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mcDandy-more_math/tests/test_more_math.py
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mcDandy e673ba4349 Add tests
2025-12-05 20:41:58 +01:00

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#!/usr/bin/env python
"""Tests for `more_math` package."""
import unittest
import torch
from more_math.ConditioningMathNode import ConditioningMathNode
from more_math.LatentMathNode import LatentMathNode
from more_math.LatentMathNode import LatentMathNode
from more_math.ImageMathNode import ImageMathNode
from more_math.FloatMathNode import FloatMathNode
import tokenize
from io import StringIO
def tokenize_expression(expr):
if not expr.endswith('\n'):
expr = expr + '\n'
f = StringIO(expr)
tokens = tokenize.generate_tokens(f.readline)
filtered_tokens = []
for toktype, tokval, _, _, _ in tokens:
token_name = tokenize.tok_name[toktype]
# Opravíme ERRORTOKEN na OP pro !, &, |, ^
if token_name == 'ERRORTOKEN' and tokval in {'!', '&', '|', '^'}:
token_name = 'OP'
if token_name in {'COMMENT', 'NL', 'NEWLINE', 'INDENT', 'DEDENT'}:
continue
filtered_tokens.append((token_name, tokval.strip()))
return filtered_tokens
class TestMoreMath(unittest.TestCase):
def test_conditioning_math_node_initialization(self):
node = ConditioningMathNode()
self.assertIsInstance(node, ConditioningMathNode)
def test_conditioning_math_node_metadata(self):
self.assertEqual(ConditioningMathNode.RETURN_TYPES, ["CONDITIONING"])
self.assertEqual(ConditioningMathNode.FUNCTION, "EXECUTE_NORMALIZED")
self.assertEqual(ConditioningMathNode.CATEGORY, "More math")
def test_latent_math_node_initialization(self):
node = LatentMathNode()
self.assertIsInstance(node, LatentMathNode)
def test_latent_math_node_metadata(self):
self.assertEqual(LatentMathNode.RETURN_TYPES, ["LATENT"])
self.assertEqual(LatentMathNode.FUNCTION, "EXECUTE_NORMALIZED")
self.assertEqual(LatentMathNode.CATEGORY, "More math")
def test_image_math_node_initialization(self):
node = ImageMathNode()
self.assertIsInstance(node, ImageMathNode)
def test_image_math_node_metadata(self):
self.assertEqual(ImageMathNode.RETURN_TYPES, ["IMAGE"])
self.assertEqual(ImageMathNode.FUNCTION, "EXECUTE_NORMALIZED")
self.assertEqual(ImageMathNode.CATEGORY, "More math")
def test_fft_invertibility(self):
# 1. Create random input latent (Batch, Channel, Height, Width)
input_tensor = torch.randn(1, 4, 32, 32, dtype=torch.float32)
input_dict = {"samples": input_tensor}
# 2. Execute ifft(fft(a))
# Note: execute is a classmethod
result = LatentMathNode.execute(
Latent="ifft(fft(a))",
a=input_dict
)
# 3. Get output tensor
output_tensor = result[0]["samples"]
# 4. Check correctness (approximate equality)
self.assertTrue(torch.allclose(input_tensor, output_tensor, atol=1e-5), \
f"Max difference: {(input_tensor - output_tensor).abs().max()}")
#!/usr/bin/env python
"""Tests for `more_math` package."""
