def __init__(self, sess, args):
self.sess = sess
self.phase = args.phase
self.continue_train = args.continue_train
self.data_dir = args.data_dir
self.log_dir = args.log_dir
self.ckpt_dir = args.ckpt_dir
self.sample_dir = args.sample_dir
self.test_dir = args.test_dir
self.epoch = args.epoch
self.batch_size = args.batch_size
self.input_size = args.input_size
self.image_c = args.image_c
self.label_n = args.label_n
self.nf = args.nf
self.lr = args.lr
self.beta1 = args.beta1
self.sample_step = args.sample_step
self.log_step = args.log_step
self.ckpt_step = args.ckpt_step
# hyper parameter for building module
OPTIONS = namedtuple('options',
['batch_size', 'nf', 'label_n', 'phase'])
self.options = OPTIONS(self.batch_size, self.nf, self.label_n,
self.phase)
# build model & make checkpoint saver
self.build_model()
self.saver = tf.train.Saver()
# labels
self.labels_dic = util.get_labels(os.path.join('data', 'labels.xlsx'))
def eval(self, batches):
loss = 0.0
count = 0
(tp, fp, tn, fn) = (0, 0, 0, 0)
print("Evaluator ... ")
for batch in batches:
pairs = util.get_tuples(batch, volatile=True)
labels = util.get_labels(batch, volatile=True)
print("batch {}: \n".format(count))
print("\t pairs: ", pairs)
# print("\t labels: ",labels)
# try:
score = self.model.forward(pairs).squeeze()
print("\t computed score: {}".format(score))
print("\t actual labels : {}".format(labels))
(tp, fp, tn, fn) = self.compare(count, score, labels,
(tp, fp, tn, fn))
print("score (at batch {}): {}".format(count,
score).encode('utf-8'))
loss += self.criterion(score, labels).data.cpu().numpy()[0]
print("loss (after {} batches): {}".format(count,
loss).encode('utf-8'))
# except:
# print("Error in evaluation {}".format(sys.exc_info()))
count += 1
print("Counts: (TP,FP,TN,FN): ", (tp, fp, tn, fn))
print("Precision: ", tp / (tp + fp))
print("Recall: ", tp / (tp + fn))
return loss / count
def generate_toy(num_x):
# get the x features
df = DataFrame(np.random.randn(cts.num_examples, num_x), columns=util.get_feature_names(cts.x, num_x))
df = concat([df, df.apply(lambda row: Series(util.get_labels(row)), axis=1)], axis=1)
return df
def main():
model_name = 'generator_e_59'
model_name = os.path.join(pp.MODEL_SAVES, model_name)
model = Generator()
chainer.serializers.load_npz(model_name, model)
num_features = util.get_number_of_features(pp.CELEB_FACES_FC6_TEST)
all_names = np.array(util.get_names_h5_file(pp.FC6_TEST_H5))
y_tmp = np.zeros((num_features, 32 * 32 * 3), dtype=np.float32)
target_tmp = np.zeros((num_features, 32 * 32 * 3), dtype=np.float32)
save_list_names = os.listdir('/home/gabi/Documents/temp_datasets/test_celeba_reconstruction_m99')
save_list_names = [i.split('_')[0]+'.jpg' for i in save_list_names]
# save_list = random.sample(xrange(num_features), 100)
# save_list_names = [''] * 100
cnt = 0
# for i in save_list:
# save_list_names[cnt] = util.sed_line(pp.CELEB_FACES_FC6_TEST, i).strip().split(',')[0]
# cnt += 1
cnt = 0
for i in all_names:
features = util.get_features_h5_in_batches([i], train=False)
features = util.to_correct_input(features)
labels = util.get_labels([i])
labels = np.asarray(labels, dtype=np.float32)
target_tmp[cnt] = labels
with chainer.using_config('train', False):
f = np.expand_dims(features[0], 0)
prediction = model(f)
y_tmp[cnt] = prediction.data[0]
if i in save_list_names:
util.save_image(prediction, i, epoch=0)
print("image '%s' saved" % i)
cnt += 1
# calculate validation loss
y_tmp.astype(np.float32)
target_tmp.astype(np.float32)
loss = chainer.functions.mean_absolute_error(y_tmp, target_tmp)
print('model: ', model_name, ' loss model: ', loss)
def fprop(self,batch, volatile=False):
pairs= util.get_tuples(batch,volatile)
labels = util.get_labels(batch,volatile)
# print("fprop ==> pairs: {}; labels {}".format(pairs,labels))
score = self.model.forward(pairs).squeeze()
# print("fprop ==> score: {}".format(score))
# print("fprop ==> score: ",score)
loss = self.criterion(score,labels)
# print("fprop ==> loss: {}".format(loss))
# print("fprop ==> loss: ",loss)
return loss
num_svms=6 width=0.5 svmList = [None]*num_svms trainfeatList = [None]*num_svms traindatList = [None]*num_svms trainlabList = [None]*num_svms trainlabsList = [None]*num_svms kernelList = [None]*num_svms for i in range(num_svms): pos=util.get_realdata(True) neg=util.get_realdata(False) traindatList[i] = concatenate((pos, neg), axis=1) trainfeatList[i] = util.get_realfeatures(pos, neg) trainlabsList[i] = util.get_labels(True) trainlabList[i] = util.get_labels() kernelList[i] = GaussianKernel(trainfeatList[i], trainfeatList[i], width) svmList[i] = LibSVM(10, kernelList[i], trainlabList[i]) for i in range(num_svms): print "Training svm nr. %d" % (i) currentSVM = svmList[i] currentSVM.train() print currentSVM.get_num_support_vectors() print "Done." x, y, z=util.compute_output_plot_isolines( currentSVM, kernelList[i], trainfeatList[i]) subplot(num_svms/2, 2, i+1) pcolor(x, y, z, shading='interp') contour(x, y, z, linewidths=1, colors='black', hold=True)
def main():
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--data_dir",
default="",
type=str,
required=True,
help=
"The input data dir. Should contain the training files for the regard classification task.",
)
parser.add_argument(
"--test_file",
default=None,
type=str,
required=False,
help="Test file, if None, defaults to `test.tsv` file in data_dir.")
