-
Notifications
You must be signed in to change notification settings - Fork 3
/
ACC_classification_builder.py
267 lines (211 loc) · 14.2 KB
/
ACC_classification_builder.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
import utils.interpolations as interpolations
import tqdm
from utils.storage import *
from tensorflow.contrib import slim
from dagan_networks_wgan_with_matchingclassifier import *
from utils.sampling_with_matchingclassifier import *
import sys
# sys.path.append("./models/research/slim")
# from nets.mobilenet import mobilenet_v2
slim = tf.contrib.slim
from densenet_classifier import densenet_classifier
# pretrained_resnet = './models/pretrained_model/resnet_v2_50_2017_04_14/resnet_v2_50.ckpt'
pretrained_resnet = './models/pretrained_model/resnet_v1_50.ckpt'
checkpoint_exclude_scopes = 'resnet_v1_50/logits'
model_name = 'resnet_v1_50'
checkpoint_path = './models/pretrained_model/resnet_v1_50.ckpt'
checkpoint_exclude_scopes = 'resnet_v1_50/logits'
#### for mobilenet
class ExperimentBuilder(object):
def __init__(self, parser, data):
tf.reset_default_graph()
args = parser.parse_args()
self.continue_from_epoch = args.continue_from_epoch
self.experiment_name = args.experiment_title
self.saved_models_filepath, self.log_path, self.save_image_path = build_experiment_folder(self.experiment_name)
self.num_gpus = args.num_of_gpus
self.batch_size = args.batch_size
gen_depth_per_layer = args.generator_inner_layers
discr_depth_per_layer = args.discriminator_inner_layers
self.z_dim = args.z_dim
self.num_generations = args.num_generations
self.dropout_rate_value = args.dropout_rate_value
self.data = data
self.reverse_channels = False
self.support_number = args.support_number
self.classification_total_epoch = args.classification_total_epoch
image_channel = data.image_channel
self.use_wide_connections = args.use_wide_connections
self.pretrain = args.pretrain
generator_layers = [64, 64, 128, 128]
self.discriminator_layers = [64, 64, 128, 128]
gen_inner_layers = [gen_depth_per_layer, gen_depth_per_layer, gen_depth_per_layer, gen_depth_per_layer]
self.discr_inner_layers = [discr_depth_per_layer, discr_depth_per_layer, discr_depth_per_layer,
discr_depth_per_layer]
generator_layer_padding = ["SAME", "SAME", "SAME", "SAME"]
image_height = self.data.image_width
image_width = self.data.image_width
image_channels = self.data.image_channel
self.classes = tf.placeholder(tf.int32)
self.input_x_i = tf.placeholder(tf.float32,
[self.num_gpus, self.batch_size * self.data.selected_classes, image_height,
image_width,
image_channels], 'inputs-1')
self.input_y = tf.placeholder(tf.float32, [self.num_gpus, self.batch_size * self.data.selected_classes,
self.data.selected_classes],
'y_inputs-1')
self.input_y_general = tf.placeholder(tf.float32, [self.num_gpus, self.batch_size * self.data.selected_classes,
self.data.testing_classes],
'y_inputs-1')
self.input_x_j = tf.placeholder(tf.float32,
[self.num_gpus, self.batch_size, self.support_number, image_height, image_width,
image_channels], 'inputs-2-same-class')
self.z_input = tf.placeholder(tf.float32, [self.batch_size, self.z_dim], 'z-input')
self.z_input_2 = tf.placeholder(tf.float32, [self.batch_size, self.z_dim], 'z-input_2')
self.training_phase = tf.placeholder(tf.bool, name='training-flag')
self.random_rotate = tf.placeholder(tf.bool, name='rotation-flag')
self.dropout_rate = tf.placeholder(tf.float32, name='dropout-prob')
self.matching = args.matching
self.fce = args.fce
self.full_context_unroll_k = args.full_context_unroll_k
self.average_per_class_embeddings = args.average_per_class_embeddings
self.total_train_batches = data.training_data_size / (self.batch_size * self.num_gpus)
self.total_val_batches = data.validation_data_size / (self.batch_size * self.num_gpus)
self.total_test_batches = 5 * 545 / (self.batch_size * self.num_gpus)
self.total_gen_batches = data.generation_data_size / (self.batch_size * self.num_gpus)
self.spherical_interpolation = True
self.is_fewshot_setting = args.is_fewshot_setting
# self.Nway = self.data.selected_classes
# self.kshot = args.general_classification_samples
self.tensorboard_update_interval = int(self.total_test_batches / 10 / self.num_gpus)
classifier = densenet_classifier(input_x_i=self.input_x_i, input_y=self.input_y_general,
classes=self.data.testing_classes,
batch_size=self.batch_size, layer_sizes=self.discriminator_layers,
inner_layers=self.discr_inner_layers, num_gpus=self.num_gpus,
use_wide_connections=self.use_wide_connections,
is_training=self.training_phase, augment=self.random_rotate,
dropout_rate=self.dropout_rate)
# print('{}_way_{}_shot'.format(self.data.selected_classes, self.data.general_classification_samples))
print('{}_classification'.format(self.data.testing_classes))
self.summary, self.losses, self.accuracy, self.graph_ops = classifier.init_train()
self.init = tf.global_variables_initializer()
self.training = args.is_training
def run_experiment(self):
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
sess.run(self.init)
