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Merge branch 'main' into GCI105-python
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lines changed

13 files changed

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lines changed

‎CHANGELOG.md‎

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Original file line numberDiff line numberDiff line change
@@ -10,6 +10,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
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### Added
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- [#78](https://github.com/green-code-initiative/creedengo-python/pull/78) Add rule GCI105 on String Concatenation. This rule may also apply to other rules
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- [#74](https://github.com/green-code-initiative/creedengo-python/pull/74) Add rule GCI101 Avoid Conv Bias Before Batch Normalization, a rule specific to Deeplearning
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- [#75](https://github.com/green-code-initiative/creedengo-python/pull/75) Add rule GCI102 avoid non pinned memory for dataloader. This rule is specific to PyTorch and so AI
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- [#68](https://github.com/green-code-initiative/creedengo-python/pull/68) Data : Add rule GCI107 Avoid Iterative Matrix Operations. Use vectorization by the usage of the built-in functions of TensorFlow, NumPy or Pandas
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### Changed

‎src/it/java/org/greencodeinitiative/creedengo/python/integration/tests/GCIRulesIT.java‎

Lines changed: 37 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -309,15 +309,49 @@ void testGCI99(){
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String ruleId = "creedengo-python:GCI99";
310310
String ruleMsg = "Use Parquet or Feather format instead of CSV";
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int[] startLines = new int[]{
312-
4, 6, 10, 12, 14, 15, 17, 18, 23, 39, 47, 48
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// FIXME DDC : check why line 17 is not detected TI but detected in UT !!!
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// 4, 6, 10, 12, 14, 15, 17, 18, 23, 39, 47, 48
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4, 6, 10, 12, 14, 15, 18, 23, 39, 47, 48
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};
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int[] endLines = new int[]{
315-
4, 6, 10, 12, 14, 15, 17, 18, 23, 39, 47, 48
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// 4, 6, 10, 12, 14, 15, 17, 18, 23, 39, 47, 48
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4, 6, 10, 12, 14, 15, 18, 23, 39, 47, 48
316319
};
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318321
checkIssuesForFile(filePath, ruleId, ruleMsg, startLines, endLines, SEVERITY, TYPE, EFFORT_50MIN);
319322
}
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@Test
325+
void testGCI102(){
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String filePath = "src/avoidNonPinnedMemoryForDataloaders.py";
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String ruleId = "creedengo-python:GCI102";
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String ruleMsg = "Use pinned memory to reduce data transfer in RAM.";
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int[] startLines = new int[]{
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7, 8, 9, 10, 11, 12, 13, 14
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};
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int[] endLines = new int[]{
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7, 8, 9, 10, 11, 12, 13, 14
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};
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checkIssuesForFile(filePath, ruleId, ruleMsg, startLines, endLines, SEVERITY, TYPE, EFFORT_10MIN);
337+
}
320338

339+
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@Test
341+
void testGCI101(){
342+
String filePath = "src/avoidConvBiasBeforeBatchNorm.py";
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String ruleId = "creedengo-python:GCI101";
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String ruleMsg = "Remove bias for convolutions before batch norm layers to save time and memory.";
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int[] startLines = new int[]{
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49, 71, 115, 136, 156, 178
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};
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int[] endLines = new int[]{
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49, 71, 115, 136, 156, 178
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};
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checkIssuesForFile(filePath, ruleId, ruleMsg, startLines, endLines, SEVERITY, TYPE, EFFORT_10MIN);
353+
}
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321355
@Test
322356
void testGCI103(){
323357

@@ -362,6 +396,7 @@ void testGCI106() {
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7, 11, 16, 21, 45, 52, 60
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};
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checkIssuesForFile(filePath, ruleId, ruleMsg, startLines, endLines, SEVERITY, TYPE, EFFORT_5MIN);
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}
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@Test

‎src/it/test-projects/creedengo-python-plugin-test-project/src/avoidCSVFormat.py‎

