def save_video_to_milvus(video_path, video_name, table_name=const.MILVUS_KEYFRAME_TABLE):
client = milvus_util.milvus_client()
frames_path, duration = extract_frame(file_path=video_path, fps=5, video_name=video_name)
print(frames_path)
feats, names, urls = feature_extract_batch(database_path=frames_path, model=VGGNet())
duration_time = [re.split('[TF.]', time)[len(re.split('[TF.]', time)) - 3] for time in names]
status, feats_ids = milvus_util.insert_vectors(client=client, table_name=table_name, vectors=feats)
return status, feats_ids, urls, duration_time, duration
def run_validation(checkpoint_file, model_name):
test_data, test_labels, _ = get_inputs_crop_flip('val')
print("Retrieved validation data successfully")
sample_size = get_size('val')
keep_prob = 1.
if model_name == 'AlexNet':
model = AlexNetSmall(keep_prob)
elif model_name == 'VGGNet':
model = VGGNet(keep_prob)
else:
raise Exception('Not a valid model name')
x = tf.placeholder(TYPE, shape=(BATCH_SIZE, IMAGE_FINAL_SIZE, IMAGE_FINAL_SIZE, NUM_CHANNELS), name='input')
logits, variables = model.model(x)
prediction = tf.nn.softmax(logits)
batch_data, batch_labels = tf.train.batch(
[test_data, test_labels],
batch_size=BATCH_SIZE,
capacity=5*BATCH_SIZE
)
if USE_GPU:
config = tf.ConfigProto()
else:
config = tf.ConfigProto(device_count={'GPU': 0})
saver = tf.train.Saver()
with tf.Session(config=config) as sess:
# Restore variables
saver.restore(sess=sess, save_path=checkpoint_file)
print("Restored variables from checkpoint %s" % checkpoint_file)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
predictions = []
labels = []
for step in range(sample_size // BATCH_SIZE):
_data, _labels = sess.run([batch_data, batch_labels])
_data = np.swapaxes(_data,0,1)
single_prediction = []
for i in range(8):
test_feed_dict = {x: _data[i]}
test_predictions = sess.run(prediction, feed_dict=test_feed_dict)
single_prediction.append(test_predictions)
# single_prediction is 8 * BATCH_SIZE * NUM_CAT
single_prediction = np.array(single_prediction)
single_prediction = np.swapaxes(single_prediction, 0, 1)
#single prediction is now BATCH_SIZE * 8 * NUM_CAT
# _labels is BATCH_SIZE
labels.extend(_labels)
predictions.extend(single_prediction.tolist())
# NUM_IMAGES * 8 * NUM_CAT
print('Processing batch number: %d / %d' % (step, sample_size//BATCH_SIZE))
# Save predictions in pickle file
filename = checkpoint_file.split('/')
if filename[-1] == '':
prefix = filename[-2]
else:
prefix = filename[-1]
prediction_file = PREDICTIONS_DIR + 'val_prediction__' + prefix
label_file = PREDICTIONS_DIR + 'val_labels__' + prefix
with open(prediction_file, 'wb') as f:
pickle.dump(predictions, f)
with open(label_file, 'wb') as f:
pickle.dump(labels, f)
print('Created new pickle file with predictions in %s' % prediction_file)
coord.request_stop()
coord.join(threads)
return (prediction_file, label_file)
def run_test(checkpoint_file, model_name):
# Get test data
test_data, test_labels, _ = get_inputs_crop_flip('test')
print("Retrieved test data successfully")
sample_size = get_size('test')
# Initialize model
keep_prob = 1.
