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TEST SCRIPT: deeplabv3

Browse source 
ref https://keras.io/examples/vision/deeplabv3_plus/
dataset ref https://drive.google.com/uc?id=1B9A9UCJYMwTL4oBEo4RZfbMZMaZhKJaz

(the code is *terrible* spaghetti....!)
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Starbeamrainbowlabs 2022-12-12 19:19:24 +00:00
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# @source https://keras.io/examples/vision/deeplabv3_plus/
# Required dataset: https://drive.google.com/uc?id=1B9A9UCJYMwTL4oBEo4RZfbMZMaZhKJaz [instance-level-human-parsing.zip]
from datetime import datetime
import os
import cv2
import numpy as np
from glob import glob
from scipy.io import loadmat
import matplotlib.pyplot as plt
import tensorflow as tf
IMAGE_SIZE = 512
BATCH_SIZE = 4
NUM_CLASSES = 20
DATA_DIR = "./instance-level_human_parsing/instance-level_human_parsing/Training"
NUM_TRAIN_IMAGES = 1000
NUM_VAL_IMAGES = 50
DIR_OUTPUT=f"output/{datetime.utcnow().date().isoformat()}_deeplabv3plus_TEST"
os.makedirs(DIR_OUTPUT)
train_images = sorted(glob(os.path.join(DATA_DIR, "Images/*")))[:NUM_TRAIN_IMAGES]
train_masks = sorted(glob(os.path.join(DATA_DIR, "Category_ids/*")))[:NUM_TRAIN_IMAGES]
val_images = sorted(glob(os.path.join(DATA_DIR, "Images/*")))[
NUM_TRAIN_IMAGES : NUM_VAL_IMAGES + NUM_TRAIN_IMAGES
]
val_masks = sorted(glob(os.path.join(DATA_DIR, "Category_ids/*")))[
NUM_TRAIN_IMAGES : NUM_VAL_IMAGES + NUM_TRAIN_IMAGES
]
def read_image(image_path, mask=False):
image = tf.io.read_file(image_path)
if mask:
image = tf.image.decode_png(image, channels=1)
image.set_shape([None, None, 1])
image = tf.image.resize(images=image, size=[IMAGE_SIZE, IMAGE_SIZE])
else:
image = tf.image.decode_png(image, channels=3)
image.set_shape([None, None, 3])
image = tf.image.resize(images=image, size=[IMAGE_SIZE, IMAGE_SIZE])
image = image / 127.5 - 1
return image
def load_data(image_list, mask_list):
image = read_image(image_list)
mask = read_image(mask_list, mask=True)
return image, mask
def data_generator(image_list, mask_list):
dataset = tf.data.Dataset.from_tensor_slices((image_list, mask_list))
dataset = dataset.map(load_data, num_parallel_calls=tf.data.AUTOTUNE)
dataset = dataset.batch(BATCH_SIZE, drop_remainder=True)
return dataset
train_dataset = data_generator(train_images, train_masks)
val_dataset = data_generator(val_images, val_masks)
print("Train Dataset:", train_dataset)
print("Val Dataset:", val_dataset)
# ███ ███ ██████ ██████ ███████ ██
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def convolution_block(
block_input,
num_filters=256,
kernel_size=3,
dilation_rate=1,
padding="same",
use_bias=False,
):
x = layers.Conv2D(
num_filters,
kernel_size=kernel_size,
dilation_rate=dilation_rate,
padding="same",
use_bias=use_bias,
kernel_initializer=keras.initializers.HeNormal(),
)(block_input)
x = layers.BatchNormalization()(x)
return tf.nn.relu(x)
def DilatedSpatialPyramidPooling(dspp_input):
dims = dspp_input.shape
x = layers.AveragePooling2D(pool_size=(dims[-3], dims[-2]))(dspp_input)
x = convolution_block(x, kernel_size=1, use_bias=True)
out_pool = 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)
x = 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):
model_input = keras.Input(shape=(image_size, image_size, 3))
