def _forward(self, x):
branch1x1 = self.branch1x1(x)
branch3x3 = self.branch3x3_1(x)
branch3x3 = [
self.branch3x3_2a(branch3x3),
self.branch3x3_2b(branch3x3),
]
branch3x3 = torch.cat(branch3x3, 1)
branch3x3dbl = self.branch3x3dbl_1(x)
branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
branch3x3dbl = [
self.branch3x3dbl_3a(branch3x3dbl),
self.branch3x3dbl_3b(branch3x3dbl),
]
branch3x3dbl = torch.cat(branch3x3dbl, 1)
if not self.use_softpool:
branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)
else:
if self.pad:
branch_pool = F.pad(x, (1, 1, 1, 1), 'constant', 0)
else:
branch_pool = F.pad(x, (0, 0, 0, 0), 'constant', 0)
branch_pool = soft_pool2d(branch_pool, kernel_size=3, stride=1)
branch_pool = self.branch_pool(branch_pool)
outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool]
return outputs
def _forward(self, x):
branch1x1 = self.branch1x1(x)
branch7x7 = self.branch7x7_1(x)
branch7x7 = self.branch7x7_2(branch7x7)
branch7x7 = self.branch7x7_3(branch7x7)
branch7x7dbl = self.branch7x7dbl_1(x)
branch7x7dbl = self.branch7x7dbl_2(branch7x7dbl)
branch7x7dbl = self.branch7x7dbl_3(branch7x7dbl)
branch7x7dbl = self.branch7x7dbl_4(branch7x7dbl)
branch7x7dbl = self.branch7x7dbl_5(branch7x7dbl)
if not self.use_softpool:
branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)
else:
if self.pad:
branch_pool = F.pad(x, (1, 1, 1, 1), 'constant', 0)
else:
branch_pool = F.pad(x, (0, 0, 0, 0), 'constant', 0)
branch_pool = soft_pool2d(branch_pool, kernel_size=3, stride=1)
branch_pool = self.branch_pool(branch_pool)
outputs = [branch1x1, branch7x7, branch7x7dbl, branch_pool]
return outputs
def _forward(self, x):
branch3x3 = self.branch3x3(x)
branch3x3dbl = self.branch3x3dbl_1(x)
branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl)
if not self.use_softpool:
branch_pool = F.max_pool2d(x, kernel_size=3, stride=2)
else:
branch_pool = soft_pool2d(x, kernel_size=3, stride=2)
outputs = [branch3x3, branch3x3dbl, branch_pool]
return outputs
def forward(self, x):
# N x 768 x 17 x 17
if not self.use_softpool:
x = F.avg_pool2d(x, kernel_size=5, stride=3)
else:
x = soft_pool2d(x, kernel_size=5, stride=3)
# N x 768 x 5 x 5
x = self.conv0(x)
# N x 128 x 5 x 5
x = self.conv1(x)
# N x 768 x 1 x 1
# Adaptive average pooling
x = F.adaptive_avg_pool2d(x, (1, 1))
# N x 768 x 1 x 1
x = torch.flatten(x, 1)
# N x 768
x = self.fc(x)
# N x 1000
return x
except Exception as e:
print('\033[91m' + '> FAILED' + '\033[0m')
print(e)
print('\033[93m' + '> Checking 1D CPU-GPU output similarities ...' + '\033[0m')
try:
check_close_enough(pl_1d_cpu.data, pl_1d_gpu.data)
print('\033[92m' + '> PASSED' + '\033[0m' + '\n')
except Exception as e:
print('\033[91m' + '> FAILED' + '\033[0m')
print(e, '\n')
################## 2D FORWARD ##################
print('\033[93m' + '> Checking 2D CPU ...' + '\033[0m')
try:
pl_2d_cpu = soft_pool2d(x_2d)
print('\033[92m' + '> PASSED' + '\033[0m')
except Exception as e:
print('\033[91m' + '> FAILED' + '\033[0m')
print(e)
print('\033[93m' + '> Checking 2D GPU ...' + '\033[0m')
try:
pl_2d_gpu = soft_pool2d(x_2d.cuda())
print('\033[92m' + '> PASSED' + '\033[0m')
except Exception as e:
print('\033[91m' + '> FAILED' + '\033[0m')
print(e)
print('\033[93m' + '> Checking 2D CPU-GPU output similarities ...' + '\033[0m')
try: