The implementation code of Thesis project which entitled "Photo-to-Emoji Transformation with TraVeLGAN and Perceptual Loss" as a final project in my master study.
You could call me Richard. I'm an Indonesian and currently living in Taiwan working as an Engineer.
Hello! Thanks for amazing idea and work.
Training wont start with the following error:
File "/content/TraVeLGAN_with_perceptual_loss/losses.py", line 71, in forward
v_o = e_o[pairs[:, 0]] - e_o[pairs[:, 1]]
IndexError: too many indices for array: array is 1-dimensional, but 2 were indexed
I added few "print"'s for debug purposes as follows to the TraVeLGAN_with_perceptual_loss/losses.py file:
class TravelLoss(nn.Module):
def init(self):
super(TravelLoss, self).init()
self.pair_selector = NegativePairSelector()
self.angle_dist = nn.CosineSimilarity()
self.mag_dist = nn.MSELoss(reduction='mean')
# embedding_network = siamese network
def forward(self, x_o, x_t, embedding_network):
**print('x_o, x_o.size(0)', x_o, x_o.size(0))**
pairs = self.pair_selector(x_o.size(0))
**print('pairs',pairs)**
e_o = embedding_network(x_o)
**print('e_o',e_o)**
v_o = e_o[pairs[:, 0]] - e_o[pairs[:, 1]]
e_t = embedding_network(x_t)
v_t = e_t[pairs[:, 0]] - e_t[pairs[:, 1]]
return (self.mag_dist(v_o, v_t) - self.angle_dist(v_o, v_t)).mean()
and here is full output:
/content/TraVeLGAN_with_perceptual_loss
Namespace(hparams='config', input_size=1024, log='./log_photo2emoji/', project_name='photo2emoji')
Random Seed: 6203
Loading data..
Domain A edge-promoting start!!
Domain B edge-promoting start!!
Model loaded on device : cuda
saved model opts.log: ./log_photo2emoji/
Start training..
/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at /pytorch/c10/core/TensorImpl.h:1156.)
return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)
x_o, x_o.size(0) tensor([[[[1., 1., 1., ..., 1., 1., 1.],
[1., 1., 1., ..., 1., 1., 1.],
[1., 1., 1., ..., 1., 1., 1.],
...,
[1., 1., 1., ..., 1., 1., 1.],
[1., 1., 1., ..., 1., 1., 1.],
[1., 1., 1., ..., 1., 1., 1.]],
[[1., 1., 1., ..., 1., 1., 1.],
[1., 1., 1., ..., 1., 1., 1.],
[1., 1., 1., ..., 1., 1., 1.],
...,
[1., 1., 1., ..., 1., 1., 1.],
[1., 1., 1., ..., 1., 1., 1.],
[1., 1., 1., ..., 1., 1., 1.]],
[[1., 1., 1., ..., 1., 1., 1.],
[1., 1., 1., ..., 1., 1., 1.],
[1., 1., 1., ..., 1., 1., 1.],
...,
[1., 1., 1., ..., 1., 1., 1.],
[1., 1., 1., ..., 1., 1., 1.],
[1., 1., 1., ..., 1., 1., 1.]]]], device='cuda:0') 1
pairs []
e_o tensor([[[[-0.2140, -0.1830, -0.1830, ..., -0.1830, -0.1830, -0.1962],
[-0.0688, -0.0689, -0.0689, ..., -0.0689, -0.0689, -0.0770],
[-0.0688, -0.0689, -0.0689, ..., -0.0689, -0.0689, -0.0770],
...,
[-0.0688, -0.0689, -0.0689, ..., -0.0689, -0.0689, -0.0770],
