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test_gpleiss_efficient_densenet_pytorch.py
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test_gpleiss_efficient_densenet_pytorch.py
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import sys
_module = sys.modules[__name__]
del sys
demo = _module
models = _module
densenet = _module
from _paritybench_helpers import _mock_config, patch_functional
from unittest.mock import mock_open, MagicMock
from torch.autograd import Function
from torch.nn import Module
import abc, collections, copy, enum, functools, inspect, itertools, logging, math, matplotlib, numbers, numpy, pandas, queue, random, re, scipy, sklearn, string, tensorflow, time, torch, torchaudio, torchtext, torchvision, types, typing, uuid, warnings
import numpy as np
from torch import Tensor
patch_functional()
open = mock_open()
yaml = logging = sys = argparse = MagicMock()
ArgumentParser = argparse.ArgumentParser
_global_config = args = argv = cfg = config = params = _mock_config()
argparse.ArgumentParser.return_value.parse_args.return_value = _global_config
yaml.load.return_value = _global_config
sys.argv = _global_config
__version__ = '1.0.0'
xrange = range
wraps = functools.wraps
import time
import torch
from torchvision import datasets
from torchvision import transforms
import math
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as cp
from collections import OrderedDict
def _bn_function_factory(norm, relu, conv):
def bn_function(*inputs):
concated_features = torch.cat(inputs, 1)
bottleneck_output = conv(relu(norm(concated_features)))
return bottleneck_output
return bn_function
class _DenseLayer(nn.Module):
def __init__(self, num_input_features, growth_rate, bn_size, drop_rate, efficient=False):
super(_DenseLayer, self).__init__()
self.add_module('norm1', nn.BatchNorm2d(num_input_features)),
self.add_module('relu1', nn.ReLU(inplace=True)),
self.add_module('conv1', nn.Conv2d(num_input_features, bn_size * growth_rate, kernel_size=1, stride=1, bias=False)),
self.add_module('norm2', nn.BatchNorm2d(bn_size * growth_rate)),
self.add_module('relu2', nn.ReLU(inplace=True)),
self.add_module('conv2', nn.Conv2d(bn_size * growth_rate, growth_rate, kernel_size=3, stride=1, padding=1, bias=False)),
self.drop_rate = drop_rate
self.efficient = efficient
def forward(self, *prev_features):
bn_function = _bn_function_factory(self.norm1, self.relu1, self.conv1)
if self.efficient and any(prev_feature.requires_grad for prev_feature in prev_features):
bottleneck_output = cp.checkpoint(bn_function, *prev_features)
else:
bottleneck_output = bn_function(*prev_features)
new_features = self.conv2(self.relu2(self.norm2(bottleneck_output)))
if self.drop_rate > 0:
new_features = F.dropout(new_features, p=self.drop_rate, training=self.training)
return new_features
class _Transition(nn.Sequential):
def __init__(self, num_input_features, num_output_features):
super(_Transition, self).__init__()
self.add_module('norm', nn.BatchNorm2d(num_input_features))
self.add_module('relu', nn.ReLU(inplace=True))
self.add_module('conv', nn.Conv2d(num_input_features, num_output_features, kernel_size=1, stride=1, bias=False))
self.add_module('pool', nn.AvgPool2d(kernel_size=2, stride=2))
class _DenseBlock(nn.Module):
def __init__(self, num_layers, num_input_features, bn_size, growth_rate, drop_rate, efficient=False):
super(_DenseBlock, self).__init__()
for i in range(num_layers):
layer = _DenseLayer(num_input_features + i * growth_rate, growth_rate=growth_rate, bn_size=bn_size, drop_rate=drop_rate, efficient=efficient)
self.add_module('denselayer%d' % (i + 1), layer)
def forward(self, init_features):
features = [init_features]
for name, layer in self.named_children():
new_features = layer(*features)
features.append(new_features)
return torch.cat(features, 1)
class DenseNet(nn.Module):
"""Densenet-BC model class, based on
`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
Args:
growth_rate (int) - how many filters to add each layer (`k` in paper)
block_config (list of 3 or 4 ints) - how many layers in each pooling block
num_init_features (int) - the number of filters to learn in the first convolution layer
bn_size (int) - multiplicative factor for number of bottle neck layers
(i.e. bn_size * k features in the bottleneck layer)
drop_rate (float) - dropout rate after each dense layer
num_classes (int) - number of classification classes
small_inputs (bool) - set to True if images are 32x32. Otherwise assumes images are larger.
efficient (bool) - set to True to use checkpointing. Much more memory efficient, but slower.
