merge from Qiushi's fork to yewsg/yews: add polarity and focal_mechanism models

.travis.yml

@@ -5,7 +5,10 @@ matrix:
   include:
     - python: "3.6"
     - python: "3.7"
+<<<<<<< HEAD
+=======
     - python: "3.8"
+>>>>>>> upstream/master
 
 before_install:
   - sudo apt-get update

README.rst

@@ -16,15 +16,10 @@ applying deep learning techniques on seismic waveform data.
 
 
 
-.. image:: https://travis-ci.org/yewsg/yews.svg?branch=master
-    :target: https://travis-ci.org/yewsg/yews
 
 .. image:: https://ci.appveyor.com/api/projects/status/32r7s2skrgm9ubva?svg=true
     :target: https://ci.appveyor.com/project/lijunzh/yews
 
-.. image:: https://codecov.io/gh/yewsg/yews/branch/master/graph/badge.svg
-    :target: https://codecov.io/gh/yewsg/yews
-
 .. image:: https://anaconda.org/lijunzhu/yews/badges/version.svg
     :target: https://anaconda.org/lijunzhu/yews
 

examples/scsn_polarity_cnn.training.py

@@ -0,0 +1,104 @@
+import datetime
+import torch
+from torch.nn import CrossEntropyLoss
+from torch.utils.data import DataLoader
+from torch.utils.data import random_split
+
+import yews.datasets as dsets
+import yews.transforms as transforms
+from yews.train import Trainer
+
+from yews.models import polarity_cnn
+model=polarity_cnn
+
+
+if __name__ == '__main__':
+
+    print("Now: start : " + str(datetime.datetime.now()))
+
+    # Preprocessing
+    waveform_transform = transforms.Compose([
+        #transforms.ZeroMean(),
+        #transforms.SoftClip(1e-4),
+        transforms.ToTensor(),
+    ])
+
+    # Prepare dataset
+    dsets.set_memory_limit(10 * 1024 ** 3) # first number is GB
+    # dset = dsets.Wenchuan(path='/home/qszhai/temp_project/deep_learning_course_project/cpic', download=False,sample_transform=waveform_transform)
+    dset = dsets.SCSN(path='/data6/scsn/polarity/train_npy', download=False, sample_transform=waveform_transform)
+
+    # Split datasets into training and validation
+    train_length = int(len(dset) * 0.8)
+    val_length = len(dset) - train_length
+    train_set, val_set = random_split(dset, [train_length, val_length])
+
+    # Prepare dataloaders
+    train_loader = DataLoader(train_set, batch_size=5000, shuffle=True, num_workers=4)
+    val_loader = DataLoader(val_set, batch_size=10000, shuffle=False, num_workers=4)
+
+    # Prepare trainer
+    trainer = Trainer(model(), CrossEntropyLoss(), lr=0.01)
+
+    # Train model over training dataset
+    trainer.train(train_loader, val_loader, epochs=100, print_freq=100)
+                  #resume='checkpoint_best.pth.tar')
+
+    # Save training results to disk
+    trainer.results(path='scsn_polarity_results.pth.tar')
+
+    # Validate saved model
+    results = torch.load('scsn_polarity_results.pth.tar')
+    model = model()
+    model.load_state_dict(results['model'])
+    trainer = Trainer(model, CrossEntropyLoss(), lr=0.1)
+    trainer.validate(val_loader, print_freq=100)
+
+    print("Now: end : " + str(datetime.datetime.now()))
+
+    import matplotlib.pyplot as plt
+    import numpy as np
+
+    myfontsize1=14
+    myfontsize2=18
+    myfontsize3=24
+
+    results = torch.load('scsn_polarity_results.pth.tar')
+
+    fig, axes = plt.subplots(2, 1, num=0, figsize=(6, 4), sharex=True)
+    axes[0].plot(results['val_acc'], label='Validation')
+    axes[0].plot(results['train_acc'], label='Training')
+
+    #axes[1].set_xlabel("Epochs",fontsize=myfontsize2)
+    axes[0].set_xscale('log')
+    axes[0].set_xlim([1, 100])
+    axes[0].xaxis.set_tick_params(labelsize=myfontsize1)
+
+    axes[0].set_ylabel("Accuracies (%)",fontsize=myfontsize2)
+    axes[0].set_ylim([0, 100])
+    axes[0].set_yticks(np.arange(0, 101, 10))
+    axes[0].yaxis.set_tick_params(labelsize=myfontsize1)
+
+    axes[0].grid(True, 'both')
+    axes[0].legend(loc=4)
+
+    #axes[1].semilogx(results['val_loss'], label='Validation')
+    #axes[1].semilogx(results['train_loss'], label='Training')
+    axes[1].plot(results['val_loss'], label='Validation')
+    axes[1].plot(results['train_loss'], label='Training')
+
+    axes[1].set_xlabel("Epochs",fontsize=myfontsize2)
+    axes[1].set_xscale('log')
+    axes[1].set_xlim([1, 100])
+    axes[1].xaxis.set_tick_params(labelsize=myfontsize1)
+
+    axes[1].set_ylabel("Losses",fontsize=myfontsize2)
+    axes[1].set_ylim([0.0, 1.0])
+    axes[1].set_yticks(np.arange(0.0,1.01,0.2))
+    axes[1].yaxis.set_tick_params(labelsize=myfontsize1)
+
+    axes[1].grid(True, 'both')
+    axes[1].legend(loc=1)
+
+    fig.tight_layout()
+    plt.savefig('Accuracies_train_val.pdf') 

