Added The argument can be an int or a tuple

Author: Laicheng0830

tensorlayerx/backend/ops/mindspore_nn.py

@@ -529,13 +529,12 @@ class Conv2D(Cell):
     def __init__(self, strides, padding, data_format='NHWC', dilations=None, out_channel=None, k_size=None):
         super(Conv2D, self).__init__()
         self.data_format, self.padding = preprocess_2d_format(data_format, padding)
-
         if self.data_format is 'NHWC':
-            self.ms_stride = strides[1]
-            self.ms_dilation = dilations[1]
+            self._stride = (strides[1], strides[2])
+            self._dilation = (dilations[1], dilations[2])
         elif self.data_format is 'NCHW':
-            self.ms_stride = strides[2]
-            self.ms_dilation = dilations[2]
+            self._stride = (strides[2], strides[3])
+            self._dilation = (dilations[2], dilations[3])
 
         self.conv2d = P.Conv2D(
             out_channel=out_channel, kernel_size=k_size, pad_mode=self.padding, stride=self.ms_stride,
@@ -582,12 +581,12 @@ def __init__(self, strides, padding, data_format='NDHWC', dilations=None, out_ch
         self.data_format, self.padding = preprocess_3d_format(data_format, padding)
 
         if self.data_format is 'NDHWC':
-            self.ms_stride = strides[1]
-            self.ms_dilation = dilations[1]
+            self.ms_stride = (strides[1], strides[2], strides[3])
+            self.ms_dilation = (dilations[1], dilations[2], dilations[3])
             raise NotImplementedError("The optional value for data format. Currently only support “NCDHW”.")
         elif self.data_format is 'NCDHW':
-            self.ms_stride = strides[2]
-            self.ms_dilation = dilations[2]
+            self.ms_stride = (strides[2], strides[3], strides[4])
+            self.ms_dilation = (dilations[2], dilations[3], dilations[4])
 
         self.conv3d = P.Conv3D(
             out_channel=out_channel, kernel_size=k_size, pad_mode=self.padding, stride=self.ms_stride,

tensorlayerx/backend/ops/paddle_backend.py

@@ -11,40 +11,61 @@
 from .paddle_nn import nchw_to_nhwc, nhwc_to_nchw, preprocess_2d_format, preprocess_1d_format, preprocess_3d_format
 import random
 
-
-_dtypeDict = {
-    'DType': paddle.dtype,
-    'float16': paddle.float16,
-    'float32': paddle.float32,
-    'float64': paddle.float64,
-    'int8': paddle.int8,
-    'int16': paddle.int16,
-    'int32': paddle.int32,
-    'int64': paddle.int64,
-    'uint8': paddle.uint8,
-    'uint16': None,
-    'uint32': None,
-    'uint64': None,
-    'bool': paddle.bool,
-    'complex64': paddle.complex64,
-    'complex128': paddle.complex128
-}
-# TODO NotImplemented
-DType = paddle.dtype
-float16 = paddle.float16
-float32 = paddle.float32
-float64 = paddle.float64
-int8 = paddle.int8
-int16 = paddle.int16
-int32 = paddle.int32
-int64 = paddle.int64
-uint8 = paddle.uint8
-uint16 = None
-uint32 = None
-uint64 = None
-bool = paddle.bool
-complex64 = paddle.complex64
-complex128 = paddle.complex128
+if paddle.__version__ < '2.2.2':
+    _dtypeDict = [
+        "float16", "float32", "float64", "int8", "int16", "int32", "int64", "uint8", "uint16", "uint32", "uint64", "bool",
+        "complex64", "complex128"
+    ]
+    # TODO NotImplemented
+    DType = None
+    float16 = "float16"
+    float32 = "float32"
+    float64 = "float64"
+    int8 = "int8"
+    int16 = "int16"
+    int32 = "int32"
+    int64 = "int64"
+    uint8 = "uint8"
+    uint16 = "uint16"
+    uint32 = "uint32"
+    uint64 = "uint64"
+    bool = "bool"
+    complex64 = "complex64"
+    complex128 = "complex128"
+else:
+    _dtypeDict = {
+        'DType': paddle.dtype ,
+        'float16': paddle.float16,
+        'float32': paddle.float32,
+        'float64': paddle.float64,
+        'int8': paddle.int8,
+        'int16': paddle.int16,
+        'int32': paddle.int32,
+        'int64': paddle.int64,
+        'uint8': paddle.uint8,
+        'uint16': None,
+        'uint32': None,
+        'uint64': None,
+        'bool': paddle.bool,
+        'complex64': paddle.complex64,
+        'complex128': paddle.complex128
+    }
+    # TODO NotImplemented
+    DType = paddle.dtype
+    float16 = paddle.float16
+    float32 = paddle.float32
+    float64 = paddle.float64
+    int8 = paddle.int8
+    int16 = paddle.int16
+    int32 = paddle.int32
+    int64 = paddle.int64
+    uint8 = paddle.uint8
+    uint16 = None
+    uint32 = None
+    uint64 = None
+    bool = paddle.bool
+    complex64 = paddle.complex64
+    complex128 = paddle.complex128
 
