From 4b1d0c89de9cb838ee77df2128d2c9f27f75a863 Mon Sep 17 00:00:00 2001
From: Guy Jacob <guy.jacob@intel.com>
Date: Sun, 10 Feb 2019 12:52:07 +0200
Subject: [PATCH] Load different random subset of dataset on each epoch (#149)
* For CIFAR-10 / ImageNet only
* Refactor data_loaders.py, reduce code duplication
* Implemented custom sampler
* Integrated in image classification sample
* Since we now shuffle the test set, had to update expected results
in 2 full_flow_tests that do evaluation
---
apputils/data_loaders.py | 195 ++++++++++--------
.../resnet20_filters.schedule_agp.yaml | 2 +-
.../resnet20_filters.schedule_agp_3.yaml | 2 +-
.../resnet20_filters.schedule_agp_4.yaml | 2 +-
.../resnet50.schedule_agp.1x1x8-blocks.yaml | 2 +-
.../resnet50.schedule_agp.filters.yaml | 2 +-
.../resnet50.schedule_agp.filters_2.yaml | 2 +-
.../resnet50.schedule_agp.filters_3.yaml | 2 +-
...resnet50.schedule_agp.filters_with_FC.yaml | 2 +-
.../compress_classifier.py | 6 +-
examples/classifier_compression/parser.py | 31 ++-
.../resnet20.network_surgery.yaml | 2 +-
.../resnet50.network_surgery.yaml | 2 +-
.../resnet50.network_surgery2.yaml | 2 +-
.../resnet50.filters.activation_apoz_agp.yaml | 2 +-
tests/full_flow_tests.py | 4 +-
16 files changed, 147 insertions(+), 113 deletions(-)
diff --git a/apputils/data_loaders.py b/apputils/data_loaders.py
index 0771bef..96802af 100755
--- a/apputils/data_loaders.py
+++ b/apputils/data_loaders.py
@@ -23,13 +23,14 @@ import os
import torch
import torchvision.transforms as transforms
import torchvision.datasets as datasets
-from torch.utils.data.sampler import SubsetRandomSampler
+from torch.utils.data.sampler import Sampler
import numpy as np
DATASETS_NAMES = ['imagenet', 'cifar10']
-def load_data(dataset, data_dir, batch_size, workers, valid_size=0.1, deterministic=False):
+def load_data(dataset, data_dir, batch_size, workers, validation_split=0.1, deterministic=False,
+ effective_train_size=1., effective_valid_size=1., effective_test_size=1.):
"""Load a dataset.
Args:
@@ -37,33 +38,22 @@ def load_data(dataset, data_dir, batch_size, workers, valid_size=0.1, determinis
data_dir: the directory where the datset resides
batch_size: the batch size
workers: the number of worker threads to use for loading the data
- valid_size: portion of training dataset to set aside for validation
+ validation_split: portion of training dataset to set aside for validation
deterministic: set to True if you want the data loading process to be deterministic.
Note that deterministic data loading suffers from poor performance.
+ effective_train/valid/test_size: portion of the datasets to load on each epoch.
+ The subset is chosen randomly each time. For the training and validation sets, this is applied AFTER
+ the split to those sets according to the validation_split parameter
"""
- assert dataset in DATASETS_NAMES
- if dataset == 'cifar10':
- return cifar10_load_data(data_dir, batch_size, workers, valid_size=valid_size, deterministic=deterministic)
- if dataset == 'imagenet':
- return imagenet_load_data(data_dir, batch_size, workers, valid_size=valid_size, deterministic=deterministic)
- print("FATAL ERROR: load_data does not support dataset %s" % dataset)
- exit(1)
+ if dataset not in DATASETS_NAMES:
+ raise ValueError('load_data does not support dataset %s" % dataset')
+ datasets_fn = cifar10_get_datasets if dataset == 'cifar10' else imagenet_get_datasets
+ return get_data_loaders(datasets_fn, data_dir, batch_size, workers, validation_split=validation_split,
+ deterministic=deterministic, effective_train_size=effective_train_size,
+ effective_valid_size=effective_valid_size, effective_test_size=effective_test_size)
-def __image_size(dataset):
- # un-squeeze is used here to add the batch dimension (value=1), which is missing
- return dataset[0][0].unsqueeze(0).size()
-
-
-def __deterministic_worker_init_fn(worker_id, seed=0):
- import random
- import numpy
- random.seed(seed)
- numpy.random.seed(seed)
- torch.manual_seed(seed)
-
-
-def cifar10_load_data(data_dir, batch_size, num_workers, valid_size=0.1, deterministic=False):
+def cifar10_get_datasets(data_dir):
"""Load the CIFAR10 dataset.
