Please provide the following information when requesting support.
Error is for the output folder
PermissionError: [Errno 13] Permission denied: ‘~/tao-getting-started_v5.2.0/notebooks/tao_launcher_starter_kit/classification_tf1/output’
Execution status: FAIL
• Hardware (T4/V100/Xavier/Nano/etc) A6000
• Network Type classification resnet and mobilenet_v2
• TLT Version (Please run “tlt info --verbose” and share “docker_tag” here)
• Training spec file(If have, please share here)
• How to reproduce the issue ? try the tao train classification_tf1
I have tried many ways to fix this issue and I keep getting it. I have done chmod 777 for that folder and for higher up folders. I have tried using python 3.6.9 and 3.8.19
model_config {
Model Architecture can be chosen from:
[‘resnet’, ‘vgg’, ‘googlenet’, ‘alexnet’]
arch: “resnet”
for resnet → n_layers can be [10, 18, 50]
for vgg → n_layers can be [16, 19]
n_layers: 101
use_batch_norm: True
use_bias: False
all_projections: False
use_pooling: True
retain_head: True
resize_interpolation_method: BICUBIC
if you want to use the pretrained model,
image size should be “3,224,224”
otherwise, it can be “3, X, Y”, where X,Y >= 16
input_image_size: “3,224,224”
}
train_config {
train_dataset_path: “~/tao-getting-started_v5.2.0/notebooks/tao_launcher_starter_kit/classification_tf1/data/split/train”
val_dataset_path: “~/tao-getting-started_v5.2.0/notebooks/tao_launcher_starter_kit/classification_tf1/data/split/val”
pretrained_model_path: ~/tao-getting-started_v5.2.0/notebooks/tao_launcher_starter_kit/classification_tf1/classification_tf1/pretrained_resnet18/pretrained_classification_vresnet18/resnet_18.hdf5"
Only [‘sgd’, ‘adam’] are supported for optimizer
optimizer {
sgd {
lr: 0.01
decay: 0.0
momentum: 0.9
nesterov: False
}
}
batch_size_per_gpu: 50
n_epochs: 150
Number of CPU cores for loading data
n_workers: 16
regularizer
reg_config {
# regularizer type can be “L1”, “L2” or “None”.
type: “L2”
# if the type is not “None”,
# scope can be either “Conv2D” or “Dense” or both.
scope: “Conv2D,Dense”
# 0 < weight decay < 1
weight_decay: 0.000015
}
learning_rate
lr_config {
cosine {
learning_rate: 0.04
soft_start: 0.0
}
}
enable_random_crop: True
enable_center_crop: True
enable_color_augmentation: True
mixup_alpha: 0.2
label_smoothing: 0.1
preprocess_mode: “caffe”
image_mean {
key: ‘b’
value: 103.9
}
image_mean {
key: ‘g’
value: 116.8
}
image_mean {
key: ‘r’
value: 123.7
}
}
eval_config {
eval_dataset_path: “/path/to/your/test/data”
model_path: “/workspace/tao-experiments/classification/weights/resnet_080.tlt”
top_k: 3
batch_size: 256
n_workers: 8
enable_center_crop: True
}