475 lines
15 KiB
Python
475 lines
15 KiB
Python
#!/usr/bin/env python
|
|
|
|
"""Tests for `more_math` package."""
|
|
|
|
import os
|
|
import sys
|
|
|
|
# Ensure test runner (Visual Studio) can import the package regardless of working dir.
|
|
# If repository uses `src/` layout, add that to sys.path; otherwise add project root.
|
|
_here = os.path.abspath(os.path.dirname(__file__))
|
|
_project_root = os.path.abspath(os.path.join(_here, os.pardir))
|
|
_src_path = os.path.join(_project_root, "src")
|
|
|
|
if os.path.isdir(_src_path) and _src_path not in sys.path:
|
|
sys.path.insert(0, _src_path)
|
|
elif _project_root not in sys.path:
|
|
sys.path.insert(0, _project_root)
|
|
|
|
# Add ComfyUI root to path to find 'comfy' and 'comfy_api' packages
|
|
_comfy_root = os.path.abspath(os.path.join(_here, "../../.."))
|
|
if _comfy_root not in sys.path:
|
|
sys.path.insert(0, _comfy_root)
|
|
|
|
import torch
|
|
from more_math.ConditioningMathNode import ConditioningMathNode
|
|
from more_math.LatentMathNode import LatentMathNode
|
|
from more_math.ImageMathNode import ImageMathNode
|
|
from more_math.FloatMathNode import FloatMathNode
|
|
|
|
|
|
# ==========================================
|
|
# Node Initialization and Metadata Tests
|
|
# ==========================================
|
|
|
|
|
|
def test_conditioning_math_node_initialization():
|
|
node = ConditioningMathNode()
|
|
assert isinstance(node, ConditioningMathNode)
|
|
|
|
|
|
def test_conditioning_math_node_metadata():
|
|
assert ConditioningMathNode.RETURN_TYPES == ["CONDITIONING"]
|
|
assert ConditioningMathNode.FUNCTION == "EXECUTE_NORMALIZED"
|
|
assert ConditioningMathNode.CATEGORY == "More math"
|
|
|
|
|
|
def test_latent_math_node_initialization():
|
|
node = LatentMathNode()
|
|
assert isinstance(node, LatentMathNode)
|
|
|
|
|
|
def test_latent_math_node_metadata():
|
|
assert LatentMathNode.RETURN_TYPES == ["LATENT"]
|
|
assert LatentMathNode.FUNCTION == "EXECUTE_NORMALIZED"
|
|
assert LatentMathNode.CATEGORY == "More math"
|
|
|
|
|
|
def test_image_math_node_initialization():
|
|
node = ImageMathNode()
|
|
assert isinstance(node, ImageMathNode)
|
|
|
|
|
|
def test_image_math_node_metadata():
|
|
assert ImageMathNode.RETURN_TYPES == ["IMAGE"]
|
|
assert ImageMathNode.FUNCTION == "EXECUTE_NORMALIZED"
|
|
assert ImageMathNode.CATEGORY == "More math"
|
|
|
|
|
|
# ==========================================
|
|
# FFT Tests
|
|
# ==========================================
|
|
|
|
|
|
def test_fft_invertibility():
|
|
# Create random input latent (Batch, Channel, Height, Width)
|
|
input_tensor = torch.randn(1, 4, 32, 32, dtype=torch.float32)
|
|
input_dict = {"samples": input_tensor}
|
|
# Execute ifft(fft(a))
|
|
result = LatentMathNode.execute(Latent="ifft(fft(a))", a=input_dict)
|
|
output_tensor = result[0]["samples"]
|
|
assert torch.allclose(input_tensor, output_tensor, atol=1e-5), f"Max difference: {(input_tensor - output_tensor).abs().max()}"
