Hello all,
I would appreciate if anyone whats to help me out with the following issue. I’ve been following this guide in order to create a faster-rcnn model. I’ve managed to do this successfully once before and wanted to use the same code i’ve written before but fit it on a different dataset. However this time I get the following error thrown at me:
File "run.py", line 122, in <module>
train()
File "run.py", line 81, in train
loss_dict = model(images, targets)
File "/Users/Luuk/Desktop/thesisvenv/venv/lib/python3.7/site-packages/torch/nn/modules/module.py", line 550, in __call__
result = self.forward(*input, **kwargs)
File "/Users/Luuk/Desktop/thesisvenv/venv/lib/python3.7/site-packages/torchvision/models/detection/generalized_rcnn.py", line 71, in forward
detections, detector_losses = self.roi_heads(features, proposals, images.image_sizes, targets)
File "/Users/Luuk/Desktop/thesisvenv/venv/lib/python3.7/site-packages/torch/nn/modules/module.py", line 550, in __call__
result = self.forward(*input, **kwargs)
File "/Users/Luuk/Desktop/thesisvenv/venv/lib/python3.7/site-packages/torchvision/models/detection/roi_heads.py", line 748, in forward
proposals, matched_idxs, labels, regression_targets = self.select_training_samples(proposals, targets)
File "/Users/Luuk/Desktop/thesisvenv/venv/lib/python3.7/site-packages/torchvision/models/detection/roi_heads.py", line 659, in select_training_samples
sampled_inds = self.subsample(labels)
File "/Users/Luuk/Desktop/thesisvenv/venv/lib/python3.7/site-packages/torchvision/models/detection/roi_heads.py", line 610, in subsample
sampled_pos_inds, sampled_neg_inds = self.fg_bg_sampler(labels)
File "/Users/Luuk/Desktop/thesisvenv/venv/lib/python3.7/site-packages/torchvision/models/detection/_utils.py", line 67, in __call__
pos_idx_per_image = positive[perm1]
IndexError: index 1 is out of bounds for dimension 0 with size 1
This is what my train functions looks like up until the point of error:
def train():
# Train on GPU or CPU
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
print(f'Running on: {device}')
# Initiate training and test datasets
train_data = FacesDataset(df, IMG_DIR, transform=get_transforms(train=False))
val_data = FacesDataset(df, IMG_DIR, transform=get_transforms(train=False))
# Split dataset in train & test (use 250 images for test)
indices = torch.randperm(len(train_data)).tolist()
train_data = torch.utils.data.Subset(train_data, indices[:-250])
val_data = torch.utils.data.Subset(val_data, indices[-250:])
print(f'Number of training examples: {len(train_data)}, Number of validation examples: {len(val_data)}')
# Initiate training and test data loaders
data_loader_train = torch.utils.data.DataLoader(train_data, batch_size=BATCH_SIZE,
shuffle=True, num_workers=NUM_WORKERS, collate_fn=collate_fn)
data_loader_val = torch.utils.data.DataLoader(val_data, batch_size=BATCH_SIZE,
shuffle=False, num_workers=NUM_WORKERS, collate_fn=collate_fn)
# Initiate model
model = initiate_model(3)
model.to(device)
# Get the learnable parameters of our model
params = [p for p in model.parameters() if p.requires_grad]
# Initaite optimizer on learnable parameters
optimizer = torch.optim.SGD(params, lr=0.005, momentum=0.9, weight_decay=0.0005)
# Set up a dynamic learning rate in order to converage faser
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer,
step_size=3,
gamma=0.1)
# Initiate averager class to keep track of losses
loss_hist = Averager()
val_loss_hist = Averager()
# Set to high value on purpose
least_loss = 10e6
# Allow model to learn parameters
model.train()
print(f'Starting training....')
for epoch in range(NUM_EPOCS):
loss_hist.reset()
val_loss_hist.reset()
itr = 1
for images, targets, img_id in data_loader_train:
images = list(image.to(device) for image in images)
targets = [{k: v.to(device) for k, v in t.items()} for t in targets]
loss_dict = model(images, targets)
In addition, this is what my dataset class looks like
class FacesDataset(Dataset):
"""Data class for facial images"""
def __init__(self, dataframe, img_dir, transform=None):
self.dataframe = dataframe
self.img_dir = img_dir
self.images = dataframe['image_id'].unique()
self.transform = transform
def __getitem__(self, idx):
target = {}
img_id = self.images[idx]
img_arr = cv2.imread(f'{self.img_dir}/{img_id}', cv2.IMREAD_COLOR)
img_arr = cv2.cvtColor(img_arr, cv2.COLOR_BGR2RGB).astype(np.uint8)
# img_arr = img_arr / 255
boxes_array = np.array(self.dataframe.loc[self.dataframe['image_id'] == img_id, ['x0', 'y0', 'x1', 'y1']])
boxes = torch.tensor(boxes_array, dtype=torch.float32)
target['boxes'] = boxes
area = []
for box in boxes:
width = box[2] - box[0]
height = box[3] - box[1]
area.append(width * height)
target['area'] = torch.tensor(area)
labels_array = np.array(self.dataframe.loc[self.dataframe['image_id'] == img_id, ['label']])
labels = torch.tensor(labels_array, dtype=torch.int64)
target['labels'] = labels
#occlusion_array = np.array(self.dataframe.loc[self.dataframe['image_id'] == img_id, ['occlusion']])
#occlusion = torch.tensor(occlusion_array, dtype=torch.float32)
#target['occlusion'] = occlusion
iscrowd = np.array(self.dataframe.loc[self.dataframe['image_id'] == img_id])
target['iscrowd'] = torch.zeros((len(iscrowd),), dtype=torch.int64)
if self.transform is not None:
img_arr = self.transform(img_arr)
return img_arr, target, img_id
def __len__(self):
return len(self.images)
The issue looks similar to this post however implementing that solution does not seem to resolve the issue for me.
