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utest.py
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utest.py
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from networks import create_Generator, BasicBlock, ResBlock
import torch
import torch.nn as nn
from torch.autograd import Variable
import pytest, random
def assert_configs(g, res, epc, dpc, init):
assert g.learning_residual == res
assert g.encoder_partial_conv == epc
assert g.decoder_partial_conv == dpc
assert g.init_type == init
@pytest.mark.parametrize('block', ['basic', 'residual'])
def test_create_generator(block):
x = Variable(torch.randn(1, 3, 512, 512))
mask = Variable(torch.bernoulli(torch.rand(1, 1, 512, 512)))
# only conv
print('only conv')
config = {'g_input': 'masked_X', 'decoder_partial_conv': False, 'init_type': 'kaiming', \
'learning_residual': False, 'block': block, 'progressive_growing': False, 'rdpcl': False}
g = create_Generator(config)
assert_configs(g, False, False, False, 'kaiming')
out1 = g(x)
assert out1.size() == x.size()
# only partial conv on encoder
print('only partial conv on encoder')
config = {'g_input': 'masked_X+mask', 'decoder_partial_conv': False, 'init_type': 'kaiming', \
'learning_residual': False, 'block': block, 'progressive_growing': False, 'rdpcl': False}
g = create_Generator(config)
assert_configs(g, False, True, False, 'kaiming')
out2 = g(x, mask)
assert out2.size() == x.size()
# full partial conv
print('full partial conv')
config = {'g_input': 'masked_X+mask', 'decoder_partial_conv': True, 'init_type': 'kaiming', \
'learning_residual': False, 'block': block, 'progressive_growing': False, 'rdpcl': False}
g = create_Generator(config)
assert_configs(g, False, True, True, 'kaiming')
out3 = g(x, mask)
assert out3.size() == x.size()
# conv + learning residual
print('conv + learning residual')
config = {'g_input': 'masked_X', 'decoder_partial_conv': False, 'init_type': 'kaiming', \
'learning_residual': True, 'block': block, 'progressive_growing': False, 'rdpcl': False}
g = create_Generator(config)
assert_configs(g, True, False, False, 'kaiming')
out4 = g(x)
assert out4.size() == x.size()
# only partial conv on encoder + learning residual
print('only partial conv on encoder + learning residual')
config = {'g_input': 'masked_X+mask', 'decoder_partial_conv': False, 'init_type': 'kaiming', \
'learning_residual': True, 'block': block, 'progressive_growing': False, 'rdpcl': False}
g = create_Generator(config)
assert_configs(g, True, True, False, 'kaiming')
out5 = g(x, mask)
assert out5.size() == x.size()
# full partial conv + learning residual
print('full partial conv + learning residual')
config = {'g_input': 'masked_X+mask', 'decoder_partial_conv': True, 'init_type': 'kaiming', \
'learning_residual': True, 'block': block, 'progressive_growing': False, 'rdpcl': False}
g = create_Generator(config)
assert_configs(g, True, True, True, 'kaiming')
out6 = g(x, mask)
assert out6.size() == x.size()
def assert_block_contains(block, module_names):
_module_names = module_names.copy()
def check_module_in(m):
name = m.__class__.__name__
if name in _module_names:
_module_names.remove(name)
block.apply(check_module_in)
assert len(_module_names) == 0
def assert_block_not_contains(block, module_names):
def check_module_not_in(m):
name = m.__class__.__name__
assert name not in module_names
block.apply(check_module_not_in)
def assert_size_match(out_size, gt_size):
assert len(out_size) == len(gt_size)
assert all([out_size[i]==gt_size[i] for i in range(len(out_size))])
def test_basic_block():
block = BasicBlock
x = Variable(torch.randn(1, 3, 64, 64))
mask = Variable(torch.bernoulli(torch.rand(1, 1, 64, 64)))
mask2 = Variable(torch.bernoulli(torch.rand(1, 1, 128, 128)))
# without upsample
cfg = {'in_channels': 3, 'out_channels': 4, 'kernel_size': 3, 'stride': 2, 'padding': 1}
# conv-bn-relu
print('conv-bn-relu')
b1 = block(False, False, False, **cfg)
out1 = b1(x)