 
 def _getter(init_fn, **kwargs):

tensorlayerx/backend/ops/paddle_nn.py

@@ -548,10 +548,10 @@ class Conv3D(object):
 
     def __init__(self, strides, padding, data_format='NDHWC', dilations=None, out_channel=None, k_size=None):
         self.data_format, self.padding = preprocess_3d_format(data_format, padding)
-        if data_format is 'NDHWC':
+        if self.data_format is 'NDHWC':
             self._strides = (strides[1], strides[2], strides[3])
             self._dilations = (dilations[1], dilations[2], dilations[3])
-        elif data_format is 'NCDHW':
+        elif self.data_format is 'NCDHW':
             self._strides = (strides[2], strides[3], strides[4])
             self._dilations = (dilations[2], dilations[3], dilations[4])
 

tensorlayerx/backend/ops/tensorflow_nn.py

@@ -54,9 +54,9 @@ def preprocess_1d_format(data_format, padding):
     -------
         str "NWC" or "NCW" and "SAME" or "VALID"
     """
-    if data_format in ["channels_last", "NWC"]:
+    if data_format in ["channels_last", "NWC", 'NLC']:
         data_format = "NWC"
-    elif data_format in ["channels_first", "NCW"]:
+    elif data_format in ["channels_first", "NCW", 'NCL']:
         data_format = "NCW"
     elif data_format == None:
         data_format = None
@@ -399,9 +399,11 @@ class BiasAdd(object):
         A Tensor with the same type as value.
     """
 
-    def __init__(self, data_format=None):
-        self.data_format, _ = preprocess_2d_format(data_format, None)
-
+    def __init__(self, data_format='channels_last'):
+        if data_format in ['channels_first', 'NCL', 'NCW', 'NCHW', 'NCDHW']:
+            self.data_format = "NCHW"
+        elif data_format in ['channels_last', 'NLC', 'NWC', 'NHWC', 'NDHWC']:
+            self.data_format = "NHWC"
     def __call__(self, x, bias):
         return tf.nn.bias_add(x, bias, data_format=self.data_format)
 

tensorlayerx/backend/ops/torch_nn.py

@@ -573,9 +573,13 @@ def same_padding(input, weight, strides, dilations):
 class Conv2D(object):
 
     def __init__(self, strides, padding, data_format='NHWC', dilations=None, out_channel=None, k_size=None, groups=1):
-        self.strides = (strides[1], strides[2])
-        self.dilations = (dilations[1], dilations[2])
         self.data_format, self.padding = preprocess_2d_format(data_format, padding)
+        if self.data_format is 'NHWC':
+            self.strides = (strides[1], strides[2])
+            self.dilations = (dilations[1], dilations[2])
+        elif self.data_format is 'NCHW':
+            self.strides = (strides[2], strides[3])
+            self.dilations = (dilations[2], dilations[3])
         self.groups = groups
 
     def __call__(self, input, filters):
@@ -644,13 +648,14 @@ def conv2d(input, filters, strides, padding, data_format='NHWC', dilations=None)
 class Conv3D(object):
 
