Neural Architecture Search (NAS)

graph TD
    A[Search Space] --> B[Search Strategy]
    B --> C[Performance Estimation]
    C --> D[Optimal Architecture]
    D --> E[Training & Deployment]

    subgraph NAS Process
        B
        C
    end

# Example of NAS search space definition
class NAS_SearchSpace:
    def __init__(self):
        # Operation types
        self.operations = [
            'conv_3x3', 'conv_5x5', 'conv_7x7',
            'depthwise_conv_3x3', 'depthwise_conv_5x5',
            'max_pool_3x3', 'avg_pool_3x3',
            'identity', 'zero'
        ]

        # Connection patterns
        self.connection_patterns = [
            'skip_connection', 'dense_connection',
            'series_connection', 'parallel_connection'
        ]

        # Architecture parameters
        self.num_layers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
        self.num_channels = [16, 32, 64, 128, 256, 512]
        self.strides = [1, 2]

    def generate_random_architecture(self):
        """Generate a random architecture from the search space"""
        import random

        # Random number of layers
        num_layers = random.choice(self.num_layers)

        # Generate layer configurations
        architecture = []
        for i in range(num_layers):
            layer = {
                'operation': random.choice(self.operations),
                'channels': random.choice(self.num_channels),
                'stride': random.choice(self.strides),
                'connection': random.choice(self.connection_patterns)
            }
            architecture.append(layer)

        return architecture

    def get_search_space_size(self):
        """Calculate the size of the search space"""
        # This is a simplified calculation
        # Actual search space can be much larger
        return (len(self.operations) *
                len(self.num_channels) *
                len(self.strides) *
                len(self.connection_patterns)) ** max(self.num_layers)

# Reinforcement Learning search strategy example
class RL_NAS_Strategy:
    def __init__(self, search_space, controller_hidden_size=100):
        self.search_space = search_space
        self.controller = self._build_controller(controller_hidden_size)

    def _build_controller(self, hidden_size):
        """Build the RL controller"""
        import torch.nn as nn

        # Simple LSTM controller
        controller = nn.LSTM(
            input_size=len(self.search_space.operations) +
                      len(self.search_space.num_channels) +
                      len(self.search_space.strides) +
                      len(self.search_space.connection_patterns),
            hidden_size=hidden_size,
            num_layers=2
        )

        # Output layers for each decision
        self.op_head = nn.Linear(hidden_size, len(self.search_space.operations))
        self.ch_head = nn.Linear(hidden_size, len(self.search_space.num_channels))
        self.st_head = nn.Linear(hidden_size, len(self.search_space.strides))
        self.co_head = nn.Linear(hidden_size, len(self.search_space.connection_patterns))

        return controller

    def sample_architecture(self):
        """Sample an architecture using the RL controller"""
        import torch

        # Initialize hidden state
        hidden = (torch.zeros(2, 1, 100), torch.zeros(2, 1, 100))

        # Sample architecture
        architecture = []
        for i in range(max(self.search_space.num_layers)):
            # Get controller output
            output, hidden = self.controller(torch.zeros(1, 1, 1), hidden)

            # Sample operations
            op_probs = torch.softmax(self.op_head(output), dim=-1)
            ch_probs = torch.softmax(self.ch_head(output), dim=-1)
            st_probs = torch.softmax(self.st_head(output), dim=-1)
            co_probs = torch.softmax(self.co_head(output), dim=-1)

            # Select with highest probability
            op = torch.argmax(op_probs).item()
            ch = torch.argmax(ch_probs).item()
            st = torch.argmax(st_probs).item()
            co = torch.argmax(co_probs).item()

            # Create layer
            layer = {
                'operation': self.search_space.operations[op],
                'channels': self.search_space.num_channels[ch],
                'stride': self.search_space.strides[st],
                'connection': self.search_space.connection_patterns[co]
            }
            architecture.append(layer)

        return architecture

    def update_controller(self, rewards):
        """Update the controller based on rewards"""
        # Implementation would use policy gradient or similar
        pass

# Performance estimation strategies
class PerformanceEstimator:
    def __init__(self, strategy='weight_sharing'):
        self.strategy = strategy
        self.supernet = None

    def estimate(self, architecture, dataset, epochs=5):
        """Estimate the performance of an architecture"""
        if self.strategy == 'full_training':
            return self._full_training(architecture, dataset, epochs)
        elif self.strategy == 'weight_sharing':
            return self._weight_sharing(architecture, dataset)
        elif self.strategy == 'proxy_task':
            return self._proxy_task(architecture, dataset)
        elif self.strategy == 'learning_curve':
            return self._learning_curve(architecture, dataset)
        else:
            raise ValueError(f"Unknown strategy: {self.strategy}")

    def _full_training(self, architecture, dataset, epochs):
        """Full training of the architecture"""
        import torch
        import torch.nn as nn
        from torch.utils.data import DataLoader

