debug util

configs/_base_/default_exp.py

@@ -6,9 +6,7 @@
 _C = CN()
 _C.ckp_dir = 'checkpoint/'
 _C.weight_file = ''
-_C.gpus = [
-    0,
-]
+_C.gpus = [0]
 _C.num_workers = 8
 _C.batch_size = 32
 _C.num_epochs = 200

configs/_base_/models/pn_transformer/vn_pn_transformer.py

@@ -8,12 +8,9 @@
 _C.pc_feat_dim = 48  # use a smaller one because VN feature is 3xC
 
 _C.encoder = 'vn-pointnet'
-_C.encoder_pool1 = 'mean'  # pooling for the input edge-conv
-_C.encoder_pool2 = 'max'  # pooling for the global feature
 
 _C.transformer_heads = 4
 _C.transformer_layers = 4
-_C.transformer_relu = True
 
 
 def get_cfg_defaults():

docs/config.md

@@ -89,8 +89,8 @@ We follow the below style to name each training config:
 {model}_{batch_per_gpu x gpu}_{schedule}_{dataset}.py
 ```
 
-To inspect one specific config file, simply call our privided script:
+To inspect one specific config file, simply call our provided script:
 
 ```
-python script/print_cfg.py --cfg_file $CFG
+python scripts/print_cfg.py --cfg_file $CFG
 ```

docs/usage.md

@@ -5,14 +5,14 @@
 To train a model, simply run:
 
 ```
-python script/train.py --cfg_file $CFG --other_args ...
+python scripts/train.py --cfg_file $CFG --other_args ...
 ```
 
 For example, to train the Global baseline model on PartNet chair, replace `$CFG` with `configs/global/global-32x1-cosine_200e-partnet_chair.py`.
 Other optional arguments include:
 
 -   `--category`: train the model only on a subset of data, e.g. `Chair`, `Table`, `Lamp` on PartNet
--   `--gpus`: setting training GPUs, note that by default we are using DP training. Please modify `script/train.py` to enable DDP training
+-   `--gpus`: setting training GPUs, note that by default we are using DP training. Please modify `scripts/train.py` to enable DDP training
 -   `--weight`: loading pre-trained weights
 -   `--fp16`: FP16 mixed precision training
 -   `--cudnn`: setting `cudnn.benchmark = True`
@@ -28,24 +28,24 @@ Please set up your account on the machine before running training commands.
 Script for configuring and submitting jobs to cluster SLURM system:
 
 ```
-GPUS=1 CPUS_PER_TASK=8 MEM_PER_CPU=5 QOS=normal ./script/sbatch_run.sh $PARTITION $JOB_NAME ./script/train.py --cfg_file $CFG --other_args...
+GPUS=1 CPUS_PER_TASK=8 MEM_PER_CPU=5 QOS=normal ./scripts/sbatch_run.sh $PARTITION $JOB_NAME ./scripts/train.py --cfg_file $CFG --other_args...
 ```
 
 Script for running a job multiple times over different random seeds:
 
 ```
-GPUS=1 CPUS_PER_TASK=8 MEM_PER_CPU=5 QOS=normal REPEAT=$NUM_REPEAT ./script/dup_run_sbatch.sh $PARTITION $JOB_NAME ./script/train.py $CFG --other_args...
+GPUS=1 CPUS_PER_TASK=8 MEM_PER_CPU=5 QOS=normal REPEAT=$NUM_REPEAT ./scripts/dup_run_sbatch.sh $PARTITION $JOB_NAME ./scripts/train.py $CFG --other_args...
 ```
 
 We also provide scripts for training on single/all categories of the Breaking-Bad dataset's `everyday` subset.
-See [train_everyday_categories.sh](../script/train_everyday_categories.sh) and [train_one_category.sh](../script/train_one_category.sh).
+See [train_everyday_categories.sh](../scripts/train_everyday_categories.sh) and [train_one_category.sh](../scripts/train_one_category.sh).
 
 ## Testing
 
 Similar to training, to test a pre-trained weight, simply run:
 
 ```
-python script/test.py --cfg_file $CFG --weight path/to/weight
+python scripts/test.py --cfg_file $CFG --weight path/to/weight
 ```
 
 Optional auguments:
@@ -57,15 +57,15 @@ Optional auguments:
 If you want to get per-category result of this model (currently only support `everyday` subset of the Breaking-Bad dataset), and report performance averaged over all the categories (used in the paper), run:
 
 ```
-python script/test.py --cfg_file $CFG --weight path/to/weight --category all
+python scripts/test.py --cfg_file $CFG --weight path/to/weight --category all
 ```
 
 We will print the metrics on each category and the averaged results.
 
 We also provide script to test your per-category trained models (currently only support `everyday` subset of the Breaking-Bad dataset). Suppose you train the models by running `./scrips/train_everyday_categories.sh $COMMAND $CFG.py`. Then the model checkpoint will be saved in `checkpoint/$CFG-$CATEGORY-dup$X`. To collect the performance on each category, run:
 
