def main():
device = torch_mlir.mlir_device()
model = Net()
tensor = torch.randn((64, 1, 28, 28), requires_grad=True)
# CHECK: PASS! fwd check
fwd_path = test.check_ref(model, tensor)
target = torch.ones((64), dtype=torch.long)
loss = F.nll_loss
# CHECK: PASS! back check
test.check_back(fwd_path, target, loss)
# CHECK: PASS! fc1_weight_grad check
test.compare(model.fc1.weight.grad, fwd_path[0].fc1.weight.grad,
"fc1_weight_grad")
Cout = 4
w = 10
h = 10
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(Cin, Cout, (3,3))
def forward(self, x):
x = self.conv1(x)
output = F.log_softmax(x, dim=1)
return output
model = Net()
tensor = torch.randn(N, Cin, h, w)
# CHECK: PASS! fwd check
fwd_path = test.check_ref(model, tensor)
loss = torch.nn.NLLLoss()
target = torch.empty(N, 8, 8, dtype=torch.long).random_(0, Cout)
# CHECK: PASS! back check
test.check_back(fwd_path, target, loss)
# CHECK: PASS! weight_grad check
test.compare(model.conv1.weight.grad, fwd_path[0].conv1.weight.grad, "weight_grad")
# CHECK: PASS! bias_grad check
test.compare(model.conv1.bias.grad, fwd_path[0].conv1.bias.grad, "bias_grad")
# -*- Python -*-
# This file is licensed under a pytorch-style license
# See frontends/pytorch/LICENSE for license information.
import torch
import npcomp.frontends.pytorch as torch_mlir
import npcomp.frontends.pytorch.test as test
# RUN: python %s | FileCheck %s
model = torch.nn.Conv2d(2,16,7,stride=[2,2], padding=[3,3],
dilation=1, groups=1, bias=True)
tensor = torch.randn((1,2,128,128))
# CHECK: PASS! fwd check
test.check_ref(model, tensor)