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experiment_v2.py
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from collections import Counter
import numpy as np
import torch.nn as nn
import torch
import torch.optim as optim
from torch.optim.lr_scheduler import StepLR
from torch.utils.data import TensorDataset, DataLoader
import matplotlib.pyplot as plt
import random
from utils import TripletLossWeighted, TripletLoss, TripletDataset, calc_embeddings
import pandas as pd
import torch.nn.functional as F
from torch.utils.data.sampler import BatchSampler
from itertools import combinations
from torchvision import transforms
dt_name_to_cols_to_encode = {
'cmc': [1,2,6,7],
'dermatology': list(range(0,10)) + list(range(10,33)),
'hayes-roth':[0,1,2,3],
'new_vehicle': [],
'new_yeast': [5],
'1czysty-cut': [],
'2delikatne-cut': [],
'3mocniej-cut': [],
'4delikatne-bezover-cut': [],
'balance-scale': [0,1,2,3],
'cleveland': [2,6,10,11,12],
'cleveland_v2': [2, 6, 10, 11, 12],
'glass': [],
'new_ecoli': [],
'new_led7digit': [],
'new_winequality-red': [],
'thyroid-newthyroid': []
}
dt_name_minority_classes = {
'1czysty-cut': [1, 2],
'2delikatne-cut': [1,2],
'3mocniej-cut': [1,2],
'4delikatne-bezover-cut': [1,2],
'balance-scale': [0],
'cleveland': [1,2,3,4],
'cleveland_v2': [1,2,3],
'cmc': [1],
'dermatology': [5],
'glass': [5,2,4],
'hayes-roth': [2],
'new_vehicle': [0,2],
'new_yeast': [2,3,4,5,6],
'new_ecoli': [4,2,3],
'new_led7digit': [1,4],
'new_winequality-red': [3,2],
'thyroid-newthyroid': [2,1]
}
def config_tuned_for_lda(config):
config['cmc'] = {'nn_config': {'units_1st_layer': 17,
'units_2nd_layer': 256,
'units_3rd_layer': 128,
'units_latent_layer': 8},
'weighted_triplet_loss': True,
'lr': 0.0002,
'batch_size': 16,
'gamma': 0.99,
'epochs': 55}
config['dermatology'] = {'nn_config': {'units_1st_layer': 97,
'units_2nd_layer': 512,
'units_3rd_layer': 256,
'units_latent_layer': 16},
'weighted_triplet_loss': True,
'lr': 0.0010,
'batch_size': 16,
'gamma': 0.99,
'epochs': 45}
config['hayes-roth'] = {'nn_config': {'units_1st_layer': 11,
'units_2nd_layer': 128,
'units_3rd_layer': 64,
'units_latent_layer': 16},
'weighted_triplet_loss': True,
'lr': 0.0015,
'batch_size': 16,
'gamma': 0.99,
'epochs': 300}
config['new_vehicle'] = {'nn_config': {'units_1st_layer': 18,
'units_2nd_layer': 256,
'units_3rd_layer': 128,
'units_latent_layer': 16},
'weighted_triplet_loss': True,
'lr': 0.001,
'batch_size': 16,
'gamma': 0.99,
'epochs': 150}
config['new_yeast'] = {'nn_config': {'units_1st_layer': 9,
'units_2nd_layer': 300,
'units_3rd_layer': 200,
'units_latent_layer': 12},
'weighted_triplet_loss': True,
'lr': 0.0004,
'batch_size': 32,
'gamma': 0.99,
'epochs': 60}
config['balance-scale'] = {'nn_config': {'units_1st_layer': 16,
'units_2nd_layer': 256,
'units_3rd_layer': 128,
'units_latent_layer': 10},
'weighted_triplet_loss': True,
'lr': 0.007,
'batch_size': 16,
'gamma': 0.99,
'epochs': 100}
config['cleveland'] = {'nn_config': {'units_1st_layer': 24,
'units_2nd_layer': 72,
'units_3rd_layer': 48,
'units_latent_layer': 16},
'weighted_triplet_loss': True,
'lr': 0.00002,
'batch_size': 16,
'gamma': 0.99,
'epochs': 120}
config['cleveland_v2'] = {'nn_config': {'units_1st_layer': 23,
