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Add get_debug_values for vqvae_3_hardswitch

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James Betker 2021-02-03 14:12:24 -07:00
parent 1405ff06b8
commit b980028ca8
1 changed files with 36 additions and 2 deletions
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38
codes/models/vqvae/vqvae_3_hardswitch.py
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@ -1,6 +1,10 @@
import os
from time import time
import torch import torch
import torchvision import torchvision
from torch import nn from torch import nn
from tqdm import tqdm
from models.switched_conv.switched_conv_hard_routing import SwitchedConvHardRouting, \ from models.switched_conv.switched_conv_hard_routing import SwitchedConvHardRouting, \
convert_conv_net_state_dict_to_switched_conv convert_conv_net_state_dict_to_switched_conv
@ -146,7 +150,21 @@ class VQVAE3HardSwitch(nn.Module):
def visual_dbg(self, step, path): 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]] 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): 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) self.save_attention_to_image_rgb(os.path.join(path, "%i_selector_%i.png" % (step, i+1)), c.last_select, 16)
def get_debug_values(self, step, __):
switched_convs = [('enc_b_blk2', self.enc_b.blocks[2]),
('enc_b_blk4', self.enc_b.blocks[4]),
('enc_t_blk2', self.enc_t.blocks[2]),
('dec_t_blk0', self.dec_t.blocks[0]),
('dec_t_blk-1', self.dec_t.blocks[-1].conv),
('dec_blk0', self.dec.blocks[0]),
('dec_blk-1', self.dec.blocks[-1].conv),
('dec_blk-3', self.dec.blocks[-3].conv)]
logs = {}
for name, swc in switched_convs:
logs[f'{name}_histogram_switch_usage'] = swc.latest_masks
return logs
def encode(self, input): def encode(self, input):
fea = self.initial_conv(input) fea = self.initial_conv(input)
@ -205,7 +223,23 @@ def register_vqvae3_hard_switch(opt_net, opt):
return VQVAE3HardSwitch(**kw) return VQVAE3HardSwitch(**kw)
def performance_test():
net = VQVAE3HardSwitch().to('cuda')
loss = nn.L1Loss()
opt = torch.optim.Adam(net.parameters(), lr=1e-4)
started = time()
for j in tqdm(range(10)):
inp = torch.rand((8, 3, 256, 256), device='cuda')
res = net(inp)[0]
l = loss(res, inp)
l.backward()
opt.step()
net.zero_grad()
print("Elapsed: ", (time()-started))
if __name__ == '__main__': if __name__ == '__main__':
#v = VQVAE3HardSwitch() #v = VQVAE3HardSwitch()
#print(v(torch.randn(1,3,128,128))[0].shape) #print(v(torch.randn(1,3,128,128))[0].shape)
convert_weights("../../../experiments/test_vqvae3.pth") #convert_weights("../../../experiments/test_vqvae3.pth")
performance_test()