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train_eval.py
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train_eval.py
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import time
import os
import math
import multiprocessing as mp
import numpy as np
import networkx as nx
import torch
import torch.nn.functional as F
from torch import tensor
from torch.optim import Adam
from sklearn.model_selection import StratifiedKFold
from torch_geometric.data import DataLoader, DenseDataLoader as DenseLoader
from tqdm import tqdm
import pdb
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
from util_functions import PyGGraph_to_nx
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
def train_multiple_epochs(train_dataset,
test_dataset,
model,
epochs,
batch_size,
lr,
lr_decay_factor,
lr_decay_step_size,
weight_decay,
ARR=0,
test_freq=1,
logger=None,
continue_from=None,
res_dir=None):
rmses = []
if train_dataset.__class__.__name__ == 'MyDynamicDataset':
num_workers = mp.cpu_count()
else:
num_workers = 2
train_loader = DataLoader(train_dataset, batch_size, shuffle=True,
num_workers=num_workers)
if test_dataset.__class__.__name__ == 'MyDynamicDataset':
num_workers = mp.cpu_count()
else:
num_workers = 2
test_loader = DataLoader(test_dataset, batch_size, shuffle=False,
num_workers=num_workers)
model.to(device).reset_parameters()
optimizer = Adam(model.parameters(), lr=lr, weight_decay=weight_decay)
start_epoch = 1
if continue_from is not None:
model.load_state_dict(
torch.load(os.path.join(res_dir, 'model_checkpoint{}.pth'.format(continue_from)))
)
optimizer.load_state_dict(
torch.load(os.path.join(res_dir, 'optimizer_checkpoint{}.pth'.format(continue_from)))
)
start_epoch = continue_from + 1
epochs -= continue_from
if torch.cuda.is_available():
torch.cuda.synchronize()
batch_pbar = len(train_dataset) >= 100000
t_start = time.perf_counter()
if not batch_pbar:
pbar = tqdm(range(start_epoch, epochs + start_epoch))
else:
pbar = range(start_epoch, epochs + start_epoch)
for epoch in pbar:
train_loss = train(model, optimizer, train_loader, device, regression=True, ARR=ARR,
show_progress=batch_pbar, epoch=epoch)
if epoch % test_freq == 0:
rmses.append(eval_rmse(model, test_loader, device, show_progress=batch_pbar))
else:
rmses.append(np.nan)
eval_info = {
'epoch': epoch,
'train_loss': train_loss,
'test_rmse': rmses[-1],
}
if not batch_pbar:
pbar.set_description(
'Epoch {}, train loss {:.6f}, test rmse {:.6f}'.format(*eval_info.values())
)
else:
print('Epoch {}, train loss {:.6f}, test rmse {:.6f}'.format(*eval_info.values()))
if epoch % lr_decay_step_size == 0:
for param_group in optimizer.param_groups:
param_group['lr'] = lr_decay_factor * param_group['lr']
if logger is not None:
logger(eval_info, model, optimizer)
if torch.cuda.is_available():
torch.cuda.synchronize()
t_end = time.perf_counter()
duration = t_end - t_start
print('Final Test RMSE: {:.6f}, Duration: {:.6f}'.
format(rmses[-1],
duration))
return rmses[-1]
def test_once(test_dataset,
model,
batch_size,
logger=None,
ensemble=False,
checkpoints=None):
test_loader = DataLoader(test_dataset, batch_size, shuffle=False)
model.to(device)
t_start = time.perf_counter()
if ensemble and checkpoints:
rmse = eval_rmse_ensemble(model, checkpoints, test_loader, device, show_progress=True)
else:
rmse = eval_rmse(model, test_loader, device, show_progress=True)
t_end = time.perf_counter()
duration = t_end - t_start
print('Test Once RMSE: {:.6f}, Duration: {:.6f}'.format(rmse, duration))
epoch_info = 'test_once' if not ensemble else 'ensemble'
eval_info = {
'epoch': epoch_info,
'train_loss': 0,
'test_rmse': rmse,
}
if logger is not None:
logger(eval_info, None, None)
return rmse
def num_graphs(data):
if data.batch is not None:
return data.num_graphs
else:
return data.x.size(0)
def train(model, optimizer, loader, device, regression=False, ARR=0,
show_progress=False, epoch=None):
model.train()
total_loss = 0
if show_progress:
pbar = tqdm(loader)
else:
pbar = loader
for data in pbar:
optimizer.zero_grad()
data = data.to(device)
out = model(data)
if regression:
loss = F.mse_loss(out, data.y.view(-1))
else:
loss = F.nll_loss(out, data.y.view(-1))
if show_progress:
pbar.set_description('Epoch {}, batch loss: {}'.format(epoch, loss.item()))
if ARR != 0:
for gconv in model.convs:
w = torch.matmul(
gconv.att,
gconv.basis.view(gconv.num_bases, -1)
).view(gconv.num_relations, gconv.in_channels, gconv.out_channels)
reg_loss = torch.sum((w[1:, :, :] - w[:-1, :, :])**2)
loss += ARR * reg_loss
loss.backward()
total_loss += loss.item() * num_graphs(data)
optimizer.step()
torch.cuda.empty_cache()
return total_loss / len(loader.dataset)
def eval_loss(model, loader, device, regression=False, show_progress=False):
model.eval()
loss = 0
if show_progress:
print('Testing begins...')
pbar = tqdm(loader)
else:
pbar = loader
for data in pbar:
data = data.to(device)
with torch.no_grad():
out = model(data)
if regression:
loss += F.mse_loss(out, data.y.view(-1), reduction='sum').item()
else:
loss += F.nll_loss(out, data.y.view(-1), reduction='sum').item()
torch.cuda.empty_cache()
return loss / len(loader.dataset)
def eval_rmse(model, loader, device, show_progress=False):
mse_loss = eval_loss(model, loader, device, True, show_progress)
rmse = math.sqrt(mse_loss)
return rmse
def eval_loss_ensemble(model, checkpoints, loader, device, regression=False, show_progress=False):
loss = 0
Outs = []
for i, checkpoint in enumerate(checkpoints):
if show_progress:
print('Testing begins...')
pbar = tqdm(loader)
else:
pbar = loader
model.load_state_dict(torch.load(checkpoint))
model.eval()
outs = []
if i == 0:
ys = []
for data in pbar:
data = data.to(device)
if i == 0:
ys.append(data.y.view(-1))
with torch.no_grad():
out = model(data)
outs.append(out)
if i == 0:
ys = torch.cat(ys, 0)
outs = torch.cat(outs, 0).view(-1, 1)
Outs.append(outs)
Outs = torch.cat(Outs, 1).mean(1)
if regression:
loss += F.mse_loss(Outs, ys, reduction='sum').item()
else:
loss += F.nll_loss(Outs, ys, reduction='sum').item()
torch.cuda.empty_cache()
return loss / len(loader.dataset)
def eval_rmse_ensemble(model, checkpoints, loader, device, show_progress=False):
mse_loss = eval_loss_ensemble(model, checkpoints, loader, device, True, show_progress)
rmse = math.sqrt(mse_loss)
return rmse
def visualize(model, graphs, res_dir, data_name, class_values, num=5, sort_by='prediction'):
model.eval()
model.to(device)
R = []
Y = []
graph_loader = DataLoader(graphs, 50, shuffle=False)
for data in tqdm(graph_loader):
data = data.to(device)
r = model(data).detach()
y = data.y
R.extend(r.view(-1).tolist())
Y.extend(y.view(-1).tolist())
if sort_by == 'true': # sort graphs by their true ratings
order = np.argsort(Y).tolist()
elif sort_by == 'prediction':
order = np.argsort(R).tolist()
elif sort_by == 'random': # randomly select graphs to visualize
order = np.random.permutation(range(len(R))).tolist()
highest = [PyGGraph_to_nx(graphs[i]) for i in order[-num:][::-1]]
lowest = [PyGGraph_to_nx(graphs[i]) for i in order[:num]]
highest_scores = [R[i] for i in order[-num:][::-1]]
lowest_scores = [R[i] for i in order[:num]]
highest_ys = [Y[i] for i in order[-num:][::-1]]
lowest_ys = [Y[i] for i in order[:num]]
scores = highest_scores + lowest_scores
ys = highest_ys + lowest_ys
type_to_label = {0: 'u0', 1: 'v0', 2: 'u1', 3: 'v1', 4: 'u2', 5: 'v2'}
type_to_color = {0: 'xkcd:red', 1: 'xkcd:blue', 2: 'xkcd:orange',
3: 'xkcd:lightblue', 4: 'y', 5: 'g'}
plt.axis('off')
f = plt.figure(figsize=(20, 10))
axs = f.subplots(2, num)
cmap = plt.cm.get_cmap('rainbow')
vmin, vmax = min(class_values), max(class_values)
sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=vmin, vmax=vmax))
sm.set_array([])
for i, g in enumerate(highest + lowest):
u_nodes = [x for x, y in g.nodes(data=True) if y['type'] % 2 == 0]
u0, v0 = 0, len(u_nodes)
pos = nx.drawing.layout.bipartite_layout(g, u_nodes)
bottom_u_node = min(pos, key=lambda x: (pos[x][0], pos[x][1]))
bottom_v_node = min(pos, key=lambda x: (-pos[x][0], pos[x][1]))
# swap u0 and v0 with bottom nodes if they are not already
if u0 != bottom_u_node:
pos[u0], pos[bottom_u_node] = pos[bottom_u_node], pos[u0]
if v0 != bottom_v_node:
pos[v0], pos[bottom_v_node] = pos[bottom_v_node], pos[v0]
labels = {x: type_to_label[y] for x, y in nx.get_node_attributes(g, 'type').items()}
node_colors = [type_to_color[y] for x, y in nx.get_node_attributes(g, 'type').items()]
edge_types = nx.get_edge_attributes(g, 'type')
edge_types = [class_values[edge_types[x]] for x in g.edges()]
axs[i//num, i%num].axis('off')
nx.draw_networkx(g, pos,
#labels=labels,
with_labels=False,
node_size=150,
node_color=node_colors, edge_color=edge_types,
ax=axs[i//num, i%num], edge_cmap=cmap, edge_vmin=vmin, edge_vmax=vmax,
)
# make u0 v0 on top of other nodes
nx.draw_networkx_nodes(g, {u0: pos[u0]}, nodelist=[u0], node_size=150,
node_color='xkcd:red', ax=axs[i//num, i%num])
nx.draw_networkx_nodes(g, {v0: pos[v0]}, nodelist=[v0], node_size=150,
node_color='xkcd:blue', ax=axs[i//num, i%num])
axs[i//num, i%num].set_title('{:.4f} ({:})'.format(
scores[i], ys[i]), x=0.5, y=-0.05, fontsize=20
)
f.subplots_adjust(right=0.85)
cbar_ax = f.add_axes([0.88, 0.15, 0.02, 0.7])
if len(class_values) > 20:
class_values = np.linspace(min(class_values), max(class_values), 20, dtype=int).tolist()
cbar = plt.colorbar(sm, cax=cbar_ax, ticks=class_values)
cbar.ax.tick_params(labelsize=22)
f.savefig(os.path.join(res_dir, "visualization_{}_{}.pdf".format(data_name, sort_by)),
interpolation='nearest', bbox_inches='tight')