154 lines
4.2 KiB
Python
154 lines
4.2 KiB
Python
import os
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import time
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import numpy as np
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import pytest
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from torch.utils.data import DataLoader
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from torchvision import transforms
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import tests.base.utils as tutils
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from pytorch_lightning import Trainer, LightningModule, seed_everything
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from tests.base.datasets import TrialMNIST
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class ParityMNIST(LightningModule):
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def __init__(self):
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super(ParityMNIST, self).__init__()
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self.c_d1 = nn.Linear(in_features=28 * 28, out_features=128)
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self.c_d1_bn = nn.BatchNorm1d(128)
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self.c_d1_drop = nn.Dropout(0.3)
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self.c_d2 = nn.Linear(in_features=128, out_features=10)
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def forward(self, x):
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x = x.view(x.size(0), -1)
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x = self.c_d1(x)
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x = torch.tanh(x)
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x = self.c_d1_bn(x)
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x = self.c_d1_drop(x)
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x = self.c_d2(x)
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return x
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def training_step(self, batch, batch_nb):
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x, y = batch
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y_hat = self(x)
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loss = F.cross_entropy(y_hat, y)
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return {'loss': loss}
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def configure_optimizers(self):
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return torch.optim.Adam(self.parameters(), lr=0.02)
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def train_dataloader(self):
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return DataLoader(TrialMNIST(train=True,
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download=True,
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num_samples=500,
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digits=list(range(5))),
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batch_size=128)
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@pytest.mark.skipif(not torch.cuda.is_available(), reason="test requires GPU machine")
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def test_pytorch_parity(tmpdir):
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"""
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Verify that the same pytorch and lightning models achieve the same results
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:param tmpdir:
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:return:
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"""
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num_epochs = 2
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num_rums = 3
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lightning_outs, pl_times = lightning_loop(ParityMNIST, num_rums, num_epochs)
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manual_outs, pt_times = vanilla_loop(ParityMNIST, num_rums, num_epochs)
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# make sure the losses match exactly to 5 decimal places
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for pl_out, pt_out in zip(lightning_outs, manual_outs):
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np.testing.assert_almost_equal(pl_out, pt_out, 5)
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# the fist run initialize dataset (download & filter)
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tutils.assert_speed_parity(pl_times[1:], pt_times[1:], num_epochs)
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def vanilla_loop(MODEL, num_runs=10, num_epochs=10):
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"""
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Returns an array with the last loss from each epoch for each run
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"""
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device = torch.device('cuda' if torch.cuda.is_available() else "cpu")
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errors = []
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times = []
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torch.backends.cudnn.deterministic = True
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for i in range(num_runs):
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time_start = time.perf_counter()
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# set seed
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seed = i
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seed_everything(seed)
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# init model parts
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model = MODEL()
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dl = model.train_dataloader()
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optimizer = model.configure_optimizers()
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# model to GPU
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model = model.to(device)
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epoch_losses = []
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for epoch in range(num_epochs):
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# run through full training set
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for j, batch in enumerate(dl):
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x, y = batch
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x = x.cuda(0)
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y = y.cuda(0)
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batch = (x, y)
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loss_dict = model.training_step(batch, j)
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loss = loss_dict['loss']
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loss.backward()
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optimizer.step()
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optimizer.zero_grad()
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# track last epoch loss
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epoch_losses.append(loss.item())
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time_end = time.perf_counter()
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times.append(time_end - time_start)
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errors.append(epoch_losses[-1])
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return errors, times
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def lightning_loop(MODEL, num_runs=10, num_epochs=10):
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errors = []
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times = []
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for i in range(num_runs):
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time_start = time.perf_counter()
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# set seed
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seed = i
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seed_everything(seed)
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model = MODEL()
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# init model parts
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trainer = Trainer(
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max_epochs=num_epochs,
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progress_bar_refresh_rate=0,
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weights_summary=None,
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gpus=1,
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early_stop_callback=False,
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checkpoint_callback=False,
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deterministic=True,
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)
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trainer.fit(model)
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final_loss = trainer.running_loss.last().item()
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errors.append(final_loss)
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time_end = time.perf_counter()
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times.append(time_end - time_start)
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return errors, times
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