diff --git a/configs/firstorder_vox_256.yaml b/configs/firstorder_vox_256.yaml new file mode 100755 index 0000000000000000000000000000000000000000..884eb9ba12fffc59e27bd9aa95698bb1967d25d7 --- /dev/null +++ b/configs/firstorder_vox_256.yaml @@ -0,0 +1,125 @@ +epochs: 100 +output_dir: output_dir + +dataset: + train: + name: FirstOrderDataset + batch_size: 8 + num_workers: 4 + use_shared_memory: False + phase: train + dataroot: data/first_order/Voxceleb/ + frame_shape: [256, 256, 3] + id_sampling: True + pairs_list: None + time_flip: True + num_repeats: 75 + create_frames_folder: False + transforms: + - name: PairedRandomHorizontalFlip + prob: 0.5 + keys: [image, image] + - name: PairedColorJitter + brightness: 0.1 + contrast: 0.1 + saturation: 0.1 + hue: 0.1 + keys: [image, image] + test: + name: FirstOrderDataset + dataroot: data/first_order/Voxceleb/ + phase: test + batch_size: 1 + num_workers: 1 + time_flip: False + id_sampling: False + create_frames_folder: False + frame_shape: [ 256, 256, 3 ] + + +model: + name: FirstOrderModel + common_params: + num_kp: 10 + num_channels: 3 + estimate_jacobian: True + generator: + name: FirstOrderGenerator + kp_detector_cfg: + temperature: 0.1 + block_expansion: 32 + max_features: 1024 + scale_factor: 0.25 + num_blocks: 5 + generator_cfg: + block_expansion: 64 + max_features: 512 + num_down_blocks: 2 + num_bottleneck_blocks: 6 + estimate_occlusion_map: True + dense_motion_params: + block_expansion: 64 + max_features: 1024 + num_blocks: 5 + scale_factor: 0.25 + discriminator: + name: FirstOrderDiscriminator + discriminator_cfg: + scales: [1] + block_expansion: 32 + max_features: 512 + num_blocks: 4 + sn: True + train_params: + num_epochs: 100 + scales: [1, 0.5, 0.25, 0.125] + checkpoint_freq: 50 + transform_params: + sigma_affine: 0.05 + sigma_tps: 0.005 + points_tps: 5 + loss_weights: + generator_gan: 0 + discriminator_gan: 1 + feature_matching: [10, 10, 10, 10] + perceptual: [10, 10, 10, 10, 10] + equivariance_value: 10 + equivariance_jacobian: 10 + +lr_scheduler: + name: MultiStepDecay + epoch_milestones: [237360, 356040] + lr_generator: 2.0e-4 + lr_discriminator: 2.0e-4 + lr_kp_detector: 2.0e-4 + +reconstruction_params: + num_videos: 1000 + format: '.mp4' + +animate_params: + num_pairs: 50 + format: '.mp4' + normalization_params: + adapt_movement_scale: False + use_relative_movement: True + use_relative_jacobian: True + +visualizer_params: + kp_size: 5 + draw_border: True + colormap: 'gist_rainbow' + +log_config: + interval: 10 + visiual_interval: 10 + +validate: + interval: 10 + save_img: false + +snapshot_config: + interval: 10 + +optimizer: + name: Adam diff --git a/docs/en_US/tutorials/motion_driving.md b/docs/en_US/tutorials/motion_driving.md index b8c6719960db4bda0a2ec03510a9e9d2a3855244..ed03b10313175fb05cfcc65895709cde7196090a 100644 --- a/docs/en_US/tutorials/motion_driving.md +++ b/docs/en_US/tutorials/motion_driving.md @@ -34,7 +34,9 @@ python -u tools/first-order-demo.py \ --ratio 0.4 \ --relative --adapt_scale ``` -- multi face + +- multi face： +``` cd applications/ python -u tools/first-order-demo.py \ --driving_video ../docs/imgs/fom_dv.mp4 \ @@ -43,6 +45,7 @@ python -u tools/first-order-demo.py \ --relative --adapt_scale \ --multi_person + **params:** - driving_video: driving video, the motion of the driving video is to be migrated. - source_image: source_image, support single people and multi-person in the image, the image will be animated according to the motion of the driving video. diff --git a/docs/zh_CN/tutorials/motion_driving.md b/docs/zh_CN/tutorials/motion_driving.md index 866fd1ad55ecce05a08f293c367678d3040ae623..ad36f1ddf7a863d726938016e650bc2c4cc33aa0 100644 --- a/docs/zh_CN/tutorials/motion_driving.md +++ b/docs/zh_CN/tutorials/motion_driving.md @@ -41,7 +41,8 @@ python -u tools/first-order-demo.py \ --ratio 0.4 \ --relative --adapt_scale ``` -- 多人脸 +- 多人脸： +``` cd applications/ python -u tools/first-order-demo.py \ --driving_video ../docs/imgs/fom_dv.mp4 \ diff --git a/ppgan/apps/first_order_predictor.py b/ppgan/apps/first_order_predictor.py index cfc7c62976d87d48d8d925ee4a3a8a5f238fef0b..fe1a49d6e53c6364d0d9d8101960acda8a0b61af 100644 --- a/ppgan/apps/first_order_predictor.py +++ b/ppgan/apps/first_order_predictor.py @@ -163,7 +163,8 @@ class FirstOrderPredictor(BasePredictor): imageio.mimsave(os.path.join(self.output, self.filename), [ cv2.resize((frame * 255.0).astype('uint8'), (h, w)) for frame in predictions - ]) + ], + fps=fps) return