'units_2nd_layer': 256,
'units_3rd_layer': 128,
'units_latent_layer': 16},
'weighted_triplet_loss': True,
'lr': 0.00002,
'batch_size': 16,
'gamma': 0.99,
'epochs': 100}
config['glass'] = {'nn_config': {'units_1st_layer': 9,
'units_2nd_layer': 256,
'units_3rd_layer': 128,
'units_latent_layer': 12},
'weighted_triplet_loss': True,
'lr': 0.001,
'batch_size': 16,
'gamma': 0.99,
'epochs': 120}
config['thyroid-newthyroid'] = {'nn_config': {'units_1st_layer': 5,
'units_2nd_layer': 64,
'units_3rd_layer': 32,
'units_latent_layer': 8},
'weighted_triplet_loss': True,
'lr': 0.001,
'batch_size': 16,
'gamma': 0.99,
'epochs': 120}
config['new_ecoli'] = {'nn_config': {'units_1st_layer': 7,
'units_2nd_layer': 128,
'units_3rd_layer': 64,
'units_latent_layer': 12},
'weighted_triplet_loss': True,
'lr': 0.0001,
'batch_size': 16,
'gamma': 0.99,
'epochs': 100}
config['new_led7digit'] = {'nn_config': {'units_1st_layer': 7,
'units_2nd_layer': 64,
'units_3rd_layer': 32,
'units_latent_layer': 8},
'weighted_triplet_loss': True,
'lr': 0.001,
'batch_size': 16,
'gamma': 0.99,
'epochs': 100}
config['new_winequality-red'] = {'nn_config': {'units_1st_layer': 11,
'units_2nd_layer': 128,
'units_3rd_layer': 64,
'units_latent_layer': 12},
'weighted_triplet_loss': True,
'lr': 0.003,
'batch_size': 16,
'gamma': 0.99,
'epochs': 100}
config['4delikatne-bezover-cut'] = {'nn_config': {'units_1st_layer': 2,
'units_2nd_layer': 128,
'units_3rd_layer': 64,
'units_latent_layer': 8},
'weighted_triplet_loss': True,
'lr': 0.004,
'batch_size': 16,
'gamma': 0.99,
'epochs': 120}
config['3mocniej-cut'] = {'nn_config': {'units_1st_layer': 2,
'units_2nd_layer': 128,
'units_3rd_layer': 64,
'units_latent_layer': 10},
'weighted_triplet_loss': True,
'lr': 0.04,
'batch_size': 16,
'gamma': 0.99,
'epochs': 90}
config['1czysty-cut'] = {'nn_config': {'units_1st_layer': 2,
'units_2nd_layer': 64,
'units_3rd_layer': 32,
'units_latent_layer': 8},
'weighted_triplet_loss': True,
'lr': 0.004,
'batch_size': 16,
'gamma': 0.99,
'epochs': 130}
config['2delikatne-cut'] = {'nn_config': {'units_1st_layer': 2,
'units_2nd_layer': 128,
'units_3rd_layer': 64,
'units_latent_layer': 12},
'weighted_triplet_loss': True,
'lr': 0.04,
'batch_size': 16,
'gamma': 0.99,
'epochs': 90}
return config
def config_tuned_for_dt(config):
config['cmc'] = {'nn_config': {'units_1st_layer': 17,
'units_2nd_layer': 256,
'units_3rd_layer': 128,
'units_latent_layer': 8},
'weighted_triplet_loss': True,
'lr': 0.0002,
'batch_size': 16,
'gamma': 0.99,
'epochs': 55}
config['dermatology'] = {'nn_config': {'units_1st_layer': 97,
'units_2nd_layer': 512,
'units_3rd_layer': 256,
'units_latent_layer': 8},
'weighted_triplet_loss': True,
'lr': 0.0010,
'batch_size': 16,
'gamma': 0.99,
'epochs': 70}
config['hayes-roth'] = {'nn_config': {'units_1st_layer': 11,
'units_2nd_layer': 128,
'units_3rd_layer': 64,
'units_latent_layer': 16},
'weighted_triplet_loss': True,
'lr': 0.0015,
'batch_size': 16,
'gamma': 0.99,
'epochs': 300}
config['new_vehicle'] = {'nn_config': {'units_1st_layer': 18,
'units_2nd_layer': 256,
'units_3rd_layer': 128,
'units_latent_layer': 16},
'weighted_triplet_loss': True,
'lr': 0.001,
'batch_size': 16,
