def main(_):
set_memory_growth()
cfg = load_yaml(FLAGS.cfg_path)
model = ArcFaceModel(size=cfg['input_size'],
backbone_type=cfg['backbone_type'],
num_classes=cfg['num_classes'],
head_type=cfg['head_type'],
embd_shape=cfg['embd_shape'],
w_decay=cfg['w_decay'],
training=True)
learning_rate = tf.constant(cfg['base_lr'])
optimizer = tf.keras.optimizers.SGD(learning_rate=learning_rate,
momentum=0.9,
nesterov=True)
loss_fn = SoftmaxLoss()
ckpt_path = tf.train.latest_checkpoint('./checkpoints/train_' +
cfg['sub_name'])
if ckpt_path is not None:
print("[*] load ckpt from {}".format(ckpt_path))
model.load_weights(ckpt_path)
else:
print("[*] training from scratch.")
model.compile(optimizer=optimizer, loss=loss_fn)
# resnet_model = tf.keras.Model(inputs=model.get_layer('resnet50').input,
# outputs=model.get_layer('resnet50').output)
# resnet_head = tf.keras.Model(inputs=resnet_model.input,
# outputs=resnet_model.get_layer('conv2_block1_add').output)
# resnet_tail = split(resnet_model, 18, 1000) # conv2_block1_out
# output_model = tf.keras.Model(inputs=model.get_layer('OutputLayer').input,
# outputs=model.get_layer('OutputLayer').output)
# archead = tf.keras.Model(inputs=model.get_layer('ArcHead').input,
# outputs=model.get_layer('ArcHead').output)
temp1 = np.ones((62, 112, 3))
temp2 = np.zeros((50, 112, 3))
masked_img = np.concatenate([temp1, temp2], axis=0).reshape(1, 112, 112, 3)
temp1 = np.ones((1, 18, 28, 256))
temp2 = np.zeros((1, 10, 28, 256))
masked = np.concatenate([temp1, temp2], axis=1)
# inputs = Input((112, 112, 3))
# labels = Input([])
# s = resnet_head(inputs)
# s = tf.keras.layers.Multiply()([s, tf.constant(masked, dtype=tf.float32)])
# s = resnet_tail(s)
# s = output_model(s)
# s = archead([s, labels])
# new_model = Model((inputs, labels), s)
# new_model.summary()
# new_model.compile(optimizer=optimizer, loss=loss_fn)
path_to_data = '/home/anhdq23/Desktop/nguyen/data/AR/test2'
anchor_names = get_gallery_pr2(path_to_data) # From 1 to 100
name_dicts = get_probe_pr2(
path_to_data) # Dictionary: {anchor_name:[name_image, ...]}
augment = ImgAugTransform()
import faiss
if FLAGS.mode == 'eager_tf':
top_left_all = [0.012]
best_acc = 0.8
for epochs in range(cfg['epochs']):
logging.info("Shuffle ms1m dataset.")