"""
def __init__(self, growth_rate=12, block_config=(16, 16, 16), compression=0.5, num_init_features=24, bn_size=4, drop_rate=0, num_classes=10, small_inputs=True, efficient=False):
super(DenseNet, self).__init__()
assert 0 < compression <= 1, 'compression of densenet should be between 0 and 1'
if small_inputs:
self.features = nn.Sequential(OrderedDict([('conv0', nn.Conv2d(3, num_init_features, kernel_size=3, stride=1, padding=1, bias=False))]))
else:
self.features = nn.Sequential(OrderedDict([('conv0', nn.Conv2d(3, num_init_features, kernel_size=7, stride=2, padding=3, bias=False))]))
self.features.add_module('norm0', nn.BatchNorm2d(num_init_features))
self.features.add_module('relu0', nn.ReLU(inplace=True))
self.features.add_module('pool0', nn.MaxPool2d(kernel_size=3, stride=2, padding=1, ceil_mode=False))
num_features = num_init_features
for i, num_layers in enumerate(block_config):
block = _DenseBlock(num_layers=num_layers, num_input_features=num_features, bn_size=bn_size, growth_rate=growth_rate, drop_rate=drop_rate, efficient=efficient)
self.features.add_module('denseblock%d' % (i + 1), block)
num_features = num_features + num_layers * growth_rate
if i != len(block_config) - 1:
trans = _Transition(num_input_features=num_features, num_output_features=int(num_features * compression))
self.features.add_module('transition%d' % (i + 1), trans)
num_features = int(num_features * compression)
self.features.add_module('norm_final', nn.BatchNorm2d(num_features))
self.classifier = nn.Linear(num_features, num_classes)
for name, param in self.named_parameters():
if 'conv' in name and 'weight' in name:
n = param.size(0) * param.size(2) * param.size(3)
param.data.normal_().mul_(math.sqrt(2.0 / n))
elif 'norm' in name and 'weight' in name:
param.data.fill_(1)
elif 'norm' in name and 'bias' in name:
param.data.fill_(0)
elif 'classifier' in name and 'bias' in name:
param.data.fill_(0)
def forward(self, x):
features = self.features(x)
out = F.relu(features, inplace=True)
out = F.adaptive_avg_pool2d(out, (1, 1))
out = torch.flatten(out, 1)
out = self.classifier(out)
return out
import torch
from torch.nn import MSELoss, ReLU
from _paritybench_helpers import _mock_config, _mock_layer, _paritybench_base, _fails_compile
TESTCASES = [
# (nn.Module, init_args, forward_args, jit_compiles)
(DenseNet,
lambda: ([], {}),
lambda: ([torch.rand([4, 3, 64, 64])], {}),
False),
(_DenseBlock,
lambda: ([], {'num_layers': 1, 'num_input_features': 4, 'bn_size': 4, 'growth_rate': 4, 'drop_rate': 0.5}),
lambda: ([torch.rand([4, 4, 4, 4])], {}),
False),
(_Transition,
lambda: ([], {'num_input_features': 4, 'num_output_features': 4}),
lambda: ([torch.rand([4, 4, 4, 4])], {}),
True),
]
class Test_gpleiss_efficient_densenet_pytorch(_paritybench_base):
def test_000(self):
self._check(*TESTCASES[0])
def test_001(self):
self._check(*TESTCASES[1])
def test_002(self):
self._check(*TESTCASES[2])