tensor-sensor

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import sys
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import torch
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import numpy as np
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import graphviz
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import tempfile
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import matplotlib.patches as patches
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import matplotlib.pyplot as plt
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import matplotlib.font_manager as fm
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# print('\n'.join(str(f) for f in fm.fontManager.ttflist))
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import tsensor
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# from tsensor.viz import pyviz, astviz
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import torch
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import tsensor
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n = 200         # number of instances
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d = 764         # number of instance features
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nhidden = 256
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Whh = torch.eye(nhidden, nhidden)  # Identity matrix
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Uxh = torch.randn(nhidden, d)
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bh  = torch.zeros(nhidden, 1)
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h = torch.randn(nhidden, 1)         # fake previous hidden state h
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# r = torch.randn(nhidden, 1)         # fake this computation
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r = torch.randn(nhidden, 3)         # fake this computation
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X = torch.rand(n,d)                 # fake input
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# Following code raises an exception
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with tsensor.explain(savefig="/Users/parrt/Desktop/toomany.png") as e:
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    h = torch.tanh(Whh @ (r*h) + Uxh @ X.T + bh)  # state vector update equation
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exit()
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def foo():
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    # W = torch.rand(size=(2000, 2000))
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    W = torch.rand(size=(2000, 2000, 10, 8))
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    b = torch.rand(size=(2000, 1))
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    h = torch.rand(size=(1_000_000,))
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    x = torch.rand(size=(2000, 1))
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    # g = tsensor.astviz("b = W@b + (h+3).dot(h) + torch.abs(torch.tensor(34))",
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    #                    sys._getframe())
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    frame = sys._getframe()
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    frame = None
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    g = tsensor.astviz("b = W[:,:,0,0]@b + (h+3).dot(h) + torch.abs(torch.tensor(34))",
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                       frame)
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    g.view()
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#foo()
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class Linear:
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    def __init__(self, d, n_neurons):
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        self.W = torch.randn(n_neurons,  d)
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        self.b = torch.zeros(n_neurons, 1)
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    def __call__(self, x):
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        return self.W@x + self.b
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n = 200         # number of instances
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d = 764         # number of instance features
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n_neurons = 100 # how many neurons in this layer?
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# L = Linear(d,n_neurons)
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#
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# import tensorflow as tf
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# X = tf.random.uniform((n,d))
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# with tsensor.clarify(hush_errors=False):
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#     Y = L(X)
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# g = tsensor.pyviz("Y = L(X)", hush_errors=False)
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# g.show()
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class GRU:
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    def __init__(self):
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        self.W = torch.rand(size=(2,20,2000,10))
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        self.b = torch.rand(size=(20,1))
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        # self.x = torch.tensor([4, 5]).reshape(2, 1)
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        self.h = torch.rand(size=(1_000_000,))
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        self.a = 3
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        print(self.W.shape)
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        print(self.W[:, :, 1].shape)
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    def get(self):
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        return torch.tensor([[1, 2], [3, 4]])
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# W = torch.tensor([[1, 2], [3, 4]])
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b = torch.rand(size=(2000,1))
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h = torch.rand(size=(1_000_000,2))
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x = torch.rand(size=(1_000_000,2))
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a = 3
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# foo = torch.rand(size=(2000,))
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# torch.relu(foo)
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g = GRU()
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# with tsensor.clarify():
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#     tf.constant([1,2]) @ tf.constant([1,3])
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code = "b = g.W[0,:,:,1]@b+torch.zeros(200,1)+(h+3).dot(h)"
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# code = "torch.relu(foo)"
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# code = "np.dot(b,b)"
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# code = "b.T"
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g = tsensor.pyviz(code, fontname='Courier New', fontsize=16, dimfontsize=9,
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                  char_sep_scale=1.8, hush_errors=False)
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plt.tight_layout()
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plt.savefig("/tmp/t.svg", dpi=200, bbox_inches='tight', pad_inches=0)
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# W = torch.tensor([[1, 2], [3, 4]])
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# x = torch.tensor([4, 5]).reshape(2, 1)
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# with tsensor.explain():
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#     b = torch.rand(size=(2000,))
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#     torch.relu(b)
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# g = GRU()
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#
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# g1 = tsensor.astviz("b = g.W@b + torch.eye(3,3)")
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# g1.view()
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# g1 = tsensor.pyviz("b = g.W@b")
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# g1.view()
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# g2 = tsensor.astviz("b = g.W@b + g.h.dot(g.h) + torch.abs(torch.tensor(34))")
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