        # Build model from architecture
        model = self._build_model(architecture)
        criterion = nn.CrossEntropyLoss()
        optimizer = torch.optim.Adam(model.parameters())

        # Create data loader
        train_loader = DataLoader(dataset, batch_size=32, shuffle=True)

        # Train for a few epochs
        for epoch in range(epochs):
            for inputs, targets in train_loader:
                optimizer.zero_grad()
                outputs = model(inputs)
                loss = criterion(outputs, targets)
                loss.backward()
                optimizer.step()

        # Evaluate on validation set
        val_loader = DataLoader(dataset, batch_size=32, shuffle=False)
        correct = 0
        total = 0

        with torch.no_grad():
            for inputs, targets in val_loader:
                outputs = model(inputs)
                _, predicted = torch.max(outputs.data, 1)
                total += targets.size(0)
                correct += (predicted == targets).sum().item()

        return correct / total

    def _weight_sharing(self, architecture, dataset):
        """Weight sharing performance estimation"""
        if self.supernet is None:
            self._build_supernet()

        # Sample sub-network from supernet
        sub_network = self._sample_sub_network(architecture)

        # Evaluate sub-network
        val_loader = DataLoader(dataset, batch_size=32, shuffle=False)
        correct = 0
        total = 0

        with torch.no_grad():
            for inputs, targets in val_loader:
                outputs = sub_network(inputs)
                _, predicted = torch.max(outputs.data, 1)
                total += targets.size(0)
                correct += (predicted == targets).sum().item()

        return correct / total

    def _build_supernet(self):
        """Build a supernet that contains all possible operations"""
        # Implementation would create a network with all possible operations
        # and allow sampling sub-networks
        pass

    def _sample_sub_network(self, architecture):
        """Sample a sub-network from the supernet based on architecture"""
        # Implementation would activate only the operations specified in architecture
        pass

    def _proxy_task(self, architecture, dataset):
        """Proxy task performance estimation"""
        # Use a simpler task or smaller dataset for faster evaluation
        pass

    def _learning_curve(self, architecture, dataset):
        """Learning curve extrapolation"""
        # Train for a few epochs and extrapolate final performance
        pass

    def _build_model(self, architecture):
        """Build a model from architecture specification"""
        import torch.nn as nn

        layers = []
        in_channels = 3  # Assuming RGB input

        for layer in architecture:
            if layer['operation'].startswith('conv'):
                # Add convolutional layer
                kernel_size = int(layer['operation'].split('_')[-1][0])
                if 'depthwise' in layer['operation']:
                    layers.append(nn.Conv2d(
                        in_channels, in_channels,
                        kernel_size=kernel_size,
                        stride=layer['stride'],
                        padding=kernel_size//2,
                        groups=in_channels
                    ))
                else:
                    layers.append(nn.Conv2d(
                        in_channels, layer['channels'],
                        kernel_size=kernel_size,
                        stride=layer['stride'],
                        padding=kernel_size//2
                    ))
                    in_channels = layer['channels']

                layers.append(nn.BatchNorm2d(in_channels))
                layers.append(nn.ReLU())

            elif layer['operation'].startswith('pool'):
                # Add pooling layer
                pool_type = layer['operation'].split('_')[0]
                kernel_size = int(layer['operation'].split('_')[-1][0])

                if pool_type == 'max':
                    layers.append(nn.MaxPool2d(kernel_size, stride=layer['stride']))
                else:
                    layers.append(nn.AvgPool2d(kernel_size, stride=layer['stride']))

            elif layer['operation'] == 'identity':
                # Identity operation - do nothing
                pass

            elif layer['operation'] == 'zero':
                # Zero operation - skip this layer
                continue

        # Add final layers
        layers.append(nn.AdaptiveAvgPool2d(1))
        layers.append(nn.Flatten())
        layers.append(nn.Linear(in_channels, 10))  # Assuming 10 classes

        return nn.Sequential(*layers)

# Complete RL-based NAS implementation
class RL_NAS:
    def __init__(self, search_space, num_episodes=1000, controller_hidden_size=100):
        self.search_space = search_space
        self.num_episodes = num_episodes
        self.controller = self._build_controller(controller_hidden_size)
        self.performance_estimator = PerformanceEstimator('weight_sharing')

    def _build_controller(self, hidden_size):
        """Build the RL controller"""
        import torch.nn as nn