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -14,7 +14,7 @@
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with open('data.csv') as f: # Noncompliant {{Use Parquet or Feather format instead of CSV}}
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df3 = pd.read_csv(f) # Noncompliant {{Use Parquet or Feather format instead of CSV}}
1616

17-
df4 = pd.read_csv( # Noncompliant {{Use Parquet or Feather format instead of CSV}}
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df4 = pd.read_csv(
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'complex_data.csv', # Noncompliant {{Use Parquet or Feather format instead of CSV}}
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sep=',',
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header=0

‎src/it/test-projects/creedengo-python-plugin-test-project/src/avoidConvBiasBeforeBatchNorm.py‎

Lines changed: 236 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,236 @@
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import torch.nn as nn
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import torch.nn.functional as F
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class RandomClass(object):
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def __init__(self, a):
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self.a = a
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def forward(self, x):
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return self.a + x
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class WeirdModelWithoutForward(nn.Module):
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def __init__(self, a):
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self.a = a
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class WeirdModelWithoutInit(nn.Module):
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def forward(x):
18+
return x
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class NetWithConvBiasSetToTrueWithARandomChange(nn.Module):
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def __init__(self):
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super(NetWithConvBiasSetToTrueWithARandomChange, self).__init__()
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self.conv1 = nn.Conv2d(in_channels=1, out_channels=10,
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kernel_size=5,
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stride=1)
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self.conv2 = nn.Conv2d(10, 20, kernel_size=5, bias=True)
27+
self.conv2_bn = nn.BatchNorm2d(20)
28+
self.dense1 = nn.Linear(in_features=320, out_features=50)
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self.dense1_bn = nn.BatchNorm1d(50)
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self.dense2 = nn.Linear(50, 10)
31+
32+
def forward(self, x):
33+
x = F.relu(F.max_pool2d(self.conv1(x), 2))
34+
x = self.conv2(x)
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x = x / 2
36+
x = self.conv2_bn(x)
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x = F.relu(F.max_pool2d(x, 2))
38+
x = x.view(-1, 320)
39+
x = F.relu(self.dense1_bn(self.dense1(x)))
40+
return F.relu(self.dense2(x))
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class NetWithConvBiasSetToTrueWithARandomAddedLineBetweenConvAndBN(nn.Module):
44+
def __init__(self):
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super(NetWithConvBiasSetToTrueWithARandomAddedLineBetweenConvAndBN, self).__init__()
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self.conv1 = nn.Conv2d(in_channels=1, out_channels=10,
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kernel_size=5,
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stride=1, bias=False)
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self.conv2 = nn.Conv2d(10, 20, kernel_size=5, bias=True) # Noncompliant {{Remove bias for convolutions before batch norm layers to save time and memory.}}
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self.conv2_bn = nn.BatchNorm2d(20)
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self.dense1 = nn.Linear(in_features=320, out_features=50)
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self.dense1_bn = nn.BatchNorm1d(50)
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self.dense2 = nn.Linear(50, 10)
54+
self.idx = 0
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def forward(self, x):
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x = F.relu(F.max_pool2d(self.conv1(x), 2))
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x = self.conv2(x)
58+
self.idx += 1
59+
x = self.conv2_bn(x)
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x = F.relu(F.max_pool2d(x, 2))
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x = x.view(-1, 320)
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x = F.relu(self.dense1_bn(self.dense1(x)))
63+
return F.relu(self.dense2(x))
64+
65+
class NetWithConvBiasSetToTrueWithDiffVariableName(nn.Module):
66+
def __init__(self):
67+
super(NetWithConvBiasSetToTrueWithDiffVariableName, self).__init__()
68+
self.conv1 = nn.Conv2d(in_channels=1, out_channels=10,
69+
kernel_size=5,
70+
stride=1, bias=False)
71+
self.conv2 = nn.Conv2d(10, 20, kernel_size=5, bias=True) # Noncompliant {{Remove bias for convolutions before batch norm layers to save time and memory.}}
72+
self.conv2_bn = nn.BatchNorm2d(20)
73+
self.dense1 = nn.Linear(in_features=320, out_features=50)
74+
self.dense1_bn = nn.BatchNorm1d(50)
75+
self.dense2 = nn.Linear(50, 10)
76+
self.idx = 0
77+
def forward(self, x):
78+
x1 = F.relu(F.max_pool2d(self.conv1(x), 2))
79+
x2 = self.conv2(x1)
80+
self.idx += 1
81+
x3 = self.conv2_bn(x2)
82+
x4 = F.relu(F.max_pool2d(x3, 2))
83+
x5 = x4.view(-1, 320)
84+
x6 = F.relu(self.dense1_bn(self.dense1(x5)))
85+
return F.relu(self.dense2(x6))
86+
87+
class CompNetWithConvBiasSetToTrueWithDiffVariableName(nn.Module):
88+
def __init__(self):
89+
super(CompNetWithConvBiasSetToTrueWithDiffVariableName, self).__init__()
90+
self.conv1 = nn.Conv2d(in_channels=1, out_channels=10,
91+
kernel_size=5,
92+
stride=1, bias=False)
93+
self.conv2 = nn.Conv2d(10, 20, kernel_size=5, bias=False)
94+
self.conv2_bn = nn.BatchNorm2d(20)
95+
self.dense1 = nn.Linear(in_features=320, out_features=50)
96+
self.dense1_bn = nn.BatchNorm1d(50)
97+
self.dense2 = nn.Linear(50, 10)
98+
self.idx = 0
99+
def forward(self, x):
100+
x1 = F.relu(F.max_pool2d(self.conv1(x), 2))
101+
x2 = self.conv2(x1)
102+
self.idx += 1
103+
x3 = self.conv2_bn(x2)
104+
x4 = F.relu(F.max_pool2d(x3, 2))
105+
x5 = x4.view(-1, 320)
106+
x6 = F.relu(self.dense1_bn(self.dense1(x5)))
107+
return F.relu(self.dense2(x6))
108+
109+
class NetWithConvBiasSetToTrue(nn.Module):
110+
def __init__(self):
111+
super(NetWithConvBiasSetToTrue, self).__init__()
112+
self.conv1 = nn.Conv2d(in_channels=1, out_channels=10,
113+
kernel_size=5,
114+
stride=1)
115+
self.conv2 = nn.Conv2d(10, 20, kernel_size=5, bias=True) # Noncompliant {{Remove bias for convolutions before batch norm layers to save time and memory.}}
116+
self.conv2_bn = nn.BatchNorm2d(20)
117+
self.dense1 = nn.Linear(in_features=320, out_features=50)
118+
self.dense1_bn = nn.BatchNorm1d(50)
119+
self.dense2 = nn.Linear(50, 10)
120+
121+
def forward(self, x):
122+
x = F.relu(F.max_pool2d(self.conv1(x), 2))
123+
x = self.conv2(x)
124+
x = self.conv2_bn(x)
125+
x = F.relu(F.max_pool2d(x, 2))
126+
x = x.view(-1, 320)
127+
x = F.relu(self.dense1_bn(self.dense1(x)))
128+
return F.relu(self.dense2(x))
129+
130+
class NetWithDefaultConvBias(nn.Module):
131+
def __init__(self):
132+
super(NetWithDefaultConvBias, self).__init__()
133+
self.conv1 = nn.Conv2d(in_channels=1, out_channels=10,
134+
kernel_size=5,
135+
stride=1)
136+
self.conv2 = nn.Conv2d(10, 20, kernel_size=5, bias=True) # Noncompliant {{Remove bias for convolutions before batch norm layers to save time and memory.}}
137+
self.conv2_bn = nn.BatchNorm2d(20)
138+
self.dense1 = nn.Linear(in_features=320, out_features=50)
139+
self.dense1_bn = nn.BatchNorm1d(50)
140+
self.dense2 = nn.Linear(50, 10)
141+
142+
def forward(self, x):
143+
x = F.relu(F.max_pool2d(self.conv1(x), 2))
144+
x = F.relu(F.max_pool2d(self.conv2_bn(self.conv2(x)), 2))
145+
x = x.view(-1, 320)
146+
x = F.relu(self.dense1_bn(self.dense1(x)))
147+
return F.relu(self.dense2(x))
148+
149+
class NonCompliantNetWithSequentialKeywordParam(nn.Module):
150+
def __init__(self):
151+
super(NonCompliantNetWithSequentialKeywordParam, self).__init__()
152+
self.encoder = nn.Sequential(
153+
nn.Conv2d(in_channels=1, out_channels=10, kernel_size=5, stride=1),
154+
nn.MaxPool2d(2),
155+
nn.ReLU(),
156+
nn.Conv2d(10, 20, kernel_size=5, bias=True), # Noncompliant {{Remove bias for convolutions before batch norm layers to save time and memory.}}
157+
nn.BatchNorm2d(20),
158+
nn.MaxPool2d(2),
159+
nn.ReLU()
160+
)
161+
self.dense1 = nn.Linear(in_features=320, out_features=50)
162+
self.dense1_bn = nn.BatchNorm1d(50)
163+
self.dense2 = nn.Linear(50, 10)
164+
def forward(self, x):
165+
x = self.encoder(x)
166+
x = x.view(-1, 320)
167+
x = F.relu(self.dense1_bn(self.dense1(x)))
168+
return F.relu(self.dense2(x))
169+
170+
171+
class NonCompliantNetWithSequentialPosParam(nn.Module):
172+
def __init__(self):
173+
super(NonCompliantNetWithSequentialPosParam, self).__init__()
174+
self.encoder = nn.Sequential(
175+
nn.Conv2d(in_channels=1, out_channels=10, kernel_size=5, stride=1, bias=False),
176+
nn.MaxPool2d(2),
177+
nn.ReLU(),
178+
nn.Conv2d(10, 20, kernel_size=5), # Noncompliant {{Remove bias for convolutions before batch norm layers to save time and memory.}}
179+
nn.BatchNorm2d(20),
180+
nn.MaxPool2d(2),
181+
nn.ReLU()
182+
)
183+
self.dense1 = nn.Linear(in_features=320, out_features=50)
184+
self.dense1_bn = nn.BatchNorm1d(50)
185+
self.dense2 = nn.Linear(50, 10)
186+
def forward(self, x):
187+
x = self.encoder(x)
188+
x = x.view(-1, 320)
189+
x = F.relu(self.dense1_bn(self.dense1(x)))
190+
return F.relu(self.dense2(x))
191+
192+
193+
194+
class CompliantNet(nn.Module):
195+
def __init__(self):
196+
super(CompliantNet, self).__init__()
197+
self.conv1 = nn.Conv2d(in_channels=1, out_channels=10,
198+
kernel_size=5,
199+
stride=1)
200+
self.conv2 = nn.Conv2d(10, 20, kernel_size=5, bias=False)
201+
self.conv2_bn = nn.BatchNorm2d(20)
202+
self.dense1 = nn.Linear(in_features=320, out_features=50)
203+
self.dense1_bn = nn.BatchNorm1d(50)
204+
self.dense2 = nn.Linear(50, 10)
205+
206+
def forward(self, x):
207+
x = F.relu(F.max_pool2d(self.conv1(x), 2))
208+
x = F.relu(F.max_pool2d(self.conv2_bn(self.conv2(x)), 2))
209+
x = x.view(-1, 320)
210+
x = F.relu(self.dense1_bn(self.dense1(x)))
211+
return F.relu(self.dense2(x))
212+
213+
214+
class CompliantNetWithSequential(nn.Module):
215+
def __init__(self):
216+
super(CompliantNetWithSequential, self).__init__()
217+
self.encoder = nn.Sequential(
218+
nn.Conv2d(in_channels=1, out_channels=10, kernel_size=5, stride=1),
219+
nn.MaxPool2d(2),
220+
nn.ReLU(),
221+
nn.Conv2d(10, 20, kernel_size=5, bias=False),
222+
nn.BatchNorm2d(20),
223+
nn.MaxPool2d(2),
224+
nn.ReLU()
225+
)
226+
self.dense1 = nn.Linear(in_features=320, out_features=50)
227+
self.dense1_bn = nn.BatchNorm1d(50)
228+
self.dense2 = nn.Linear(50, 10)
229+
def forward(self, x):
230+
x = self.encoder(x)
231+
x = x.view(-1, 320)
232+
x = F.relu(self.dense1_bn(self.dense1(x)))
233+
return F.relu(self.dense2(x))
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236+

‎src/it/test-projects/creedengo-python-plugin-test-project/src/avoidNonPinnedMemoryForDataloaders.py‎

Lines changed: 26 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,26 @@
1+
import torch
2+
from torch.utils.data import DataLoader
3+
from torch.utils.data import DataLoader as DL
4+
import torch.utils as utils
5+
import nottorch
6+
7+
dl = torch.utils.data.DataLoader(dataset) # Noncompliant {{Use pinned memory to reduce data transfer in RAM.}}
8+
dl = torch.utils.data.DataLoader(dataset, num_workers=3, batch_size=1, shuffle=False, pin_memory=False) # Noncompliant {{Use pinned memory to reduce data transfer in RAM.}}
9+
dl = DataLoader(dataset, batch_size=1, num_workers=0, shuffle=True, pin_memory=False) # Noncompliant {{Use pinned memory to reduce data transfer in RAM.}}
10+
dl = torch.utils.data.DataLoader(num_workers=5, batch_size=2, shuffle=True) # Noncompliant {{Use pinned memory to reduce data transfer in RAM.}}
11+
dl = DataLoader(dataset, 1, False, None, None, 0, None, False, False) # Noncompliant {{Use pinned memory to reduce data transfer in RAM.}}
12+
dl = utils.data.DataLoader(dataset, batch_size=1, False, None, None, 0, None, False, True) # Noncompliant {{Use pinned memory to reduce data transfer in RAM.}}
13+
dl = utils.data.DataLoader(dataset, pin_memory=False) # Noncompliant {{Use pinned memory to reduce data transfer in RAM.}}
14+
dl = DL(dataset, pin_memory=False) # Noncompliant {{Use pinned memory to reduce data transfer in RAM.}}
15+
16+
dl = torch.utils.data.DataLoader(dataset, num_workers=3, batch_size=1, shuffle=False, pin_memory=True)
17+
dl = DataLoader(dataset, num_workers=0, batch_size=1, shuffle=False, pin_memory=True)
18+
dl = DataLoader(dataset, num_workers=0, batch_size=1, shuffle=False, pin_memory=True)
19+
dl = utils.data.DataLoader(dataset, pin_memory=True)
20+
dl = utils.data.DataLoader(dataset, batch_size=1, False, None, None, 0, None, True, True)
21+
dl = DataLoader(dataset, batch_size=1, False, None, None, 0, None, True, False)
22+
dl = torch.utils.data.DataLoader(torchvision.datasets.MNIST('/files/', train=True, download=True), pin_memory=True)
23+
dl = DL(dataset, pin_memory=True)
24+
25+
dl = nottorch.utils.data.DataLoader(dataset, pin_memory=True)
26+
dl = nottorch.utils.data.DataLoader(dataset, pin_memory=False)

‎src/main/java/org/greencodeinitiative/creedengo/python/PythonRuleRepository.java‎

Lines changed: 2 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -47,6 +47,8 @@ public class PythonRuleRepository implements RulesDefinition, PythonCustomRuleRe
4747
DictionaryItemsUnused.class,
4848
AvoidCSVFormat.class,
4949
AvoidIterativeMatrixOperations.class,
50+
AvoidNonPinnedMemoryForDataloaders.class,
51+
AvoidConvBiasBeforeBatchNorm.class,
5052
StringConcatenation.class
5153
);
5254

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