Remove fluid in OCR

ocr/data.py

@@ -15,8 +15,7 @@ import logging
 logger = logging.getLogger(__name__)
 
 import paddle
-from paddle import fluid
-from paddle.fluid.dygraph.parallel import ParallelEnv
+from paddle.distributed import ParallelEnv
 
 DATA_MD5 = "7256b1d5420d8c3e74815196e58cdad5"
 DATA_URL = "http://paddle-ocr-data.bj.bcebos.com/data.tar.gz"
@@ -97,7 +96,7 @@ class PadTarget(object):
         return samples
 
 
-class BatchSampler(fluid.io.BatchSampler):
+class BatchSampler(paddle.io.BatchSampler):
     def __init__(self,
                  dataset,
                  batch_size,

ocr/eval.py

@@ -17,7 +17,6 @@ import argparse
 import functools
 
 import paddle
-import paddle.fluid as fluid
 
 from paddle.static import InputSpec as Input
 from paddle.vision.transforms import BatchCompose
@@ -47,7 +46,7 @@ add_arg('dynamic',           bool,  False,              "Whether to use dygraph.
 
 def main(FLAGS):
     device = paddle.set_device("gpu" if FLAGS.use_gpu else "cpu")
-    fluid.enable_dygraph(device) if FLAGS.dynamic else None
+    paddle.disable_static(device) if FLAGS.dynamic else None
 
     # yapf: disable
     inputs = [
@@ -79,7 +78,7 @@ def main(FLAGS):
         batch_size=FLAGS.batch_size,
         drop_last=False,
         shuffle=False)
-    test_loader = fluid.io.DataLoader(
+    test_loader = paddle.io.DataLoader(
         test_dataset,
         batch_sampler=test_sampler,
         places=device,
@@ -94,7 +93,7 @@ def main(FLAGS):
 
 def beam_search(FLAGS):
     device = set_device("gpu" if FLAGS.use_gpu else "cpu")
-    fluid.enable_dygraph(device) if FLAGS.dynamic else None
+    paddle.disable_static(device) if FLAGS.dynamic else None
 
     # yapf: disable
     inputs = [
@@ -128,7 +127,7 @@ def beam_search(FLAGS):
         batch_size=FLAGS.batch_size,
         drop_last=False,
         shuffle=False)
-    test_loader = fluid.io.DataLoader(
+    test_loader = paddle.io.DataLoader(
         test_dataset,
         batch_sampler=test_sampler,
         places=device,

ocr/predict.py

@@ -23,7 +23,6 @@ import functools
 from PIL import Image
 
 import paddle
-import paddle.fluid as fluid
 
 from paddle.static import InputSpec as Input
 from paddle.vision.datasets.folder import ImageFolder
@@ -53,7 +52,7 @@ add_arg('dynamic',           bool,  False,   "Whether to use dygraph.")
 
 def main(FLAGS):
     device = paddle.set_device("gpu" if FLAGS.use_gpu else "cpu")
-    fluid.enable_dygraph(device) if FLAGS.dynamic else None
+    paddle.disable_static(device) if FLAGS.dynamic else None
 
     inputs = [Input([None, 1, 48, 384], "float32", name="pixel"), ]
     model = paddle.Model(
@@ -71,7 +70,7 @@ def main(FLAGS):
     fn = lambda p: Image.open(p).convert('L')
     test_dataset = ImageFolder(FLAGS.image_path, loader=fn)
     test_collate_fn = BatchCompose([data.Resize(), data.Normalize()])
-    test_loader = fluid.io.DataLoader(
+    test_loader = paddle.io.DataLoader(
         test_dataset,
         places=device,
         num_workers=0,

ocr/seq2seq_attn.py

@@ -16,11 +16,10 @@ from __future__ import print_function
 import numpy as np
 
 import paddle
-import paddle.fluid as fluid
-import paddle.fluid.layers as layers
-from paddle.fluid.layers import BeamSearchDecoder
-
-from paddle.text import RNNCell, RNN, DynamicDecode
+import paddle.nn as nn
+import paddle.nn.functional as F
+#from paddle.text import RNNCell, RNN, DynamicDecode
+from paddle.text import DynamicDecode, BeamSearchDecoder
 