if model_name == 'AlexNet':
model = AlexNetSmall(keep_prob)
elif model_name == 'VGGNet':
model = VGGNet(keep_prob)
else:
raise Exception('Not a valid model name')
x = tf.placeholder(TYPE, shape=(BATCH_SIZE, IMAGE_FINAL_SIZE, IMAGE_FINAL_SIZE, NUM_CHANNELS), name='input')
logits, variables = model.model(x)
prediction = tf.nn.softmax(logits)
batch_data = tf.train.batch(
test_data,
batch_size=BATCH_SIZE,
capacity=5*BATCH_SIZE)
if USE_GPU:
config = tf.ConfigProto()
else:
config = tf.ConfigProto(device_count={'GPU': 0})
saver = tf.train.Saver()
with tf.Session(config=config) as sess:
# Restore variables
saver.restore(sess=sess, save_path=checkpoint_file)
print("Restored variables from checkpoint %s" % checkpoint_file)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
predictions = []
for step in range(sample_size // BATCH_SIZE):
_data = sess.run(batch_data)
single_prediction = []
for i in range(8):
test_feed_dict = {x: _data[i]}
test_predictions = sess.run(prediction, feed_dict=test_feed_dict)
single_prediction.append(test_predictions)
# single_prediction is 8 * BATCH_SIZE * NUM_CAT
single_prediction = np.array(single_prediction)
single_prediction = np.swapaxes(single_prediction, 0, 1)
#single prediction is now BATCH_SIZE * 8 * NUM_CAT
# average_prediction = np.prod(np.array(single_prediction), axis=0)
# flat_prediction = np.array(single_prediction).flatten()
# ind = np.argpartition(flat_prediction, -40)[-40:]
# indices = ind[np.argsort(flat_prediction[ind])][::-1]
# indices = np.unique(indices%100)[:5]
# average_prediction = np.zeros(100)
# for i in range(5):
# average_prediction[indices[i]] = 6-i
predictions.extend(single_prediction.tolist())
# NUM_IMAGES * 8 * NUM_CAT
print('Processing batch number: %d / %d' % (step, sample_size//BATCH_SIZE))
# Save predictions in pickle file
filename = checkpoint_file.split('/')
if filename[-1] == '':
prefix = filename[-2]
else:
prefix = filename[-1]
prediction_file = PREDICTIONS_DIR + 'prediction__' + prefix
with open(prediction_file, 'wb') as f:
pickle.dump(predictions, f)
print('Created new pickle file with predictions in %s' % prediction_file)
coord.request_stop()
coord.join(threads)
return prediction_file
(checkpoint_prefix + "-" + str(step)))
saver.save(sess,
checkpoint_prefix,
global_step=step,
write_meta_graph=False)
print("\tSuccess!\n")
coord.request_stop()
coord.join(threads)
if __name__ == '__main__':
# optimizer = tf.train.AdamOptimizer(0.001)
optimizer = tf.train.AdagradOptimizer(0.01)
target_categories = []
# target_categories = ['playground', 'abbey', 'amphitheater', 'baseball_field', 'bedroom', 'cemetery', 'courtyard', 'kitchen', 'mountain', 'shower']
# target_categories = ALL_CATEGORIES[:10]
keep_prob = tf.placeholder(
tf.float32,
name='keep_prob') # we need to define a probability for the dropout
### Example when running BrianNet
#run(target_categories, optimizer, {}, {}, model=BrianNet())
# model = AlexNetSmall(keep_prob)
model = VGGNet(keep_prob)
run(target_categories,
optimizer, {keep_prob: 1.}, {keep_prob: KEEP_PROB},
model=model)
device = args.gpu
n_epoch = 81
# 保存先をチェックする
if os.path.exists('models/' + args.dir):
print('selected dir exists!')
else:
print('selected dir does not exits! make dir.')
os.mkdir('models/' + args.dir)
# 各種必要なパラメータを読み込み
# VGGmodelを読み込む
if args.usevgg == 1:
print('loading VGG model...')
vgg = VGGNet()
serializers.load_hdf5('/tmp/VGG.model', vgg)
print('loaded VGG!')
# ############学習の高速化のためにパラメータを事前に整理しておく###################
if args.usevgg == 1:
print('preprocessing vgg data')
vgg_img_param = []
start = time.time()
if os.path.isfile('features/vggparam_yahoo.pickle'):
print('vgg checkpoint pickle is exist! loading pickle')
with open('features/vggparam_yahoo.pickle', mode='rb') as f:
vgg_img_param = pickle.load(f)
else:
text, prob = classifier(img_name)
print("------------------------------")
print(f"Label: {text:<75}")
print(f"Probability: {prob:.6f}.")
