mirror of
https://github.com/sbrl/research-rainfallradar
synced 2024-12-22 22:25:01 +00:00
spaces → spaces
This commit is contained in:
parent
56a501f8a9
commit
67b8a2c6c0
GPG key ID:
1BE5172E637709C2
1 changed files with 86 additions and 86 deletions
|
|
@ -60,68 +60,68 @@ logger.info("Validation Dataset:", dataset_validate)
|
|||
# ██ ██ ██████ ██████ ███████ ███████
|
||||
|
||||
def convolution_block(
|
||||
block_input,
|
||||
num_filters=256,
|
||||
kernel_size=3,
|
||||
dilation_rate=1,
|
||||
padding="same",
|
||||
use_bias=False,
|
||||
block_input,
|
||||
num_filters=256,
|
||||
kernel_size=3,
|
||||
dilation_rate=1,
|
||||
padding="same",
|
||||
use_bias=False,
|
||||
):
|
||||
x = tf.keras.layers.Conv2D(
|
||||
num_filters,
|
||||
kernel_size=kernel_size,
|
||||
dilation_rate=dilation_rate,
|
||||
padding="same",
|
||||
use_bias=use_bias,
|
||||
kernel_initializer=tf.keras.initializers.HeNormal(),
|
||||
)(block_input)
|
||||
x = tf.keras.layers.BatchNormalization()(x)
|
||||
return tf.nn.relu(x)
|
||||
x = tf.keras.layers.Conv2D(
|
||||
num_filters,
|
||||
kernel_size=kernel_size,
|
||||
dilation_rate=dilation_rate,
|
||||
padding="same",
|
||||
use_bias=use_bias,
|
||||
kernel_initializer=tf.keras.initializers.HeNormal(),
|
||||
)(block_input)
|
||||
x = tf.keras.layers.BatchNormalization()(x)
|
||||
return tf.nn.relu(x)
|
||||
|
||||
|
||||
def DilatedSpatialPyramidPooling(dspp_input):
|
||||
dims = dspp_input.shape
|
||||
x = tf.keras.layers.AveragePooling2D(pool_size=(dims[-3], dims[-2]))(dspp_input)
|
||||
x = convolution_block(x, kernel_size=1, use_bias=True)
|
||||
out_pool = tf.keras.layers.UpSampling2D(
|
||||
size=(dims[-3] // x.shape[1], dims[-2] // x.shape[2]), interpolation="bilinear",
|
||||
)(x)
|
||||
dims = dspp_input.shape
|
||||
x = tf.keras.layers.AveragePooling2D(pool_size=(dims[-3], dims[-2]))(dspp_input)
|
||||
x = convolution_block(x, kernel_size=1, use_bias=True)
|
||||
out_pool = tf.keras.layers.UpSampling2D(
|
||||
size=(dims[-3] // x.shape[1], dims[-2] // x.shape[2]), interpolation="bilinear",
|
||||
)(x)
|
||||
|
||||
out_1 = convolution_block(dspp_input, kernel_size=1, dilation_rate=1)
|
||||
out_6 = convolution_block(dspp_input, kernel_size=3, dilation_rate=6)
|
||||
out_12 = convolution_block(dspp_input, kernel_size=3, dilation_rate=12)
|
||||
out_18 = convolution_block(dspp_input, kernel_size=3, dilation_rate=18)
|
||||
out_1 = convolution_block(dspp_input, kernel_size=1, dilation_rate=1)
|
||||
out_6 = convolution_block(dspp_input, kernel_size=3, dilation_rate=6)
|
||||
out_12 = convolution_block(dspp_input, kernel_size=3, dilation_rate=12)
|
||||
out_18 = convolution_block(dspp_input, kernel_size=3, dilation_rate=18)
|
||||
|
||||
x = tf.keras.layers.Concatenate(axis=-1)([out_pool, out_1, out_6, out_12, out_18])
|
||||
output = convolution_block(x, kernel_size=1)
|
||||
return output
|
||||
x = tf.keras.layers.Concatenate(axis=-1)([out_pool, out_1, out_6, out_12, out_18])
|
||||
output = convolution_block(x, kernel_size=1)
|
||||
return output
|
||||
|
||||
|
||||
def DeeplabV3Plus(image_size, num_classes, num_channels=3):
|
||||
model_input = tf.keras.Input(shape=(image_size, image_size, num_channels))
