def test():
#declare the main named dimension variables using tsalib api
#recall these values anywhere in the program using `get_dim_vars`
from tsalib import dim_vars
dim_vars('Batch(b):10 Length(t):100 Hidden(d):1024')
from tsanley.dynamic import init_analyzer
init_analyzer(trace_func_names=['f*'], show_updates=True)
test_func()
def test2():
#declare the named dimension variables using the tsalib api
from tsalib import dim_vars
dim_vars('Batch(b):10 Length(t):100 Hidden(d):1024')
# initialize tsanley's dynamic shape analyzer
from tsanley.dynamic import init_analyzer
init_analyzer(trace_func_names=['foo'], show_updates=True,
debug=False) #check_tsa=True, debug=False
test_foo()
def test_resnet ():
# declare dim vars: required for checking
B, C, Ci, Co = dim_vars('Batch(b):10 Channels(c):3 ChannelsIn(ci) ChannelsOut(co)')
H, W, Ex = dim_vars('Height(h):224 Width(w):224 BlockExpansion(e):1')
rs18 = resnet18()
x: 'bchw' = torch.ones(10, 3, 224, 224)
from tsanley.dynamic import init_analyzer
#init_analyzer(trace_func_names=['ResNet.forward', 'BasicBlock.forward'])
init_analyzer(trace_func_names=['ResNet.forward'])
out = rs18.forward(x)
print (out.size())
def setup_named_dims():
from tsanley.dynamic import init_analyzer
init_analyzer(trace_func_names=['Net.forward'],
show_updates=True) #check_tsa=True, debug=False
def test_gnn ():
from tsanley.dynamic import init_analyzer
init_analyzer(['GatedGraphNeuralNetwork.compute_node_representations'])
main()
def test_effnet ():
eff = EffNet()
x: 'bchw' = torch.ones(B, C, H, W)
init_analyzer(['EffNet.forward'])
out = eff.forward(x)
print (out.size())
def setup_named_dims():
from tsanley.dynamic import init_analyzer
init_analyzer(trace_func_names=['Net.forward', 'AGNNConv.forward'],
show_updates=True)
