From b2a1a55c151b687ac303d3379153ff6394266d10 Mon Sep 17 00:00:00 2001
From: Jannis Klinkenberg <j.klinkenberg@itc.rwth-aachen.de>
Date: Sat, 9 Nov 2024 18:42:43 +0100
Subject: [PATCH] implemented resizing
---
tensorflow/cifar10/train_model.py | 39 +++++++++++-------
tensorflow/cifar10_distributed/train_model.py | 40 ++++++++++++-------
2 files changed, 49 insertions(+), 30 deletions(-)
diff --git a/tensorflow/cifar10/train_model.py b/tensorflow/cifar10/train_model.py
index 1251e61..edf6ba4 100644
--- a/tensorflow/cifar10/train_model.py
+++ b/tensorflow/cifar10/train_model.py
@@ -50,13 +50,28 @@ def load_dataset(args):
x_train -= x_train_mean
x_test -= x_train_mean
- print("x_train shape:", x_train.shape)
- print("y_train shape:", y_train.shape)
- print(x_train.shape[0], "train samples")
- print(x_test.shape[0], "test samples")
- sys.stdout.flush()
+ # dimensions
+ print(f"original train_shape: {x_train.shape}")
+ print(f"original test_shape: {x_test.shape}")
+ n_train, n_test = x_train.shape[0], x_test.shape[0]
+ resize_size = 224 # use bigger images with ResNet
- return (x_train, y_train), (x_test, y_test)
+ # Generating input pipelines
+ ds_train = (tf.data.Dataset.from_tensor_slices((x_train, y_train))
+ .map(lambda image, label: (tf.image.resize(image, [resize_size, resize_size]), label))
+ .shuffle(n_train).cache().batch(args.global_batches).prefetch(tf.data.AUTOTUNE)
+ )
+ ds_test = (tf.data.Dataset.from_tensor_slices((x_test, y_test))
+ .map(lambda image, label: (tf.image.resize(image, [resize_size, resize_size]), label))
+ .shuffle(n_test).cache().batch(args.global_batches).prefetch(tf.data.AUTOTUNE)
+ )
+
+ # get updated shapes
+ train_shape, test_shape = ds_train.element_spec[0].shape, ds_test.element_spec[0].shape
+ print(f"final train_shape: {train_shape}")
+ print(f"final test_shape: {test_shape}")
+
+ return ds_train, ds_test, train_shape
def setup(args):
if args.num_intraop_threads:
@@ -84,19 +99,13 @@ def main():
# run setup (e.g., create distributed environment if desired)
setup(args)
- # data set loading
- (x_train, y_train), (x_test, y_test) = load_dataset(args)
- n_train, n_test = x_train.shape[0], x_test.shape[0]
- input_shape = x_train.shape[1:]
-
- # Generating input pipelines
- ds_train = tf.data.Dataset.from_tensor_slices((x_train, y_train)).shuffle(n_train).cache().batch(args.batch_size).prefetch(tf.data.AUTOTUNE)
- ds_test = ds_test = tf.data.Dataset.from_tensor_slices((x_test, y_test)).shuffle(n_test).cache().batch(args.batch_size).prefetch(tf.data.AUTOTUNE)
+ # loading desired dataset
+ ds_train, ds_test, train_shape = load_dataset(args)
# callbacks to register
callbacks = []
- model = applications.ResNet50(weights=None, input_shape=input_shape, classes=args.num_classes)
+ model = applications.ResNet50(weights=None, input_shape=train_shape[1:], classes=args.num_classes)
# model.summary() # display the model architecture
cur_optimizer = Adam(0.001)
model.compile(loss="categorical_crossentropy", optimizer=cur_optimizer, metrics=["accuracy"])
diff --git a/tensorflow/cifar10_distributed/train_model.py b/tensorflow/cifar10_distributed/train_model.py
index ab0c3c0..42932f2 100644
--- a/tensorflow/cifar10_distributed/train_model.py
+++ b/tensorflow/cifar10_distributed/train_model.py
@@ -68,14 +68,30 @@ def load_dataset(args):
x_train -= x_train_mean
x_test -= x_train_mean
+ # dimensions
if args.world_rank == 0:
- print("x_train shape:", x_train.shape)
- print("y_train shape:", y_train.shape)
- print(x_train.shape[0], "train samples")
- print(x_test.shape[0], "test samples")
- sys.stdout.flush()
+ print(f"original train_shape: {x_train.shape}")
+ print(f"original test_shape: {x_test.shape}")
+ n_train, n_test = x_train.shape[0], x_test.shape[0]
+ resize_size = 224 # use bigger images with ResNet
- return (x_train, y_train), (x_test, y_test)
+ # Generating input pipelines
+ ds_train = (tf.data.Dataset.from_tensor_slices((x_train, y_train))
+ .map(lambda image, label: (tf.image.resize(image, [resize_size, resize_size]), label))
+ .shuffle(n_train).cache().batch(args.global_batches).prefetch(tf.data.AUTOTUNE)
+ )
+ ds_test = (tf.data.Dataset.from_tensor_slices((x_test, y_test))
+ .map(lambda image, label: (tf.image.resize(image, [resize_size, resize_size]), label))
+ .shuffle(n_test).cache().batch(args.global_batches).prefetch(tf.data.AUTOTUNE)
+ )
+
+ # get updated shapes
+ train_shape, test_shape = ds_train.element_spec[0].shape, ds_test.element_spec[0].shape
+ if args.world_rank == 0:
+ print(f"final train_shape: {train_shape}")
+ print(f"final test_shape: {test_shape}")
+
+ return ds_train, ds_test, train_shape
def setup(args):
if args.num_intraop_threads:
@@ -115,20 +131,14 @@ def main():
# run setup (e.g., create distributed environment if desired)
strategy = setup(args)
- # data set loading
- (x_train, y_train), (x_test, y_test) = load_dataset(args)
- n_train, n_test = x_train.shape[0], x_test.shape[0]
- input_shape = x_train.shape[1:]
-
- # Generating input pipelines
- ds_train = tf.data.Dataset.from_tensor_slices((x_train, y_train)).shuffle(n_train).cache().batch(args.global_batches).prefetch(tf.data.AUTOTUNE)
- ds_test = ds_test = tf.data.Dataset.from_tensor_slices((x_test, y_test)).shuffle(n_test).cache().batch(args.global_batches).prefetch(tf.data.AUTOTUNE)
+ # loading desired dataset
+ ds_train, ds_test, train_shape = load_dataset(args)
# callbacks to register
callbacks = []
with strategy.scope():
- model = applications.ResNet50(weights=None, input_shape=input_shape, classes=args.num_classes)
+ model = applications.ResNet50(weights=None, input_shape=train_shape[1:], classes=args.num_classes)
# model.summary() # display the model architecture
cur_optimizer = Adam(0.001)
model.compile(loss="categorical_crossentropy", optimizer=cur_optimizer, metrics=["accuracy"])
--
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