generated from fastai/nbdev_template
/
datagenerator.py
216 lines (179 loc) · 6.32 KB
/
datagenerator.py
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import os
import pathlib
import random
import time
from functools import partial
from glob import glob
from pathlib import Path
from typing import Callable, Union
import tensorflow as tf
from typeguard import check_argument_types, typechecked
from chitra.image.tf_image import read_image, resize_image
def benchmark(dataset, num_epochs=2, fake_infer_time=0.001):
"""Use this function to benchmark your Dataset loading time."""
start_time = time.perf_counter()
for _ in range(num_epochs):
for _ in dataset:
# Performing a training step
time.sleep(fake_infer_time)
tf.print(
f"Execution time for {num_epochs} epochs: {time.perf_counter() - start_time :0.3f} seconds"
)
def get_filenames(root_dir):
root_dir = pathlib.Path(root_dir)
return glob(str(root_dir / "*/*"))
def get_label(filename):
return filename.split("/")[-2]
class ImageSizeList:
def __init__(self, img_sz_list=None):
if (
isinstance(img_sz_list, (list, tuple))
and len(img_sz_list) != 0
and not isinstance(img_sz_list[0], (list, tuple))
):
img_sz_list = [img_sz_list][:]
self.start_size = None
self.last_size = None
self.curr_size = None
self.img_sz_list = img_sz_list
try:
self.start_size = img_sz_list[0]
self.last_size = img_sz_list[-1]
self.curr_size = img_sz_list[0]
except (IndexError, TypeError):
print("No item present in the image size list")
self.curr_size = None # no item present in the list
def get_size(self):
img_sz_list = self.img_sz_list
try:
self.curr_size = img_sz_list.pop(0)
except (IndexError, AttributeError):
print(f"Returning the last set size which is: {self.curr_size}")
return self.curr_size
# Cell
class Pipeline:
@typechecked
def __init__(self, funcs: Union[Callable, list, tuple] = None):
if not check_argument_types():
raise AssertionError
if isinstance(funcs, list):
self.funcs = funcs
elif callable(funcs):
self.funcs = [funcs]
else:
self.funcs = []
@typechecked
def add(self, func: Callable):
if not check_argument_types():
raise AssertionError
self.funcs.append(func)
def __call__(self, item):
try:
for func in self.funcs:
item = func(item)
except Exception as e:
print("Error while applying function in pipeline!")
raise e
return item
class Dataset:
MAPPINGS = {
"PY_TO_TF": {str: tf.string, int: tf.int32, float: tf.float32},
}
def __init__(
self,
train_dir: Union[str, Path],
image_size=None,
transforms=None,
default_encode=True,
**kwargs,
):
"""
Create a Dataset object that can generate tf.data.Dataset
Args:
train_dir:
image_size:
transforms:
default_encode:
**kwargs:
"""
self.get_filenames = get_filenames
self.read_image = read_image
self.get_label = get_label
self.transforms = transforms
self.root_dir = train_dir
self.default_encode = default_encode
self.filenames = self.get_filenames(train_dir)
self.num_files = len(self.filenames)
self.image_size = image_size
self.img_sz_list = ImageSizeList(self.image_size)
self.labels = kwargs.get("labels", self.get_labels())
def __len__(self):
return len(self.filenames)
def _process(self, filename):
image = self.read_image(filename)
label = self.get_label(filename)
return image, label
def _reload(self):
image_size = self.image_size[:]
self.filenames = self.get_filenames(self.root_dir)
self.num_files = len(self.filenames)
self.img_sz_list = ImageSizeList(image_size)
self.labels = self.get_labels()
def _capture_return_types(self):
return_types = []
for e in self.generator():
outputs = e
break
if isinstance(outputs, tuple):
for ret_type in outputs:
return_types.append(
ret_type.dtype
if tf.is_tensor(ret_type)
else Dataset.MAPPINGS["PY_TO_TF"][type(ret_type)]
)
else:
raise UserWarning("Unable to capture return type!")
return tuple(return_types)
def __getitem__(self, idx):
filename = self.filenames[idx]
return self._process(filename)
def update_component(self, component_name, new_component):
setattr(self, component_name, new_component)
print(f"{component_name} updated with {new_component}")
self._reload()
def get_labels(self):
# get labels should also update self.num_classes
root_dir = self.root_dir
labels = set()
folders = glob(f"{root_dir}/*")
for folder in folders:
labels.add(os.path.basename(folder))
labels = sorted(labels)
self.NUM_CLASSES = len(labels)
self.label_to_idx = {label: i for i, label in enumerate(labels)}
return labels
def label_encoder(self, label):
idx = self.label_to_idx.get(label, None)
if idx is None:
raise AssertionError(f"Error while converting label={label} to index!")
return idx
def generator(self, shuffle=False):
if shuffle:
random.shuffle(self.filenames)
img_sz = self.img_sz_list.get_size()
n = len(self.filenames)
for i in range(n):
image, label = self.__getitem__(i)
if img_sz:
image = resize_image(image, img_sz)
if self.transforms:
image = self.transforms(image)
if self.default_encode is True:
label = self.label_encoder(label)
yield image, label
def get_tf_dataset(self, output_shape=None, shuffle=True):
return_types = self._capture_return_types()
self._reload()
generator = partial(self.generator, shuffle=shuffle)
datagen = tf.data.Dataset.from_generator(generator, return_types, output_shape)
return datagen