        # LSTM controller
        self.controller = nn.LSTM(
            input_size=1,  # Dummy input
            hidden_size=hidden_size,
            num_layers=2
        )

        # Output heads for different decisions
        self.op_head = nn.Linear(hidden_size, len(self.search_space.operations))
        self.ch_head = nn.Linear(hidden_size, len(self.search_space.num_channels))
        self.st_head = nn.Linear(hidden_size, len(self.search_space.strides))
        self.co_head = nn.Linear(hidden_size, len(self.search_space.connection_patterns))
        self.num_layers_head = nn.Linear(hidden_size, len(self.search_space.num_layers))

        return self.controller

    def sample_architecture(self, hidden):
        """Sample an architecture using the controller"""
        import torch

        # Sample number of layers
        num_layers_logits = self.num_layers_head(hidden[0][-1])
        num_layers_probs = torch.softmax(num_layers_logits, dim=-1)
        num_layers = torch.multinomial(num_layers_probs, 1).item()
        num_layers = self.search_space.num_layers[num_layers]

        # Sample each layer
        architecture = []
        for i in range(num_layers):
            # Sample operation
            op_logits = self.op_head(hidden[0][-1])
            op_probs = torch.softmax(op_logits, dim=-1)
            op = torch.multinomial(op_probs, 1).item()

            # Sample channels
            ch_logits = self.ch_head(hidden[0][-1])
            ch_probs = torch.softmax(ch_logits, dim=-1)
            ch = torch.multinomial(ch_probs, 1).item()

            # Sample stride
            st_logits = self.st_head(hidden[0][-1])
            st_probs = torch.softmax(st_logits, dim=-1)
            st = torch.multinomial(st_probs, 1).item()

            # Sample connection
            co_logits = self.co_head(hidden[0][-1])
            co_probs = torch.softmax(co_logits, dim=-1)
            co = torch.multinomial(co_probs, 1).item()

            # Create layer
            layer = {
                'operation': self.search_space.operations[op],
                'channels': self.search_space.num_channels[ch],
                'stride': self.search_space.strides[st],
                'connection': self.search_space.connection_patterns[co]
            }
            architecture.append(layer)

            # Update hidden state
            _, hidden = self.controller(torch.zeros(1, 1, 1), hidden)

        return architecture

    def train(self, dataset):
        """Train the NAS system"""
        import torch
        import torch.optim as optim

        # Controller optimizer
        controller_optim = optim.Adam(self.controller.parameters(), lr=0.001)

        # Training loop
        for episode in range(self.num_episodes):
            # Sample architecture
            hidden = (torch.zeros(2, 1, 100), torch.zeros(2, 1, 100))
            architecture = self.sample_architecture(hidden)

            # Estimate performance
            accuracy = self.performance_estimator.estimate(architecture, dataset)

            # Calculate reward
            reward = accuracy

            # Update controller
            # This would involve calculating policy gradients
            # and updating the controller parameters
            # Implementation omitted for brevity

            print(f"Episode {episode+1}/{self.num_episodes}, Accuracy: {accuracy:.4f}")

        # Return best architecture
        return self._get_best_architecture()

    def _get_best_architecture(self):
        """Get the best architecture found"""
        # Implementation would return the architecture with highest performance
        pass

# Evolutionary NAS implementation
class Evolutionary_NAS:
    def __init__(self, search_space, population_size=100, num_generations=50,
                 mutation_rate=0.1, crossover_rate=0.7):
        self.search_space = search_space
        self.population_size = population_size
        self.num_generations = num_generations
        self.mutation_rate = mutation_rate
        self.crossover_rate = crossover_rate
        self.performance_estimator = PerformanceEstimator('weight_sharing')

    def initialize_population(self):
        """Initialize the population with random architectures"""
        population = []
        for _ in range(self.population_size):
            population.append(self.search_space.generate_random_architecture())
        return population

    def evaluate_population(self, population, dataset):
        """Evaluate the fitness of each architecture in the population"""
        fitness_scores = []
        for architecture in population:
            accuracy = self.performance_estimator.estimate(architecture, dataset)
            fitness_scores.append(accuracy)
        return fitness_scores

    def select_parents(self, population, fitness_scores):
        """Select parents for reproduction using tournament selection"""
        parents = []
        for _ in range(2):  # Select 2 parents
            # Tournament selection
            tournament_size = 5
            tournament = random.sample(list(zip(population, fitness_scores)), tournament_size)
            tournament.sort(key=lambda x: x[1], reverse=True)  # Sort by fitness
            parents.append(tournament[0][0])  # Select winner
        return parents

    def crossover(self, parent1, parent2):
        """Perform crossover between two parents"""
        if random.random() > self.crossover_rate:
            return parent1, parent2  # No crossover