assert_block_contains(b1, ['Conv2d', 'BatchNorm2d', 'ReLU'])
assert_block_not_contains(b1, ['Upsample'])
assert_size_match(out1.size(), [1, 4, 32, 32])
# conv-relu
print('conv-relu')
b2 = block(False, False, True, **cfg)
out2 = b2(x)
assert_block_contains(b2, ['Conv2d', 'ReLU'])
assert_block_not_contains(b2, ['BatchNorm2d', 'Upsample'])
assert_size_match(out2.size(), [1, 4, 32, 32])
# pconv-in-lrelu
print('pconv-in-lrelu')
b3 = block(False, True, False, **cfg, norm=nn.InstanceNorm2d, activation=nn.LeakyReLU(0.2))
out3, _mask_ = b3(x, mask)
assert_block_contains(b3, ['PartialConv2d', 'InstanceNorm2d', 'LeakyReLU'])
assert_block_not_contains(b3, ['BatchNorm2d', 'ReLU', 'Upsample'])
assert_size_match(out3.size(), [1, 4, 32, 32]) and assert_size_match(_mask_.size(), [1, 1, 32, 32])
# pconv-sigmoid
print('pconv-sigmoid')
b4 = block(False, True, True, **cfg, activation=nn.Sigmoid())
out4, _mask_ = b4(x, mask)
assert_block_contains(b4, ['PartialConv2d', 'Sigmoid'])
assert_block_not_contains(b4, ['BatchNorm2d', 'ReLU', 'Upsample'])
assert_size_match(out4.size(), [1, 4, 32, 32]) and assert_size_match(_mask_.size(), [1, 1, 32, 32])
# with upsample
cfg = {'in_channels': 3, 'out_channels': 4, 'kernel_size': 3, 'stride': 1, 'padding': 1}
# conv-bn-relu
print('upsample + conv-bn-relu')
b1 = block(True, False, False, **cfg)
out1 = b1(x)
assert_block_contains(b1, ['Conv2d', 'BatchNorm2d', 'ReLU', 'Upsample'])
assert_size_match(out1.size(), [1, 4, 128, 128])
# conv-relu
print('upsample + conv-relu')
b2 = block(True, False, True, **cfg)
out2 = b2(x)
assert_block_contains(b2, ['Conv2d', 'ReLU', 'Upsample'])
assert_block_not_contains(b2, ['BatchNorm2d'])
assert_size_match(out2.size(), [1, 4, 128, 128])
# pconv-in-lrelu
print('upsample + pconv-in-lrelu')
b3 = block(True, True, False, **cfg, norm=nn.InstanceNorm2d, activation=nn.LeakyReLU(0.2))
out3, _mask_ = b3(x, mask2)
assert_block_contains(b3, ['PartialConv2d', 'InstanceNorm2d', 'LeakyReLU', 'Upsample'])
assert_block_not_contains(b3, ['BatchNorm2d', 'ReLU'])
assert_size_match(out3.size(), [1, 4, 128, 128]) and assert_size_match(_mask_.size(), [1, 1, 128, 128])
# pconv-sigmoid
print('upsample + pconv-sigmoid')
b4 = block(True, True, True, **cfg, activation=nn.Sigmoid())
out4, _mask_ = b4(x, mask2)
assert_block_contains(b4, ['PartialConv2d', 'Sigmoid', 'Upsample'])
assert_block_not_contains(b4, ['BatchNorm2d', 'ReLU'])
assert_size_match(out4.size(), [1, 4, 128, 128]) and assert_size_match(_mask_.size(), [1, 1, 128, 128])
def test_res_block():
block = ResBlock
x = Variable(torch.randn(1, 3, 64, 64))
mask = Variable(torch.bernoulli(torch.rand(1, 1, 64, 64)))
mask2 = Variable(torch.bernoulli(torch.rand(1, 1, 128, 128)))
# without upsample
cfg = {'in_channels': 3, 'out_channels': 4, 'kernel_size': 3, 'stride': 2, 'padding': 1}
# resblock with conv and bn and relu
print('resblock with conv and bn and relu')
b1 = block(False, False, False, **cfg)
out1 = b1(x)
assert_block_contains(b1, ['Conv2d', 'BatchNorm2d', 'ReLU'])
assert_block_not_contains(b1, ['Upsample'])
assert_size_match(out1.size(), [1, 4, 32, 32])
# resblock with pconv and in and lrelu
print('resblock with pconv and in and lrelu')
b2 = block(False, True, False, **cfg, norm=nn.InstanceNorm2d, activation=nn.LeakyReLU(0.2))
out2, _mask_ = b2(x, mask)
assert_block_contains(b2, ['PartialConv2d', 'InstanceNorm2d', 'LeakyReLU', 'ReLU'])
assert_block_not_contains(b2, ['BatchNorm2d', 'Upsample'])
assert_size_match(out2.size(), [1, 4, 32, 32]) and assert_size_match(_mask_.size(), [1, 1, 32, 32])
# resblock with pconv and bn and sigmoid
print('resblock with pconv and bn and sigmoid')
b3 = block(False, True, True, **cfg, activation=nn.Sigmoid())
out3, _mask_ = b3(x, mask)
assert_block_contains(b3, ['PartialConv2d', 'Sigmoid', 'ReLU', 'BatchNorm2d'])
assert_block_not_contains(b3, ['Upsample'])
assert_size_match(out3.size(), [1, 4, 32, 32]) and assert_size_match(_mask_.size(), [1, 1, 32, 32])