parser.add_argument(
"--model_version",
default=2,
type=int,
required=False,
help="1 or 2.",
)
parser.add_argument(
"--model_type",
default=None,
type=str,
required=True,
help="Model type selected in the list: " +
", ".join(MODEL_CLASSES.keys()),
)
parser.add_argument(
"--model_name_or_path",
default=None,
type=str,
required=True,
help="Path to pre-trained model or shortcut name selected in the list: "
+ ", ".join(ALL_MODELS),
)
parser.add_argument(
"--output_dir",
default=None,
type=str,
required=True,
help=
"The output directory where the model predictions and checkpoints will be written.",
)
# Other parameters
parser.add_argument(
"--config_name",
default="",
type=str,
help="Pretrained config name or path if not the same as model_name")
parser.add_argument(
"--tokenizer_name",
default="",
type=str,
help="Pretrained tokenizer name or path if not the same as model_name",
)
parser.add_argument(
"--cache_dir",
default="",
type=str,
help=
"Where do you want to store the pre-trained models downloaded from s3",
)
parser.add_argument(
"--max_seq_length",
default=128,
type=int,
help=
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.",
)
parser.add_argument("--do_train",
action="store_true",
help="Whether to run training.")
parser.add_argument("--do_eval",
action="store_true",
help="Whether to run eval on the dev set.")
parser.add_argument("--do_predict",
action="store_true",
help="Whether to run predictions on the test set.")
parser.add_argument(
"--evaluate_during_training",
action="store_true",
help="Whether to run evaluation during training at each logging step.",
)
parser.add_argument(
"--do_lower_case",
action="store_true",
help="Set this flag if you are using an uncased model.")
parser.add_argument("--per_gpu_train_batch_size",
default=8,
type=int,
help="Batch size per GPU/CPU for training.")
parser.add_argument("--per_gpu_eval_batch_size",
default=8,
type=int,
help="Batch size per GPU/CPU for evaluation.")
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=1,
help=
"Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument("--learning_rate",
default=5e-5,
type=float,
help="The initial learning rate for Adam.")
parser.add_argument("--weight_decay",
default=0.0,
type=float,
help="Weight decay if we apply some.")
parser.add_argument("--adam_epsilon",
default=1e-8,
type=float,
help="Epsilon for Adam optimizer.")
parser.add_argument("--max_grad_norm",
default=1.0,
type=float,
help="Max gradient norm.")
parser.add_argument("--num_train_epochs",
default=3.0,
type=float,
help="Total number of training epochs to perform.")
parser.add_argument(
"--max_steps",
default=-1,
type=int,
help=
"If > 0: set total number of training steps to perform. Override num_train_epochs.",
)
parser.add_argument("--warmup_steps",
default=0,
type=int,
help="Linear warmup over warmup_steps.")
parser.add_argument("--logging_steps",
type=int,
default=50,
help="Log every X updates steps.")
parser.add_argument("--save_steps",
type=int,
default=50,
help="Save checkpoint every X updates steps.")
parser.add_argument(
"--eval_all_checkpoints",
action="store_true",
help=
"Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number",
)
parser.add_argument("--no_cuda",
action="store_true",
help="Avoid using CUDA when available")
parser.add_argument("--overwrite_output_dir",
action="store_true",
help="Overwrite the content of the output directory")
parser.add_argument(
"--overwrite_cache",
action="store_true",
help="Overwrite the cached training and evaluation sets")
parser.add_argument("--seed",
type=int,
default=42,
help="random seed for initialization")
parser.add_argument(
"--fp16",
action="store_true",
help=
"Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit",
)
parser.add_argument(
"--fp16_opt_level",
type=str,
default="O1",
help=
"For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
"See details at https://nvidia.github.io/apex/amp.html",
)
parser.add_argument("--local_rank",
type=int,
default=-1,
help="For distributed training: local_rank")
parser.add_argument("--server_ip",
type=str,
default="",
help="For distant debugging.")
parser.add_argument("--server_port",
type=str,
default="",
help="For distant debugging.")
args = parser.parse_args()
if (os.path.exists(args.output_dir) and os.listdir(args.output_dir)
and args.do_train and not args.overwrite_output_dir):
raise ValueError(
"Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome."