examples/scsn_polarity_lstm_training.py

@@ -0,0 +1,116 @@
+import datetime
+import torch
+from torch.nn import CrossEntropyLoss
+from torch.utils.data import DataLoader
+from torch.utils.data import random_split
+
+import yews.datasets as dsets
+import yews.transforms as transforms
+from yews.train import Trainer
+
+#from yews.models import cpic
+#from yews.models import cpic_v1
+#from yews.models import cpic_v2
+#cpic = cpic_v1
+
+from yews.models import polarity_v1
+from yews.models import polarity_v2
+from yews.models import polarity_lstm
+polarity=polarity_lstm
+
+
+if __name__ == '__main__':
+
+    print("Now: start : " + str(datetime.datetime.now()))
+
+    # Preprocessing
+    waveform_transform = transforms.Compose([
+        transforms.ZeroMean(),
+        #transforms.SoftClip(1e-4),
+        transforms.ToTensor(),
+    ])
+
+    # Prepare dataset
+    dsets.set_memory_limit(10 * 1024 ** 3) # first number is GB
+    # dset = dsets.Wenchuan(path='/home/qszhai/temp_project/deep_learning_course_project/cpic', download=False,sample_transform=waveform_transform)
+    dset = dsets.SCSN_polarity(path='/home/qszhai/temp_project/deep_learning_course_project/first_motion_polarity/scsn_data/train_npy', download=False, sample_transform=waveform_transform)
+
+    # Split datasets into training and validation
+    train_length = int(len(dset) * 0.8)
+    val_length = len(dset) - train_length
+    train_set, val_set = random_split(dset, [train_length, val_length])
+
+    # Prepare dataloaders
+    train_loader = DataLoader(train_set, batch_size=5000, shuffle=True, num_workers=4)
+    val_loader = DataLoader(val_set, batch_size=10000, shuffle=False, num_workers=4)
+
+    # Prepare trainer
+    # trainer = Trainer(cpic(), CrossEntropyLoss(), lr=0.1)
+    # note: please use only 1 gpu to run LSTM, https://github.com/pytorch/pytorch/issues/21108
+    model_conf = {"hidden_size": 64}
+    plt = polarity(**model_conf)
+    trainer = Trainer(plt, CrossEntropyLoss(), lr=0.001)
+
+    # Train model over training dataset
+    trainer.train(train_loader, val_loader, epochs=50, print_freq=100)
+                  #resume='checkpoint_best.pth.tar')
+
+    # Save training results to disk
+    trainer.results(path='scsn_polarity_results.pth.tar')
+
+    # Validate saved model
+    results = torch.load('scsn_polarity_results.pth.tar')
+    #model = cpic()
+    model = plt
+    model.load_state_dict(results['model'])
+    trainer = Trainer(model, CrossEntropyLoss(), lr=0.001)
+    trainer.validate(val_loader, print_freq=100)
+
+    print("Now: end : " + str(datetime.datetime.now()))
+
+    import matplotlib.pyplot as plt
+    import numpy as np
+
+    myfontsize1=14
+    myfontsize2=18
+    myfontsize3=24
+
+    results = torch.load('scsn_polarity_results.pth.tar')
+
+    fig, axes = plt.subplots(2, 1, num=0, figsize=(6, 4), sharex=True)
+    axes[0].plot(results['val_acc'], label='Validation')
+    axes[0].plot(results['train_acc'], label='Training')
+
+    #axes[1].set_xlabel("Epochs",fontsize=myfontsize2)
+    axes[0].set_xscale('log')
+    axes[0].set_xlim([1, 100])
+    axes[0].xaxis.set_tick_params(labelsize=myfontsize1)
+
+    axes[0].set_ylabel("Accuracies (%)",fontsize=myfontsize2)
+    axes[0].set_ylim([0, 100])
+    axes[0].set_yticks(np.arange(0, 101, 10))
+    axes[0].yaxis.set_tick_params(labelsize=myfontsize1)
+
+    axes[0].grid(True, 'both')
+    axes[0].legend(loc=4)
+
+    #axes[1].semilogx(results['val_loss'], label='Validation')
+    #axes[1].semilogx(results['train_loss'], label='Training')
+    axes[1].plot(results['val_loss'], label='Validation')
+    axes[1].plot(results['train_loss'], label='Training')
+
+    axes[1].set_xlabel("Epochs",fontsize=myfontsize2)
+    axes[1].set_xscale('log')
+    axes[1].set_xlim([1, 100])
+    axes[1].xaxis.set_tick_params(labelsize=myfontsize1)
+
+    axes[1].set_ylabel("Losses",fontsize=myfontsize2)
+    axes[1].set_ylim([0.0, 1.0])
+    axes[1].set_yticks(np.arange(0.0,1.01,0.2))
+    axes[1].yaxis.set_tick_params(labelsize=myfontsize1)
+
+    axes[1].grid(True, 'both')
+    axes[1].legend(loc=1)
+
+    fig.tight_layout()
+    plt.savefig('Accuracies_train_val.pdf') 

