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test_convgru.py
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test_convgru.py
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import torch
from datetime import time
import unittest
from convgru import ConvGRUFullyGated,SampleConvGRUFullyGatedNet,SampleConvGRUTye2Net
import torch.optim as optim
from torchvision import datasets, transforms
from torch.optim.lr_scheduler import StepLR
import config
from utils import train, test
class TestConvGRUFullyGated(unittest.TestCase):
def setUp(self):
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.batch_size = 16
self.input_width = self.input_height=32
self.in_channels = 3
self.out_channels = self.in_channels
self.hidden_channels = 10
self.kernel_size = [3,3]
self.stride = [1,1]
self.bias = False
self.time_steps = 5
self.conv_gru = ConvGRUFullyGated(
in_channels=self.in_channels,
hidden_channels=self.hidden_channels,
kernel_size=self.kernel_size,
stride=self.stride,
in_size=[self.input_width,self.input_height],
time_steps=self.time_steps
).to(self.device)
def test_forward(self):
x = torch.rand([self.batch_size,self.in_channels,self.input_width,self.input_height]).to(self.device)
self.assertTrue(
self.conv_gru(x).size()
==
torch.Size(
(
self.batch_size,self.hidden_channels,self.input_width,self.input_height
)
)
)
stride = [2,3]
self.conv_gru = ConvGRUFullyGated(
in_channels=self.in_channels,
hidden_channels=self.hidden_channels,
kernel_size=self.kernel_size,
stride=stride,
in_size=[self.input_width,self.input_height],
time_steps=self.time_steps
).to(self.device)
self.assertTrue(
self.conv_gru(x).size()
==
torch.Size(
(
self.batch_size,self.hidden_channels,self.input_width//stride[0],self.input_height//stride[1]
)
)
)
kernel_size=[7,7]
self.conv_gru = ConvGRUFullyGated(
in_channels=self.in_channels,
hidden_channels=self.hidden_channels,
kernel_size=kernel_size,
stride=stride,
in_size=[self.input_width,self.input_height],
time_steps=self.time_steps
).to(self.device)
self.assertTrue(
self.conv_gru(x).size()
==
torch.Size(
(
self.batch_size,self.hidden_channels,self.input_width//stride[0],self.input_height//stride[1]
)
)
)
kernel_size=[5,5]
stride = [7,4]
self.conv_gru = ConvGRUFullyGated(
in_channels=self.in_channels,
hidden_channels=self.hidden_channels,
kernel_size=kernel_size,
stride=stride,
in_size=[self.input_width,self.input_height],
time_steps=self.time_steps
).to(self.device)
self.assertTrue(
self.conv_gru(x).size()
==
torch.Size(
(
self.batch_size,self.hidden_channels,self.input_width//stride[0],self.input_height//stride[1]
)
)
)
class TestSampleConvGRUFullyGatedNet(unittest.TestCase):
def setUp(self):
print()
self.in_channels = 1
self.hidden_channels = 32
self.kernel_size = [3,3]
self.stride = [2,2]
self.in_size = [28,28]
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.model = SampleConvGRUFullyGatedNet(
in_channels=self.in_channels,
hidden_channels=self.hidden_channels,
kernel_size=self.kernel_size,
stride=self.stride,
in_size=self.in_size
).to(self.device)
torch.manual_seed(config.seed)
train_kwargs = {'batch_size': config.batch_size}
test_kwargs = {'batch_size': config.test_batch_size}
if self.device.type=="cuda":
cuda_kwargs = {'num_workers': 0,
'pin_memory': True,
'shuffle': True}
train_kwargs.update(cuda_kwargs)
cuda_kwargs = {'num_workers': 0,
'pin_memory': True,
'shuffle': False}
test_kwargs.update(cuda_kwargs)
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])
dataset1 = datasets.MNIST('../data', train=True, download=True,
transform=transform)
dataset2 = datasets.MNIST('../data', train=False,
transform=transform)
self.train_loader = torch.utils.data.DataLoader(dataset1,**train_kwargs)
self.test_loader = torch.utils.data.DataLoader(dataset2, **test_kwargs)
def test_train_and_test(self):
optimizer = optim.Adadelta(self.model.parameters(), lr=config.lr)
scheduler = StepLR(optimizer, step_size=1, gamma=config.gamma)
for epoch in range(1, config.epochs + 1):
train(self.model, self.device, self.train_loader, optimizer, epoch)
scheduler.step()
print()
test(self.model, self.device, self.test_loader)
class TestSampleConvGRUType2Net(unittest.TestCase):
def setUp(self):
print()
self.in_channels = 1
self.hidden_channels = 32
self.kernel_size = [3,3]
self.stride = [2,2]
self.in_size = [28,28]
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.model = SampleConvGRUTye2Net(
in_channels=self.in_channels,
hidden_channels=self.hidden_channels,
kernel_size=self.kernel_size,
stride=self.stride,
in_size=self.in_size
).to(self.device)
torch.manual_seed(config.seed)
train_kwargs = {'batch_size': config.batch_size}
test_kwargs = {'batch_size': config.test_batch_size}
if self.device.type=="cuda":
cuda_kwargs = {'num_workers': 1,
'pin_memory': True,
'shuffle': True}
train_kwargs.update(cuda_kwargs)
test_kwargs.update(cuda_kwargs)
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])
dataset1 = datasets.MNIST('../data', train=True, download=True,
transform=transform)
dataset2 = datasets.MNIST('../data', train=False,
transform=transform)
self.train_loader = torch.utils.data.DataLoader(dataset1,**train_kwargs)
self.test_loader = torch.utils.data.DataLoader(dataset2, **test_kwargs)
def test_train_and_test(self):
optimizer = optim.Adadelta(self.model.parameters(), lr=config.lr)
scheduler = StepLR(optimizer, step_size=1, gamma=config.gamma)
for epoch in range(1, config.epochs + 1):
train(self.model, self.device, self.train_loader, optimizer, epoch)
scheduler.step()
print()
test(self.model, self.device, self.test_loader)
if __name__=="__main__":
unittest.main()