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基于Flask框架的write-notes网站:提升笔记书写的便捷性

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在这个知识点的梳理中,我们会围绕以下几点深入探讨: 1. Flask框架基础 2. 网站登录功能实现 3. 动态内容创建与管理 首先,我们来谈谈Flask框架。Flask是一个用Python编写的轻量级Web应用框架,它能够协助开发者快速构建web应用和web服务。其设计哲学是尽量保持简单而不失灵活,使得开发者可以轻松地部署应用和扩展功能。 知识点1:Flask框架基础 1.1 Flask应用结构:通常,一个Flask应用会包含一个或多个Python文件。在这个项目中,我们可能看到一个名为`app.py`的文件,这很可能是主文件,因为它通常用来初始化Flask应用实例。 1.2 路由和视图:路由在Flask中用于映射URL到相应的处理函数。这在`app.py`中可能会被定义,比如`@app.route('/login')`装饰器来定义一个登录页面的路由。 1.3 模板渲染:Flask使用Jinja2模板引擎来渲染HTML文件。开发者会将HTML代码放在一个单独的文件夹(通常是`templates`文件夹)中,并在Python代码中调用`render_template('template_name.html')`来加载和渲染HTML模板。 1.4 请求和响应:Flask应用会处理客户端的请求,并返回相应的响应。这通常涉及到`request`对象,它是全局请求对象,允许开发者访问客户端发送的数据。 1.5 用户会话管理:为了实现登录状态保持,Flask提供了`session`对象,它基于签名的cookies来存储跨请求的信息。在用户登录成功后,可以通过设置`session['logged_in'] = True`来标记用户为登录状态。 接下来,我们来关注网站登录功能的实现。 知识点2:网站登录功能实现 2.1 用户认证:网站登录功能通常包含用户认证,也就是验证用户的身份是否合法。在这个过程中,可能需要处理用户提交的用户名和密码,然后与存储在数据库中的信息进行比对。 2.2 密码安全:为了安全起见,密码不会以明文形式存储。在Flask项目中,通常会使用例如werkzeug的`generate_password_hash`函数来加密密码,并在验证时使用`check_password_hash`函数。 2.3 登录状态管理:登录成功后,通常会设置session中的某些值来标记用户已经通过认证。当用户访问需要认证的页面时,可以检查这些session值来决定是否允许访问。 2.4 错误处理:在登录过程中,如果用户输入不正确或者出现其他问题,应该给用户正确的反馈。在Flask中,可以通过返回特定的HTTP状态码或自定义错误消息来实现。 最后,我们将探讨动态内容的创建与管理。 知识点3:动态内容创建与管理 3.1 CRUD操作:在笔记应用中,CRUD操作是核心功能,即创建(Create)、读取(Read)、更新(Update)和删除(Delete)。这些操作将与后端数据库进行交互。 3.2 数据库设计:为了保存笔记数据,Flask应用需要连接到一个数据库,如SQLite。数据库中会有相应的表格来存储笔记内容。 3.3 内容编辑界面:为了编辑或创建笔记,应该有一个Web界面来接收用户输入的笔记内容,并提供相应的按钮来保存和更新笔记。 3.4 数据验证与存储:在笔记保存到数据库之前,应该进行必要的数据验证,例如检查是否为空或者长度限制等。之后,将数据存储到数据库中,可以使用ORM(如SQLAlchemy)或直接SQL语句来完成。 3.5 内容展示:笔记内容需要以一种友好的方式展示给用户,可以通过遍历数据库中存储的笔记信息,并将它们渲染到HTML页面中。 【压缩包子文件的文件名称列表】中提到的`write-notes-master`,暗示了我们这是一个含有多个文件和文件夹的项目结构。它可能包含以下内容: - `app.py`:Flask应用的入口文件,包含路由定义、视图函数以及程序运行的启动点。 - `templates`文件夹:存放所有HTML模板文件。 - `static`文件夹:存放静态文件,比如CSS样式表、JavaScript脚本和图片等。 - `venv`文件夹:虚拟环境文件夹,如果使用虚拟环境来管理Python包和依赖的话。 - `requirements.txt`文件:列出项目的所有Python依赖,用于安装项目所需的包。 这个项目结构非常符合传统的Flask项目布局,使得其他开发者可以快速理解和上手。

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# LSTM-Based Classical Music Generator using PyTorch (Extended with deeper model and audio modeling hooks) # Ensure you have installed: torch, pandas, numpy, pretty_midi, librosa import torch import torch.nn as nn import pandas as pd import numpy as np import pretty_midi import librosa from sklearn.preprocessing import MinMaxScaler from sklearn.model_selection import train_test_split from torch.utils.data import Dataset, DataLoader import os import glob # Set device device = torch.device("cpu") # force CPU only ### 1. Load and preprocess data ### data = pd.read_csv('/kaggle/input/musicnet-dataset/musicnet_metadata.csv') # Load preprocessed solo_piano data (already filtered) note_min = 21 solo_piano['note'] = solo_piano['note'] - note_min features = solo_piano[['composer', 'start_beat', 'end_beat', 'seconds']] # Target: note features = data_train[['composer', 'start_beat', 'end_beat', 'seconds']] note_min = 21 solo_piano['note'] = solo_piano['note'] - note_min features = solo_piano[['composer', 'start_beat', 'end_beat', 'seconds']] target = solo_piano['note'] scaler = MinMaxScaler() X_scaled = scaler.fit_transform(features) y = target.values # Create sequences SEQ_LEN = 50 def create_sequences(X, y, seq_len): sequences = [] labels = [] for i in range(len(X) - seq_len): sequences.append(X[i:i+seq_len]) labels.append(y[i+seq_len]) return