TEST: use tensorboard for stuff

.gitignore

@@ -1,7 +1,9 @@
 baseline-experiments/
+pretrained/
 synchronous/
 npydata/
 **/__pycache__/**
 slurm-*
 save/
+save_file/
 .vscode/

_DDPShA-train.sh

@@ -0,0 +1,36 @@
+#!/bin/sh
+#SBATCH -N 1
+#SBATCH -n 1
+#SBATCH --partition=Teach-Standard
+#SBATCH --gres=gpu:4
+#SBATCH --mem=24000
+#SBATCH --time=3-00:00:00
+
+set -e
+
+export CUDA_HOME=/opt/cuda-9.0.176.1/
+export CUDNN_HOME=/opt/cuDNN-7.0/
+export STUDENT_ID=$(whoami)
+
+export LD_LIBRARY_PATH=${CUDNN_HOME}/lib64:${CUDA_HOME}/lib64:${LD_LIBRARY_PATH}
+export LIBRARY_PATH=${CUDNN_HOME}/lib64:${LIBRARY_PATH}
+export CPATH=${CUDNN_HOME}/include:$CPATH
+export PATH=${CUDA_HOME}/bin:${PATH}
+export PYTHON_PATH=$PATH
+
+mkdir -p /disk/scratch/${STUDENT_ID}
+export TMPDIR=/disk/scratch/${STUDENT_ID}/
+export TMP=/disk/scratch/${STUDENT_ID}/
+
+source /home/${STUDENT_ID}/miniconda3/bin/activate mlp-cuda
+
+python train.py \
+	--pth_tar './pretrained/deit_base_patch16_384-8de9b5d1.pth' \
+	--model 'stn' \
+	--save_path ./save/DDP-ShanghaiA-stn-$(date -Iminutes) \
+	--batch_size 4 \
+	--use_ddp True \
+	--ddp_world_size 4 \
+	--gpus 0,1,2,3 \
+	--print_freq 100
+

_DDPShB-base-train.sh

@@ -0,0 +1,36 @@
+#!/bin/sh
+#SBATCH -N 1
+#SBATCH -n 1
+#SBATCH --partition=Teach-Standard
+#SBATCH --gres=gpu:4
+#SBATCH --mem=24000
+#SBATCH --time=3-00:00:00
+
+set -e
+
+export CUDA_HOME=/opt/cuda-9.0.176.1/
+export CUDNN_HOME=/opt/cuDNN-7.0/
+export STUDENT_ID=$(whoami)
+
+export LD_LIBRARY_PATH=${CUDNN_HOME}/lib64:${CUDA_HOME}/lib64:${LD_LIBRARY_PATH}
+export LIBRARY_PATH=${CUDNN_HOME}/lib64:${LIBRARY_PATH}
+export CPATH=${CUDNN_HOME}/include:$CPATH
+export PATH=${CUDA_HOME}/bin:${PATH}
+export PYTHON_PATH=$PATH
+
+mkdir -p /disk/scratch/${STUDENT_ID}
+export TMPDIR=/disk/scratch/${STUDENT_ID}/
+export TMP=/disk/scratch/${STUDENT_ID}/
+
+source /home/${STUDENT_ID}/miniconda3/bin/activate mlp-cuda
+
+python train.py \
+	--use_ddp True \
+	--ddp_world_size 4 \
+	--pth_tar './pretrained/deit_base_patch16_384-8de9b5d1.pth' \
+	--train_dataset 'ShanghaiB' \
+	--save_path ./save/DDP-ShanghaiB-base-$(date -Iminutes) \
+	--batch_size 4 \
+	--gpus 0,1,2,3 \
+	--print_freq 100
+

_ShA-base-train.sh

@@ -2,7 +2,7 @@
 #SBATCH -N 1
 #SBATCH -n 1
 #SBATCH --partition=Teach-Standard
-#SBATCH --gres=gpu:6
+#SBATCH --gres=gpu:4
 #SBATCH --mem=24000
 #SBATCH --time=3-00:00:00
 
@@ -25,9 +25,9 @@ export TMP=/disk/scratch/${STUDENT_ID}/
 source /home/${STUDENT_ID}/miniconda3/bin/activate mlp-cuda
 
 python train.py \
-    --debug True \
+	--pth_tar './pretrained/deit_base_patch16_384-8de9b5d1.pth' \
-    --model 'stn' \
+	--save_path ./save/ShanghaiA-base-$(date -Iminutes) \
-    --save_path ./save_file/ShanghaiA \
+	--batch_size 4 \
-    --batch_size 4 \
+	--gpus 0,1,2,3 \
-    --gpus 0,1,2,3,4,5 \
+	--print_freq 100
-    --print_freq 100
+

