I am trying to shape my own custom image dataset into the correct input shape for the pretrained MobileNet model on Tensorflow using their tutorial here. My code:
batch_size = 256
epochs = 15
IMG_HEIGHT = 160
IMG_WIDTH = 160
BATCH_SIZE = 256
SHUFFLE_BUFFER_SIZE = 1000
traindir = pathlib.Path('/train')
valdir = pathlib.Path('/validation')
list_ds = tf.data.Dataset.list_files(str(traindir/'*/*'))
val_list_ds = tf.data.Dataset.list_files(str(valdir/'*/*'))
CLASS_NAMES = np.array([item.name for item in valdir.glob('*') if item.name != "LICENSE.txt"])
def get_label(file_path):
# convert the path to a list of path components
parts = tf.strings.split(file_path, os.path.sep)
# The second to last is the class-directory
return parts[-2] == CLASS_NAMES
def decode_img(img):
# convert the compressed string to a 3D uint8 tensor
img = tf.image.decode_jpeg(img, channels=3)
# Use `convert_image_dtype` to convert to floats in the [0,1] range.
img = tf.image.convert_image_dtype(img, tf.float32)
# resize the image to the desired size.
return tf.image.resize(img, [IMG_HEIGHT, IMG_WIDTH])
def process_path(file_path):
label = get_label(file_path)
# load the raw data from the file as a string
img = tf.io.read_file(file_path)
img = decode_img(img)
return img, label
# Set `num_parallel_calls` so multiple images are loaded/processed in parallel.
labeled_ds = list_ds.map(process_path, num_parallel_calls=5)
labeled_val_ds = val_list_ds.map(process_path, num_parallel_calls=5)
train_batches = labeled_ds.shuffle(SHUFFLE_BUFFER_SIZE).batch(BATCH_SIZE)
validation_batches = labeled_val_ds.batch(BATCH_SIZE)
for image_batch, label_batch in train_batches.take(1):
pass
image_batch.shape
After which I continue with the TF tutorial on transfer learning here. However, I ran into this problem where I suspect the JPEG image is corrupted or there is a lack of/problem with the iterator?:
Epoch 1/10
21/330 [>.............................] - ETA: 14:02 - loss: 3.9893 - accuracy: 0.0326
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
<ipython-input-87-11afdc6d5aef> in <module>
1 history = model.fit(train_batches,
2 epochs=initial_epochs,
----> 3 validation_data=validation_batches)
~AppDataRoamingPythonPython37site-packagestensorflowpythonkerasenginetraining.py in _method_wrapper(self, *args, **kwargs)
64 def _method_wrapper(self, *args, **kwargs):
65 if not self._in_multi_worker_mode(): # pylint: disable=protected-access
---> 66 return method(self, *args, **kwargs)
67
68 # Running inside `run_distribute_coordinator` already.
~AppDataRoamingPythonPython37site-packagestensorflowpythonkerasenginetraining.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)
846 batch_size=batch_size):
847 callbacks.on_train_batch_begin(step)
--> 848 tmp_logs = train_function(iterator)
849 # Catch OutOfRangeError for Datasets of unknown size.
850 # This blocks until the batch has finished executing.
~AppDataRoamingPythonPython37site-packagestensorflowpythoneagerdef_function.py in __call__(self, *args, **kwds)
578 xla_context.Exit()
579 else:
--> 580 result = self._call(*args, **kwds)
581
582 if tracing_count == self._get_tracing_count():
~AppDataRoamingPythonPython37site-packagestensorflowpythoneagerdef_function.py in _call(self, *args, **kwds)
609 # In this case we have created variables on the first call, so we run the
610 # defunned version which is guaranteed to never create variables.
--> 611 return self._stateless_fn(*args, **kwds) # pylint: disable=not-callable
612 elif self._stateful_fn is not None:
613 # Release the lock early so that multiple threads can perform the call
~AppDataRoamingPythonPython37site-packagestensorflowpythoneagerfunction.py in __call__(self, *args, **kwargs)
2418 with self._lock:
2419 graph_function, args, kwargs = self._maybe_define_function(args, kwargs)
-> 2420 return graph_function._filtered_call(args, kwargs) # pylint: disable=protected-access
2421
2422 @property
~AppDataRoamingPythonPython37site-packagestensorflowpythoneagerfunction.py in _filtered_call(self, args, kwargs)
1663 if isinstance(t, (ops.Tensor,
1664 resource_variable_ops.BaseResourceVariable))),
-> 1665 self.captured_inputs)
1666
1667 def _call_flat(self, args, captured_inputs, cancellation_manager=None):
~AppDataRoamingPythonPython37site-packagestensorflowpythoneagerfunction.py in _call_flat(self, args, captured_inputs, cancellation_manager)
1744 # No tape is watching; skip to running the function.
1745 return self._build_call_outputs(self._inference_function.call(
-> 1746 ctx, args, cancellation_manager=cancellation_manager))
1747 forward_backward = self._select_forward_and_backward_functions(
1748 args,
~AppDataRoamingPythonPython37site-packagestensorflowpythoneagerfunction.py in call(self, ctx, args, cancellation_manager)
596 inputs=args,
597 attrs=attrs,
--> 598 ctx=ctx)
599 else:
600 outputs = execute.execute_with_cancellation(
~AppDataRoamingPythonPython37site-packagestensorflowpythoneagerexecute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
58 ctx.ensure_initialized()
59 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
---> 60 inputs, attrs, num_outputs)
61 except core._NotOkStatusException as e:
62 if name is not None:
InvalidArgumentError: 2 root error(s) found.
(0) Invalid argument: Invalid JPEG data or crop window, data size 34228
[[{{node DecodeJpeg}}]]
[[IteratorGetNext]]
(1) Invalid argument: Invalid JPEG data or crop window, data size 34228
[[{{node DecodeJpeg}}]]
[[IteratorGetNext]]
[[IteratorGetNext/_4]]
0 successful operations.
0 derived errors ignored. [Op:__inference_train_function_30787]
Function call stack:
train_function -> train_function
Thank you for your time! Edit: After re-running the code a few times, it seems it will produce the same errors with different data sizes like 16384….
Edit: Yes, the problem lies with the fact that some .jpeg are actually .png in disguise, or they are just plain corrupted. I highly recommend checking data integrity before training any model with the data.
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Answer
I faced similar problem. there is a problem in some of your training data. you can use code below to check which jpeg image is corrupted and delete it.
from struct import unpack
from tqdm import tqdm
import os
marker_mapping = {
0xffd8: "Start of Image",
0xffe0: "Application Default Header",
0xffdb: "Quantization Table",
0xffc0: "Start of Frame",
0xffc4: "Define Huffman Table",
0xffda: "Start of Scan",
0xffd9: "End of Image"
}
class JPEG:
def __init__(self, image_file):
with open(image_file, 'rb') as f:
self.img_data = f.read()
def decode(self):
data = self.img_data
while(True):
marker, = unpack(">H", data[0:2])
# print(marker_mapping.get(marker))
if marker == 0xffd8:
data = data[2:]
elif marker == 0xffd9:
return
elif marker == 0xffda:
data = data[-2:]
else:
lenchunk, = unpack(">H", data[2:4])
data = data[2+lenchunk:]
if len(data)==0:
break
bads = []
for img in tqdm(images):
image = osp.join(root_img,img)
image = JPEG(image)
try:
image.decode()
except:
bads.append(img)
for name in bads:
os.remove(osp.join(root_img,name))
I used yasoob script to decode jpeg image.