np.array(sequences), np.array(labels) X_seq, y_seq = create_sequences(X_scaled, y, SEQ_LEN) # Train/Test split X_train, X_val, y_train, y_val = train_test_split(X_seq, y_seq, test_size=0.1, random_state=42) # Custom Dataset with optional audio features class PianoDataset(Dataset): def __init__(self, X, y, audio_folder=None, ids=None): self.X = torch.tensor(X, dtype=torch.float32) self.y = torch.tensor(y, dtype=torch.float32) self.audio_folder = audio_folder self.ids = ids def __len__(self): return len(self.X) def __getitem__(self, idx): sample = self.X[idx], self.y[idx] return sample train_loader = DataLoader(PianoDataset(X_train, y_train), batch_size=64, shuffle=True) val_loader = DataLoader(PianoDataset(X_val, y_val), batch_size=64) ### 2. Define LSTM Model (deeper, with LayerNorm) ### class MusicLSTM(nn.Module): def __init__(self, input_size, hidden_size, num_classes, num_layers=4): super(MusicLSTM, self).__init__() self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True, dropout=0.3) self.norm = nn.LayerNorm(hidden_size) self.dropout = nn.Dropout(0.4) self.fc = nn.Linear(hidden_size, 1) def forward(self, x): out, _ = self.lstm(x) out = self.norm(out[:, -1, :]) out = self.dropout(out) out = self.fc(out) return out model = MusicLSTM(input_size=4, hidden_size=384, num_classes=1) # run on CPU # reduced hidden_size for lower GPU memory use criterion = nn.MSELoss() optimizer = torch.optim.Adam(model.parameters(), lr=0.001) scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.5, patience=3) ### 3. Train model (with early stopping and LR scheduler) ### epochs = 50 best_val_loss = float('inf') patience = 7 patience_counter = 0 for epoch in range(epochs): model.train() total_loss = 0 for batch_X, batch_y in train_loader: batch_X, batch_y = batch_X, batch_y # stay on CPU optimizer.zero_grad() outputs = model(batch_X) loss = criterion(outputs, batch_y) loss.backward() optimizer.step() total_loss += loss.item() # Validation model.eval() val_loss = 0 with torch.no_grad(): for batch_X, batch_y in val_loader: batch_X, batch_y = batch_X.to(device), batch_y.to(device) outputs = model(batch_X) loss = criterion(outputs, batch_y) val_loss += loss.item() scheduler.step(val_loss) print(f"Epoch {epoch+1}/{epochs}, Train Loss: {total_loss:.4f}, Val Loss: {val_loss:.4f}") if val_loss < best_val_loss: best_val_loss = val_loss torch.save(model.state_dict(), "best_model.pt") patience_counter = 0 else: patience_counter += 1 if patience_counter >= patience: print("Early stopping triggered.") break ### 4. Generate music sequence ### model.load_state_dict(torch.load("best_model.pt", map_location=torch.device('cpu'))) model.eval() seed = torch.tensor(X_val[0:1], dtype=torch.float32) generated_notes = [] input_seq = seed.clone() for _ in range(100): output = model(input_seq) pred_note = round(output.item()) generated_notes.append(pred_note) next_input = input_seq[:, 1:, :] next_step = torch.tensor(X_val[_:_+1], dtype=torch.float32) input_seq = torch.cat([next_input, next_step], dim=1) ### 5. Write MIDI ### midi = pretty_midi.PrettyMIDI() instrument = pretty_midi.Instrument(program=0) start = 0.0 for note_num in generated_notes: note = pretty_midi.Note(velocity=100, pitch=note_num + note_min, start=start, end=start + 0.5) instrument.notes.append(note) start += 0.5 midi.instruments.append(instrument) midi.write("generated_music.mid") ### 6. (Optional) Hook: Extract audio feature from WAV ### def extract_mel_spectrogram(wav_path): y, sr = librosa.load(wav_path, sr=None) mel = librosa.feature.melspectrogram(y=y, sr=sr, n_mels=128) mel_db = librosa.power_to_db(mel, ref=np.max) return mel_db # Example usage: # mel_spec = extract_mel_spectrogram('path/to/audio.wav') 修改成能在kaggle的T4*2上跑的代码