|
|
|
|
|
|
def test_image_fft_dims():
|
|
# Image input is (Batch, Height, Width, Channel)
|
|
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]
|
|
assert input_tensor.shape == output_tensor.shape
|
|
assert torch.allclose(input_tensor, output_tensor, atol=1e-5), (
|
|
f"Image FFT round trip failed. Max diff: {(input_tensor - output_tensor).abs().max()}"
|
|
)
|
|
|
|
|
|
# ==========================================
|
|
# Latent Math Basic Functions (Evaluated on Tensors)
|
|
# ==========================================
|
|
|
|
|
|
def test_latent_lerp():
|
|
node = LatentMathNode()
|
|
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"]
|
|
assert torch.allclose(res_lerp, torch.full_like(res_lerp, 5.0))
|
|
|
|
|
|
def test_latent_step_true():
|
|
node = LatentMathNode()
|
|
# 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"]
|
|
assert torch.allclose(res_step, torch.ones_like(res_step))
|
|
|
|
|
|
def test_latent_step_false():
|
|
node = LatentMathNode()
|
|
# 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"]
|
|
assert torch.allclose(res_step2, torch.zeros_like(res_step2))
|
|
|
|
|
|
def test_latent_swap():
|
|
node = LatentMathNode()
|
|
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}
|
|
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)
|
|
assert torch.allclose(res_swap, expected)
|
|
|
|
|
|
def test_latent_relu():
|
|
node = LatentMathNode()
|
|
l_a = {"samples": torch.zeros(1, 4, 32, 32)}
|
|
res_relu = node.execute("relu(-5.0)", a=l_a)[0]["samples"]
|
|
assert torch.allclose(res_relu, torch.zeros_like(res_relu))
|
|
|
|
|
|
def test_latent_sign():
|
|
node = LatentMathNode()
|
|
l_a = {"samples": torch.zeros(1, 4, 32, 32)}
|
|
res_sign = node.execute("sign(-5.0)", a=l_a)[0]["samples"]
|
|
assert torch.allclose(res_sign, torch.full_like(res_sign, -1.0))
|
|
|
|
|
|
def test_latent_fract():
|
|
node = LatentMathNode()
|
|
l_a = {"samples": torch.zeros(1, 4, 32, 32)}
|
|
res_fract = node.execute("fract(1.5)", a=l_a)[0]["samples"]
|
|
assert torch.allclose(res_fract, torch.full_like(res_fract, 0.5))
|
|
|
|
|
|
# ==========================================
|
|
# Float Math Basic Functions (Evaluated on Scalars)
|
|
# ==========================================
|
|
|
|
|
|
def test_float_lerp():
|
|
node = FloatMathNode()
|
|
res = node.execute("lerp(a, b, 0.5)", a=0.0, b=10.0)[0]
|
|
assert abs(res - 5.0) < 1e-5
|
|
|
|
|
|
def test_float_step():
|
|
node = FloatMathNode()
|
|
res = node.execute("step(0.5, a)", a=0.8)[0]
|
|
assert abs(res - 1.0) < 1e-5
|
|
|
|
|
|
def test_float_relu():
|
|
node = FloatMathNode()
|
|
res = node.execute("relu(a)", a=-5.0)[0]
|
|
assert abs(res - 0.0) < 1e-5
|
|
|
|
|
|
def test_float_smoothstep():
|
|
node = FloatMathNode()
|
|
res = node.execute("smoothstep(0, 1, a)", a=0.5)[0]
|
|
assert abs(res - 0.5) < 1e-5
|
|
|
|
|
|
# ==========================================
|
|
# Float Math Extended Functions
|
|
