adding files

load_data.py

@@ -0,0 +1,9 @@
+import numpy as np
+
+
+def load_data(size=2000,height=256,width=256,channels=3):
+
+   inputs = np.random.randn((size,height,width,channels))
+
+   return inputs
+

model_run.py

@@ -0,0 +1,49 @@
+import torch,time
+from ptflops import get_model_complexity_info
+
+
+def run(point):
+
+   batch_size = point['batch_size']
+   height = point['height']
+   width = point['width']
+   in_channels = point['in_channels']
+   out_channels = point['out_channels']
+   kernel_size = (point['kernel_size'],point['kernel_size'])
+
+
+   inputs = torch.arange(batch_size * height * width * in_channels,dtype=torch.float).view((batch_size,in_channels,height,width))
+
+   layer = torch.nn.Conv2d(in_channels,out_channels,kernel_size,stride=1)
+   flops, params = get_model_complexity_info(layer, tuple(inputs.shape[1:]),as_strings=False)
+   print(flops)
+
+   outputs = layer(inputs)
+
+   runs = 5
+   tot_time = 0.
+   tt = time.time()
+   for _ in range(runs):
+      outputs = layer(inputs)
+      tot_time += time.time() - tt
+      tt = time.time()
+
+   ave_time = tot_time / runs
+
+   ave_flops = flops / ave_time
+
+   return ave_flops
+
+
+if __name__ == '__main__':
+   point = {
+      'batch_size': 10,
+      'height': 512,
+      'width': 512,
+      'in_channels': 3,
+      'out_channels': 64,
+      'kernel_size': 4
+   }
+
+   print('flops for this setting =',run(point))
+

problem.py

@@ -0,0 +1,14 @@
+from deephyper.benchmark import HpProblem
+
+Problem = HpProblem()
+Problem.add_dim('batch_size',(1,128))
+Problem.add_dim('height',(128,512))
+Problem.add_dim('width',(128,512))
+Problem.add_dim('in_channels',(2,16))
+Problem.add_dim('out_channels',(2,16))
+Problem.add_dim('kernel_size',(2,8))
+
+Problem.add_starting_point(batch_size=10,height=128,width=128,in_channels=3,out_channels=16,kernel_size=3)
+
+if __name__ == '__main__':
+   print(Problem)