fix(unittest): remove the torch dependency in test_conv

GitOrigin-RevId: 36dcab2b95

python_module/test/unit/module/test_conv.py

@@ -9,12 +9,9 @@
 import itertools
 
 import numpy as np
-import pytest
-import torch
 
-import megengine as mge
 from megengine import Parameter, tensor
-from megengine.module import Conv2d, ConvTranspose2d
+from megengine.module import ConvTranspose2d
 from megengine.test import assertTensorClose
 
 
@@ -37,28 +34,19 @@ def test_conv_transpose2d():
     weight = np.random.normal(size=(IC, OC, KH, KW)).astype(np.float32)
     bias = np.random.normal(size=(1, OC, 1, 1)).astype(np.float32)
 
+    # naive calculation use numpy
     for n, ic, ih, iw in itertools.product(*map(range, [N, IC, IH, IW])):
         oh, ow = ih * SH, iw * SW
         out[n, :, oh : oh + KH, ow : ow + KW] += inp[n, ic, ih, iw] * weight[ic]
-
     out = out[:, :, PH : OH - PH, PW : OW - PW]
     if BIAS:
         out += bias
+
+    # megengine conv_transpose2d calculation
     conv_transpose2d = ConvTranspose2d(IC, OC, (KH, KW), (SH, SW), (PH, PW), bias=BIAS)
     conv_transpose2d.weight = Parameter(weight, dtype=np.float32)
     if BIAS:
         conv_transpose2d.bias = Parameter(bias, dtype=np.float32)
-
     y = conv_transpose2d(tensor(inp))
-    assertTensorClose(out, y.numpy(), max_err=2e-6)
 
-    torch_conv_transpose2d = torch.nn.ConvTranspose2d(
-        IC, OC, (KH, KW), stride=(SH, SW), padding=(PH, PW), bias=BIAS
-    )
-    torch_conv_transpose2d.weight = torch.nn.parameter.Parameter(torch.Tensor(weight))
-    if BIAS:
-        torch_conv_transpose2d.bias = torch.nn.parameter.Parameter(
-            torch.Tensor(bias).reshape(OC)
-        )
-    torch_y = torch_conv_transpose2d(torch.Tensor(inp))
-    assertTensorClose(torch_y.detach().numpy(), y.numpy(), max_err=2e-6)
+    assertTensorClose(out, y.numpy(), max_err=2e-6)