'gamma': 0.99,
'epochs': 150}
config['new_yeast'] = {'nn_config': {'units_1st_layer': 9,
'units_2nd_layer': 300,
'units_3rd_layer': 200,
'units_latent_layer': 12},
'weighted_triplet_loss': True,
'lr': 0.0004,
'batch_size': 32,
'gamma': 0.99,
'epochs': 60}
config['balance-scale'] = {'nn_config': {'units_1st_layer': 16,
'units_2nd_layer': 256,
'units_3rd_layer': 128,
'units_latent_layer': 10},
'weighted_triplet_loss': True,
'lr': 0.007,
'batch_size': 16,
'gamma': 0.99,
'epochs': 100}
config['cleveland'] = {'nn_config': {'units_1st_layer': 24,
'units_2nd_layer': 72,
'units_3rd_layer': 48,
'units_latent_layer': 8},
'weighted_triplet_loss': True,
'lr': 0.00002,
'batch_size': 16,
'gamma': 0.99,
'epochs': 120}
config['cleveland_v2'] = {'nn_config': {'units_1st_layer': 23,
'units_2nd_layer': 256,
'units_3rd_layer': 128,
'units_latent_layer': 16},
'weighted_triplet_loss': True,
'lr': 0.00002,
'batch_size': 16,
'gamma': 0.99,
'epochs': 100}
config['glass'] = {'nn_config': {'units_1st_layer': 9,
'units_2nd_layer': 256,
'units_3rd_layer': 128,
'units_latent_layer': 12},
'weighted_triplet_loss': True,
'lr': 0.001,
'batch_size': 16,
'gamma': 0.99,
'epochs': 120}
config['thyroid-newthyroid'] = {'nn_config': {'units_1st_layer': 5,
'units_2nd_layer': 64,
'units_3rd_layer': 32,
'units_latent_layer': 8},
'weighted_triplet_loss': True,
'lr': 0.001,
'batch_size': 16,
'gamma': 0.99,
'epochs': 120}
config['new_ecoli'] = {'nn_config': {'units_1st_layer': 7,
'units_2nd_layer': 128,
'units_3rd_layer': 64,
'units_latent_layer': 12},
'weighted_triplet_loss': True,
'lr': 0.0001,
'batch_size': 16,
'gamma': 0.99,
'epochs': 100}
config['new_led7digit'] = {'nn_config': {'units_1st_layer': 7,
'units_2nd_layer': 64,
'units_3rd_layer': 32,
'units_latent_layer': 8},
'weighted_triplet_loss': True,
'lr': 0.001,
'batch_size': 16,
'gamma': 0.99,
'epochs': 100}
config['new_winequality-red'] = {'nn_config': {'units_1st_layer': 11,
'units_2nd_layer': 128,
'units_3rd_layer': 64,
'units_latent_layer': 8},
'weighted_triplet_loss': True,
'lr': 0.003,
'batch_size': 16,
'gamma': 0.99,
'epochs': 100}
config['4delikatne-bezover-cut'] = {'nn_config': {'units_1st_layer': 2,
'units_2nd_layer': 128,
'units_3rd_layer': 64,
'units_latent_layer': 8},
'weighted_triplet_loss': True,
'lr': 0.004,
'batch_size': 16,
'gamma': 0.99,
'epochs': 120}
config['3mocniej-cut'] = {'nn_config': {'units_1st_layer': 2,
'units_2nd_layer': 128,
'units_3rd_layer': 64,
'units_latent_layer': 4},
'weighted_triplet_loss': True,
'lr': 0.04,
'batch_size': 16,
'gamma': 0.99,
'epochs': 90}
config['1czysty-cut'] = {'nn_config': {'units_1st_layer': 2,
'units_2nd_layer': 64,
'units_3rd_layer': 32,
'units_latent_layer': 4},
'weighted_triplet_loss': True,
'lr': 0.004,
'batch_size': 16,
'gamma': 0.99,
'epochs': 130}
config['2delikatne-cut'] = {'nn_config': {'units_1st_layer': 2,
'units_2nd_layer': 128,
'units_3rd_layer': 64,
'units_latent_layer': 12},
'weighted_triplet_loss': True,
'lr': 0.04,
'batch_size': 16,
'gamma': 0.99,
'epochs': 90}
return config
def config_tuned_for_knn(config):
config['cmc'] = {'nn_config': {'units_1st_layer': 17,
'units_2nd_layer': 256,
'units_3rd_layer': 128,
'units_latent_layer': 8},