import unittest
import torch
from more_math.ConditioningMathNode import ConditioningMathNode
from more_math.LatentMathNode import LatentMathNode
from more_math.ImageMathNode import ImageMathNode
import tokenize
from io import StringIO
def tokenize_expression(expr):
if not expr.endswith('\n'):
expr = expr + '\n'
f = StringIO(expr)
tokens = tokenize.generate_tokens(f.readline)
filtered_tokens = []
for toktype, tokval, _, _, _ in tokens:
token_name = tokenize.tok_name[toktype]
# Opravíme ERRORTOKEN na OP pro !, &, |, ^
if token_name == 'ERRORTOKEN' and tokval in {'!', '&', '|', '^'}:
token_name = 'OP'
if token_name in {'COMMENT', 'NL', 'NEWLINE', 'INDENT', 'DEDENT'}:
continue
filtered_tokens.append((token_name, tokval.strip()))
return filtered_tokens
class TestMoreMath(unittest.TestCase):
def test_conditioning_math_node_initialization(self):
node = ConditioningMathNode()
self.assertIsInstance(node, ConditioningMathNode)
def test_conditioning_math_node_metadata(self):
self.assertEqual(ConditioningMathNode.RETURN_TYPES, ["CONDITIONING"])
self.assertEqual(ConditioningMathNode.FUNCTION, "EXECUTE_NORMALIZED")
self.assertEqual(ConditioningMathNode.CATEGORY, "More math")
def test_latent_math_node_initialization(self):
node = LatentMathNode()
self.assertIsInstance(node, LatentMathNode)
def test_latent_math_node_metadata(self):
self.assertEqual(LatentMathNode.RETURN_TYPES, ["LATENT"])
self.assertEqual(LatentMathNode.FUNCTION, "EXECUTE_NORMALIZED")
self.assertEqual(LatentMathNode.CATEGORY, "More math")
def test_image_math_node_initialization(self):
node = ImageMathNode()
self.assertIsInstance(node, ImageMathNode)
def test_image_math_node_metadata(self):
self.assertEqual(ImageMathNode.RETURN_TYPES, ["IMAGE"])
self.assertEqual(ImageMathNode.FUNCTION, "EXECUTE_NORMALIZED")
self.assertEqual(ImageMathNode.CATEGORY, "More math")
def test_fft_invertibility(self):
# 1. Create random input latent (Batch, Channel, Height, Width)
input_tensor = torch.randn(1, 4, 32, 32, dtype=torch.float32)
input_dict = {"samples": input_tensor}
# 2. Execute ifft(fft(a))
# Note: execute is a classmethod
result = LatentMathNode.execute(
Latent="ifft(fft(a))",
a=input_dict
)
# 3. Get output tensor
output_tensor = result[0]["samples"]
# 4. Check correctness (approximate equality)
self.assertTrue(torch.allclose(input_tensor, output_tensor, atol=1e-5), \
f"Max difference: {(input_tensor - output_tensor).abs().max()}")
def test_image_fft_dims(self):
# Image input is (Batch, Height, Width, Channel)
# Verify 2D FFT works by doing a round trip
input_tensor = torch.randn(1, 32, 32, 3, dtype=torch.float32)
result = ImageMathNode.execute(
Image="ifft(fft(a))",
a=input_tensor
)
output_tensor = result[0]
self.assertEqual(input_tensor.shape, output_tensor.shape)
self.assertTrue(torch.allclose(input_tensor, output_tensor, atol=1e-5), \
f"Image FFT round trip failed. Max diff: {(input_tensor - output_tensor).abs().max()}")
def test_new_math_functions(self):
node = LatentMathNode()
# Test Lerp
# lerp(a, b, 0.5) where a=0, b=10 -> 5
l_a = {"samples": torch.zeros(1, 4, 32, 32)}
l_b = {"samples": torch.full((1, 4, 32, 32), 10.0)}
res_lerp = node.execute("lerp(a, b, 0.5)", a=l_a, b=l_b)[0]["samples"]
self.assertTrue(torch.allclose(res_lerp, torch.full_like(res_lerp, 5.0)))
# Test Step
# step(0.5, a) where a=0.8 -> 1
res_step = node.execute("step(0.5, a)", a={"samples": torch.full((1,1,1,1), 0.8)})[0]["samples"]
self.assertTrue(torch.allclose(res_step, torch.ones_like(res_step)))
# step(0.5, a) where a=0.2 -> 0
res_step2 = node.execute("step(0.5, a)", a={"samples": torch.full((1,1,1,1), 0.2)})[0]["samples"]
self.assertTrue(torch.allclose(res_step2, torch.zeros_like(res_step2)))