# ==========================================
|
|
|
|
|
|
def test_float_fract():
|
|
node = FloatMathNode()
|
|
res = node.execute("fract(a)", a=1.5)[0]
|
|
assert abs(res - 0.5) < 1e-5
|
|
|
|
|
|
def test_float_softplus():
|
|
node = FloatMathNode()
|
|
res = node.execute("softplus(a)", a=0.0)[0]
|
|
assert abs(res - 0.69314718) < 1e-5
|
|
|
|
|
|
def test_float_sign_negative():
|
|
node = FloatMathNode()
|
|
assert node.execute("sign(a)", a=-10.0)[0] == -1.0
|
|
|
|
|
|
def test_float_sign_positive():
|
|
node = FloatMathNode()
|
|
assert node.execute("sign(a)", a=10.0)[0] == 1.0
|
|
|
|
|
|
def test_float_sign_zero():
|
|
node = FloatMathNode()
|
|
assert node.execute("sign(a)", a=0.0)[0] == 0.0
|
|
|
|
|
|
def test_float_gelu():
|
|
node = FloatMathNode()
|
|
assert node.execute("gelu(a)", a=0.0)[0] == 0.0
|
|
|
|
|
|
# ==========================================
|
|
# Latent Math Extended Functions
|
|
# ==========================================
|
|
|
|
|
|
def test_latent_smoothstep():
|
|
node = LatentMathNode()
|
|
l_a = {"samples": torch.zeros(1, 4, 32, 32)}
|
|
res = node.execute("smoothstep(0, 1, 0.5)", a=l_a)[0]["samples"]
|
|
assert torch.allclose(res, torch.full_like(res, 0.5))
|
|
|
|
|
|
def test_latent_softplus():
|
|
node = LatentMathNode()
|
|
l_a = {"samples": torch.zeros(1, 4, 32, 32)}
|
|
res = node.execute("softplus(0.0)", a=l_a)[0]["samples"]
|
|
assert torch.allclose(res, torch.full_like(res, 0.69314718))
|
|
|
|
|
|
def test_latent_gelu():
|
|
node = LatentMathNode()
|
|
l_a = {"samples": torch.zeros(1, 4, 32, 32)}
|
|
res = node.execute("gelu(0.0)", a=l_a)[0]["samples"]
|
|
assert torch.allclose(res, torch.zeros_like(res))
|
|
|
|
|
|
# ==========================================
|
|
# Image Math Operations
|
|
# ==========================================
|
|
|
|
|
|
def test_image_lerp():
|
|
node = ImageMathNode()
|
|
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)
|
|
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)
|
|
assert torch.allclose(res_blend, expected)
|
|
|
|
|
|
def test_image_swap():
|
|
node = ImageMathNode()
|
|
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)
|
|
res_swap = node.execute("swap(a, 1, 0, 2)", a=img_red)[0]
|
|
assert torch.allclose(res_swap, img_blue)
|
|
|
|
|
|
# ==========================================
|
|
# Nested Expressions
|
|
# ==========================================
|
|
|
|
|
|
def test_float_nested_expressions_true():
|
|
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]
|
|
assert res == 10.0
|
|
|
|
|
|
def test_float_nested_expressions_false():
|
|
node = FloatMathNode()
|
|
# 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]
|
|
assert res2 == 0.0
|
|
|
|
|
|
# ==========================================
|
|
# 5D Tensor Support
|
|
# ==========================================
|
|
|
|
|
|
def test_5d_tensors_identity():
|
|
node = LatentMathNode()
|
|
samples = torch.randn(1, 5, 4, 32, 32)
|
|
l_in = {"samples": samples}
|
|
