-
Notifications
You must be signed in to change notification settings - Fork 0
/
train_utils.py
478 lines (409 loc) · 19.8 KB
/
train_utils.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
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
import os
from abc import ABC
import numpy as np
from sklearn.metrics import accuracy_score
from utils.utils import feature_scaling
from utils.shallow_classifiers import shallow_clf_accuracy
from scipy.spatial.distance import cdist
import time
# suppress TF low level logging info
os.environ['TF_CPP_MIN_LOG_LEVEL'] = "3"
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from models.model import get_model, get_callbacks
from data_utils import get_dataset
import tensorflow as tf
# from utils.ramps import linear_rampup
# from data_utils.SS_generator import combined_pseudo_generator
template1 = "Labeled= {} selection={}% iterations= {}"
template2 = 'total selected based on percentile {} having accuracy {:.2f}%'
template3 = 'Length of predicted {}, unlabeled {}'
def set_dataset(dataset, lt, semi=True, scale=True):
"""
Create dataset object containing labeled, unlabeled, and test datasets
:param dataset: Name of dataset. mnist, fashion_mnist, svhn_cropped, cifar10, plant32, plant64 or plant96
:param lt: One of cross-entropy, triplet, arcface, or contrastive
:param semi: semi=true means N-labelled, semi=False means all-labelled
:param scale: between 0...1
:return: dataset object and dataset details
"""
one_hot = True if lt.lower() == "arcface" else False
dso, data_config = get_dataset.read_data_sets(dataset, one_hot, semi, scale=scale)
return dso, data_config
def set_model(arch, data_config, weights, loss_type="", opt="adam", lr=1e-3):
"""
setup a model compiled based on given loss function
:rtype: tf.keras.model
"""
model = get_model(arch, data_config, weights, loss_type, opt, lr)
model.summary()
return model
def get_log_name(flags, data_config, prefix=""):
"""
Creates a name for target directory and file for saving training logs
:param flags: training flags
:param data_config: dataset details
:param prefix: string to be added
:return: path of directory and name of file for saving logs
"""
path = prefix + flags.lt + '_logs/' + flags.dataset + '/' + flags.network + '/'
log_name = str(data_config.n_label) + '-'
weights = '-w' if flags.weights else ''
if flags.lt != "cross-entropy":
log_name += flags.lbl + '-'
log_name += flags.opt.lower() + weights
if flags.self_training:
log_name = log_name + '-self-training-'
if flags.lt != "cross-entropy":
log_name += flags.confidence_measure
return path, log_name
def compute_supervised_accuracy(model, imgs, lbls, ret_labels=False, v=0, bs=100):
"""
Evaluates accuracy of the model from softmax probabilities.
:param model: tf.keras.model
:param imgs: test images
:param lbls: test labels
:param ret_labels: return predicted labels or not
:param v: verbose. By default 0.
:param bs: size of mini-batch
:return: accuracy or accuracy and predicted labels
"""
accuracy = model.evaluate(imgs, lbls, verbose=v, batch_size=bs)
if isinstance(accuracy, list):
accuracy = accuracy[1]
accuracy = np.round(accuracy * 100., 2)
if ret_labels:
pred_lbls = model.predict(imgs, verbose=v, batch_size=bs)
return accuracy, pred_lbls
return accuracy
def get_network_embeddings(model, input_images, lt, bs=100):
"""
Returns network embeddings, can be used for accuracy calculation for metric learning losses.
:param model: tf.keras.model
:param input_images: input images
:param lt: loss_type: One of cross-entropy, triplet, arcface, or contrastive
:param bs: size of mini-batch
:return: embeddings
"""
emb = model.predict(input_images, batch_size=bs)
if isinstance(emb, list):
emb = emb[-1]
return emb
# if 'face' in lt:
# inp = model.input[0] # input placeholder
# layer = -3
# else:
# inp = model.input # input placeholder
# layer = -2
# outputs = model.layers[layer].output # all layer outputs
# emb = models.Model(inp, outputs)
# feat = emb.predict(input_images, batch_size=bs)
#
# return feat
def get_network_output(model, input_images, lt="", scaling=False, v=0, bs=100):
"""
Returns the output of network model on given input images.
:param model: tf.keras.model
:param input_images: input images
:param lt: loss_type: One of cross-entropy, triplet, arcface, or contrastive
:param scaling: perform feature scaling
:param v: verbosity
:param bs: size of mini-batch
:return: output features
"""
if 'cross-entropy' in lt:
feat = model.predict(input_images, verbose=v, batch_size=bs)
else:
feat = get_network_embeddings(model, input_images, lt)
if scaling:
feat, _, _ = feature_scaling(feat)
return feat
def compute_embeddings_accuracy(model, imgs, lbls, test_imgs, test_lbls, labelling="knn", loss_type="",
ret_labels=False, scaling=True):
"""
Calculate accuracy from embeddings for metric learning losses.
