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pytorch-image-models/tests/test_models.py

71 lines
2.9 KiB

import pytest
import torch
from timm import list_models, create_model
@pytest.mark.timeout(120)
@pytest.mark.parametrize('model_name', list_models())
@pytest.mark.parametrize('batch_size', [1])
def test_model_forward(model_name, batch_size):
"""Run a single forward pass with each model"""
model = create_model(model_name, pretrained=False)
model.eval()
input_size = model.default_cfg['input_size']
if any([x > 448 for x in input_size]):
# cap forward test at max res 448 * 448 to keep resource down
input_size = tuple([min(x, 448) for x in input_size])
inputs = torch.randn((batch_size, *input_size))
outputs = model(inputs)
assert outputs.shape[0] == batch_size
assert not torch.isnan(outputs).any(), 'Output included NaNs'
@pytest.mark.timeout(120)
@pytest.mark.parametrize('model_name', list_models(exclude_filters='dla*')) # DLA models have an issue TBD
@pytest.mark.parametrize('batch_size', [2])
def test_model_backward(model_name, batch_size):
"""Run a single forward pass with each model"""
model = create_model(model_name, pretrained=False, num_classes=42)
num_params = sum([x.numel() for x in model.parameters()])
model.eval()
input_size = model.default_cfg['input_size']
if any([x > 128 for x in input_size]):
# cap backward test at 128 * 128 to keep resource usage down
input_size = tuple([min(x, 128) for x in input_size])
inputs = torch.randn((batch_size, *input_size))
outputs = model(inputs)
outputs.mean().backward()
num_grad = sum([x.grad.numel() for x in model.parameters() if x.grad is not None])
assert outputs.shape[-1] == 42
assert num_params == num_grad, 'Some parameters are missing gradients'
assert not torch.isnan(outputs).any(), 'Output included NaNs'
@pytest.mark.timeout(120)
@pytest.mark.parametrize('model_name', list_models())
@pytest.mark.parametrize('batch_size', [1])
def test_model_default_cfgs(model_name, batch_size):
"""Run a single forward pass with each model"""
model = create_model(model_name, pretrained=False)
model.eval()
state_dict = model.state_dict()
cfg = model.default_cfg
classifier = cfg['classifier']
first_conv = cfg['first_conv']
pool_size = cfg['pool_size']
input_size = model.default_cfg['input_size']
if all([x <= 448 for x in input_size]):
# pool size only checked if default res <= 448 * 448 to keep resource down
input_size = tuple([min(x, 448) for x in input_size])
outputs = model.forward_features(torch.randn((batch_size, *input_size)))
assert outputs.shape[-1] == pool_size[-1] and outputs.shape[-2] == pool_size[-2]
assert any([k.startswith(cfg['classifier']) for k in state_dict.keys()]), f'{classifier} not in model params'
assert any([k.startswith(cfg['first_conv']) for k in state_dict.keys()]), f'{first_conv} not in model params'