通过timm下载模型

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
201
202
203
204
205
206
import os
import warnings
from pprint import pprint
from glob import glob
from tqdm import tqdm

import torch
import torch.optim as optim
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import torchvision.transforms as T
from box import Box
from timm import create_model
from sklearn.model_selection import StratifiedKFold
from torchvision.io import read_image
from torch.utils.data import DataLoader, Dataset
from pytorch_grad_cam import GradCAMPlusPlus
from pytorch_grad_cam.utils.image import show_cam_on_image


import pytorch_lightning as pl
from pytorch_lightning.utilities.seed import seed_everything
from pytorch_lightning import callbacks
from pytorch_lightning.callbacks.progress import ProgressBarBase
from pytorch_lightning.callbacks.early_stopping import EarlyStopping
from pytorch_lightning.loggers import TensorBoardLogger
from pytorch_lightning import LightningDataModule, LightningModule

config = {'seed': 20211111,
          'root': './input/petfinder-pawpularity-score/', 
          'n_splits': 5,
          'epoch': 60,
          'trainer': {
              'gpus': 1,
              'accumulate_grad_batches': 1,
              'progress_bar_refresh_rate': 1,
              'fast_dev_run': False,
              'num_sanity_val_steps': 0,
              'resume_from_checkpoint': None,
          },
          'transform':{
              'name': 'get_default_transforms',
              'image_size': 600
          },
          'train_loader':{
              'batch_size': 2,
              'shuffle': True,
              'num_workers': 4,
              'pin_memory': False,
              'drop_last': True,
          },
          'val_loader': {
              'batch_size': 2,
              'shuffle': False,
              'num_workers': 4,
              'pin_memory': False,
              'drop_last': False
         },
          'model':{
              'name': 'vit_large_patch16_384',
              'output_dim': 1
          },
          'optimizer':{
              'name': 'optim.AdamW',
              'params':{
                  'lr': 1e-5
              },
          },
          'scheduler':{
              'name': 'optim.lr_scheduler.CosineAnnealingWarmRestarts',
              'params':{
                  'T_0': 20,
                  'eta_min': 1e-4,
              }
          },
          'loss': 'nn.BCEWithLogitsLoss',
}
config = Box(config)
pprint(config)


class Model(pl.LightningModule):
    def __init__(self, cfg):
        super().__init__()
        self.cfg = cfg
        self.__build_model()
        self._criterion = eval(self.cfg.loss)()
        self.transform = get_default_transforms()
        self.save_hyperparameters(cfg)

    def __build_model(self):
        self.backbone = create_model(
            self.cfg.model.name, pretrained=True, num_classes=0, in_chans=3
        )
        num_features = self.backbone.num_features
        self.fc = nn.Sequential(
            nn.Dropout(0.5), nn.Linear(num_features, self.cfg.model.output_dim)
        )

    def forward(self, x):
        f = self.backbone(x)
        out = self.fc(f)
        return out

    def training_step(self, batch, batch_idx):
        loss, pred, labels = self.__share_step(batch, 'train')
        return {'loss': loss, 'pred': pred, 'labels': labels}
        
    def validation_step(self, batch, batch_idx):
        loss, pred, labels = self.__share_step(batch, 'val')
        return {'pred': pred, 'labels': labels}
    
    def __share_step(self, batch, mode):
        images, labels = batch
        labels = labels.float() / 100.0
        images = self.transform[mode](images)
        
        if torch.rand(1)[0] < 0.5 and mode == 'train':
            mix_images, target_a, target_b, lam = mixup(images, labels, alpha=0.5)
            logits = self.forward(mix_images).squeeze(1)
            loss = self._criterion(logits, target_a) * lam + 
                (1 - lam) * self._criterion(logits, target_b)
        else:
            logits = self.forward(images).squeeze(1)
            loss = self._criterion(logits, labels)
        
        pred = logits.sigmoid().detach().cpu() * 100.
        labels = labels.detach().cpu() * 100.
        return loss, pred, labels
        
    def training_epoch_end(self, outputs):
        self.__share_epoch_end(outputs, 'train')

    def validation_epoch_end(self, outputs):
        self.__share_epoch_end(outputs, 'val')    
        
    def __share_epoch_end(self, outputs, mode):
        preds = []
        labels = []
        for out in outputs:
            pred, label = out['pred'], out['labels']
            preds.append(pred)
            labels.append(label)
        preds = torch.cat(preds)
        labels = torch.cat(labels)
        metrics = torch.sqrt(((labels - preds) ** 2).mean())
        self.log(f'{mode}_loss', metrics)
    
    def check_gradcam(self, dataloader, target_layer, target_category, reshape_transform=None):
        cam = GradCAMPlusPlus(
            model=self,
            #target_layer=target_layer, 
            target_layers=[target_layer], 
            use_cuda=self.cfg.trainer.gpus, 
            reshape_transform=reshape_transform)
        
        org_images, labels = iter(dataloader).next()
        cam.batch_size = len(org_images)
        images = self.transform['val'](org_images)
        images = images.to(self.device)
        logits = self.forward(images).squeeze(1)
        pred = logits.sigmoid().detach().cpu().numpy() * 100
        labels = labels.cpu().numpy()
        
        grayscale_cam = cam(input_tensor=images, target_category=target_category, eigen_smooth=True)
        org_images = org_images.detach().cpu().numpy().transpose(0, 2, 3, 1) / 255.
        return org_images, grayscale_cam, pred, labels

    def configure_optimizers(self):
        optimizer = eval(self.cfg.optimizer.name)(
            self.parameters(), **self.cfg.optimizer.params
        )
        scheduler = eval(self.cfg.scheduler.name)(
            optimizer,
            **self.cfg.scheduler.params
        )
        return [optimizer], [scheduler]

IMAGENET_MEAN = [0.485, 0.456, 0.406]  # RGB
IMAGENET_STD = [0.229, 0.224, 0.225]  # RGB
def get_default_transforms():
    transform = {
        "train": T.Compose(
            [
                T.RandomHorizontalFlip(),
                T.RandomVerticalFlip(),
                T.RandomAffine(15, translate=(0.1, 0.1), scale=(0.9, 1.1)),
                T.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1),
                T.ConvertImageDtype(torch.float),
                T.Normalize(mean=IMAGENET_MEAN, std=IMAGENET_STD),
            ]
        ),
        "val": T.Compose(
            [
                T.ConvertImageDtype(torch.float),
                T.Normalize(mean=IMAGENET_MEAN, std=IMAGENET_STD),
            ]
        ),
    }
    return transform

model = Model(config)