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Running
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Zero
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import torch
from torch import nn
from networks.encoder import Encoder
from networks.decoder import Decoder
import numpy as np
from tqdm import tqdm
from einops import rearrange, repeat
class Generator(nn.Module):
def __init__(self, size, style_dim=512, motion_dim=40, scale=1):
super(Generator, self).__init__()
style_dim = style_dim * scale
# encoder
self.enc = Encoder(style_dim, motion_dim, scale)
self.dec = Decoder(style_dim, motion_dim, scale)
@property
def device(self):
if self._device is None:
self._device = next(self.parameters()).device
return self._device
def get_alpha(self, x):
return self.enc.enc_motion(x)
def edit_img(self, img_source, d_l, v_l):
z_s2r, feat_rgb = self.enc.enc_2r(img_source)
alpha_r2s = self.enc.enc_r2t(z_s2r)
alpha_r2s[:, d_l] = alpha_r2s[:, d_l] + torch.FloatTensor(v_l).unsqueeze(0).to('cuda')
img_recon = self.dec(z_s2r, [alpha_r2s], feat_rgb)
return img_recon
def animate(self, img_source, vid_target, d_l, v_l):
alpha_start = self.get_alpha(vid_target[:, 0, :, :, :])
vid_target_recon = []
z_s2r, feat_rgb = self.enc.enc_2r(img_source)
alpha_r2s = self.enc.enc_r2t(z_s2r)
alpha_r2s[:, d_l] = alpha_r2s[:, d_l] + torch.FloatTensor(v_l).unsqueeze(0).to('cuda')
for i in tqdm(range(vid_target.size(1))):
img_target = vid_target[:, i, :, :, :]
alpha = self.enc.enc_transfer_vid(alpha_r2s, img_target, alpha_start)
img_recon = self.dec(z_s2r, alpha, feat_rgb)
vid_target_recon.append(img_recon.unsqueeze(2))
vid_target_recon = torch.cat(vid_target_recon, dim=2) # BCTHW
return vid_target_recon
def animate_batch(self, img_source, vid_target, d_l, v_l, chunk_size):
b,t,c,h,w = vid_target.size()
alpha_start = self.get_alpha(vid_target[:, 0, :, :, :]) # 1x40
vid_target_recon = []
z_s2r, feat_rgb = self.enc.enc_2r(img_source)
alpha_r2s = self.enc.enc_r2t(z_s2r)
alpha_r2s[:, d_l] = alpha_r2s[:, d_l] + torch.FloatTensor(v_l).unsqueeze(0).to('cuda')
bs = chunk_size
chunks = t//bs
alpha_start_r = repeat(alpha_start, 'b c -> (repeat b) c', repeat=bs)
alpha_r2s_r = repeat(alpha_r2s, 'b c -> (repeat b) c', repeat=bs)
feat_rgb_r = [repeat(feat, 'b c h w -> (repeat b) c h w', repeat=bs) for feat in feat_rgb]
z_s2r_r = repeat(z_s2r, 'b c -> (repeat b) c', repeat=bs)
for i in range(chunks+1):
if i == chunks:
img_target = vid_target[:, i*bs:, :, :, :]
bs = t-i*bs
alpha_start_r = alpha_start_r[:bs]
alpha_r2s_r = alpha_r2s_r[:bs]
feat_rgb_r = [feat[:bs] for feat in feat_rgb_r]
z_s2r_r = z_s2r_r[:bs]
else:
img_target = vid_target[:, i*bs:(i+1)*bs, :, :, :]
alpha = self.enc.enc_transfer_vid(alpha_r2s_r, img_target.squeeze(0), alpha_start_r)
img_recon = self.dec(z_s2r_r, alpha, feat_rgb_r) # bs x 3 x h x w
vid_target_recon.append(img_recon)
vid_target_recon = torch.cat(vid_target_recon, dim=0).unsqueeze(0) # 1xTCHW
vid_target_recon = rearrange(vid_target_recon, 'b t c h w -> b c t h w')
return vid_target_recon # BCTHW
def edit_vid(self, vid_target, d_l, v_l):
img_source = vid_target[:, 0, :, :, :]
alpha_start = self.get_alpha(vid_target[:, 0, :, :, :])
vid_target_recon = []
z_s2r, feat_rgb = self.enc.enc_2r(img_source)
alpha_r2s = self.enc.enc_r2t(z_s2r)
alpha_r2s[:, d_l] = alpha_r2s[:, d_l] + torch.FloatTensor(v_l).unsqueeze(0).to('cuda')
for i in tqdm(range(vid_target.size(1))):
img_target = vid_target[:, i, :, :, :]
alpha = self.enc.enc_transfer_vid(alpha_r2s, img_target, alpha_start)
img_recon = self.dec(z_s2r, alpha, feat_rgb)
vid_target_recon.append(img_recon.unsqueeze(2))
vid_target_recon = torch.cat(vid_target_recon, dim=2) # BCTHW
return vid_target_recon
def edit_vid_batch(self, vid_target, d_l, v_l, chunk_size):
b,t,c,h,w = vid_target.size()
img_source = vid_target[:, 0, :, :, :]
alpha_start = self.get_alpha(img_source) # 1x40
vid_target_recon = []
