-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathTrain.py
200 lines (173 loc) · 9.94 KB
/
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
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
import logging
import os
import os.path
import pandas as pd
import numpy as np
from accelerate import Accelerator
from PIL import Image
from sklearn.model_selection import train_test_split
from sing import SING
import torch
import torch.nn as nn
from torch.utils.tensorboard import SummaryWriter
import lightning.pytorch as pl
from tqdm import tqdm
from transformers import get_cosine_schedule_with_warmup, ViTForImageClassification
from ffcv.writer import DatasetWriter
from ffcv.fields import RGBImageField, IntField
from ffcv.loader import Loader, OrderOption
import ffcv.transforms as fftr
from ffcv.fields.decoders import IntDecoder, RandomResizedCropRGBImageDecoder, SimpleRGBImageDecoder
batch_size = 64
grad_acc = 1
lr = 5e-4
weight_decay = 5e-3
writer = SummaryWriter(f"./logs/Dino_sing/lr{lr}_wd{weight_decay}_bs{batch_size*grad_acc}")
workers = 8
accelerator = Accelerator(gradient_accumulation_steps=grad_acc)
torch.backends.cuda.matmul.allow_tf32 = True
torch.set_float32_matmul_precision('high')
pl.seed_everything(42, workers=True)
class IdemiaDataset():
def __init__(self, data, img_dir, size):
self.img_dir = img_dir
self.data = data
self.size = size
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
img_name, label, genre, _ = self.data.iloc[idx]
img_path = os.path.join(self.img_dir, img_name)
image = Image.open(img_path)
image = image.resize((self.size, self.size), Image.Resampling.BICUBIC)
return image, label, genre
def get_loaders(labels, size, Imagenet=False):
if Imagenet:
data_mean, data_std = np.array([0.485, 0.456, 0.406]) * 255, np.array([0.229, 0.224, 0.225]) * 255
else:
data_mean, data_std = np.array([0.5550244450569153, 0.4250235855579376, 0.36004188656806946]) * 255, np.array([0.28600722551345825, 0.24972566962242126, 0.23863893747329712]) * 255
data_train, data_val = train_test_split(labels, stratify=labels.stratif, test_size=0.2, random_state=35, shuffle=True)
trainset = IdemiaDataset(data_train, "./train/", size)
valset = IdemiaDataset(data_val, "./train/", size)
# Convert datasets to ffcv
file_train = f"./ffcv/train_{size}_Imagenet.beton" if Imagenet else f"./ffcv/train_{size}.beton"
file_val = f"./ffcv/val_{size}_Imagenet.beton" if Imagenet else f"./ffcv/val_{size}.beton"
if not os.path.isfile(file_train):
writer_train = DatasetWriter(file_train, {
'image': RGBImageField(write_mode='jpg', jpeg_quality=100),
'label': IntField(),
'genre': IntField()}, num_workers=workers)
writer_train.from_indexed_dataset(trainset)
if not os.path.isfile(file_val):
writer_val = DatasetWriter(file_val, {
'image': RGBImageField(write_mode='jpg', jpeg_quality=100),
'label': IntField(),
'genre': IntField()}, num_workers=workers)
writer_val.from_indexed_dataset(valset)
decoder = RandomResizedCropRGBImageDecoder((size, size), scale=(0.5, 1))
decoder_val = SimpleRGBImageDecoder()
normalize = fftr.NormalizeImage(mean=data_mean, std=data_std, type=np.float32)
eraser_mean = list((np.random.rand(3,) * 255).astype(int))
image_train_pipeline = [decoder, fftr.RandomHorizontalFlip(), fftr.Rotate(angle=0.5),
fftr.RandomErasing(erase_prob=0.5, scale=(0.02, 0.4), ratio=(0.15, 6), mean=eraser_mean),
fftr.RandomColorJitter(jitter_prob=0.5, brightness=[1.0, 1.5], contrast=0, saturation=[1.0, 1.5], hue=0),
fftr.GaussianBlur(0.5), fftr.ToTensor(), fftr.ToTorchImage(), fftr.ToDevice(accelerator.device, non_blocking=True), normalize]
image_val_pipeline = [decoder_val, fftr.ToTensor(), fftr.ToTorchImage(), fftr.ToDevice(accelerator.device, non_blocking=True), normalize]
label_pipeline = [IntDecoder(), fftr.ToTensor(), fftr.Convert(torch.float16), fftr.ToDevice(accelerator.device, non_blocking=True), fftr.Squeeze()]
# Pipeline for each data field
pipeline_train = {
'image': image_train_pipeline, 'label': label_pipeline, 'genre': label_pipeline
}
pipeline_val = {
'image': image_val_pipeline, 'label': label_pipeline, 'genre': label_pipeline
}
trainloader = Loader(file_train, batch_size=batch_size, num_workers=workers,
order=OrderOption.RANDOM, pipelines=pipeline_train)
valloader = Loader(file_val, batch_size=batch_size, num_workers=workers,
order=OrderOption.SEQUENTIAL, pipelines=pipeline_val)
return trainloader, valloader
def evaluate(model, valloader):
valloader = tqdm(valloader, leave=False)
with torch.no_grad():
loss, count, global_acc = 0, 0, 0
acc_g0, acc_g1, count_g0, count_g1 = 0, 0, 0, 0
