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main.py
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main.py
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import torchvision
from torchvision import datasets, transforms , models
from torch import Tensor, save
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import os
import torch
import torch.nn as nn
import cv2
import matplotlib.pyplot as plt
from torch.utils.data import Dataset, DataLoader, ConcatDataset, random_split
import copy
import tqdm
from PIL import Image
from qiskit import execute
from qiskit.circuit import Parameter, ControlledGate
from qiskit import Aer
import qiskit
import numpy as np
import torch
from torch.autograd import Function
import torch.optim as optim
import torch.nn as nn
import torch.nn.functional as F
# if torch.cuda.is_available():
# device = torch.device("cuda:0")
# print("Running on the GPU")
# else:
# device = torch.device("cpu")
# print("Running on the CPU")
device = torch.device("cpu")
from qiskit import execute
from qiskit.circuit import Parameter, ControlledGate
from qiskit import Aer
import qiskit
import numpy as np
from tqdm import tqdm
from matplotlib import pyplot as plt
import torchvision.datasets as dset
np.random.seed = 42
NUM_QUBITS = 4
NUM_SHOTS = 1000
SHIFT = np.pi / 2
LEARNING_RATE = 0.01
MOMENTUM = 0.5
SIMULATOR = Aer.get_backend('qasm_simulator')
# create list of all possible outputs of quantum circuit (2**NUM_QUBITS possible)
import itertools
def create_QC_OUTPUTS():
measurements = list(itertools.product([0, 1], repeat=NUM_QUBITS))
return [
''.join([str(bit) for bit in measurement])
for measurement in measurements
]
QC_OUTPUTS = create_QC_OUTPUTS()
print(QC_OUTPUTS)
class QiskitCircuit():
def __init__(self, n_qubits, backend, shots):
# --- Circuit definition ---
self.circuit = qiskit.QuantumCircuit(n_qubits)
self.n_qubits = n_qubits
self.thetas = {
k: Parameter('Theta' + str(k))
for k in range(self.n_qubits)
}
all_qubits = [i for i in range(n_qubits)]
self.circuit.h(all_qubits)
self.circuit.cx(0, 1)
self.circuit.cx(2, 3)
self.circuit.cx(1, 2)
self.circuit.barrier()
for k in range(n_qubits):
self.circuit.ry(self.thetas[k], k)
# # Apply controlled-unitary
# # uc=ry(self.theta4, 4).to_gate().control(4)
# # self.circuit.append(uc, [0,1,2,3,4])
# self.circuit.ry(self.theta4, 4).to_gate().control(4)
self.circuit.measure_all()
# ---------------------------
self.backend = backend
self.shots = shots
# check = perc
# for i in range(nr_qubits):
# check *= (float(key[i])-1/2)*2
# expects += check
def N_qubit_expectation_Z(self, counts, shots, nr_qubits):
expects = np.zeros(len(QC_OUTPUTS))
for k in range(len(QC_OUTPUTS)):
key = QC_OUTPUTS[k]
perc = counts.get(key, 0) / shots
expects[k] = perc
return expects
def run(self, i):
params = i
# print('params = {}'.format(len(params)))
backend = Aer.get_backend('qasm_simulator')
job_sim = execute(self.circuit,
self.backend,
shots=self.shots,
parameter_binds=[{
self.thetas[k]: params[k].item()
for k in range(NUM_QUBITS)
}])
#
result_sim = job_sim.result()
counts = result_sim.get_counts(self.circuit)
return self.N_qubit_expectation_Z(counts, self.shots, NUM_QUBITS)
circuit = QiskitCircuit(NUM_QUBITS, SIMULATOR, NUM_SHOTS)
print('Expected value for rotation [pi/4]: {}'.format(
circuit.run(torch.Tensor([np.pi / 4] * NUM_QUBITS))))
#circuit.circuit.draw(output='mpl', filename='Figures/{}-qubit circuit ryN.jpg'.format(NUM_QUBITS))
class TorchCircuit(Function):
@staticmethod
def forward(ctx, i):
if not hasattr(ctx, 'QiskitCirc'):
ctx.QiskitCirc = QiskitCircuit(NUM_QUBITS,
SIMULATOR,
shots=NUM_SHOTS)
exp_value = ctx.QiskitCirc.run(i)
result = torch.tensor([exp_value])
ctx.save_for_backward(result.to(device), i)
return result
def backward(ctx, grad_output):
forward_tensor, i = ctx.saved_tensors
# print('forward_tensor = {}'.format(forward_tensor))
input_numbers = i.to(device)
# print('input_numbers = {}'.format(input_numbers))
gradients = torch.Tensor().to(device)
for k in range(NUM_QUBITS):
shift_right = input_numbers.detach().clone()
shift_right[k] = shift_right[k] + SHIFT
shift_left = input_numbers.detach().clone()
shift_left[k] = shift_left[k] - SHIFT
# print('shift_right = {}, shift_left = {}'.format(shift_right, shift_left))
expectation_right = ctx.QiskitCirc.run(shift_right)
expectation_left = ctx.QiskitCirc.run(shift_left)
# print('expectation_right = {}, \nexpectation_left = {}'.format(expectation_right, expectation_left))
gradient = torch.tensor([expectation_right]) - torch.tensor(
[expectation_left])
# rescale gradient
# gradient = gradient / torch.norm(gradient)
# print('gradient for k={}: {}'.format(k, gradient))
gradients = torch.cat((gradients, gradient.float().to(device)))
# print(gradients)
result = gradients.clone()
# print('gradients = {}'.format(result))
# print('grad_output = {}'.format(grad_output))
ret = (result.float() * grad_output.to(device).float()).T.to(device)
# print(ret)
return ret
x = torch.tensor([np.pi / 4] * NUM_QUBITS, requires_grad=True).to(device)
qc = TorchCircuit.apply
y1 = qc(x).to(device)
print('y1 after quantum layer: {}'.format(y1.float()))
y1 = nn.Linear(2**NUM_QUBITS, 1).to(device)(y1.float()).to(device)
y1.backward()
print('x.grad = {}'.format(x.grad))
x = torch.randn(3, 4, 5)
qc = TorchCircuit.apply
def cost(x):
target = -1
expval = qc(x)[0]
# simple linear layer: average all outputs of quantum layer
# print(expval)
val = sum([(i + 1) * expval[i]
for i in range(2**NUM_QUBITS)]) / 2**NUM_QUBITS
# print(val)
return torch.abs(val - target)**2, expval
x = torch.tensor([-np.pi / 4] * NUM_QUBITS, requires_grad=True)
opt = torch.optim.Adam([x], lr=0.1)
num_epoch = 100
loss_list = []
expval_list = []
for i in tqdm(range(num_epoch)):
# for i in range(num_epoch):
opt.zero_grad()
loss, expval = cost(x)
loss.backward()
opt.step()
loss_list.append(loss.item())
expval_list.append(expval)
plt.plot(loss_list)
plt.show()
img_dimensions = 300
batch_size = 1
data_dir = './data/'
# TODO: Define transforms for the training data and testing data
train_transforms = transforms.Compose([transforms.RandomRotation(30),
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])])
test_transforms = transforms.Compose([transforms.Resize(255),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])])
# Pass transforms in here, then run the next cell to see how the transforms look
train_data = datasets.ImageFolder('D:/Studio_Lufter/Ibmq_camp2020/catdog/data/train', transform=train_transforms)
test_data = datasets.ImageFolder('D:/Studio_Lufter/Ibmq_camp2020/catdog/data/test', transform=test_transforms)
train_data_loader = torch.utils.data.DataLoader(train_data, batch_size=1, shuffle=True)
test_data_loader = torch.utils.data.DataLoader(test_data, batch_size=1)