|
||||
resnet50 = tf.keras.applications.ResNet50(
|
||||
weights="imagenet" if num_channels == 3 else None,
|
||||
model_input = tf.keras.Input(shape=(image_size, image_size, num_channels))
|
||||
resnet50 = tf.keras.applications.ResNet50(
|
||||
weights="imagenet" if num_channels == 3 else None,
|
||||
include_top=False, input_tensor=model_input
|
||||
)
|
||||
x = resnet50.get_layer("conv4_block6_2_relu").output
|
||||
x = DilatedSpatialPyramidPooling(x)
|
||||
)
|
||||
x = resnet50.get_layer("conv4_block6_2_relu").output
|
||||
x = DilatedSpatialPyramidPooling(x)
|
||||
|
||||
input_a = tf.keras.layers.UpSampling2D(
|
||||
size=(image_size // 4 // x.shape[1], image_size // 4 // x.shape[2]),
|
||||
interpolation="bilinear",
|
||||
)(x)
|
||||
input_b = resnet50.get_layer("conv2_block3_2_relu").output
|
||||
input_b = convolution_block(input_b, num_filters=48, kernel_size=1)
|
||||
input_a = tf.keras.layers.UpSampling2D(
|
||||
size=(image_size // 4 // x.shape[1], image_size // 4 // x.shape[2]),
|
||||
interpolation="bilinear",
|
||||
)(x)
|
||||
input_b = resnet50.get_layer("conv2_block3_2_relu").output
|
||||
input_b = convolution_block(input_b, num_filters=48, kernel_size=1)
|
||||
|
||||
x = tf.keras.layers.Concatenate(axis=-1)([input_a, input_b])
|
||||
x = convolution_block(x)
|
||||
x = convolution_block(x)
|
||||
x = tf.keras.layers.UpSampling2D(
|
||||
size=(image_size // x.shape[1], image_size // x.shape[2]),
|
||||
interpolation="bilinear",
|
||||
)(x)
|
||||
model_output = tf.keras.layers.Conv2D(num_classes, kernel_size=(1, 1), padding="same")(x)
|
||||
return tf.keras.Model(inputs=model_input, outputs=model_output)
|
||||
x = tf.keras.layers.Concatenate(axis=-1)([input_a, input_b])
|
||||
x = convolution_block(x)
|
||||
x = convolution_block(x)
|
||||
x = tf.keras.layers.UpSampling2D(
|
||||
size=(image_size // x.shape[1], image_size // x.shape[2]),
|
||||
interpolation="bilinear",
|
||||
)(x)
|
||||
model_output = tf.keras.layers.Conv2D(num_classes, kernel_size=(1, 1), padding="same")(x)
|
||||
return tf.keras.Model(inputs=model_input, outputs=model_output)
|
||||
|
||||
|
||||
model = DeeplabV3Plus(image_size=IMAGE_SIZE, num_classes=NUM_CLASSES, num_channels=8)
|
||||
|
|
@ -138,9 +138,9 @@ summarywriter(model, os.path.join(DIR_OUTPUT, "summary.txt"))
|
|||
|
||||
loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
|
||||
model.compile(
|
||||
optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
|
||||
loss=loss,
|
||||
metrics=["accuracy"],
|
||||
optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
|
||||
loss=loss,
|
||||
metrics=["accuracy"],
|
||||
)
|
||||
logger.info(">>> Beginning training")
|
||||
history = model.fit(dataset_train,
|
||||
|
|
@ -195,59 +195,59 @@ plt.close()
|
|||
|
||||
# Loading the Colormap
|
||||
colormap = loadmat(
|
||||
PATH_COLOURMAP
|
||||
PATH_COLOURMAP
|
||||
)["colormap"]
|
||||
colormap = colormap * 100
|
||||
colormap = colormap.astype(np.uint8)
|
||||
|
||||
|
||||
def infer(model, image_tensor):
|
||||
predictions = model.predict(tf.expand_dims((image_tensor), axis=0))
|
||||
predictions = tf.squeeze(predictions)
|
||||
predictions = tf.argmax(predictions, axis=2)
|
||||
return predictions
|
||||
predictions = model.predict(tf.expand_dims((image_tensor), axis=0))
|
||||
predictions = tf.squeeze(predictions)
|
||||
predictions = tf.argmax(predictions, axis=2)
|
||||
return predictions
|
||||
|
||||
|
||||
def decode_segmentation_masks(mask, colormap, n_classes):
|
||||
r = np.zeros_like(mask).astype(np.uint8)
|
||||
g = np.zeros_like(mask).astype(np.uint8)
|
||||
b = np.zeros_like(mask).astype(np.uint8)
|
||||
for l in range(0, n_classes):
|
||||
idx = mask == l
|
||||
r[idx] = colormap[l, 0]
|
||||
g[idx] = colormap[l, 1]
|
||||
b[idx] = colormap[l, 2]
|
||||
rgb = np.stack([r, g, b], axis=2)
|
||||
return rgb
|
||||
r = np.zeros_like(mask).astype(np.uint8)
|
||||
g = np.zeros_like(mask).astype(np.uint8)
|
||||
b = np.zeros_like(mask).astype(np.uint8)
|
||||
for l in range(0, n_classes):
|
||||
idx = mask == l
|
||||
r[idx] = colormap[l, 0]
|
||||
g[idx] = colormap[l, 1]
|
||||
b[idx] = colormap[l, 2]
|
||||
rgb = np.stack([r, g, b], axis=2)
|
||||
return rgb
|
||||
|
||||
|
||||
def get_overlay(image, coloured_mask):
|
||||
image = tf.keras.preprocessing.image.array_to_img(image)
|
||||
image = np.array(image).astype(np.uint8)
|
||||
overlay = cv2.addWeighted(image, 0.35, coloured_mask, 0.65, 0)
|
||||
return overlay
|
||||
image = tf.keras.preprocessing.image.array_to_img(image)
|
||||
image = np.array(image).astype(np.uint8)
|
||||
overlay = cv2.addWeighted(image, 0.35, coloured_mask, 0.65, 0)
|
||||
return overlay
|
||||
|
||||
|
||||
def plot_samples_matplotlib(filepath, display_list, figsize=(5, 3)):
|
||||
_, axes = plt.subplots(nrows=1, ncols=len(display_list), figsize=figsize)
|
||||
for i in range(len(display_list)):
|
||||
if display_list[i].shape[-1] == 3:
|
||||
axes[i].imshow(tf.keras.preprocessing.image.array_to_img(display_list[i]))
|
||||
else:
|
||||
axes[i].imshow(display_list[i])
|
||||
plt.savefig(filepath)
|
||||
_, axes = plt.subplots(nrows=1, ncols=len(display_list), figsize=figsize)
|
||||
for i in range(len(display_list)):
|
||||
if display_list[i].shape[-1] == 3:
|
||||
axes[i].imshow(tf.keras.preprocessing.image.array_to_img(display_list[i]))
|
||||
else:
|
||||
axes[i].imshow(display_list[i])
|
||||
plt.savefig(filepath)
|
||||
|
||||
|
||||
def plot_predictions(filepath, input_items, colormap, model):
|
||||
for input_tensor in input_items:
|
||||
prediction_mask = infer(image_tensor=input_tensor, model=model)
|
||||
prediction_colormap = decode_segmentation_masks(prediction_mask, colormap, 20)
|
||||
overlay = get_overlay(input_tensor, prediction_colormap)
|
||||
plot_samples_matplotlib(
|
||||
for input_tensor in input_items:
|
||||
prediction_mask = infer(image_tensor=input_tensor, model=model)
|
||||
prediction_colormap = decode_segmentation_masks(prediction_mask, colormap, 20)
|
||||
overlay = get_overlay(input_tensor, prediction_colormap)
|
||||
plot_samples_matplotlib(
|
||||
filepath,
|
||||
[input_tensor, overlay, prediction_colormap],
|
||||
[input_tensor, overlay, prediction_colormap],
|
||||
figsize=(18, 14)
|
||||
)
|
||||
)
|
||||
|
||||
def get_items_from_batched(dataset, count):
|
||||
result = []
|
||||
|
|
|
|||
Loading…
Reference in a new issue