for batch in valloader:
X_val, y_val, genre = batch
count += X_val.shape[0]
count_g0 += torch.sum(genre == -1)
count_g1 += torch.sum(genre == 1)
mask0 = (genre == -1).nonzero(as_tuple=True)[0]
mask1 = (genre == 1).nonzero(as_tuple=True)[0]
output = model(X_val).logits
y_smoothed = torch.where(nn.functional.one_hot(y_val.long(), num_classes=2) == 1, 0.9, 0.1)
loss += nn.functional.binary_cross_entropy_with_logits(output, y_smoothed, reduction="sum")
y_pred = torch.argmax(output, dim=1)
global_acc += torch.sum(y_pred == y_val)
acc_g0 += torch.sum(y_pred[mask0] == y_val[mask0])
acc_g1 += torch.sum(y_pred[mask1] == y_val[mask1])
valloader.set_description(f"Val Loss: {loss/count:.4f} - Val acc: {global_acc/count:.4f} - Acc g0: {acc_g0/count_g0:.4f} - Acc g1: {acc_g1/count_g1:.4f}")
acc_g0, acc_g1 = acc_g0 / count_g0, acc_g1 / count_g1
score = 0.5 * (acc_g0 + acc_g1) - abs(acc_g0 - acc_g1)
return loss / count, global_acc / count, acc_g0, acc_g1, score
def train(epoch, trainloader, valloader, model, optimizer, scheduler):
epochs = tqdm(range(epoch))
best_acc = 0
for e in epochs:
model.train()
losses, count = 0, 0
trainloader = tqdm(trainloader, leave=False)
for idx, batch in enumerate(trainloader):
with accelerator.accumulate(model):
X, y, _ = batch
count += X.shape[0]
y_pred = model(X).logits
y_smoothed = torch.where(nn.functional.one_hot(y.long(), num_classes=2) == 1, 0.9, 0.1)
loss = nn.functional.binary_cross_entropy_with_logits(y_pred, y_smoothed, reduction="sum")
losses += loss.item()
accelerator.backward(loss)
optimizer.step()
scheduler.step()
grads = [param.grad.detach().flatten() for param in model.parameters() if param.grad is not None]
norm = torch.cat(grads).norm()
optimizer.zero_grad()
# Observe evolution of gradient norm at each step
writer.add_scalar('Dino_sing/gradient_norm', norm, idx + e * len(trainloader) // grad_acc)
trainloader.set_description(f"Train Iter: {idx+1}/{len(trainloader)} - LR: {scheduler.get_last_lr()[0]:e} - Gradient norm: {norm:e} - Total Loss: {losses/count:.4f}")
model.eval()
valloss, valacc, acc_g0, acc_g1, score = evaluate(model, valloader)
epochs.set_description(f"Epoch: {e+1}/{epoch} - Train Loss: {losses/count:.4f} - Val loss: {valloss:.4f} - Val accuracy: {valacc:.4f} - Acc g0:{acc_g0:.4f} - Acc g1:{acc_g1:.4f} - Val score: {score:.4f}")
writer.add_scalars('Dino_sing/learning_curves', {'train': losses / count, 'val': valloss}, e)
writer.add_scalar('Dino_sing/learning_rate', scheduler.get_last_lr()[0], e)
writer.add_scalars('Dino_sing/score', {'acc_g0': acc_g0, 'acc_g1': acc_g1, 'score': score}, e)
if valacc > best_acc:
best_acc = valacc
torch.save({'epoch': e + 1, 'state_dict': model.state_dict(), 'score': score, 'accuracy': valacc,
'optimizer': optimizer.state_dict(), 'scheduler': scheduler.state_dict()},
f"./results/Dino_sing/lr{lr}_wd{weight_decay}_bs{batch_size*grad_acc}_epoch{e}.pth")
return best_acc
def main():
logging.basicConfig(style='{', format='{asctime} : {message}', datefmt="%c", level=logging.WARNING)
labels = pd.read_csv('./train.txt', sep='\t', header=None, names=['image', 'label', 'genre'])
labels['label'] = labels['label'].map(lambda x: 0 if x == -1 else x)
# Brutal hack : https://stackoverflow.com/a/51525992
labels['stratif'] = labels['label'].astype(str) + labels['genre'].astype(str)
trainloader, valloader = get_loaders(labels, 224, Imagenet=True)
epoch = 10
model = ViTForImageClassification.from_pretrained('facebook/dino-vits8', num_labels=2,
cache_dir="dino-vits8/", ignore_mismatched_sizes=True)
no_decay_list = ["bias", "bn", "norm", "layernorm"]
decay = [param for name, param in model.named_parameters() if not any(word in name for word in no_decay_list)]
no_decay = [param for name, param in model.named_parameters() if any(word in name for word in no_decay_list)]
parameters = [{"params": decay, "weight_decay": weight_decay}, {"params": no_decay, "weight_decay": 0.0}]
optim = SING(parameters, lr=lr)
scheduler = get_cosine_schedule_with_warmup(optim, 0.05 * len(trainloader) * epoch / grad_acc, len(trainloader) * epoch / grad_acc)
model, optim, scheduler = accelerator.prepare(model, optim, scheduler)
# Juste in case : https://github.com/AdrienCourtois/SING/tree/main#further-recommandations
# for module in model.modules():
# if isinstance(module, nn.LayerNorm):
# module.weight.requires_grad_(False)
# module.bias.requires_grad_(False)
best_acc = train(epoch, trainloader, valloader, model, optim, scheduler)
print(f"Best accuracy on the validation set: {best_acc}")
if __name__ == "__main__":
main()