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target_h = bby2 - boundaryy1 |
if target_h == 0: |
target_h += 1 |
target_w = bbx2 - boundaryx1 |
if target_w == 0: |
target_w += 1 |
return boundaryx1, boundaryy1, target_h, target_w |
@tf.function |
def cutmix(train_ds_one, train_ds_two): |
(image1, label1), (image2, label2) = train_ds_one, train_ds_two |
alpha = [0.25] |
beta = [0.25] |
# Get a sample from the Beta distribution |
lambda_value = sample_beta_distribution(1, alpha, beta) |
# Define Lambda |
lambda_value = lambda_value[0][0] |
# Get the bounding box offsets, heights and widths |
boundaryx1, boundaryy1, target_h, target_w = get_box(lambda_value) |
# Get a patch from the second image (`image2`) |
crop2 = tf.image.crop_to_bounding_box( |
image2, boundaryy1, boundaryx1, target_h, target_w |
) |
# Pad the `image2` patch (`crop2`) with the same offset |
image2 = tf.image.pad_to_bounding_box( |
crop2, boundaryy1, boundaryx1, IMG_SIZE, IMG_SIZE |
) |
# Get a patch from the first image (`image1`) |
crop1 = tf.image.crop_to_bounding_box( |
image1, boundaryy1, boundaryx1, target_h, target_w |
) |
# Pad the `image1` patch (`crop1`) with the same offset |
img1 = tf.image.pad_to_bounding_box( |
crop1, boundaryy1, boundaryx1, IMG_SIZE, IMG_SIZE |
) |
# Modify the first image by subtracting the patch from `image1` |
# (before applying the `image2` patch) |
image1 = image1 - img1 |
# Add the modified `image1` and `image2` together to get the CutMix image |
image = image1 + image2 |
# Adjust Lambda in accordance to the pixel ration |
lambda_value = 1 - (target_w * target_h) / (IMG_SIZE * IMG_SIZE) |
lambda_value = tf.cast(lambda_value, tf.float32) |
# Combine the labels of both images |
label = lambda_value * label1 + (1 - lambda_value) * label2 |
return image, label |
Note: we are combining two images to create a single one. |
Visualize the new dataset after applying the CutMix augmentation |
# Create the new dataset using our `cutmix` utility |
train_ds_cmu = ( |
train_ds.shuffle(1024) |
.map(cutmix, num_parallel_calls=AUTO) |
.batch(BATCH_SIZE) |
.prefetch(AUTO) |
) |
# Let's preview 9 samples from the dataset |
image_batch, label_batch = next(iter(train_ds_cmu)) |
plt.figure(figsize=(10, 10)) |
for i in range(9): |
ax = plt.subplot(3, 3, i + 1) |
plt.title(class_names[np.argmax(label_batch[i])]) |
plt.imshow(image_batch[i]) |
plt.axis(\"off\") |
png |
Define a ResNet-20 model |
def resnet_layer( |
inputs, |
num_filters=16, |
kernel_size=3, |
strides=1, |
activation=\"relu\", |
batch_normalization=True, |
conv_first=True, |
): |
conv = keras.layers.Conv2D( |
num_filters, |
kernel_size=kernel_size, |
strides=strides, |
padding=\"same\", |
kernel_initializer=\"he_normal\", |
kernel_regularizer=keras.regularizers.l2(1e-4), |
) |
x = inputs |
if conv_first: |
x = conv(x) |
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