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Visual dbg in vqvae3hs

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James Betker 2021-02-02 23:50:01 -07:00
parent f5f91850fd
commit b0a8fa00bc
1 changed files with 14 additions and 0 deletions
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codes/models/vqvae/vqvae_3_hardswitch.py
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@ -133,6 +133,20 @@ class VQVAE3HardSwitch(nn.Module):
return dec, diff return dec, diff
def save_attention_to_image_rgb(self, output_file, attention_out, attention_size, cmap_discrete_name='viridis'):
from matplotlib import cm
magnitude, indices = torch.topk(attention_out, 3, dim=1)
indices = indices.cpu()
colormap = cm.get_cmap(cmap_discrete_name, attention_size)
img = torch.tensor(colormap(indices[:, 0, :, :].detach().numpy())) # TODO: use other k's
img = img.permute((0, 3, 1, 2))
torchvision.utils.save_image(img, output_file)
def visual_dbg(self, step, path):
convs = [self.dec.blocks[-1].conv, self.dec_t.blocks[-1].conv, self.enc_b.blocks[-4], self.enc_t.blocks[-4]]
for i, c in enumerate(convs):
self.save_attention_to_image_rgb(os.path.join(path, "%i_selector_%i.png" % (step, i+1)), c.last_select, self.breadth)
def encode(self, input): def encode(self, input):
fea = self.initial_conv(input) fea = self.initial_conv(input)
enc_b = checkpoint(self.enc_b, fea) enc_b = checkpoint(self.enc_b, fea)