diff --git a/op_list.md b/op_list.md index 3c4002c3ba8d7f2bf1ab88ee183f46d675af9b03..a69a54bef1ad763602a350dd894f816279a7d531 100644 --- a/op_list.md +++ b/op_list.md @@ -1,5 +1,5 @@ # X2Paddle支持OP列表 -> 目前X2Paddle支持40+的TensorFlow OP，30+的Caffe Layer，覆盖了大部分CV分类模型常用的操作。我们在如下列表中给出了目前X2Paddle支持的全部OP。 +> 目前X2Paddle支持50+的TensorFlow OP，30+的Caffe Layer，覆盖了大部分CV分类模型常用的操作。我们在如下列表中给出了目前X2Paddle支持的全部OP。 **注：** 目前，部分OP暂未支持，如您在转换过程中出现OP不支持的情况，可自行添加或反馈给我们。欢迎通过[ISSUE反馈](https://github.com/PaddlePaddle/X2Paddle/issues/new)的方式告知我们(模型名，代码实现或模型获取方式)，我们会及时跟进：） @@ -20,7 +20,7 @@ | 41 | Cast | 42 | Split | 43 | Squeeze | 44 | ResizeNearestNeighbor | | 45 | Softmax | 46 | Range | 47 | ConcatV2 | 48 | MirrorPad | | 49 | Identity | 50 | GreaterEqual | 51 | StopGradient | 52 | Minimum | -| 53 | RadnomUniform | | | | | | | +| 53 | RadnomUniform | 54 | Fill | 55 | Floor | 56 | DepthToSpace | ## Caffe diff --git a/x2paddle/op_mapper/tf_op_mapper_nhwc.py b/x2paddle/op_mapper/tf_op_mapper_nhwc.py index 307226d6267f07c5a4414bd56b4aaa93d1e7a524..5184a9fbe21757835503abebe6442c15c4542234 100644 --- a/x2paddle/op_mapper/tf_op_mapper_nhwc.py +++ b/x2paddle/op_mapper/tf_op_mapper_nhwc.py @@ -46,7 +46,8 @@ class TFOpMapperNHWC(OpMapper): 'Softplus': ['softplus'], 'LeakyRelu': ['leaky_relu', { 'alpha': 'alpha' - }] + }], + 'Floor': ['floor'] } elementwise_ops = { 'Add': 'elementwise_add', @@ -54,6 +55,7 @@ class TFOpMapperNHWC(OpMapper): 'RealDiv': 'elementwise_div', 'Sub': 'elementwise_sub', 'Maximum': 'elementwise_max', + 'Minimum': 'elementwise_min', 'Mul': 'elementwise_mul', 'FloorDiv': 'elementwise_floordiv' } @@ -202,6 +204,52 @@ class TFOpMapperNHWC(OpMapper): node.fluid_code.add_layer( "transpose", inputs=input, output=node, param_attr=attr) + def Fill(self, node): + dims = self.graph.get_node(node.layer.input[0], copy=True) + input_value = self.graph.get_node(node.layer.input[1], copy=True) + + assert input_value.layer_type == "Const", "Value of fill OP should be Const" + + self.add_omit_nodes(input_value.layer_name, node.layer_name) + input_value = input_value.value + input_dtype = string(input_value.dtype) + attr = {'value': input_value, 'dtype': input_dtype} + + node.fluid_code.add_layer( + "fill_constant", inputs=dims, output=node, param_attr=attr) + + def DepthToSpace(self, node): + input = self.graph.get_node(node.layer.input[0], copy=True) + + block_size = node.get_attr("block_size") + data_format = node.get_attr("data_format").decode() + + if data_format == "NHWC": + attr = {"perm": [0, 3, 1, 2]} + node.fluid_code.add_layer( + "transpose", inputs=input, output=input, param_attr=attr) + n, h, w, c = input.out_shapes[0] + + attr = {'shape': [0, block_size * block_size, -1, h, w]} + node.fluid_code.add_layer( + "reshape", inputs=input, output=input, param_attr=attr) + + attr = {'perm': [0, 2, 1, 3, 4]} + node.fluid_code.add_layer( + "transpose", inputs=input, output=input, param_attr=attr) + attr = {'shape': [0, c, h, w]} + node.fluid_code.add_layer( + "reshape", inputs=input, output=input, param_attr=attr) + + attr = {'upscale_factor': block_size} + node.fluid_code.add_layer( + "pixel_shuffle", inputs=input, output=node, param_attr=attr) + + if data_format == "NHWC": + attr = {"perm": [0, 2, 3, 1]} + node.fluid_code.add_layer( + "transpose", inputs=node, output=node, param_attr=attr) + def MaxPool(self, node): input = self.graph.get_node(node.layer.input[0], copy=True) @@ -236,6 +284,7 @@ class TFOpMapperNHWC(OpMapper): def Conv2D(self, node): input = self.graph.get_node(node.layer.input[0], copy=True) kernel = self.graph.get_node(node.layer.input[1], copy=True) + self.add_omit_nodes(kernel.layer_name, node.layer_name) k_size = kernel.out_shapes[0] strides = node.get_attr("strides") @@ -245,10 +294,17 @@ class TFOpMapperNHWC(OpMapper): channel_first = data_format == "NCHW" if kernel.layer_type == 'Const': - self.add_omit_nodes(kernel.layer_name, node.layer_name) kernel_value = kernel.value - self.weights[kernel.layer_name.replace('/', '_')] = numpy.transpose( - kernel_value, (3, 2, 0, 1)) + kernel_weight_name = kernel.layer_name.replace('/', '_') + else: + kernel_value = self.decoder.infer_tensor(kernel) + if kernel.layer_type == 'Split': + kernel_weight_name = "{}_{}_kernel".format(node.layer_name, + kernel.layer_name) + else: + kernel_weight_name = kernel.layer_name.replace('/', '_') + self.weights[kernel_weight_name] = numpy.transpose(kernel_value, + (3, 2, 0, 1)) if not channel_first: strides = [strides[i] for i in [0, 3, 1, 2]] @@ -257,10 +313,9 @@ class TFOpMapperNHWC(OpMapper): node.fluid_code.add_layer( "transpose", inputs=input, output=node, param_attr=attr) input = node - attr = { "bias_attr": False, - "param_attr": string(kernel.layer_name), + "param_attr": string(kernel_weight_name), "num_filters": k_size[3], "filter_size": k_size[0:2], "stride": strides[2:4], @@ -700,13 +755,16 @@ class TFOpMapperNHWC(OpMapper): input = self.graph.get_node(node.layer.input[2], copy=True) assert kernel.layer_type == "Const", "Kernel of Conv2DBackpropInput should be Const" - assert out_shape.layer_type == "Const", "Out_shape of Conv2DBackpropInput should be Const" self.add_omit_nodes(kernel.layer_name, node.layer_name) - - out_shape = out_shape.value.tolist() self.add_omit_nodes(out_shape.layer_name, node.layer_name) + if out_shape.layer_type == "Const": + out_shape = out_shape.value.tolist() + else: + out_shape = self.decoder.infer_shape_tensor(out_shape, + node.out_shapes[0]) + in_shape = input.out_shapes[0] if in_shape.count(-1) > 2: in_shape = self.decoder.infer_tensor(input).shape diff --git a/x2paddle_model_zoo.md b/x2paddle_model_zoo.md index d44960fafde1eb81d25e70aa34257d9ac70fa547..d2a95c735b1a80a9680e0b9b39958bed1b6702f2 100644 --- a/x2paddle_model_zoo.md +++ b/x2paddle_model_zoo.md @@ -1,5 +1,5 @@ # X2Paddle模型测试库 -> 目前X2Paddle支持40+的TensorFlow OP，40+的Caffe Layer，覆盖了大部分CV分类模型常用的操作。我们在如下模型列表中测试了X2Paddle的转换。 +> 目前X2Paddle支持50+的TensorFlow OP，40+的Caffe Layer，覆盖了大部分CV分类模型常用的操作。我们在如下模型列表中测试了X2Paddle的转换。 **注：** 受限于不同框架的差异，部分模型可能会存在目前无法转换的情况，如TensorFlow中包含控制流的模型，NLP模型等。对于CV常见的模型，如若您发现无法转换或转换失败，存在较大diff等问题，欢迎通过[ISSUE反馈](https://github.com/PaddlePaddle/X2Paddle/issues/new)的方式告知我们(模型名，代码实现或模型获取方式)，我们会及时跟进：） @@ -22,7 +22,8 @@ | UNet | [code1](https://github.com/jakeret/tf_unet )/[code2](https://github.com/lyatdawn/Unet-Tensorflow) |-| |MTCNN | [code](https://github.com/AITTSMD/MTCNN-Tensorflow) |-| |YOLO-V3| [code](https://github.com/YunYang1994/tensorflow-yolov3) | 转换需要关闭NHWC->NCHW的优化，见[文档Q2](FAQ.md) | -|Inception_ResNet_V2| [code](https://github.com/tensorflow/models/tree/master/research/slim/nets) | - | +| FALSR | [code](https://github.com/xiaomi-automl/FALSR) | - | +| DCSCN | [code](https://modelzoo.co/model/dcscn-super-resolution) | - | ## Caffe