From bdac9501929317e77e01f2f6742e4fd4be1ffe55 Mon Sep 17 00:00:00 2001 From: ceci3 Date: Tue, 24 Dec 2019 15:17:32 +0800 Subject: [PATCH] fix nas print best_tokens (#9) --- docs/docs/table_latency.md | 2 +- paddleslim/analysis/latency.py | 53 ++++++++++++++++-------------- paddleslim/common/sa_controller.py | 3 +- 3 files changed, 31 insertions(+), 27 deletions(-) diff --git a/docs/docs/table_latency.md b/docs/docs/table_latency.md index 1d95077c..cc8d524b 100644 --- a/docs/docs/table_latency.md +++ b/docs/docs/table_latency.md @@ -81,7 +81,7 @@ op_type,active_type,n_in,c_in,h_in,w_in\tlatency **字段解释** - **op_type(str)** - 当前op类型。 -- **active_type (string)** - 激活函数类型，包含：relu, prelu, sigmoid, relu6, tanh。 +- **active_type (string|None)** - 激活函数类型，包含：relu, prelu, sigmoid, relu6, tanh。 - **n_in (int)** - 输入 Tensor 的批尺寸 (batch size)。 - **c_in (int)** - 输入 Tensor 的通道 (channel) 数。 - **h_in (int)** - 输入 Tensor 的特征高度。 diff --git a/paddleslim/analysis/latency.py b/paddleslim/analysis/latency.py index ec9df492..fea255c7 100644 --- a/paddleslim/analysis/latency.py +++ b/paddleslim/analysis/latency.py @@ -12,6 +12,8 @@ # See the License for the specific language governing permissions and # limitations under the License. +from paddle.fluid import Program +from ..core import GraphWrapper, OpWrapper __all__ = ["LatencyEvaluator", "TableLatencyEvaluator"] @@ -28,33 +30,33 @@ class LatencyEvaluator(object): i = 0 for op in graph.ops(): if op.type() in ['conv2d', 'depthwise_conv2d']: - tmp = _conv_op_args(op) + tmp = self._conv_op_args(op) elif op.type() in [ 'elementwise_add', 'elementwise_mul', 'elementwise_max' ]: - tmp = _eltwise_op_args(op) + tmp = self._eltwise_op_args(op) elif op.type() in [ 'relu', 'prelu', 'sigmoid', 'relu6', 'elu', 'brelu', 'leaky_relu' ]: - tmp = _activation_op_args(op) + tmp = self._activation_op_args(op) elif op.type() == 'batch_norm': - tmp = _batch_norm_op_args(op) + tmp = self._batch_norm_op_args(op) elif op.type() == 'pool2d': - tmp = _pooling_op_args(op) + tmp = self._pooling_op_args(op) elif op.type() == 'batch_norm': - tmp = _batch_norm_op_args(op) + tmp = self._batch_norm_op_args(op) elif op.type() == 'softmax': - tmp = _softmax_op_args(op) + tmp = self._softmax_op_args(op) elif op.type() == 'mul': - tmp = _fc_op_args(op) + tmp = self._fc_op_args(op) else: tmp = None if tmp: ops.append(tmp) return ops - def _conv_op_args(op): + def _conv_op_args(self, op): assert isinstance(op, OpWrapper) tmp, res = [], [] # op_name @@ -69,11 +71,11 @@ class LatencyEvaluator(object): # batch size tmp.append(1) # channels, height, width - in_shapes = op.inputs('Input')[0].shape + in_shapes = op.inputs('Input')[0].shape() tmp = tmp + [int(in_shapes[1]), int(in_shapes[2]), int(in_shapes[3])] # output channels - w_shapes = op.inputs('Filter')[0].shape + w_shapes = op.inputs('Filter')[0].shape() tmp.append(int(w_shapes[0])) # group @@ -104,7 +106,7 @@ class LatencyEvaluator(object): tmp = tmp + res return tmp - def _batch_norm_op_args(op): + def _batch_norm_op_args(self, op): tmp = [] # op name tmp.append('batch_norm') @@ -116,11 +118,11 @@ class LatencyEvaluator(object): # batch size tmp.append(1) # input channels, height, width - in_shapes = op.inputs("X")[0].shape + in_shapes = op.inputs("X")[0].shape() tmp = tmp + [int(in_shapes[1]), int(in_shapes[2]), int(in_shapes[3])] return