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Merge pull request #475 from mv1388/trainloop-end-2-end-test
train loop end2end train history and loss
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tests/test_torchtrain/test_train_loop/test_e2e_train_loop/test_end2end_train_loop.py
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import unittest | ||
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import random | ||
import numpy as np | ||
import torch | ||
import torch.nn as nn | ||
import torch.nn.functional as F | ||
import torch.optim as optim | ||
from torch.utils.data.dataloader import DataLoader | ||
from torch.utils.data.dataset import TensorDataset | ||
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from aitoolbox.torchtrain.train_loop import TrainLoop | ||
from aitoolbox.torchtrain.model import TTModel | ||
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class FFNet(TTModel): | ||
def __init__(self): | ||
super().__init__() | ||
self.ff_1 = nn.Linear(50, 100) | ||
self.ff_2 = nn.Linear(100, 100) | ||
self.ff_3 = nn.Linear(100, 10) | ||
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def forward(self, batch_data): | ||
ff_out = F.relu(self.ff_1(batch_data)) | ||
ff_out = F.relu(self.ff_2(ff_out)) | ||
ff_out = self.ff_3(ff_out) | ||
out_softmax = F.log_softmax(ff_out, dim=1) | ||
return out_softmax | ||
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def get_loss(self, batch_data, criterion, device): | ||
input_data, target = batch_data | ||
input_data = input_data.to(device) | ||
target = target.to(device) | ||
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predicted = self(input_data) | ||
loss = criterion(predicted, target) | ||
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return loss | ||
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def get_predictions(self, batch_data, device): | ||
input_data, target = batch_data | ||
input_data = input_data.to(device) | ||
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predicted = self(input_data) | ||
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return predicted.cpu(), target, {'example_feat_sum': input_data.sum(dim=1).tolist()} | ||
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class TestEnd2EndTrainLoop(unittest.TestCase): | ||
def test_e2e_ff_net_train_loop(self): | ||
self.set_seeds() | ||
batch_size = 10 | ||
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train_dataset = TensorDataset(torch.randn(100, 50), torch.randint(low=0, high=10, size=(100,))) | ||
val_dataset = TensorDataset(torch.randn(30, 50), torch.randint(low=0, high=10, size=(30,))) | ||
test_dataset = TensorDataset(torch.randn(30, 50), torch.randint(low=0, high=10, size=(30,))) | ||
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train_dataloader = DataLoader(train_dataset, batch_size=batch_size) | ||
val_dataloader = DataLoader(val_dataset, batch_size=batch_size) | ||
test_dataloader = DataLoader(test_dataset, batch_size=batch_size) | ||
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model = FFNet() | ||
optimizer = optim.Adam(model.parameters(), lr=0.001, betas=(0.9, 0.999)) | ||
criterion = nn.NLLLoss() | ||
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train_loop = TrainLoop( | ||
model, | ||
train_dataloader, val_dataloader, test_dataloader, | ||
optimizer, criterion | ||
) | ||
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train_loop.fit(num_epochs=5) | ||
tl_history = train_loop.train_history.train_history | ||
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self.assertEqual(train_loop.epoch, 4) | ||
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result_approx = { | ||
'loss': [2.224587655067444, 2.1440203189849854, 2.0584306001663206, 1.962017869949341, 1.8507084131240845], | ||
'accumulated_loss': [2.3059947967529295, 2.1976317405700683, 2.114974856376648, 2.0259472250938417, 1.9252637863159179], | ||
'val_loss': [2.330514828364054, 2.345397472381592, 2.363233725229899, 2.3853348096211753, 2.4111196994781494], | ||
'train_end_test_loss': [2.31626296043396] | ||
} | ||
self.assertEqual(sorted(tl_history.keys()), sorted(result_approx.keys())) | ||
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for metric, results_list in result_approx.items(): | ||
self.assertEqual(len(results_list), len(tl_history[metric])) | ||
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for correct_result, tl_result in zip(results_list, tl_history[metric]): | ||
self.assertAlmostEqual(correct_result, tl_result, places=6) | ||
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# linux_result = { | ||
# 'loss': [2.224587655067444, 2.1440203189849854, 2.0584306001663206, | ||
# 1.962017869949341, 1.8507084846496582], | ||
# 'accumulated_loss': [2.3059947967529295, 2.1976317405700683, 2.114974856376648, | ||
# 2.0259472012519835, 1.9252637863159179], | ||
# 'val_loss': [2.330514828364054, 2.345397472381592, 2.363233804702759, | ||
# 2.3853348096211753, 2.4111196994781494], | ||
# 'train_end_test_loss': [2.31626296043396] | ||
# } | ||
# self.assertEqual(train_loop.train_history.train_history, linux_result) | ||
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def test_e2e_ff_net_train_loop_loss(self): | ||