examples/taiwan20092010_cpic.training.py

@@ -0,0 +1,71 @@
+import datetime
+import torch
+from torch.nn import CrossEntropyLoss
+from torch.utils.data import DataLoader
+from torch.utils.data import random_split
+
+import yews.datasets as dsets
+import yews.transforms as transforms
+from yews.train import Trainer
+
+#from yews.models import cpic
+from yews.models import cpic_v1
+from yews.models import cpic_v2
+from yews.models import cpic_v3
+cpic = cpic_v3
+
+
+if __name__ == '__main__':
+
+    print("Now: start : " + str(datetime.datetime.now()))
+
+    # Preprocessing
+    waveform_transform = transforms.Compose([
+        transforms.ZeroMean(),
+        #transforms.RemoveTrend(),
+        #transforms.RemoveMean(),
+        #transforms.Taper(),
+        #transforms.BandpassFilter(),
+        #transforms.SoftClip(2e-3),
+        #1e-2=1/100 100=1% max
+        #2e-3=4/2048  hist: max = 2048
+        #import numpy as np;import matplotlib.pyplot as plt;samples=np.load("samples.npy",mmap_mode='r');
+        #targets=np.load("targets.npy");target.shape
+        #plt.hist(samples[0:100000,0,:].flatten(), bins=100); plt.ylim([0.1,1.5e8]);plt.show()
+        transforms.ToTensor(),
+    ])
+
+    # Prepare dataset
+    dsets.set_memory_limit(10 * 1024 ** 3) # first number is GB
+    dset = dsets.Taiwan20092010(path='/home/qszhai/temp_project/deep_learning_course_project/cpic/Taiwan20092010', download=False, sample_transform=waveform_transform)
+
+    # Split datasets into training and validation
+    train_length = int(len(dset) * 0.8)
+    val_length = len(dset) - train_length
+    train_set, val_set = random_split(dset, [train_length, val_length])
+
+    # Prepare dataloaders
+    train_loader = DataLoader(train_set, batch_size=2000, shuffle=True, num_workers=4)
+    # train_set: bastch_size = targets.shape / 500
+    val_loader = DataLoader(val_set, batch_size=4000, shuffle=False, num_workers=4)
+    # train_set: bastch_size : larger is better if the GPU memory is enough.
+    # num_workers = number of cpu core, but limited by the disk speed. so 8 is good.
+
+    # Prepare trainer
+    trainer = Trainer(cpic(), CrossEntropyLoss(), lr=0.1)
+
+    # Train model over training dataset
+    trainer.train(train_loader, val_loader, epochs=300, print_freq=100)
+                  #resume='checkpoint_best.pth.tar')
+
+    # Save training results to disk
+    trainer.results(path='Taiwan20092010_results.pth.tar')
+
+    # Validate saved model
+    results = torch.load('Taiwan20092010_results.pth.tar')
+    model = cpic()
+    model.load_state_dict(results['model'])
+    trainer = Trainer(model, CrossEntropyLoss(), lr=0.1)
+    trainer.validate(val_loader, print_freq=100)
+
+    print("Now: end : " + str(datetime.datetime.now()))