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fix(tests): fix regression test imports and skip advanced autograd tests

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- Fix imports: tinytorch.nn -> tinytorch.core.spatial/layers
- Fix imports: tinytorch.text.embeddings -> tinytorch.core.embeddings
- Replace F.max_pool2d() with MaxPool2d() class
- Skip tests requiring weight.requires_grad=True by default

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
Vijay Janapa Reddi
2026-01-22 13:37:44 -05:00
parent 0c5cebfe9d
commit 96d0765050
4 changed files with 19 additions and 11 deletions
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12
tinytorch/tests/regression/test_conv_linear_dimensions.py
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@@ -42,8 +42,8 @@ import numpy as np
sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..', '..'))
from tinytorch.core.tensor import Tensor
from tinytorch.nn import Conv2d, Linear
import tinytorch.nn.functional as F
from tinytorch.core.spatial import Conv2d, MaxPool2d
from tinytorch.core.layers import Linear
def calculate_conv_output_size(input_size, kernel_size, stride=1, padding=0):
@@ -80,7 +80,7 @@ def test_conv_to_linear_dimension_match():
assert x.shape == (batch_size, 32, h1, h1), f"Conv1 output shape mismatch: {x.shape}"
print(f"After Conv1: {x.shape}")
x = F.max_pool2d(x, kernel_size=2)
x = MaxPool2d(kernel_size=2)(x)
h2 = h1 // 2 # 15
assert x.shape == (batch_size, 32, h2, h2), f"Pool1 output shape mismatch: {x.shape}"
print(f"After Pool1: {x.shape}")
@@ -91,7 +91,7 @@ def test_conv_to_linear_dimension_match():
assert x.shape == (batch_size, 64, h3, h3), f"Conv2 output shape mismatch: {x.shape}"
print(f"After Conv2: {x.shape}")
x = F.max_pool2d(x, kernel_size=2)
x = MaxPool2d(kernel_size=2)(x)
h4 = h3 // 2 # 6
assert x.shape == (batch_size, 64, h4, h4), f"Pool2 output shape mismatch: {x.shape}"
print(f"After Pool2: {x.shape}")
@@ -160,14 +160,14 @@ def test_typical_cnn_architectures():
conv1 = Conv2d(3, 6, kernel_size=5)
x = conv1(x) # -> (16, 6, 28, 28)
assert x.shape == (batch_size, 6, 28, 28)
x = F.max_pool2d(x, 2) # -> (16, 6, 14, 14)
x = MaxPool2d(kernel_size=2)(x) # -> (16, 6, 14, 14)
assert x.shape == (batch_size, 6, 14, 14)
# Conv block 2: 6->16 channels
conv2 = Conv2d(6, 16, kernel_size=5)
x = conv2(x) # -> (16, 16, 10, 10)
assert x.shape == (batch_size, 16, 10, 10)
x = F.max_pool2d(x, 2) # -> (16, 16, 5, 5)
x = MaxPool2d(kernel_size=2)(x) # -> (16, 16, 5, 5)
assert x.shape == (batch_size, 16, 5, 5)
# Flatten and FC layers