The original training dataset is split into training and validation sets (code is
@@ -81,116 +71,139 @@ def cifar10_load_data(data_dir, batch_size, num_workers, valid_size=0.1, determi
[1] C.-Y. Lee, S. Xie, P. Gallagher, Z. Zhang, and Z. Tu. Deeply Supervised Nets.
arXiv:1409.5185, 2014
"""
- transform = transforms.Compose([
+ train_transform = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
- transform_test = transforms.Compose([
+ train_dataset = datasets.CIFAR10(root=data_dir, train=True,
+ download=True, transform=train_transform)
+
+ test_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
- train_dataset = datasets.CIFAR10(root=data_dir, train=True,
- download=True, transform=transform)
+ test_dataset = datasets.CIFAR10(root=data_dir, train=False,
+ download=True, transform=test_transform)
- num_train = len(train_dataset)
- indices = list(range(num_train))
- split = int(np.floor(valid_size * num_train))
+ return train_dataset, test_dataset
- np.random.shuffle(indices)
- train_idx, valid_idx = indices[split:], indices[:split]
- train_sampler = SubsetRandomSampler(train_idx)
+def imagenet_get_datasets(data_dir):
+ """
+ Load the ImageNet dataset.
+ """
+ train_dir = os.path.join(data_dir, 'train')
+ test_dir = os.path.join(data_dir, 'val')
+ normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
+ std=[0.229, 0.224, 0.225])
- worker_init_fn = __deterministic_worker_init_fn if deterministic else None
+ train_transform = transforms.Compose([
+ transforms.RandomResizedCrop(224),
+ transforms.RandomHorizontalFlip(),
+ transforms.ToTensor(),
+ normalize,
+ ])
- train_loader = torch.utils.data.DataLoader(train_dataset,
- batch_size=batch_size, sampler=train_sampler,
- num_workers=num_workers, pin_memory=True,
- worker_init_fn=worker_init_fn)
+ train_dataset = datasets.ImageFolder(train_dir, train_transform)
- valid_loader = None
- if split > 0:
- valid_sampler = SubsetRandomSampler(valid_idx)
- valid_loader = torch.utils.data.DataLoader(train_dataset,
- batch_size=batch_size, sampler=valid_sampler,
- num_workers=num_workers, pin_memory=True,
- worker_init_fn=worker_init_fn)
+ test_transform = transforms.Compose([
+ transforms.Resize(256),
+ transforms.CenterCrop(224),
+ transforms.ToTensor(),
+ normalize,
+ ])
- testset = datasets.CIFAR10(root=data_dir, train=False,
- download=True, transform=transform_test)
+ test_dataset = datasets.ImageFolder(test_dir, test_transform)
- test_loader = torch.utils.data.DataLoader(
- testset, batch_size=batch_size, shuffle=False,
- num_workers=num_workers, pin_memory=True)
+ return train_dataset, test_dataset
- input_shape = __image_size(train_dataset)
- # If validation split was 0 we use the test set as the validation set
- return train_loader, valid_loader or test_loader, test_loader, input_shape
+def __image_size(dataset):
+ # un-squeeze is used here to add the batch dimension (value=1), which is missing
+ return dataset[0][0].unsqueeze(0).size()
+
+
+def __deterministic_worker_init_fn(worker_id, seed=0):
+ import random
+ import numpy
+ random.seed(seed)
+ numpy.random.seed(seed)
+ torch.manual_seed(seed)
+
+
+def __split_list(l, ratio):
+ split_idx = int(np.floor(ratio * len(l)))
+ return l[:split_idx], l[split_idx:]
-def imagenet_load_data(data_dir, batch_size, num_workers, valid_size=0.1, deterministic=False):
- """Load the ImageNet dataset.
+class SwitchingSubsetRandomSampler(Sampler):
+ """Samples a random subset of elements from a data source, without replacement.
- Somewhat unconventionally, we use the ImageNet validation dataset as our test dataset,
- and split the training dataset for training and validation (90/10 by default).