print("------------------------------")
if args.echo:
self.echo(str(text), prob)
def echo(self, text, prob):
QMessageBox.information(
self, "Message",
f"Label :{str(text):<75}\nProbability: {prob:.6f}")
if __name__ == "__main__":
model = VGGNet.from_pretrained(args.model_name)
model.eval()
if args.image_size is None:
args.image_size = VGGNet.get_image_size(args.model_name)
# Preprocess image
tfms = transforms.Compose([
transforms.Resize(args.image_size),
transforms.CenterCrop(args.image_size),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
])
# Load class names
labels_map = json.load(open(args.labels_map))
labels_map = [labels_map[str(i)] for i in range(args.num_classes)]
# Open image
img = Image.open('panda.jpg')
# Preprocess image
tfms = transforms.Compose([
transforms.Resize(image_size),
transforms.CenterCrop(image_size),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
])
img = tfms(img).unsqueeze(0)
# Load class names
labels_map = json.load(open('labels_map.txt'))
labels_map = [labels_map[str(i)] for i in range(1000)]
# Classify with AlexNet
print("=> loading checkpoint 'vggnet-b11'.")
model = VGGNet.from_pretrained('vggnet-b11')
print("=> loaded checkpoint 'vggnet-b11'.")
model.eval()
with torch.no_grad():
logits = model(img)
preds = torch.topk(logits, k=5).indices.squeeze(0).tolist()
print('-----')
for idx in preds:
label = labels_map[idx]
prob = torch.softmax(logits, dim=1)[0, idx].item()
print('{:<75} ({:.2f}%)'.format(label, prob * 100))
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
from vggnet import VGGNet
# Integer value represents output channel after performing the convolution layer
# 'M' represents the max pooling layer
# After convolution blocks; flatten the output and use 4096x4096x1000 Linear Layers
# with soft-max at the end
VGG_types = {
'VGG11': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
'VGG13':
[64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
'VGG16': [
64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M',
512, 512, 512, 'M'
],
'VGG19': [
64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512,
512, 'M', 512, 512, 512, 512, 'M'
],
}
if __name__ == "__main__":
# test VGGNet16
model = VGGNet(name="VGGNet16",
architecture=VGG_types["VGG16"],
input_shape=(224, 224, 3),
classes=1000)
model.summary()
# import argparse
# from PIL import ImageFile
from chainer import cuda, serializers, Variable
from tinynet import wordQueryNet
from tinynet_novgg import wordQueryNetNoVGG
from wordparam import word2vector
from vggparam import vggparamater
from vggnet import VGGNet
import pickle
import argparse
import cv2 as cv
# import chainer
# import chainer.functions as F
vgg = VGGNet()
serializers.load_hdf5('/tmp/VGG.model', vgg)
mean = np.array([103.939, 116.779, 123.68])
img = cv.imread(image).astype(np.float32)
img -= mean
img = cv.resize(img, (224, 224)).transpose((2, 0, 1))
img = img[np.newaxis, :, :, :]
gpu = 0
if gpu >= 0:
cuda.get_device(gpu).use()
vgg.to_gpu()
img = cuda.cupy.asarray(img, dtype=np.float32)
pred = vgg(Variable(img), None)
def main_worker(gpu, ngpus_per_node, args):
global best_acc1
args.gpu = gpu
if args.gpu is not None:
print("Use GPU: {} for training".format(args.gpu))
if args.distributed:
if args.dist_url == "env://" and args.rank == -1:
args.rank = int(os.environ["RANK"])
if args.multiprocessing_distributed:
# For multiprocessing distributed training, rank needs to be the
# global rank among all the processes
args.rank = args.rank * ngpus_per_node + gpu
dist.init_process_group(backend=args.dist_backend,
init_method=args.dist_url,
world_size=args.world_size,
rank=args.rank)
# create model
if 'vgg' in args.arch: # NEW
if args.pretrained:
model = VGGNet.from_pretrained(args.arch,
num_classes=args.num_classes)
print("=> using pre-trained model '{}'".format(args.arch))
else:
print("=> creating model '{}'".format(args.arch))
model = VGGNet.from_name(args.arch)
else:
if args.pretrained:
print("=> using pre-trained model '{}'".format(args.arch))
model = models.__dict__[args.arch](pretrained=True)
else:
print("=> creating model '{}'".format(args.arch))
model = models.__dict__[args.arch]()
if args.distributed:
