Python mxnet.ndarray.zeros_like() Examples

The following are 16 code examples of mxnet.ndarray.zeros_like(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module mxnet.ndarray , or try the search function .
Example #1
Source File: gradcam.py    From dynamic-training-with-apache-mxnet-on-aws with Apache License 2.0 6 votes vote down vote up 
def backward(self, req, out_grad, in_data, out_data, in_grad, aux):
        if ReluOp.guided_backprop:
            # Get output and gradients of output
            y = out_data[0]
            dy = out_grad[0]
            # Zero out the negatives in the gradients of the output
            dy_positives = nd.maximum(dy, nd.zeros_like(dy))
            # What output values were greater than 0?
            y_ones = y.__gt__(0)
            # Mask out the values for which at least one of dy or y is negative
            dx = dy_positives * y_ones
            self.assign(in_grad[0], req[0], dx)
        else:
            # Regular backward for ReLU
            x = in_data[0]
            x_gt_zero = x.__gt__(0)
            dx = out_grad[0] * x_gt_zero
            self.assign(in_grad[0], req[0], dx)
Example #2
Source File: test_autograd.py    From dynamic-training-with-apache-mxnet-on-aws with Apache License 2.0 6 votes vote down vote up 
def test_out_grads():
    x = nd.ones((3, 5))
    dx = nd.zeros_like(x)
    mark_variables([x], [dx])
    da = None
    db = nd.array([1,2,3,4,5])
    dc = nd.array([5,4,3,2,1])

    with record():
        a, b, c = nd.split(x, axis=0, num_outputs=3, squeeze_axis=True)
        backward([a, b, c], [da, db, dc])

    assert (dx.asnumpy() == np.array(
        [[1,1,1,1,1],
         [1,2,3,4,5],
         [5,4,3,2,1]])).all()
Example #3
Source File: gradcam.py    From training_results_v0.6 with Apache License 2.0 6 votes vote down vote up 
def backward(self, req, out_grad, in_data, out_data, in_grad, aux):
        if ReluOp.guided_backprop:
            # Get output and gradients of output
            y = out_data[0]
            dy = out_grad[0]
            # Zero out the negatives in the gradients of the output
            dy_positives = nd.maximum(dy, nd.zeros_like(dy))
            # What output values were greater than 0?
            y_ones = y.__gt__(0)
            # Mask out the values for which at least one of dy or y is negative
            dx = dy_positives * y_ones
            self.assign(in_grad[0], req[0], dx)
        else:
            # Regular backward for ReLU
            x = in_data[0]
            x_gt_zero = x.__gt__(0)
            dx = out_grad[0] * x_gt_zero
            self.assign(in_grad[0], req[0], dx)
Example #4
Source File: test_autograd.py    From SNIPER-mxnet with Apache License 2.0 6 votes vote down vote up 
def test_out_grads():
    x = nd.ones((3, 5))
    dx = nd.zeros_like(x)
    mark_variables([x], [dx])
    da = None
    db = nd.array([1,2,3,4,5])
    dc = nd.array([5,4,3,2,1])

    with record():
        a, b, c = nd.split(x, axis=0, num_outputs=3, squeeze_axis=True)
        backward([a, b, c], [da, db, dc])

    assert (dx.asnumpy() == np.array(
        [[1,1,1,1,1],
         [1,2,3,4,5],
         [5,4,3,2,1]])).all()
Example #5
Source File: test_contrib_autograd.py    From SNIPER-mxnet with Apache License 2.0 6 votes vote down vote up 
def test_out_grads():
    x = nd.ones((3, 5))
    dx = nd.zeros_like(x)
    mark_variables([x], [dx])
    da = None
    db = nd.array([1,2,3,4,5])
    dc = nd.array([5,4,3,2,1])

    with train_section():
        a, b, c = nd.split(x, axis=0, num_outputs=3, squeeze_axis=True)
        backward([a, b, c], [da, db, dc])