dataset_len = cfg['num_samples']
steps_per_epoch = dataset_len // cfg['batch_size']
train_dataset = dataset.load_tfrecord_dataset(
cfg['train_dataset'],
cfg['batch_size'],
cfg['binary_img'],
is_ccrop=cfg['is_ccrop'])
sub_train_dataset = dataset.load_tfrecord_dataset(
cfg['train_dataset'],
cfg['batch_size'],
cfg['binary_img'],
is_ccrop=cfg['is_ccrop'])
for batch, ((x, y), (sub_x, sub_y)) in enumerate(
zip(train_dataset, sub_train_dataset)):
x0_new = np.array(x[0], dtype=np.uint8)
x1_new = np.array(x[1], dtype=np.float32)
for i in np.arange(len(x0_new)):
x0_new[i] = augment(x0_new[i])
temp = np.array(x0_new, dtype=np.float32) / 255.0
temp = np.multiply(temp, masked_img)
sub_x0_new = np.array(sub_x[0], dtype=np.uint8)
sub_x1_new = np.array(sub_x[1], dtype=np.float32)
for i in np.arange(len(sub_x0_new)):
sub_x0_new[i] = augment(sub_x0_new[i])
sub_temp = np.array(sub_x0_new, dtype=np.float32) / 255.0
model.train_on_batch(*((sub_temp, sub_x1_new), sub_x1_new))
loss = model.train_on_batch(*((temp, x1_new), x1_new))
if batch % 50 == 0:
verb_str = "Epoch {}/{}: {}/{}, loss={:.6f}, lr={:.6f}"
print(
verb_str.format(
epochs, cfg['epochs'], batch, steps_per_epoch,
loss, cfg['base_lr'] / (1.0 + cfg['w_decay'] *
(epochs * 45489 + batch))))
if batch % cfg['save_steps'] == 0:
resnet_model = tf.keras.Model(
inputs=model.get_layer('resnet50').input,
outputs=model.get_layer('resnet50').output)
output_model = tf.keras.Model(
inputs=model.get_layer('OutputLayer').input,
outputs=model.get_layer('OutputLayer').output)
database_image_names = []
database_feature_list = []
for anchor_name in anchor_names:
img1 = Image.open(
os.path.join(path_to_data, anchor_name))
img1 = img1.resize((112, 112))
img1 = np.array(img1) / 255.0
img1 = np.multiply(img1, masked_img)
fc1 = resnet_model.predict(
img1.reshape((1, 112, 112, 3)))
fc1 = output_model.predict(fc1)
norm_fc1 = preprocessing.normalize(fc1.reshape(
(1, 512)),
norm='l2',
axis=1)
database_image_names.append(anchor_name)
database_feature_list.append(norm_fc1)
index_flat = faiss.IndexFlatL2(512)
gpu_index_flat = index_flat
gpu_index_flat.add(
np.array(database_feature_list).reshape(
(-1, 512))) # add vectors to the index
count = 0
for key in list(name_dicts.keys()):
for name in name_dicts[key]:
img2 = Image.open(
os.path.join(path_to_data, name)).resize(
(112, 112))
img2 = img2.resize((112, 112))
img2 = np.array(img2) / 255.0
img2 = np.multiply(img2, masked_img)
fc2 = resnet_model.predict(
img2.reshape((1, 112, 112, 3)))
fc2 = output_model.predict(fc2)
norm_fc2 = preprocessing.normalize(fc2.reshape(
(1, 512)),
norm='l2',
axis=1)
D, I = gpu_index_flat.search(
norm_fc2, k=1) # actual search
if name[0:5] == database_image_names[I[0]
[0]][0:5]:
count += 1
acc = count / 600.0
if acc > best_acc:
best_acc = acc
print('[*] save ckpt file!')
model.save_weights(
'checkpoints/{}/e_{}_b_{}.ckpt'.format(
cfg['sub_name'], epochs,
batch % steps_per_epoch))
print("The current acc: {:.6f} ".format(acc))
print("The best acc: {:.6f} ".format(best_acc))
model.save_weights('checkpoints/train_{}/{}.ckpt'.format(
cfg['sub_name'], cfg['sub_name']))
def main(_):
set_memory_growth()
cfg = load_yaml(FLAGS.cfg_path)
model = ArcFaceModel(size=cfg['input_size'],
backbone_type=cfg['backbone_type'],
num_classes=cfg['num_classes'],
head_type=cfg['head_type'],
embd_shape=cfg['embd_shape'],
w_decay=cfg['w_decay'],
training=True)
model.summary()
learning_rate = tf.constant(cfg['base_lr'])
optimizer = tf.keras.optimizers.SGD(learning_rate=learning_rate,
momentum=0.9,
nesterov=True)
loss_fn = SoftmaxLoss()
ckpt_path = tf.train.latest_checkpoint('./checkpoints/' + cfg['sub_name'])
if ckpt_path is not None:
print("[*] load ckpt from {}".format(ckpt_path))
model.load_weights(ckpt_path)
else:
print("[*] training from scratch.")