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.vgg = models.vgg19_bn(pretrained = True)
for param in self.vgg.features.parameters():
param.requires_grad = False
self.vgg.classifier = nn.Sequential(nn.Linear(25088, 4096),
nn.ReLU(),
nn.Dropout(0.5),
nn.Linear(4096, 512),
nn.ReLU(),
nn.Dropout(0.5),
nn.Linear(512, 256)
)
#self.conv1 = nn.Conv2d(3, 10, kernel_size=5)
#self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
#self.conv2_drop = nn.Dropout2d()
#self.fc1 = nn.Linear(320, 50)
self.fc2 = nn.Linear(256, NUM_QUBITS)
self.qc = TorchCircuit.apply
self.fc3 = nn.Linear(2**NUM_QUBITS, 2)
#self.qc = TorchCircuit.apply
def forward(self, x):
#x = F.relu(F.max_pool2d(self.conv1(x), 2))
#x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
#x = x.view(-1, 320)
#x = F.relu(self.fc1(x))
#x = F.dropout(x, training=self.training)
x = self.vgg(x)
x = self.fc2(x)
x = np.pi * torch.tanh(x)
# print('params to QC: {}'.format(x))
x = qc(x[0]) # QUANTUM LAYER
# print('output of QC = {}'.format(x))
x = torch.Tensor(x.float()).to(device)
x = self.fc3(x)
#x = qc(x[0])
#x = torch.Tensor(x.float()).to(device)
#x.float()
x = torch.softmax(x, dim=1)
return x
#def predict(self, x):
# apply softmax
#pred = self.forward(x)
# print(pred)
#ans = torch.argmax(pred[0]).item()
#return torch.tensor(ans)
network = Net().to(device)
optimizer = optim.Adam(network.parameters(), lr=0.001)
# optimizer = optim.Adam(network.parameters(), lr=learning_rate)
epochs = 20
loss_list = []
loss_func = nn.CrossEntropyLoss().to(device)
for epoch in range(epochs):
total_loss = []
for batch_idx, (data, target) in enumerate(train_data_loader):
data, target = data.to(device), target.to(device)
# print(batch_idx)
optimizer.zero_grad()
# Forward pass
#print(data.dtype)
output = network(data).to(device)
# Calculating loss
loss = loss_func(output, target)
# Backward pass
loss.backward()
# Optimize the weights
optimizer.step()
total_loss.append(loss.item())
loss_list.append(sum(total_loss) / len(total_loss))
print('Training [{:.0f}%]\tLoss: {:.4f}'.format(
100. * (epoch + 1) / epochs, loss_list[-1]))
plt.plot(loss_list)
plt.title('Hybrid NN Training Convergence for {}-qubit'.format(NUM_QUBITS))
plt.xlabel('Training Iterations')
plt.ylabel('Cross Entropy Loss')
plt.savefig('Figures/{}-qubit Loss Curve ryN.jpg'.format(NUM_QUBITS))
accuracy = 0
number = 0
for batch_idx, (data, target) in enumerate(test_loader):
number += 1
output = network.predict(data).item()
accuracy += (output == target[0].item()) * 1
print("Performance on test data is is: {}/{} = {}%".format(
accuracy, number, 100 * accuracy / number))
n_samples_shape = (8, 6)
count = 0
fig, axes = plt.subplots(nrows=n_samples_shape[0],
ncols=n_samples_shape[1],
figsize=(10, 2 * n_samples_shape[0]))
network.eval()
with torch.no_grad():
for batch_idx, (data, target) in enumerate(test_data_loader):
if count == n_samples_shape[0] * n_samples_shape[1]:
break
pred = network.predict(data).item()
axes[count // n_samples_shape[1]][count % n_samples_shape[1]].imshow(
data[0].numpy().squeeze(), cmap='gray')
axes[count // n_samples_shape[1]][count %
n_samples_shape[1]].set_xticks([])
axes[count // n_samples_shape[1]][count %
n_samples_shape[1]].set_yticks([])
axes[count // n_samples_shape[1]][count %
n_samples_shape[1]].set_title(
'Predicted {}'.format(pred))
count += 1