def load_model():
model_save_path = constant.load_model_path
state = torch.load(model_save_path,
map_location=lambda storage, location: storage)
constant.arg = state['config']
load_settings()
if constant.model == "LSTM":
model = LstmModel(vocab=vocab,
embedding_size=constant.emb_dim,
hidden_size=constant.hidden_dim,
num_layers=constant.n_layers,
is_bidirectional=constant.bidirec,
input_dropout=constant.drop,
layer_dropout=constant.drop,
attentive=constant.attn)
elif constant.model == "UTRS":
model = UTransformer(vocab=vocab,
embedding_size=constant.emb_dim,
hidden_size=constant.hidden_dim,
num_layers=constant.hop,
num_heads=constant.heads,
total_key_depth=constant.depth,
total_value_depth=constant.depth,
filter_size=constant.filter,
act=constant.act)
elif constant.model == "ELMO":
model = ELMoEncoder(C=4)
else:
print("Model is not defined")
exit(0)
model = model.load_state_dict(state['model'])
return model
def load_model():
model_load_path = constant.load_model_path
model_save_path = constant.save_path
state = torch.load(model_load_path,
map_location=lambda storage, location: storage)
arg = state['config']
load_settings(arg)
data_loaders_train, data_loaders_val, data_loaders_test, vocab = prepare_data_loaders(
num_split=1,
batch_size=constant.batch_size,
hier=False,
elmo=constant.elmo,
dev_with_label=False,
include_test=True)
if constant.model == "LSTM":
model = LstmModel(vocab=vocab,
embedding_size=constant.emb_dim,
hidden_size=constant.hidden_dim,
num_layers=constant.n_layers,
is_bidirectional=constant.bidirec,
input_dropout=constant.drop,
layer_dropout=constant.drop,
attentive=constant.attn)
elif constant.model == "UTRS":
model = UTransformer(vocab=vocab,
embedding_size=constant.emb_dim,
hidden_size=constant.hidden_dim,
num_layers=constant.hop,
num_heads=constant.heads,
total_key_depth=constant.depth,
total_value_depth=constant.depth,
filter_size=constant.filter,
act=constant.act)
elif constant.model == "ELMO":
model = ELMoEncoder(C=4)
else:
print("Model is not defined")
exit(0)
model.load_state_dict(state['model'])
return model, data_loaders_test, vocab, model_save_path
#from models import Zilpzalp
#from models import Hawk
from models import LstmModel
from utils import avg_score, maxwindow_score, get_top5_prediction
app = Flask(__name__)
# Initiate the model
model = LstmModel(time_axis=216, freq_axis=256, no_classes=100)
# Load the state of model from checkpoint
checkpoint_path = 'model/checkpoint_Lstm_29-03'
checkpoint = torch.load(checkpoint_path, map_location='cpu')
state = checkpoint['state_dict']
model.load_state_dict(state)
model.eval()
# Add the dictionary with the species info
label_dict = {}
reader = csv.DictReader(open('model/top100_codes_translated.csv'))
for row in reader:
label_dict[int(row['id1'])] = {
'name': row['english'],
'img_source': row['img_source'],
'img_link': row['img_link'],
'wiki_link': row['wiki_link'],
}
@app.route("/")