self.set_seeds() | ||
batch_size = 10 | ||
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train_dataset = TensorDataset(torch.randn(100, 50), torch.randint(low=0, high=10, size=(100,))) | ||
val_dataset = TensorDataset(torch.randn(30, 50), torch.randint(low=0, high=10, size=(30,))) | ||
test_dataset = TensorDataset(torch.randn(30, 50), torch.randint(low=0, high=10, size=(30,))) | ||
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train_dataloader = DataLoader(train_dataset, batch_size=batch_size) | ||
val_dataloader = DataLoader(val_dataset, batch_size=batch_size) | ||
test_dataloader = DataLoader(test_dataset, batch_size=batch_size) | ||
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model = FFNet() | ||
optimizer = optim.Adam(model.parameters(), lr=0.001, betas=(0.9, 0.999)) | ||
criterion = nn.NLLLoss() | ||
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train_loop = TrainLoop( | ||
model, | ||
train_dataloader, val_dataloader, test_dataloader, | ||
optimizer, criterion | ||
) | ||
train_loop.fit(num_epochs=5) | ||
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self.assertAlmostEqual(train_loop.evaluate_loss_on_train_set(), 1.8507084131240845, places=6) | ||
self.assertAlmostEqual(train_loop.evaluate_loss_on_validation_set(), 2.4111196994781494, places=6) | ||
self.assertAlmostEqual(train_loop.evaluate_loss_on_test_set(), 2.31626296043396, places=6) | ||
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def test_e2e_ff_net_train_loop_predictions(self): | ||
self.set_seeds() | ||
batch_size = 10 | ||
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train_dataset = TensorDataset(torch.randn(100, 50), torch.randint(low=0, high=10, size=(100,))) | ||
val_dataset = TensorDataset(torch.randn(30, 50), torch.randint(low=0, high=10, size=(30,))) | ||
test_dataset = TensorDataset(torch.randn(30, 50), torch.randint(low=0, high=10, size=(30,))) | ||
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train_dataloader = DataLoader(train_dataset, batch_size=batch_size) | ||
val_dataloader = DataLoader(val_dataset, batch_size=batch_size) | ||
test_dataloader = DataLoader(test_dataset, batch_size=batch_size) | ||
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model = FFNet() | ||
optimizer = optim.Adam(model.parameters(), lr=0.001, betas=(0.9, 0.999)) | ||
criterion = nn.NLLLoss() | ||
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train_loop = TrainLoop( | ||
model, | ||
train_dataloader, val_dataloader, test_dataloader, | ||
optimizer, criterion | ||
) | ||
train_loop.fit(num_epochs=5) | ||
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train_pred, train_target, train_meta = train_loop.predict_on_train_set() | ||
self.assertEqual( | ||
train_pred.argmax(dim=1).tolist(), | ||
[0, 4, 8, 0, 1, 1, 6, 1, 6, 1, 8, 3, 0, 0, 8, 8, 1, 8, 8, 1, 8, 0, 8, 0, 0, 1, 3, 4, 8, 8, 0, 8, 8, 1, 8, 8, | ||
5, 5, 1, 8, 8, 8, 8, 8, 9, 0, 8, 8, 5, 8, 8, 1, 1, 8, 5, 1, 8, 8, 5, 5, 1, 8, 8, 8, 8, 0, 0, 1, 1, 0, 8, 8, | ||
3, 0, 5, 0, 8, 9, 1, 8, 8, 8, 5, 8, 5, 8, 0, 1, 8, 5, 8, 6, 5, 1, 8, 1, 0, 8, 1, 8] | ||
) | ||
self.assertEqual(train_target.tolist(), train_dataset.tensors[1].tolist()) | ||
self.assertEqual(train_dataset.tensors[0].sum(dim=1).tolist(), train_meta['example_feat_sum']) | ||
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val_pred, val_target, val_meta = train_loop.predict_on_validation_set() | ||
self.assertEqual( | ||
val_pred.argmax(dim=1).tolist(), | ||
[1, 1, 1, 1, 5, 8, 0, 8, 1, 1, 5, 8, 8, 1, 8, 8, 1, 8, 8, 8, 1, 0, 0, 8, 0, 1, 1, 0, 1, 8] | ||
) | ||
self.assertEqual(val_target.tolist(), val_dataset.tensors[1].tolist()) | ||
self.assertEqual(val_dataset.tensors[0].sum(dim=1).tolist(), val_meta['example_feat_sum']) | ||
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test_pred, test_target, test_meta = train_loop.predict_on_test_set() | ||
self.assertEqual( | ||
test_pred.argmax(dim=1).tolist(), | ||
[4, 8, 0, 8, 1, 8, 1, 1, 8, 8, 8, 8, 8, 0, 8, 8, 5, 8, 8, 5, 8, 1, 0, 5, 1, 8, 8, 8, 8, 1] | ||
) | ||
self.assertEqual(test_target.tolist(), test_dataset.tensors[1].tolist()) | ||
self.assertEqual(test_dataset.tensors[0].sum(dim=1).tolist(), test_meta['example_feat_sum']) | ||
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@staticmethod | ||
def set_seeds(): | ||
manual_seed = 0 | ||
np.random.seed(manual_seed) | ||
random.seed(manual_seed) | ||
torch.manual_seed(manual_seed) | ||
# if you are suing GPU | ||
torch.cuda.manual_seed(manual_seed) | ||
torch.cuda.manual_seed_all(manual_seed) | ||
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torch.backends.cudnn.enabled = False | ||
torch.backends.cudnn.benchmark = False | ||
torch.backends.cudnn.deterministic = True |