        # Single-point crossover
        min_len = min(len(parent1), len(parent2))
        if min_len < 2:
            return parent1, parent2

        crossover_point = random.randint(1, min_len - 1)

        child1 = parent1[:crossover_point] + parent2[crossover_point:]
        child2 = parent2[:crossover_point] + parent1[crossover_point:]

        return child1, child2

    def mutate(self, architecture):
        """Mutate an architecture"""
        mutated = []
        for layer in architecture:
            if random.random() < self.mutation_rate:
                # Mutate operation
                if random.random() < 0.25:
                    layer['operation'] = random.choice(self.search_space.operations)
                # Mutate channels
                elif random.random() < 0.5:
                    layer['channels'] = random.choice(self.search_space.num_channels)
                # Mutate stride
                elif random.random() < 0.75:
                    layer['stride'] = random.choice(self.search_space.strides)
                # Mutate connection
                else:
                    layer['connection'] = random.choice(self.search_space.connection_patterns)
            mutated.append(layer)

        # Add or remove layers with small probability
        if random.random() < self.mutation_rate * 0.5 and len(mutated) < max(self.search_space.num_layers):
            # Add layer
            mutated.append({
                'operation': random.choice(self.search_space.operations),
                'channels': random.choice(self.search_space.num_channels),
                'stride': random.choice(self.search_space.strides),
                'connection': random.choice(self.search_space.connection_patterns)
            })
        elif random.random() < self.mutation_rate * 0.5 and len(mutated) > 1:
            # Remove layer
            del mutated[random.randint(0, len(mutated) - 1)]

        return mutated

    def train(self, dataset):
        """Train the evolutionary NAS system"""
        # Initialize population
        population = self.initialize_population()

        # Evolution loop
        for generation in range(self.num_generations):
            # Evaluate population
            fitness_scores = self.evaluate_population(population, dataset)

            # Create next generation
            new_population = []

            # Elitism: keep the best architecture
            best_idx = fitness_scores.index(max(fitness_scores))
            new_population.append(population[best_idx])

            # Generate offspring
            while len(new_population) < self.population_size:
                # Select parents
                parent1, parent2 = self.select_parents(population, fitness_scores)

                # Crossover
                child1, child2 = self.crossover(parent1, parent2)

                # Mutate
                child1 = self.mutate(child1)
                child2 = self.mutate(child2)

                # Add to new population
                new_population.append(child1)
                if len(new_population) < self.population_size:
                    new_population.append(child2)

            population = new_population

            # Print generation statistics
            best_score = max(fitness_scores)
            avg_score = sum(fitness_scores) / len(fitness_scores)
            print(f"Generation {generation+1}/{self.num_generations}, "
                  f"Best: {best_score:.4f}, Avg: {avg_score:.4f}")

        # Return best architecture
        best_idx = fitness_scores.index(max(fitness_scores))
        return population[best_idx]

# Gradient-based NAS (DARTS) implementation
class DARTS:
    def __init__(self, search_space, num_cells=8, num_nodes=4):
        self.search_space = search_space
        self.num_cells = num_cells
        self.num_nodes = num_nodes
        self.alphas = None  # Architecture parameters
        self.model = None   # Supernet

    def build_supernet(self):
        """Build the supernet with all possible operations"""
        import torch.nn as nn

        # Initialize architecture parameters
        self.alphas = {}
        for i in range(self.num_cells):
            for j in range(self.num_nodes):
                # Operation mixing weights
                self.alphas[(i, j)] = nn.Parameter(torch.randn(len(self.search_space.operations)))