.format(args.output_dir))
# Setup distant debugging if needed
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print("Waiting for debugger attach")
ptvsd.enable_attach(address=(args.server_ip, args.server_port),
redirect_output=True)
ptvsd.wait_for_attach()
# Setup CUDA, GPU & distributed training
if args.local_rank == -1 or args.no_cuda:
device = torch.device("cuda" if torch.cuda.is_available()
and not args.no_cuda else "cpu")
args.n_gpu = torch.cuda.device_count()
else: # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
torch.distributed.init_process_group(backend="nccl")
args.n_gpu = 1
args.device = device
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN,
)
logger.warning(
"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
args.local_rank,
device,
args.n_gpu,
bool(args.local_rank != -1),
args.fp16,
)
# Set seed
set_seed(args)
# Prepare regard classification task
labels = get_labels(model_version=args.model_version)
num_labels = len(labels)
# Use cross entropy ignore index as padding label id so that only real label ids contribute to the loss later
pad_token_label_id = CrossEntropyLoss().ignore_index
# Load pretrained model and tokenizer
if args.local_rank not in [-1, 0]:
torch.distributed.barrier(
) # Make sure only the first process in distributed training will download model & vocab
args.model_type = args.model_type.lower()
config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
config = config_class.from_pretrained(
args.config_name if args.config_name else args.model_name_or_path,
num_labels=num_labels,
cache_dir=args.cache_dir if args.cache_dir else None,
)
tokenizer = tokenizer_class.from_pretrained(
args.tokenizer_name
if args.tokenizer_name else args.model_name_or_path,
do_lower_case=args.do_lower_case,
cache_dir=args.cache_dir if args.cache_dir else None,
)
model = model_class.from_pretrained(
args.model_name_or_path,
from_tf=bool(".ckpt" in args.model_name_or_path),
config=config,
cache_dir=args.cache_dir if args.cache_dir else None,
)
if args.local_rank == 0:
torch.distributed.barrier(
) # Make sure only the first process in distributed training will download model & vocab
model.to(args.device)
logger.info("Training/evaluation parameters %s", args)
# Training
if args.do_train:
train_dataset = load_and_cache_examples(args,
tokenizer,
labels,
pad_token_label_id,
data_file=TRAIN_FILE_PATTERN,
is_test=False)
global_step, tr_loss = train(args, train_dataset, model, tokenizer,
labels, pad_token_label_id)
logger.info(" global_step = %s, average loss = %s", global_step,
tr_loss)
# Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained()
if args.do_train and (args.local_rank == -1
or torch.distributed.get_rank() == 0):
# Create output directory if needed
if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
os.makedirs(args.output_dir)
logger.info("Saving model checkpoint to %s", args.output_dir)
# Save a trained model, configuration and tokenizer using `save_pretrained()`.
# They can then be reloaded using `from_pretrained()`
model_to_save = (model.module if hasattr(model, "module") else model
) # Take care of distributed/parallel training
model_to_save.save_pretrained(args.output_dir)
tokenizer.save_pretrained(args.output_dir)
# Good practice: save your training arguments together with the trained model
torch.save(args, os.path.join(args.output_dir, "training_args.bin"))
# Evaluation
results = {}
if args.do_eval and args.local_rank in [-1, 0]:
tokenizer = tokenizer_class.from_pretrained(
args.output_dir, do_lower_case=args.do_lower_case)
checkpoints = [args.output_dir]
if args.eval_all_checkpoints:
checkpoints = list(
os.path.dirname(c) for c in sorted(
glob.glob(args.output_dir + "/**/" + WEIGHTS_NAME,
recursive=True)))
logging.getLogger("pytorch_transformers.modeling_utils").setLevel(
logging.WARN) # Reduce logging
logger.info("Evaluate the following checkpoints: %s", checkpoints)
for checkpoint in checkpoints:
global_step = checkpoint.split(
"-")[-1] if len(checkpoints) > 1 else ""
model = model_class.from_pretrained(checkpoint)
model.to(args.device)
result, _ = evaluate(args,
model,
tokenizer,
labels,
pad_token_label_id,
mode=DEV_FILE_PATTERN,
prefix=global_step,
is_test=False)
if global_step:
result = {
"{}_{}".format(global_step, k): v
for k, v in result.items()
}
results.update(result)
output_eval_file = os.path.join(args.output_dir, "eval_results.txt")
with open(output_eval_file, "w") as writer:
for key in sorted(results.keys()):
writer.write("{} = {}\n".format(key, str(results[key])))
if args.do_predict and args.local_rank in [-1, 0]:
tokenizer = tokenizer_class.from_pretrained(
args.output_dir, do_lower_case=args.do_lower_case)
model = model_class.from_pretrained(args.model_name_or_path)
model.to(args.device)
if args.test_file:
test_file = args.test_file
elif os.path.exists(os.path.join(args.data_dir, TEST_FILE_PATTERN)):
test_file = TEST_FILE_PATTERN
else:
raise NotImplementedError(
"No test_file provided and %s DNE." %
os.path.join(args.data_dir, TEST_FILE_PATTERN))
result, predictions = evaluate(args,
model,
tokenizer,
labels,
pad_token_label_id,
mode=test_file,
is_test=True)
test_file_basename = os.path.basename(test_file).split('.')[0]
# Save predictions
output_test_predictions_file = os.path.join(
args.output_dir, test_file_basename + "_predictions.txt")
with open(output_test_predictions_file, "w") as writer:
with open(os.path.join(args.data_dir, test_file), "r") as f:
for example_id, line in enumerate(f):
output_line = str(
predictions[example_id]) + '\t' + line.split(
'\t')[-1].strip() + "\n"
writer.write(output_line)
return results
import util
util.set_title('KernelRidgeRegression')
width = 20
# positive examples
pos = util.get_realdata(True)
plot(pos[0, :], pos[1, :], "r.")