res = node.execute("a * 1.0", a=l_in)[0]["samples"]
|
|
assert res.shape == (1, 5, 4, 32, 32)
|
|
assert torch.allclose(res, samples)
|
|
|
|
|
|
def test_5d_tensors_variable_T():
|
|
node = LatentMathNode()
|
|
samples = torch.randn(1, 5, 4, 32, 32)
|
|
l_in = {"samples": samples}
|
|
# In 5D, T maps to dim -4 (size 5)
|
|
res_t = node.execute("a + T", a=l_in)[0]["samples"]
|
|
assert torch.allclose(res_t, samples + 5.0)
|
|
|
|
|
|
def test_5d_tensors_fft():
|
|
node = LatentMathNode()
|
|
samples = torch.randn(1, 5, 4, 32, 32)
|
|
l_in = {"samples": samples}
|
|
res_fft = node.execute("ifft(fft(a))", a=l_in)[0]["samples"]
|
|
assert torch.allclose(res_fft, samples, atol=1e-5)
|
|
|
|
|
|
# ==========================================
|
|
# Noise Math Node 5D Support
|
|
# ==========================================
|
|
|
|
|
|
def test_noise_math_5d():
|
|
from more_math.NoiseMathNode import NoiseMathNode
|
|
|
|
class MockNoise:
|
|
def __init__(self, tensor):
|
|
self.tensor = tensor
|
|
|
|
def generate_noise(self, input_latent):
|
|
return self.tensor
|
|
|
|
node = NoiseMathNode()
|
|
samples = torch.randn(1, 5, 4, 32, 32)
|
|
noise_a = MockNoise(samples)
|
|
|
|
result_executor = node.execute("a + T", a=noise_a)[0]
|
|
dummy_latent = {"samples": samples}
|
|
res = result_executor.generate_noise(dummy_latent)
|
|
|
|
assert res.shape == (1, 5, 4, 32, 32)
|
|
assert torch.allclose(res, samples + 5.0)
|
|
|
|
|
|
# ==========================================
|
|
# NestedTensor Support
|
|
# ==========================================
|
|
|
|
|
|
def test_nested_tensor_support():
|
|
try:
|
|
from comfy.nested_tensor import NestedTensor
|
|
except ImportError:
|
|
assert False, "Could not import comfy.nested_tensor."
|
|
|
|
node = LatentMathNode()
|
|
t1 = torch.full((1, 4, 32, 32), 1.0)
|
|
t2 = torch.full((2, 4, 32, 32), 2.0)
|
|
nt_in = NestedTensor([t1, t2])
|
|
l_in = {"samples": nt_in}
|
|
|
|
res_lat = node.execute("a + 1.0", a=l_in)[0]["samples"]
|
|
|
|
assert getattr(res_lat, "is_nested", False)
|
|
res_list = res_lat.unbind()
|
|
assert len(res_list) == 2
|
|
assert torch.allclose(res_list[0], torch.full_like(t1, 2.0))
|
|
assert torch.allclose(res_list[1], torch.full_like(t2, 3.0))
|
|
|
|
|
|
# ==========================================
|
|
# Comprehensive Math Function Tests
|
|
# ==========================================
|
|
|
|
|
|
def test_trig_functions():
|
|
node = FloatMathNode()
|
|
# Sin/Cos checks
|
|
# sin(0) = 0, cos(0) = 1
|
|
assert abs(node.execute("sin(0)", a=0.0)[0] - 0.0) < 1e-5
|
|
assert abs(node.execute("cos(0)", a=0.0)[0] - 1.0) < 1e-5
|
|
# tan(0) = 0
|
|
assert abs(node.execute("tan(0)", a=0.0)[0] - 0.0) < 1e-5
|
|
|
|
|
|
def test_inverse_trig_functions():
|
|
node = FloatMathNode()
|
|
# asin(0) = 0, acos(1) = 0, atan(0) = 0
|
|
assert abs(node.execute("asin(0)", a=0.0)[0] - 0.0) < 1e-5
|
|
assert abs(node.execute("acos(1)", a=0.0)[0] - 0.0) < 1e-5
|
|