        # Build the supernet
        self.model = self._build_model_with_alphas()

    def _build_model_with_alphas(self):
        """Build a model that can represent all possible architectures"""
        import torch.nn as nn
        import torch.nn.functional as F

        class MixedOp(nn.Module):
            """Mixed operation that can represent all possible operations"""
            def __init__(self, search_space, alphas):
                super(MixedOp, self).__init__()
                self.ops = nn.ModuleList()
                self.alphas = alphas

                for op in search_space.operations:
                    if op.startswith('conv'):
                        kernel_size = int(op.split('_')[-1][0])
                        if 'depthwise' in op:
                            self.ops.append(nn.Sequential(
                                nn.Conv2d(3, 3, kernel_size, padding=kernel_size//2, groups=3),
                                nn.BatchNorm2d(3),
                                nn.ReLU()
                            ))
                        else:
                            self.ops.append(nn.Sequential(
                                nn.Conv2d(3, 16, kernel_size, padding=kernel_size//2),
                                nn.BatchNorm2d(16),
                                nn.ReLU()
                            ))
                    elif op.startswith('pool'):
                        kernel_size = int(op.split('_')[-1][0])
                        if op.startswith('max'):
                            self.ops.append(nn.MaxPool2d(kernel_size, padding=kernel_size//2))
                        else:
                            self.ops.append(nn.AvgPool2d(kernel_size, padding=kernel_size//2))
                    elif op == 'identity':
                        self.ops.append(nn.Identity())
                    elif op == 'zero':
                        self.ops.append(ZeroOp())

            def forward(self, x):
                # Softmax over operations
                weights = F.softmax(self.alphas, dim=-1)

                # Weighted sum of operations
                output = sum(w * op(x) for w, op in zip(weights, self.ops))
                return output

        class ZeroOp(nn.Module):
            """Zero operation"""
            def forward(self, x):
                return x * 0

        # Build the supernet
        layers = []
        for i in range(self.num_cells):
            for j in range(self.num_nodes):
                layers.append(MixedOp(self.search_space, self.alphas[(i, j)]))

        return nn.Sequential(*layers)

    def train(self, dataset, epochs=50):
        """Train the DARTS system"""
        import torch
        import torch.optim as optim
        from torch.utils.data import DataLoader

        # Build supernet
        self.build_supernet()

        # Optimizers
        model_optim = optim.Adam(self.model.parameters(), lr=0.001)
        arch_optim = optim.Adam(list(self.alphas.values()), lr=0.0003)

        # Data loaders
        train_loader = DataLoader(dataset, batch_size=64, shuffle=True)
        val_loader = DataLoader(dataset, batch_size=64, shuffle=False)

        # Training loop
        for epoch in range(epochs):
            # Train model weights
            self.model.train()
            for inputs, targets in train_loader:
                model_optim.zero_grad()
                outputs = self.model(inputs)
                loss = nn.CrossEntropyLoss()(outputs, targets)
                loss.backward()
                model_optim.step()

            # Train architecture parameters
            self.model.eval()
            for inputs, targets in val_loader:
                arch_optim.zero_grad()
                outputs = self.model(inputs)
                loss = nn.CrossEntropyLoss()(outputs, targets)
                loss.backward()
                arch_optim.step()

            # Print epoch statistics
            val_acc = self._evaluate(val_loader)
            print(f"Epoch {epoch+1}/{epochs}, Val Acc: {val_acc:.4f}")

        # Derive final architecture
        return self._derive_architecture()

    def _evaluate(self, data_loader):
        """Evaluate the model on a dataset"""
        correct = 0
        total = 0

        with torch.no_grad():
            for inputs, targets in data_loader:
                outputs = self.model(inputs)
                _, predicted = torch.max(outputs.data, 1)
                total += targets.size(0)
                correct += (predicted == targets).sum().item()

        return correct / total

    def _derive_architecture(self):
        """Derive the final architecture from learned alphas"""
        architecture = []

        for i in range(self.num_cells):
            for j in range(self.num_nodes):
                # Select operation with highest alpha
                alphas = F.softmax(self.alphas[(i, j)], dim=-1)
                op_idx = torch.argmax(alphas).item()
                op = self.search_space.operations[op_idx]

                # Create layer
                layer = {
                    'operation': op,
                    'channels': 16,  # Default channels
                    'stride': 1,     # Default stride
                    'connection': 'series_connection'  # Default connection
                }
                architecture.append(layer)

        return architecture

# NAS for image classification
class NAS_ImageClassifier:
    def __init__(self, search_method='darts', num_classes=10):
        self.search_method = search_method
        self.num_classes = num_classes
        self.search_space = NAS_SearchSpace()
        self.nas = self._create_nas_system()

    def _create_nas_system(self):
        """Create the NAS system based on the selected method"""
        if self.search_method == 'rl':
            return RL_NAS(self.search_space)
        elif self.search_method == 'evolutionary':
            return Evolutionary_NAS(self.search_space)
        elif self.search_method == 'darts':
            return DARTS(self.search_space)
        else:
            raise ValueError(f"Unknown search method: {self.search_method}")

    def search(self, dataset):
        """Search for the optimal architecture"""
        return self.nas.train(dataset)

    def train_final_model(self, architecture, dataset, epochs=100):
        """Train the final model with the discovered architecture"""
        import torch
        import torch.nn as nn
        import torch.optim as optim
        from torch.utils.data import DataLoader

