Predict on images that are different size than training data

From the steps in the ‘DeepPoseKit Step 3 - Train a model’ I have trained a model for locust pose mapping. However, the data used in the annotation set is 160x160. When following the steps in the ‘DeepPoseKit Step 4b - Predict on new data’ notebook, I get the following issue:

reader = VideoReader(HOME + '/Data/crop.mp4', batch_size=50, gray=True) predictions = model.predict(reader, verbose=1) reader.close()

`--------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-11-d79f7abc10a7> in <module> 1 reader = VideoReader(HOME + ‘/Data/crop.mp4’, batch_size=50, gray=True) ----> 2 predictions = model.predict(reader, verbose=1) 3 reader.close()

~\Anaconda3\lib\site-packages\tensorflow_core\python\keras\engine\training.py in predict(self, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing) 907 max_queue_size=max_queue_size, 908 workers=workers, –> 909 use_multiprocessing=use_multiprocessing) 910 911 def reset_metrics(self):

~\Anaconda3\lib\site-packages\tensorflow_core\python\keras\engine\training_generator.py in predict(self, model, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing) 646 max_queue_size=max_queue_size, 647 workers=workers, –> 648 use_multiprocessing=use_multiprocessing) 649 650

~\Anaconda3\lib\site-packages\tensorflow_core\python\keras\engine\training_generator.py in model_iteration(model, data, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch, mode, batch_size, steps_name, **kwargs) 263 264 is_deferred = not model._is_compiled –> 265 batch_outs = batch_function(*batch_data) 266 if not isinstance(batch_outs, list): 267 batch_outs = [batch_outs]

~\Anaconda3\lib\site-packages\tensorflow_core\python\keras\engine\training_generator.py in predict_on_batch(x, y, sample_weights) 533 # 1, 2, or 3-tuples from generator 534 def predict_on_batch(x, y=None, sample_weights=None): # pylint: disable=unused-argument –> 535 return model.predict_on_batch(x) 536 537 f = predict_on_batch

~\Anaconda3\lib\site-packages\tensorflow_core\python\keras\engine\training.py in predict_on_batch(self, x) 1142 # Validate and standardize user data. 1143 inputs, _, _ = self._standardize_user_data( -> 1144 x, extract_tensors_from_dataset=True) 1145 # If self._distribution_strategy is True, then we are in a replica context 1146 # at this point.

~\Anaconda3\lib\site-packages\tensorflow_core\python\keras\engine\training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, batch_size, check_steps, steps_name, steps, validation_split, shuffle, extract_tensors_from_dataset) 2470 feed_input_shapes, 2471 check_batch_axis=False, # Don’t enforce the batch size. -> 2472 exception_prefix=‘input’) 2473 2474 # Get typespecs for the input data and sanitize it if necessary.

~\Anaconda3\lib\site-packages\tensorflow_core\python\keras\engine\training_utils.py in standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix) 572 ': expected ’ + names[i] + ’ to have shape ’ + 573 str(shape) + ’ but got array with shape ’ + –> 574 str(data_shape)) 575 return data 576

ValueError: Error when checking input: expected input_1 to have shape (160, 160, 1) but got array with shape (512, 512, 1)`

I assume that this is because the model was trained on 160x160 pixel images, while I would like to predict on 512x512 images. I have tried to resize the densenet model with Keras using the following methods:

from kerassurgeon.operations import delete_layer, insert_layer my_input_tensor = Input(input_shape=(512, 512, 1)) from tfkerassurgeon import delete_layer, insert_layer model = delete_layer(model.layers[0]) model = insert_layer(model.layers[0], my_input_tensor) along with keras.add, model.layers, and model.inputs to no avail.

Is there an easy way to resize the model for inference/prediction? Or will I have to re-train the model on 512x512 images?

Thanks!

[jgraving]

Hi, currently the API only supports predicting on images that are the same size as the training images, but this will be changed in a coming update. However, this is not hard to do using the Keras API. Here is some code that should accomplish what you want:

from deepposekit.models import load_model
import tensorflow as tf

model = load_model("/path/to/saved/model.h5")
predict_model = model.predict_model
predict_model.layers.pop(0) # remove current input layer

inputs = tf.keras.layers.Input((512, 512, 1))
outputs = predict_model(inputs)
predict_model = tf.keras.Model(inputs, outputs)

x = np.random.randint(0, 255, (16, 512, 512, 1), dtype=np.uint8) 
prediction = predict_model.predict(x, verbose=True)

[jgraving]

Hi, I’m not sure exactly what you mean, but currently all of the images in the training set must be the same size.