:param model: tf.keras.model
:param imgs: labeled images
:param lbls: labels of labeled images
:param test_imgs: test images
:param test_lbls: labels of test images
:param labelling: type of shallow classifier. can be one of knn: k-nearest-neighbor, lda: linear discriminant
analysis, rf: random forest, and lr: logistic regression
:param loss_type: loss_type: One of cross-entropy, triplet, arcface, or contrastive
:param ret_labels: whether to return predicted labels
:param scaling: apply feature scaling or not
:return: test accuracy
"""
if lbls.ndim > 1: # for Arcface, convert one-hot encodings to simple
lbls = np.argmax(lbls, 1)
test_lbls = np.argmax(test_lbls, 1)
labels, accuracy = shallow_clf_accuracy(get_network_output(model, imgs, loss_type, scaling=scaling), lbls,
get_network_output(model, test_imgs, loss_type, scaling=scaling),
test_lbls, labelling)
accuracy = np.round(accuracy * 100., 2)
if ret_labels:
return accuracy, labels
return accuracy
def compute_accuracy(model, train_images, train_labels, test_images, test_labels, loss_type="cross-entropy",
labelling="knn"):
"""
computes test accuracy from either softmax probabilities or from embeddings by training a shallow classifier.
:rtype: accuracy
"""
if 'cross-entropy' in loss_type:
ac = compute_supervised_accuracy(model, test_images, test_labels)
else:
ac = compute_embeddings_accuracy(model, train_images, train_labels, test_images, test_labels,
loss_type=loss_type, labelling=labelling)
return ac
def log_accuracy(model, dso, loss_type="", semi=True, labelling="knn"):
"""
computes test accuracy from dataset object either by using softmax probabilities or embeddings by training a
shallow classifier.
:rtype: accuracy
"""
if semi:
acc = compute_accuracy(model, dso.train.labeled_ds.images, dso.train.labeled_ds.labels, dso.test.images,
dso.test.labels, loss_type=loss_type, labelling=labelling)
else:
acc = compute_accuracy(model, dso.train.images, dso.train.labels, dso.test.images, dso.test.labels,
loss_type=loss_type, labelling=labelling)
return acc
def start_training(model, dso, epochs=100, semi=True, bs=100, verb=1):
"""
Starts training
:param model: tf.keras.model
:param dso: dataset object containing train.labeled, train.unlabeled, and test datasets
:param epochs: training for the number of epochs
:param semi: semi=True : N-labelled, semi=False: All-labelled
param verb:
:param bs:
"""
if semi: # N-labelled
images, labels = dso.train.labeled_ds.images, dso.train.labeled_ds.labels
else: # all-labelled examples
images, labels = dso.train.images, dso.train.labels,
do_training(model, images, labels, dso.test.images, dso.test.labels, train_iter=epochs, batch_size=bs, verb=verb)
def do_training(model, images, labels, test_images, test_labels, train_iter=10, batch_size=100, print_test_error=False,
verb=1, hflip=True, vf=1, iter=''):
calls = get_callbacks(verb)
csv_path = "./csvs/{}-{}-supervised-{}-{}.csv".format(iter, str(len(labels)), time.strftime("%d-%m-%Y-%H%M%S"),
os.uname()[1])
print("saving losses at ", csv_path)
csv = tf.keras.callbacks.CSVLogger(csv_path)
calls.append(csv)
aug = ImageDataGenerator(width_shift_range=0.125, height_shift_range=0.125, fill_mode='nearest',
horizontal_flip=hflip)
test_aug = ImageDataGenerator()
test_generator = test_aug.flow(test_images, test_labels, batch_size=batch_size)
train_generator = aug.flow(images, labels, batch_size=batch_size)
steps_per_epoch = int(len(labels) / batch_size)
if print_test_error:
history = model.fit(train_generator, epochs=train_iter, verbose=verb,
steps_per_epoch=steps_per_epoch, validation_data=test_generator,
validation_freq=vf, callbacks=calls)
else:
history = model.fit(train_generator, epochs=train_iter, verbose=verb, steps_per_epoch=steps_per_epoch,
callbacks=calls)
return history
class CustomModel(tf.keras.Model):
def __init__(self, base_model, lt, emb_size=64, dropout_rate=0.2, num_classes=10):
super(CustomModel, self).__init__()
self.base_model = base_model
self.classification_head = tf.keras.Sequential([
tf.keras.layers.Dropout(dropout_rate, name="dropout_out"),
tf.keras.layers.Dense(emb_size, name="embeddings")
])
if 'triplet' in lt:
self.classification_head.add(tf.keras.layers.Lambda(lambda x: tf.math.l2_normalize(x, axis=1),
name="l2-normalisation")) # l2-normalisation
else: # default cross-entropy loss
self.classification_head.add(tf.keras.layers.Dense(num_classes, name="fc_out"))
self.loss_tracker = tf.keras.metrics.Mean(name="loss")
self.acc_tracker = tf.keras.metrics.SparseCategoricalAccuracy(name="acc")
# self.val_loss_tracker = tf.keras.metrics.Mean(name="val-loss")
# self.val_acc_tracker = tf.keras.metrics.SparseCategoricalAccuracy(name="val-acc")
@property
def metrics(self):
return [self.loss_tracker, self.acc_tracker]
def call(self, inputs):
x = self.base_model(inputs)
x = self.classification_head(x)
return x
def train_step(self, data):
