PyTorch: Batch size=1 but model gives 2 outputs - deep-learning

Question

Here is some part of my PyTorch code:
test_loader = DataLoader(dataset = test_loader_hibiscus, batch_size = 1, shuffle=False, num_workers=0)
test_losses = []
y_pred_list = []
feat_list = []
with torch.no_grad():
model.eval()
test_loss = 0.0
if expe_temoin == False :
for test_dwi,test_adc,test_tmax,test_cbf,test_cbv,test_label in test_loader :
test_dwi = test_dwi.to(device)
test_adc = test_adc.to(device)
test_tmax = test_tmax.to(device)
test_cbf = test_cbf.to(device)
test_cbv = test_cbv.to(device)
in_imgs = torch.cat((train_dwi,train_adc,train_tmax,train_cbf,train_cbv), dim=1)
out_recon, my_feat = model(in_imgs)
print("my_feat", my_feat[0].shape)
But it prints:
my_feat torch.Size([2, 512, 1, 24, 24])
Could someone please tell me why 2 (batch?) Thanks!
Hint: When I run with test data size: 26, its OK, when run with data size: 25, mess up the batch! Is there something about being odd and even?!
Here is the Unet 3d model for 3D reconstruction and segmentation:
class Abstract3DUNet(nn.Module):
def __init__(self, in_channels, out_channels, final_sigmoid, basic_module, f_maps=64, layer_order='gcr',
num_groups=8, num_levels=4, is_segmentation=True, testing=False,
conv_kernel_size=3, pool_kernel_size=2, conv_padding=1, **kwargs):
super(Abstract3DUNet, self).__init__()
self.testing = testing
if isinstance(f_maps, int):
f_maps = number_of_features_per_level(f_maps, num_levels=num_levels)
assert isinstance(f_maps, list) or isinstance(f_maps, tuple)
assert len(f_maps) > 1, "Required at least 2 levels in the U-Net"
# create encoder path
self.encoders = create_encoders(in_channels, f_maps, basic_module, conv_kernel_size, conv_padding, layer_order,
num_groups, pool_kernel_size)
# create decoder path
self.decoders = create_decoders(f_maps, basic_module, conv_kernel_size, conv_padding, layer_order, num_groups,
upsample=True)
# in the last layer a 1×1 convolution reduces the number of output
# channels to the number of labels
self.final_conv = nn.Conv3d(f_maps[0], out_channels, 1)
if is_segmentation:
# semantic segmentation problem
if final_sigmoid:
self.final_activation = nn.Sigmoid()
else:
self.final_activation = nn.Softmax(dim=1)
else:
# regression problem
self.final_activation = None
def forward(self, x):
# encoder part
encoders_features = []
my_feat =[]
for encoder in self.encoders:
x = encoder(x)
# reverse the encoder outputs to be aligned with the decoder
encoders_features.insert(0, x)
# remove the last encoder's output from the list
# !!remember: it's the 1st in the list
my_feat = encoders_features[0:]
encoders_features = encoders_features[1:]
# decoder part
for decoder, encoder_features in zip(self.decoders, encoders_features):
# pass the output from the corresponding encoder and the output
# of the previous decoder
x = decoder(encoder_features, x)
x = self.final_conv(x)
# apply final_activation (i.e. Sigmoid or Softmax) only during prediction. During training the network outputs
# logits and it's up to the user to normalize it before visualising with tensorboard or computing validation metric
if self.testing and self.final_activation is not None:
x = self.final_activation(x)
return x, my_feat
class UNet3D(Abstract3DUNet):
def __init__(self, in_channels, out_channels, final_sigmoid=True, f_maps=64, layer_order='gcr',
num_groups=8, num_levels=4, is_segmentation=True, conv_padding=1, **kwargs):
super(UNet3D, self).__init__(in_channels=in_channels,
out_channels=out_channels,
final_sigmoid=final_sigmoid,
basic_module=DoubleConv,
f_maps=f_maps,
layer_order=layer_order,
num_groups=num_groups,
num_levels=num_levels,
is_segmentation=is_segmentation,
conv_padding=conv_padding,
**kwargs)

Answer 1

My train batch size was 3! When I changed it into 2 or 4 the problem solved!