+ The subset of elements is re-chosen randomly each time the sampler is enumerated
+
+ Args:
+ data_source (Dataset): dataset to sample from
+ subset_size (float): value in (0..1], representing the portion of dataset to sample at each enumeration.
"""
- train_dir = os.path.join(data_dir, 'train')
- test_dir = os.path.join(data_dir, 'val')
- normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
- std=[0.229, 0.224, 0.225])
+ def __init__(self, data_source, subset_size):
+ if subset_size <= 0 or subset_size > 1:
+ raise ValueError('subset_size must be in (0..1]')
+ self.data_source = data_source
+ self.subset_length = int(np.floor(len(self.data_source) * subset_size))
+
+ def __iter__(self):
+ # Randomizing in the same way as in torch.utils.data.sampler.SubsetRandomSampler to maintain
+ # reproducibility with the previous data loaders implementation
+ indices = torch.randperm(len(self.data_source))
+ subset_indices = indices[:self.subset_length]
+ return (self.data_source[i] for i in subset_indices)
+
+ def __len__(self):
+ return self.subset_length
+
- train_dataset = datasets.ImageFolder(
- train_dir,
- transforms.Compose([
- transforms.RandomResizedCrop(224),
- transforms.RandomHorizontalFlip(),
- transforms.ToTensor(),
- normalize,
- ]))
+def get_data_loaders(datasets_fn, data_dir, batch_size, num_workers, validation_split=0.1, deterministic=False,
+ effective_train_size=1., effective_valid_size=1., effective_test_size=1.):
+ train_dataset, test_dataset = datasets_fn(data_dir)
+
+ worker_init_fn = __deterministic_worker_init_fn if deterministic else None
num_train = len(train_dataset)
indices = list(range(num_train))
- split = int(np.floor(valid_size * num_train))
- # Note! We must shuffle the imagenet data because the files are ordered
- # by class. If we don't shuffle, the train and validation datasets will
- # by mutually-exclusive
- np.random.shuffle(indices)
+ # TODO: Switch to torch.utils.data.datasets.random_split()
- train_idx, valid_idx = indices[split:], indices[:split]
- train_sampler = SubsetRandomSampler(train_idx)
-
- input_shape = __image_size(train_dataset)
+ # We shuffle indices here in case the data is arranged by class, in which case we'd would get mutually
+ # exclusive datasets if we didn't shuffle
+ np.random.shuffle(indices)
- worker_init_fn = __deterministic_worker_init_fn if deterministic else None
+ valid_indices, train_indices = __split_list(indices, validation_split)
+ train_sampler = SwitchingSubsetRandomSampler(train_indices, effective_train_size)
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=batch_size, sampler=train_sampler,
num_workers=num_workers, pin_memory=True,
worker_init_fn=worker_init_fn)
valid_loader = None
- if split > 0:
- valid_sampler = SubsetRandomSampler(valid_idx)
+ if valid_indices:
+ valid_sampler = SwitchingSubsetRandomSampler(valid_indices, effective_valid_size)
valid_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=batch_size, sampler=valid_sampler,
num_workers=num_workers, pin_memory=True,
worker_init_fn=worker_init_fn)
- test_loader = torch.utils.data.DataLoader(
- datasets.ImageFolder(test_dir, transforms.Compose([
- transforms.Resize(256),
- transforms.CenterCrop(224),
- transforms.ToTensor(),
- normalize,
- ])),
- batch_size=batch_size, shuffle=False,
- num_workers=num_workers, pin_memory=True)
+ test_indices = list(range(len(test_dataset)))
+ test_sampler = SwitchingSubsetRandomSampler(test_indices, effective_test_size)
+ test_loader = torch.utils.data.DataLoader(test_dataset,
+ batch_size=batch_size, sampler=test_sampler,
+ num_workers=num_workers, pin_memory=True)
+
+ input_shape = __image_size(train_dataset)
# If validation split was 0 we use the test set as the validation set
return train_loader, valid_loader or test_loader, test_loader, input_shape