# negative examples
neg = util.get_realdata(False)
plot(neg[0, :], neg[1, :], "b.")
# train krr
labels = util.get_labels(type='regression')
train = util.get_realfeatures(pos, neg)
gk = GaussianKernel(train, train, width)
krr = KernelRidgeRegression()
krr.set_labels(labels)
krr.set_kernel(gk)
krr.set_tau(1e-3)
krr.train()
# compute output plot iso-lines
x, y, z = util.compute_output_plot_isolines(krr, gk, train, regression=True)
pcolor(x, y, z)
contour(x, y, z, linewidths=1, colors='black', hold=True)
connect('key_press_event', util.quit)
from util import get_labels
import lightgbm as lgb
from datetime import datetime
import matplotlib.pylab as plt
#matplotlib inline
from matplotlib.pylab import rcParams
rcParams['figure.figsize'] = 12, 4
import numpy as np
import pandas as pd
np.random.seed(seed=SEED)
n_estimators = 100
labels = get_labels()
for net in networks.keys():
print(f'Loading training data for {net}...')
with open(f'bottleneck_features_avg/{net}_avg_features_train.npy',
'rb') as f:
x_train = np.load(f)
print(f'Features shape: {x_train.shape}')
le = LabelEncoder()
le.fit(labels['breed'])
y_train = le.transform(labels['breed'])
print('Creating train/val split...')
x_train, x_val, y_train, y_val = train_test_split(x_train,
y_train,
import util
util.set_title('KernelRidgeRegression')
width=20
# positive examples
pos=util.get_realdata(True)
plot(pos[0,:], pos[1,:], "r.")
# negative examples
neg=util.get_realdata(False)
plot(neg[0,:], neg[1,:], "b.")
# train svm
labels = util.get_labels(type='regression')
train = util.get_realfeatures(pos, neg)
gk=GaussianKernel(train, train, width)
krr = KernelRidgeRegression()
krr.set_labels(labels)
krr.set_kernel(gk)
krr.set_tau(1e-3)
krr.train()
# compute output plot iso-lines
x, y, z=util.compute_output_plot_isolines(krr, gk, train, regression=True)
pcolor(x, y, z, shading='interp')
contour(x, y, z, linewidths=1, colors='black', hold=True)
connect('key_press_event', util.quit)
return G, node_sentences_pair
if __name__ == '__main__':
print('### Tokenizing and Preprocessing ###')
G, node_sentences_pair = GenerateGraph(
train_input_h='corpora/assin+msr/train/train-h.txt',
train_input_t='corpora/assin+msr/train/train-t.txt',
test_input_h='corpora/assin+msr/test/test-h.txt',
test_input_t='corpora/assin+msr/test/test-t.txt',
).create_graph()
print(len(node_sentences_pair))
train_labels = util.get_labels('corpora/assin+msr/labels-train.txt')
test_labels = util.get_labels('corpora/assin+msr/labels-test.txt')
reg = Regularization()
reg.regulariza(G,
node_sentences_pair,
train_labels,
'',
total_pre_anotados=0.4,
method='llgc')
df_train = pd.read_csv("features_2800_pre_anotados_train.csv")
df_test = pd.read_csv("features_2800_pre_anotados_test.csv")
clf = MLPClassifier(solver='adam',
hidden_layer_sizes=(20, 20),
random_state=42,
"""
train, valid, test = get_dis(data_dir, prefix, params.corpus)
word_vec = build_vocab(train['s1'] + train['s2'] +
valid['s1'] + valid['s2'] +
test['s1'] + test['s2'], glove_path)
# unknown words instead of map to <unk>, this directly takes them out
for split in ['s1', 's2']:
for data_type in ['train', 'valid', 'test']:
eval(data_type)[split] = np.array([['<s>'] +
[word for word in sent.split() if word in word_vec] +
['</s>'] for sent in eval(data_type)[split]])
params.word_emb_dim = 300
dis_labels = get_labels(params.corpus)
label_size = len(dis_labels)
"""
MODEL
"""
# model config
config_dis_model = {
'n_words': len(word_vec),
'word_emb_dim': params.word_emb_dim,
'enc_lstm_dim': params.enc_lstm_dim,
'n_enc_layers': params.n_enc_layers,
'dpout_emb': params.dpout_emb,
'dpout_model': params.dpout_model,
'dpout_fc': params.dpout_fc,
'fc_dim': params.fc_dim,
num_svms = 6
width = 0.5
svmList = [None] * num_svms
trainfeatList = [None] * num_svms
traindatList = [None] * num_svms
trainlabList = [None] * num_svms
trainlabsList = [None] * num_svms
kernelList = [None] * num_svms
for i in range(num_svms):
pos = util.get_realdata(True)
neg = util.get_realdata(False)
traindatList[i] = concatenate((pos, neg), axis=1)
trainfeatList[i] = util.get_realfeatures(pos, neg)
trainlabsList[i] = util.get_labels(True)
trainlabList[i] = util.get_labels()
kernelList[i] = GaussianKernel(trainfeatList[i], trainfeatList[i], width)
svmList[i] = LibSVM(10, kernelList[i], trainlabList[i])
for i in range(num_svms):
print "Training svm nr. %d" % (i)
currentSVM = svmList[i]
currentSVM.train()
print currentSVM.get_num_support_vectors()
print "Done."
