def test_embeddings():
model = Siamese('dummy', input_shape=(6, 8, 3), embedding_size=3)
model.make_embeddings('data_tiny/train',
'data_tiny/train.csv',
batch_size=1)
emb = pd.read_pickle(os.path.join(model.cache_dir, 'embeddings.pkl'))
print(emb)
model.predict('data_tiny/train')
pred = pd.read_pickle(os.path.join(model.cache_dir, 'predictions.pkl'))
print(pred)
from core.siamese import Siamese
model = Siamese('shallow_mnist', input_shape=(28, 28, 3), embedding_size=64, strategy='batch_all')
model.load_weights('cache/cache-190213-131256/training/checkpoint-07.h5')
model.make_embeddings('data_mnist/train.csv', 'data_mnist/train', batch_size=200)
model.predict('data_mnist/train_subset')
model.make_csv('cache/cache-190213-131256/idx_to_whales_mapping.npy')
# model.load_embeddings('cache/cache-190205-070856/embeddings.pkl')
# model.load_predictions('cache/cache-190205-072026/predictions.pkl')
# model.make_kaggle_csv('cache/cache-190205-065005/idx_to_whales_mapping.npy')
# model.draw_tsne(model.predictions.values[:, 1:])
from core.siamese import Siamese
model = Siamese('mobilenet_like',
input_shape=(672, 896, 3),
embedding_size=128)
model.load_weights('trained/final_weights.h5')
#model.make_embeddings('data/train', 'data/train.csv', batch_size=5, meta_dir='data/meta')
model.load_embeddings('trained/embeddings.pkl')
model.predict('data/test', meta_dir='data/meta')
#model.load_predictions('trained/predictions.pkl')
model.make_kaggle_csv('data/meta/idx_to_whales_mapping.npy')
import sys
sys.path.insert(0, '../')
from core.siamese import Siamese
#model = Siamese(input_shape=(224, 224, 3), train_hidden_layers=False, n_classes=8, mode='classification')
model = Siamese(input_shape=(224, 224, 3),
train_hidden_layers=True,
n_classes=8,
mode='arcface')
#model.load_weights('../trained/final_weights.h5')
model.load_weights('trained/final_weights.h5')
model.train('../data/train',
'train.csv',
meta_dir='../data/meta',
epochs=70,
batch_size=5,
learning_rate=0.0001)
from core.siamese import Siamese
# using softmax loss classification
# model = Siamese(input_shape=(224, 224, 3), n_classes=5004)
# model.load_weights('trained/final_weights.h5')
# model.predict('data/test')
# model.make_kaggle_csv('data/meta/idx_to_whales_mapping.npy')
# using cos_angular embeddings
model = Siamese(input_shape=(224, 224, 3),
n_classes=5004,
mode='cosface',
train=False)
model.load_weights('trained/final_weights.h5')
model.make_embeddings('data/train',
'data/train.csv',
batch_size=25,
meta_dir='data/meta')
#model.load_embeddings('trained/embeddings.pkl')
model.predict_using_embeddings('data/test', meta_dir='data/meta')
#model.load_predictions('trained/predictions.pkl')
model.make_kaggle_csv('data/meta/idx_to_whales_mapping.npy')
import sys
sys.path.insert(0, '../')
from core.siamese import Siamese
model = Siamese('mobilenet_like', input_shape=(672, 896, 3), embedding_size=128, train_hidden_layers=True)
#model.load_weights('../trained/final_weights.h5')
model.load_weights('trained/final_weights.h5')
model.train('../data/train', 'train.csv', meta_dir='../data/meta', epochs=40, batch_size=20, learning_rate=0.001, margin=1.0)
from core.siamese import Siamese
model = Siamese('mobilenet_like',
input_shape=(672, 896, 3),
embedding_size=128,
strategy='batch_all')
# model.load_weights('cache/cache-190208-093118/training/checkpoint-03.h5')
# TODO mark pretrained mobilenet
model.load_weights('cache/cache-190208-093118/training/checkpoint-03.h5')
model.train('data/train.csv',
'data/train',
epochs=20,
batch_size=32,
learning_rate=0.0001,
margin=2.0)
# inference to get cos angle embeddings
import sys
sys.path.insert(0, '../')
from core.siamese import Siamese
model = Siamese(input_shape=(224, 224, 3),
n_classes=8,
mode='arcface',
train=False)
model.load_weights('trained/final_weights.h5')
model.make_embeddings(
'../data/train',
'train.csv',
mappings_filename='../data/meta/whales_to_idx_mapping.npy',
batch_size=25)
#model.load_embeddings('trained/embeddings.pkl')
model.predict_using_embeddings('../data/train', 'val.csv')
#model.predict_using_embeddings('../data/train', 'train.csv')
from core.siamese import Siamese
model = Siamese('resnet_like_33', input_shape=(384, 512, 3), embedding_size=128, strategy='batch_all')
model.load_weights('trained/checkpoint-05.h5')
model.load_embeddings('trained/embeddings.pkl')
model.load_predictions('trained/predictions.pkl')
model.make_kaggle_csv('trained/idx_to_whales_mapping.npy')
from core.siamese import Siamese
model = Siamese(input_shape=(224, 224, 3), n_classes=5004)
model.load_weights('data/new_whalizer.h5')
model.predict_new_whales('data/test', 'data/submission_no_new_whales.csv')