diff --git a/examples/agp-pruning/resnet20_filters.schedule_agp.yaml b/examples/agp-pruning/resnet20_filters.schedule_agp.yaml
index 708c141..aa04ed5 100755
--- a/examples/agp-pruning/resnet20_filters.schedule_agp.yaml
+++ b/examples/agp-pruning/resnet20_filters.schedule_agp.yaml
@@ -14,7 +14,7 @@
# Total sparsity: 41.10
# # of parameters: 120,000 (=55.7% of the baseline parameters)
#
-# time python3 compress_classifier.py --arch resnet20_cifar ../../../data.cifar10 -p=50 --lr=0.3 --epochs=180 --compress=../agp-pruning/resnet20_filters.schedule_agp.yaml -j=1 --deterministic --resume=../ssl/checkpoints/checkpoint_trained_dense.pth.tar --validation-size=0
+# time python3 compress_classifier.py --arch resnet20_cifar ../../../data.cifar10 -p=50 --lr=0.3 --epochs=180 --compress=../agp-pruning/resnet20_filters.schedule_agp.yaml -j=1 --deterministic --resume=../ssl/checkpoints/checkpoint_trained_dense.pth.tar --validation-split=0
#
# Parameters:
# +----+-------------------------------------+----------------+---------------+----------------+------------+------------+----------+----------+----------+------------+---------+----------+------------+
diff --git a/examples/agp-pruning/resnet20_filters.schedule_agp_3.yaml b/examples/agp-pruning/resnet20_filters.schedule_agp_3.yaml
index 9ac92a2..3879858 100755
--- a/examples/agp-pruning/resnet20_filters.schedule_agp_3.yaml
+++ b/examples/agp-pruning/resnet20_filters.schedule_agp_3.yaml
@@ -14,7 +14,7 @@
# Total sparsity: 56.41%
# # of parameters: 95922 (=35.4% of the baseline parameters ==> 64.6% sparsity)
#
-# time python3 compress_classifier.py --arch resnet20_cifar ../../../data.cifar10 -p=50 --lr=0.4 --epochs=180 --compress=../agp-pruning/resnet20_filters.schedule_agp_3.yaml -j=1 --deterministic --resume=../ssl/checkpoints/checkpoint_trained_dense.pth.tar --validation-size=0
+# time python3 compress_classifier.py --arch resnet20_cifar ../../../data.cifar10 -p=50 --lr=0.4 --epochs=180 --compress=../agp-pruning/resnet20_filters.schedule_agp_3.yaml -j=1 --deterministic --resume=../ssl/checkpoints/checkpoint_trained_dense.pth.tar --validation-split=0
#
# Parameters:
# +----+-------------------------------------+----------------+---------------+----------------+------------+------------+----------+----------+----------+------------+---------+----------+------------+
diff --git a/examples/agp-pruning/resnet20_filters.schedule_agp_4.yaml b/examples/agp-pruning/resnet20_filters.schedule_agp_4.yaml
index 816ac04..3f85c2d 100755
--- a/examples/agp-pruning/resnet20_filters.schedule_agp_4.yaml
+++ b/examples/agp-pruning/resnet20_filters.schedule_agp_4.yaml
@@ -14,7 +14,7 @@
# Total sparsity: 39.66
# # of parameters: 78,776 (=29.1% of the baseline parameters)
#
-# time python3 compress_classifier.py --arch resnet20_cifar ../../../data.cifar10 -p=50 --lr=0.3 --epochs=180 --compress=../agp-pruning/resnet20_filters.schedule_agp_4.yaml -j=1 --deterministic --resume=../ssl/checkpoints/checkpoint_trained_dense.pth.tar --validation-size=0
+# time python3 compress_classifier.py --arch resnet20_cifar ../../../data.cifar10 -p=50 --lr=0.3 --epochs=180 --compress=../agp-pruning/resnet20_filters.schedule_agp_4.yaml -j=1 --deterministic --resume=../ssl/checkpoints/checkpoint_trained_dense.pth.tar --validation-split=0
#
# Parameters:
# +----+-------------------------------------+----------------+---------------+----------------+------------+------------+----------+----------+----------+------------+---------+----------+------------+
diff --git a/examples/agp-pruning/resnet50.schedule_agp.1x1x8-blocks.yaml b/examples/agp-pruning/resnet50.schedule_agp.1x1x8-blocks.yaml
index 342492c..c7cf5c0 100755