_ShA-train.sh

@@ -0,0 +1,34 @@
+#!/bin/sh
+#SBATCH -N 1
+#SBATCH -n 1
+#SBATCH --partition=Teach-Standard
+#SBATCH --gres=gpu:4
+#SBATCH --mem=24000
+#SBATCH --time=3-00:00:00
+
+set -e
+
+export CUDA_HOME=/opt/cuda-9.0.176.1/
+export CUDNN_HOME=/opt/cuDNN-7.0/
+export STUDENT_ID=$(whoami)
+
+export LD_LIBRARY_PATH=${CUDNN_HOME}/lib64:${CUDA_HOME}/lib64:${LD_LIBRARY_PATH}
+export LIBRARY_PATH=${CUDNN_HOME}/lib64:${LIBRARY_PATH}
+export CPATH=${CUDNN_HOME}/include:$CPATH
+export PATH=${CUDA_HOME}/bin:${PATH}
+export PYTHON_PATH=$PATH
+
+mkdir -p /disk/scratch/${STUDENT_ID}
+export TMPDIR=/disk/scratch/${STUDENT_ID}/
+export TMP=/disk/scratch/${STUDENT_ID}/
+
+source /home/${STUDENT_ID}/miniconda3/bin/activate mlp-cuda
+
+python train.py \
+	--pth_tar './pretrained/deit_base_patch16_384-8de9b5d1.pth' \
+	--model 'stn' \
+	--save_path ./save/ShanghaiA-stn-$(date -Iminutes) \
+	--batch_size 4 \
+	--gpus 0,1,2,3 \
+	--print_freq 100
+

_ShB-base-train.sh

@@ -0,0 +1,34 @@
+#!/bin/sh
+#SBATCH -N 1
+#SBATCH -n 1
+#SBATCH --partition=Teach-Standard
+#SBATCH --gres=gpu:4
+#SBATCH --mem=24000
+#SBATCH --time=3-00:00:00
+
+set -e
+
+export CUDA_HOME=/opt/cuda-9.0.176.1/
+export CUDNN_HOME=/opt/cuDNN-7.0/
+export STUDENT_ID=$(whoami)
+
+export LD_LIBRARY_PATH=${CUDNN_HOME}/lib64:${CUDA_HOME}/lib64:${LD_LIBRARY_PATH}
+export LIBRARY_PATH=${CUDNN_HOME}/lib64:${LIBRARY_PATH}
+export CPATH=${CUDNN_HOME}/include:$CPATH
+export PATH=${CUDA_HOME}/bin:${PATH}
+export PYTHON_PATH=$PATH
+
+mkdir -p /disk/scratch/${STUDENT_ID}
+export TMPDIR=/disk/scratch/${STUDENT_ID}/
+export TMP=/disk/scratch/${STUDENT_ID}/
+
+source /home/${STUDENT_ID}/miniconda3/bin/activate mlp-cuda
+
+python train.py \
+	--pth_tar './pretrained/deit_base_patch16_384-8de9b5d1.pth' \
+	--train_dataset 'ShanghaiB' \
+	--save_path ./save/ShanghaiB-base-$(date -Iminutes) \
+	--batch_size 4 \
+	--gpus 0,1,2,3 \
+	--print_freq 100
+

_ShB-train.sh

@@ -0,0 +1,35 @@
+#!/bin/sh
+#SBATCH -N 1
+#SBATCH -n 1
+#SBATCH --partition=Teach-Standard
+#SBATCH --gres=gpu:4
+#SBATCH --mem=24000
+#SBATCH --time=3-00:00:00
+
+set -e
+
+export CUDA_HOME=/opt/cuda-9.0.176.1/
+export CUDNN_HOME=/opt/cuDNN-7.0/
+export STUDENT_ID=$(whoami)
+
+export LD_LIBRARY_PATH=${CUDNN_HOME}/lib64:${CUDA_HOME}/lib64:${LD_LIBRARY_PATH}
+export LIBRARY_PATH=${CUDNN_HOME}/lib64:${LIBRARY_PATH}
+export CPATH=${CUDNN_HOME}/include:$CPATH
+export PATH=${CUDA_HOME}/bin:${PATH}
+export PYTHON_PATH=$PATH
+
+mkdir -p /disk/scratch/${STUDENT_ID}
+export TMPDIR=/disk/scratch/${STUDENT_ID}/
+export TMP=/disk/scratch/${STUDENT_ID}/
+
+source /home/${STUDENT_ID}/miniconda3/bin/activate mlp-cuda
+
+python train.py \
+	--pth_tar './pretrained/deit_base_patch16_384-8de9b5d1.pth' \
+	--model 'stn' \
+	--train_dataset 'ShanghaiB' \
+	--save_path ./save/ShanghaiB-stn-$(date -Iminutes) \
+	--batch_size 4 \
+	--gpus 0,1,2,3 \
+	--print_freq 100
+

preprocess_data.py

@@ -25,6 +25,7 @@ import cv2
 import scipy.io as io
 import scipy.sparse as sparse
 import h5py
+from tqdm.auto import tqdm
 
 CWD = os.getcwd()
 
@@ -51,7 +52,7 @@ def pre_dataset_sh():
 
