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test.py
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test.py
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team_name="IonQnia"
import cirq
import numpy as np
import pickle
import json
import os
import sys
from collections import Counter
from sklearn.metrics import mean_squared_error
import os
os.environ['KMP_DUPLICATE_LIB_OK']='True'
# OpenMP: number of parallel threads.
# Plotting
import matplotlib.pyplot as plt
# PyTorch
import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
import torchvision
from torchvision import datasets, models, transforms
# Pennylane
import pennylane as qml
from pennylane import numpy as np
# Other tools
import time
import os
import copy
import numpy as np
import os
import random
import matplotlib.pyplot as plt
if len(sys.argv) > 1:
data_path = sys.argv[1]
else:
data_path = '.'
def count_gates(num_qubits, depth):
"""Returns the number of 1-qubit gates, number of 2-qubit gates, number of 3-qubit gates...."""
counter = num_qubits * depth
return counter
def H_layer(nqubits):
"""Layer of single-qubit Hadamard gates.
"""
for idx in range(nqubits):
qml.Hadamard(wires=idx)
def RY_layer(w):
"""Layer of parametrized qubit rotations around the y axis.
"""
for idx, element in enumerate(w):
qml.RY(element, wires=idx)
def entangling_layer(nqubits):
"""Layer of CNOTs followed by another shifted layer of CNOT.
"""
# In other words it should apply something like :
#CNOT CNOT CNOT CNOT... CNOT
# CNOT CNOT CNOT... CNOT
for i in range(0, nqubits - 1, 2): #loop over even indices: i=0,2,...N-2
qml.CNOT(wires=[i, i + 1])
for i in range(1, nqubits - 1, 2): #loop over odd indices: i=1,3,...N-3
qml.CNOT(wires=[i, i + 1])
print("finish functions and packages loading")
n_qubits = 4 # Number of qubits
quantum = True # If set to "False", the dressed quantum circuit is replaced by
# An enterily classical net (defined by the next parameter).
classical_model = '512_nq_2' # Possible choices: '512_2','512_nq_2','551_512_2'.
step = 0.0004 # Learning rate
batch_size = 4 # Number of samples for each training step
num_epochs = 30 # Number of training epochs
q_depth = 6 # Depth of the quantum circuit (number of variational layers)
gamma_lr_scheduler = 0.1 # Learning rate reduction applied every 10 epochs.
max_layers = 15 # Keep 15 even if not all are used.
q_delta = 0.01 # Initial spread of random quantum weights
rng_seed = 0 # Seed for random number generator
start_time = time.time() # Start of the computation timer
dev = qml.device('default.qubit', wires=n_qubits)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print("finish parameters loading!")
data_transforms = {
'train': transforms.Compose([
#transforms.RandomResizedCrop(224), # uncomment for data augmentation
#transforms.RandomHorizontalFlip(), # uncomment for data augmentation
transforms.Resize(28),
transforms.CenterCrop(224),
transforms.ToTensor(),
# Normalize input channels using mean values and standard deviations of ImageNet.
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
'val': transforms.Compose([
transforms.Resize(28),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
}
# Load images and labels
images=np.load(data_path+'/images.npy')
labels=np.load(data_path+'/labels.npy')
# Calculate number of images in each dataset
num_images = len(images)
num_train = int(1 * num_images)
num_val = num_images
# Split images and labels into train and val datasets
train_images = images[:num_train]
val_images = images[:num_train]
train_labels = labels[:num_train]
val_labels = labels[:num_train]
# Make directories for train and val datasets
os.makedirs("data/class_3/train", exist_ok=True)
os.makedirs("data/class_3/val", exist_ok=True)
# Make directories for True and False labels in train and val datasets
os.makedirs("data/class_3/train/True", exist_ok=True)
os.makedirs("data/class_3/train/False", exist_ok=True)
os.makedirs("data/class_3/val/True", exist_ok=True)
os.makedirs("data/class_3/val/False", exist_ok=True)
# Save train images to corresponding True/False directories
for i, (image, label) in enumerate(zip(train_images, train_labels)):
plt.imsave(f"data/class_3/train/{label}/{i}.png", image, cmap='gray')
# Save val images to corresponding True/False directories
for i, (image, label) in enumerate(zip(val_images, val_labels)):
plt.imsave(f"data/class_3/val/{label}/{i}.png", image, cmap='gray')
data_transforms = {
'train': transforms.Compose([
#transforms.RandomResizedCrop(224), # uncomment for data augmentation
#transforms.RandomHorizontalFlip(), # uncomment for data augmentation
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
# Normalize input channels using mean values and standard deviations of ImageNet.
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
'val': transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
}
data_dir = 'data/class_3'
image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),
data_transforms[x]) for x in ['train', 'val']}
dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}
class_names = image_datasets['train'].classes
# Initialize dataloader
dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x],
batch_size=batch_size, shuffle=True) for x in ['train', 'val']}
print("finish image data transformation!")