x, y, z = util.compute_output_plot_isolines(currentSVM, kernelList[i],
trainfeatList[i])
subplot(num_svms / 2, 2, i + 1)
pcolor(x, y, z)
contour(x, y, z, linewidths=1, colors='black', hold=True)
from keras.applications import inception_v3
from keras.models import load_model
from tqdm import tqdm
from util import get_labels, get_images
import csv
import numpy as np
# Define constants
INPUT_SIZE = 299
fname = 'model1_finetune.h5'
nr_predictions = 10357
# Get ids, label names and images
print('Load data...')
labels = get_labels().sort_values(by=['breed']).breed.unique()
ids = []
images = np.zeros((nr_predictions, INPUT_SIZE, INPUT_SIZE, 3), dtype='float16')
for i, (img, img_id) in tqdm(enumerate(get_images('test', INPUT_SIZE))):
x = inception_v3.preprocess_input(np.expand_dims(img, axis=0))
images[i] = x
ids.append(img_id)
# Load model weights
print(f'Load model from {fname}')
model = load_model(fname)
# Make predictions on input images
print('Predict...')
predictions = model.predict(images, verbose=1)
def training():
print('setting up...')
if pc.TRAIN:
num_features = util.get_number_of_features(pp.CELEB_FACES_FC6_TRAIN)
# num_features = util.get_number_of_features_from_train(pp.CELEB_FACES_FC6_TRAIN) # for server
all_names = np.array(util.get_names_h5_file(pp.FC6_TRAIN_H5))
path_images = pp.CELEB_FACES_FC6_TRAIN
else:
num_features = util.get_number_of_features(pp.CELEB_FACES_FC6_TEST)
all_names = np.array(util.get_names_h5_file(pp.FC6_TEST_H5))
path_images = pp.CELEB_FACES_FC6_TEST
total_steps = num_features / pc.BATCH_SIZE
mask_L_sti = util.get_L_sti_mask()
# ----------------------------------------------------------------
# GENERATOR
generator = Generator()
# generator = GeneratorPaper()
generator_train_loss = np.zeros(pc.EPOCHS)
generator_optimizer = chainer.optimizers.Adam(alpha=0.0002,
beta1=0.9,
beta2=0.999,
eps=10**-8)
generator_optimizer.setup(generator)
# ----------------------------------------------------------------
# DISCRIMINATOR
discriminator = Discriminator()
# discriminator = DiscriminatorPaper()
discriminator_train_loss = np.zeros(pc.EPOCHS)
discriminator_optimizer = chainer.optimizers.Adam(alpha=0.0002,
beta1=0.9,
beta2=0.999,
eps=10**-8)
discriminator_optimizer.setup(discriminator)
# ----------------------------------------------------------------
# VGG16 FOR FEATURE LOSS
vgg16 = VGG16Layers()
# ----------------------------------------------------------------
save_list = random.sample(xrange(num_features), 20)
save_list_names = [''] * 20
cnt = 0
for i in save_list:
save_list_names[cnt] = util.sed_line(path_images,
i).strip().split(',')[0]
cnt += 1
ones1 = util.make_ones(generator)
zeros = util.make_zeros(generator)
print('training...')
for epoch in range(pc.EPOCHS):
# shuffle training instances
order = range(num_features)
random.shuffle(order)
names_order = all_names[order]
train_gen = True
train_dis = True
print('epoch %d' % epoch)
for step in range(total_steps):
names = names_order[step * pc.BATCH_SIZE:(step + 1) *
pc.BATCH_SIZE]
features = util.get_features_h5_in_batches(names, train=pc.TRAIN)
features = util.to_correct_input(features)
labels_32, labels_224 = util.get_labels(names)
# labels_32 = util.get_labels(names)
# vgg16_features = util.get_features_h5_in_batches(names, train=pc.TRAIN, which_features='vgg16')
# vgg16_features = util.to_correct_input(vgg16_features)
# labels_32 = np.asarray(labels_32, dtype=np.float32)
with chainer.using_config('train', train_gen):
generator.cleargrads()
prediction = generator(chainer.Variable(features))
with chainer.using_config('train', train_dis):
discriminator.cleargrads()
print('prediction shape', np.shape(prediction.data))
data = np.reshape(
generator(chainer.Variable(features)).data,
(pc.BATCH_SIZE, 32, 32, 3))
data = np.transpose(data, (0, 3, 1, 2))
fake_prob = discriminator(chainer.Variable(data))
other_data = np.reshape(labels_32, (pc.BATCH_SIZE, 32, 32, 3))
other_data = np.transpose(other_data, (0, 3, 1, 2))
real_prob = discriminator(chainer.Variable(other_data))
feature_truth = vgg16(labels_224,
layers=['conv3_3'])['conv3_3']
feature_reconstruction = vgg16(util.fix_prediction_for_vgg16(
prediction, vgg16),
layers=['conv3_3'])['conv3_3']
# feature_reconstruction = None
# ----------------------------------------------------------------
# CALCULATE LOSS
lambda_adv = 10**2
lambda_sti = 2 * (10**-6)
lambda_fea = 10**-2
l_adv = lambda_adv * F.sigmoid_cross_entropy(
fake_prob, ones1.data)
# TODO: mask is probably breaking the graph, fix this
thing_1 = util.apply_mask(labels_32, mask_L_sti)
