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weiwenjiangweiwenjiang
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‎session_4/EX_6_QF_MixNN_V_U.ipynb

Lines changed: 71 additions & 108 deletions
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@@ -21,9 +21,9 @@
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": null,
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"source": [
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"is_colab = False\n",
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"is_colab = True\n",
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"import sys\n",
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"if is_colab:\n",
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" !pip install -q torch==1.9.0\n",
@@ -35,10 +35,10 @@
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"\n",
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"\n",
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"import torch\n",
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"from qiskit import QuantumCircuit, ClassicalRegister\n",
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"from qiskit import QuantumCircuit, ClassicalRegister\n",
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"import numpy as np\n",
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"import functools\n",
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"from qfnn.qf_fb.q_output import fire_ibmq,analyze,add_measure\n",
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"from qfnn.qf_fb.q_output import fire_ibmq, analyze, add_measure\n",
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"from qfnn.qf_circ.f_lyr_circ import F_LYR_Circ\n",
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"from qfnn.qf_circ.v_lyr_circ import V_LYR_Circ\n",
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"from qfnn.qf_net.utils import binarize\n",
@@ -53,11 +53,11 @@
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"# remember to change the path on your computer\n",
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"######################################################\n",
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"if is_colab:\n",
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" data_path = '/content/data' #mnist path\n",
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" resume_path = '/content/model/v16_u2/model_best.tar' #model path\n",
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" data_path = '/content/data' # mnist path\n",
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" resume_path = '/content/model/v16_u2/model_best.tar' # model path\n",
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"else:\n",
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" data_path = '/home/hzr/Software/quantum/qc_mnist/pytorch/data' #mnist path\n",
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" resume_path = '/home/hzr/Software/quantum/QuantumFlow_Tutorial/model/v16_u2/model_best.tar' #model path"
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" data_path = '/home/hzr/Software/quantum/qc_mnist/pytorch/data' # mnist path\n",
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" resume_path = '/home/hzr/Software/quantum/QuantumFlow_Tutorial/model/v16_u2/model_best.tar' # model path"
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],
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"outputs": [],
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"metadata": {
@@ -69,14 +69,14 @@
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": null,
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"source": [
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"################ Zhirui on 12-30-2020 ################\n",
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"# Parameters of settings\n",
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"######################################################\n",
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"interest_num = [3,6]\n",
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"img_size = 4\n",
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"batch_size = 1# how many samples per batch to load\n",
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"batch_size = 1 # how many samples per batch to load\n",
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"inference_batch_size = 1\n",
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"isppd = False #is prepared data\n",
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"\n"
@@ -93,9 +93,9 @@
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": null,
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"source": [
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"train_loader, test_loader = load_data(interest_num,data_path,isppd,img_size,batch_size,inference_batch_size,False)\n",
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"train_loader, test_loader = load_data(interest_num, data_path, isppd, img_size, batch_size,inference_batch_size,False)\n",
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"\n",
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"\n",
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"for batch_idx, (data, target) in enumerate(test_loader):\n",
@@ -104,23 +104,7 @@
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" quantum_matrix = to_quantum_matrix(data)\n",
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" break"
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],
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"C:\\Users\\wjiang8\\AppData\\Roaming\\Python\\Python38\\site-packages\\torchvision\\datasets\\mnist.py:498: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at ..\\torch\\csrc\\utils\\tensor_numpy.cpp:180.)\n",
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" return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Batch Id: 0, Target: tensor([1])\n"
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]
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}
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],
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"outputs": [],
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"metadata": {}
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},
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{
@@ -139,7 +123,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": null,
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"source": [
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"################ hzr on 12-30-2020 ################\n",
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"# Get the parameters of the trained model\n",
@@ -152,29 +136,47 @@