'weighted_triplet_loss': True,
'lr': 0.0002,
'batch_size': 16,
'gamma': 0.99,
'epochs': 55}
config['dermatology'] = {'nn_config': {'units_1st_layer': 97,
'units_2nd_layer': 512,
'units_3rd_layer': 256,
'units_latent_layer': 8},
'weighted_triplet_loss': True,
'lr': 0.0010,
'batch_size': 16,
'gamma': 0.99,
'epochs': 45}
config['hayes-roth'] = {'nn_config': {'units_1st_layer': 11,
'units_2nd_layer': 128,
'units_3rd_layer': 64,
'units_latent_layer': 16},
'weighted_triplet_loss': True,
'lr': 0.0015,
'batch_size': 16,
'gamma': 0.99,
'epochs': 300}
config['new_vehicle'] = {'nn_config': {'units_1st_layer': 18,
'units_2nd_layer': 256,
'units_3rd_layer': 128,
'units_latent_layer': 16},
'weighted_triplet_loss': True,
'lr': 0.001,
'batch_size': 16,
'gamma': 0.99,
'epochs': 150}
config['new_yeast'] = {'nn_config': {'units_1st_layer': 9,
'units_2nd_layer': 300,
'units_3rd_layer': 200,
'units_latent_layer': 12},
'weighted_triplet_loss': True,
'lr': 0.0004,
'batch_size': 32,
'gamma': 0.99,
'epochs': 60}
config['balance-scale'] = {'nn_config': {'units_1st_layer': 16,
'units_2nd_layer': 256,
'units_3rd_layer': 128,
'units_latent_layer': 10},
'weighted_triplet_loss': True,
'lr': 0.007,
'batch_size': 16,
'gamma': 0.99,
'epochs': 100}
config['cleveland'] = {'nn_config': {'units_1st_layer': 24,
'units_2nd_layer': 72,
'units_3rd_layer': 48,
'units_latent_layer': 16},
'weighted_triplet_loss': True,
'lr': 0.00002,
'batch_size': 16,
'gamma': 0.99,
'epochs': 120}
config['cleveland_v2'] = {'nn_config': {'units_1st_layer': 23,
'units_2nd_layer': 128,
'units_3rd_layer': 64,
'units_latent_layer': 16},
'weighted_triplet_loss': True,
'lr': 0.00002,
'batch_size': 16,
'gamma': 0.99,
'epochs': 100}
config['glass'] = {'nn_config': {'units_1st_layer': 9,
'units_2nd_layer': 256,
'units_3rd_layer': 128,
'units_latent_layer': 12},
'weighted_triplet_loss': True,
'lr': 0.001,
'batch_size': 16,
'gamma': 0.99,
'epochs': 120}
config['thyroid-newthyroid'] = {'nn_config': {'units_1st_layer': 5,
'units_2nd_layer': 64,
'units_3rd_layer': 32,
'units_latent_layer': 8},
'weighted_triplet_loss': True,
'lr': 0.001,
'batch_size': 16,
'gamma': 0.99,
'epochs': 120}
config['new_ecoli'] = {'nn_config': {'units_1st_layer': 7,
'units_2nd_layer': 128,
'units_3rd_layer': 64,
'units_latent_layer': 8},
'weighted_triplet_loss': True,
'lr': 0.0001,
'batch_size': 16,
'gamma': 0.99,
'epochs': 100}
config['new_led7digit'] = {'nn_config': {'units_1st_layer': 7,
'units_2nd_layer': 64,
'units_3rd_layer': 32,
'units_latent_layer': 8},
'weighted_triplet_loss': True,
'lr': 0.003,
'batch_size': 16,
'gamma': 0.99,
'epochs': 100}
config['new_winequality-red'] = {'nn_config': {'units_1st_layer': 11,
'units_2nd_layer': 128,
'units_3rd_layer': 64,
'units_latent_layer': 8},
'weighted_triplet_loss': True,
'lr': 0.003,
'batch_size': 16,
'gamma': 0.99,
'epochs': 100}
config['4delikatne-bezover-cut'] = {'nn_config': {'units_1st_layer': 2,
'units_2nd_layer': 128,
'units_3rd_layer': 64,
'units_latent_layer': 8},
'weighted_triplet_loss': True,
'lr': 0.004,
'batch_size': 16,
'gamma': 0.99,
'epochs': 120}
config['3mocniej-cut'] = {'nn_config': {'units_1st_layer': 2,
'units_2nd_layer': 128,
'units_3rd_layer': 64,