thing_2 = util.apply_mask(prediction.data, mask_L_sti)
l_sti = lambda_sti * F.mean_squared_error(thing_1, thing_2)
l_fea = lambda_fea * F.mean_squared_error(
feature_truth, feature_reconstruction)
generator_loss = l_adv + l_sti + l_fea
generator_loss.backward()
generator_optimizer.update()
generator_train_loss[epoch] += generator_loss.data
lambda_dis = 10**2
discriminator_loss = lambda_dis * (
F.sigmoid_cross_entropy(real_prob, ones1.data) +
F.sigmoid_cross_entropy(fake_prob, zeros.data))
discriminator_loss.backward()
discriminator_optimizer.update()
discriminator_train_loss[epoch] += discriminator_loss.data
# ----------------------------------------------------------------
# when to suspend / resume training
dis_adv_ratio = discriminator_loss.data / l_adv.data
if dis_adv_ratio < 0.1:
train_dis = False
if dis_adv_ratio > 0.5:
train_dis = True
if dis_adv_ratio > 10:
train_gen = False
if dis_adv_ratio < 2:
train_gen = True
# print('%d/%d %d/%d generator: %f l_adv: %f l_sti: %f discriminator: %f l3: %f l4: %f' % (
# epoch, pc.EPOCHS, step, total_steps, generator_loss.data, l_adv.data, l_sti.data, discriminator_loss.data,
# l3.data, l4.data))
print(
'%d/%d %d/%d generator: %f l_adv: %f l_sti: %f l_fea: %f discriminator: %f dis/adv: %f'
% (epoch, pc.EPOCHS, step, total_steps,
generator_loss.data, l_adv.data, l_sti.data, l_fea.data,
discriminator_loss.data, dis_adv_ratio))
# information = util.update_information(information1, step, generator_loss.data, l_adv.data, l_sti.data)
# information = util.update_information(information2, step, discriminator_loss.data, l3.data, l4.data)
# visualizing loss
# prev_max_ax1 = util.plot_everything(information1, fig1, lines1, ax1, prev_max_ax1, step)
# prev_max_ax2 = util.plot_everything(information2, fig2, lines2, ax2, prev_max_ax2, step)
with chainer.using_config('train', False):
for i in range(len(names)):
if names[i] in save_list_names:
f = np.expand_dims(features[i], 0)
prediction = generator(f)
util.save_image(prediction, names[i], epoch,
pp.RECONSTRUCTION_FOLDER)
print("image '%s' saved" % names[i])
# if (epoch+1) % pc.SAVE_EVERY_N_STEPS == 0:
# util.save_model(generator, epoch)
generator_train_loss[epoch] /= total_steps
print(generator_train_loss[epoch])
discriminator_train_loss[epoch] /= total_steps
print(discriminator_train_loss[epoch])
def create_db(self, rpt_file, db_file):
import sqlite3
con = sqlite3.connect(db_file)
# con.execute("PRAGMA foreign_keys = ON")
# Create table
con.execute("""
CREATE TABLE IF NOT EXISTS nodes
(id INTEGER PRIMARY KEY,
parent INTEGER,
last INTEGER,
name TEXT,
cell TEXT,
internal REAL,
switching REAL,
leakage REAL,
total REAL,
FOREIGN KEY(parent) REFERENCES nodes(id),
FOREIGN KEY(last) REFERENCES nodes(id))
""")
header = []
with open(rpt_file) as f:
hier = []
rows = []
skip_header = True
for k, line in enumerate(f):
line_num = k + 1
if skip_header:
if "----" in line:
skip_header = False
temp = []
for header_line in reversed(header):
if header_line.strip() == '':
break
temp.append(header_line)
labels = get_labels("\n".join(reversed(temp)))
print(labels)
else:
header.append(line)
continue
if line == "1\n":
print(f"Done on line {line_num}")
break
info = line.split()
if len(info) == 6:
cell = None
name, internal, switching, leakage, total, percent = info
elif len(info) == 7:
name, cell, internal, switching, leakage, total, percent = info
elif len(info) == 10:
cell = None
name, internal, switching, leakage, peak_power, peak_time, glitch_power, x_tran_power, total, percent = info
elif len(info) == 11:
name, cell, internal, switching, leakage, peak_power, peak_time, glitch_power, x_tran_power, total, percent = info
else:
raise NotImplementedError(line)
if cell is not None:
cell = cell.lstrip("(").rstrip(")")
if total == "N/A":
total = None
info = {
"indent": len(line) - len(line.lstrip(' ')),
"id": line_num,
"name": name,
"cell": cell,
"internal": internal,
"switching": switching,
"leakage": leakage,
"total": total,
}
while len(hier) > 0 and info["indent"] <= hier[-1]["indent"]:
node = hier.pop()
node["last"] = line_num - 1
node["parent"] = hier[-1]["id"] if len(hier) > 0 else None
rows.append((
node["id"],
node["parent"],
node["last"],
node["name"],
node["cell"],
node["internal"],
node["switching"],
node["leakage"],
node["total"],
))
if len(rows) > self.batch_size:
con.executemany(
"INSERT INTO nodes VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)",
rows)
rows.clear()
hier.append(info)
while len(hier) > 0:
node = hier.pop()
