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11.py
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11.py
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#import ads3 as ads3
import ads3_pt_profile as ads3
import torch
import os
from pathlib import Path
from torch.utils import data as D
from nvidia.dali.plugin.pytorch import DALIGenericIterator, LastBatchPolicy
from util_11 import create_pipeline_perspective, create_pipeline_no_perspective
torch.manual_seed(0)
INPUT_SIZE = 224
root = Path("data")
file_train = root / "train.txt"
folder_images = root / "image"
images_train = root / "image_train"
images_valid = root / "image_valid"
class DALIDataset():
def __init__(self, path, batch_size, num_workers):
self.path = path
self.batch_size = batch_size
self.num_workers = num_workers
pipeline = create_pipeline_no_perspective(
self.batch_size,
self.num_workers,
self.path
)
self.dataset = DALIGenericIterator(
pipeline,
["data", "label"],
reader_name="Reader",
last_batch_policy=LastBatchPolicy.PARTIAL,
)
def __len__(self):
return sum([len(files) for r, d, files in os.walk(self.path)])
if __name__ == "__main__":
"""Initialise dataset"""
labels = ads3.get_labels()
log_name = "results/11.csv"
loader_train = DALIDataset(images_train, 80, 1)
loader_valid = DALIDataset(images_valid, 80, 1)
ads3.run_experiment(
loader_train, loader_valid, log_name
) # For profiling feel free to lower epoch count via epoch=X