'units_latent_layer': 4},
'weighted_triplet_loss': True,
'lr': 0.04,
'batch_size': 16,
'gamma': 0.99,
'epochs': 90}
config['1czysty-cut'] = {'nn_config': {'units_1st_layer': 2,
'units_2nd_layer': 64,
'units_3rd_layer': 32,
'units_latent_layer': 4},
'weighted_triplet_loss': True,
'lr': 0.004,
'batch_size': 16,
'gamma': 0.99,
'epochs': 130}
config['2delikatne-cut'] = {'nn_config': {'units_1st_layer': 2,
'units_2nd_layer': 128,
'units_3rd_layer': 64,
'units_latent_layer': 12},
'weighted_triplet_loss': True,
'lr': 0.04,
'batch_size': 16,
'gamma': 0.99,
'epochs': 90}
return config
def one_hot_encode_all(datasets):
ds_names = list(dt_name_to_cols_to_encode.keys())
for ds_name in ds_names:
k = ds_name
df = pd.DataFrame(data=datasets[k]['data'])
encoded = pd.get_dummies(df, columns=dt_name_to_cols_to_encode[ds_name], drop_first=True)
datasets[f"{k}_encoded"] = {'data': encoded.values, 'target': datasets[k]['target']}
return datasets
def config_calculation_strategy1(datasets):
config = {}
for dataset_name in datasets:
data, target = datasets[dataset_name]['data'], datasets[dataset_name]['target']
neural_net_config = {
"units_1st_layer": data.shape[1],
"units_2nd_layer": data.shape[1] * 2,
"units_3rd_layer": data.shape[1],
"units_latent_layer": data.shape[1] // 2
}
config[dataset_name] = {
"nn_config": neural_net_config,
"weighted_triplet_loss": True
}
return config
def config_calculation_strategy2(datasets):
config = {}
for dataset_name in datasets:
data, target = datasets[dataset_name]['data'], datasets[dataset_name]['target']
neural_net_config = {
"units_1st_layer": data.shape[1],
"units_2nd_layer": max(16, data.shape[1] * 3),
"units_3rd_layer": max(data.shape[1] * 2, 8),
"units_latent_layer": max(4, data.shape[1] // 2)
}
config[dataset_name] = {
"nn_config": neural_net_config,
"weighted_triplet_loss": True,
"lr": 1e-4,
"batch_size": 32,
"gamma": 0.95,
"epochs": 100
}
return config
def config_calculation_strategy3(datasets):
config = {}
for dataset_name in datasets:
data, target = datasets[dataset_name]['data'], datasets[dataset_name]['target']
neural_net_config = {
"units_1st_layer": data.shape[1],
"units_2nd_layer": max(16, data.shape[1] * 3),
"units_3rd_layer": max(data.shape[1] * 2, 8),
"units_latent_layer": max(4, data.shape[1] // 2),
"units_decision_layer": np.unique(target).size
}
config[dataset_name] = {
"nn_config": neural_net_config,
"weighted_triplet_loss": True,
"lr": 0.003,
"batch_size": 16,
"gamma": 0.99,
"epochs": 100
}
return config
def weights_calculation_strategy1(X_train, y_train):
# Inverse class frequencies, normalized
cards = Counter(y_train)
# weights = {c: (1/v) * 100 for c,v in cards.items()}
weights = {c: 1/v for c, v in cards.items()}
weights_normalized = {c: weights[c]/sum(weights.values()) for c in cards.keys()}
print(f"Class cardinalities: {cards}")
print(f"Weights: {weights_normalized}")
return weights_normalized
class EmbeddingNet(nn.Module):
def __init__(self, nn_config):
super(EmbeddingNet, self).__init__()
self.fc = nn.Sequential(nn.Linear(nn_config["units_1st_layer"], nn_config["units_2nd_layer"]),
nn.PReLU(),
nn.Linear(nn_config["units_2nd_layer"], nn_config["units_3rd_layer"]),
nn.PReLU(),