model.compile(optimizer=optimizer, loss=loss_fn)
data_path = 'data'
lfw, lfw_issame = get_val_pair(data_path, 'lfw_align_112/lfw')
lfw = np.transpose(lfw, [0, 2, 3, 1]) * 0.5 + 0.5
image_1 = lfw[0::2]
image_2 = lfw[1::2]
augment = ImgAugTransform()
if FLAGS.mode == 'eager_tf':
top_left_all = [0.008807]
for epochs in range(cfg['epochs']):
logging.info("Shuffle ms1m dataset.")
dataset_len = cfg['num_samples']
steps_per_epoch = dataset_len // cfg['batch_size']
train_dataset = dataset.load_tfrecord_dataset(
cfg['train_dataset'],
cfg['batch_size'],
cfg['binary_img'],
is_ccrop=cfg['is_ccrop'])
for batch, (x, y) in enumerate(train_dataset):
x0_new = np.array(x[0], dtype=np.uint8)
x1_new = np.array(x[1], dtype=np.float32)
for i in np.arange(len(x0_new)):
x0_new[i] = augment(x0_new[i])
temp = np.array(x0_new, dtype=np.float32) / 255.0
loss = model.train_on_batch(*((temp, x1_new), x1_new))
if batch % 50 == 0:
verb_str = "Epoch {}/{}: {}/{}, loss={:.6f}, lr={:.6f}"
print(
verb_str.format(
epochs, cfg['epochs'], batch, steps_per_epoch,
loss, cfg['base_lr'] / (1.0 + cfg['w_decay'] *
(epochs * 45489 + batch))))
if batch % cfg['save_steps'] == 0:
resnet_model = tf.keras.Model(
inputs=model.get_layer('resnet50').input,
outputs=model.get_layer('resnet50').output)
output_model = tf.keras.Model(
inputs=model.get_layer('OutputLayer').input,
outputs=model.get_layer('OutputLayer').output)
dist_all = []
top_left_batch = []
for idx in range(0, len(lfw_issame),
cfg['batch_size']):
tem = resnet_model.predict(
image_1[idx:idx + cfg['batch_size']])
embeds_1 = output_model.predict(tem)
norm_embeds_1 = preprocessing.normalize(embeds_1,
norm='l2',
axis=1)
tem = resnet_model.predict(
image_2[idx:idx + cfg['batch_size']])
embeds_2 = output_model.predict(tem)
norm_embeds_2 = preprocessing.normalize(embeds_2,
norm='l2',
axis=1)
diff = np.subtract(norm_embeds_1, norm_embeds_2)
dist = np.sqrt(np.sum(np.square(diff), 1)) / 2
dist_all.extend(dist)
thresholds = np.arange(0, 1, 0.01)
for thr in thresholds:
tpr, fpr, _ = calculate_accuracy(
thr, np.array(dist_all), lfw_issame)
top_left = np.sqrt((1 - tpr)**2 + fpr**2)
top_left_batch.append(top_left)
print(
"The current top left: {:.6f} Threshold: {:.2f}"
.format(np.min(top_left_batch),
0.01 * np.argmin(top_left_batch)))
if not len(top_left_all):
print(
"The best top left: {:.6f} Threshold: {:.2f}"
.format(np.min(top_left_batch),
0.01 * np.argmin(top_left_batch)))
else:
print("The best top left: {:.6f}".format(
top_left_all[-1]))
if not len(top_left_all):
top_left_all.append(np.min(top_left_batch))
print('[*] save ckpt file!')
model.save_weights(
'checkpoints/{}/e_{}_b_{}.ckpt'.format(
cfg['sub_name'], epochs,
batch % steps_per_epoch))
elif top_left_all[-1] > np.min(top_left_batch):
top_left_all.append(np.min(top_left_batch))
print('[*] save ckpt file!')
model.save_weights(
'checkpoints/{}/e_{}_b_{}.ckpt'.format(
cfg['sub_name'], epochs,
batch % steps_per_epoch))
model.save_weights('checkpoints/train_{}/{}.ckpt'.format(
cfg['sub_name'], cfg['sub_name']))