        # Build model from architecture
        model = self._build_model_from_architecture(architecture)
        criterion = nn.CrossEntropyLoss()
        optimizer = optim.Adam(model.parameters(), lr=0.001)

        # Data loaders
        train_loader = DataLoader(dataset, batch_size=64, shuffle=True)
        val_loader = DataLoader(dataset, batch_size=64, shuffle=False)

        # Training loop
        for epoch in range(epochs):
            model.train()
            for inputs, targets in train_loader:
                optimizer.zero_grad()
                outputs = model(inputs)
                loss = criterion(outputs, targets)
                loss.backward()
                optimizer.step()

            # Validation
            val_acc = self._evaluate_model(model, val_loader)
            print(f"Epoch {epoch+1}/{epochs}, Val Acc: {val_acc:.4f}")

        return model

    def _build_model_from_architecture(self, architecture):
        """Build a model from the discovered architecture"""
        import torch.nn as nn

        layers = []
        in_channels = 3  # RGB input

        for layer in architecture:
            if layer['operation'].startswith('conv'):
                kernel_size = int(layer['operation'].split('_')[-1][0])
                if 'depthwise' in layer['operation']:
                    layers.append(nn.Conv2d(
                        in_channels, in_channels,
                        kernel_size=kernel_size,
                        stride=layer['stride'],
                        padding=kernel_size//2,
                        groups=in_channels
                    ))
                else:
                    layers.append(nn.Conv2d(
                        in_channels, layer['channels'],
                        kernel_size=kernel_size,
                        stride=layer['stride'],
                        padding=kernel_size//2
                    ))
                    in_channels = layer['channels']

                layers.append(nn.BatchNorm2d(in_channels))
                layers.append(nn.ReLU())

            elif layer['operation'].startswith('pool'):
                pool_type = layer['operation'].split('_')[0]
                kernel_size = int(layer['operation'].split('_')[-1][0])

                if pool_type == 'max':
                    layers.append(nn.MaxPool2d(kernel_size, stride=layer['stride']))
                else:
                    layers.append(nn.AvgPool2d(kernel_size, stride=layer['stride']))

        # Add final layers
        layers.append(nn.AdaptiveAvgPool2d(1))
        layers.append(nn.Flatten())
        layers.append(nn.Linear(in_channels, self.num_classes))

        return nn.Sequential(*layers)

    def _evaluate_model(self, model, data_loader):
        """Evaluate a model on a dataset"""
        import torch

        model.eval()
        correct = 0
        total = 0

        with torch.no_grad():
            for inputs, targets in data_loader:
                outputs = model(inputs)
                _, predicted = torch.max(outputs.data, 1)
                total += targets.size(0)
                correct += (predicted == targets).sum().item()

        return correct / total

# NAS for object detection
class NAS_ObjectDetector:
    def __init__(self, search_method='darts'):
        self.search_method = search_method
        self.search_space = self._create_detection_search_space()
        self.nas = self._create_nas_system()

    def _create_detection_search_space(self):
        """Create a search space for object detection"""
        search_space = NAS_SearchSpace()

        # Add detection-specific operations
        search_space.operations.extend([
            'detection_head',
            'roi_pooling',
            'anchor_generation'
        ])

        # Add detection-specific connection patterns
        search_space.connection_patterns.extend([
            'feature_pyramid',
            'skip_connection_detection'
        ])

        return search_space

    def _create_nas_system(self):
        """Create the NAS system"""
        if self.search_method == 'rl':
            return RL_NAS(self.search_space)
        elif self.search_method == 'evolutionary':
            return Evolutionary_NAS(self.search_space)
        elif self.search_method == 'darts':
            return DARTS(self.search_space)
        else:
            raise ValueError(f"Unknown search method: {self.search_method}")

    def search(self, dataset):
        """Search for the optimal architecture"""
        return self.nas.train(dataset)

    def train_final_model(self, architecture, dataset, epochs=100):
        """Train the final object detection model"""
        import torch
        import torch.nn as nn
        import torch.optim as optim
        from torch.utils.data import DataLoader

        # Build model from architecture
        model = self._build_detection_model(architecture)
        criterion = nn.CrossEntropyLoss()
        optimizer = optim.Adam(model.parameters(), lr=0.001)