 ```
-python script/collect_test.py --cfg_file $CFG.py --num_dup $X --ckp_suffix checkpoint/$CFG-
+python scripts/collect_test.py --cfg_file $CFG.py --num_dup $X --ckp_suffix checkpoint/$CFG-
 ```
 
 You can again control the number of pieces and GPUs to use.

multi_part_assembly/models/pn_transformer/vn_network.py

@@ -34,8 +34,8 @@ def _init_encoder(self):
             self.cfg.model.encoder,
             feat_dim=self.pc_feat_dim,
             global_feat=True,
-            pool1=self.cfg.model.get('encoder_pool1', 'mean'),
-            pool2=self.cfg.model.get('encoder_pool2', 'max'),
+            pool1='mean',
+            pool2='max',
         )
         return encoder
 

scripts/debug.py

@@ -0,0 +1,23 @@
+import os
+import sys
+import importlib
+
+from multi_part_assembly.datasets import build_dataloader
+from multi_part_assembly.models import build_model
+
+
+def build(cfg_file):
+    sys.path.append(os.path.dirname(cfg_file))
+    cfg = importlib.import_module(os.path.basename(cfg_file)[:-3])
+    cfg = cfg.get_cfg_defaults()
+
+    cfg.freeze()
+    print(cfg)
+
+    # Initialize model
+    model = build_model(cfg)
+
+    # Initialize dataloaders
+    train_loader, val_loader = build_dataloader(cfg)
+
+    return model, train_loader, val_loader, cfg

script/dup_run_sbatch.sh → scripts/dup_run_sbatch.sh

@@ -5,7 +5,7 @@
 
 #######################################################################
 # An example usage:
-#     GPUS=1 CPUS_PER_TASK=8 MEM_PER_CPU=5 QOS=normal REPEAT=3 ./script/dup_run_sbatch.sh \
+#     GPUS=1 CPUS_PER_TASK=8 MEM_PER_CPU=5 QOS=normal REPEAT=3 ./scripts/dup_run_sbatch.sh \
 #       rtx6000 test-sbatch test.py config.py --fp16 --cudnn
 #######################################################################
 
@@ -27,7 +27,7 @@ do
     cfg="${CFG:0:(-3)}-dup${repeat_idx}.py"
     cp $CFG $cfg
     job_name="${JOB_NAME}-dup${repeat_idx}"
-    cmd="./script/sbatch_run.sh $PARTITION $job_name $PY_FILE --cfg_file $cfg $PY_ARGS"
+    cmd="./scripts/sbatch_run.sh $PARTITION $job_name $PY_FILE --cfg_file $cfg $PY_ARGS"
     echo $cmd
     eval $cmd
 done

script/sbatch_run.sh → scripts/sbatch_run.sh

@@ -6,8 +6,8 @@
 
 #######################################################################
 # An example usage:
-#     GPUS=1 CPUS_PER_TASK=8 MEM_PER_CPU=5 QOS=normal ./script/sbatch_run.sh rtx6000 train-sbatch \
-#       ./script/train.py --cfg_file config.py
+#     GPUS=1 CPUS_PER_TASK=8 MEM_PER_CPU=5 QOS=normal ./scripts/sbatch_run.sh rtx6000 train-sbatch \
+#       ./scripts/train.py --cfg_file config.py
 #######################################################################
 
 # read args from command line

script/train_everyday_categories.sh → scripts/train_everyday_categories.sh

@@ -4,7 +4,7 @@
 
 #######################################################################
 # An example usage:
-#     ./script/train_everyday_categories.sh "GPUS=1 CPUS_PER_TASK=8 MEM_PER_CPU=5 QOS=normal REPEAT=3 ./script/dup_run_sbatch.sh rtx6000 everyday_cat ./script/train.py config.py --fp16 --cudnn" config.py
+#     ./scripts/train_everyday_categories.sh "GPUS=1 CPUS_PER_TASK=8 MEM_PER_CPU=5 QOS=normal REPEAT=3 ./scripts/dup_run_sbatch.sh rtx6000 everyday_cat ./scripts/train.py config.py --fp16 --cudnn" config.py
 #######################################################################
 
 CMD=$1

script/train_one_category.sh → scripts/train_one_category.sh

@@ -4,7 +4,7 @@
 
 #######################################################################
 # An example usage:
-#     ./script/train_one_category.sh "GPUS=1 CPUS_PER_TASK=8 MEM_PER_CPU=5 QOS=normal REPEAT=3 ./script/dup_run_sbatch.sh rtx6000 everyday_cat ./script/train.py config.py --fp16 --cudnn" config.py Bottle
+#     ./scripts/train_one_category.sh "GPUS=1 CPUS_PER_TASK=8 MEM_PER_CPU=5 QOS=normal REPEAT=3 ./scripts/dup_run_sbatch.sh rtx6000 everyday_cat ./scripts/train.py config.py --fp16 --cudnn" config.py Bottle
 #######################################################################
 
 CMD=$1