     # np.random.seed(0)
     # random.seed(0)
-    for img_path in img_paths:
+    for _, img_path in tqdm(img_paths, desc="Preprocessing Data"):
         img_data = cv2.imread(img_path)
         mat = io.loadmat(
             img_path
@@ -169,4 +170,5 @@ def make_npydata():
 if __name__ == "__main__":
     # Download manually...
     pre_dataset_sh()    # XXX: preliminary
+    print("Storing dataset paths...")
     make_npydata()

train.py

@@ -9,6 +9,8 @@ import torch
 import torch.nn as nn
 import torch.multiprocessing as torch_mp
 from torch.utils.data import DataLoader
+from torch.utils.tensorboard import SummaryWriter
+import torchvision
 import nni
 import logging
 import numpy as np
@@ -17,10 +19,11 @@ from model.transcrowd_gap import VisionTransformerGAP
 from arguments import args, ret_args
 import dataset
 from dataset import *
-from model.transcrowd_gap import base_patch16_384_gap, stn_patch16_384_gap
+from model.transcrowd_gap import *
 from checkpoint import save_checkpoint
 
 logger = logging.getLogger("train")
+writer = SummaryWriter(args.save_path + "/tensorboard-run")
 
 def setup_process_group(
         rank: int,
@@ -196,7 +199,7 @@ def worker(rank: int, args: Namespace):
 
         # Validate
         if epoch % 5 == 0 or args.debug:
-            prec1 = valid_one_epoch(test_loader, model, device, args)
+            prec1 = valid_one_epoch(test_loader, model, device, epoch, args)
             end_valid = time.time()
             is_best = prec1 < args.best_pred
             args.best_pred = min(prec1, args.best_pred)
@@ -255,11 +258,14 @@ def train_one_epoch(
             gt_count_whole = gt_count_whole.cuda()
             device_type = "cuda"
 
+        # Desperate measure to reduce mem footprint...
         with torch.autocast(device_type):
             # fpass
             out, gt_count = model(img, kpoint)
             # loss
             loss = criterion(out, gt_count) # wrt. transformer
+            writer.add_scalar("L1-loss wrt. xformer (train)", loss, epoch * i)
+
             loss += (
                 F.mse_loss(                 # stn: info retainment
                     gt_count.view(batch_size, -1).sum(axis=1, keepdim=True),
@@ -270,6 +276,7 @@ def train_one_epoch(
                     value=loss.item()
                 )
             )
+            writer.add_scalar("Composite loss (train)", loss, epoch * i)
 
         # free grad from mem
         optimizer.zero_grad(set_to_none=True)
@@ -283,10 +290,13 @@ def train_one_epoch(
         if args.debug:
             break
 
+    # Flush writer
+    writer.flush()
+
     scheduler.step()
 
 
-def valid_one_epoch(test_loader, model, device, args):
+def valid_one_epoch(test_loader, model, device, epoch, args):
     print("[valid_one_epoch] Validating...")
     batch_size = 1
     model.eval()
@@ -295,6 +305,7 @@ def valid_one_epoch(test_loader, model, device, args):
     mse = .0
     visi = []
     index = 0
+    xformed = []
 
     for i, (fname, img, kpoint, gt_count_whole) in enumerate(test_loader):
         kpoint = kpoint.type(torch.FloatTensor)
@@ -324,6 +335,10 @@ def valid_one_epoch(test_loader, model, device, args):
         mse += diff ** 2
 
         if i % 5 == 0:
+            if isinstance(model, STNet_VisionTransformerGAP):
+                with torch.no_grad():
+                    img_xformed = model.stnet(img).to("cpu")
+                    xformed.append(img_xformed)
             print("[valid_one_epoch] {} | Gt {:.2f} Pred {:.4f} |".format(
                 fname[0],
                 torch.sum(gt_count_whole).item(),
@@ -332,10 +347,16 @@ def valid_one_epoch(test_loader, model, device, args):
 
     mae = mae * 1.0 / (len(test_loader) * batch_size)
     mse = np.sqrt(mse / (len(test_loader)) * batch_size)
+    writer.add_scalar("MAE (valid)", mae, epoch)
+    writer.add_scalar("MSE (valid)", mse, epoch)
+    if len(xformed) != 0:
+        img_grid = torchvision.utils.make_grid(xformed)
+        writer.add_image("STN: transformed image", img_grid, epoch)
     nni.report_intermediate_result(mae)
     print("* MAE {mae:.3f} | MSE {mse:.3f} *".format(
         mae=mae, mse=mse
     ))
+    writer.flush()
     return mae
 
 
@@ -353,7 +374,7 @@ if __name__ == "__main__":
             worker,
             args=(combined_params, ), # rank supplied at callee as 1st param
             # also above *has* to be 1-tuple else runtime expands Namespace.
-            nprocs=combined_params.world_size,
+            nprocs=combined_params.ddp_world_size,
         )
     else:
         # No DDP, run in current thread