tmp - def _eltwise_op_args(op): + def _eltwise_op_args(self, op): # op name tmp = ['eltwise'] # elementwise type, TODO: add more ops @@ -133,7 +135,7 @@ class LatencyEvaluator(object): # batch size tmp.append(1) # input channels, height, width - in_shapes = op.inputs('X')[0].shape + in_shapes = op.inputs('X')[0].shape() while len(in_shapes) < 4: in_shapes = in_shapes + (1, ) @@ -141,14 +143,14 @@ class LatencyEvaluator(object): tmp.append(int(in_shapes[i])) return tmp - def _activation_op_args(op): + def _activation_op_args(self, op): tmp = [] # activation type tmp.append(op.type()) # batch size tmp.append(1) # input channels, height, width - in_shapes = op.inputs('X')[0].shape + in_shapes = op.inputs('X')[0].shape() while len(in_shapes) < 4: in_shapes = in_shapes + (1, ) @@ -156,7 +158,7 @@ class LatencyEvaluator(object): tmp.append(int(in_shapes[i])) return tmp - def _pooling_op_args(op): + def _pooling_op_args(self, op): tmp, res = [], [] # op name tmp.append('pooling') @@ -165,7 +167,7 @@ class LatencyEvaluator(object): # batch size tmp.append(1) # channels, height, width - in_shapes = op.inputs('X')[0].shape + in_shapes = op.inputs('X')[0].shape() tmp = tmp + [int(in_shapes[1]), int(in_shapes[2]), int(in_shapes[3])] # kernel size ksize = op.attr('ksize') @@ -201,7 +203,7 @@ class LatencyEvaluator(object): tmp = tmp + res return tmp - def _softmax_op_args(op): + def _softmax_op_args(self, op): # op name tmp = ['softmax'] # axis @@ -209,7 +211,7 @@ class LatencyEvaluator(object): # batch size tmp.append(1) # input channels, height, width - in_shapes = op.inputs('X')[0].shape + in_shapes = op.inputs('X')[0].shape() while len(in_shapes) < 4: in_shapes = in_shapes + (1, ) @@ -218,7 +220,7 @@ class LatencyEvaluator(object): return tmp - def _fc_op_args(blocks, op): + def _fc_op_args(self, op): # op name tmp = ['conv'] # flag bias @@ -229,12 +231,12 @@ class LatencyEvaluator(object): tmp.append(1) # input channels, height, width channels = 1 - in_shape = op.inputs('X')[0].shape + in_shape = op.inputs('X')[0].shape() for i in range(1, len(in_shape)): channels *= in_shape[i] tmp = tmp + [int(channels), 1, 1] # output channels - tmp.append(int(op.outputs('Out')[0].shape[1])) + tmp.append(int(op.outputs('Out')[0].shape()[1])) # groups, kernel size, padding, stride, dilation tmp = tmp + [1, 1, 0, 1, 1] return tmp @@ -279,5 +281,6 @@ class TableLatencyEvaluator(LatencyEvaluator): graph = GraphWrapper(graph) assert isinstance(graph, GraphWrapper) for op in self._get_ops_from_graph(graph): - total_latency += self._op_latency(self._delimiter.join(op)) + total_latency += self._op_latency( + self._delimiter.join(map(lambda x: str(x), op))) return total_latency diff --git a/paddleslim/common/sa_controller.py b/paddleslim/common/sa_controller.py index 210721ea..2beb9eed 100644 --- a/paddleslim/common/sa_controller.py +++ b/paddleslim/common/sa_controller.py @@ -98,7 +98,8 @@ class SAController(EvolutionaryController): self._best_tokens = tokens _logger.info( "Controller - iter: {}; best_reward: {}, best tokens: {}, current_reward: {}; current tokens: {}". - format(self._iter, self._reward, self._tokens, reward, tokens)) + format(self._iter, self._max_reward, self._best_tokens, reward, + tokens)) if self._checkpoints != None: self._save_checkpoint(self._checkpoints) -- GitLab