diff --git a/dgcnn/dgcnn_shape_net_inference.py b/dgcnn/dgcnn_shape_net_inference.py
index 079a7c6750816dabccc4ede3943b92b3d144c196..36dcba693c6aae1a1bd0e88f22ed15611b101fdb 100644
--- a/dgcnn/dgcnn_shape_net_inference.py
+++ b/dgcnn/dgcnn_shape_net_inference.py
@@ -5,14 +5,12 @@ import torch.nn.init as init
from my_models.model_shape_net import DgcnShapeNet as DGCNN
-
-
def main():
- dgcnn = DGCNN(50)
+ dgcnn = DGCNN(50, 4)
dgcnn.eval()
# simple test
input_tensor = torch.randn(2, 128, 3)
- label_one_hot = torch.zeros((2, 16))
+ label_one_hot = torch.zeros((2, 4))
print(input_tensor.shape)
out = dgcnn(input_tensor, label_one_hot)
print(out.shape)
diff --git a/dgcnn/dgcnn_train_pl.py b/dgcnn/dgcnn_train_pl.py
index e49a9433c0b5ffcb95667ff53e70b217c4bff044..dd1366c46b2403dd38471a2e6de054ee5b580af1 100644
--- a/dgcnn/dgcnn_train_pl.py
+++ b/dgcnn/dgcnn_train_pl.py
@@ -138,7 +138,7 @@ shapenet_data_train = ShapenetDataDgcnn(
return_cls_label=True,
small_data=config['data']['small_data'],
small_data_size=config['data']['small_data_size'],
- num_classes=config['data']['just_four_classes'],
+ num_classes=config['data']['num_classes'],
split='train',
norm=config['data']['norm'],
augmnetation=config['data']['augmentation']
@@ -151,7 +151,7 @@ shapenet_data_val = ShapenetDataDgcnn(
return_cls_label=True,
small_data=config['data']['small_data'],
small_data_size=config['data']['small_data_size'],
- num_classes=config['data']['just_four_classes'],
+ num_classes=config['data']['num_classes'],
split='test',
norm=config['data']['norm']
)
@@ -163,7 +163,7 @@ shapenet_data_test = ShapenetDataDgcnn(
return_cls_label=True,
small_data=config['data']['small_data'],
small_data_size=config['data']['small_data_size'],
- num_classes=config['data']['just_four_classes'],
+ num_classes=config['data']['num_classes'],
split='test',
norm=config['data']['norm']
)
@@ -217,10 +217,8 @@ else:
)
# Initialize a model
-if config['data']['just_four_classes']:
- num_classes = 4
-else:
- num_classes = 16
+num_classes = int(config['data']['num_classes'])
+
model = DGCNNLightning(num_classes=num_classes)
if config['wandb']['use_wandb']:
diff --git a/dgcnn/dgcnn_train_shape_net.py b/dgcnn/dgcnn_train_shape_net.py
index 9d9b9b96e626f302ae832e399314fc0f723311f4..9aeb3884f3ae603004c5fe9d46580924a2fcb013 100644
--- a/dgcnn/dgcnn_train_shape_net.py
+++ b/dgcnn/dgcnn_train_shape_net.py
@@ -31,12 +31,12 @@ def train():
# get data
shapenet_data = ShapenetDataDgcnn(
root='/home/nibio/mutable-outside-world/code/oracle_gpu_runs/data/shapenet',
- npoints=1024,
+ npoints=32,
return_cls_label=True,
- small_data=False,
- small_data_size=300,
- just_four_classes=False,
- data_augmentation=True,
+ small_data=True,
+ small_data_size=10,
+ num_classes=True,
+ data_augmentation=False,
split='train',
norm=True
)
@@ -44,7 +44,7 @@ def train():
# create a dataloader
dataloader = torch.utils.data.DataLoader(
shapenet_data,
- batch_size=4,
+ batch_size=8,
shuffle=True,
num_workers=8,
drop_last=True
@@ -77,6 +77,11 @@ def train():
# print(f"Batch: {i}")
points, labels, class_name = data
+ # log data to wandb
+ if use_wandb:
+ wandb.log({"points": wandb.Object3D(points[0, :, :].cpu().numpy())})