    assert (dx.asnumpy() == np.array(
        [[1,1,1,1,1],
         [1,2,3,4,5],
         [5,4,3,2,1]])).all()
Example #6
Source File: gradcam.py    From dynamic-training-with-apache-mxnet-on-aws with Apache License 2.0 5 votes vote down vote up 
def forward(self, is_train, req, in_data, out_data, aux):
        x = in_data[0]
        y = nd.maximum(x, nd.zeros_like(x))
        self.assign(out_data[0], req[0], y)
Example #7
Source File: test_autograd.py    From dynamic-training-with-apache-mxnet-on-aws with Apache License 2.0 5 votes vote down vote up 
def grad_and_loss(func, argnum=None):
    """Return function that computes both gradient of arguments and loss value.

    Parameters
    ----------
    func: a python function
        The forward (loss) function.
    argnum: an int or a list of int
        The index of argument to calculate gradient for.

    Returns
    -------
    grad_and_loss_func: a python function
        A function that would compute both the gradient of arguments and loss value.
    """
    @functools.wraps(func)
    def wrapped(*args):
        """Wrapped function."""
        variables = args
        if argnum is not None:
            argnum_ = argnum if isinstance(argnum, list) else [argnum]
            variables = [args[i] for i in argnum_]
        for x in variables:
            assert isinstance(x, NDArray), "type of autograd input should NDArray."
        grads = [zeros_like(x) for x in variables]
        mark_variables(variables, grads)
        with record():
            outputs = func(*args)
        backward([outputs] if isinstance(outputs, NDArray) else outputs)
        return grads, outputs
    return wrapped
Example #8
Source File: test_autograd.py    From dynamic-training-with-apache-mxnet-on-aws with Apache License 2.0 5 votes vote down vote up 
def test_detach_updated_grad():
    x = nd.ones((2, 2))
    dx = nd.zeros_like(x)
    y = nd.ones_like(x)
    dy = nd.zeros_like(x)
    mark_variables([x, y], [dx, dy])
    assert x._fresh_grad == False
    assert y._fresh_grad == False

    with record():
        x2 = x + 2
        y2  = x2 + y
        y2.backward()
    assert (dx.asnumpy() == 1).all()
    assert x._fresh_grad == True
    assert y._fresh_grad == True

    dx[:] = 0
    x._fresh_grad = False
    y._fresh_grad = False
    assert x._fresh_grad == False
    assert y._fresh_grad == False
    with record():
        x2 = x + 2
        x2 = x2.detach()
        y2  = x2 + y
        y2.backward()
    assert (dx.asnumpy() == 0).all()
    assert y._fresh_grad == True
    assert x._fresh_grad == False
Example #9
Source File: test_contrib_autograd.py    From dynamic-training-with-apache-mxnet-on-aws with Apache License 2.0 5 votes vote down vote up 
def test_detach_updated_grad():
    x = nd.ones((2, 2))
    dx = nd.zeros_like(x)
    y = nd.ones_like(x)
    dy = nd.zeros_like(x)
    mark_variables([x, y], [dx, dy])
    assert x._fresh_grad == False
    assert y._fresh_grad == False

    with train_section():
        x2 = x + 2
        y2  = x2 + y
        y2.backward()
    assert (dx.asnumpy() == 1).all()
    assert x._fresh_grad == True
    assert y._fresh_grad == True

    dx[:] = 0
    x._fresh_grad = False
    y._fresh_grad = False
    assert x._fresh_grad == False
    assert y._fresh_grad == False
    with train_section():
        x2 = x + 2
        x2 = x2.detach()
        y2  = x2 + y
        y2.backward()
    assert (dx.asnumpy() == 0).all()
    assert y._fresh_grad == True
    assert x._fresh_grad == False
Example #10
Source File: parall_module_local_v1.py    From insightface with MIT License 5 votes vote down vote up 
def kv_push(self, key, value):
      #if value.context!=mx.cpu():
      #  value = value.as_in_context(mx.cpu())
      if not key in self._kvinit:
        self._distkv.init(key, nd.zeros_like(value))
        self._kvinit[key] = 1
      self._distkv.push(key, value)