# Test Swap
# tensor 1x3: [0, 1, 2]. Swap(dim=1, 0, 2) -> [2, 1, 0]
t = torch.tensor([[[0.0, 1.0, 2.0]]]) # 1x1x3
l_t = {"samples": t}
# dim 2 (channel dim in 1x1x3? No, dims are B,C,H,W usually but here shape is 1x1x3)
# LatentMathNode uses "samples" directly.
# eval_single_tensor exposes 'a'.
# Let's use simple Latent 1x4x1x1 (B,C,H,W)
t_lat = torch.tensor([0.0, 10.0, 20.0, 30.0]).view(1,4,1,1)
# Swap channels 0 and 3 -> 30, 10, 20, 0
l_swap = {"samples": t_lat}
# swap(a, 1, 0, 3) . Dim 1 is channel (B=0, C=1)
res_swap = node.execute("swap(a, 1, 0, 3)", a=l_swap)[0]["samples"]
expected = torch.tensor([30.0, 10.0, 20.0, 0.0]).view(1,4,1,1)
self.assertTrue(torch.allclose(res_swap, expected))
# Test Relu, Sign, Fract
res_relu = node.execute("relu(-5.0)", a=l_a)[0]["samples"]
self.assertTrue(torch.allclose(res_relu, torch.zeros_like(res_relu)))
res_sign = node.execute("sign(-5.0)", a=l_a)[0]["samples"]
self.assertTrue(torch.allclose(res_sign, torch.full_like(res_sign, -1.0)))
res_fract = node.execute("fract(1.5)", a=l_a)[0]["samples"]
self.assertTrue(torch.allclose(res_fract, torch.full_like(res_fract, 0.5)))
def test_float_math_functions(self):
node = FloatMathNode()
# Test Lerp: lerp(0, 10, 0.5) -> 5.0
res = node.execute("lerp(a, b, 0.5)", a=0.0, b=10.0)[0]
self.assertAlmostEqual(res, 5.0)
# Test Step: step(0.5, 0.8) -> 1.0
res = node.execute("step(0.5, a)", a=0.8)[0]
self.assertAlmostEqual(res, 1.0)
# Test Relu: relu(-5) -> 0.0
res = node.execute("relu(a)", a=-5.0)[0]
self.assertAlmostEqual(res, 0.0)
# Test Smoothstep: smoothstep(0, 1, 0.5) -> 0.5
res = node.execute("smoothstep(0, 1, a)", a=0.5)[0]
self.assertAlmostEqual(res, 0.5)
def test_float_math_extended(self):
node = FloatMathNode()
# Test Fract: fract(1.5) -> 0.5
res = node.execute("fract(a)", a=1.5)[0]
self.assertAlmostEqual(res, 0.5)
# Test Softplus: softplus(0) = log(1+exp(0)) = log(2) ~= 0.6931
res = node.execute("softplus(a)", a=0.0)[0]
self.assertAlmostEqual(res, 0.69314718, places=5)
# Test Sign: sign(-10) -> -1.0, sign(10) -> 1.0, sign(0) -> 0.0
self.assertEqual(node.execute("sign(a)", a=-10.0)[0], -1.0)
self.assertEqual(node.execute("sign(a)", a=10.0)[0], 1.0)
self.assertEqual(node.execute("sign(a)", a=0.0)[0], 0.0)
# Test Gelu: gelu(0) -> 0.0
self.assertEqual(node.execute("gelu(a)", a=0.0)[0], 0.0)
def test_latent_math_extended(self):
node = LatentMathNode()
l_a = {"samples": torch.zeros(1, 4, 32, 32)}
