mirror of
https://github.com/iperov/DeepFaceLab.git
synced 2024-12-25 23:41:12 -08:00
479 lines
16 KiB
Python
479 lines
16 KiB
Python
import numpy as np
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from core.leras import nn
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tf = nn.tf
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from tensorflow.python.ops import array_ops, random_ops, math_ops, sparse_ops, gradients
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from tensorflow.python.framework import sparse_tensor
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def tf_get_value(tensor):
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return nn.tf_sess.run (tensor)
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nn.tf_get_value = tf_get_value
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def batch_set_value(tuples):
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if len(tuples) != 0:
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with nn.tf.device('/CPU:0'):
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assign_ops = []
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feed_dict = {}
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for x, value in tuples:
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if isinstance(value, nn.tf.Operation) or \
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isinstance(value, nn.tf.Variable):
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assign_ops.append(value)
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else:
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value = np.asarray(value, dtype=x.dtype.as_numpy_dtype)
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assign_placeholder = nn.tf.placeholder( x.dtype.base_dtype, shape=[None]*value.ndim )
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assign_op = nn.tf.assign (x, assign_placeholder )
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assign_ops.append(assign_op)
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feed_dict[assign_placeholder] = value
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nn.tf_sess.run(assign_ops, feed_dict=feed_dict)
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nn.batch_set_value = batch_set_value
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def init_weights(weights):
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ops = []
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ca_tuples_w = []
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ca_tuples = []
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for w in weights:
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initializer = w.initializer
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for input in initializer.inputs:
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if "_cai_" in input.name:
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ca_tuples_w.append (w)
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ca_tuples.append ( (w.shape.as_list(), w.dtype.as_numpy_dtype) )
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break
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else:
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ops.append (initializer)
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if len(ops) != 0:
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nn.tf_sess.run (ops)
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if len(ca_tuples) != 0:
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nn.batch_set_value( [*zip(ca_tuples_w, nn.initializers.ca.generate_batch (ca_tuples))] )
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nn.init_weights = init_weights
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def tf_gradients ( loss, vars ):
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grads = gradients.gradients(loss, vars, colocate_gradients_with_ops=True )
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gv = [*zip(grads,vars)]
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for g,v in gv:
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if g is None:
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raise Exception(f"Variable {v.name} is declared as trainable, but no tensors flow through it.")
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return gv
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nn.gradients = tf_gradients
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def average_gv_list(grad_var_list, tf_device_string=None):
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if len(grad_var_list) == 1:
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return grad_var_list[0]
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e = tf.device(tf_device_string) if tf_device_string is not None else None
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if e is not None: e.__enter__()
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result = []
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for i, (gv) in enumerate(grad_var_list):
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for j,(g,v) in enumerate(gv):
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g = tf.expand_dims(g, 0)
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if i == 0:
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result += [ [[g], v] ]
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else:
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result[j][0] += [g]
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for i,(gs,v) in enumerate(result):
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result[i] = ( tf.reduce_mean( tf.concat (gs, 0), 0 ), v )
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if e is not None: e.__exit__(None,None,None)
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return result
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nn.average_gv_list = average_gv_list
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def average_tensor_list(tensors_list, tf_device_string=None):
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if len(tensors_list) == 1:
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return tensors_list[0]
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e = tf.device(tf_device_string) if tf_device_string is not None else None
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if e is not None: e.__enter__()
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result = tf.reduce_mean(tf.concat ([tf.expand_dims(t, 0) for t in tensors_list], 0), 0)
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if e is not None: e.__exit__(None,None,None)
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return result
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nn.average_tensor_list = average_tensor_list
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def concat (tensors_list, axis):
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"""
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Better version.
