diff --git a/README.md b/README.md index 9c94f6143cd1cda99b08129ea7d0bdebe92c6d63..ae24f6b1f2b9f56b9de3ab0982c99f7c114478a9 100644 --- a/README.md +++ b/README.md @@ -10,7 +10,7 @@ X2Paddle在多个主流的CV模型上，测试过TensorFlow/Caffe/ONNX模型的 ## 环境依赖 python == 2.7 | python >= 3.5 -paddlepaddle >= 1.5.0 +paddlepaddle >= 1.6.0 **按需安装以下依赖** tensorflow ： tensorflow == 1.14.0 diff --git a/x2paddle/convert.py b/x2paddle/convert.py index c71dabdd2c98ef1f86f37fed21d986b5fa40dfad..75a562159edfc868819c1e4cbe1618a9304a94b9 100644 --- a/x2paddle/convert.py +++ b/x2paddle/convert.py @@ -188,7 +188,7 @@ def main(): if args.version: import x2paddle - print("x2paddle-{} with python>=3.5, paddlepaddle>=1.5.0\n".format( + print("x2paddle-{} with python>=3.5, paddlepaddle>=1.6.0\n".format( x2paddle.__version__)) return @@ -198,8 +198,8 @@ def main(): try: import paddle v0, v1, v2 = paddle.__version__.split('.') - if int(v0) != 1 or int(v1) < 5: - print("paddlepaddle>=1.5.0 is required") + if int(v0) != 1 or int(v1) < 6: + print("paddlepaddle>=1.6.0 is required") return except: print("paddlepaddle not installed, use \"pip install paddlepaddle\"") diff --git a/x2paddle/op_mapper/tf_op_mapper.py b/x2paddle/op_mapper/tf_op_mapper.py index bf2d5519ebd0a738b1680088e6b28a2470e366bc..b36635c51bfc8bdb429d882aefbd93575f53081f 100644 --- a/x2paddle/op_mapper/tf_op_mapper.py +++ b/x2paddle/op_mapper/tf_op_mapper.py @@ -278,6 +278,7 @@ class TFOpMapper(OpMapper): 'name': string(node.layer_name), 'append_batch_size': False } + if shape[0] < 0: self.batch_node = node @@ -382,7 +383,6 @@ class TFOpMapper(OpMapper): data_format = node.get_attr("data_format").decode() pad_mode = node.get_attr("padding").decode() channel_first = data_format == "NCHW" - padding = 0 if not channel_first: in_shape = [in_shape[i] for i in [0, 3, 1, 2]] @@ -391,22 +391,10 @@ class TFOpMapper(OpMapper): else: self.graph.data_format_propagation(node) - if pad_mode == "SAME": - pad_h = get_same_padding(in_shape[2], k_size[2], strides[2]) - pad_w = get_same_padding(in_shape[3], k_size[3], strides[3]) - pad_h = pad_h[0] + pad_h[1] - pad_w = pad_w[0] + pad_w[1] - if pad_h != 0 or pad_w != 0: - attr = {"paddings": [0, pad_h, 0, pad_w], "pad_value": -10000.0} - node.fluid_code.add_layer("pad2d", - inputs=input, - output=node, - param_attr=attr) - input = node attr = { "pool_size": k_size[2:4], "pool_type": string("max"), - "pool_padding": padding, + "pool_padding": string(pad_mode), "pool_stride": strides[2:4] } node.fluid_code.add_layer("pool2d", @@ -432,7 +420,6 @@ class TFOpMapper(OpMapper): data_format = node.get_attr("data_format").decode() pad_mode = node.get_attr("padding").decode() channel_first = data_format == "NCHW" - padding = 0 self.weights[kernel.layer_name.replace('/', '_')] = numpy.transpose( kernel.value, (3, 2, 0, 1)) @@ -444,18 +431,6 @@ class TFOpMapper(OpMapper): else: self.graph.data_format_propagation(node) - if pad_mode == "SAME": - pad_h = get_same_padding(in_shape[2], k_size[0], strides[2]) - pad_w = get_same_padding(in_shape[3], k_size[1], strides[3]) - if pad_h[0] == pad_h[1] and pad_w[0] == pad_w[1]: - padding = [pad_h[0], pad_w[0]] - else: - attr = {"paddings": pad_h + pad_w, "pad_value": 0.0} - node.fluid_code.add_layer("pad2d", - inputs=input, - output=node, - param_attr=attr) - input = node attr = { "bias_attr": False, "param_attr": string(kernel.layer_name), @@ -463,7 +438,7 @@ class TFOpMapper(OpMapper): "filter_size": k_size[0:2], "stride": strides[2:4], "dilation": dilations[2:4], - "padding": padding + "padding": string(pad_mode) } node.fluid_code.add_layer("conv2d", inputs=input, @@ -535,7 +510,6 @@ class TFOpMapper(OpMapper): data_format = node.get_attr("data_format").decode() pad_mode = node.get_attr("padding").decode() channel_first = data_format == "NCHW" - padding = 0 self.weights[kernel.layer_name.replace('/', '_')] = numpy.transpose( kernel.value, (2, 3, 0, 1)) @@ -547,19 +521,6 @@ class TFOpMapper(OpMapper): else: self.data_format_propagation(node) - if pad_mode == "SAME": - pad_h = get_same_padding(in_shape[2], k_size[0], strides[2]) - pad_w = get_same_padding(in_shape[3], k_size[1], strides[3]) - if pad_h[0] == pad_h[1] and pad_w[0] == pad_w[1]: - padding = [pad_h[0], pad_w[0]] - else: - attr = {"paddings": pad_h + pad_w, "pad_value": 0.0} - node.fluid_code.add_layer("pad2d", - inputs=input, - output=node, - param_attr=attr) - input = node - attr = { "bias_attr": False, "param_attr": string(kernel.layer_name), @@ -569,7 +530,7 @@ class TFOpMapper(OpMapper): "dilation": dilations[2:4], "groups": k_size[3] * in_shape[1], "use_cudnn": False, - "padding": padding + "padding": string(pad_mode) } node.fluid_code.add_layer("conv2d", inputs=input, @@ -691,14 +652,9 @@ class TFOpMapper(OpMapper): attr = { "pool_size": k_size[2:4], "pool_type": string("avg"), - "pool_stride": strides[2:4] + "pool_stride": strides[2:4], + "pool_padding": string(pad_mode) } - if pad_mode == "SAME": - pad_h = get_same_padding(in_shape[2], k_size[2], strides[2]) - pad_w = get_same_padding(in_shape[3], k_size[3], strides[3]) - assert pad_h[0] == pad_h[1] and pad_w[0] == pad_w[ - 1], "Cannot map AvgPool" - attr["pool_padding"] = [pad_h[0], pad_w[0]] node.fluid_code.add_layer("pool2d", inputs=input, output=node, @@ -993,20 +949,6 @@ class TFOpMapper(OpMapper): else: self.data_format_propagation(node) - padding = 0 - if pad_mode == "SAME": - pad_h = get_same_padding(in_shape[2], k_size[0], strides[2]) - pad_w = get_same_padding(in_shape[3], k_size[1], strides[3]) - if pad_h[0] == pad_h[1] and pad_w[0] == pad_w[1]: - padding = [pad_h[0], pad_w[0]] - else: - attr = {"paddings": pad_h + pad_w, "pad_value": 0.0} - node.fluid_code.add_layer("pad2d", - inputs=input, - output=node, - param_attr=attr) - input = node - attr = { "bias_attr": False, "param_attr": string(kernel.layer_name), @@ -1014,29 +956,14 @@ class TFOpMapper(OpMapper): "filter_size": k_size[0:2], "stride": strides[2:4], "dilation": dilations[2:4], - "padding": padding + "padding": string(pad_mode), + "output_size": out_shape[1:3] } node.fluid_code.add_layer("conv2d_transpose", inputs=input, output=node, param_attr=attr) - if pad_mode == "SAME": - if node.tf_data_format == "NHWC": - out_shape = [out_shape[i] for i in [0, 3, 1, 2]] - for i in range(4): - if out_shape[i] < 0: - out_shape[i] = 999999 - attr = { - "axes": [0, 1, 2, 3], - "starts": [0, 0, 0, 0], - "ends": out_shape - } - node.fluid_code.add_layer("slice", - inputs=node, - output=node, - param_attr=attr) - def Max(self, node): input = self.graph.get_node(node.layer.input[0], copy=True) reduce_idx = self.graph.get_node(node.layer.input[1], copy=True) diff --git a/x2paddle/op_mapper/tf_op_mapper_nhwc.py b/x2paddle/op_mapper/tf_op_mapper_nhwc.py index 06fec6923cca4ffdfee4b04070ba2c7f1bcd9906..ee31281306c9c657dc71b2066ba6a1598fc29399 100644 --- a/x2paddle/op_mapper/tf_op_mapper_nhwc.py +++ b/x2paddle/op_mapper/tf_op_mapper_nhwc.py @@ -321,22 +321,11 @@ class TFOpMapperNHWC(OpMapper): k_size = [k_size[i] for i in [0, 3, 1, 2]] input = node - if pad_mode == "SAME": - pad_h = get_same_padding(in_shape[2], k_size[2], strides[2]) - pad_w = get_same_padding(in_shape[3], k_size[3], strides[3]) - pad_h = pad_h[0] + pad_h[1] - pad_w = pad_w[0] + pad_w[1] - attr = {"paddings": [0, pad_h, 0, pad_w], "pad_value": -10000.0} - if pad_h + pad_w != 0: - node.fluid_code.add_layer("pad2d", - inputs=input, - output=node, - param_attr=attr) - input = node attr = { "pool_size": k_size[2:4], "pool_type": string("max"), - "pool_stride": strides[2:4] + "pool_stride": strides[2:4], + "pool_padding": string(pad_mode) } node.fluid_code.add_layer("pool2d", inputs=input, @@ -368,7 +357,6 @@ class TFOpMapperNHWC(OpMapper): data_format = node.get_attr("data_format").decode() pad_mode = node.get_attr("padding").decode() channel_first = data_format == "NCHW" - padding = 0 self.weights[kernel.layer_name.replace('/', '_')] = numpy.transpose( kernel.value, (3, 2, 0, 1)) @@ -384,18 +372,6 @@ class TFOpMapperNHWC(OpMapper): param_attr=attr) input = node - if pad_mode == "SAME": - pad_h = get_same_padding(in_shape[2], k_size[0], strides[2]) - pad_w = get_same_padding(in_shape[3], k_size[1], strides[3]) - if pad_h[0] == pad_h[1] and pad_w[0] == pad_w[1]: - padding = [pad_h[0], pad_w[0]] - else: - attr = {"paddings": pad_h + pad_w, "pad_value": 0.0} - node.fluid_code.add_layer("pad2d", - inputs=input, - output=node, - param_attr=attr) - input = node attr = { "bias_attr": False, "param_attr": string(kernel.layer_name), @@ -403,7 +379,7 @@ class TFOpMapperNHWC(OpMapper): "filter_size": k_size[0:2], "stride": strides[2:4], "dilation": dilations[2:4], - "padding": padding + "padding": string(pad_mode) } node.fluid_code.add_layer("conv2d", inputs=input, @@ -490,7 +466,6 @@ class TFOpMapperNHWC(OpMapper): data_format = node.get_attr("data_format").decode() pad_mode = node.get_attr("padding").decode() channel_first = data_format == "NCHW" - padding = 0 self.weights[kernel.layer_name.replace('/', '_')] = numpy.transpose( kernel.value, (2, 3, 0, 1)) @@ -506,19 +481,6 @@ class TFOpMapperNHWC(OpMapper): param_attr=attr) input = node - if pad_mode == "SAME": - pad_h = get_same_padding(in_shape[2], k_size[0], strides[2]) - pad_w = get_same_padding(in_shape[3], k_size[1], strides[3]) - if pad_h[0] == pad_h[1] and pad_w[0] == pad_w[1]: - padding = [pad_h[0], pad_w[0]] - else: - attr = {"paddings": pad_h + pad_w, "pad_value": 0.0} - node.fluid_code.add_layer("pad2d", - inputs=input, - output=node, - param_attr=attr) - input = node - attr = { "bias_attr": False, "param_attr": string(kernel.layer_name), @@ -528,7 +490,7 @@ class TFOpMapperNHWC(OpMapper): "dilation": dilations[2:4], "groups": k_size[3] * in_shape[1], "use_cudnn": False, - "padding": padding + "padding": string(pad_mode) } node.fluid_code.add_layer("conv2d", inputs=input, @@ -623,14 +585,9 @@ class TFOpMapperNHWC(OpMapper): attr = { "pool_size": k_size[2:4], "pool_type": string("avg"), - "pool_stride": strides[2:4] + "pool_stride": strides[2:4], + "pool_padding": string(pad_mode) } - if pad_mode == "SAME": - pad_h = get_same_padding(in_shape[2], k_size[2], strides[2]) - pad_w = get_same_padding(in_shape[3], k_size[3], strides[3]) - assert pad_h[0] == pad_h[1] and pad_w[0] == pad_w[ - 1], "Cannot map AvgPool" - attr["pool_padding"] = [pad_h[0], pad_w[0]] node.fluid_code.add_layer("pool2d", inputs=input, output=node, @@ -990,20 +947,6 @@ class TFOpMapperNHWC(OpMapper): else: self.data_format_propagation(node) - padding = 0 - if pad_mode == "SAME": - pad_h = get_same_padding(in_shape[2], k_size[0], strides[2]) - pad_w = get_same_padding(in_shape[3], k_size[1], strides[3]) - if pad_h[0] == pad_h[1] and pad_w[0] == pad_w[1]: - padding = [pad_h[0], pad_w[0]] - else: - attr = {"paddings": pad_h + pad_w, "pad_value": 0.0} - node.fluid_code.add_layer("pad2d", - inputs=input, - output=node, - param_attr=attr) - input = node - attr = { "bias_attr": False, "param_attr": string(kernel.layer_name), @@ -1011,29 +954,14 @@ class TFOpMapperNHWC(OpMapper): "filter_size": k_size[0:2], "stride": strides[2:4], "dilation": dilations[2:4], - "padding": padding + "padding": string(pad_mode), + "output_size": out_shape[1:3] } node.fluid_code.add_layer("conv2d_transpose", inputs=input, output=node, param_attr=attr) - if pad_mode == "SAME": - if node.tf_data_format == "NHWC": - out_shape = [out_shape[i] for i in [0, 3, 1, 2]] - for i in range(4): - if out_shape[i] < 0: - out_shape[i] = 999999 - attr = { - "axes": [0, 1, 2, 3], - "starts": [0, 0, 0, 0], - "ends": out_shape - } - node.fluid_code.add_layer("slice", - inputs=node, - output=node, - param_attr=attr) - if not channel_first: attr = {"perm": [0, 2, 3, 1]} node.fluid_code.add_layer("transpose", @@ -1181,6 +1109,7 @@ class TFOpMapperNHWC(OpMapper): else: shape = self.decoder.infer_shape_tensor(shape) attr = {"shape": shape, "min": 0.0, "max": 0.9999} + if shape[0] < 0: input = self.batch_node node.fluid_code.add_layer("uniform_random_batch_size_like",