z_s2r, feat_rgb = self.enc.enc_2r(img_source)
alpha_r2s = self.enc.enc_r2t(z_s2r)
alpha_r2s[:, d_l] = alpha_r2s[:, d_l] + torch.FloatTensor(v_l).unsqueeze(0).to('cuda')
bs = chunk_size
chunks = t//bs
alpha_start_r = repeat(alpha_start, 'b c -> (repeat b) c', repeat=bs)
alpha_r2s_r = repeat(alpha_r2s, 'b c -> (repeat b) c', repeat=bs)
feat_rgb_r = [repeat(feat, 'b c h w -> (repeat b) c h w', repeat=bs) for feat in feat_rgb]
z_s2r_r = repeat(z_s2r, 'b c -> (repeat b) c', repeat=bs)
for i in range(chunks+1):
if i == chunks:
img_target = vid_target[:, i*bs:, :, :, :]
bs = t-i*bs
alpha_start_r = alpha_start_r[:bs]
alpha_r2s_r = alpha_r2s_r[:bs]
feat_rgb_r = [feat[:bs] for feat in feat_rgb_r]
z_s2r_r = z_s2r_r[:bs]
else:
img_target = vid_target[:, i*bs:(i+1)*bs, :, :, :]
alpha = self.enc.enc_transfer_vid(alpha_r2s_r, img_target.squeeze(0), alpha_start_r)
img_recon = self.dec(z_s2r_r, alpha, feat_rgb_r) # bs x 3 x h x w
vid_target_recon.append(img_recon)
vid_target_recon = torch.cat(vid_target_recon, dim=0).unsqueeze(0) # 1xTCHW
vid_target_recon = rearrange(vid_target_recon, 'b t c h w -> b c t h w')
return vid_target_recon # BCTHW
def interpolate_img(self, img_source, d_l, v_l):
vid_target_recon = []
step = 16
v_start = np.array([0.] * len(v_l))
v_end = np.array(v_l)
stride = (v_end - v_start) / step
z_s2r, feat_rgb = self.enc.enc_2r(img_source)
v_tmp = v_start
for i in range(step):
v_tmp = v_tmp + stride
alpha = self.enc.enc_transfer_img(z_s2r, d_l, v_tmp)
img_recon = self.dec(z_s2r, alpha, feat_rgb)
vid_target_recon.append(img_recon.unsqueeze(2))
for i in range(step):
v_tmp = v_tmp - stride
alpha = self.enc.enc_transfer_img(z_s2r, d_l, v_tmp)
img_recon = self.dec(z_s2r, alpha, feat_rgb)
vid_target_recon.append(img_recon.unsqueeze(2))
if (v_l[6]!=0) or (v_l[7]!=0) or (v_l[8]!=0) or (v_l[9]!=0):
for i in range(step):
v_tmp = v_tmp + stride
alpha = self.enc.enc_transfer_img(z_s2r, d_l, v_tmp)
img_recon = self.dec(z_s2r, alpha, feat_rgb)
vid_target_recon.append(img_recon.unsqueeze(2))
for i in range(step):
v_tmp = v_tmp - stride
alpha = self.enc.enc_transfer_img(z_s2r, d_l, v_tmp)
img_recon = self.dec(z_s2r, alpha, feat_rgb)
vid_target_recon.append(img_recon.unsqueeze(2))
else:
for i in range(step):
v_tmp = v_tmp - stride
alpha = self.enc.enc_transfer_img(z_s2r, d_l, v_tmp)
img_recon = self.dec(z_s2r, alpha, feat_rgb)
vid_target_recon.append(img_recon.unsqueeze(2))
for i in range(step):
v_tmp = v_tmp + stride
alpha = self.enc.enc_transfer_img(z_s2r, d_l, v_tmp)
img_recon = self.dec(z_s2r, alpha, feat_rgb)
vid_target_recon.append(img_recon.unsqueeze(2))
vid_target_recon = torch.cat(vid_target_recon, dim=2) # BCTHW
return vid_target_recon
def enc_img(self, img_source, d_l, v_l):
"""Core edit_img logic without timing - can be compiled"""
z_s2r, feat_rgb = self.enc.enc_2r(img_source)
alpha_r2s = self.enc.enc_r2t(z_s2r)
# Create tensor directly on the same device as alpha_r2s
v_l_tensor = torch.tensor(v_l, device=alpha_r2s.device, dtype=alpha_r2s.dtype).unsqueeze(0)
alpha_r2s[:, d_l] = alpha_r2s[:, d_l] + v_l_tensor
return z_s2r, alpha_r2s, feat_rgb
def dec_img(self, z_s2r, alpha_r2s, feat_rgb):
return self.dec(z_s2r, [alpha_r2s], feat_rgb)
def dec_vid(self, z_s2r, alpha_r2s, feat_rgb, img_start, img_target_batch):
# z_s2r: BC
# alpha_r2s: BC
# feat: BCHW
# alpha_start: BC
bs = img_target_batch.size(0)
alpha_start = self.get_alpha(img_start)
alpha_start_r = repeat(alpha_start, 'b c -> (repeat b) c', repeat=bs)
alpha_r2s_r = repeat(alpha_r2s, 'b c -> (repeat b) c', repeat=bs)
feat_rgb_r = [repeat(feat, 'b c h w -> (repeat b) c h w', repeat=bs) for feat in feat_rgb]
z_s2r_r = repeat(z_s2r, 'b c -> (repeat b) c', repeat=bs)
alpha = self.enc.enc_transfer_vid(alpha_r2s_r, img_target_batch, alpha_start_r)
img_batch_recon = self.dec(z_s2r_r, alpha, feat_rgb_r) # bs x 3 x h x w
return img_batch_recon
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