        # Data loaders
        train_loader = DataLoader(dataset, batch_size=16, shuffle=True)
        val_loader = DataLoader(dataset, batch_size=16, shuffle=False)

        # Training loop
        for epoch in range(epochs):
            model.train()
            for inputs, targets in train_loader:
                optimizer.zero_grad()
                outputs = model(inputs)
                loss = criterion(outputs, targets)
                loss.backward()
                optimizer.step()

            # Validation
            val_acc = self._evaluate_model(model, val_loader)
            print(f"Epoch {epoch+1}/{epochs}, Val Acc: {val_acc:.4f}")

        return model

    def _build_detection_model(self, architecture):
        """Build an object detection model from architecture"""
        import torch.nn as nn

        # Build backbone
        backbone = []
        in_channels = 3

        for layer in architecture:
            if layer['operation'] in ['detection_head', 'roi_pooling', 'anchor_generation']:
                continue  # Skip detection-specific layers for backbone

            if layer['operation'].startswith('conv'):
                kernel_size = int(layer['operation'].split('_')[-1][0])
                if 'depthwise' in layer['operation']:
                    backbone.append(nn.Conv2d(
                        in_channels, in_channels,
                        kernel_size=kernel_size,
                        stride=layer['stride'],
                        padding=kernel_size//2,
                        groups=in_channels
                    ))
                else:
                    backbone.append(nn.Conv2d(
                        in_channels, layer['channels'],
                        kernel_size=kernel_size,
                        stride=layer['stride'],
                        padding=kernel_size//2
                    ))
                    in_channels = layer['channels']

                backbone.append(nn.BatchNorm2d(in_channels))
                backbone.append(nn.ReLU())

            elif layer['operation'].startswith('pool'):
                pool_type = layer['operation'].split('_')[0]
                kernel_size = int(layer['operation'].split('_')[-1][0])

                if pool_type == 'max':
                    backbone.append(nn.MaxPool2d(kernel_size, stride=layer['stride']))
                else:
                    backbone.append(nn.AvgPool2d(kernel_size, stride=layer['stride']))

        backbone = nn.Sequential(*backbone)

        # Build detection head
        detection_head = []
        for layer in architecture:
            if layer['operation'] == 'detection_head':
                detection_head.append(nn.Conv2d(in_channels, 9 * 5, kernel_size=1))
                # 9 anchors per location, 5 values per anchor (4 coords + 1 confidence)
            elif layer['operation'] == 'roi_pooling':
                detection_head.append(nn.AdaptiveMaxPool2d((7, 7)))
            elif layer['operation'] == 'anchor_generation':
                # Anchor generation would be implemented here
                pass

        detection_head = nn.Sequential(*detection_head)

        # Combine backbone and detection head
        class DetectionModel(nn.Module):
            def __init__(self, backbone, detection_head):
                super(DetectionModel, self).__init__()
                self.backbone = backbone
                self.detection_head = detection_head

            def forward(self, x):
                features = self.backbone(x)
                outputs = self.detection_head(features)
                return outputs

        return DetectionModel(backbone, detection_head)

    def _evaluate_model(self, model, data_loader):
        """Evaluate the detection model"""
        import torch

        model.eval()
        correct = 0
        total = 0

        with torch.no_grad():
            for inputs, targets in data_loader:
                outputs = model(inputs)
                # For detection, we would calculate mAP instead of accuracy
                # This is simplified for illustration
                _, predicted = torch.max(outputs.data, 1)
                total += targets.size(0)
                correct += (predicted == targets).sum().item()

        return correct / total

# NAS for medical imaging
class NAS_MedicalImaging:
    def __init__(self, search_method='darts', in_channels=1, num_classes=2):
        self.search_method = search_method
        self.in_channels = in_channels
        self.num_classes = num_classes
        self.search_space = self._create_medical_search_space()
        self.nas = self._create_nas_system()

    def _create_medical_search_space(self):
        """Create a search space for medical imaging"""
        search_space = NAS_SearchSpace()

        # Add medical imaging specific operations
        search_space.operations.extend([
            '3d_conv_3x3x3',
            '3d_conv_5x5x5',
            '3d_max_pool_2x2x2',
            '3d_avg_pool_2x2x2',
            'attention_gate',
            'multi_scale_fusion'
        ])

        # Add medical imaging specific connection patterns
        search_space.connection_patterns.extend([
            'skip_connection_3d',
            'dense_connection_3d'
        ])

        return search_space

    def _create_nas_system(self):
        """Create the NAS system"""
        if self.search_method == 'rl':
            return RL_NAS(self.search_space)
        elif self.search_method == 'evolutionary':
            return Evolutionary_NAS(self.search_space)
        elif self.search_method == 'darts':
            return DARTS(self.search_space)
        else:
            raise ValueError(f"Unknown search method: {self.search_method}")

    def search(self, dataset):
        """Search for the optimal architecture"""
        return self.nas.train(dataset)

    def train_final_model(self, architecture, dataset, epochs=100):
        """Train the final medical imaging model"""
        import torch
        import torch.nn as nn
        import torch.optim as optim
        from torch.utils.data import DataLoader

        # Build model from architecture
        model = self._build_medical_model(architecture)
        criterion = nn.CrossEntropyLoss()
        optimizer = optim.Adam(model.parameters(), lr=0.001)

        # Data loaders
        train_loader = DataLoader(dataset, batch_size=16, shuffle=True)
        val_loader = DataLoader(dataset, batch_size=16, shuffle=False)

        # Training loop
        for epoch in range(epochs):
            model.train()
            for inputs, targets in train_loader:
                optimizer.zero_grad()
                outputs = model(inputs)
                loss = criterion(outputs, targets)
                loss.backward()
                optimizer.step()

            # Validation
            val_acc = self._evaluate_model(model, val_loader)
            print(f"Epoch {epoch+1}/{epochs}, Val Acc: {val_acc:.4f}")

        return model

    def _build_medical_model(self, architecture):
        """Build a medical imaging model from architecture"""
        import torch.nn as nn

        layers = []
        in_channels = self.in_channels

        for layer in architecture:
            if layer['operation'].startswith('3d_conv'):
                kernel_size = int(layer['operation'].split('_')[-1][0])
                layers.append(nn.Conv3d(
                    in_channels, layer['channels'],
                    kernel_size=kernel_size,
                    stride=layer['stride'],
                    padding=kernel_size//2
                ))
                in_channels = layer['channels']
                layers.append(nn.BatchNorm3d(in_channels))
                layers.append(nn.ReLU())

            elif layer['operation'].startswith('3d_pool'):
                pool_type = layer['operation'].split('_')[1]
                kernel_size = int(layer['operation'].split('_')[-1][0])

                if pool_type == 'max':
                    layers.append(nn.MaxPool3d(kernel_size, stride=layer['stride']))
                else:
                    layers.append(nn.AvgPool3d(kernel_size, stride=layer['stride']))

            elif layer['operation'] == 'attention_gate':
                layers.append(AttentionGate(in_channels))

            elif layer['operation'] == 'multi_scale_fusion':
                layers.append(MultiScaleFusion(in_channels))

        # Add final layers
        layers.append(nn.AdaptiveAvgPool3d(1))
        layers.append(nn.Flatten())
        layers.append(nn.Linear(in_channels, self.num_classes))

        return nn.Sequential(*layers)

    def _evaluate_model(self, model, data_loader):
        """Evaluate the model on a dataset"""
        import torch

        model.eval()
        correct = 0
        total = 0

        with torch.no_grad():
            for inputs, targets in data_loader:
                outputs = model(inputs)
                _, predicted = torch.max(outputs.data, 1)
                total += targets.size(0)
                correct += (predicted == targets).sum().item()

        return correct / total

class AttentionGate(nn.Module):
    """Attention gate for medical imaging"""
    def __init__(self, in_channels):
        super(AttentionGate, self).__init__()
        self.conv = nn.Sequential(
            nn.Conv3d(in_channels, in_channels, kernel_size=1),
            nn.BatchNorm3d(in_channels),
            nn.ReLU(),
            nn.Conv3d(in_channels, 1, kernel_size=1),
            nn.Sigmoid()
        )

    def forward(self, x):
        attention = self.conv(x)
        return x * attention

class MultiScaleFusion(nn.Module):
    """Multi-scale feature fusion for medical imaging"""
    def __init__(self, in_channels):
        super(MultiScaleFusion, self).__init__()
        self.conv1 = nn.Conv3d(in_channels, in_channels, kernel_size=1)
        self.conv3 = nn.Conv3d(in_channels, in_channels, kernel_size=3, padding=1)
        self.conv5 = nn.Conv3d(in_channels, in_channels, kernel_size=5, padding=2)

    def forward(self, x):
        x1 = self.conv1(x)
        x3 = self.conv3(x)
        x5 = self.conv5(x)
        return x1 + x3 + x5