     def __init__(self, strides, padding, data_format='NDHWC', dilations=None, out_channel=None, k_size=None):
-        if data_format is 'NDHWC':
+        self.data_format, self.padding = preprocess_3d_format(data_format, padding)
+        if self.data_format is 'NDHWC':
             self._strides = (strides[1], strides[2], strides[3])
             self._dilations = (dilations[1], dilations[2], dilations[3])
-        elif data_format is 'NCDHW':
+        elif self.data_format is 'NCDHW':
             self._strides = (strides[2], strides[3], strides[4])
             self._dilations = (dilations[2], dilations[3], dilations[4])
-        self.data_format, self.padding = preprocess_3d_format(data_format, padding)
+
 
     def __call__(self, input, filters):
         if self.data_format == 'NDHWC':

tensorlayerx/nn/core/common.py

@@ -39,6 +39,22 @@
 }
 
 
+def check_parameter(parameter, dim='2d'):
+    if dim == '2d':
+        if isinstance(parameter, int):
+            out = (parameter, parameter)
+        else:
+            out = parameter
+    elif dim == '3d':
+        if isinstance(parameter, int):
+            out = (parameter, parameter, parameter)
+        else:
+            out = parameter
+    else:
+        raise ("dim must be 2d or 3d.")
+    return out
+
+
 def str2init(initializer):
     if isinstance(initializer, str):
         if initializer not in _initializers_dict.keys():
@@ -507,6 +523,19 @@ def construct_graph(inputs, outputs):
     return node_by_depth, all_layers
 
 
+def select_attrs(obj):
+    attrs_dict = obj.__dict__
+    attrs = {}
+    _select_key = ['kernel_size', 'stride', 'act', 'padding', 'data_format', 'concat_dim']
+    for k in _select_key:
+        if k in attrs_dict:
+            if k == 'act':
+                attrs[k] = attrs_dict[k].__class__.__name__
+            else:
+                attrs[k] = attrs_dict[k]
+    return attrs
+
+
 class ModuleNode(object):
     """
     The class :class:`ModuleNode` class represents a conceptional node for a layer.
@@ -537,7 +566,7 @@ class ModuleNode(object):
         (1) Forwarding through the layer. (2) Update its input/output tensors.
     """
 
-    def __init__(self, layer, node_index, in_nodes, in_tensors, out_tensors, in_tensor_idxes):
+    def __init__(self, layer, node_index, in_nodes, in_tensors, out_tensors, in_tensor_idxes, attr):
         self.layer = layer
         self.node_index = node_index
         self.in_nodes = in_nodes
@@ -547,6 +576,7 @@ def __init__(self, layer, node_index, in_nodes, in_tensors, out_tensors, in_tens
         self.node_name = layer.name + "_node_{}".format(node_index)
 
         self.in_tensors_idxes = in_tensor_idxes
+        self.attr = attr
         self.visited = False
 
     def __call__(self, inputs, **kwargs):

tensorlayerx/nn/core/core_mindspore.py

@@ -1,7 +1,7 @@
 #! /usr/bin/python
 # -*- coding: utf-8 -*-
 
-from .common import str2act, str2init, random_normal, tolist, construct_graph, ModuleNode
+from .common import check_parameter, str2act, str2init, random_normal, tolist, construct_graph, ModuleNode, select_attrs
 from .common import _save_weights, _load_weights, _save_standard_weights_dict, _load_standard_weights_dict
 from mindspore.nn import Cell
 import tensorlayerx as tlx
@@ -105,7 +105,7 @@ def _get_weights(self, var_name, shape, init=random_normal(), trainable=True, tr
         if len(shape) == 3:
             shape = shape[::-1]
         if len(shape) == 4:
-            if not transposed and self.data_format == 'NHWC':
+            if not transposed and self.data_format in ['NHWC', 'channels_last']:
                 shape = (shape[3], shape[0], shape[1], shape[2])
             else:
                 shape = (shape[3], shape[2], shape[0], shape[1])
@@ -265,6 +265,9 @@ def all_weights(self):
     def str_to_init(self, initializer):
         return str2init(initializer)
 
+    def check_param(self, param, dim='2d'):
+        return check_parameter(param, dim)
+
     def insert_child_to_layer(self, child_name, child):
         """
         Adds a child layer to the current layer.
@@ -333,7 +336,7 @@ def _add_node(self, input_tensors, output_tensors):
             in_tensor_idxes = [tensor._info[1] for tensor in inputs_list]
         node_index = len(_global_layer_node)
 