+ # wandb.log({"labels": labels.cpu().numpy()})
+ # wandb.log({"class_name": class_name.cpu().numpy()})
label_one_hot = np.zeros((class_name.shape[0], 16))
for idx in range(class_name.shape[0]):
label_one_hot[idx, class_name[idx]] = 1
diff --git a/dgcnn/dgcnn_train_shape_net_cat_1-4.py b/dgcnn/dgcnn_train_shape_net_cat_1-4.py
new file mode 100644
index 0000000000000000000000000000000000000000..3c25b49d3e93d7aa2c111b4e54b6358317e88699
--- /dev/null
+++ b/dgcnn/dgcnn_train_shape_net_cat_1-4.py
@@ -0,0 +1,122 @@
+import random
+import numpy as np
+import torch
+import os
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.nn.init as init
+from matplotlib import pyplot as plt
+from shapenet_data_dgcnn import ShapenetDataDgcnn
+# import IoU from torchmetrics
+from torchmetrics import Accuracy
+from torchmetrics import JaccardIndex as IoU
+from torch.optim.lr_scheduler import StepLR
+
+import wandb
+
+use_wandb = False
+num_classes = 4
+batch_size = 4
+
+# create a wandb run
+if use_wandb:
+ run = wandb.init(project="dgcnn", entity="maciej-wielgosz-nibio")
+
+
+from my_models.model_shape_net import DgcnShapeNet as DGCNN
+
+
+def train():
+ seg_num_all = 50
+ dgcnn = DGCNN(
+ seg_num_all=seg_num_all,
+ num_classes=num_classes
+ ).cuda()
+ dgcnn.train()
+
+ # get data
+ shapenet_data = ShapenetDataDgcnn(
+ root='/home/nibio/mutable-outside-world/code/oracle_gpu_runs/data/shapenet',
+ npoints=32,
+ return_cls_label=True,
+ small_data=True,
+ small_data_size=10,
+ num_classes=num_classes,
+ data_augmentation=False,
+ split='train',
+ norm=True
+ )
+
+ # create a dataloader
+ dataloader = torch.utils.data.DataLoader(
+ shapenet_data,
+ batch_size=batch_size,
+ shuffle=True,
+ num_workers=8,
+ drop_last=True
+ )
+
+ # create a optimizer
+ optimizer = torch.optim.Adam(dgcnn.parameters(), lr=0.001, weight_decay=1e-4)
+ scheduler = StepLR(optimizer, step_size=20, gamma=0.5)
+ if use_wandb:
+ # create a config wandb
+ wandb.config.update({
+ "batch_size": batch_size,
+ "learning_rate": 0.01,
+ "optimizer": "Adam",
+ "loss_function": "cross_entropy"
+ })
+
+ # train
+ iou = IoU(num_classes=50, task='multiclass', average='macro').cuda()
+ acc = Accuracy(num_classes=50, compute_on_step=False, dist_sync_on_step=False, task='multiclass').cuda()
+
+ for epoch in range(500):
+ iou.reset()
+ print(f"Epoch: {epoch}")
+ if use_wandb:
+ wandb.log({"epoch": epoch})
+ for i, data in enumerate(dataloader, 0):
+ # print(f"Batch: {i}")
+
+ points, labels, class_name = data
+ print('class_name', class_name)
+ label_one_hot = np.zeros((class_name.shape[0], num_classes))
+ for idx in range(class_name.shape[0]):
+
+ label_one_hot[idx, class_name[idx]] = 1
+ label_one_hot = torch.from_numpy(label_one_hot.astype(np.float32))
+
+ points = points.cuda()
+ labels = labels.cuda()
+ label_one_hot = label_one_hot.cuda()
+
+ optimizer.zero_grad()
+ pred = dgcnn(points, label_one_hot)
+
+ loss = F.cross_entropy(
+ pred, labels, reduction='mean', ignore_index=255)
+ loss.backward()
+ optimizer.step()
+ if optimizer.param_groups[0]['lr'] > 1e-5:
+ scheduler.step()
+
+ # print lose every 10 batches
+ if i % 100 == 0:
+ print(loss.item())
+ pred_softmax = F.softmax(pred, dim=1)