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"theta = checkpoint['state_dict']['fc0.theta']\n",
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"weight = checkpoint['state_dict']['fc1.weight']\n"
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],
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"outputs": [
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{
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"ename": "FileNotFoundError",
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"evalue": "[Errno 2] No such file or directory: '/home/hzr/Software/quantum/QuantumFlow_Tutorial/model/v16_u2/model_best.tar'",
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"output_type": "error",
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"traceback": [
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"\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
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"\u001B[1;31mFileNotFoundError\u001B[0m Traceback (most recent call last)",
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"\u001B[1;32m~\\AppData\\Local\\Temp/ipykernel_29852/3962706902.py\u001B[0m in \u001B[0;36m<module>\u001B[1;34m\u001B[0m\n\u001B[0;32m 4\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 5\u001B[0m \u001B[1;31m# Model initialization\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m----> 6\u001B[1;33m \u001B[0mcheckpoint\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mtorch\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mload\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mresume_path\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mmap_location\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;34m'cpu'\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m 7\u001B[0m \u001B[0mprint\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mcheckpoint\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;34m'state_dict'\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;34m'fc0.theta'\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 8\u001B[0m \u001B[0mprint\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mcheckpoint\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;34m'state_dict'\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;34m'fc1.weight'\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
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"\u001B[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\torch\\serialization.py\u001B[0m in \u001B[0;36mload\u001B[1;34m(f, map_location, pickle_module, **pickle_load_args)\u001B[0m\n\u001B[0;32m 592\u001B[0m \u001B[0mpickle_load_args\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;34m'encoding'\u001B[0m\u001B[1;33m]\u001B[0m \u001B[1;33m=\u001B[0m \u001B[1;34m'utf-8'\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 593\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 594\u001B[1;33m \u001B[1;32mwith\u001B[0m \u001B[0m_open_file_like\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mf\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;34m'rb'\u001B[0m\u001B[1;33m)\u001B[0m \u001B[1;32mas\u001B[0m \u001B[0mopened_file\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m 595\u001B[0m \u001B[1;32mif\u001B[0m \u001B[0m_is_zipfile\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mopened_file\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 596\u001B[0m \u001B[1;31m# The zipfile reader is going to advance the current file position.\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
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"\u001B[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\torch\\serialization.py\u001B[0m in \u001B[0;36m_open_file_like\u001B[1;34m(name_or_buffer, mode)\u001B[0m\n\u001B[0;32m 228\u001B[0m \u001B[1;32mdef\u001B[0m \u001B[0m_open_file_like\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mname_or_buffer\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mmode\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 229\u001B[0m \u001B[1;32mif\u001B[0m \u001B[0m_is_path\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mname_or_buffer\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 230\u001B[1;33m \u001B[1;32mreturn\u001B[0m \u001B[0m_open_file\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mname_or_buffer\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mmode\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m 231\u001B[0m \u001B[1;32melse\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 232\u001B[0m \u001B[1;32mif\u001B[0m \u001B[1;34m'w'\u001B[0m \u001B[1;32min\u001B[0m \u001B[0mmode\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
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"\u001B[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\torch\\serialization.py\u001B[0m in \u001B[0;36m__init__\u001B[1;34m(self, name, mode)\u001B[0m\n\u001B[0;32m 209\u001B[0m \u001B[1;32mclass\u001B[0m \u001B[0m_open_file\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0m_opener\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 210\u001B[0m \u001B[1;32mdef\u001B[0m \u001B[0m__init__\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mname\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mmode\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 211\u001B[1;33m \u001B[0msuper\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0m_open_file\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m__init__\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mopen\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mname\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mmode\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m 212\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 213\u001B[0m \u001B[1;32mdef\u001B[0m \u001B[0m__exit__\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;33m*\u001B[0m\u001B[0margs\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
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"\u001B[1;31mFileNotFoundError\u001B[0m: [Errno 2] No such file or directory: '/home/hzr/Software/quantum/QuantumFlow_Tutorial/model/v16_u2/model_best.tar'"
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]
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}
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],
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"outputs": [],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"### classical inference"
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],
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"metadata": {}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"source": [
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"neural_in_layers = 'v:16,u:2'\n",
159+
