Commit 48deb5bb authored by Verena Praher's avatar Verena Praher
Browse files

add dataloaders to resnet, make resnet experiment work

parent abf2d01d
from utils import CURR_RUN_PATH, USE_GPU, logger
from pytorch_lightning import Trainer
from pytorch_lightning.callbacks import EarlyStopping
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from test_tube import Experiment
from models.resnet18 import Network
import os
def run():
......@@ -13,21 +14,30 @@ def run():
# callbacks
early_stop = EarlyStopping(
monitor='val_loss', # TODO: check if this exists
monitor='val_loss',
patience=50,
verbose=True,
mode='min' # TODO: check if correct
mode='min'
)
checkpoint_callback = ModelCheckpoint(
filepath=os.path.join(CURR_RUN_PATH, 'best.ckpt'),
save_best_only=True,
verbose=True,
monitor='val_loss',
mode='min'
)
if USE_GPU:
trainer = Trainer(gpus=[0], distributed_backend='ddp',
experiment=exp, max_nb_epochs=500, train_percent_check=1.0,
fast_dev_run=False, early_stop_callback=early_stop)
fast_dev_run=False, early_stop_callback=early_stop,
checkpoint_callback=checkpoint_callback)
else:
trainer = Trainer(experiment=exp, max_nb_epochs=1, train_percent_check=0.1,
fast_dev_run=True)
model = Network() # TODO num_tags
model = Network(56) # TODO num_tags
print(model)
......
from utils import *
from datasets import MelSpecDataset
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
import pytorch_lightning as pl
from models.shared_stuff import tng_dataloader, val_dataloader, test_dataloader, \
validation_end, training_step, validation_step
from sklearn.metrics import roc_auc_score
# TODO pr-auc
# TODO f1-score
from torchvision.models import resnet18
from models.resnet_arch import ResNet, BasicBlock
class Network(pl.LightningModule):
def __init__(self, num_tags):
super(Network, self).__init__()
self.num_tags = num_tags
self.model = resnet18(False) # TODO: need to check if optimizer recognizes these parameters
num_features = self.model.fc.in_features
self.model.fc = nn.Linear(num_features, self.num_tags) # overwriting fc layer
self.sig = nn.Sigmoid(self.num_tags)
self.model = nn.Sequential(
ResNet(BasicBlock, [2, 2, 2, 2], num_classes=self.num_tags),
nn.Sigmoid())
# TODO: need to check if optimizer recognizes these parameters
# num_features = self.model.fc.in_features
# self.model.fc = nn.Linear(num_features, self.num_tags) # overwriting fc layer
# self.sig = nn.Sigmoid()
def forward(self, x):
x = self.model(x)
x = self.sig(x)
# x = self.sig(x)
return x
def my_loss(self, y_hat, y):
return F.binary_cross_entropy(y_hat, y)
def configure_optimizers(self):
return [torch.optim.Adam(self.parameters(), lr=0.001)]
\ No newline at end of file
return [torch.optim.Adam(self.parameters(), lr=0.001)]
def training_step(self, data_batch, batch_nb):
return training_step(self, data_batch, batch_nb)
def validation_step(self, data_batch, batch_nb):
return validation_step(self, data_batch, batch_nb)
def validation_end(self, outputs):
return validation_end(outputs)
@pl.data_loader
def tng_dataloader(self):
return tng_dataloader()
@pl.data_loader
def val_dataloader(self):
return val_dataloader()
@pl.data_loader
def test_dataloader(self):
return test_dataloader()
import torch.nn as nn
import torch
def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=dilation, groups=groups, bias=False, dilation=dilation)
def conv1x1(in_planes, out_planes, stride=1):
"""1x1 convolution"""
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
class BasicBlock(nn.Module):
expansion = 1
__constants__ = ['downsample']
def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
base_width=64, dilation=1, norm_layer=None):
super(BasicBlock, self).__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm2d
if groups != 1 or base_width != 64:
raise ValueError('BasicBlock only supports groups=1 and base_width=64')
if dilation > 1:
raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
# Both self.conv1 and self.downsample layers downsample the input when stride != 1
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = norm_layer(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes)
self.bn2 = norm_layer(planes)
self.downsample = downsample
self.stride = stride
def forward(self, x):
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
out = self.relu(out)
return out
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
base_width=64, dilation=1, norm_layer=None):
super(Bottleneck, self).__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm2d
width = int(planes * (base_width / 64.)) * groups
# Both self.conv2 and self.downsample layers downsample the input when stride != 1
self.conv1 = conv1x1(inplanes, width)
self.bn1 = norm_layer(width)
self.conv2 = conv3x3(width, width, stride, groups, dilation)
self.bn2 = norm_layer(width)
self.conv3 = conv1x1(width, planes * self.expansion)
self.bn3 = norm_layer(planes * self.expansion)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x):
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
out = self.relu(out)
return out
class ResNet(nn.Module):
def __init__(self, block, layers, num_classes=1000, zero_init_residual=False,
groups=1, width_per_group=64, replace_stride_with_dilation=None,
norm_layer=None):
super(ResNet, self).__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm2d
self._norm_layer = norm_layer
self.inplanes = 64
self.dilation = 1
if replace_stride_with_dilation is None:
# each element in the tuple indicates if we should replace
# the 2x2 stride with a dilated convolution instead
replace_stride_with_dilation = [False, False, False]
if len(replace_stride_with_dilation) != 3:
raise ValueError("replace_stride_with_dilation should be None "
"or a 3-element tuple, got {}".format(replace_stride_with_dilation))
self.groups = groups
self.base_width = width_per_group
self.conv1 = nn.Conv2d(1, self.inplanes, kernel_size=7, stride=2, padding=3,
bias=False)
self.bn1 = norm_layer(self.inplanes)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2,
dilate=replace_stride_with_dilation[0])
self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
dilate=replace_stride_with_dilation[1])
self.layer4 = self._make_layer(block, 512, layers[3], stride=2,
dilate=replace_stride_with_dilation[2])
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(512 * block.expansion, num_classes)
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
# Zero-initialize the last BN in each residual branch,
# so that the residual branch starts with zeros, and each residual block behaves like an identity.
# This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
if zero_init_residual:
for m in self.modules():
if isinstance(m, Bottleneck):
nn.init.constant_(m.bn3.weight, 0)
elif isinstance(m, BasicBlock):
nn.init.constant_(m.bn2.weight, 0)
def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
norm_layer = self._norm_layer
downsample = None
previous_dilation = self.dilation
if dilate:
self.dilation *= stride
stride = 1
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
conv1x1(self.inplanes, planes * block.expansion, stride),
norm_layer(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
self.base_width, previous_dilation, norm_layer))
self.inplanes = planes * block.expansion
for _ in range(1, blocks):
layers.append(block(self.inplanes, planes, groups=self.groups,
base_width=self.base_width, dilation=self.dilation,
norm_layer=norm_layer))
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = torch.flatten(x, 1)
x = self.fc(x)
return x
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