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/* 3966 * Notes on Program-Order guarantees on SMP systems. 3967 * 3968 * MIGRATION 3969 * 3970 * The basic program-order guarantee on SMP systems is that when a task [t] 3971 * migrates, all its activity on its old CPU [c0] happens-before any subsequent 3972 * execution on its new CPU [c1]. 3973 * 3974 * For migration (of runnable tasks) this is provided by the following means: 3975 * 3976 * A) UNLOCK of the rq(c0)->lock scheduling out task t 3977 * B) migration for t is required to synchronize *both* rq(c0)->lock and 3978 * rq(c1)->lock (if not at the same time, then in that order). 3979 * C) LOCK of the rq(c1)->lock scheduling in task 3980 * 3981 * Release/acquire chaining guarantees that B happens after A and C after B. 3982 * Note: the CPU doing B need not be c0 or c1 3983 * 3984 * Example: 3985 * 3986 * CPU0 CPU1 CPU2 3987 * 3988 * LOCK rq(0)->lock 3989 * sched-out X 3990 * sched-in Y 3991 * UNLOCK rq(0)->lock 3992 * 3993 * LOCK rq(0)->lock // orders against CPU0 3994 * dequeue X 3995 * UNLOCK rq(0)->lock 3996 * 3997 * LOCK rq(1)->lock 3998 * enqueue X 3999 * UNLOCK rq(1)->lock 4000 * 4001 * LOCK rq(1)->lock // orders against CPU2 4002 * sched-out Z 4003 * sched-in X 4004 * UNLOCK rq(1)->lock 4005 * 4006 * 4007 * BLOCKING -- aka. SLEEP + WAKEUP 4008 * 4009 * For blocking we (obviously) need to provide the same guarantee as for 4010 * migration. However the means are completely different as there is no lock 4011 * chain to provide order. Instead we do: 4012 * 4013 * 1) smp_store_release(X->on_cpu, 0) -- finish_task() 4014 * 2) smp_cond_load_acquire(!X->on_cpu) -- try_to_wake_up() 4015 * 4016 * Example: 4017 * 4018 * CPU0 (schedule) CPU1 (try_to_wake_up) CPU2 (schedule) 4019 * 4020 * LOCK rq(0)->lock LOCK X->pi_lock 4021 * dequeue X 4022 * sched-out X 4023 * smp_store_release(X->on_cpu, 0); 4024 * 4025 * smp_cond_load_acquire(&X->on_cpu, !VAL); 4026 * X->state = WAKING 4027 * set_task_cpu(X,2) 4028 * 4029 * LOCK rq(2)->lock 4030 * enqueue X 4031 * X->state = RUNNING 4032 * UNLOCK rq(2)->lock 4033 * 4034 * LOCK rq(2)->lock // orders against CPU1 4035 * sched-out Z 4036 * sched-in X 4037 * UNLOCK rq(2)->lock 4038 * 4039 * UNLOCK X->pi_lock 4040 * UNLOCK rq(0)->lock 4041 * 4042 * 4043 * However, for wakeups there is a second guarantee we must provide, namely we 4044 * must ensure that CONDITION=1 done by the caller can not be reordered with 4045 * accesses to the task state; see try_to_wake_up() and set_current_state(). 4046 */ 4047 4048 /** 4049 * try_to_wake_up - wake up a thread 4050 * @p: the thread to be awakened 4051 * @state: the mask of task states that can be woken 4052 * @wake_flags: wake modifier flags (WF_*) 4053 * 4054 * Conceptually does: 4055 * 4056 * If (@state & @p->state) @p->state = TASK_RUNNING. 4057 * 4058 * If the task was not queued/runnable, also place it back on a runqueue. 4059 * 4060 * This function is atomic against schedule() which would dequeue the task. 4061 * 4062 * It issues a full memory barrier before accessing @p->state, see the comment 4063 * with set_current_state(). 4064 * 4065 * Uses p->pi_lock to serialize against concurrent wake-ups. 4066 * 4067 * Relies on p->pi_lock stabilizing: 4068 * - p->sched_class 4069 * - p->cpus_ptr 4070 * - p->sched_task_group 4071 * in order to do migration, see its use of select_task_rq()/set_task_cpu(). 4072 * 4073 * Tries really hard to only take one task_rq(p)->lock for performance. 4074 * Takes rq->lock in: 4075 * - ttwu_runnable() -- old rq, unavoidable, see comment there; 4076 * - ttwu_queue() -- new rq, for enqueue of the task; 4077 * - psi_ttwu_dequeue() -- much sadness :-( accounting will kill us. 4078 * 4079 * As a consequence we race really badly with just about everything. See the 4080 * many memory barriers and their comments for details. 4081 * 4082 * Return: %true if @p->state changes (an actual wakeup was done), 4083 * %false otherwise. 4084 */ 4085 static int 4086 try_to_wake_up(struct task_struct *p, unsigned int state, int wake_flags) 4087 { 4088 unsigned long flags; 4089 int cpu, success = 0; 4090 4091 preempt_disable(); 4092 if (p == current) { 4093 /* 4094 * We're waking current, this means 'p->on_rq' and 'task_cpu(p) 4095 * == smp_processor_id()'. Together this means we can special 4096 * case the whole 'p->on_rq && ttwu_runnable()' case below 4097 * without taking any locks. 4098 * 4099 * In particular: 4100 * - we rely on Program-Order guarantees for all the ordering, 4101 * - we're serialized against set_special_state() by virtue of 4102 * it disabling IRQs (this allows not taking ->pi_lock). 4103 */ 4104 if (!ttwu_state_match(p, state, &success)) 4105 goto out; 4106 4107 trace_sched_waking(p); 4108 WRITE_ONCE(p->__state, TASK_RUNNING); 4109 trace_sched_wakeup(p); 4110 goto out; 4111 } 4112 4113 /* 4114 * If we are going to wake up a thread waiting for CONDITION we 4115 * need to ensure that CONDITION=1 done by the caller can not be 4116 * reordered with p->state check below. This pairs with smp_store_mb() 4117 * in set_current_state() that the waiting thread does. 4118 */ 4119 raw_spin_lock_irqsave(&p->pi_lock, flags); 4120 smp_mb__after_spinlock(); 4121 if (!ttwu_state_match(p, state, &success)) 4122 goto unlock; 4123 4124 #ifdef CONFIG_FREEZER 4125 /* 4126 * If we're going to wake up a thread which may be frozen, then 4127 * we can only do so if we have an active CPU which is capable of 4128 * running it. This may not be the case when resuming from suspend, 4129 * as the secondary CPUs may not yet be back online. See __thaw_task() 4130 * for the actual wakeup. 4131 */ 4132 if (unlikely(frozen_or_skipped(p)) && 4133 !cpumask_intersects(cpu_active_mask, task_cpu_possible_mask(p))) 4134 goto unlock; 4135 #endif 4136 4137 trace_sched_waking(p); 4138 4139 /* 4140 * Ensure we load p->on_rq _after_ p->state, otherwise it would 4141 * be possible to, falsely, observe p->on_rq == 0 and get stuck 4142 * in smp_cond_load_acquire() below. 4143 * 4144 * sched_ttwu_pending() try_to_wake_up() 4145 * STORE p->on_rq = 1 LOAD p->state 4146 * UNLOCK rq->lock 4147 * 4148 * __schedule() (switch to task 'p') 4149 * LOCK rq->lock smp_rmb(); 4150 * smp_mb__after_spinlock(); 4151 * UNLOCK rq->lock 4152 * 4153 * [task p] 4154 * STORE p->state = UNINTERRUPTIBLE LOAD p->on_rq 4155 * 4156 * Pairs with the LOCK+smp_mb__after_spinlock() on rq->lock in 4157 * __schedule(). See the comment for smp_mb__after_spinlock(). 4158 * 4159 * A similar smb_rmb() lives in try_invoke_on_locked_down_task(). 