assert abs(node.execute("atan(0)", a=0.0)[0] - 0.0) < 1e-5
|
|
|
|
|
|
def test_pow_log_functions():
|
|
node = FloatMathNode()
|
|
# pow(2, 3) = 8
|
|
assert abs(node.execute("pow(2, 3)", a=0.0)[0] - 8.0) < 1e-5
|
|
# sqrt(4) = 2
|
|
assert abs(node.execute("sqrt(4)", a=0.0)[0] - 2.0) < 1e-5
|
|
# exp(0) = 1
|
|
assert abs(node.execute("exp(0)", a=0.0)[0] - 1.0) < 1e-5
|
|
# log(100) = 2 (base 10)
|
|
assert abs(node.execute("log(100)", a=0.0)[0] - 2.0) < 1e-5
|
|
# ln(e) = 1. Using 'e' constant logic check or approx 2.718
|
|
assert abs(node.execute("ln(2.7182818)", a=0.0)[0] - 1.0) < 1e-4
|
|
|
|
|
|
def test_min_max_functions():
|
|
import sys
|
|
|
|
node = FloatMathNode()
|
|
print("Testing tmin...", flush=True)
|
|
# tmin(2, 5) = 2, tmax(2, 5) = 5
|
|
assert abs(node.execute("tmin(2, 5)", a=0.0)[0] - 2.0) < 1e-5
|
|
print("Testing tmax...", flush=True)
|
|
assert abs(node.execute("tmax(2, 5)", a=0.0)[0] - 5.0) < 1e-5
|
|
|
|
# smin/smax (Smooth min/max? Or just multi-arg min/max? TensorEvalVisitor uses stack.min/max)
|
|
# smin(1, 2, 3) = 1
|
|
print("Testing smin...", flush=True)
|
|
res_smin = node.execute("smin(1, 2, 3)", a=0.0)[0]
|
|
print(f"smin result: {res_smin} type: {type(res_smin)}", flush=True)
|
|
assert abs(res_smin - 1.0) < 1e-5
|
|
|
|
print("Testing smax...", flush=True)
|
|
res_smax = node.execute("smax(1, 2, 3)", a=0.0)[0]
|
|
print(f"smax result: {res_smax} type: {type(res_smax)}", flush=True)
|
|
assert abs(res_smax - 3.0) < 1e-5
|
|
|
|
|
|
def test_basic_utilities():
|
|
node = FloatMathNode()
|
|
# abs(-5) = 5
|
|
assert abs(node.execute("abs(-5)", a=0.0)[0] - 5.0) < 1e-5
|
|
# floor(1.9) = 1
|
|
assert abs(node.execute("floor(1.9)", a=0.0)[0] - 1.0) < 1e-5
|
|
# ceil(1.1) = 2
|
|
assert abs(node.execute("ceil(1.1)", a=0.0)[0] - 2.0) < 1e-5
|
|
# round(1.6) = 2, round(1.4) = 1
|
|
assert abs(node.execute("round(1.6)", a=0.0)[0] - 2.0) < 1e-5
|
|
assert abs(node.execute("round(1.4)", a=0.0)[0] - 1.0) < 1e-5
|
|
# clamp(10, 0, 5) = 5, clamp(-5, 0, 5) = 0
|
|
assert abs(node.execute("clamp(10, 0, 5)", a=0.0)[0] - 5.0) < 1e-5
|
|
assert abs(node.execute("clamp(-5, 0, 5)", a=0.0)[0] - 0.0) < 1e-5
|
|
|
|
|
|
def test_advanced_activations():
|
|
node = FloatMathNode()
|
|
# sigm(0) = 0.5
|
|
assert abs(node.execute("sigm(0)", a=0.0)[0] - 0.5) < 1e-5
|
|
|
|
|
|
if __name__ == "__main__":
|
|
import sys
|
|
|
|
try:
|
|
test_conditioning_math_node_initialization()
|
|
test_conditioning_math_node_metadata()
|
|
test_latent_math_node_initialization()
|
|
test_latent_math_node_metadata()
|
|
test_image_math_node_initialization()
|
|
test_image_math_node_metadata()
|
|
test_trig_functions()
|
|
test_inverse_trig_functions()
|
|
test_pow_log_functions()
|
|
test_min_max_functions()
|
|
test_basic_utilities()
|
|
test_advanced_activations()
|
|
print("All tests passed!")
|
|
except Exception as e:
|
|
import traceback
|
|
|
|
traceback.print_exc()
|
|
sys.exit(1)
|
|
print("All tests in test_more_math.py passed!")
|