add automatic weight download (#146)

* add automatic weight download

applications/tools/wav2lip.py

@@ -10,17 +10,16 @@ parser = argparse.ArgumentParser(
 parser.add_argument('--checkpoint_path',
                     type=str,
                     help='Name of saved checkpoint to load weights from',
-                    required=True)
-
-parser.add_argument('--face',
-                    type=str,
-                    help='Filepath of video/image that contains faces to use',
-                    required=True)
+                    default=None)
 parser.add_argument(
     '--audio',
     type=str,
     help='Filepath of video/audio file to use as raw audio source',
     required=True)
+parser.add_argument('--face',
+                    type=str,
+                    help='Filepath of video/image that contains faces to use',
+                    required=True)
 parser.add_argument('--outfile',
                     type=str,
                     help='Video path to save result. See default for an e.g.',

ppgan/apps/wav2lip_predictor.py

@@ -6,11 +6,13 @@ import json, subprocess, random, string
 from tqdm import tqdm
 from glob import glob
 import paddle
+from paddle.utils.download import get_weights_path_from_url
 from ppgan.faceutils import face_detection
 from ppgan.utils import audio
 from ppgan.models.generators.wav2lip import Wav2Lip
 from .base_predictor import BasePredictor
 
+WAV2LIP_WEIGHT_URL = 'https://paddlegan.bj.bcebos.com/models/wav2lip_hq.pdparams'
 mel_step_size = 16
 
 
@@ -216,7 +218,11 @@ class Wav2LipPredictor(BasePredictor):
         gen = self.datagen(full_frames.copy(), mel_chunks)
 
         model = Wav2Lip()
-        weights = paddle.load(self.args.checkpoint_path)
+        if self.args.checkpoint_path is None:
+            model_weights_path = get_weights_path_from_url(WAV2LIP_WEIGHT_URL)
+            weights = paddle.load(model_weights_path)
+        else:
+            weights = paddle.load(self.args.checkpoint_path)
         model.load_dict(weights)
         model.eval()
         print("Model loaded")

ppgan/models/wav2lip_hq_model.py

@@ -14,6 +14,7 @@
 
 import paddle
 import paddle.nn.functional as F
+from paddle.utils.download import get_weights_path_from_url
 from .base_model import BaseModel
 
 from .builder import MODELS
@@ -25,7 +26,7 @@ from .wav2lip_model import cosine_loss, get_sync_loss
 from ..solver import build_optimizer
 from ..modules.init import init_weights
 
-lipsync_weight_path = '/workspace/PaddleGAN/lipsync_expert.pdparams'
+SYNCNET_WEIGHT_URL = 'https://paddlegan.bj.bcebos.com/models/syncnet.pdparams'
 
 
 @MODELS.register()
@@ -65,7 +66,8 @@ class Wav2LipModelHq(BaseModel):
                      distribution='uniform')
         if self.is_train:
             self.nets['netDS'] = build_discriminator(discriminator_sync)
-            params = paddle.load(lipsync_weight_path)
+            weights_path = get_weights_path_from_url(SYNCNET_WEIGHT_URL)
+            params = paddle.load(weights_path)
             self.nets['netDS'].load_dict(params)
 
             self.nets['netDH'] = build_discriminator(discriminator_hq)

ppgan/models/wav2lip_model.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 
 import paddle
+from paddle.utils.download import get_weights_path_from_url
 from .base_model import BaseModel
 
 from .builder import MODELS
@@ -22,6 +23,7 @@ from .discriminators.builder import build_discriminator
 from ..solver import build_optimizer
 from ..modules.init import init_weights
 
+SYNCNET_WEIGHT_URL = 'https://paddlegan.bj.bcebos.com/models/syncnet.pdparams'
 syncnet_T = 5
 syncnet_mel_step_size = 16
 
@@ -74,7 +76,8 @@ class Wav2LipModel(BaseModel):
         init_weights(self.nets['netG'], distribution='uniform')
         if self.is_train:
             self.nets['netD'] = build_discriminator(discriminator)
-            params = paddle.load(lipsync_weight_path)
+            weights_path = get_weights_path_from_url(SYNCNET_WEIGHT_URL)
+            params = paddle.load(weights_path)
             self.nets['netD'].load_dict(params)
 
         if self.is_train: