移动doc_dewarp

services/paddle_services/doc_dewarp/weight_init.py

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+import math
+
+import numpy as np
+import paddle
+import paddle.nn.initializer as init
+from scipy import special
+
+
+def weight_init_(
+    layer, func, weight_name=None, bias_name=None, bias_value=0.0, **kwargs
+):
+    """
+    In-place params init function.
+    Usage:
+    .. code-block:: python
+
+        import paddle
+        import numpy as np
+
+        data = np.ones([3, 4], dtype='float32')
+        linear = paddle.nn.Linear(4, 4)
+        input = paddle.to_tensor(data)
+        print(linear.weight)
+        linear(input)
+
+        weight_init_(linear, 'Normal', 'fc_w0', 'fc_b0', std=0.01, mean=0.1)
+        print(linear.weight)
+    """
+
+    if hasattr(layer, "weight") and layer.weight is not None:
+        getattr(init, func)(**kwargs)(layer.weight)
+        if weight_name is not None:
+            # override weight name
+            layer.weight.name = weight_name
+
+    if hasattr(layer, "bias") and layer.bias is not None:
+        init.Constant(bias_value)(layer.bias)
+        if bias_name is not None:
+            # override bias name
+            layer.bias.name = bias_name
+
+
+def _no_grad_trunc_normal_(tensor, mean, std, a, b):
+    def norm_cdf(x):
+        # Computes standard normal cumulative distribution function
+        return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0
+
+    if (mean < a - 2 * std) or (mean > b + 2 * std):
+        print(
+            "mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
+            "The distribution of values may be incorrect."
+        )
+
+    with paddle.no_grad():
+        # Values are generated by using a truncated uniform distribution and
+        # then using the inverse CDF for the normal distribution.
+        # Get upper and lower cdf values
+        lower = norm_cdf((a - mean) / std)
+        upper = norm_cdf((b - mean) / std)
+
+        # Uniformly fill tensor with values from [l, u], then translate to [2l-1, 2u-1].
+        tmp = np.random.uniform(
+            2 * lower - 1, 2 * upper - 1, size=list(tensor.shape)
+        ).astype(np.float32)
+
+        # Use inverse cdf transform for normal distribution to get truncated
+        # standard normal
+        tmp = special.erfinv(tmp)
+
+        # Transform to proper mean, std
+        tmp *= std * math.sqrt(2.0)
+        tmp += mean
+
+        # Clamp to ensure it's in the proper range
+        tmp = np.clip(tmp, a, b)
+        tensor.set_value(paddle.to_tensor(tmp))
+
+        return tensor
+
+
+def _calculate_fan_in_and_fan_out(tensor):
+    dimensions = tensor.dim()
+    if dimensions < 2:
+        raise ValueError(
+            "Fan in and fan out can not be computed for tensor "
+            "with fewer than 2 dimensions"
+        )
+
+    num_input_fmaps = tensor.shape[1]
+    num_output_fmaps = tensor.shape[0]
+    receptive_field_size = 1
+    if tensor.dim() > 2:
+        receptive_field_size = tensor[0][0].numel()
+    fan_in = num_input_fmaps * receptive_field_size
+    fan_out = num_output_fmaps * receptive_field_size
+
+    return fan_in, fan_out
+
+
+def trunc_normal_(tensor, mean=0.0, std=1.0, a=-2.0, b=2.0):
+    return _no_grad_trunc_normal_(tensor, mean, std, a, b)
+
+
+def kaiming_normal_(tensor, a=0.0, mode="fan_in", nonlinearity="leaky_relu"):
+    def _calculate_correct_fan(tensor, mode):
+        mode = mode.lower()
+        valid_modes = ["fan_in", "fan_out"]
+        if mode not in valid_modes:
+            raise ValueError(
+                "Mode {} not supported, please use one of {}".format(mode, valid_modes)
+            )
+
+        fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor)
+        return fan_in if mode == "fan_in" else fan_out
+
+    def calculate_gain(nonlinearity, param=None):
+        linear_fns = [
+            "linear",
+            "conv1d",
+            "conv2d",
+            "conv3d",
+            "conv_transpose1d",
+            "conv_transpose2d",
+            "conv_transpose3d",
+        ]
+        if nonlinearity in linear_fns or nonlinearity == "sigmoid":
+            return 1
+        elif nonlinearity == "tanh":
+            return 5.0 / 3
+        elif nonlinearity == "relu":
+            return math.sqrt(2.0)
+        elif nonlinearity == "leaky_relu":
+            if param is None:
+                negative_slope = 0.01
+            elif (
+                not isinstance(param, bool)
+                and isinstance(param, int)
+                or isinstance(param, float)
+            ):
+                negative_slope = param
+            else:
+                raise ValueError("negative_slope {} not a valid number".format(param))
+            return math.sqrt(2.0 / (1 + negative_slope**2))
+        else:
+            raise ValueError("Unsupported nonlinearity {}".format(nonlinearity))
+
+    fan = _calculate_correct_fan(tensor, mode)
+    gain = calculate_gain(nonlinearity, a)
+    std = gain / math.sqrt(fan)
+    with paddle.no_grad():
+        paddle.nn.initializer.Normal(0, std)(tensor)
+        return tensor