torch.manual_seed(rng_seed)
dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x],
batch_size=batch_size,shuffle=True) for x in ['train', 'val']}
@qml.qnode(dev, interface='torch')
def q_net(q_in, q_weights_flat):
# Reshape weights
q_weights = q_weights_flat.reshape(max_layers, n_qubits)
# Start from state |+> , unbiased w.r.t. |0> and |1>
H_layer(n_qubits)
# Embed features in the quantum node
RY_layer(q_in)
# Sequence of trainable variational layers
for k in range(q_depth):
entangling_layer(n_qubits)
RY_layer(q_weights[k + 1])
# Expectation values in the Z basis
return [qml.expval(qml.PauliZ(j)) for j in range(n_qubits)]
class Quantumnet(nn.Module):
def __init__(self):
super().__init__()
self.pre_net = nn.Linear(512, n_qubits)
self.q_params = nn.Parameter(q_delta * torch.randn(max_layers * n_qubits))
self.post_net = nn.Linear(n_qubits, 2)
def forward(self, input_features):
pre_out = self.pre_net(input_features)
q_in = torch.tanh(pre_out) * np.pi / 2.0
# Apply the quantum circuit to each element of the batch and append to q_out
q_out = torch.Tensor(0, n_qubits)
q_out = q_out.to(device)
for elem in q_in:
q_out_elem = q_net(elem,self.q_params).float().unsqueeze(0)
q_out = torch.cat((q_out, q_out_elem))
return self.post_net(q_out)
model_hybrid = torchvision.models.resnet18(pretrained=True)
for param in model_hybrid.parameters():
param.requires_grad = False
if quantum:
model_hybrid.fc = Quantumnet()
elif classical_model == '512_2':
model_hybrid.fc = nn.Linear(512, 2)
elif classical_model == '512_nq_2':
model_hybrid.fc = nn.Sequential(nn.Linear(512, n_qubits), torch.nn.ReLU(), nn.Linear(n_qubits, 2))
elif classical_model == '551_512_2':
model_hybrid.fc = nn.Sequential(nn.Linear(512, 512), torch.nn.ReLU(), nn.Linear(512, 2))
# Use CUDA or CPU according to the "device" object.
model_hybrid = model_hybrid.to(device)
criterion = nn.CrossEntropyLoss()
optimizer_hybrid = optim.Adam(model_hybrid.fc.parameters(), lr=step)
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_hybrid, step_size=10, gamma=gamma_lr_scheduler)
def train_model(model, criterion, optimizer, scheduler, num_epochs):
val_acc_list = []
train_acc_list = []
since = time.time()
best_model_wts = copy.deepcopy(model.state_dict())
best_acc = 0.0
best_loss = 10000.0 # Large arbitrary number
best_acc_train = 0.0
best_loss_train = 10000.0 # Large arbitrary number
print('Training started:')
for epoch in range(num_epochs):
# Each epoch has a training and validation phase
for phase in ['train', 'val']:
if phase == 'train':
scheduler.step()
# Set model to training mode
model.train()
else:
# Set model to evaluate mode
model.eval()
running_loss = 0.0
running_corrects = 0
# Iterate over data.
n_batches = dataset_sizes[phase] // batch_size
it = 0
for inputs, labels in dataloaders[phase]:
since_batch = time.time()
batch_size_ = len(inputs)
inputs = inputs.to(device)
labels = labels.to(device)
optimizer.zero_grad()
# Track/compute gradient and make an optimization step only when training
with torch.set_grad_enabled(phase == 'train'):
outputs = model(inputs)
_, preds = torch.max(outputs, 1)
loss = criterion(outputs, labels)
if phase == 'train':
loss.backward()
optimizer.step()
# Print iteration results
running_loss += loss.item() * batch_size_
batch_corrects = torch.sum(preds == labels.data).item()
running_corrects += batch_corrects
print('Phase: {} Epoch: {}/{} Iter: {}/{} Batch time: {:.4f}'.format(phase, epoch + 1, num_epochs, it + 1, n_batches + 1, time.time() - since_batch), end='\r', flush=True)
it += 1
# Print epoch results
epoch_loss = running_loss / dataset_sizes[phase]
epoch_acc = running_corrects / dataset_sizes[phase]
print('Phase: {} Epoch: {}/{} Loss: {:.4f} Acc: {:.4f} '.format('train' if phase == 'train' else 'val ', epoch + 1, num_epochs, epoch_loss, epoch_acc))
if phase == 'train':
train_acc_list.append(epoch_acc)
else:
val_acc_list.append(epoch_acc)
# Check if this is the best model wrt previous epochs
if phase == 'val' and epoch_acc > best_acc:
best_acc = epoch_acc
best_model_wts = copy.deepcopy(model.state_dict())
if phase == 'val' and epoch_loss < best_loss:
best_loss = epoch_loss
if phase == 'train' and epoch_acc > best_acc_train:
best_acc_train = epoch_acc
if phase == 'train' and epoch_loss < best_loss_train:
best_loss_train = epoch_loss
# Print final results
model.load_state_dict(best_model_wts)
time_elapsed = time.time() - since
print('Training completed in {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))
print('Best test loss: {:.4f} | Best test accuracy: {:.4f}'.format(best_loss, best_acc))
return model, best_acc, train_acc_list, val_acc_list
print("start to run transfer learning! (May take time to run.)")
model_hybrid = train_model(model_hybrid, criterion, optimizer_hybrid,exp_lr_scheduler, num_epochs=4)
part1_score= max(model_hybrid[2])
part2_score= max(model_hybrid[3])
gatecount= count_gates(n_qubits, q_depth)
print()
print("=======Result=========")
print("Number of Qubits:", n_qubits, "Depth of VQC:", q_depth, "Gate Count:", gatecount)
print("Part1 Grade evaluated by 2-qubits gate and accuracy:",part1_score*(0.999**gatecount))
print("Part2 Grade evaluated by 2-qubits gate and accuracy:",part2_score*(0.999**gatecount))
print("Presented by Team IonQnia with PennyLane")