    #get fc1 and partial fc7
Example #11
Source File: tensor.py    From dgl with Apache License 2.0 5 votes vote down vote up 
def zeros_like(input):
    return nd.zeros_like(input)
Example #12
Source File: gradcam.py    From training_results_v0.6 with Apache License 2.0 5 votes vote down vote up 
def forward(self, is_train, req, in_data, out_data, aux):
        x = in_data[0]
        y = nd.maximum(x, nd.zeros_like(x))
        self.assign(out_data[0], req[0], y)
Example #13
Source File: parall_module_local_v1.py    From 1.FaceRecognition with MIT License 5 votes vote down vote up 
def kv_push(self, key, value):
      #if value.context!=mx.cpu():
      #  value = value.as_in_context(mx.cpu())
      if not key in self._kvinit:
        self._distkv.init(key, nd.zeros_like(value))
        self._kvinit[key] = 1
      self._distkv.push(key, value)

    #get fc1 and partial fc7
Example #14
Source File: test_autograd.py    From SNIPER-mxnet with Apache License 2.0 5 votes vote down vote up 
def grad_and_loss(func, argnum=None):
    """Return function that computes both gradient of arguments and loss value.

    Parameters
    ----------
    func: a python function
        The forward (loss) function.
    argnum: an int or a list of int
        The index of argument to calculate gradient for.

    Returns
    -------
    grad_and_loss_func: a python function
        A function that would compute both the gradient of arguments and loss value.
    """
    @functools.wraps(func)
    def wrapped(*args):
        """Wrapped function."""
        variables = args
        if argnum is not None:
            argnum_ = argnum if isinstance(argnum, list) else [argnum]
            variables = [args[i] for i in argnum_]
        for x in variables:
            assert isinstance(x, NDArray), "type of autograd input should NDArray."
        grads = [zeros_like(x) for x in variables]
        mark_variables(variables, grads)
        with record():
            outputs = func(*args)
        backward([outputs] if isinstance(outputs, NDArray) else outputs)
        return grads, outputs
    return wrapped
Example #15
Source File: test_autograd.py    From SNIPER-mxnet with Apache License 2.0 5 votes vote down vote up 
def test_detach_updated_grad():
    x = nd.ones((2, 2))
    dx = nd.zeros_like(x)
    y = nd.ones_like(x)
    dy = nd.zeros_like(x)
    mark_variables([x, y], [dx, dy])
    assert x._fresh_grad == False
    assert y._fresh_grad == False

    with record():
        x2 = x + 2
        y2  = x2 + y
        y2.backward()
    assert (dx.asnumpy() == 1).all()
    assert x._fresh_grad == True
    assert y._fresh_grad == True

    dx[:] = 0
    x._fresh_grad = False
    y._fresh_grad = False
    assert x._fresh_grad == False
    assert y._fresh_grad == False
    with record():
        x2 = x + 2
        x2 = x2.detach()
        y2  = x2 + y
        y2.backward()
    assert (dx.asnumpy() == 0).all()
    assert y._fresh_grad == True
    assert x._fresh_grad == False
Example #16
Source File: test_contrib_autograd.py    From SNIPER-mxnet with Apache License 2.0 5 votes vote down vote up 
def test_detach_updated_grad():
    x = nd.ones((2, 2))
    dx = nd.zeros_like(x)
    y = nd.ones_like(x)
    dy = nd.zeros_like(x)
    mark_variables([x, y], [dx, dy])
    assert x._fresh_grad == False
    assert y._fresh_grad == False

    with train_section():
        x2 = x + 2
        y2  = x2 + y
        y2.backward()
    assert (dx.asnumpy() == 1).all()
    assert x._fresh_grad == True
    assert y._fresh_grad == True

    dx[:] = 0
    x._fresh_grad = False
    y._fresh_grad = False
    assert x._fresh_grad == False
    assert y._fresh_grad == False
    with train_section():
        x2 = x + 2
        x2 = x2.detach()
        y2  = x2 + y
        y2.backward()
    assert (dx.asnumpy() == 0).all()
    assert y._fresh_grad == True
    assert x._fresh_grad == False