--- a/examples/agp-pruning/resnet50.schedule_agp.1x1x8-blocks.yaml
+++ b/examples/agp-pruning/resnet50.schedule_agp.1x1x8-blocks.yaml
@@ -4,7 +4,7 @@
#
# Best Top1: 76.358 (epoch 72) vs. 76.15 baseline (+0.2%)
#
-# time python3 compress_classifier.py -a=resnet50 --pretrained -p=50 ../../../data.imagenet/ -j=22 --epochs=100 --lr=0.0005 --compress=../agp-pruning/resnet50.schedule_agp.1x1x8-blocks.yaml --validation-size=0 --num-best-scores=10
+# time python3 compress_classifier.py -a=resnet50 --pretrained -p=50 ../../../data.imagenet/ -j=22 --epochs=100 --lr=0.0005 --compress=../agp-pruning/resnet50.schedule_agp.1x1x8-blocks.yaml --validation-split=0 --num-best-scores=10
#
# Parameters:
# +----+-------------------------------------+--------------------+---------------+----------------+------------+------------+----------+----------+----------+------------+---------+----------+------------+
diff --git a/examples/agp-pruning/resnet50.schedule_agp.filters.yaml b/examples/agp-pruning/resnet50.schedule_agp.filters.yaml
index a654ba6..04e3500 100755
--- a/examples/agp-pruning/resnet50.schedule_agp.filters.yaml
+++ b/examples/agp-pruning/resnet50.schedule_agp.filters.yaml
@@ -5,7 +5,7 @@
# No. of Parameters: 12,335,296 (of 25,502,912) = 43.37% dense (56.63% sparse)
# Total MACs: 1,822,031,872 (of 4,089,184,256) = 44.56% compute = 2.24x
#
-# time python3 compress_classifier.py -a=resnet50 --pretrained -p=50 ../../../data.imagenet/ -j=22 --epochs=100 --lr=0.0005 --compress=resnet50.schedule_agp.filters.yaml --validation-size=0 --num-best-scores=10 --name="resnet50_filters_v3.2"
+# time python3 compress_classifier.py -a=resnet50 --pretrained -p=50 ../../../data.imagenet/ -j=22 --epochs=100 --lr=0.0005 --compress=resnet50.schedule_agp.filters.yaml --validation-split=0 --num-best-scores=10 --name="resnet50_filters_v3.2"
#
# Parameters:
# +----+-------------------------------------+--------------------+---------------+----------------+------------+------------+----------+----------+----------+------------+---------+----------+------------+
diff --git a/examples/agp-pruning/resnet50.schedule_agp.filters_2.yaml b/examples/agp-pruning/resnet50.schedule_agp.filters_2.yaml
index e571319..a9b6505 100755
--- a/examples/agp-pruning/resnet50.schedule_agp.filters_2.yaml
+++ b/examples/agp-pruning/resnet50.schedule_agp.filters_2.yaml
@@ -5,7 +5,7 @@
# No. of Parameters: 12,671,168 (of 25,502,912) = 49.69% dense (50.31% sparse)
# Total MACs: 2,037,186,560 (of 4,089,184,256) = 49.82% compute = 2.01x
#
-# time python3 compress_classifier.py -a=resnet50 --pretrained -p=50 ../../../data.imagenet/ -j=22 --epochs=100 --lr=0.0005 --compress=resnet50.schedule_agp.filters_2.yaml --validation-size=0 --num-best-scores=10
+# time python3 compress_classifier.py -a=resnet50 --pretrained -p=50 ../../../data.imagenet/ -j=22 --epochs=100 --lr=0.0005 --compress=resnet50.schedule_agp.filters_2.yaml --validation-split=0 --num-best-scores=10
#
# Parameters:
# +----+-------------------------------------+--------------------+---------------+----------------+------------+------------+----------+----------+----------+------------+---------+----------+------------+
diff --git a/examples/agp-pruning/resnet50.schedule_agp.filters_3.yaml b/examples/agp-pruning/resnet50.schedule_agp.filters_3.yaml
index d68c3fc..8c21bbe 100755
--- a/examples/agp-pruning/resnet50.schedule_agp.filters_3.yaml
+++ b/examples/agp-pruning/resnet50.schedule_agp.filters_3.yaml
@@ -5,7 +5,7 @@
# No. of Parameters: 17,329,344 (of 25,502,912) = 67.95% dense (32.05% sparse)
# Total MACs: 2,753,298,432 (of 4,089,184,256) = 67.33% compute = 1.49x
#
-# time python3 compress_classifier.py -a=resnet50 --pretrained -p=50 ../../../data.imagenet/ -j=22 --epochs=100 --lr=0.0005 --compress=resnet50.schedule_agp.filters_3.yaml --validation-size=0 --num-best-scores=10
