-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathforcnn-resnet-train.py
170 lines (142 loc) · 6.94 KB
/
forcnn-resnet-train.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
import os
import pickle
import pandas as pd
import torch
from typing import Optional
from data import utils
from data.loader import DatasetLoader
from models.resnet import ResNet50
dataset_directory = 'data/datasets'
large_checkpoint_directory = 'experiments/checkpoints/forcnn-resnet/large/state_dict_model.pt'
small_checkpoint_directory_finetuned = 'experiments/checkpoints/forcnn-resnet/small-finetuned/state_dict_model.pt'
small_checkpoint_directory = 'experiments/checkpoints/forcnn-resnet/small/state_dict_model.pt'
large_configs_directory = 'experiments/configs/datasets/large'
small_configs_directory = 'experiments/configs/datasets/small'
device_id = 0
timeframe_size = 28
noise_percentage = 0.0
quantiles = [0.02, 0.1, 0.25, 0.5, 0.75, 0.9, 0.98]
lr = 0.001
finetuning_lr = 0.0005
batch_size = 64
epochs = 600
early_stopping_patience = 200
autosave = True
def train(
train_df: pd.DataFrame,
eval_df: pd.DataFrame,
test_df: pd.DataFrame,
device: torch.device,
learning_rate: float,
checkpoint_directory: str,
previous_checkpoint_directory: Optional[str] = None
) -> (ResNet50, pd.DataFrame, pd.DataFrame):
def train_model() -> (ResNet50, pd.DataFrame):
print('Converting timeseries to images...')
x_train, y_train = utils.construct_image_dataset(df=train_df, timeframe_size=timeframe_size)
x_eval, y_eval = utils.construct_image_dataset(df=eval_df, timeframe_size=timeframe_size)
print(f'Constructed {x_train.shape[0]} train and {x_eval.shape} eval images of size: {x_train.shape[1:]}')
model = ResNet50(
num_channels=x_train.shape[1],
image_size=(timeframe_size, timeframe_size),
quantiles=quantiles,
device=device
)
if previous_checkpoint_directory is not None:
model.load(previous_checkpoint_directory)
train_loss_per_epoch, eval_loss_per_epoch, eval_mse_per_epoch, eval_mae_per_epoch = model.train_model(
x_train=x_train,
y_train=y_train,
x_eval=x_eval,
y_eval=y_eval,
lr=learning_rate,
batch_size=batch_size,
epochs=epochs,
early_stopping_patience=early_stopping_patience,
autosave=True,
checkpoint_directory=checkpoint_directory
)
history_df = pd.DataFrame({
'epochs': range(1, len(train_loss_per_epoch) + 1),
'train-loss': train_loss_per_epoch,
'eval-loss': eval_loss_per_epoch,
'eval-mse': eval_mae_per_epoch,
'eval_mae': eval_mse_per_epoch
})
return model, history_df
def evaluate_model(model: ResNet50) -> pd.DataFrame:
evaluation_dict = {'exchange': [], 'symbol': [], 'loss': [], 'mse': [], 'mae': []}
for _, df_group in test_df.groupby(['exchange', 'symbol']):
exchange = df_group.iloc[0]['exchange']
symbol = df_group.iloc[0]['symbol']
x_test, y_test = utils.construct_image_dataset(df=df_group, timeframe_size=timeframe_size)
test_dataloader = utils.construct_dataloader(x=x_test, y=y_test, batch_size=batch_size, shuffle=False)
loss, mse, mae = model.eval_model(eval_dataloader=test_dataloader, desc_prefix=f'Evaluating: {exchange}-{symbol}')
evaluation_dict['exchange'].append(exchange)
evaluation_dict['symbol'].append(symbol)
evaluation_dict['loss'].append(loss)
evaluation_dict['mse'].append(mse)
evaluation_dict['mae'].append(mae)
return pd.DataFrame(evaluation_dict)
model, history_df = train_model()
evaluation_df = evaluate_model(model=model)
return model, history_df, evaluation_df
def main():
device = torch.device('cuda') if torch.cuda.is_available() else 'cpu'
dataset_loader = DatasetLoader(dataset_directory=dataset_directory)
# Loading large dataset (train-eval-test)
with open(f'{large_configs_directory}/train.pkl', 'rb') as dictfile:
train_df = dataset_loader.load_datasets(dataset_configs=pickle.load(dictfile), noise_percentage=noise_percentage)
with open(f'{large_configs_directory}/eval.pkl', 'rb') as dictfile:
eval_df = dataset_loader.load_datasets(dataset_configs=pickle.load(dictfile))
with open(f'{large_configs_directory}/test.pkl', 'rb') as dictfile:
test_df = dataset_loader.load_datasets(dataset_configs=pickle.load(dictfile))
print(f'Loaded {train_df.shape[0]} train samples, {eval_df.shape[0]} eval samples and {test_df.shape[0]} test samples from large dataset.')
# Train ResNet50-Large model
print('\n#--- Training ResNet50-Large ---\n')
_, history_df, evaluation_df = train(
train_df=train_df,
eval_df=eval_df,
test_df=test_df,
device=device,
learning_rate=lr,
checkpoint_directory=large_checkpoint_directory,
previous_checkpoint_directory=None
)
history_df.to_csv('experiments/results/forcnn-resnet/large_history.csv', index=False)
evaluation_df.to_csv('experiments/results/forcnn-resnet/large_evaluation.csv', index=False)
# Load Small Dataset
with open(f'{small_configs_directory}/train.pkl', 'rb') as dictfile:
train_df = dataset_loader.load_datasets(dataset_configs=pickle.load(dictfile), noise_percentage=noise_percentage)
with open(f'{small_configs_directory}/eval.pkl', 'rb') as dictfile:
eval_df = dataset_loader.load_datasets(dataset_configs=pickle.load(dictfile))
# Train ResNet50-Small-Finetuned
print('\n#--- Training ResNet50-Small-Finetuned ---\n')
_, history_df, evaluation_df = train(
train_df=train_df,
eval_df=eval_df,
test_df=eval_df,
device=device,
learning_rate=finetuning_lr,
checkpoint_directory=small_checkpoint_directory_finetuned,
previous_checkpoint_directory=large_checkpoint_directory
)
history_df.to_csv('experiments/results/forcnn-resnet/small_finetuned_history.csv', index=False)
evaluation_df.to_csv('experiments/results/forcnn-resnet/small_finetuned_evaluation.csv', index=False)
print('\n#--- Training ResNet50-Small-No-Finetuned ---\n')
_, history_df, evaluation_df = train(
train_df=train_df,
eval_df=eval_df,
test_df=eval_df,
device=device,
learning_rate=lr,
checkpoint_directory=small_checkpoint_directory,
previous_checkpoint_directory=None
)
history_df.to_csv('experiments/results/forcnn-resnet/small_history.csv', index=False)
evaluation_df.to_csv('experiments/results/forcnn-resnet/small_evaluation.csv', index=False)
if __name__ == "__main__":
if not 0 <= device_id <= 1:
raise RuntimeError(f'Maximum 2 GPUs are supported with ids 0 or 1, got {device_id}')
os.environ['CUDA_VISIBLE_DEVICES'] = str(device_id)
main()