Deci-AI
/
super-gradients
connected to https://github.com/Deci-AI/super-gradients.git


  
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

	
            import unittest

import numpy as np
import torch
from torch.optim import SGD
from torch.optim.lr_scheduler import ReduceLROnPlateau, MultiStepLR
from torchmetrics import F1Score

from super_gradients import Trainer
from super_gradients.training import models
from super_gradients.training.dataloaders.dataloaders import classification_test_dataloader
from super_gradients.training.metrics import Accuracy, Top5, ToyTestClassificationMetric
from super_gradients.training.utils.callbacks import LRSchedulerCallback, Phase


class TrainWithInitializedObjectsTest(unittest.TestCase):
    """
    Unit test for training with initialized objects passed as parameters.
    """

    def test_train_with_external_criterion(self):
        trainer = Trainer("external_criterion_test")
        dataloader = classification_test_dataloader(batch_size=10)

        model = models.get("resnet18", arch_params={"num_classes": 5})
        train_params = {"max_epochs": 2, "lr_updates": [1], "lr_decay_factor": 0.1, "lr_mode": "step",
                        "lr_warmup_epochs": 0, "initial_lr": 0.1, "loss": torch.nn.CrossEntropyLoss(),
                        "optimizer": "SGD",
                        "criterion_params": {}, "optimizer_params": {"weight_decay": 1e-4, "momentum": 0.9},
                        "train_metrics_list": [Accuracy()], "valid_metrics_list": [Accuracy()],
                        "metric_to_watch": "Accuracy",
                        "greater_metric_to_watch_is_better": True}
        trainer.train(model=model, training_params=train_params, train_loader=dataloader, valid_loader=dataloader)

    def test_train_with_external_optimizer(self):
        trainer = Trainer("external_optimizer_test")
        dataloader = classification_test_dataloader(batch_size=10)

        model = models.get("resnet18", arch_params={"num_classes": 5})
        optimizer = SGD(params=model.parameters(), lr=0.1)
        train_params = {"max_epochs": 2, "lr_updates": [1], "lr_decay_factor": 0.1, "lr_mode": "step",
                        "lr_warmup_epochs": 0, "initial_lr": 0.1, "loss": "cross_entropy", "optimizer": optimizer,
                        "criterion_params": {}, "optimizer_params": {"weight_decay": 1e-4, "momentum": 0.9},
                        "train_metrics_list": [Accuracy(), Top5()], "valid_metrics_list": [Accuracy(), Top5()],
                        "metric_to_watch": "Accuracy",
                        "greater_metric_to_watch_is_better": True}
        trainer.train(model=model, training_params=train_params, train_loader=dataloader, valid_loader=dataloader)

    def test_train_with_external_scheduler(self):
        trainer = Trainer("external_scheduler_test")
        dataloader = classification_test_dataloader(batch_size=10)

        lr = 0.3
        model = models.get("resnet18", arch_params={"num_classes": 5})
        optimizer = SGD(params=model.parameters(), lr=lr)
        lr_scheduler = MultiStepLR(optimizer=optimizer, milestones=[1, 2], gamma=0.1)
        phase_callbacks = [LRSchedulerCallback(lr_scheduler, Phase.TRAIN_EPOCH_END)]

        train_params = {"max_epochs": 2, "phase_callbacks": phase_callbacks,
                        "lr_warmup_epochs": 0, "initial_lr": lr, "loss": "cross_entropy", "optimizer": optimizer,
                        "criterion_params": {},
                        "train_metrics_list": [Accuracy(), Top5()], "valid_metrics_list": [Accuracy(), Top5()],
                        "metric_to_watch": "Accuracy",
                        "greater_metric_to_watch_is_better": True}
        trainer.train(model=model, training_params=train_params, train_loader=dataloader, valid_loader=dataloader)
        self.assertTrue(lr_scheduler.get_last_lr()[0] == lr * 0.1 * 0.1)

    def test_train_with_external_scheduler_class(self):
        trainer = Trainer("external_scheduler_test")
        dataloader = classification_test_dataloader(batch_size=10)

        model = models.get("resnet18", arch_params={"num_classes": 5})
        optimizer = SGD  # a class - not an instance