+# time python3 compress_classifier.py -a=resnet50 --pretrained -p=50 ../../../data.imagenet/ -j=22 --epochs=100 --lr=0.0005 --compress=resnet50.schedule_agp.filters_3.yaml --validation-split=0 --num-best-scores=10
#
# Parameters:
# +----+-------------------------------------+--------------------+---------------+----------------+------------+------------+----------+----------+----------+------------+---------+----------+------------+
diff --git a/examples/agp-pruning/resnet50.schedule_agp.filters_with_FC.yaml b/examples/agp-pruning/resnet50.schedule_agp.filters_with_FC.yaml
index 15cc334..1b20fa1 100755
--- a/examples/agp-pruning/resnet50.schedule_agp.filters_with_FC.yaml
+++ b/examples/agp-pruning/resnet50.schedule_agp.filters_with_FC.yaml
@@ -6,7 +6,7 @@
# Best Top1: 74.564 (epoch 84) vs. 76.15 baseline (-1.6%)
# No. of Parameters: 10,901,696 (of 25,502,912) = 42.74% dense (57.26% sparse)
# Total MACs: 1,822,031,872 (of 4,089,184,256) = 44.56% compute = 2.24x
-# time python3 compress_classifier.py -a=resnet50 --pretrained -p=50 ~/datasets/imagenet/ -j=22 --epochs=100 --lr=0.0005 --compress=resnet50.schedule_agp.filters.yaml --validation-size=0 --num-best-scores=10
+# time python3 compress_classifier.py -a=resnet50 --pretrained -p=50 ~/datasets/imagenet/ -j=22 --epochs=100 --lr=0.0005 --compress=resnet50.schedule_agp.filters.yaml --validation-split=0 --num-best-scores=10
#
# Parameters:
# +----+-------------------------------------+--------------------+---------------+----------------+------------+------------+----------+----------+----------+------------+---------+----------+------------+
diff --git a/examples/classifier_compression/compress_classifier.py b/examples/classifier_compression/compress_classifier.py
index 076ddf9..98ec9de 100755
--- a/examples/classifier_compression/compress_classifier.py
+++ b/examples/classifier_compression/compress_classifier.py
@@ -194,7 +194,8 @@ def main():
# substring "_cifar", then cifar10 is used.
train_loader, val_loader, test_loader, _ = apputils.load_data(
args.dataset, os.path.expanduser(args.data), args.batch_size,
- args.workers, args.validation_size, args.deterministic)
+ args.workers, args.validation_split, args.deterministic,
+ args.effective_train_size, args.effective_valid_size, args.effective_test_size)
msglogger.info('Dataset sizes:\n\ttraining=%d\n\tvalidation=%d\n\ttest=%d',
len(train_loader.sampler), len(val_loader.sampler), len(test_loader.sampler))
@@ -644,7 +645,8 @@ def automated_deep_compression(model, criterion, optimizer, loggers, args):
train_loader, val_loader, test_loader, _ = apputils.load_data(
args.dataset, os.path.expanduser(args.data), args.batch_size,
- args.workers, args.validation_size, args.deterministic)
+ args.workers, args.validation_split, args.deterministic,
+ args.effective_train_size, args.effective_valid_size, args.effective_test_size)
args.display_confusion = True
validate_fn = partial(validate, val_loader=test_loader, criterion=criterion,
diff --git a/examples/classifier_compression/parser.py b/examples/classifier_compression/parser.py
index 435c8a7..0bad76b 100755
--- a/examples/classifier_compression/parser.py
+++ b/examples/classifier_compression/parser.py
@@ -15,6 +15,7 @@
#
import argparse
+import operator
import distiller
import models
@@ -79,8 +80,19 @@ def getParser():
'Flag set => overrides the --gpus flag')
parser.add_argument('--name', '-n', metavar='NAME', default=None, help='Experiment name')
parser.add_argument('--out-dir', '-o', dest='output_dir', default='logs', help='Path to dump logs and checkpoints')
