I'm building a U-Net model to detect breast cancer, I got the dataset from here: https://www.kaggle.com/datasets/aryashah2k/breast-ultrasound-images-dataset Even though, all images are of format png, when trying to training my model an error occurs that my images are not of adequate format. here is the error: --------------------------------------------------------------------------- InvalidArgumentError Traceback (most recent call last) Cell In[51], line 7 5 train_dataset = image_ds.cache().shuffle(BUFFER_SIZE).batch(BATCH_SIZE) 6 print(image_ds.element_spec) ----> 7 model_history = unet.fit(train_dataset, epochs=EPOCHS) File ~\anaconda3\Lib\site-packages\keras\src\utils\traceback_utils.py:122, in filter_traceback.<locals>.error_handler(*args, **kwargs) 119 filtered_tb = _process_traceback_frames(e.__traceback__) 120 # To get the full stack trace, call: 121 # `keras.config.disable_traceback_filtering()` --> 122 raise e.with_traceback(filtered_tb) from None 123 finally: 124 del filtered_tb File ~\anaconda3\Lib\site-packages\tensorflow\python\eager\execute.py:53, in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name) 51 try: 52 ctx.ensure_initialized() ---> 53 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name, 54 inputs, attrs, num_outputs) 55 except core._NotOkStatusException as e: 56 if name is not None: InvalidArgumentError: Graph execution error: Detected at node decode_image/DecodeImage defined at (most recent call last): <stack traces unavailable> Error in user-defined function passed to MapDataset:3 transformation with iterator: Iterator::Root:refetch::BatchV2::Shuffle::MemoryCacheImpl::Filter:arallelMapV2: Unknown image file format. One of JPEG, PNG, GIF, BMP required. [[{{node decode_image/DecodeImage}}]] [[IteratorGetNext]] [Op:__inference_one_step_on_iterator_9520] The erro only occurs when trying to train the model, and it refers to this function: def preprocess_image(image, mask, target_size=(256, 256)): try: # Decode the image and mask safely image = tf.io.decode_image(image, channels=3, expand_animations=False) mask = tf.io.decode_image(mask, channels=1, expand_animations=False) # Check for undefined or zero dimensions if image.shape is None or image.shape[0] == 0 or image.shape[1] == 0: print(f"Error: Image has undefined or zero dimensions: {image.shape}") return None, None if mask.shape is None or mask.shape[0] == 0 or mask.shape[1] == 0: print(f"Error: Mask has undefined or zero dimensions: {mask.shape}") return None, None # Ensure the image has exactly 3 channels (RGB) if image.shape[-1] != 3: print(f"Error: Image does not have 3 channels (found {image.shape[-1]}).") return None, None # Normalize the image to range [0, 1] image = tf.image.convert_image_dtype(image, tf.float32) # Resize image and mask to target dimensions (256*256) image = tf.image.resize(image, target_size, method='nearest') mask = tf.image.resize(mask, target_size, method='nearest') # Convert mask to a binary (0 or 1) format for classification tasks mask = tf.cast(tf.math.reduce_max(mask, axis=-1, keepdims=True) > 0, tf.float32) # Ensure binary mask return image, mask except Exception as e: print(f"Error during preprocessing: {str(e)}") return None, None # Apply the preprocessing function to the dataset image_ds = dataset.map(preprocess_image) # Filter out None values returned by preprocess_image image_ds = image_ds.filter(lambda img, mask: img is not None and mask is not None) I tried multiple ways trying to fix this using chatgpt but nothing seems to work. Continue reading...