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"""
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if len(tensors_list) == 1:
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return tensors_list[0]
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return tf.concat(tensors_list, axis)
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nn.concat = concat
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def gelu(x):
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cdf = 0.5 * (1.0 + tf.nn.tanh((np.sqrt(2 / np.pi) * (x + 0.044715 * tf.pow(x, 3)))))
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return x * cdf
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nn.gelu = gelu
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def upsample2d(x, size=2):
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if nn.data_format == "NCHW":
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x = tf.transpose(x, (0,2,3,1))
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x = tf.image.resize_nearest_neighbor(x, (x.shape[1]*size, x.shape[2]*size) )
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x = tf.transpose(x, (0,3,1,2))
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# b,c,h,w = x.shape.as_list()
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# x = tf.reshape (x, (-1,c,h,1,w,1) )
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# x = tf.tile(x, (1,1,1,size,1,size) )
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# x = tf.reshape (x, (-1,c,h*size,w*size) )
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return x
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else:
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return tf.image.resize_nearest_neighbor(x, (x.shape[1]*size, x.shape[2]*size) )
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nn.upsample2d = upsample2d
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def resize2d_bilinear(x, size=2):
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h = x.shape[nn.conv2d_spatial_axes[0]].value
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w = x.shape[nn.conv2d_spatial_axes[1]].value
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if nn.data_format == "NCHW":
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x = tf.transpose(x, (0,2,3,1))
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if size > 0:
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new_size = (h*size,w*size)
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else:
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new_size = (h//-size,w//-size)
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x = tf.image.resize(x, new_size, method=tf.image.ResizeMethod.BILINEAR)
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if nn.data_format == "NCHW":
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x = tf.transpose(x, (0,3,1,2))
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return x
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nn.resize2d_bilinear = resize2d_bilinear
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def resize2d_nearest(x, size=2):
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if size in [-1,0,1]:
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return x
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if size > 0:
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raise Exception("")
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else:
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if nn.data_format == "NCHW":
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x = x[:,:,::-size,::-size]
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else:
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x = x[:,::-size,::-size,:]
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return x
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h = x.shape[nn.conv2d_spatial_axes[0]].value
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w = x.shape[nn.conv2d_spatial_axes[1]].value
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if nn.data_format == "NCHW":
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x = tf.transpose(x, (0,2,3,1))
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if size > 0:
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new_size = (h*size,w*size)
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else:
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new_size = (h//-size,w//-size)
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x = tf.image.resize(x, new_size, method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
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if nn.data_format == "NCHW":
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x = tf.transpose(x, (0,3,1,2))
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return x
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nn.resize2d_nearest = resize2d_nearest
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def flatten(x):
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if nn.data_format == "NHWC":
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# match NCHW version in order to switch data_format without problems
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x = tf.transpose(x, (0,3,1,2) )
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return tf.reshape (x, (-1, np.prod(x.shape[1:])) )
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nn.flatten = flatten
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def max_pool(x, kernel_size=2, strides=2):
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if nn.data_format == "NHWC":
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return tf.nn.max_pool(x, [1,kernel_size,kernel_size,1], [1,strides,strides,1], 'SAME', data_format=nn.data_format)
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else:
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return tf.nn.max_pool(x, [1,1,kernel_size,kernel_size], [1,1,strides,strides], 'SAME', data_format=nn.data_format)
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nn.max_pool = max_pool
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def reshape_4D(x, w,h,c):
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if nn.data_format == "NHWC":
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# match NCHW version in order to switch data_format without problems
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x = tf.reshape (x, (-1,c,h,w))
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x = tf.transpose(x, (0,2,3,1) )
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return x
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else:
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return tf.reshape (x, (-1,c,h,w))
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nn.reshape_4D = reshape_4D
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def random_binomial(shape, p=0.0, dtype=None, seed=None):
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if dtype is None:
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dtype=tf.float32
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if seed is None:
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seed = np.random.randint(10e6)
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return array_ops.where(
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random_ops.random_uniform(shape, dtype=tf.float16, seed=seed) < p,
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array_ops.ones(shape, dtype=dtype), array_ops.zeros(shape, dtype=dtype))
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nn.random_binomial = random_binomial
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def gaussian_blur(input, radius=2.0):
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def gaussian(x, mu, sigma):
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return np.exp(-(float(x) - float(mu)) ** 2 / (2 * sigma ** 2))
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def make_kernel(sigma):
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kernel_size = max(3, int(2 * 2 * sigma))
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if kernel_size % 2 == 0:
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kernel_size += 1
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mean = np.floor(0.5 * kernel_size)
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kernel_1d = np.array([gaussian(x, mean, sigma) for x in range(kernel_size)])
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np_kernel = np.outer(kernel_1d, kernel_1d).astype(np.float32)
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kernel = np_kernel / np.sum(np_kernel)
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return kernel, kernel_size
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gauss_kernel, kernel_size = make_kernel(radius)
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padding = kernel_size//2
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if padding != 0:
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if nn.data_format == "NHWC":
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padding = [ [0,0], [padding,padding], [padding,padding], [0,0] ]
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else:
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padding = [ [0,0], [0,0], [padding,padding], [padding,padding] ]
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else:
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padding = None
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gauss_kernel = gauss_kernel[:,:,None,None]
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x = input
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k = tf.tile (gauss_kernel, (1,1,x.shape[nn.conv2d_ch_axis],1) )
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x = tf.pad(x, padding )
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x = tf.nn.depthwise_conv2d(x, k, strides=[1,1,1,1], padding='VALID', data_format=nn.data_format)
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return x
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nn.gaussian_blur = gaussian_blur
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def style_loss(target, style, gaussian_blur_radius=0.0, loss_weight=1.0, step_size=1):
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def sd(content, style, loss_weight):
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content_nc = content.shape[ nn.conv2d_ch_axis ]
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style_nc = style.shape[nn.conv2d_ch_axis]
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if content_nc != style_nc:
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raise Exception("style_loss() content_nc != style_nc")
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c_mean, c_var = tf.nn.moments(content, axes=nn.conv2d_spatial_axes, keep_dims=True)
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s_mean, s_var = tf.nn.moments(style, axes=nn.conv2d_spatial_axes, keep_dims=True)
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c_std, s_std = tf.sqrt(c_var + 1e-5), tf.sqrt(s_var + 1e-5)
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mean_loss = tf.reduce_sum(tf.square(c_mean-s_mean), axis=[1,2,3])
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std_loss = tf.reduce_sum(tf.square(c_std-s_std), axis=[1,2,3])
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return (mean_loss + std_loss) * ( loss_weight / content_nc.value )
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if gaussian_blur_radius > 0.0:
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target = gaussian_blur(target, gaussian_blur_radius)
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style = gaussian_blur(style, gaussian_blur_radius)
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return sd( target, style, loss_weight=loss_weight )
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nn.style_loss = style_loss
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def dssim(img1,img2, max_val, filter_size=11, filter_sigma=1.5, k1=0.01, k2=0.03):
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if img1.dtype != img2.dtype:
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raise ValueError("img1.dtype != img2.dtype")
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not_float32 = img1.dtype != tf.float32
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if not_float32:
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img_dtype = img1.dtype
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img1 = tf.cast(img1, tf.float32)
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img2 = tf.cast(img2, tf.float32)
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filter_size = max(1, filter_size)
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kernel = np.arange(0, filter_size, dtype=np.float32)
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kernel -= (filter_size - 1 ) / 2.0
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kernel = kernel**2
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kernel *= ( -0.5 / (filter_sigma**2) )
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kernel = np.reshape (kernel, (1,-1)) + np.reshape(kernel, (-1,1) )
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kernel = tf.constant ( np.reshape (kernel, (1,-1)), dtype=tf.float32 )
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kernel = tf.nn.softmax(kernel)
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kernel = tf.reshape (kernel, (filter_size, filter_size, 1, 1))
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kernel = tf.tile (kernel, (1,1, img1.shape[ nn.conv2d_ch_axis ] ,1))
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def reducer(x):
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return tf.nn.depthwise_conv2d(x, kernel, strides=[1,1,1,1], padding='VALID', data_format=nn.data_format)
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c1 = (k1 * max_val) ** 2
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c2 = (k2 * max_val) ** 2
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mean0 = reducer(img1)
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mean1 = reducer(img2)
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num0 = mean0 * mean1 * 2.0
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den0 = tf.square(mean0) + tf.square(mean1)
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luminance = (num0 + c1) / (den0 + c1)
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num1 = reducer(img1 * img2) * 2.0
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den1 = reducer(tf.square(img1) + tf.square(img2))
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c2 *= 1.0 #compensation factor
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cs = (num1 - num0 + c2) / (den1 - den0 + c2)
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ssim_val = tf.reduce_mean(luminance * cs, axis=nn.conv2d_spatial_axes )
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dssim = (1.0 - ssim_val ) / 2.0
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if not_float32:
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dssim = tf.cast(dssim, img_dtype)
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return dssim
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nn.dssim = dssim
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def space_to_depth(x, size):
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if nn.data_format == "NHWC":
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# match NCHW version in order to switch data_format without problems
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b,h,w,c = x.shape.as_list()
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oh, ow = h // size, w // size
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x = tf.reshape(x, (-1, size, oh, size, ow, c))
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x = tf.transpose(x, (0, 2, 4, 1, 3, 5))
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x = tf.reshape(x, (-1, oh, ow, size* size* c ))
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return x
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else:
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return tf.space_to_depth(x, size, data_format=nn.data_format)
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nn.space_to_depth = space_to_depth
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def depth_to_space(x, size):
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if nn.data_format == "NHWC":
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# match NCHW version in order to switch data_format without problems
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b,h,w,c = x.shape.as_list()
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oh, ow = h * size, w * size
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oc = c // (size * size)
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x = tf.reshape(x, (-1, h, w, size, size, oc, ) )
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x = tf.transpose(x, (0, 1, 3, 2, 4, 5))
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x = tf.reshape(x, (-1, oh, ow, oc, ))
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return x
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else:
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cfg = nn.getCurrentDeviceConfig()
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if not cfg.cpu_only:
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return tf.depth_to_space(x, size, data_format=nn.data_format)
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b,c,h,w = x.shape.as_list()
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oh, ow = h * size, w * size
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oc = c // (size * size)
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x = tf.reshape(x, (-1, size, size, oc, h, w, ) )
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x = tf.transpose(x, (0, 3, 4, 1, 5, 2))
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x = tf.reshape(x, (-1, oc, oh, ow))
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return x
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nn.depth_to_space = depth_to_space
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def rgb_to_lab(srgb):
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srgb_pixels = tf.reshape(srgb, [-1, 3])
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linear_mask = tf.cast(srgb_pixels <= 0.04045, dtype=tf.float32)
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exponential_mask = tf.cast(srgb_pixels > 0.04045, dtype=tf.float32)
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rgb_pixels = (srgb_pixels / 12.92 * linear_mask) + (((srgb_pixels + 0.055) / 1.055) ** 2.4) * exponential_mask
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rgb_to_xyz = tf.constant([
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# X Y Z
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[0.412453, 0.212671, 0.019334], # R
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[0.357580, 0.715160, 0.119193], # G
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[0.180423, 0.072169, 0.950227], # B
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])
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xyz_pixels = tf.matmul(rgb_pixels, rgb_to_xyz)
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xyz_normalized_pixels = tf.multiply(xyz_pixels, [1/0.950456, 1.0, 1/1.088754])
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epsilon = 6/29
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linear_mask = tf.cast(xyz_normalized_pixels <= (epsilon**3), dtype=tf.float32)
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exponential_mask = tf.cast(xyz_normalized_pixels > (epsilon**3), dtype=tf.float32)
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fxfyfz_pixels = (xyz_normalized_pixels / (3 * epsilon**2) + 4/29) * linear_mask + (xyz_normalized_pixels ** (1/3)) * exponential_mask
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fxfyfz_to_lab = tf.constant([
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# l a b
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[ 0.0, 500.0, 0.0], # fx
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[116.0, -500.0, 200.0], # fy
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[ 0.0, 0.0, -200.0], # fz
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])
