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Use cached download to reduce google drive load #31

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Use cached download to reduce google drive load #31

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20 changes: 15 additions & 5 deletions PULSE.py
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Expand Up @@ -19,9 +19,14 @@ def __init__(self, cache_dir, verbose=True):

cache_dir = Path(cache_dir)
cache_dir.mkdir(parents=True, exist_ok = True)
if self.verbose: print("Loading Synthesis Network")
with open_url("https://drive.google.com/uc?id=1TCViX1YpQyRsklTVYEJwdbmK91vklCo8", cache_dir=cache_dir, verbose=verbose) as f:
self.synthesis.load_state_dict(torch.load(f))
if self.verbose:
print("Loading Synthesis Network")
synthesis_cached = f"{cache_dir}/synthesis.pt"
if Path(synthesis_cached).exists():
self.synthesis.load_state_dict(torch.load(synthesis_cached))
else:
with open_url("https://drive.google.com/uc?id=1TCViX1YpQyRsklTVYEJwdbmK91vklCo8", cache_dir=cache_dir, verbose=verbose) as f:
self.synthesis.load_state_dict(torch.load(f))

for param in self.synthesis.parameters():
param.requires_grad = False
Expand All @@ -31,10 +36,15 @@ def __init__(self, cache_dir, verbose=True):
if Path("gaussian_fit.pt").exists():
self.gaussian_fit = torch.load("gaussian_fit.pt")
else:
if self.verbose: print("\tLoading Mapping Network")
if self.verbose:
print("\tLoading Mapping Network")
mapping = G_mapping().cuda()

with open_url("https://drive.google.com/uc?id=14R6iHGf5iuVx3DMNsACAl7eBr7Vdpd0k", cache_dir=cache_dir, verbose=verbose) as f:
mapping_cached = f"{cache_dir}/mapping.pt"
if Path(mapping_cached).exists():
mapping.load_state_dict(torch.load(mapping_cached))
else:
with open_url("https://drive.google.com/uc?id=14R6iHGf5iuVx3DMNsACAl7eBr7Vdpd0k", cache_dir=cache_dir, verbose=verbose) as f:
mapping.load_state_dict(torch.load(f))

if self.verbose: print("\tRunning Mapping Network")
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12 changes: 9 additions & 3 deletions align_face.py
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Expand Up @@ -30,9 +30,15 @@
output_dir = Path(args.output_dir)
output_dir.mkdir(parents=True,exist_ok=True)

print("Downloading Shape Predictor")
f=open_url("https://drive.google.com/uc?id=1huhv8PYpNNKbGCLOaYUjOgR1pY5pmbJx", cache_dir=cache_dir, return_path=True)
predictor = dlib.shape_predictor(f)
predictor_cached = f"{cache_dir}/shape_predictor_68_face_landmarks.dat"

if Path(predictor_cached).exists():
print("Using cached Shape Predictor")
predictor = dlib.shape_predictor(predictor_cached)
else:
print("Downloading Shape Predictor")
with open_url("https://drive.google.com/uc?id=1huhv8PYpNNKbGCLOaYUjOgR1pY5pmbJx", cache_dir=cache_dir, return_path=True) as f:
predictor = dlib.shape_predictor(f)

for im in Path(args.input_dir).glob("*.*"):
faces = align_face(str(im),predictor)
Expand Down