+ pred_argmax = torch.argmax(pred_softmax, dim=1)
+ gt_one_hot = F.one_hot(labels, num_classes=50)
+ pred_one_hot = F.one_hot(pred_argmax, num_classes=50)
+ print("loss : ", loss.item())
+ print("IoU : ", iou(pred_one_hot, gt_one_hot))
+ print("Acc : ", acc(pred_argmax, labels))
+ if use_wandb:
+ wandb.log({"loss": loss.item()})
+ wandb.log({"iou": iou(pred, labels)})
+ wandb.log({"acc": acc(pred, labels)})
+
+
+if __name__ == '__main__':
+ train()
\ No newline at end of file
diff --git a/dgcnn/my_models/model_shape_net.py b/dgcnn/my_models/model_shape_net.py
index fc275ac8734d6df2cc9547bc14d46b4fb0b5b8eb..244f4eaf70db12d8a5ee9a9d4b5e55785e62ff37 100644
--- a/dgcnn/my_models/model_shape_net.py
+++ b/dgcnn/my_models/model_shape_net.py
@@ -43,7 +43,7 @@ class DgcnShapeNet(nn.Module):
- def forward(self, x, class_label):
+ def forward(self, x, class_label_one_hot):
# Apply Transform_Net on input point cloud
trans_matrix = self.transform_net(x)
@@ -63,10 +63,10 @@ class DgcnShapeNet(nn.Module):
x = self.conv5(x5) # (batch_size, 1024, num_points)
x = x.max(dim=-1, keepdim=True)[0] # (batch_size, 1024)
- class_label = class_label.view(batch_size, -1, 1) # (batch_size, num_categoties, 1)
- class_label = self.conv7(class_label) # (batch_size, num_categoties, 1) -> (batch_size, 64, 1)
+ class_label_one_hot = class_label_one_hot.view(batch_size, -1, 1) # (batch_size, num_categoties, 1)
+ class_label_one_hot = self.conv7(class_label_one_hot) # (batch_size, num_categoties, 1) -> (batch_size, 64, 1)
- x = torch.cat((x, class_label), dim=1) # (batch_size, 1088, 1)
+ x = torch.cat((x, class_label_one_hot), dim=1) # (batch_size, 1088, 1)
x = x.repeat(1, 1, num_points) # (batch_size, 1088, num_points)
x = torch.cat((x, x1, x2), dim=1) # (batch_size, 1088+64*3, num_points)
diff --git a/dgcnn/shapenet_data_dgcnn.py b/dgcnn/shapenet_data_dgcnn.py
index d518a639e1fb2a97cd4ed6379664b5ff2820188f..71fb5f89c8282419bc70f5c1562c32417897a0a5 100644
--- a/dgcnn/shapenet_data_dgcnn.py
+++ b/dgcnn/shapenet_data_dgcnn.py
@@ -17,7 +17,7 @@ class ShapenetDataDgcnn(object):
small_data=False,
small_data_size=10,
return_cls_label=False,
- num_classes=1, # None - all classes (50), 1 - one class, 2 - two classes, max 4
+ num_classes=1, # you can choose 1, 2, 3, 4, or 16
norm=False,
augmnetation=False,
data_augmentation=False
@@ -241,12 +241,15 @@ class ShapenetDataDgcnn(object):
class_name = self.val_data_file[index].split('/')[-2]
# apply the mapper
- # if self.num_classes:
- # class_name = self.class_mapper_4_classes(class_name)
- # else:
- # class_name = self.class_mapper(class_name)
+ if self.num_classes in range(1, 5):
+ class_name = self.class_mapper_4_classes(class_name)
+ elif self.num_classes == 16:
+ class_name = self.class_mapper(class_name)
+ else:
+ raise ValueError('num_classes not in range, should be in range 1-4 or 16')
+
- class_name = self.class_mapper(class_name)
+ # class_name = self.class_mapper(class_name)
# convert the class name to a number
class_name = np.array(class_name, dtype=np.int64)
@@ -254,6 +257,7 @@ class ShapenetDataDgcnn(object):
# map to tensor
# class_name = torch.from_numpy(class_name)
+
if self.return_cls_label:
return point_set, labels, class_name
else: