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L40S
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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
import time
from contextlib import contextmanager
@contextmanager
def timing_context(label, enabled=True):
"""Context manager for timing that doesn't break torch.compile"""
if not enabled:
yield
return
start = time.time()
yield
end = time.time()
print(f"[Generator.edit_img] {label} took: {(end - start) * 1000:.2f} ms")
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)
# Pre-allocate commonly used tensors to avoid repeated allocations
self._device = None
self._cached_tensors = {}
@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):
return self._edit_img_core(img_source, d_l, v_l)
def edit_img_with_timing(self, img_source, d_l, v_l):
"""Version with timing for debugging - not compiled"""
start_time = time.time()
print(f"[Generator.edit_img] Starting image editing...")
with timing_context("enc_2r encoding"):
z_s2r, feat_rgb = self.enc.enc_2r(img_source)
with timing_context("enc_r2t encoding"):
alpha_r2s = self.enc.enc_r2t(z_s2r)
with timing_context("Alpha modification"):
# 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
with timing_context("Decoding"):
img_recon = self.dec(z_s2r, [alpha_r2s], feat_rgb)
# Total time
end_time = time.time()
total_time_ms = (end_time - start_time) * 1000
print(f"[Generator.edit_img] Total execution time: {total_time_ms:.2f} ms")
print(f"[Generator.edit_img] ----------------------------------------")
return img_recon
def _edit_img_core(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
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)
# Optimized alpha modification
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
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)
# Optimized alpha modification
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
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)
# Optimized alpha modification
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
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)
# Optimized alpha modification
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
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 |