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47cac2b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 | import torch
import numpy as np
import os
from residual_diffusion import ResidualDiffusion
from transformer_denoiser import TransformerDenoiser
@torch.no_grad()
def generate_predictions(diffusion, model, sample, device, bounds, mean_r, std_r, num_preds=10, clip_len=750, clips_per_call=8, amp_dtype=torch.float16, guidance_weight=1.0):
L = sample["length"]
audio = sample["speaker_audio"] #(L, 768)
dmm = sample["speaker_3dmm"] #(L, 58)
emo = sample["speaker_emotion"] #(L, 25)
if L % clip_len == 0:
b = L // clip_len
lengths = torch.tensor([clip_len] * b, dtype=torch.long)
else:
b = (L // clip_len) + 1
pad = clip_len - (L % clip_len)
audio = torch.cat((audio, torch.zeros(pad, audio.shape[-1])), dim=0)
dmm = torch.cat((dmm, torch.zeros(pad, dmm.shape[-1])), dim=0)
emo = torch.cat((emo, torch.zeros(pad, emo.shape[-1])), dim=0)
lengths = torch.tensor([clip_len] * (b - 1) + [L % clip_len], dtype=torch.long)
audio_c = audio.reshape(b, clip_len, -1).to(device)
dmm_c = dmm.reshape(b, clip_len, -1).to(device)
emo_c = emo.reshape(b, clip_len, -1).to(device)
lengths = lengths.to(device)
use_amp = (device == "cuda")
per_clip = [None] * b
for start in range(0, b, clips_per_call):
stop = min(start + clips_per_call, b)
g = stop - start
audio_k = audio_c[start:stop].repeat(num_preds, 1, 1)
dmm_k = dmm_c[start:stop].repeat(num_preds, 1, 1)
emo_k = emo_c[start:stop].repeat(num_preds, 1, 1)
lengths_k = lengths[start:stop].repeat(num_preds)
with torch.amp.autocast("cuda", dtype=amp_dtype, enabled=use_amp):
x = diffusion.ddim_sample(
model=model,
shape=(num_preds*g, clip_len, 25),
speaker_audio=audio_k,
speaker_3dmm=dmm_k,
speaker_emotion=emo_k,
mean_r=mean_r,
std_r=std_r,
motion_length=lengths_k,
guidance_weight=guidance_weight,
)
x = x.float().reshape(num_preds, g, clip_len, 25)
for i in range(g):
per_clip[i + start] = x[:, i, :, :] #(num_preds, clip_len, 25)
preds = torch.cat(per_clip, dim=1)[:, :L, :].cpu()
preds[:, :, :15] = torch.round(preds[:, :, :15])
preds = torch.clamp(
preds,
min=torch.cat([torch.zeros(15), bounds["lower"]]),
max=torch.cat([torch.ones(15), bounds["upper"]])
)
return preds
def load_data(modality, role, target_dim=None):
path = os.path.join("sample_data", modality, role + ".npy")
t = np.load(path).astype(np.float32)
t = torch.from_numpy(t).squeeze()
if target_dim is not None:
assert t.dim() == target_dim
return t
def main():
if torch.cuda.is_available():
device = "cuda"
print("Connected to GPU")
else:
device = "cpu"
print("Unable to connected to GPU; connected to CPU")
diffusion = ResidualDiffusion()
model = TransformerDenoiser().to(device).eval()
ckpt = torch.load("./checkpoints/residual_diffusion_best.pt", map_location="cpu", weights_only=False)
if isinstance(ckpt, dict) and "model" in ckpt:
state_dict = ckpt["model"]
elif isinstance(ckpt, dict) and "state_dict" in ckpt:
state_dict = ckpt["state_dict"]
else:
state_dict = ckpt["model"]
model.load_state_dict(state_dict)
with open("./checkpoints/guidance_weight.txt", "r") as f:
guidance_weight = f.readline()
gw = float(guidance_weight.strip())
bounds = torch.load("./checkpoints/bounds.pt")
distribution = torch.load("./checkpoints/residual_distribution.pt")
mean_r = distribution["mean"]
std_r = distribution["std"]
mean_face = np.load("./external_tools/mean_face.npy").astype(np.float32)
std_face = np.load("./external_tools/std_face.npy").astype(np.float32)
mean_face = torch.from_numpy(mean_face).unsqueeze(0)
std_face = torch.from_numpy(std_face).unsqueeze(0)
sp_audio = load_data("audio-features", "speaker", target_dim=2)
sp_dmm = load_data("coefficients", "speaker", target_dim=2)
sp_emo = load_data("facial-attributes", "speaker", target_dim=2)
T = min(sp_audio.size(0), sp_dmm.size(0), sp_emo.size(0))
sp_audio = sp_audio[:T]
sp_dmm = sp_dmm[:T]
sp_emo = sp_emo[:T]
sp_dmm = (sp_dmm - mean_face) / std_face
sample = {
"length":T,
"speaker_audio":sp_audio,
"speaker_3dmm": sp_dmm,
"speaker_emotion": sp_emo
}
print("Generating predictions\n\n")
preds = generate_predictions(diffusion, model, sample, device, bounds, mean_r, std_r, guidance_weight=gw)
print("Generation complete")
print("Preds shape: ", preds.shape)
np.save("results/generated_reactions.npy", preds.numpy())
print("Saved successfully to results/generated_reactions.npy")
if __name__ == "__main__":
main() |