- parser.add_argument('--validation-size', '--vs', type=float_range, default=0.1,
+ parser.add_argument('--validation-split', '--valid-size', '--vs', dest='validation_split',
+ type=float_range(exc_max=True), default=0.1,
help='Portion of training dataset to set aside for validation')
+ parser.add_argument('--effective-train-size', '--etrs', type=float_range(exc_min=True), default=1.,
+ help='Portion of training dataset to be used in each epoch. '
+ 'NOTE: If --validation-split is set, then the value of this argument is applied '
+ 'AFTER the train-validation split according to that argument')
+ parser.add_argument('--effective-valid-size', '--evs', type=float_range(exc_min=True), default=1.,
+ help='Portion of validation dataset to be used in each epoch. '
+ 'NOTE: If --validation-split is set, then the value of this argument is applied '
+ 'AFTER the train-validation split according to that argument')
+ parser.add_argument('--effective-test-size', '--etes', type=float_range(exc_min=True), default=1.,
+ help='Portion of test dataset to be used in each epoch')
parser.add_argument('--adc', dest='ADC', action='store_true', help='temp HACK')
parser.add_argument('--adc-params', dest='ADC_params', default=None, help='temp HACK')
parser.add_argument('--confusion', dest='display_confusion', default=False, action='store_true',
@@ -134,8 +146,15 @@ def getParser():
return parser
-def float_range(val_str):
- val = float(val_str)
- if val < 0 or val >= 1:
- raise argparse.ArgumentTypeError('Must be >= 0 and < 1 (received {0})'.format(val_str))
- return val
+def float_range(min_val=0., max_val=1., exc_min=False, exc_max=False):
+ def checker(val_str):
+ val = float(val_str)
+ min_op, min_op_str = (operator.gt, '>') if exc_min else (operator.ge, '>=')
+ max_op, max_op_str = (operator.lt, '<') if exc_max else (operator.le, '<=')
+ if min_op(val, min_val) and max_op(val, max_val):
+ return val
+ raise argparse.ArgumentTypeError(
+ 'Value must be {} {} and {} {} (received {})'.format(min_op_str, min_val, max_op_str, max_val, val))
+ if min_val >= max_val:
+ raise ValueError('min_val must be less than max_val')
+ return checker
diff --git a/examples/network_surgery/resnet20.network_surgery.yaml b/examples/network_surgery/resnet20.network_surgery.yaml
index ba8740e..1188ec8 100755
--- a/examples/network_surgery/resnet20.network_surgery.yaml
+++ b/examples/network_surgery/resnet20.network_surgery.yaml
@@ -31,7 +31,7 @@
# Total sparsity: 69.1%
# # of parameters: 83,671
#
-# time python3 compress_classifier.py --arch resnet20_cifar ../../../data.cifar10 -p=50 --lr=0.01 --epochs=180 --compress=../network_surgery/resnet20.network_surgery.yaml -j=1 --deterministic --validation-size=0 --resume=../ssl/checkpoints/checkpoint_trained_dense.pth.tar --masks-sparsity --num-best-scores=10
+# time python3 compress_classifier.py --arch resnet20_cifar ../../../data.cifar10 -p=50 --lr=0.01 --epochs=180 --compress=../network_surgery/resnet20.network_surgery.yaml -j=1 --deterministic --validation-split=0 --resume=../ssl/checkpoints/checkpoint_trained_dense.pth.tar --masks-sparsity --num-best-scores=10
#
# Parameters:
# +----+-------------------------------------+----------------+---------------+----------------+------------+------------+----------+----------+----------+------------+---------+----------+------------+
diff --git a/examples/network_surgery/resnet50.network_surgery.yaml b/examples/network_surgery/resnet50.network_surgery.yaml
index d7d0588..9822701 100755
--- a/examples/network_surgery/resnet50.network_surgery.yaml
+++ b/examples/network_surgery/resnet50.network_surgery.yaml
@@ -5,7 +5,7 @@