node["last"] = line_num - 1
node["parent"] = hier[-1]["id"] if len(hier) > 0 else None
rows.append((
node["id"],
node["parent"],
node["last"],
node["name"],
node["cell"],
node["internal"],
node["switching"],
node["leakage"],
node["total"],
))
con.executemany(
"INSERT INTO nodes VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)", rows)
con.commit()
foreign_key_failures = con.execute(
"PRAGMA foreign_key_check").fetchall()
if len(foreign_key_failures) > 0:
raise sqlite3.IntegrityError(
f"Failed foreign key checks: {foreign_key_failures}")
# print(f"split: {time_split}")
# print(f"update: {time_update}")
return con
def create_df(self, rpt_file, db_file):
header = []
with open(rpt_file) as f:
hier = []
rows = []
skip_header = True
for k, line in enumerate(f):
line_num = k + 1
if skip_header:
if "----" in line:
skip_header = False
temp = []
for header_line in reversed(header):
if header_line.strip() == '':
break
temp.append(header_line)
labels = get_labels("\n".join(reversed(temp)))
print(labels)
else:
header.append(line)
continue
if line == "1\n":
print(f"Done on line {line_num}")
break
info = line.split()
if len(info) == 6:
cell = None
name, internal, switching, leakage, total, percent = info
elif len(info) == 7:
name, cell, internal, switching, leakage, total, percent = info
elif len(info) == 10:
cell = None
name, internal, switching, leakage, peak_power, peak_time, glitch_power, x_tran_power, total, percent = info
elif len(info) == 11:
name, cell, internal, switching, leakage, peak_power, peak_time, glitch_power, x_tran_power, total, percent = info
else:
raise NotImplementedError(line)
if cell is not None:
cell = cell.lstrip("(").rstrip(")")
info = {
"indent": len(line) - len(line.lstrip(' ')),
"id": line_num,
"name": name,
"cell": cell,
"internal": None if internal == "N/A" else float(internal),
"switching":
None if switching == "N/A" else float(switching),
"leakage": None if leakage == "N/A" else float(leakage),
"total": None if total == "N/A" else float(total),
}
while len(hier) > 0 and info["indent"] <= hier[-1]["indent"]:
node = hier.pop()
node["last"] = line_num - 1
node["parent"] = hier[-1]["id"] if len(hier) > 0 else None
rows.append(node)
hier.append(info)
while len(hier) > 0:
node = hier.pop()
node["last"] = line_num - 1
node["parent"] = hier[-1]["id"] if len(hier) > 0 else None
rows.append(node)
return pd.DataFrame(rows)
n_pre_epochs = 10
n_epochs = 100
batch_size = 32
n_images = 300
USE_PCA = True
USE_GENSEL = False
USE_AUTOENC = False #TODO
USE_ICA = True
USE_CANNY = True
USE_CORNERHARRIS = True
TRAIN = False
CHANNELS = 3
# Load labels
print('Load labels...')
labels = get_labels()[:n_images]
# Load training data
print('Load training data...')
x_train = np.zeros((n_images, INPUT_SIZE, INPUT_SIZE, 3), dtype=K.floatx())
for i, (img, img_id) in tqdm(enumerate(get_images('train', INPUT_SIZE, amount=n_images))):
x = inception_v3.preprocess_input(np.expand_dims(img, axis=0))
x_train[i] = x
y_train = one_hot(labels['breed'].values, num_classes=NUM_CLASSES)
# Arguments of ImageDataGenerator define types of augmentation to be performed
# E.g: Horizontal flip, rotation, etc...
# no fitting required since we don't use centering/normalization/whitening
datagen = ImageDataGenerator(
rotation_range=20,
width_shift_range=.2,
def preprocess(input, segmentation, extraction, use_letters, dim):
segmented = segmentation.segment(input)
feature_vector = extraction.extract(segmented)
labels = get_labels(input.shape[1], input.shape[0], dim, dim, use_letters)
return [feature_vector, labels]
class Classification:
def __init__(self, model, features, labels):
self.model = joblib.load(model)
self.x = features
self.y = labels
def classifier(self):
y_pred = self.model.predict(self.x)
return classification_report(self.y, y_pred)
if __name__ == '__main__':
# algorithms = ['bayes.pkl', 'lre.pkl', 'nn.pkl', 'svm.pkl', 'tree.pkl']
algorithms = ['svm.pkl']
# model = 'trained-model/assin_res_mod_skip300'
model = 'trained-model/assin+msr_mod_glove50'
# model = 'trained-model/mod_'
# test = 'features/test/features-test-all1.txt'
print('### Extracting features ###')
features, _ = ExtractFeatures(
model='model/glove50.txt',
input_h='assin+msr/test/test-h.txt',
input_t='assin+msr/test/test-t.txt').extract_features()
# test = 'baseline/test/features-test.txt'
labels = util.get_labels('assin+msr/labels-test.txt')
print('### Classifying ###')
for a in algorithms:
print(Classification(model + a, features, labels).classifier())
def training():
print('setting up...')