# Smoothstep
# smoothstep(0, 1, 0.5) -> 0.5
res = node.execute("smoothstep(0, 1, 0.5)", a=l_a)[0]["samples"]
self.assertTrue(torch.allclose(res, torch.full_like(res, 0.5)))
# Softplus
res = node.execute("softplus(0.0)", a=l_a)[0]["samples"]
self.assertTrue(torch.allclose(res, torch.full_like(res, 0.69314718)))
# Gelu
res = node.execute("gelu(0.0)", a=l_a)[0]["samples"]
self.assertTrue(torch.allclose(res, torch.zeros_like(res)))
def test_image_math_operations(self):
node = ImageMathNode()
# Image is (Batch, Height, Width, Channel)
# Create Red image [1, 0, 0] and Blue image [0, 0, 1]
img_red = torch.tensor([1.0, 0.0, 0.0]).view(1, 1, 1, 3)
img_blue = torch.tensor([0.0, 0.0, 1.0]).view(1, 1, 1, 3)
# Test Lerp (Blend)
# lerp(red, blue, 0.5) -> [0.5, 0.0, 0.5] (Purple)
res_blend = node.execute("lerp(a, b, 0.5)", a=img_red, b=img_blue)[0]
expected = torch.tensor([0.5, 0.0, 0.5]).view(1, 1, 1, 3)
self.assertTrue(torch.allclose(res_blend, expected))
# Test Swap Channels (Red <-> Blue)
# Input: Red [1, 0, 0]. Swap R(0) and B(2). Result: [0, 0, 1] (Blue)
# Note: In ImageMathNode, Channel is dim 1 (internally permuted to B,C,H,W)
res_swap = node.execute("swap(a, 1, 0, 2)", a=img_red)[0]
self.assertTrue(torch.allclose(res_swap, img_blue))
def test_nested_expressions(self):
node = FloatMathNode()
# lerp(0, 10, step(0.5, 0.8)) -> lerp(0, 10, 1) -> 10
res = node.execute("lerp(0, 10, step(0.5, 0.8))", a=0.0)[0]
self.assertEqual(res, 10.0)
# lerp(0, 10, step(0.5, 0.2)) -> lerp(0, 10, 0) -> 0
res2 = node.execute("lerp(0, 10, step(0.5, 0.2))", a=0.0)[0]
self.assertEqual(res2, 0.0)
def test_5d_tensors(self):
# Test support for 5D tensors: (Batch, Time, Channel, Height, Width)
# LatentMathNode expects this structure if ndim >= 5.
node = LatentMathNode()
# Create a 5D tensor: 1 Batch, 5 Frames, 4 Channels, 32 Height, 32 Width
# Shape: (1, 5, 4, 32, 32)
samples = torch.randn(1, 5, 4, 32, 32)
l_in = {"samples": samples}
# Test 1: Identity/Passthrough using a basic operation
# a * 1.0 should return same shape and values
res = node.execute("a * 1.0", a=l_in)[0]["samples"]
self.assertEqual(res.shape, (1, 5, 4, 32, 32))
self.assertTrue(torch.allclose(res, samples))
# Test 2: Accessing 'T' (batch_count/time) variable
# In 5D, T maps to dim -4 (size 5)
# Expression: "a + T" -> Adds 5.0 to every element
res_t = node.execute("a + T", a=l_in)[0]["samples"]
self.assertTrue(torch.allclose(res_t, samples + 5.0))
# Test 3: FFT on 5D tensor
# FFT logic operates on dims [2:] -> (Channel, Height, Width) for 5D input??
# Wait, LatentMathNode.eval_single_tensor sets:
# channel_dim = -3, height_dim = -2, width = -1.
# So for (B, T, C, H, W), it processes C, H, W as spatial?
# Let's verify standard roundtrip
res_fft = node.execute("ifft(fft(a))", a=l_in)[0]["samples"]
self.assertTrue(torch.allclose(res_fft, samples, atol=1e-5))
def test_noise_math_5d(self):
# NoiseMathNode takes objects that have a 'generate_noise' method.
# We need to mock this for testing.
from more_math.NoiseMathNode import NoiseMathNode
class MockNoise:
def __init__(self, tensor):
self.tensor = tensor
def generate_noise(self, input_latent):
# Return the stored tensor, ignoring input_latent for this test
return self.tensor
node = NoiseMathNode()
# Create 5D tensor (1, 5, 4, 32, 32)
samples = torch.randn(1, 5, 4, 32, 32)
# In NoiseMathNode, 'input_latent' is passed to generate_noise.
# The node execution logic calls generate_noise on inputs a,b,c,d.
# But wait, NoiseMathNode.execute returns a NoiseExecutor object.
# The actual calculation happens when NoiseExecutor.generate_noise() is called.
noise_a = MockNoise(samples)
# 1. Execute node to get executor
# Noise="a + T"
# We need to verify that T is 5 (frames) not 1 (batch)
result_executor = node.execute("a + T", a=noise_a)[0]
# 2. Call generate_noise on the result executor
# We need to pass a dummy input_latent because generate_noise expects it.
# The input_latent structure dictates dimensions if a,b,c,d don't override?
# Actually NoiseMathNode uses the 'samples' from input_latent as 'merged_samples' usually?
# Let's check logic:
# samples = input_latent["samples"]
# a_val = self.a.generate_noise()
# ...
# if samples is nested...
# else: merged_samples = samples.
#
# So 'merged_samples' comes from 'input_latent' passed to generate_noise!
# The 'a' input is just a generator.
# So to test 5D support, we must pass a 5D latent to generate_noise.
dummy_latent = {"samples": samples} # This sets the context dimensions
# If we pass a MockNoise that returns 'samples', then a_val will be 'samples'.
# And merged_samples will be 'samples'.
# So a + T -> samples + frame_count.
res = result_executor.generate_noise(dummy_latent)
self.assertEqual(res.shape, (1, 5, 4, 32, 32))
res = result_executor.generate_noise(dummy_latent)
self.assertEqual(res.shape, (1, 5, 4, 32, 32))
self.assertTrue(torch.allclose(res, samples + 5.0),
f"Expected samples + 5.0, got {res[0,0,0,0,0]} vs {samples[0,0,0,0,0]+5.0}")
def test_nested_tensor_support(self):
# Test compatibility with comfy.nested_tensor.NestedTensor
try:
from comfy.nested_tensor import NestedTensor
except ImportError:
# If comfy is not in path or fails to import, we might need to skip or mock
# But run_tests.py adds the root, so it should work.
# If it fails, fail the test to alert us.
self.fail("Could not import comfy.nested_tensor. Make sure ComfyUI root is in sys.path")
node = LatentMathNode()
# Create two tensors with different dimensions to simulate nesting
# Tensor 1: (1, 4, 32, 32)
t1 = torch.full((1, 4, 32, 32), 1.0)
# Tensor 2: (2, 4, 32, 32)
t2 = torch.full((2, 4, 32, 32), 2.0)
# Create NestedTensor
nt_in = NestedTensor([t1, t2])
l_in = {"samples": nt_in}
# Operation: a + 1.0
# Expected: t1 becomes 2.0s, t2 becomes 3.0s
res_lat = node.execute("a + 1.0", a=l_in)[0]["samples"]
# Verify result is NestedTensor
self.assertTrue(getattr(res_lat, 'is_nested', False), "Result should be a NestedTensor")
# Verify contents
res_list = res_lat.unbind()
self.assertEqual(len(res_list), 2)
self.assertTrue(torch.allclose(res_list[0], torch.full_like(t1, 2.0)))
self.assertTrue(torch.allclose(res_list[1], torch.full_like(t2, 3.0)))
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