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tcorollertcoroller
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Merge pull request #84 from Novartis/83-update-notebook
fixed example
2 parents 6ed7d8c + 179b634 commit 55cac81

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‎docs/notebooks/helpers_momentum.py‎

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@@ -163,6 +163,7 @@ def train_dataloader(self):
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num_workers=self.num_workers,
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persistent_workers=True,
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shuffle=True,
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drop_last=True,
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)
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def val_dataloader(self):

‎docs/notebooks/momentum.ipynb‎

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@@ -45,6 +45,7 @@
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"source": [
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"import matplotlib.pyplot as plt\n",
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"import torch\n",
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"import copy\n",
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"import lightning as L\n",
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"from torchvision.models import resnet18\n",
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"from torchvision.transforms import v2\n",
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"execution_count": null,
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"id": "794004c5-588c-4590-ae96-c6d9e52109ff",
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"metadata": {},
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"outputs": [],
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"source": [
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"EPOCHS = 2 # number of epochs to train\n",
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"FAST_DEV_RUN = 5 # Quick prototype, comment line for full epochs training"
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"FAST_DEV_RUN = None # Quick prototype, comment line for full epochs training"
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]
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},
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{
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"outputs": [],
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"source": [
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"# Train first model (regular training) using our backbone\n",
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"model_regular = LitMNIST(backbone=resnet)"
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"model_regular = LitMNIST(backbone=copy.deepcopy(resnet))"
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]
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},
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{
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"TPU available: False, using: 0 TPU cores\n",
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"IPU available: False, using: 0 IPUs\n",
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"HPU available: False, using: 0 HPUs\n",
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"Running in `fast_dev_run` mode: will run the requested loop using 3 batch(es). Logging and checkpointing is suppressed.\n"
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"Running in `fast_dev_run` mode: will run the requested loop using 10 batch(es). Logging and checkpointing is suppressed.\n"
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]
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}
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],
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Epoch 0: 100%|██████████| 3/3 [00:50<00:00, 0.06it/s, loss_step=274.0, val_loss_step=260.0, cindex_step=0.615, val_loss_epoch=261.0, cindex_epoch=0.618, loss_epoch=300.0]"
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"Epoch 0: 100%|██████████| 10/10 [06:00<00:00, 0.03it/s, loss_step=242.0, val_loss_step=260.0, cindex_step=0.585, val_loss_epoch=260.0, cindex_epoch=0.579, loss_epoch=265.0]"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"`Trainer.fit` stopped: `max_steps=3` reached.\n"
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"`Trainer.fit` stopped: `max_steps=10` reached.\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Epoch 0: 100%|██████████| 3/3 [00:50<00:00, 0.06it/s, loss_step=274.0, val_loss_step=260.0, cindex_step=0.615, val_loss_epoch=261.0, cindex_epoch=0.618, loss_epoch=300.0]\n"
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"Epoch 0: 100%|██████████| 10/10 [06:00<00:00, 0.03it/s, loss_step=242.0, val_loss_step=260.0, cindex_step=0.585, val_loss_epoch=260.0, cindex_epoch=0.579, loss_epoch=265.0]\n"
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]
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}
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],
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Testing DataLoader 0: 100%|██████████| 3/3 [00:20<00:00, 0.15it/s]\n",
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"Testing DataLoader 0: 100%|██████████| 10/10 [03:09<00:00, 0.05it/s]\n",
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"────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
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" Test metric DataLoader 0\n",
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"────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
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" cindex_epoch 0.612917959690094\n",
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" val_loss_epoch 34.167057037353516\n",
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" cindex_epoch 0.5841928124427795\n",
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" val_loss_epoch -90.17676544189453\n",
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"────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"[{'val_loss_epoch': 34.167057037353516, 'cindex_epoch': 0.612917959690094}]"
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"[{'val_loss_epoch': -90.17676544189453, 'cindex_epoch': 0.5841928124427795}]"
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]
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},
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"execution_count": 15,
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"outputs": [],
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"source": [
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"FACTOR = 10 # Number of batch to keep in memory. Increase our training batch size artificially by factor of 10 here\n",
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"resnet_momentum = Momentum(resnet, neg_partial_log_likelihood, steps=FACTOR, rate=0.999)\n",
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"resnet_momentum = Momentum(copy.deepcopy(resnet), neg_partial_log_likelihood, steps=FACTOR, rate=0.999)\n",
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"model_momentum = LitMomentum(backbone=resnet_momentum)\n",
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"\n",
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"# By using momentum, we can in theory reduce our batch size by factor and still have the same effective sample size\n",
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"TPU available: False, using: 0 TPU cores\n",
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"IPU available: False, using: 0 IPUs\n",
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"HPU available: False, using: 0 HPUs\n",
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"Running in `fast_dev_run` mode: will run the requested loop using 3 batch(es). Logging and checkpointing is suppressed.\n",
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"Running in `fast_dev_run` mode: will run the requested loop using 10 batch(es). Logging and checkpointing is suppressed.\n",
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"\n",
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" | Name | Type | Params\n",
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"-----------------------------------\n",
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Epoch 0: 100%|██████████| 3/3 [00:15<00:00, 0.19it/s, loss_step=44.70, val_loss_step=74.10, cindex_step=0.614, val_loss_epoch=74.00, cindex_epoch=0.631, loss_epoch=23.30]"
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"Epoch 0: 100%|██████████| 10/10 [01:27<00:00, 0.11it/s, loss_step=65.60, val_loss_step=67.00, cindex_step=0.524, val_loss_epoch=66.40, cindex_epoch=0.519, loss_epoch=52.70]"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"`Trainer.fit` stopped: `max_steps=3` reached.\n"
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"`Trainer.fit` stopped: `max_steps=10` reached.\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Epoch 0: 100%|██████████| 3/3 [00:15<00:00, 0.19it/s, loss_step=44.70, val_loss_step=74.10, cindex_step=0.614, val_loss_epoch=74.00, cindex_epoch=0.631, loss_epoch=23.30]\n"
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"Epoch 0: 100%|██████████| 10/10 [01:27<00:00, 0.11it/s, loss_step=65.60, val_loss_step=67.00, cindex_step=0.524, val_loss_epoch=66.40, cindex_epoch=0.519, loss_epoch=52.70]\n"
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]
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}
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],
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Testing DataLoader 0: 100%|██████████| 3/3 [00:04<00:00, 0.61it/s]\n",
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"Testing DataLoader 0: 100%|██████████| 10/10 [00:29<00:00, 0.34it/s]\n",
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"────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
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" Test metric DataLoader 0\n",
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"────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
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" cindex_epoch 0.609957754611969\n",
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" val_loss_epoch 65.41207122802734\n",
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" cindex_epoch 0.521008312702179\n",
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" val_loss_epoch 66.2925796508789\n",
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"────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"[{'val_loss_epoch': 65.41207122802734, 'cindex_epoch': 0.609957754611969}]"
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"[{'val_loss_epoch': 66.2925796508789, 'cindex_epoch': 0.521008312702179}]"
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]
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},
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"execution_count": 18,
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Cindex (regular) = 0.637220025062561\n",
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"Cindex (momentum) = 0.6211331486701965\n",
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"Compare (p-value) = 0.9429321885108948\n"
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"Cindex (regular) = 0.5925866365432739\n",
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"Cindex (momentum) = 0.5304314494132996\n",
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"Compare (p-value) = 0.9395550489425659\n"
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]
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}
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],

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