocabulary, Vocabulary):
"""
Load train, dev and optionally test data as specified in configuration.
Vocabularies are created from the training set with a limit of `voc_limit`
tokens and a minimum token frequency of `voc_min_freq`
(specified in the configuration dictionary).
The training data is filtered to include sentences up to `max_sent_length`
on source and target side.
:param data_cfg: configuration dictionary for data
("data" part of configuation file)
:return:
- train_data: training dataset
- dev_data: development dataset
- test_data: testdata set if given, otherwise None
- src_vocab: source vocabulary extracted from training data
- trg_vocab: target vocabulary extracted from training data
"""
data_cfg = cfg["data"]
# Source, Target and Files postfixes
src_lang = data_cfg["src"]
trg_lang = data_cfg["trg"]
files_lang = data_cfg.get("files", "files")
# Train, Dev and Test Path
train_path = data_cfg["train"]
test_path = data_cfg["test"]
if os.path.isfile(test_path + ".pth"):
os.unlink(test_path + ".pth")
level = "word"
lowercase = False
max_sent_length = data_cfg["max_sent_length"]
# Target size is plus one due to the counter required for the model
trg_size = cfg["model"]["trg_size"] + 1
# Skip frames is used to skip a set proportion of target frames, to simplify the model requirements
skip_frames = data_cfg.get("skip_frames", 1)
EOS_TOKEN = "</s>"
tok_fun = lambda s: list(s) if level == "char" else s.split()
# Source field is a tokenised version of the source words
src_field = data.Field(
init_token=None,
eos_token=EOS_TOKEN,
pad_token=PAD_TOKEN,
tokenize=tok_fun,
batch_first=True,
lower=lowercase,
unk_token=UNK_TOKEN,
include_lengths=True,
)
# Files field is just a raw text field
files_field = data.RawField()
def tokenize_features(features):
features = torch.as_tensor(features)
ft_list = torch.split(features, 1, dim=0)
return [ft.squeeze() for ft in ft_list]
def stack_features(features, something):
return torch.stack([torch.stack(ft, dim=0) for ft in features], dim=0)
# Creating a regression target field
# Pad token is a vector of output size, containing the constant TARGET_PAD
reg_trg_field = data.Field(
sequential=True,
use_vocab=False,
dtype=torch.float32,
batch_first=True,
include_lengths=False,
pad_token=torch.ones((trg_size,)) * TARGET_PAD,
preprocessing=tokenize_features,
postprocessing=stack_features,
)
# Create the Training Data, using the SignProdDataset
train_data = SignProdDataset(
path=train_path,
exts=("." + src_lang, "." + trg_lang, "." + files_lang),
fields=(src_field, reg_trg_field, files_field),
trg_size=trg_size,
skip_frames=skip_frames,
filter_pred=lambda x: len(vars(x)["src"]) <= max_sent_length
and len(vars(x)["trg"]) <= max_sent_length,
)
src_max_size = data_cfg.get("src_voc_limit", sys.maxsize)
src_min_freq = data_cfg.get("src_voc_min_freq", 1)
src_vocab_file = data_cfg.get("src_vocab", None)
if os.path.isfile("src_vocab.pth"):
# print("loading binary src_vocab")
with open("src_vocab.pth", "rb") as f:
src_vocab = pickle.load(f)
# print("done")
else:
# print("build_vocab")
src_vocab = build_vocab(
field="src",
min_freq=src_min_freq,
max_size=src_max_size,
dataset=train_data,
vocab_file=src_vocab_file,
)
# print("saving src_vocab")
with open("src_vocab.pth", "wb") as f:
pickle.dump(src_vocab, f)
# print("done")
# Create a target vocab just as big as the required target vector size -
# So that len(trg_vocab) is # of joints + 1 (for the counter)
trg_vocab = [None] * trg_size
# Create the Testing Data
test_data = SignProdDataset(
path=test_path,
exts=("." + src_lang, "." + trg_lang, "." + files_lang),
trg_size=trg_size,
fields=(src_field, reg_trg_field, files_field),
skip_frames=skip_frames,
)
src_field.vocab = src_vocab
return test_data, src_vocab, trg_vocab
cfg_file = "Configs/Base.yaml"
print("Loading saved model")
# Load the config file
cfg = load_config(cfg_file)
# Load the model directory and checkpoint
model_dir = cfg["training"]["model_dir"]
# when checkpoint is not specified, take latest (best) from model dir
ckpt = None
if ckpt is None:
# print("get_latest_checkpoint")
ckpt = get_latest_checkpoint(model_dir, post_fix="_best")
if ckpt is None:
raise FileNotFoundError(
"No checkpoint found in directory {}.".format(model_dir)
)
batch_size = cfg["training"].get("eval_batch_size", cfg["training"]["batch_size"])
batch_type = cfg["training"].get(
"eval_batch_type", cfg["training"].get("batch_type", "sentence")
)
use_cuda = cfg["training"].get("use_cuda", False)
eval_metric = cfg["training"]["eval_metric"]
max_output_length = cfg["training"].get("max_output_length", None)
# load the data
# print("load_data")
test_data, src_vocab, trg_vocab = load_data(cfg=cfg)
# To produce testing results
data_to_predict = {"test": test_data}
# To produce validation results
# data_to_predict = {"dev": dev_data}
# Load model state from disk
# print("load_checkpoint")
model_checkpoint = load_checkpoint(ckpt, use_cuda=use_cuda)
# Build model and load parameters into it
# print("build_model")
model = build_model(cfg, src_vocab=src_vocab, trg_vocab=trg_vocab)
model.load_state_dict(model_checkpoint["model_state"])
# If cuda, set model as cuda
if use_cuda:
model.cuda()
# print("save binary")
# torch.save(model, "model.binary")
# print("saved model")
# Set up trainer to produce videos
# print("TrainManager")
trainer = TrainManager(model=model, config=cfg, test=True)
app = Flask(__name__)
@app.route("/text", methods=["GET", "POST"])
def speech():
if request.method == "POST":
data = str(request.data)
print(data)
input_text = request.form["query"]
print(input_text)
# print("inside test()")
text = re.sub(r"[^\w\s]", "", input_text)
text = text.lower()
if text[0] == " ":
text = text[1:]
if text[-1] == " ":
text = text[:-1]
if text[-1] != ".":
text += " ."
with open("../data_aud_text/test.text", "w") as f:
f.write(text)
with open("../data_aud_text/test.file", "w") as f:
f.write("inconnect_input")
test_data, src_vocab, trg_vocab = load_data(cfg=cfg)
# To produce testing results
data_to_predict = {"test": test_data}
print("data_to_predict", data_to_predict)
# For each of the required data, produce results
for data_set_name, data_set in data_to_predict.items():
# print("data_set_name", data_set_name)
# print("data_set", data_set)
# Validate for this data set
print("Generating poses")
score, loss, references, hypotheses, inputs, all_dtw_scores, file_paths = (
validate_on_data(
model=trainer.model,
data=data_set,
batch_size=batch_size,
max_output_length=max_output_length,
eval_metric=eval_metric,
loss_function=None,
batch_type=batch_type,
type="val",
)
)
# Set which sequences to produce video for
display = list(range(len(hypotheses)))
# Produce videos for the produced hypotheses
print("Producing video")
video = trainer.produce_validation_video(
output_joints=hypotheses,
inputs=inputs,
references=references,
model_dir=model_dir,
display=display,
type="test",
file_paths=file_paths,
text=input_text,
)
print("Video saved to", video)
return video