# num_features = util.get_number_of_features(pp.CELEB_FACES_FC6_TRAIN)
num_features = util.get_number_of_features(pp.CELEB_FACES_FC6_TEST)
total_steps = num_features / pc.BATCH_SIZE
# all_names = np.array(util.get_names_h5_file(pp.FC6_TRAIN_H5))
all_names = np.array(util.get_names_h5_file(pp.FC6_TEST_H5))
generator = Generator()
train_loss = np.zeros(pc.EPOCHS)
# generator = GeneratorPaper()
optimizer = chainer.optimizers.Adam(alpha=0.0002,
beta1=0.5,
beta2=0.999,
eps=10e-8)
optimizer.setup(generator)
save_list = random.sample(xrange(num_features), 20)
save_list_names = [''] * 20
cnt = 0
for i in save_list:
# save_list_names[cnt] = util.sed_line(pp.CELEB_FACES_FC6_TRAIN, i).strip().split(',')[0]
save_list_names[cnt] = util.sed_line(pp.CELEB_FACES_FC6_TEST,
i).strip().split(',')[0]
cnt += 1
print('training...')
for epoch in range(pc.EPOCHS):
# shuffle training instances
order = range(num_features)
random.shuffle(order)
names_order = all_names[order]
print('epoch %d' % epoch)
for step in range(total_steps):
# names, features = util.get_features_in_batches(step, train=True)
names = names_order[step * pc.BATCH_SIZE:(step + 1) *
pc.BATCH_SIZE]
# features = util.get_features_h5_in_batches(names, train=True)
features = util.get_features_h5_in_batches(names, train=False)
features = util.to_correct_input(features)
labels = util.get_labels(names)
labels = np.asarray(labels, dtype=np.float32)
with chainer.using_config('train', True):
generator.cleargrads()
prediction = generator(features)
loss = chainer.functions.mean_absolute_error(
prediction, labels)
# print('loss', loss.data)
print('%d/%d %d/%d loss: %f' %
(epoch, pc.EPOCHS, step, total_steps, float(loss.data)))
loss.backward()
optimizer.update()
train_loss[epoch] += loss.data
# with chainer.using_config('train', False):
# for i in range(len(names)):
# if names[i] in save_list_names:
# f = np.expand_dims(features[i], 0)
# prediction = generator(f)
# util.save_image(prediction, names[i], epoch)
# print("image '%s' saved" % names[i])
if (epoch + 1) % pc.SAVE_EVERY_N_STEPS == 0:
util.save_model(generator, epoch)
train_loss[epoch] /= total_steps
print(train_loss[epoch])
from pylab import plot,grid,title,subplot,xlabel,ylabel,text,subplots_adjust,fill_between,mean,connect,show
from shogun.Kernel import GaussianKernel
from shogun.Classifier import LibSVM, LDA
from shogun.Evaluation import PRCEvaluation
import util
util.set_title('PRC example')
util.DISTANCE=0.5
subplots_adjust(hspace=0.3)
pos=util.get_realdata(True)
neg=util.get_realdata(False)
features=util.get_realfeatures(pos, neg)
labels=util.get_labels()
# classifiers
gk=GaussianKernel(features, features, 1.0)
svm = LibSVM(1000.0, gk, labels)
svm.train()
lda=LDA(1,features,labels)
lda.train()
## plot points
subplot(211)
plot(pos[0,:], pos[1,:], "r.")
plot(neg[0,:], neg[1,:], "b.")
grid(True)
title('Data',size=10)
# plot PRC for SVM
subplot(223)
def detect_object_in( model_path , label_map_path , video_src):
PATH_TO_FROZEN_GRAPH = model_path + '/frozen_inference_graph.pb'
PATH_TO_LABELS = label_map_path
detection_graph = tf.Graph()
with detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(PATH_TO_FROZEN_GRAPH, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS, use_display_name=True)
with detection_graph.as_default():
with create_session() as sess:
# Get handles to input and output tensors
ops = tf.get_default_graph().get_operations()
all_tensor_names = {output.name for op in ops for output in op.outputs}
tensor_dict = {}
for key in [
'num_detections', 'detection_boxes', 'detection_scores',
'detection_classes', 'detection_masks'
]:
tensor_name = key + ':0'
if tensor_name in all_tensor_names:
tensor_dict[key] = tf.get_default_graph().get_tensor_by_name(tensor_name)
cap = cv2.VideoCapture(video_src)
video_running = True
while True:
ret , image_np = cap.read()
output_dict = run_inference_for_single_image(sess,image_np, detection_graph, tensor_dict)
detected_labels = get_labels(
output_dict['detection_boxes'],
output_dict['detection_classes'],
output_dict['detection_scores'],
category_index
)
print(detected_labels)
print("=================")
vis_util.visualize_boxes_and_labels_on_image_array(
image_np,
output_dict['detection_boxes'],
output_dict['detection_classes'],
output_dict['detection_scores'],
category_index,
instance_masks=output_dict.get('detection_masks'),
use_normalized_coordinates=True,
line_thickness=8)
cv_image = cv2.resize(image_np, (800, 600))
cv2.imshow("Press q to quit" , cv_image)
if cv2.waitKey(25) & 0xFF == ord("q"):
cap.release()
cv2.destroyAllWindows()
break
