From 38189e2e473f0c86498f947ab9def9875312aba8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Joakim=20Fogelstr=C3=B6m?= Date: Thu, 9 Sep 2021 13:44:17 +0200 Subject: [PATCH] new model the export in perceptilabs now also includes preprocessing, so normalisation is removed from code now --- inference.py | 2 +- model/keras_metadata.pb | 297 +++++++++--------- model/saved_model.pb | Bin 5364056 -> 5813724 bytes model/variables/variables.data-00000-of-00001 | Bin 12014083 -> 15125366 bytes model/variables/variables.index | Bin 9199 -> 10103 bytes 5 files changed, 157 insertions(+), 142 deletions(-) diff --git a/inference.py b/inference.py index a276a43..e3e34ee 100644 --- a/inference.py +++ b/inference.py @@ -60,7 +60,7 @@ async def predict(bild: bytes = File(...)): for b in range(block_rows): for a in range(block_cols): pred_in = img_array[b,a,:,:].reshape(1, 224, 224, 3) # pick out the block to be predicted - predict = np.asarray(model(pred_in / norm)['target']).reshape(224, 224, 3) # make the prediction and reshape output (omit batch) + predict = np.asarray(model(pred_in)['target']).reshape(224, 224, 3) # make the prediction and reshape output (omit batch) img_array_out[(b * 208):(b * 208) + 208, (a * 208):(a * 208) + 208, :] = predict[8:216, 8:216, :] # put the predicted block in it's right place in the new array img_out = tf.keras.preprocessing.image.array_to_img(img_array_out) # convert array to image img_out = img_out.crop((0, 0, W, H)) # crop out the padding diff --git a/model/keras_metadata.pb b/model/keras_metadata.pb index 8d18255..55bee3d 100644 --- a/model/keras_metadata.pb +++ b/model/keras_metadata.pb @@ -1,198 +1,213 @@ -Ð -root"_tf_keras_network*® -{"name": "model", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "must_restore_from_config": false, "class_name": "Functional", "config": {"name": "model", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": {"class_name": "__tuple__", "items": 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null, "min_ndim": null, "axes": {"3": 256}}, "shared_object_id": 397}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 28, 28, 256]}}2 +‘âXroot.layer_with_weights-0._layers_by_id.1630950847245.unet.layer_with_weights-0.layer-41"_tf_keras_layer*š{"name": "conv3_block1_preact_relu", "trainable": false, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Activation", "config": {"name": "conv3_block1_preact_relu", "trainable": false, "dtype": "float32", "activation": "relu"}, "inbound_nodes": [[["conv3_block1_preact_bn", 0, 0, {}]]], "shared_object_id": 111}2 ÷ -³eroot.layer_with_weights-0._layers_by_id.1630950847245.unet.layer_with_weights-0.layer_with_weights-21"_tf_keras_layer*ó {"name": "conv3_block1_1_conv", "trainable": false, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": 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