4160 */ 4161 smp_rmb(); 4162 if (READ_ONCE(p->on_rq) && ttwu_runnable(p, wake_flags)) 4163 goto unlock; 4164 4165 if (READ_ONCE(p->__state) & TASK_UNINTERRUPTIBLE) 4166 trace_sched_blocked_reason(p); 4167 4168 #ifdef CONFIG_SMP 4169 /* 4170 * Ensure we load p->on_cpu _after_ p->on_rq, otherwise it would be 4171 * possible to, falsely, observe p->on_cpu == 0. 4172 * 4173 * One must be running (->on_cpu == 1) in order to remove oneself 4174 * from the runqueue. 4175 * 4176 * __schedule() (switch to task 'p') try_to_wake_up() 4177 * STORE p->on_cpu = 1 LOAD p->on_rq 4178 * UNLOCK rq->lock 4179 * 4180 * __schedule() (put 'p' to sleep) 4181 * LOCK rq->lock smp_rmb(); 4182 * smp_mb__after_spinlock(); 4183 * STORE p->on_rq = 0 LOAD p->on_cpu 4184 * 4185 * Pairs with the LOCK+smp_mb__after_spinlock() on rq->lock in 4186 * __schedule(). See the comment for smp_mb__after_spinlock(). 4187 * 4188 * Form a control-dep-acquire with p->on_rq == 0 above, to ensure 4189 * schedule()'s deactivate_task() has 'happened' and p will no longer 4190 * care about it's own p->state. See the comment in __schedule(). 4191 */ 4192 smp_acquire__after_ctrl_dep(); 4193 4194 /* 4195 * We're doing the wakeup (@success == 1), they did a dequeue (p->on_rq 4196 * == 0), which means we need to do an enqueue, change p->state to 4197 * TASK_WAKING such that we can unlock p->pi_lock before doing the 4198 * enqueue, such as ttwu_queue_wakelist(). 4199 */ 4200 WRITE_ONCE(p->__state, TASK_WAKING); 4201 4202 /* 4203 * If the owning (remote) CPU is still in the middle of schedule() with 4204 * this task as prev, considering queueing p on the remote CPUs wake_list 4205 * which potentially sends an IPI instead of spinning on p->on_cpu to 4206 * let the waker make forward progress. This is safe because IRQs are 4207 * disabled and the IPI will deliver after on_cpu is cleared. 4208 * 4209 * Ensure we load task_cpu(p) after p->on_cpu: 4210 * 4211 * set_task_cpu(p, cpu); 4212 * STORE p->cpu = @cpu 4213 * __schedule() (switch to task 'p') 4214 * LOCK rq->lock 4215 * smp_mb__after_spin_lock() smp_cond_load_acquire(&p->on_cpu) 4216 * STORE p->on_cpu = 1 LOAD p->cpu 4217 * 4218 * to ensure we observe the correct CPU on which the task is currently 4219 * scheduling. 4220 */ 4221 if (smp_load_acquire(&p->on_cpu) && 4222 ttwu_queue_wakelist(p, task_cpu(p), wake_flags)) 4223 goto unlock; 4224 4225 /* 4226 * If the owning (remote) CPU is still in the middle of schedule() with 4227 * this task as prev, wait until it's done referencing the task. 4228 * 4229 * Pairs with the smp_store_release() in finish_task(). 4230 * 4231 * This ensures that tasks getting woken will be fully ordered against 4232 * their previous state and preserve Program Order. 4233 */ 4234 smp_cond_load_acquire(&p->on_cpu, !VAL); 4235 4236 trace_android_rvh_try_to_wake_up(p); 4237 4238 cpu = select_task_rq(p, p->wake_cpu, wake_flags | WF_TTWU); 4239 if (task_cpu(p) != cpu) { 4240 if (p->in_iowait) { 4241 delayacct_blkio_end(p); 4242 atomic_dec(&task_rq(p)->nr_iowait); 4243 } 4244 4245 wake_flags |= WF_MIGRATED; 4246 psi_ttwu_dequeue(p); 4247 set_task_cpu(p, cpu); 4248 } 4249 #else 4250 cpu = task_cpu(p); 4251 #endif /* CONFIG_SMP */ 4252 4253 ttwu_queue(p, cpu, wake_flags); 4254 unlock: 4255 raw_spin_unlock_irqrestore(&p->pi_lock, flags); 4256 out: 4257 if (success) { 4258 trace_android_rvh_try_to_wake_up_success(p); 4259 ttwu_stat(p, task_cpu(p), wake_flags); 4260 } 4261 preempt_enable(); 4262 4263 return success; 4264 } 分析并给出总结解释