-        new_node = ModuleNode(self, node_index, in_nodes, inputs_list, outputs_list, in_tensor_idxes)
+        new_node = ModuleNode(self, node_index, in_nodes, inputs_list, outputs_list, in_tensor_idxes, select_attrs(self))
         _global_layer_node.append(new_node)
         for idx, tensor in enumerate(outputs_list):
             tensor._info = (new_node, idx)

tensorlayerx/nn/core/core_paddle.py

@@ -2,7 +2,7 @@
 # -*- coding: utf-8 -*-
 
 import copy, six
-from .common import str2act, str2init, tolist, construct_graph, ModuleNode
+from .common import check_parameter, str2act, str2init, tolist, construct_graph, ModuleNode
 from .common import _save_weights, _load_weights, _save_standard_weights_dict, _load_standard_weights_dict
 from paddle.fluid import framework
 from paddle.fluid.dygraph import Layer
@@ -288,6 +288,9 @@ def load_standard_weights(self, file_path, skip=False, reshape=False, format='np
     def str_to_init(self, initializer):
         return str2init(initializer)
 
+    def check_param(self, param, dim='2d'):
+        return check_parameter(param, dim)
+
     def insert_child_to_layer(self, child_name, child):
         """
         Adds a child layer to the current layer.

tensorlayerx/nn/core/core_tensorflow.py

@@ -1,7 +1,7 @@
 #! /usr/bin/python
 # -*- coding: utf-8 -*-
 
-from .common import str2act, str2init, tolist, construct_graph, ModuleNode
+from .common import check_parameter, str2act, str2init, tolist, construct_graph, ModuleNode, select_attrs
 from .common import _save_weights, _load_weights, _save_standard_weights_dict, _load_standard_weights_dict
 from collections import OrderedDict, abc as container_abcs
 from collections import OrderedDict
@@ -582,6 +582,9 @@ def init_build(self, *inputs, **kwargs):
     def str_to_init(self, initializer):
         return str2init(initializer)
 
+    def check_param(self, param, dim='2d'):
+        return check_parameter(param, dim)
+
     def build_graph(self, *inputs, **kwargs):
         # Add nodes only when the composition is needed.
         layers = self.layers_and_names(name_prefix='')
@@ -621,7 +624,7 @@ def _add_node(self, input_tensors, output_tensors):
             in_tensor_idxes = [tensor._info[1] for tensor in inputs_list]
         node_index = len(_global_layer_node)
 
-        new_node = ModuleNode(self, node_index, in_nodes, inputs_list, outputs_list, in_tensor_idxes)
+        new_node = ModuleNode(self, node_index, in_nodes, inputs_list, outputs_list, in_tensor_idxes, select_attrs(self))
         _global_layer_node.append(new_node)
         for idx, tensor in enumerate(outputs_list):
             tensor._info = (new_node, idx)

tensorlayerx/nn/core/core_torch.py

@@ -2,7 +2,7 @@
 # -*- coding: utf-8 -*-
 
 from torch.nn import Module as T_Module
-from .common import str2act, str2init, tolist, construct_graph, ModuleNode
+from .common import check_parameter, str2act, str2init, tolist, construct_graph, ModuleNode, select_attrs
 from .common import _save_weights, _load_weights, _save_standard_weights_dict, _load_standard_weights_dict
 from torch.nn.parameter import Parameter
 from collections import OrderedDict
@@ -173,6 +173,9 @@ def load_standard_weights(self, file_path, skip=False, reshape=False, format='np
     def str_to_init(self, initializer):
         return str2init(initializer)
 
+    def check_param(self, param, dim='2d'):
+        return check_parameter(param, dim)
+
     def init_build(self, *inputs, **kwargs):
         """
         (1) This method must be called when the Layer has no input in_channels.
@@ -220,7 +223,7 @@ def _add_node(self, input_tensors, output_tensors):
             in_tensor_idxes = [tensor._info[1] for tensor in inputs_list]
         node_index = len(_global_layer_node)
 
-        new_node = ModuleNode(self, node_index, in_nodes, inputs_list, outputs_list, in_tensor_idxes)
+        new_node = ModuleNode(self, node_index, in_nodes, inputs_list, outputs_list, in_tensor_idxes, select_attrs(self))
         _global_layer_node.append(new_node)
         for idx, tensor in enumerate(outputs_list):
             tensor._info = (new_node, idx)