 
 class ConvBNPool(paddle.nn.Layer):
@@ -36,103 +35,99 @@ class ConvBNPool(paddle.nn.Layer):
 
         filter_size = 3
         std = (2.0 / (filter_size**2 * in_ch))**0.5
-        param_0 = fluid.ParamAttr(
-            initializer=fluid.initializer.Normal(0.0, std))
+        param_0 = paddle.ParamAttr(
+            initializer=paddle.nn.initializer.Normal(0.0, std))
 
         std = (2.0 / (filter_size**2 * out_ch))**0.5
-        param_1 = fluid.ParamAttr(
-            initializer=fluid.initializer.Normal(0.0, std))
-
-        self.conv0 = fluid.dygraph.Conv2D(
-            in_ch,
-            out_ch,
-            3,
-            padding=1,
-            param_attr=param_0,
-            bias_attr=False,
-            act=None,
-            use_cudnn=use_cudnn)
-        self.bn0 = fluid.dygraph.BatchNorm(out_ch, act=act)
-        self.conv1 = fluid.dygraph.Conv2D(
-            out_ch,
-            out_ch,
-            filter_size=3,
-            padding=1,
-            param_attr=param_1,
-            bias_attr=False,
-            act=None,
-            use_cudnn=use_cudnn)
-        self.bn1 = fluid.dygraph.BatchNorm(out_ch, act=act)
+        param_1 = paddle.ParamAttr(
+            initializer=paddle.nn.initializer.Normal(0.0, std))
+
+        net = [
+            nn.Conv2d(
+                in_ch,
+                out_ch,
+                3,
+                padding=1,
+                weight_attr=param_0,
+                bias_attr=False),
+            nn.BatchNorm2d(out_ch),
+        ]
+        if act == 'relu':
+            net += [nn.ReLU()]
+
+        net += [
+            nn.Conv2d(
+                out_ch,
+                out_ch,
+                kernel_size=3,
+                padding=1,
+                weight_attr=param_1,
+                bias_attr=False),
+            nn.BatchNorm2d(out_ch),
+        ]
+        if act == 'relu':
+            net += [nn.ReLU()]
 
         if self.pool:
-            self.pool = fluid.dygraph.Pool2D(
-                pool_size=2,
-                pool_type='max',
-                pool_stride=2,
-                use_cudnn=use_cudnn,
-                ceil_mode=True)
+            net += [nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True)]
+        self.net = nn.Sequential(*net)
 
     def forward(self, inputs):
-        out = self.conv0(inputs)
-        out = self.bn0(out)
-        out = self.conv1(out)
-        out = self.bn1(out)
-        if self.pool:
-            out = self.pool(out)
-        return out
+        return self.net(inputs)
 
 
 class CNN(paddle.nn.Layer):
     def __init__(self, in_ch=1, is_test=False):
         super(CNN, self).__init__()
-        self.conv_bn1 = ConvBNPool(in_ch, 16)
-        self.conv_bn2 = ConvBNPool(16, 32)
-        self.conv_bn3 = ConvBNPool(32, 64)
-        self.conv_bn4 = ConvBNPool(64, 128, pool=False)
+        net = [
+            ConvBNPool(in_ch, 16),
+            ConvBNPool(16, 32),
+            ConvBNPool(32, 64),
+            ConvBNPool(
+                64, 128, pool=False),
+        ]
+        self.net = nn.Sequential(*net)
 