nn.Linear(nn_config["units_3rd_layer"], nn_config["units_latent_layer"])
)
def forward(self, x):
output = self.fc(x)
return output
def embed(self, x):
return self.forward(x)
class TripletNet(nn.Module):
def __init__(self, embedding_net):
super(TripletNet, self).__init__()
self.embedding_net = embedding_net
def forward(self, x1, x2, x3):
output1 = self.embedding_net(x1)
output2 = self.embedding_net(x2)
output3 = self.embedding_net(x3)
return output1, output2, output3
def embed(self, x):
return self.embedding_net(x)
class ClassificationNet(nn.Module):
def __init__(self, embedding_net, nn_config):
super(ClassificationNet, self).__init__()
self.embedding_net = embedding_net
self.n_classes = nn_config["units_decision_layer"]
self.nonlinear = nn.PReLU()
self.fc1 = nn.Linear(nn_config["units_latent_layer"], nn_config["units_decision_layer"])
def forward(self, x):
output = self.embedding_net(x)
output = self.nonlinear(output)
scores = F.log_softmax(self.fc1(output), dim=-1)
return scores
def embed(self, x):
return self.embedding_net(x)
def train_classification_net(model, device, train_loader, optimizer):
model.train()
train_loss = []
for batch_idx, (data, target) in enumerate(train_loader):
data, target = data.to(device), target.to(device)
optimizer.zero_grad()
output = model(data)
loss = F.nll_loss(output, target.long())
loss.backward()
optimizer.step()
train_loss.append(loss.item())
return np.mean(train_loss)
def test_classification_net(model, device, test_loader):
model.eval()
test_loss = []
with torch.no_grad():
for data, target in test_loader:
data, target = data.to(device), target.to(device)
output = model(data)
loss = F.nll_loss(output, target.long())
test_loss.append(loss.item())
return np.mean(test_loss)
def train_tripletnet(model, device, train_loader, optimizer, epoch, weights, nn_config, log_interval=10, dry_run=False):
model.train()
train_loss = []
for batch_idx, (data, target) in enumerate(train_loader):
data = (data,)
data = tuple(d.cuda() for d in data)
target = target.cuda()
optimizer.zero_grad()
outputs = model(*data)
outputs = (outputs,)
loss_inputs = outputs
target = (target,)
loss_inputs += target
margin = 1.0
loss_fn = OnlineTripletLoss(margin=margin, triplet_selector=RandomNegativeTripletSelector(margin))
loss_outputs = loss_fn(*loss_inputs)
loss = loss_outputs[0] if type(loss_outputs) in (tuple, list) else loss_outputs
loss.backward()
optimizer.step()
train_loss.append(loss.item())
return np.mean(train_loss)
def test_tripletnet(model, device, test_loader, weights, nn_config):
model.eval()
test_loss = []
correct = 0
with torch.no_grad():
for data, target in test_loader:
# print(data)
# print(target)
data = (data,)
data = tuple(d.cuda() for d in data)
target = target.cuda()
outputs = model(*data)
outputs = (outputs,)
loss_inputs = outputs
target = (target,)
loss_inputs += target
margin = 1.0
loss_fn = OnlineTripletLoss(margin=margin, triplet_selector=RandomNegativeTripletSelector(margin))
loss_outputs = loss_fn(*loss_inputs)
loss = loss_outputs[0] if type(loss_outputs) in (tuple, list) else loss_outputs
test_loss.append(loss.item())
# print(test_loss)
return np.mean(test_loss)
