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from data.data_ukb import get_imaging_pretraining_data
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torch.multiprocessing.set_sharing_strategy("file_system")
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os.environ["CUDA_VISIBLE_DEVICES"] = "4"
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ImageFile.LOAD_TRUNCATED_IMAGES = True
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max_epochs = 100
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IMG_SIZE = 448
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PROJECTION_DIM = 128
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BATCH_SIZE = 32
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ACCUMULATE_GRAD_BATCHES = 2
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LR = 1e-3
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WEIGHT_DECAY = 1e-6
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TEMPERATURE = 0.1
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MEMORY_BANK_SIZE = 2 ** 16
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class SimCLRModel(pl.LightningModule):
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def __init__(self, num_ftrs=2048):
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super().__init__()
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# create a ResNet backbone and remove the classification head
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resnet = torchvision.models.resnet50()
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# create a simclr model based on ResNet
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self.resnet_simclr = models.SimCLR(
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torch.nn.Sequential(*list(resnet.children())[:-1]),
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num_ftrs=num_ftrs,
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out_dim=PROJECTION_DIM,
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)
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self.criterion = loss.NTXentLoss(
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temperature=TEMPERATURE, memory_bank_size=MEMORY_BANK_SIZE
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)
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def forward(self, x):
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self.resnet_simclr(x)
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def training_step(self, batch, batch_idx):
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(x0, x1), _, _ = batch
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x0, x1 = self.resnet_simclr(x0, x1)
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loss = self.criterion(x0, x1)
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self.log("train_loss_ssl", loss)
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return loss
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def configure_optimizers(self):
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global training_set_len
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optim = torch.optim.Adam(
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self.resnet_simclr.parameters(),
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LR,
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weight_decay=WEIGHT_DECAY,
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)
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scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
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optim, T_max=training_set_len, eta_min=0, last_epoch=-1
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)
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return [optim], [scheduler]
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model = SimCLRModel()
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print(model)
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dataloader, _, _ = get_imaging_pretraining_data(
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num_workers=8,
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size=IMG_SIZE,
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batch_size=BATCH_SIZE,
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train_pct=0.7,
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val_pct=0.1,
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tfms_settings="simclr",
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)
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training_set_len = len(dataloader)
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trainer = pl.Trainer(
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max_epochs=max_epochs,
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gpus=1,
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accumulate_grad_batches=ACCUMULATE_GRAD_BATCHES,
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)
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trainer.fit(model, dataloader)
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print("Finished Training")
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# <FILESEP>
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#!/usr/bin/python3
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# -*- coding: utf-8 -*-
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#
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# Copyright 2022, Tijl "Photubias" Deneut <@tijldeneut>
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" PARSING NGC DATA FROM REGISTRY """
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r'''
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Credential Providers in the Registry: SOFTWARE\Microsoft\Windows\CurrentVersion\Authentication\Credential Providers (with {D6886603-9D2F-4EB2-B667-1971041FA96B} having GUIDs)
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'''
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