diff --git a/demo/models/mobilenet.py b/demo/models/mobilenet.py index 9ae095e6bd54209a8144d73bdee07a45470855ce..f2e86e3f3b697fda699997785cda4b5923507e42 100644 --- a/demo/models/mobilenet.py +++ b/demo/models/mobilenet.py @@ -130,7 +130,6 @@ class MobileNet(): with fluid.name_scope('last_fc'): output = fluid.layers.fc(input=input, size=class_dim, - act='softmax', param_attr=ParamAttr( initializer=MSRA(), name="fc7_weights"), diff --git a/demo/models/mobilenet_v2.py b/demo/models/mobilenet_v2.py index ccfb250b79a5365d28470886624287fbc87be50c..3e97b6c9527ab4a526068c0692fdefe150d67d33 100644 --- a/demo/models/mobilenet_v2.py +++ b/demo/models/mobilenet_v2.py @@ -110,7 +110,6 @@ class MobileNetV2(): output = fluid.layers.fc(input=input, size=class_dim, - act='softmax', param_attr=ParamAttr(name='fc10_weights'), bias_attr=ParamAttr(name='fc10_offset')) return output diff --git a/demo/models/mobilenet_v3.py b/demo/models/mobilenet_v3.py index 3276b352744a199ee858d193cb46e1b5ce36bca7..eaba6ee3cbdeba3cfafe9f01a8c10060b590cdc8 100644 --- a/demo/models/mobilenet_v3.py +++ b/demo/models/mobilenet_v3.py @@ -119,7 +119,6 @@ class MobileNetV3(): conv = self.hard_swish(conv) out = fluid.layers.fc(input=conv, size=class_dim, - act='softmax', param_attr=ParamAttr(name='fc_weights'), bias_attr=ParamAttr(name='fc_offset')) return out @@ -244,8 +243,7 @@ class MobileNetV3(): if num_in_filter != num_out_filter or stride != 1: return conv2 else: - return fluid.layers.elementwise_add( - x=input_data, y=conv2, act=None) + return fluid.layers.elementwise_add(x=input_data, y=conv2, act=None) def MobileNetV3_small_x0_25(): diff --git a/demo/models/pvanet.py b/demo/models/pvanet.py index 6f5024c94f33424b7a55474431cbf48d68133093..9ffa6e1804eb9eb98878cfb95b4a666f74f50709 100644 --- a/demo/models/pvanet.py +++ b/demo/models/pvanet.py @@ -59,10 +59,8 @@ class PVANet(): block_configs=[ BlockConfig(2, '64 48-96 24-48-48 96 128', True, BLOCK_TYPE_INCEP), - BlockConfig(1, '64 64-96 24-48-48 128', True, - BLOCK_TYPE_INCEP), - BlockConfig(1, '64 64-96 24-48-48 128', True, - BLOCK_TYPE_INCEP), + BlockConfig(1, '64 64-96 24-48-48 128', True, BLOCK_TYPE_INCEP), + BlockConfig(1, '64 64-96 24-48-48 128', True, BLOCK_TYPE_INCEP), BlockConfig(1, '64 64-96 24-48-48 128', True, BLOCK_TYPE_INCEP) ], name='conv4', @@ -76,9 +74,8 @@ class PVANet(): BlockConfig(1, '64 96-128 32-64-64 196', True, BLOCK_TYPE_INCEP), BlockConfig(1, '64 96-128 32-64-64 196', True, - BLOCK_TYPE_INCEP), BlockConfig( - 1, '64 96-128 32-64-64 196', True, - BLOCK_TYPE_INCEP) + BLOCK_TYPE_INCEP), + BlockConfig(1, '64 96-128 32-64-64 196', True, BLOCK_TYPE_INCEP) ], name='conv5', end_points=end_points) @@ -89,7 +86,6 @@ class PVANet(): output = fluid.layers.fc(input=input, size=class_dim, - act='softmax', param_attr=ParamAttr( initializer=MSRA(), name="fc_weights"), bias_attr=ParamAttr(name="fc_offset")) @@ -182,9 +178,8 @@ class PVANet(): conv_stride = stride else: conv_stride = 1 - path_net = self._conv_bn_relu(path_net, num_output, - kernel_size, name + scope, - conv_stride) + path_net = self._conv_bn_relu(path_net, num_output, kernel_size, + name + scope, conv_stride) paths.append(path_net) if stride > 1: @@ -359,8 +354,8 @@ class PVANet(): name, stride=1, groups=1): - return self._conv_bn_relu(input, num_filters, filter_size, name, - stride, groups) + return self._conv_bn_relu(input, num_filters, filter_size, name, stride, + groups) def Fpn_Fusion(blocks, net): @@ -433,8 +428,7 @@ def east(input, class_num=31): out[i], k, 1, name='fusion_' + str(len(blocks))) elif j <= 4: conv = net.deconv_bn_layer( - out[i], k, 2 * j, j, j // 2, - name='fusion_' + str(len(blocks))) + out[i], k, 2 * j, j, j // 2, name='fusion_' + str(len(blocks))) else: conv = net.deconv_bn_layer( out[i], 32, 8, 4, 2, name='fusion_' + str(len(blocks)) + '_1') diff --git a/demo/models/resnet.py b/demo/models/resnet.py index 4ceaef41ecc87d7388ae05d7fcb199de1841ebc2..da5e562a86941c35dae7ae0cb6b15c5386a3eb5a 100644 --- a/demo/models/resnet.py +++ b/demo/models/resnet.py @@ -105,7 +105,6 @@ class ResNet(): out = fluid.layers.fc( input=pool, size=class_dim, - act='softmax', name=fc_name, param_attr=fluid.param_attr.ParamAttr( initializer=fluid.initializer.Uniform(-stdv, stdv))) @@ -138,8 +137,7 @@ class ResNet(): bn_name = "bn" + name[3:] else: if name.split("_")[1] == "conv1": - bn_name = name.split("_", 1)[0] + "_bn_" + name.split("_", - 1)[1] + bn_name = name.split("_", 1)[0] + "_bn_" + name.split("_", 1)[1] else: bn_name = name.split("_", 1)[0] + "_bn" + name.split("_", 1)[1][3:] diff --git a/demo/prune/train.py b/demo/prune/train.py index 9a4239872942fde3de3becc97e118117021c5ace..574f515803c7a4485bae0cf11bfc95babea9eec8 100644 --- a/demo/prune/train.py +++ b/demo/prune/train.py @@ -147,7 +147,8 @@ def compress(args): # model definition model = models.__dict__[args.model]() out = model.net(input=image, class_dim=class_dim) - avg_cost = paddle.nn.functional.loss.cross_entropy(input=out, label=label) + cost = paddle.nn.functional.loss.cross_entropy(input=out, label=label) + avg_cost = paddle.mean(x=cost) acc_top1 = paddle.metric.accuracy(input=out, label=label, k=1) acc_top5 = paddle.metric.accuracy(input=out, label=label, k=5) val_program = paddle.static.default_main_program().clone(for_test=True)