def train_triplets(X_train, y_train, X_test, y_test, weights, cfg):
seed = 3
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
seed = 7
batch_size = cfg["batch_size"]
test_batch_size = cfg["batch_size"]
use_cuda = True
lr = cfg["lr"]
gamma = cfg["gamma"]
epochs = cfg["epochs"]
save_model = True
log_interval = 20
nn_config = cfg["nn_config"]
torch.manual_seed(seed)
device = torch.device("cuda" if use_cuda else "cpu")
train_kwargs, test_kwargs = {}, {}
if use_cuda:
cuda_kwargs = {'num_workers': 1,
'pin_memory': True}
train_kwargs.update(cuda_kwargs)
test_kwargs.update(cuda_kwargs)
dataset1 = TensorDataset(torch.Tensor(X_train), torch.Tensor(y_train))
dataset1.train_data = torch.Tensor(X_train)
dataset1.train_labels = torch.Tensor(y_train)
dataset1.train = True
dataset2 = TensorDataset(torch.Tensor(X_test), torch.Tensor(y_test))
dataset2.test_data = torch.Tensor(X_test)
dataset2.test_labels = torch.Tensor(y_test)
dataset2.train = False
triplet_train_dataset = TripletDataset(dataset1)
triplet_test_dataset = TripletDataset(dataset2)
triplet_train_loader = torch.utils.data.DataLoader(triplet_train_dataset, **train_kwargs)
triplet_test_loader = torch.utils.data.DataLoader(triplet_test_dataset, **test_kwargs)
n_classes = np.unique(y_train).size
train_batch_sampler = BalancedBatchSampler(dataset1.train_labels, n_classes=n_classes, n_samples=max(1,batch_size//n_classes), name="train")
test_batch_sampler = BalancedBatchSampler(dataset2.test_labels, n_classes=n_classes, n_samples=max(1,test_batch_size//n_classes), name="test")
online_train_loader = torch.utils.data.DataLoader(dataset1, batch_sampler=train_batch_sampler, **train_kwargs)
online_test_loader = torch.utils.data.DataLoader(dataset2, batch_sampler=test_batch_sampler, **test_kwargs)
embedding_net = EmbeddingNet(nn_config)
# model = TripletNet(embedding_net).to(device)
model = embedding_net.to(device)
optimizer = optim.Adam(model.parameters(), lr=lr)
scheduler = StepLR(optimizer, step_size=1, gamma=gamma)
test_losses = []
train_losses = []
for epoch in range(1, epochs + 1):
train_losses.append(train_tripletnet(model, device, online_train_loader, optimizer, epoch, weights, nn_config, log_interval))
test_losses.append(test_tripletnet(model, device, online_test_loader, weights, nn_config))
scheduler.step()
if save_model:
torch.save(model.state_dict(), "mnist_cnn_triplet.pt")
test_loader = torch.utils.data.DataLoader(dataset2, batch_size=1)
train_loader = torch.utils.data.DataLoader(dataset1, batch_size=1)
embeddings_train, _ = calc_embeddings(model, device, train_loader)
embeddings_test, _ = calc_embeddings(model, device, test_loader)
plt.plot(test_losses, label="test losses")
plt.plot(train_losses, label="train losses")
plt.legend()
plt.show()
return embeddings_train, embeddings_test
def train_classification(X_train, y_train, X_test, y_test, cfg):
seed = 3
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
seed = 7
batch_size = cfg["batch_size"]
test_batch_size = cfg["batch_size"]
use_cuda = True
lr = cfg["lr"]
gamma = cfg["gamma"]
epochs = cfg["epochs"]
save_model = True
log_interval = 20
nn_config = cfg["nn_config"]
torch.manual_seed(seed)
device = torch.device("cuda" if use_cuda else "cpu")