# For multiprocessing distributed, DistributedDataParallel constructor
# should always set the single device scope, otherwise,
# DistributedDataParallel will use all available devices.
if args.gpu is not None:
torch.cuda.set_device(args.gpu)
model.cuda(args.gpu)
# When using a single GPU per process and per
# DistributedDataParallel, we need to divide the batch size
# ourselves based on the total number of GPUs we have
args.batch_size = int(args.batch_size / ngpus_per_node)
args.workers = int(args.workers / ngpus_per_node)
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[args.gpu])
else:
model.cuda()
# DistributedDataParallel will divide and allocate batch_size to all
# available GPUs if device_ids are not set
model = torch.nn.parallel.DistributedDataParallel(model)
elif args.gpu is not None:
torch.cuda.set_device(args.gpu)
model = model.cuda(args.gpu)
else:
# DataParallel will divide and allocate batch_size to all available GPUs
if args.arch.startswith('alexnet') or args.arch.startswith('vgg'):
model.features = torch.nn.DataParallel(model.features)
model.cuda()
else:
model = torch.nn.DataParallel(model).cuda()
# define loss function (criterion) and optimizer
criterion = nn.CrossEntropyLoss().cuda(args.gpu)
optimizer = torch.optim.SGD(model.parameters(),
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
model, optimizer = amp.initialize(model,
optimizer,
opt_level=args.opt_level)
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
best_acc1 = checkpoint['best_acc1']
if args.gpu is not None:
# best_acc1 may be from a checkpoint from a different GPU
best_acc1 = best_acc1.to(args.gpu)
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
amp.load_state_dict(checkpoint['amp'])
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
cudnn.benchmark = True
# Data loading code
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_dataset = datasets.CIFAR10(root=args.data,
train=True,
download=True,
transform=transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
]))
if args.distributed:
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_dataset)
else:
train_sampler = None
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=(train_sampler is None),
num_workers=args.workers,
pin_memory=True,
sampler=train_sampler)
test_dataset = datasets.CIFAR10(root=args.data,
train=False,
download=True,
transform=transforms.Compose([
transforms.ToTensor(),
normalize,
]))
test_loader = torch.utils.data.DataLoader(test_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
if args.evaluate:
top1, top5 = validate(test_loader, model, criterion, args)
with open("res.txt", "w") as f:
print(f"Acc@1: {top1}\tAcc@5: {top5}", file=f)
return
for epoch in range(args.start_epoch, args.epochs):
if args.distributed:
train_sampler.set_epoch(epoch)
adjust_learning_rate(optimizer, epoch, args)
# train for one epoch
train(train_loader, model, criterion, optimizer, epoch, args)
# evaluate on validation set
acc1, _ = validate(test_loader, model, criterion, args)
# remember best acc@1 and save checkpoint
is_best = acc1 > best_acc1
best_acc1 = max(acc1, best_acc1)
if not args.multiprocessing_distributed or (
args.multiprocessing_distributed
and args.rank % ngpus_per_node == 0):
save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'best_acc1': best_acc1,
'optimizer': optimizer.state_dict(),
'amp': amp.state_dict(),
}, is_best)
def save_feats_batch_to_milvus(keyframe_path, table_name):
client = milvus_util.milvus_client()
feats, names, urls= feature_extract(database_path=keyframe_path, model=VGGNet())
status, feats_ids = milvus_util.insert_vectors(client=client, table_name=table_name, vectors=feats)
return status, feats_ids, names
256,
256,
'M', # Layer 3
512,
512,
512,
512,
'M', # Layer 4
512,
512,
512,
512,
'M', # Layer 5
]
if __name__ == "__main__":
opt = Adam(lr=0.001)
# test VGGNet16
model = VGGNet(name="VGGNet16",
architecture=VGG16,
input_shape=(224, 224, 3),
classes=1000)
model.compile(optimizer=opt,
loss='categorical_crossentropy',
metrics=['accuracy'])
X_train = np.random.randn(1080, 224, 224, 3).astype('f')
Y_train = np.random.randn(1080, 1000).astype('f')
model.fit(X_train, Y_train, epochs=1, batch_size=32)
