I’m trying to convert my tensorflow model into pytorch, however, I’m a bit confused about the ordering / how things such as batch_size and channels have to be passed in which sequence.
I started creating my own dataset. Afterwards I defined my model however with receiving the following RuntimeError.
RuntimeError: Given groups=1, weight of size [28, 1, 3], expected input[1, 128, 19994] to have 1 channels, but got 128 channels instead
My current code looks like this:
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader, TensorDataset
import torch.nn.functional as F
X1 = torch.randn(1,9999, 4000)
X2 = torch.rand((1,9999, 4000))
aux1 = torch.randn(4000,)
aux2 = torch.randn(4000,)
aux3 = torch.randn(4000,)
y = torch.randn(4000,)
class Dataset:
def __init__(self, x1,
x2,
aux1,
aux2,
aux3,
y):
self.x1 = x1
self.x2= x2
self.aux1 = aux1
self.aux2 = aux2
self.aux3 = aux3
self.y = y
def __len__(self):
return self.x1.shape[-1]
def __getitem__(self, idx):
#inputs
x1 = self.x1[:,:,idx]
x2 = self.x2[:,:,idx]
aux1 = self.aux1[idx]
aux2 = self.aux2[idx]
aux3 = self.aux3[idx]
#outputs
y = self.y[idx]
return ([torch.tensor(x1, dtype=torch.float32),
torch.tensor(x2, dtype=torch.float32),
torch.tensor(aux1, dtype=torch.float32),
torch.tensor(aux2, dtype=torch.float32),
torch.tensor(aux3, dtype=torch.float32)],
torch.tensor(y, dtype=torch.long))
# train loader
dataset = Dataset(X1, X2, aux1, aux2, aux3,y)
train_loader = DataLoader(dataset, batch_size=128, shuffle=True)
class MyModel(nn.Module):
def __init__(self):
super(MyModel, self).__init__()
self.features_amp = nn.Sequential(
nn.Conv1d(1, 1, 3, 1),
)
self.features_phase = nn.Sequential(
nn.Conv1d(1, 1, 3, 1),
)
self.backbone = nn.Sequential(
nn.Conv1d(1,28,3,1),
nn.Conv1d(28,28,3,1),
nn.AvgPool1d(3),
nn.ReLU(),
)
self.classifier = nn.LazyLinear(3)
def forward(self, x1, x2, aux1, aux2, aux3):
x1 = self.features_amp(x1)
x2 = self.features_phase(x2)
x1 = x1.view(x1.size(0), -1)
x2 = x2.view(x2.size(0), -1)
x = torch.cat((x1, x2), dim=-1)
#print(x.size())
x = self.backbone(x)
x = torch.flatten(x)
#print(x.size())
x = torch.cat([x, aux1, aux2, aux3], dim=-1)
shelf_life_clf = self.classifier(x)
return shelf_life_clf
model = MyModel()#.type(torch.LongTensor)#.double()
#model = MyModel().double()
optimizer = optim.Adam(model.parameters(), lr=0.003)
def train(epoch):
model.train()
#exp_lr_scheduler.step()
arr_loss = []
#first_batch = next(iter(train_loader))
for batch_idx, (data, target) in enumerate(train_loader):
amp, phase, weight, temperture, humidity = data
#print(np.array(target).shape)
if torch.cuda.is_available():
amp = amp.cuda()
phase = phase.cuda()
weight = weight.cuda()
temperature = temperature.cuda()
humidity = humidity.cuda()
target = target.cuda()
optimizer.zero_grad()
output1 = model(*data)
loss = criterion1(output1, target)
loss.backward()
optimizer.step()
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, (batch_idx + 1) * len(data), len(train_loader.dataset),
100. * (batch_idx + 1) / len(train_loader), loss.data))
arr_loss.append(loss.data)
return arr_loss
optimizer = optim.Adam(model.parameters(), lr=0.00003)
criterion1 = nn.CrossEntropyLoss()
n_epochs = 10
for epoch in range(n_epochs):
loss = train(epoch)
What I dont understand is that the expected output of 128 corresponds to by batch size. However this is something I’m getting out of batch_idx. This corresponds also to the question on weither I’m passing my cutom dataset to the dataLoader correctly. I would be glad about some guidance.