# Unpack the data.
imgs, lbls = data
# Forward pass through the encoder and predictor.
with tf.GradientTape() as tape:
base_output = self.base_model(imgs)
output = self.classification_head(base_output)
loss = self.loss(lbls, output)
# Compute gradients and update the parameters.
learnable_params = (
self.base_model.trainable_variables + self.classification_head.trainable_variables
)
gradients = tape.gradient(loss, learnable_params)
self.optimizer.apply_gradients(zip(gradients, learnable_params))
# Monitor loss.
self.loss_tracker.update_state(loss)
self.acc_tracker.update_state(lbls, output)
return {"loss": self.loss_tracker.result(), "acc": self.acc_tracker.result()}
def test_step(self, data):
# Unpack the data.
imgs, lbls = data
base_output = self.base_model(imgs)
output = self.classification_head(base_output)
loss = self.loss(lbls, output)
self.acc_tracker.update_state(lbls, output)
self.loss_tracker.update_state(loss)
return {"loss": self.loss_tracker.result(), "acc": self.acc_tracker.result()}
def get_custom_model(arch, data_config, weights, loss_type="", opt="adam", lr=1e-3):
_, [conv_base, _, optimizer, _] = get_model(arch, data_config, weights, loss_type, opt, lr,
ret_base_model=True)
model = CustomModel(conv_base, loss_type, num_classes=data_config.nc)
if loss_type == "triplet":
# from losses.Triplet import triplet_loss
import tensorflow_addons as tfa
loss = tfa.losses.TripletSemiHardLoss() # triplet_loss
else:
loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
model.compile(optimizer, loss=loss, metrics=['acc', tf.keras.metrics.SparseCategoricalAccuracy()])
input_shape = (None, data_config.size, data_config.size, data_config.channels)
model.build(input_shape=input_shape)
model.summary()
return model
def start_self_learning(model, dso, dc, lt, i, mti, bs, logger):
self_learning(model, dso, lt, logger, i, dc.sp, mti, bs)
def pseudo_label_selection(imgs, pred_lbls, scores, orig_lbls, p=0.05):
"""
Select top p% pseudo-labels based on confidence score. Stratified selection.
:param imgs: images
:param pred_lbls: predicted labels
:param scores: confidence score
:param orig_lbls: original labels of images
:param p: selection percentile p%
:return: pseudo-labelled images and their indices
"""
to_select = int(len(pred_lbls) * p)
pseudo_images = []
pseudo_labels = []
orig_lbls_selected = []
number_classes = np.unique(pred_lbls)
per_class = to_select // len(number_classes)
args = np.argsort(scores)
indices = []
for key in number_classes: # for all classes
selected = 0
for index in args:
if pred_lbls[index] == key:
pseudo_images.append(imgs[index])
pseudo_labels.append(pred_lbls[index])
indices.append(index)
orig_lbls_selected.append(orig_lbls[index])
selected += 1
if per_class == selected:
break
orig_lbls_selected = np.array(orig_lbls_selected)
pseudo_labels = np.array(pseudo_labels)
if orig_lbls_selected.ndim > 1:
acc = accuracy_score(np.argmax(orig_lbls_selected, 1), pseudo_labels) * 100.
else:
acc = accuracy_score(orig_lbls_selected, pseudo_labels) * 100.
return np.array(pseudo_images), pseudo_labels, indices, acc
def assign_labels(model, train_labels, train_images, unlabeled_imgs, unlabeled_lbls, lt="cross-entropy"):
"""
compute labels and prediction score. For cross-entropy loss, softmax probabilities are used for label assignment and
prediction score. For metric learning losses, 1-nearest-neighbour or local learning with global consistency (LLGC)
is used for label assignment and for prediction score.
