def test_residual_block_asymmetric_pad_future_contexts(self):
# test future contexts at various values
# 0 = no future context
# 2 = limited future context
# 5 = symmetric context
# 8 = excess future context (more future context than present or past context)
future_contexts = [0, 2, 5, 8]
for future_context in future_contexts:
print(future_context)
config = self.jasper_base_config(future_context=future_context)
act = jasper.jasper_activations.get(config.pop('activation'))()
block = jasper.JasperBlock(**config, activation=act)
x = torch.randn(1, 16, 131)
xlen = torch.tensor([131])
y, ylen = block(([x], xlen))
self.check_module_exists(block, torch.nn.ConstantPad1d)
self.check_module_exists(block, jasper.MaskedConv1d)
assert isinstance(block, jasper.JasperBlock)
assert y[0].shape == torch.Size([1, config['planes'], 131])
assert ylen[0] == 131
assert block.mconv[0].pad_layer is not None
assert block.mconv[0]._padding == (config['kernel_size'][0] - 1 - future_context, future_context)
def test_residual_block(self):
config = self.jasper_base_config(residual=True)
act = jasper.jasper_activations.get(config.pop('activation'))()
block = jasper.JasperBlock(**config, activation=act)
x = torch.randn(1, 16, 131)
xlen = torch.tensor([131])
y, ylen = block(([x], xlen))
assert isinstance(block, jasper.JasperBlock)
assert y[0].shape == torch.Size([1, config['planes'], 131])
assert ylen[0] == 131
def test_basic_block_repeat_stride(self):
config = self.jasper_base_config(residual=False, repeat=3, stride=[2])
act = jasper.jasper_activations.get(config.pop('activation'))()
block = jasper.JasperBlock(**config, activation=act)
x = torch.randn(1, 16, 131)
xlen = torch.tensor([131])
y, ylen = block(([x], xlen))
assert isinstance(block, jasper.JasperBlock)
assert y[0].shape == torch.Size([1, config['planes'], 17]) # 131 // (stride ^ repeats)
assert ylen[0] == 17 # 131 // (stride ^ repeats)
assert len(block.mconv) == 3 * 3 + 1 # (3 repeats x {1 conv + 1 norm + 1 dropout} + final conv)
def test_residual_block_se(self):
config = self.jasper_base_config(se=True, se_reduction_ratio=8)
act = jasper.jasper_activations.get(config.pop('activation'))()
block = jasper.JasperBlock(**config, activation=act)
x = torch.randn(1, 16, 131)
xlen = torch.tensor([131])
y, ylen = block(([x], xlen))
self.check_module_exists(block, jasper.SqueezeExcite)
assert isinstance(block, jasper.JasperBlock)
assert y[0].shape == torch.Size([1, config['planes'], 131])
assert ylen[0] == 131
def test_basic_block_stride(self):
config = self.jasper_base_config(stride=[2], residual=False)
act = jasper.jasper_activations.get(config.pop('activation'))()
print(config)
block = jasper.JasperBlock(**config, activation=act)
x = torch.randn(1, 16, 131)
xlen = torch.tensor([131])
y, ylen = block(([x], xlen))
assert isinstance(block, jasper.JasperBlock)
assert y[0].shape == torch.Size([1, config['planes'], 66])
assert ylen[0] == 66
def test_residual_block_normalizations(self):
NORMALIZATIONS = ["batch", "layer", "group"]
for normalization in NORMALIZATIONS:
config = self.jasper_base_config(normalization=normalization)
act = jasper.jasper_activations.get(config.pop('activation'))()
block = jasper.JasperBlock(**config, activation=act)
x = torch.randn(1, 16, 131)
xlen = torch.tensor([131])
y, ylen = block(([x], xlen))
assert isinstance(block, jasper.JasperBlock)
assert y[0].shape == torch.Size([1, config['planes'], 131])
assert ylen[0] == 131
def test_residual_block_activations(self):
for activation in jasper.jasper_activations.keys():
config = self.jasper_base_config(activation=activation)
act = jasper.jasper_activations.get(config.pop('activation'))()
block = jasper.JasperBlock(**config, activation=act)
x = torch.randn(1, 16, 131)
xlen = torch.tensor([131])
y, ylen = block(([x], xlen))
self.check_module_exists(block, act.__class__)
assert isinstance(block, jasper.JasperBlock)
assert y[0].shape == torch.Size([1, config['planes'], 131])
assert ylen[0] == 131
def test_residual_block_asymmetric_pad_future_context_fallback(self):
# test future contexts at various values
# 15 = K < FC; fall back to symmetric context
future_context = 15
print(future_context)
config = self.jasper_base_config(future_context=future_context)
act = jasper.jasper_activations.get(config.pop('activation'))()
block = jasper.JasperBlock(**config, activation=act)
x = torch.randn(1, 16, 131)
xlen = torch.tensor([131])
y, ylen = block(([x], xlen))
self.check_module_exists(block, jasper.MaskedConv1d)
assert isinstance(block, jasper.JasperBlock)
assert y[0].shape == torch.Size([1, config['planes'], 131])
assert ylen[0] == 131
assert block.mconv[0].pad_layer is None
assert block.mconv[0]._padding == config['kernel_size'][0] // 2
def contrust_jasper_block(**config_kwargs):
config = TestJasperBlock.jasper_base_config(**config_kwargs)
act = jasper.jasper_activations.get(config.pop('activation'))()
block = jasper.JasperBlock(**config, activation=act)
return block