train_kwargs = {'batch_size': batch_size}
test_kwargs = {'batch_size': test_batch_size}
if use_cuda:
cuda_kwargs = {'num_workers': 1,
'pin_memory': True,
'shuffle': True}
train_kwargs.update(cuda_kwargs)
test_kwargs.update(cuda_kwargs)
dataset1 = TensorDataset(torch.Tensor(X_train), torch.Tensor(y_train))
dataset1.train_data = torch.Tensor(X_train)
dataset1.train_labels = torch.Tensor(y_train)
dataset1.train = True
dataset2 = TensorDataset(torch.Tensor(X_test), torch.Tensor(y_test))
dataset2.test_data = torch.Tensor(X_test)
dataset2.test_labels = torch.Tensor(y_test)
dataset2.train = False
train_loader = torch.utils.data.DataLoader(dataset1, **train_kwargs)
test_loader = torch.utils.data.DataLoader(dataset2, **test_kwargs)
embedding_net = EmbeddingNet(nn_config)
model = ClassificationNet(embedding_net, nn_config).to(device)
optimizer = optim.Adam(model.parameters(), lr=lr)
scheduler = StepLR(optimizer, step_size=1, gamma=gamma)
test_losses = []
train_losses = []
for epoch in range(1, epochs + 1):
train_losses.append(train_classification_net(model, device, train_loader, optimizer))
test_losses.append(test_classification_net(model, device, test_loader))
scheduler.step()
if save_model:
torch.save(model.state_dict(), "mnist_cnn.pt")
plt.plot(test_losses, label="test losses")
plt.plot(train_losses, label="train losses")
plt.legend()
plt.show()
return model
class BalancedBatchSampler(BatchSampler):
"""
samples n_classes and within these classes samples n_samples
Returns batches of size n_classes * n_samples
"""
def __init__(self, labels, n_classes, n_samples, name):
self.name=name
self.labels = labels
self.labels_set = list(set(self.labels.numpy()))
self.label_to_indices = {label: np.where(self.labels.numpy() == label)[0]
for label in self.labels_set}
for l in self.labels_set:
np.random.shuffle(self.label_to_indices[l])
self.used_label_indices_count = {label: 0 for label in self.labels_set}
self.count = 0
self.n_classes = n_classes
self.n_samples = n_samples
self.n_dataset = len(self.labels)
# print(f"{self.name}: {self.n_dataset}")
# print(f"{self.name}: {self.n_classes} x {self.n_samples}")
self.batch_size = self.n_samples * self.n_classes
def __iter__(self):
self.count = 0
while self.count + self.batch_size < self.n_dataset:
# print(self.name)
classes = np.random.choice(self.labels_set, self.n_classes, replace=False)
indices = []
for class_ in classes:
indices.extend(self.label_to_indices[class_][
self.used_label_indices_count[class_]:self.used_label_indices_count[
class_] + self.n_samples])
self.used_label_indices_count[class_] += self.n_samples
if self.used_label_indices_count[class_] + self.n_samples > len(self.label_to_indices[class_]):
np.random.shuffle(self.label_to_indices[class_])
self.used_label_indices_count[class_] = 0
yield indices
self.count += self.n_classes * self.n_samples
def __len__(self):
return self.n_dataset // self.batch_size
class OnlineTripletLoss(nn.Module):
def __init__(self, margin, triplet_selector):
super(OnlineTripletLoss, self).__init__()
self.margin = margin
self.triplet_selector = triplet_selector
def forward(self, embeddings, target):
triplets = self.triplet_selector.get_triplets(embeddings, target)