"layers = []\n",
160+
"for item1 in neural_in_layers.split(\",\"):\n",
161+
" x= item1.split(\":\")\n",
162+
" layer =[]\n",
163+
" layer.append(x[0].strip())\n",
164+
" layer.append(int(x[1].strip()))\n",
165+
" layers.append(layer)\n",
166+
"print(layers)\n",
167+
"\n",
168+
"img_size = 4\n",
169+
"model = Net(img_size,layers,False,False)\n",
170+
"model.load_state_dict(checkpoint[\"state_dict\"])\n",
171+
"to_quantum_data = ToQuantumData(img_size)\n",
172+
"output_data = to_quantum_data(data)\n",
173+
"# output = model.forward(output_data,False)\n",
174+
"output = model(output_data, False)\n",
175+
"print(\"classical inference result:\",output)"
176+
],
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"outputs": [],
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"metadata": {}
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},
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{
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"cell_type": "markdown",
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"source": [
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{
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"cell_type": "markdown",
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"source": [
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"### build the network"
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"### build the network on Quantum Circuit"
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],
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"metadata": {}
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%% md\n"
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}
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}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"source": [
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"################ hzr on 12-30-2020 ################\n",
218224
"# Generate the circuit of v-layer\n",
219225
"######################################################\n",
220226
"\n",
221-
"#define your input and repeat number\n",
227+
"# define your input and repeat number\n",
222228
"vqc = V_LYR_Circ(4,2)\n",
223-
"#add v-layer to your circuit\n",
224-
"vqc.forward(circuit,inputs,'v10',np.array(theta,dtype=np.double))\n",
229+
"# add v-layer to your circuit\n",
230+
"vqc.forward(circuit, inputs, 'v10', np.array(theta, dtype=np.double))\n",
225231
"\n",
226232
"circuit.draw('text',fold=300)"
227233
],
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"outputs": [],
229-
"metadata": {}
234+
"metadata": {
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"collapsed": false,
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"pycharm": {
237+
"name": "#%%\n"
238+
}
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
@@ -248,11 +260,9 @@
248260
"#add f-layer to your circuit\n",
249261
"f_layer.forward(circuit,binarize(weight),inputs,f_layer_out_qubits,None,aux)\n",
250262
"\n",
263+
"#add measurement to your circuit if needed\n",
251264
"add_measure(circuit,f_layer_out_qubits,'reg')\n",
252-
"\n",
253265
"circuit.barrier()\n",
254-
"#add measurement to your circuit if needed\n",
255-
"\n",
256266
"\n",
257267
"circuit.draw('text',fold=300)\n",
258268
"\n"
@@ -270,9 +280,7 @@
270280
"source": [
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"### simulation\n"
272282
],
273-
"metadata": {
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"collapsed": false
275-
}
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"metadata": {}
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},
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{
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"cell_type": "code",
@@ -283,9 +291,9 @@
283291
"######################################################\n",
284292
"\n",
285293
"\n",
286-
"qc_shots=8192\n",
287-
"opt_counts = fire_ibmq(circuit,qc_shots,True)\n",
288-
"(opt_mycount,bits) = analyze(opt_counts)\n",
294+
"qc_shots= 10000 # 8192\n",
295+
"opt_counts = fire_ibmq(circuit, qc_shots, True)\n",
296+
"(opt_mycount, bits) = analyze(opt_counts)\n",
289297
"opt_class_prob=[]\n",
290298
"for b in range(bits):\n",
291299
" opt_class_prob.append(float(opt_mycount[b])/qc_shots)\n",
@@ -301,42 +309,6 @@
301309
"print(\"=\"*30)"
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],
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"outputs": [],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
308-
}
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"### classical inference"
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],
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"metadata": {}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"source": [
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"\n",
323-
"neural_in_layers = 'v:16,u:2'\n",
324-
"layers = []\n",
325-
"for item1 in neural_in_layers.split(\",\"):\n",
326-
" x= item1.split(\":\")\n",
327-
" layer =[]\n",
328-
" layer.append(x[0].strip())\n",
329-
" layer.append(int(x[1].strip()))\n",
330-
" layers.append(layer)\n",
331-
"\n",
332-
"model = Net(img_size,layers,False,False)\n",
333-
"model.load_state_dict(checkpoint[\"state_dict\"])\n",
334-
"to_quantum_data = ToQuantumData(img_size)\n",
335-
"output_data = to_quantum_data(data)\n",
336-
"output = model.forward(output_data,False)\n",
337-
"print(\"classical inference result:\",output)"
338-
],
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"outputs": [],
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"metadata": {}
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}
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],
@@ -357,15 +329,6 @@
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"nbconvert_exporter": "python",
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"file_extension": ".py"
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},
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"pycharm": {
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"stem_cell": {
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"cell_type": "raw",
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"source": [],
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"metadata": {
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"collapsed": false
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}
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}
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},
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"interpreter": {
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"hash": "f24048f0d5bdb0ff49c5e7c8a9899a65bc3ab13b0f32660a2227453ca6b95fd8"
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}

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