:param model: tf.keras.model
:param train_labels: training labels
:param train_images: training images
:param unlabeled_imgs:
:param unlabeled_lbls:
:param lt: loss type
:return: predicted labels, prediction score and accuracy for unlabelled examples
"""
if unlabeled_lbls.ndim > 1: # if labels are one-hot encoded
train_labels = np.argmax(train_labels, 1)
unlabeled_lbls = np.argmax(unlabeled_lbls, 1)
if lt == "cross-entropy":
test_image_feat = model.predict(unlabeled_imgs)
pred_lbls = np.argmax(test_image_feat, 1)
calc_score = np.max(test_image_feat, 1)
calc_score = calc_score * -1. # negate probs for same notion as distance
else: # for other loss functions
# default to 1-NN distance as confidence score
pred_lbls = []
calc_score = []
k = 1
test_image_feat = get_network_output(model, unlabeled_imgs, lt)
current_labeled_train_feat = get_network_output(model, train_images, lt)
for j in range(len(test_image_feat)):
search_feat = np.expand_dims(test_image_feat[j], 0)
# calculate the sqeuclidean similarity and sort
dist = cdist(current_labeled_train_feat, search_feat, 'sqeuclidean')
rank = np.argsort(dist.ravel())
pred_lbls.append(train_labels[rank[:k]])
calc_score.append(dist[rank[0]])
pred_lbls = np.array(pred_lbls)
pred_lbls = pred_lbls.squeeze()
pred_acc = accuracy_score(unlabeled_lbls, pred_lbls)*100.
# print('predicted accuracy {:.2f} %'.format(pred_acc))
calc_score = np.array(calc_score)
pred_score = calc_score.squeeze()
return pred_lbls, pred_score, pred_acc
def self_learning(model, mdso, lt, logger, num_iterations=25, percentile=0.05, epochs=200, bs=100):
"""
Apply self-learning
:param model: tf.keras.model
:param mdso: dataset object containing labeled, unlabeled, and test datasets
:param lt: loss type
:param logger: for printing/saving logs
:param num_iterations: number of meta-iterations. Default 25
:param percentile: selection percentile `p%` of pseudo-labels. Default `5%`
:param epochs: number of epochs in each meta-iteration
:param bs: mini-batch size
:return: images [initially-labeled+pseudo-labelled], labels [initially-labeled+pseudo-labelled]
"""
# Initial labeled data
imgs = mdso.train.labeled_ds.images
lbls = mdso.train.labeled_ds.labels
# Initial unlabeled data
unlabeled_imgs = mdso.train.unlabeled_ds.images
unlabeled_lbls = mdso.train.unlabeled_ds.labels
if lbls.ndim > 1:
n_classes = len(np.unique(np.argmax(lbls, 1)))
else:
n_classes = len(np.unique(lbls))
n_label = len(lbls)
logger.info(template1.format(n_label, 100 * percentile, num_iterations))
logger.info("i-th meta-iteration, unlabelled accuracy, pseudo-label accuracy,test accuracy")
for i in range(num_iterations):
print('=============== Meta-iteration = ', str(i + 1), '/', num_iterations, ' =======================')
# 1- training
do_training(model, imgs, lbls, mdso.test.images, mdso.test.labels, epochs, bs, iter=str(i+1))
# 2- predict labels and confidence score
pred_lbls, pred_score, unlabeled_acc = assign_labels(model, mdso.train.labeled_ds.labels,
mdso.train.labeled_ds.images, unlabeled_imgs,
unlabeled_lbls, lt)
# 3- select top p% pseudo-labels
pseudo_label_imgs, pseudo_labels, indices_of_selected, pseudo_labels_acc = \
pseudo_label_selection(unlabeled_imgs, pred_lbls, pred_score, unlabeled_lbls, percentile)
# 4- merging new labeled for next loop iteration
imgs = np.concatenate([imgs, pseudo_label_imgs], axis=0)
if lbls.ndim > 1: # if one-hot encoded
pseudo_labels = np.eye(n_classes)[pseudo_labels]
lbls = np.concatenate([lbls, pseudo_labels], axis=0)
# 5- remove selected pseudo-labelled data from unlabelled data
unlabeled_imgs = np.delete(unlabeled_imgs, indices_of_selected, 0)
unlabeled_lbls = np.delete(unlabeled_lbls, indices_of_selected, 0)
#####################################################################################
# print/save accuracies and other information
test_acc = compute_accuracy(model, mdso.train.labeled_ds.images, mdso.train.labeled_ds.labels, mdso.test.images,
mdso.test.labels, lt)
print(template2.format(len(indices_of_selected), pseudo_labels_acc))
print(template3.format(len(lbls) - n_label, len(unlabeled_lbls)))
print("Acc: unlabeled: {:.2f} %, test {:.2f} %".format(unlabeled_acc, test_acc))
# ith meta-iteration, unlabelled accuracy, pseudo-label accuracy, test accuracy
logger.info("{},{:.2f},{:.2f},{:.2f}".format(i + 1, unlabeled_acc, pseudo_labels_acc, test_acc))
#####################################################################################
return imgs, lbls