# with upsample
cfg = {'in_channels': 3, 'out_channels': 4, 'kernel_size': 3, 'stride': 1, 'padding': 1}
# resblock with conv and bn and relu
print('upsample + resblock with conv and bn and relu')
b1 = block(True, False, False, **cfg)
out1 = b1(x)
assert_block_contains(b1, ['Conv2d', 'BatchNorm2d', 'ReLU', 'Upsample'])
assert_size_match(out1.size(), [1, 4, 128, 128])
# resblock with pconv and in and lrelu
print('upsample + resblock with pconv and in and lrelu')
b2 = block(True, True, False, **cfg, norm=nn.InstanceNorm2d, activation=nn.LeakyReLU(0.2))
out2, _mask_ = b2(x, mask2)
assert_block_contains(b2, ['PartialConv2d', 'InstanceNorm2d', 'LeakyReLU', 'ReLU', 'Upsample'])
assert_block_not_contains(b2, ['BatchNorm2d'])
assert_size_match(out2.size(), [1, 4, 128, 128]) and assert_size_match(_mask_.size(), [1, 1, 128, 128])
# resblock with pconv and bn and sigmoid
print('upsample + resblock with pconv and bn and sigmoid')
b3 = block(True, True, True, **cfg, activation=nn.Sigmoid())
out3, _mask_ = b3(x, mask2)
assert_block_contains(b3, ['PartialConv2d', 'Sigmoid', 'ReLU', 'BatchNorm2d', 'Upsample'])
assert_size_match(out3.size(), [1, 4, 128, 128]) and assert_size_match(_mask_.size(), [1, 1, 128, 128])
@pytest.mark.parametrize('block,k', [(b, _k) for b in ['basic', 'residual'] for _k in range(8)] + \
[(b, _k+random.random()) for b in ['basic', 'residual'] for _k in range(7)])
def test_create_pg_generator(block, k):
k_int = 7-int(k)
x = Variable(torch.randn(1, 3, 2**(k_int+2), 2**(k_int+2)))
mask = Variable(torch.bernoulli(torch.rand(1, 1, 2**(k_int+2), 2**(k_int+2))))
# only conv
print('only conv')
config = {'g_input': 'masked_X', 'decoder_partial_conv': False, 'init_type': 'kaiming', \
'learning_residual': False, 'block': block, 'progressive_growing': True, 'rdpcl': False}
g = create_Generator(config)
assert_configs(g, False, False, False, 'kaiming')
out1 = g(x, phase=k)
assert out1.size() == x.size()
# only partial conv on encoder
print('only partial conv on encoder')
config = {'g_input': 'masked_X+mask', 'decoder_partial_conv': False, 'init_type': 'kaiming', \
'learning_residual': False, 'block': block, 'progressive_growing': True, 'rdpcl': False}
g = create_Generator(config)
assert_configs(g, False, True, False, 'kaiming')
out2 = g(x, mask, phase=k)
assert out2.size() == x.size()
# full partial conv
print('full partial conv')
config = {'g_input': 'masked_X+mask', 'decoder_partial_conv': True, 'init_type': 'kaiming', \
'learning_residual': False, 'block': block, 'progressive_growing': True, 'rdpcl': False}
g = create_Generator(config)
assert_configs(g, False, True, True, 'kaiming')
out3 = g(x, mask, phase=k)
assert out3.size() == x.size()
# conv + learning residual
print('conv + learning residual')
config = {'g_input': 'masked_X', 'decoder_partial_conv': False, 'init_type': 'kaiming', \
'learning_residual': True, 'block': block, 'progressive_growing': True, 'rdpcl': False}
g = create_Generator(config)
assert_configs(g, True, False, False, 'kaiming')
out4 = g(x, phase=k)
assert out4.size() == x.size()
# only partial conv on encoder + learning residual
print('only partial conv on encoder + learning residual')
config = {'g_input': 'masked_X+mask', 'decoder_partial_conv': False, 'init_type': 'kaiming', \
'learning_residual': True, 'block': block, 'progressive_growing': True, 'rdpcl': False}
g = create_Generator(config)
assert_configs(g, True, True, False, 'kaiming')
out5 = g(x, mask, phase=k)
assert out5.size() == x.size()
# full partial conv + learning residual
print('full partial conv + learning residual')
config = {'g_input': 'masked_X+mask', 'decoder_partial_conv': True, 'init_type': 'kaiming', \
'learning_residual': True, 'block': block, 'progressive_growing': True, 'rdpcl': False}
g = create_Generator(config)
assert_configs(g, True, True, True, 'kaiming')
out6 = g(x, mask, phase=k)
assert out6.size() == x.size()