# self.train_writer = tf.summary.FileWriter("{}/train_classification_logs/".format(self.log_path),
# graph=tf.get_default_graph())
# self.valid_writer = tf.summary.FileWriter("{}/validation_classification_logs/".format(self.log_path),
# graph=tf.get_default_graph())
print('Load parameters from basemodel')
variables = tf.global_variables()
vars_restore = [var for var in variables
if not "Momentum" in var.name and
not "global_step" in var.name and var.name.split('/')[0] != 'logits']
# print('load weights',vars_restore)
### remove fc weights to load
# tf.contrib.framework.get_variables("logits/fc/weights")
# print('network variable', vars_restore)
saver_restore = tf.train.Saver(vars_restore, max_to_keep=10000)
checkpoint = './pretrained-resnet18model/model.ckpt'
# checkpoint = './EVALUATION/AugmentedClassifier/animals/pretrained/saved_models/Pretrain_on_Source_Domain_9_0.5907641649246216.ckpt'
# reader = tf.train.NewCheckpointReader(checkpoint)
# var_to_shape_map = reader.get_variable_to_shape_map()
# for key in var_to_shape_map:
# print("store model name: ", key)
if self.training:
fine_tune = slim.assign_from_checkpoint_fn(
checkpoint,
vars_restore,
ignore_missing_vars=True)
fine_tune(sess)
# saver_restore.restore(sess, checkpoint)
self.saver = tf.train.Saver()
self.iter_done = 0
best_d_val_loss = np.inf
lowest_d_val_accuracy = 0
with tqdm.tqdm(total=self.classification_total_epoch) as pbar_e:
for e in range(self.classification_total_epoch):
train_loss = []
train_acc = []
test_loss = []
test_acc = []
with tqdm.tqdm(total=self.total_test_batches) as pbar_samp:
for iter in range(int(self.total_test_batches)):
if self.pretrain > 0:
x_test_classification_i, x_train_classification_i, _, _,y_test, y_train = self.data.get_train_batch()
else:
x_test_classification_i, x_train_classification_i, _, _,y_test, y_train = self.data.get_test_batch()
x_train_i = np.reshape(x_train_classification_i,
[x_train_classification_i.shape[0],
x_train_classification_i.shape[1] *
x_train_classification_i.shape[2], \
x_train_classification_i.shape[3],
x_train_classification_i.shape[4],
x_train_classification_i.shape[5]])
x_test_i = np.reshape(x_test_classification_i,
[x_test_classification_i.shape[0],
x_test_classification_i.shape[1] *
x_test_classification_i.shape[2], \
x_test_classification_i.shape[3],
x_test_classification_i.shape[4],
x_test_classification_i.shape[5]])
# x_train_i = np.reshape(x_train_i,
# [x_train_i.shape[0] * x_train_i.shape[1], \
# x_train_i.shape[2], x_train_i.shape[3], x_train_i.shape[4]])
#
# x_test_i = np.reshape(x_test_i,
# [x_test_i.shape[0] * x_test_i.shape[1], \
# x_test_i.shape[2], x_test_i.shape[3], x_test_i.shape[4]])
y_train = np.reshape(y_train, [y_train.shape[0], y_train.shape[1] * y_train.shape[2],
y_train.shape[3]])
y_test = np.reshape(y_test,
[y_test.shape[0], y_test.shape[1] * y_test.shape[2], y_test.shape[3]])
if self.training:
_, train_loss_value, train_acc_value, train_summary = sess.run(
[self.graph_ops["loss_opt_op"], self.losses["losses"], self.accuracy, self.summary],
feed_dict={self.input_x_i: x_train_i,
self.input_y_general: y_train,
self.dropout_rate: self.dropout_rate_value,
self.training_phase: True, self.random_rotate: True})
train_loss.append(train_loss_value)
train_acc.append(train_acc_value)
test_loss_value, test_acc_value, test_summary = sess.run(
[self.losses["losses"], self.accuracy, self.summary],
feed_dict={self.input_x_i: x_test_i,
self.input_y_general: y_test,
self.dropout_rate: self.dropout_rate_value,
self.training_phase: False, self.random_rotate: False})
test_loss.append(test_loss_value)
test_acc.append(test_acc_value)
# if iter % (self.tensorboard_update_interval) == 0:
# self.train_writer.add_summary(train_summary, global_step=self.iter_done)
# self.valid_writer.add_summary(test_summary, global_step=self.iter_done)
if self.training:
total_train_loss_mean = np.mean(train_loss)
total_train_accuracy_mean = np.mean(train_acc)
total_test_loss_mean = np.mean(test_loss)
total_test_accuracy_mean = np.mean(test_acc)
iter_out = "{},total_test_loss: {}, total_test_accuracy: {} ".format(iter, total_test_loss_mean,
total_test_accuracy_mean)
pbar_e.set_description(iter_out)
pbar_e.update(1)
if self.training:
### validation selection, according to the loss of discriminator.
model_name = 'Pretrain_on__Unseen_Categories'
# if total_test_loss_mean < best_d_val_loss:
# best_d_val_loss = total_test_loss_mean
# val_save_path = self.saver.save(sess, "{}/{}_{}_{}.ckpt".format(
# self.saved_models_filepath,
# model_name, e, total_test_accuracy_mean))
# print("Saved current best val model at", val_save_path)
if total_test_accuracy_mean > lowest_d_val_accuracy:
lowest_d_val_accuracy = total_test_accuracy_mean
val_save_path = self.saver.save(sess, "{}/{}_{}_{}.ckpt".format(
self.saved_models_filepath,
model_name, e, total_test_accuracy_mean))
# val_save_path = self.saver.save(sess, "{}/{}_epoch{}.ckpt".format(
# self.saved_models_filepath,
# model_name, e))
# print("Saved current best val model at", val_save_path)
pbar_e.update(1)