update ZJU-MoCap finetuning

README.md

@@ -83,6 +83,12 @@ python train_net.py --cfg_file configs/enerf/dtu/scan114.yaml
 
 Fine-tuning for 3000 and 11000 iterations takes about 11 minutes and 40 minutes, respectively, on our test machine ( i9-12900K CPU, RTX 3090 GPU).
 
+### Fine-tuning on the ZJU-MoCap dataset
+
+```
+python train_net.py --cfg_file configs/enerf/zjumocap/zjumocap_train.yaml
+```
+
 
 ## Evaluation
 

configs/enerf/llff/flower.yaml

@@ -14,5 +14,8 @@ test_dataset:
  scene: flower
 train:
  epoch: 147 # pretrained epoch + 8
+ sampler_meta:
+ input_views_num: [3, 4] 
+ input_views_prob: [0.4, 0.6]
 save_ep: 1
 eval_ep: 1

configs/enerf/llff/fortress.yaml

@@ -14,5 +14,8 @@ test_dataset:
  scene: fortress
 train:
  epoch: 147 # pretrained epoch + 8
+ sampler_meta:
+ input_views_num: [3, 4] 
+ input_views_prob: [0.4, 0.6]
 save_ep: 1
 eval_ep: 1

configs/enerf/zjumocap/zjumocap_train.yaml

@@ -0,0 +1,56 @@
+parent_cfg: 'configs/enerf/dtu_pretrain.yaml'
+exp_name: zjumocap
+pretrain: dtu_pretrain
+
+train_dataset_module: lib.datasets.zjumocap.enerf
+test_dataset_module: lib.datasets.zjumocap.enerf
+network_module: lib.networks.enerf.network_human
+evaluator_module: lib.evaluators.enerf_human
+
+enerf:
+ sample_on_mask: True
+ train_input_views: [2, 3]
+ train_input_views_prob: [0.9, 0.1]
+ test_input_views: 2
+ cas_config:
+ train_img: [False, False]
+ patch_size: [-1, 64] #
+ num_rays: [4096, 16384] # 
+ num_patchs: [0, 4] # 
+ volume_planes: [32, 8]
+ render_if: [True, True]
+
+train_dataset:
+ data_root: 'zju_mocap'
+ scene: 'CoreView_313'
+ split: train
+ frames: [0, 600, 1]
+ input_views: [0, -1, 2]
+ render_views: [0, -1, 2]
+ input_ratio: 0.5
+
+test_dataset:
+ data_root: 'zju_mocap'
+ scene: 'CoreView_313'
+ split: test
+ frames: [0, 600, 100]
+ input_views: [0, -1, 2]
+ render_views: [1, -1, 2]
+ input_ratio: 0.5
+
+train:
+ batch_size: 1
+ lr: 5e-4
+ epoch: 100
+ scheduler:
+ type: 'exponential'
+ gamma: 0.5
+ decay_epochs: 10
+ sampler_meta:
+ input_views_num: [2, 3] 
+ input_views_prob: [0.9, 0.1]
+
+eval_ep: 1
+
+
+

lib/utils/net_utils.py

@@ -493,7 +493,7 @@ def save_trained_config(cfg):
  yaml.dump(cfg, open(train_config_path, 'w'))
 
 def load_pretrain(net, model_dir):
- model_dir = os.path.join('data/trained_model', cfg.task, model_dir)
+ model_dir = os.path.join(cfg.workspace, 'trained_model', cfg.task, model_dir)
  if not os.path.exists(model_dir):
  return 1