File size: 7,495 Bytes
7d43cc6 c537534 7d43cc6 d66d420 7d43cc6 d66d420 7d43cc6 d66d420 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 |
import gradio as gr
import numpy as np
import tensorflow as tf
from keras.applications.mobilenet_v2 import MobileNetV2, preprocess_input
from keras.models import Model
import matplotlib.pyplot as plt
import logging
from skimage.transform import resize
from PIL import Image
from tqdm import tqdm
class SwarmAgent:
def __init__(self, position, velocity):
self.position = position
self.velocity = velocity
self.m = np.zeros_like(position)
self.v = np.zeros_like(position)
class SwarmNeuralNetwork:
def __init__(self, num_agents, image_shape, target_image):
self.image_shape = image_shape
self.resized_shape = (64, 64, 3)
self.agents = [SwarmAgent(self.random_position(), self.random_velocity()) for _ in range(num_agents)]
self.target_image = self.load_target_image(target_image)
self.generated_image = np.random.randn(*image_shape) # Start with noise
self.mobilenet = self.load_mobilenet_model()
self.current_epoch = 0
self.noise_schedule = np.linspace(0.1, 0.002, 1000) # Noise schedule
def random_position(self):
return np.random.randn(*self.image_shape) # Use Gaussian noise
def random_velocity(self):
return np.random.randn(*self.image_shape) * 0.01
def load_target_image(self, img):
img = img.resize((self.image_shape[1], self.image_shape[0]))
img_array = np.array(img) / 127.5 - 1 # Normalize to [-1, 1]
plt.imshow((img_array + 1) / 2) # Convert back to [0, 1] for display
plt.title('Target Image')
plt.show()
return img_array
def resize_image(self, image):
return resize(image, self.resized_shape, anti_aliasing=True)
def load_mobilenet_model(self):
mobilenet = MobileNetV2(weights='imagenet', include_top=False, input_shape=self.resized_shape)
return Model(inputs=mobilenet.input, outputs=mobilenet.get_layer('block_13_expand_relu').output)
def add_positional_encoding(self, image):
h, w, c = image.shape
pos_enc = np.zeros_like(image)
for i in range(h):
for j in range(w):
pos_enc[i, j, :] = [i/h, j/w, 0]
return image + pos_enc
def multi_head_attention(self, agent, num_heads=4):
attention_scores = []
for _ in range(num_heads):
similarity = np.exp(-np.sum((agent.position - self.target_image)**2, axis=-1))
attention_score = similarity / np.sum(similarity)
attention_scores.append(attention_score)
attention = np.mean(attention_scores, axis=0)
return np.expand_dims(attention, axis=-1)
def multi_scale_perceptual_loss(self, agent_positions):
target_image_resized = self.resize_image((self.target_image + 1) / 2) # Convert to [0, 1] for MobileNet
target_image_preprocessed = preprocess_input(target_image_resized[np.newaxis, ...] * 255) # MobileNet expects [0, 255]
target_features = self.mobilenet.predict(target_image_preprocessed)
losses = []
for agent_position in agent_positions:
agent_image_resized = self.resize_image((agent_position + 1) / 2)
agent_image_preprocessed = preprocess_input(agent_image_resized[np.newaxis, ...] * 255)
agent_features = self.mobilenet.predict(agent_image_preprocessed)
loss = np.mean((target_features - agent_features)**2)
losses.append(1 / (1 + loss))
return np.array(losses)
def update_agents(self, timestep):
noise_level = self.noise_schedule[min(timestep, len(self.noise_schedule) - 1)]
for agent in self.agents:
# Predict noise
predicted_noise = agent.position - self.target_image
# Denoise
denoised = (agent.position - noise_level * predicted_noise) / (1 - noise_level)
# Add scaled noise for next step
agent.position = denoised + np.random.randn(*self.image_shape) * np.sqrt(noise_level)
# Clip values
agent.position = np.clip(agent.position, -1, 1)
def generate_image(self):
self.generated_image = np.mean([agent.position for agent in self.agents], axis=0)
# Normalize to [0, 1] range for display
self.generated_image = (self.generated_image + 1) / 2
self.generated_image = np.clip(self.generated_image, 0, 1)
def train(self, epochs):
logging.basicConfig(filename='training.log', level=logging.INFO)
for epoch in tqdm(range(epochs), desc="Training Epochs"):
self.update_agents(epoch)
self.generate_image()
mse = np.mean(((self.generated_image * 2 - 1) - self.target_image)**2)
logging.info(f"Epoch {epoch}, MSE: {mse}")
if epoch % 10 == 0:
print(f"Epoch {epoch}, MSE: {mse}")
self.display_image(self.generated_image, title=f'Epoch {epoch}')
self.current_epoch += 1
def display_image(self, image, title=''):
plt.imshow(image)
plt.title(title)
plt.axis('off')
plt.show()
def display_agent_positions(self, epoch):
fig, ax = plt.subplots()
positions = np.array([agent.position for agent in self.agents])
ax.imshow(self.generated_image, extent=[0, self.image_shape[1], 0, self.image_shape[0]])
ax.scatter(positions[:, :, 0].flatten(), positions[:, :, 1].flatten(), s=1, c='red')
plt.title(f'Agent Positions at Epoch {epoch}')
plt.show()
def save_model(self, filename):
model_state = {
'agents': self.agents,
'generated_image': self.generated_image,
'current_epoch': self.current_epoch
}
np.save(filename, model_state)
def load_model(self, filename):
model_state = np.load(filename, allow_pickle=True).item()
self.agents = model_state['agents']
self.generated_image = model_state['generated_image']
self.current_epoch = model_state['current_epoch']
def generate_new_image(self, num_steps=1000):
for agent in self.agents:
agent.position = np.random.randn(*self.image_shape)
for step in tqdm(range(num_steps), desc="Generating Image"):
self.update_agents(num_steps - step - 1) # Reverse order
self.generate_image()
return self.generated_image
# Gradio Interface
def train_snn(image, num_agents, epochs):
snn = SwarmNeuralNetwork(num_agents=num_agents, image_shape=(64, 64, 3), target_image=image)
snn.train(epochs=epochs)
snn.save_model('snn_model.npy')
return snn.generated_image
def generate_new_image():
snn = SwarmNeuralNetwork(num_agents=2000, image_shape=(64, 64, 3), target_image=None)
snn.load_model('snn_model.npy')
new_image = snn.generate_new_image()
return new_image
interface = gr.Interface(
fn=train_snn,
inputs=[
gr.Image(type="pil", label="Upload Target Image"),
gr.Slider(minimum=500, maximum=3000, value=2000, label="Number of Agents"),
gr.Slider(minimum=10, maximum=200, value=100, label="Number of Epochs")
],
outputs=gr.Image(type="numpy", label="Generated Image"),
title="Swarm Neural Network Image Generation",
description="Upload an image and set the number of agents and epochs to train the Swarm Neural Network to generate a new image."
)
interface.launch()
|