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lab_pixels = tf.matmul(fxfyfz_pixels, fxfyfz_to_lab) + tf.constant([-16.0, 0.0, 0.0])
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return tf.reshape(lab_pixels, tf.shape(srgb))
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nn.rgb_to_lab = rgb_to_lab
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def total_variation_mse(images):
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"""
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Same as generic total_variation, but MSE diff instead of MAE
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"""
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pixel_dif1 = images[:, 1:, :, :] - images[:, :-1, :, :]
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pixel_dif2 = images[:, :, 1:, :] - images[:, :, :-1, :]
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tot_var = ( tf.reduce_sum(tf.square(pixel_dif1), axis=[1,2,3]) +
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tf.reduce_sum(tf.square(pixel_dif2), axis=[1,2,3]) )
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return tot_var
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nn.total_variation_mse = total_variation_mse
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def pixel_norm(x, axes):
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return x * tf.rsqrt(tf.reduce_mean(tf.square(x), axis=axes, keepdims=True) + 1e-06)
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nn.pixel_norm = pixel_norm
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"""
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def tf_suppress_lower_mean(t, eps=0.00001):
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if t.shape.ndims != 1:
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raise ValueError("tf_suppress_lower_mean: t rank must be 1")
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t_mean_eps = tf.reduce_mean(t) - eps
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q = tf.clip_by_value(t, t_mean_eps, tf.reduce_max(t) )
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q = tf.clip_by_value(q-t_mean_eps, 0, eps)
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q = q * (t/eps)
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return q
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"""
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def _get_pixel_value(img, x, y):
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shape = tf.shape(x)
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batch_size = shape[0]
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height = shape[1]
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width = shape[2]
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batch_idx = tf.range(0, batch_size)
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batch_idx = tf.reshape(batch_idx, (batch_size, 1, 1))
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b = tf.tile(batch_idx, (1, height, width))
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indices = tf.stack([b, y, x], 3)
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return tf.gather_nd(img, indices)
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def bilinear_sampler(img, x, y):
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H = tf.shape(img)[1]
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W = tf.shape(img)[2]
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H_MAX = tf.cast(H - 1, tf.int32)
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W_MAX = tf.cast(W - 1, tf.int32)
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# grab 4 nearest corner points for each (x_i, y_i)
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x0 = tf.cast(tf.floor(x), tf.int32)
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x1 = x0 + 1
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y0 = tf.cast(tf.floor(y), tf.int32)
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y1 = y0 + 1
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# clip to range [0, H-1/W-1] to not violate img boundaries
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x0 = tf.clip_by_value(x0, 0, W_MAX)
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x1 = tf.clip_by_value(x1, 0, W_MAX)
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y0 = tf.clip_by_value(y0, 0, H_MAX)
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y1 = tf.clip_by_value(y1, 0, H_MAX)
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|
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# get pixel value at corner coords
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Ia = _get_pixel_value(img, x0, y0)
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Ib = _get_pixel_value(img, x0, y1)
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Ic = _get_pixel_value(img, x1, y0)
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Id = _get_pixel_value(img, x1, y1)
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|
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# recast as float for delta calculation
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x0 = tf.cast(x0, tf.float32)
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x1 = tf.cast(x1, tf.float32)
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y0 = tf.cast(y0, tf.float32)
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y1 = tf.cast(y1, tf.float32)
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|
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# calculate deltas
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wa = (x1-x) * (y1-y)
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wb = (x1-x) * (y-y0)
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wc = (x-x0) * (y1-y)
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wd = (x-x0) * (y-y0)
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|
|
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# add dimension for addition
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wa = tf.expand_dims(wa, axis=3)
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wb = tf.expand_dims(wb, axis=3)
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wc = tf.expand_dims(wc, axis=3)
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wd = tf.expand_dims(wd, axis=3)
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|
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# compute output
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out = tf.add_n([wa*Ia, wb*Ib, wc*Ic, wd*Id])
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|
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return out
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|
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nn.bilinear_sampler = bilinear_sampler
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|
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