# Top1 is 75.492 (on Epoch: 93) vs the published Top1: 76.15 (https://pytorch.org/docs/stable/torchvision/models.html)
# Total sparsity: 80.05
#
-# time python3 compress_classifier.py -a=resnet50 --pretrained -p=50 ../../../data.imagenet/ -j=22 --epochs=100 --lr=0.0005 --compress=resnet50.network_surgery.yaml --validation-size=0 --masks-sparsity --num-best-scores=10
+# time python3 compress_classifier.py -a=resnet50 --pretrained -p=50 ../../../data.imagenet/ -j=22 --epochs=100 --lr=0.0005 --compress=resnet50.network_surgery.yaml --validation-split=0 --masks-sparsity --num-best-scores=10
#
# Parameters:
# +----+-------------------------------------+--------------------+---------------+----------------+------------+------------+----------+----------+----------+------------+---------+----------+------------+
diff --git a/examples/network_surgery/resnet50.network_surgery2.yaml b/examples/network_surgery/resnet50.network_surgery2.yaml
index 17612b9..2f50b58 100755
--- a/examples/network_surgery/resnet50.network_surgery2.yaml
+++ b/examples/network_surgery/resnet50.network_surgery2.yaml
@@ -5,7 +5,7 @@
# Top1 is 75.518 (on Epoch: 99) vs the published Top1: 76.15 (https://pytorch.org/docs/stable/torchvision/models.html)
# Total sparsity: 82.6%
#
-# time python3 compress_classifier.py -a=resnet50 --pretrained -p=50 ../../../data.imagenet/ -j=22 --epochs=100 --lr=0.001 --compress=resnet50.network_surgery2.yaml --validation-size=0 --masks-sparsity --num-best-scores=10
+# time python3 compress_classifier.py -a=resnet50 --pretrained -p=50 ../../../data.imagenet/ -j=22 --epochs=100 --lr=0.001 --compress=resnet50.network_surgery2.yaml --validation-split=0 --masks-sparsity --num-best-scores=10
#
#
# Parameters:
diff --git a/examples/network_trimming/resnet50.filters.activation_apoz_agp.yaml b/examples/network_trimming/resnet50.filters.activation_apoz_agp.yaml
index 2a02ef5..ebced2a 100755
--- a/examples/network_trimming/resnet50.filters.activation_apoz_agp.yaml
+++ b/examples/network_trimming/resnet50.filters.activation_apoz_agp.yaml
@@ -1,7 +1,7 @@
#
# This schedule uses the average percentage of zeros (APoZ) in the activations, to rank filters.
#
-# time python3 compress_classifier.py -a=resnet50 --pretrained -p=50 ../../../data.imagenet/ -j=22 --epochs=100 --lr=0.0005 --compress=resnet50.schedule_agp.filters.yaml --validation-size=0 --num-best-scores=10 --name="resnet50_filters_v5_APoZ" --act-stats=valid
+# time python3 compress_classifier.py -a=resnet50 --pretrained -p=50 ../../../data.imagenet/ -j=22 --epochs=100 --lr=0.0005 --compress=resnet50.schedule_agp.filters.yaml --validation-split=0 --num-best-scores=10 --name="resnet50_filters_v5_APoZ" --act-stats=valid
#
# Results:
# Best Top1: 73.926 on Epoch: 88
diff --git a/tests/full_flow_tests.py b/tests/full_flow_tests.py
index c78d0fc..e361a61 100755
--- a/tests/full_flow_tests.py
+++ b/tests/full_flow_tests.py
@@ -118,13 +118,13 @@ test_configs = [
TestConfig('--arch simplenet_cifar --epochs 2', DS_CIFAR, accuracy_checker, [48.340, 92.630]),
TestConfig('-a resnet20_cifar --resume {0} --quantize-eval --evaluate'.
format(os.path.join(examples_root, 'ssl', 'checkpoints', 'checkpoint_trained_dense.pth.tar')),
- DS_CIFAR, accuracy_checker, [91.580, 99.620]),
+ DS_CIFAR, accuracy_checker, [91.640, 99.610]),
TestConfig('-a preact_resnet20_cifar --epochs 2 --compress {0}'.
format(os.path.join('full_flow_tests', 'preact_resnet20_cifar_pact_test.yaml')),
DS_CIFAR, accuracy_checker, [48.290, 94.460]),
TestConfig('-a resnet20_cifar --resume {0} --sense=filter --sense-range 0 0.10 0.05'.
format(os.path.join(examples_root, 'ssl', 'checkpoints', 'checkpoint_trained_dense.pth.tar')),
- DS_CIFAR, collateral_checker, [('sensitivity.csv', 3188), ('sensitivity.png', 96158)])
+ DS_CIFAR, collateral_checker, [('sensitivity.csv', 3165), ('sensitivity.png', 96158)])
]
--
GitLab