        train_params = {"max_epochs": 2,
                        "lr_warmup_epochs": 0, "initial_lr": 0.3, "loss": "cross_entropy", "optimizer": optimizer,
                        "criterion_params": {},
                        "train_metrics_list": [Accuracy(), Top5()], "valid_metrics_list": [Accuracy(), Top5()],
                        "metric_to_watch": "Accuracy",
                        "greater_metric_to_watch_is_better": True}
        trainer.train(model=model, training_params=train_params, train_loader=dataloader, valid_loader=dataloader)

    def test_train_with_reduce_on_plateau(self):
        trainer = Trainer("external_reduce_on_plateau_scheduler_test")
        dataloader = classification_test_dataloader(batch_size=10)

        lr = 0.3
        model = models.get("resnet18", arch_params={"num_classes": 5})
        optimizer = SGD(params=model.parameters(), lr=lr)
        lr_scheduler = ReduceLROnPlateau(optimizer=optimizer, patience=0)
        phase_callbacks = [LRSchedulerCallback(lr_scheduler, Phase.VALIDATION_EPOCH_END, "ToyTestClassificationMetric")]

        train_params = {"max_epochs": 2, "phase_callbacks": phase_callbacks,
                        "lr_warmup_epochs": 0, "initial_lr": lr, "loss": "cross_entropy", "optimizer": optimizer,
                        "criterion_params": {},
                        "train_metrics_list": [Accuracy(), Top5()],
                        "valid_metrics_list": [Accuracy(), Top5(), ToyTestClassificationMetric()],
                        "metric_to_watch": "Accuracy",
                        "greater_metric_to_watch_is_better": True}
        trainer.train(model=model, training_params=train_params, train_loader=dataloader, valid_loader=dataloader)
        self.assertTrue(lr_scheduler._last_lr[0] == lr * 0.1)

    def test_train_with_external_metric(self):
        trainer = Trainer("external_metric_test")
        dataloader = classification_test_dataloader(batch_size=10)

        model = models.get("resnet18", arch_params={"num_classes": 5})
        train_params = {"max_epochs": 2, "lr_updates": [1], "lr_decay_factor": 0.1, "lr_mode": "step",
                        "lr_warmup_epochs": 0, "initial_lr": 0.1, "loss": "cross_entropy", "optimizer": "SGD",
                        "criterion_params": {}, "optimizer_params": {"weight_decay": 1e-4, "momentum": 0.9},
                        "train_metrics_list": [F1Score()], "valid_metrics_list": [F1Score()],
                        "metric_to_watch": "F1Score",
                        "greater_metric_to_watch_is_better": True}
        trainer.train(model=model, training_params=train_params, train_loader=dataloader, valid_loader=dataloader)

    def test_train_with_external_dataloaders(self):
        trainer = Trainer("external_data_loader_test")

        batch_size = 5
        trainset = torch.utils.data.TensorDataset(torch.Tensor(np.random.random((10, 3, 32, 32))),
                                                  torch.LongTensor(np.zeros((10))))

        valset = torch.utils.data.TensorDataset(torch.Tensor(np.random.random((10, 3, 32, 32))),
                                                torch.LongTensor(np.zeros((10))))

        train_loader = torch.utils.data.DataLoader(trainset, batch_size=batch_size)
        val_loader = torch.utils.data.DataLoader(valset, batch_size=batch_size)

        model = models.get("resnet18", num_classes=5)
        train_params = {"max_epochs": 2, "lr_updates": [1], "lr_decay_factor": 0.1, "lr_mode": "step",
                        "lr_warmup_epochs": 0, "initial_lr": 0.1, "loss": "cross_entropy", "optimizer": "SGD",
                        "criterion_params": {}, "optimizer_params": {"weight_decay": 1e-4, "momentum": 0.9},
                        "train_metrics_list": [F1Score()], "valid_metrics_list": [F1Score()],
                        "metric_to_watch": "F1Score",
                        "greater_metric_to_watch_is_better": True}
        trainer.train(model=model, training_params=train_params, train_loader=train_loader, valid_loader=val_loader)


if __name__ == '__main__':
    unittest.main()