resnet50 = keras.applications.ResNet50(
weights="imagenet", include_top=False, input_tensor=model_input
)
x = resnet50.get_layer("conv4_block6_2_relu").output
x = DilatedSpatialPyramidPooling(x)
input_a = 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 = layers.Concatenate(axis=-1)([input_a, input_b])
x = convolution_block(x)
x = convolution_block(x)
x = layers.UpSampling2D(
size=(image_size // x.shape[1], image_size // x.shape[2]),
interpolation="bilinear",
)(x)
model_output = layers.Conv2D(num_classes, kernel_size=(1, 1), padding="same")(x)
return keras.Model(inputs=model_input, outputs=model_output)
model = DeeplabV3Plus(image_size=IMAGE_SIZE, num_classes=NUM_CLASSES)
model.summary()
# ████████ ██████ █████ ██ ███ ██ ██ ███ ██ ██████
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# ██ ██ ██ ██ ██ ██ ██ ████ ██ ██ ████ ██████
loss = keras.losses.SparseCategoricalCrossentropy(from_logits=True)
model.compile(
optimizer=keras.optimizers.Adam(learning_rate=0.001),
loss=loss,
metrics=["accuracy"],
)
history = model.fit(train_dataset,
validation_data=val_dataset,
epochs=25,
callbacks=[
tf.keras.callbacks.CSVLogger(
filename=os.path.join(DIR_OUTPUT, "metrics.tsv"),
separator="\t"
)
],
)
plt.plot(history.history["loss"])
plt.title("Training Loss")
plt.ylabel("loss")
plt.xlabel("epoch")
plt.savefig(os.path.join(DIR_OUTPUT, "loss.png"))
plt.plot(history.history["accuracy"])
plt.title("Training Accuracy")
plt.ylabel("accuracy")
plt.xlabel("epoch")
plt.savefig(os.path.join(DIR_OUTPUT, "acc.png"))
plt.plot(history.history["val_loss"])
plt.title("Validation Loss")
plt.ylabel("val_loss")
plt.xlabel("epoch")
plt.savefig(os.path.join(DIR_OUTPUT, "val_loss.png"))
plt.plot(history.history["val_accuracy"])
plt.title("Validation Accuracy")
plt.ylabel("val_accuracy")
plt.xlabel("epoch")
plt.savefig(os.path.join(DIR_OUTPUT, "val_acc.png"))
# ██ ███ ██ ███████ ███████ ██████ ███████ ███ ██ ██████ ███████
# ██ ████ ██ ██ ██ ██ ██ ██ ████ ██ ██ ██
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# Loading the Colormap
colormap = loadmat(
"./instance-level_human_parsing/instance-level_human_parsing/human_colormap.mat"
)["colormap"]
colormap = colormap * 100
colormap = colormap.astype(np.uint8)
def infer(model, image_tensor):
predictions = model.predict(np.expand_dims((image_tensor), axis=0))
predictions = np.squeeze(predictions)
predictions = np.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
def get_overlay(image, colored_mask):
image = tf.keras.preprocessing.image.array_to_img(image)
image = np.array(image).astype(np.uint8)
overlay = cv2.addWeighted(image, 0.35, colored_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)
def plot_predictions(filepath, images_list, colormap, model):
for image_file in images_list:
image_tensor = read_image(image_file)
prediction_mask = infer(image_tensor=image_tensor, model=model)
prediction_colormap = decode_segmentation_masks(prediction_mask, colormap, 20)
overlay = get_overlay(image_tensor, prediction_colormap)
plot_samples_matplotlib(
filepath,
[image_tensor, overlay, prediction_colormap],
figsize=(18, 14)
)
plot_predictions(os.path.join(DIR_OUTPUT, "predict_train.png"), train_images[:4], colormap, model=model)
plot_predictions(os.path.join(DIR_OUTPUT, "predict_validate.png"), val_images[:4], colormap, model=model)