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

	
141

	
142

	
143

	
144

	
145

	
146

	
147

	
148

	
149

	
150

	
151

	
152

	
153

	
154

	
155

	
156

	
157

	
158

	
            import unittest
from super_gradients import SgModel, \
    ClassificationTestDatasetInterface
from super_gradients.training.metrics import Accuracy, Top5, ToyTestClassificationMetric
from super_gradients.training.models import ResNet18
from torch.optim import SGD
from torch.optim.lr_scheduler import ReduceLROnPlateau, MultiStepLR
from super_gradients.training.utils.callbacks import LRSchedulerCallback, Phase
from torchmetrics import F1Score
import torch
import numpy as np
from super_gradients.training.datasets.dataset_interfaces import DatasetInterface


class TrainWithInitializedObjectsTest(unittest.TestCase):
    """
    Unit test for training with initialized objects passed as parameters.
    """
    def test_train_with_external_criterion(self):
        model = SgModel("external_criterion_test", model_checkpoints_location='local')
        dataset_params = {"batch_size": 10}
        dataset = ClassificationTestDatasetInterface(dataset_params=dataset_params)
        model.connect_dataset_interface(dataset)

        net = ResNet18(num_classes=5, arch_params={})
        model.build_model(net)
        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}
        model.train(train_params)

    def test_train_with_external_optimizer(self):
        model = SgModel("external_optimizer_test", model_checkpoints_location='local')
        dataset_params = {"batch_size": 10}
        dataset = ClassificationTestDatasetInterface(dataset_params=dataset_params)
        model.connect_dataset_interface(dataset)

        net = ResNet18(num_classes=5, arch_params={})
        optimizer = SGD(params=net.parameters(), lr=0.1)
        model.build_model(net)
        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()],
                        "loss_logging_items_names": ["Loss"], "metric_to_watch": "Accuracy",
                        "greater_metric_to_watch_is_better": True}
        model.train(train_params)

    def test_train_with_external_scheduler(self):
        model = SgModel("external_scheduler_test", model_checkpoints_location='local')
        dataset_params = {"batch_size": 10}
        dataset = ClassificationTestDatasetInterface(dataset_params=dataset_params)
        model.connect_dataset_interface(dataset)

        lr = 0.3
        net = ResNet18(num_classes=5, arch_params={})
        optimizer = SGD(params=net.parameters(), lr=lr)
        lr_scheduler = MultiStepLR(optimizer=optimizer, milestones=[1, 2], gamma=0.1)
        phase_callbacks = [LRSchedulerCallback(lr_scheduler, Phase.TRAIN_EPOCH_END)]
        model.build_model(net)

        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()],
                        "loss_logging_items_names": ["Loss"], "metric_to_watch": "Accuracy",
                        "greater_metric_to_watch_is_better": True}
        model.train(train_params)
        assert lr_scheduler.get_last_lr()[0] == lr * 0.1 * 0.1

    def test_train_with_external_scheduler_class(self):
        model = SgModel("external_scheduler_test", model_checkpoints_location='local')
        dataset_params = {"batch_size": 10}
        dataset = ClassificationTestDatasetInterface(dataset_params=dataset_params)
        model.connect_dataset_interface(dataset)

        net = ResNet18(num_classes=5, arch_params={})
        optimizer = SGD # a class - not an instance
        model.build_model(net)

        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()],
                        "loss_logging_items_names": ["Loss"], "metric_to_watch": "Accuracy",
                        "greater_metric_to_watch_is_better": True}
        model.train(train_params)

    def test_train_with_reduce_on_plateau(self):
        model = SgModel("external_reduce_on_plateau_scheduler_test", model_checkpoints_location='local')
        dataset_params = {"batch_size": 10}
        dataset = ClassificationTestDatasetInterface(dataset_params=dataset_params)
        model.connect_dataset_interface(dataset)

        lr = 0.3
        net = ResNet18(num_classes=5, arch_params={})
        optimizer = SGD(params=net.parameters(), lr=lr)
        lr_scheduler = ReduceLROnPlateau(optimizer=optimizer, patience=0)
        phase_callbacks = [LRSchedulerCallback(lr_scheduler, Phase.VALIDATION_EPOCH_END, "ToyTestClassificationMetric")]
        model.build_model(net)

        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()],
                        "loss_logging_items_names": ["Loss"], "metric_to_watch": "Accuracy",
                        "greater_metric_to_watch_is_better": True}
        model.train(train_params)
        assert lr_scheduler._last_lr[0] == lr * 0.1

    def test_train_with_external_metric(self):
        model = SgModel("external_metric_test", model_checkpoints_location='local')
        dataset_params = {"batch_size": 10}
        dataset = ClassificationTestDatasetInterface(dataset_params=dataset_params)
        model.connect_dataset_interface(dataset)

        net = ResNet18(num_classes=5, arch_params={})
        model.build_model(net)
        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()],
                        "loss_logging_items_names": ["Loss"], "metric_to_watch": "F1Score",
                        "greater_metric_to_watch_is_better": True}
        model.train(train_params)

    def test_train_with_external_dataloaders(self):
        model = SgModel("external_data_loader_test", model_checkpoints_location='local')

        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))))

        classes = [0, 1, 2, 3, 4]
        train_loader = torch.utils.data.DataLoader(trainset, batch_size=batch_size)
        val_loader = torch.utils.data.DataLoader(valset, batch_size=batch_size)

        dataset_interface = DatasetInterface(train_loader=train_loader, val_loader=val_loader, classes=classes)
        model.connect_dataset_interface(dataset_interface)

        net = ResNet18(num_classes=5, arch_params={})
        model.build_model(net)
        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()],
                        "loss_logging_items_names": ["Loss"], "metric_to_watch": "F1Score",
                        "greater_metric_to_watch_is_better": True}
        model.train(train_params)


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