bboxes = self.extract_bbox(source_image.copy()) @@ -175,7 +176,8 @@ class FirstOrderPredictor(BasePredictor): imageio.mimsave(os.path.join(self.output, self.filename), [ cv2.resize((frame * 255.0).astype('uint8'), (h, w)) for frame in predictions - ]) + ], + fps=fps) return # for multi person diff --git a/ppgan/datasets/firstorder_dataset.py b/ppgan/datasets/firstorder_dataset.py index 803b22d4b4c64ff859606b0a08a0a3fa18699a6f..41d5cc22135a22661dce63351bc51d037601b997 100755 --- a/ppgan/datasets/firstorder_dataset.py +++ b/ppgan/datasets/firstorder_dataset.py @@ -104,7 +104,9 @@ def read_video(name: Path, frame_shape=tuple([256, 256, 3]), saveto='folder'): if name.is_dir(): frames = sorted(name.iterdir(), key=lambda x: int(x.with_suffix('').name)) - video_array = np.array([imread(path) for path in frames]) + video_array = np.array([imread(path) for path in frames], + dtype='float32') + return video_array elif name.suffix.lower() in ['.gif', '.mp4', '.mov']: try: video = mimread(name, memtest=False) @@ -135,9 +137,9 @@ def read_video(name: Path, frame_shape=tuple([256, 256, 3]), saveto='folder'): for idx, img in enumerate(video_array_reshape): cv2.imwrite(sub_dir.joinpath('%i.png' % idx), img) name.unlink() + return video_array_reshape else: raise Exception("Unknown dataset file extensions %s" % name) - return video_array_reshape class FramesDataset(Dataset): diff --git a/ppgan/models/firstorder_model.py b/ppgan/models/firstorder_model.py index 64ecf08ef2a4b0fa41f7a89867e18128042e7a60..0b690aa65bbabb4ebddd6aee0408a4a0d31f63d2 100755 --- a/ppgan/models/firstorder_model.py +++ b/ppgan/models/firstorder_model.py @@ -64,27 +64,13 @@ class FirstOrderModel(BaseModel): generator_cfg.update({'train_params': train_params}) generator_cfg.update( {'dis_scales': discriminator.discriminator_cfg.scales}) - self.Gen_Full = build_generator(generator_cfg) + self.nets['Gen_Full'] = build_generator(generator_cfg) discriminator_cfg = discriminator discriminator_cfg.update({'common_params': common_params}) discriminator_cfg.update({'train_params': train_params}) - self.Dis = build_discriminator(discriminator_cfg) + self.nets['Dis'] = build_discriminator(discriminator_cfg) self.visualizer = Visualizer() - if isinstance(self.Gen_Full, paddle.DataParallel): - self.nets['kp_detector'] = self.Gen_Full._layers.kp_extractor - self.nets['generator'] = self.Gen_Full._layers.generator - self.nets['discriminator'] = self.Dis._layers.discriminator - else: - self.nets['kp_detector'] = self.Gen_Full.kp_extractor - self.nets['generator'] = self.Gen_Full.generator - self.nets['discriminator'] = self.Dis.discriminator - - # init params - init_weight(self.nets['kp_detector']) - init_weight(self.nets['generator']) - init_weight(self.nets['discriminator']) - def setup_lr_schedulers(self, lr_cfg): self.kp_lr = MultiStepDecay(learning_rate=lr_cfg['lr_kp_detector'], milestones=lr_cfg['epoch_milestones'], @@ -102,6 +88,21 @@ class FirstOrderModel(BaseModel): } def setup_optimizers(self, lr_cfg, optimizer): + if isinstance(self.nets['Gen_Full'], paddle.DataParallel): + self.nets['kp_detector'] = self.nets[ + 'Gen_Full']._layers.kp_extractor + self.nets['generator'] = self.nets['Gen_Full']._layers.generator + self.nets['discriminator'] = self.nets['Dis']._layers.discriminator + else: + + self.nets['kp_detector'] = self.nets['Gen_Full'].kp_extractor + self.nets['generator'] = self.nets['Gen_Full'].generator + self.nets['discriminator'] = self.nets['Dis'].discriminator + # init params + init_weight(self.nets['kp_detector']) + init_weight(self.nets['generator']) + init_weight(self.nets['discriminator']) + # define loss functions self.losses = {} @@ -124,7 +125,7 @@ class FirstOrderModel(BaseModel): def forward(self): """Run forward pass; called by both functions and .""" self.losses_generator, self.generated = \ - self.Gen_Full(self.input_data.copy(), self.nets['discriminator']) + self.nets['Gen_Full'](self.input_data.copy(), self.nets['discriminator']) self.visual_items['driving_source_gen'] = self.visualizer.visualize( self.input_data['driving'].detach(), self.input_data['source'].detach(), self.generated) @@ -136,7 +137,8 @@ class FirstOrderModel(BaseModel): loss.backward() def backward_D(self): - losses_discriminator = self.Dis(self.input_data.copy(), self.generated) + losses_discriminator = self.nets['Dis'](self.input_data.copy(), + self.generated) loss_values = [val.mean() for val in losses_discriminator.values()] loss = paddle.add_n(loss_values) loss.backward()