[-0.0688, -0.0689, -0.0689, ..., -0.0687, -0.0689, -0.0770],
[ 0.0322, 0.0649, 0.0649, ..., 0.0645, 0.0649, 0.0525]],
[[ 0.0994, 0.1673, 0.1673, ..., 0.1673, 0.1673, 0.1499],
[ 0.1358, 0.1945, 0.1945, ..., 0.1945, 0.1945, 0.1619],
[ 0.1358, 0.1945, 0.1945, ..., 0.1945, 0.1945, 0.1619],
...,
[ 0.1358, 0.1945, 0.1945, ..., 0.1945, 0.1945, 0.1619],
[ 0.1358, 0.1945, 0.1945, ..., 0.1943, 0.1945, 0.1619],
[ 0.0830, 0.1578, 0.1578, ..., 0.1570, 0.1578, 0.1395]],
[[ 0.0879, 0.1331, 0.1331, ..., 0.1331, 0.1331, 0.0967],
[ 0.0671, 0.0954, 0.0954, ..., 0.0954, 0.0954, 0.0433],
[ 0.0671, 0.0954, 0.0954, ..., 0.0954, 0.0954, 0.0433],
...,
[ 0.0671, 0.0954, 0.0954, ..., 0.0954, 0.0954, 0.0433],
[ 0.0671, 0.0954, 0.0954, ..., 0.0953, 0.0954, 0.0433],
[ 0.0205, 0.0348, 0.0348, ..., 0.0351, 0.0348, -0.0099]],
...,
[[ 0.2573, 0.2636, 0.2636, ..., 0.2636, 0.2636, 0.3076],
[ 0.2788, 0.2641, 0.2641, ..., 0.2641, 0.2641, 0.3184],
[ 0.2788, 0.2641, 0.2641, ..., 0.2641, 0.2641, 0.3184],
...,
[ 0.2788, 0.2641, 0.2641, ..., 0.2642, 0.2641, 0.3184],
[ 0.2788, 0.2641, 0.2641, ..., 0.2640, 0.2641, 0.3184],
[ 0.2795, 0.2725, 0.2725, ..., 0.2728, 0.2725, 0.2982]],
[[-0.2516, -0.2385, -0.2385, ..., -0.2385, -0.2385, -0.3603],
[-0.3489, -0.3108, -0.3108, ..., -0.3108, -0.3108, -0.4396],
[-0.3489, -0.3108, -0.3108, ..., -0.3108, -0.3108, -0.4396],
...,
[-0.3489, -0.3108, -0.3108, ..., -0.3108, -0.3108, -0.4396],
[-0.3489, -0.3108, -0.3108, ..., -0.3112, -0.3108, -0.4396],
[-0.3442, -0.3314, -0.3314, ..., -0.3307, -0.3314, -0.4594]],
[[ 0.1763, 0.1709, 0.1709, ..., 0.1709, 0.1709, 0.1492],
[ 0.1521, 0.1422, 0.1422, ..., 0.1422, 0.1422, 0.1239],
[ 0.1521, 0.1422, 0.1422, ..., 0.1422, 0.1422, 0.1239],
...,
[ 0.1521, 0.1422, 0.1422, ..., 0.1422, 0.1422, 0.1239],
[ 0.1521, 0.1422, 0.1422, ..., 0.1420, 0.1422, 0.1239],
[ 0.2000, 0.1805, 0.1805, ..., 0.1804, 0.1805, 0.1575]]]],
device='cuda:0', grad_fn=<AddBackward0>)
Traceback (most recent call last):
File "train.py", line 101, in
print('model.gen_update(x_a, x_b)',model.gen_update(x_a, x_b))
File "/content/TraVeLGAN_with_perceptual_loss/trainer.py", line 172, in gen_update
travel_loss = self.travel_loss(x_a, x_ab, self.siam) +
File "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py", line 1051, in _call_impl
return forward_call(*input, **kwargs)
File "/content/TraVeLGAN_with_perceptual_loss/losses.py", line 71, in forward
v_o = e_o[pairs[:, 0]] - e_o[pairs[:, 1]]
IndexError: too many indices for array: array is 1-dimensional, but 2 were indexed
As far as I can see, "pairs" war is empty but should not be. What is the problem?
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