     def forward(self, inputs):
-        conv = self.conv_bn1(inputs)
-        conv = self.conv_bn2(conv)
-        conv = self.conv_bn3(conv)
-        conv = self.conv_bn4(conv)
-        return conv
-
-
-class GRUCell(RNNCell):
-    def __init__(self,
-                 input_size,
-                 hidden_size,
-                 param_attr=None,
-                 bias_attr=None,
-                 gate_activation='sigmoid',
-                 candidate_activation='tanh',
-                 origin_mode=False):
-        super(GRUCell, self).__init__()
-        self.hidden_size = hidden_size
-        self.fc_layer = fluid.dygraph.Linear(
-            input_size,
-            hidden_size * 3,
-            param_attr=param_attr,
-            bias_attr=False)
-
-        self.gru_unit = fluid.dygraph.GRUUnit(
-            hidden_size * 3,
-            param_attr=param_attr,
-            bias_attr=bias_attr,
-            activation=candidate_activation,
-            gate_activation=gate_activation,
-            origin_mode=origin_mode)
-
-    def forward(self, inputs, states):
-        # step_outputs, new_states = cell(step_inputs, states)
-        # for GRUCell, `step_outputs` and `new_states` both are hidden
-        x = self.fc_layer(inputs)
-        hidden, _, _ = self.gru_unit(x, states)
-        return hidden, hidden
-
-    @property
-    def state_shape(self):
-        return [self.hidden_size]
+        return self.net(inputs)
+
+
+#class GRUCell(RNNCell):
+#    def __init__(self,
+#                 input_size,
+#                 hidden_size,
+#                 param_attr=None,
+#                 bias_attr=None,
+#                 gate_activation='sigmoid',
+#                 candidate_activation='tanh',
+#                 origin_mode=False):
+#        super(GRUCell, self).__init__()
+#        self.hidden_size = hidden_size
+#        self.fc_layer = nn.Linear(
+#            input_size,
+#            hidden_size * 3,
+#            weight_attr=param_attr,
+#            bias_attr=False)
+#
+#        self.gru_unit = fluid.dygraph.GRUUnit(
+#            hidden_size * 3,
+#            param_attr=param_attr,
+#            bias_attr=bias_attr,
+#            activation=candidate_activation,
+#            gate_activation=gate_activation,
+#            origin_mode=origin_mode)
+#
+#    def forward(self, inputs, states):
+#        # step_outputs, new_states = cell(step_inputs, states)
+#        # for GRUCell, `step_outputs` and `new_states` both are hidden
+#        x = self.fc_layer(inputs)
+#        hidden, _, _ = self.gru_unit(x, states)
+#        return hidden, hidden
+#
+#    @property
+#    def state_shape(self):
+#        return [self.hidden_size]
+#
 
 
 class Encoder(paddle.nn.Layer):
@@ -147,41 +142,41 @@ class Encoder(paddle.nn.Layer):
 
         self.backbone = CNN(in_ch=in_channel, is_test=is_test)
 
-        para_attr = fluid.ParamAttr(
-            initializer=fluid.initializer.Normal(0.0, 0.02))
-        bias_attr = fluid.ParamAttr(
-            initializer=fluid.initializer.Normal(0.0, 0.02), learning_rate=2.0)
-        self.gru_fwd = RNN(cell=GRUCell(
-            input_size=128 * 6,
-            hidden_size=rnn_hidden_size,
-            param_attr=para_attr,
-            bias_attr=bias_attr,
-            candidate_activation='relu'),
-                           is_reverse=False,
-                           time_major=False)
-        self.gru_bwd = RNN(cell=GRUCell(
-            input_size=128 * 6,
-            hidden_size=rnn_hidden_size,
-            param_attr=para_attr,
-            bias_attr=bias_attr,
-            candidate_activation='relu'),
-                           is_reverse=True,
-                           time_major=False)
-        self.encoded_proj_fc = fluid.dygraph.Linear(
+        para_attr = paddle.ParamAttr(
+            initializer=paddle.nn.initializer.Normal(0.0, 0.02))
+        bias_attr = paddle.ParamAttr(
+            initializer=paddle.nn.initializer.Normal(0.0, 0.02),
+            learning_rate=2.0)
+        self.gru_fwd = nn.RNN(
+            cell=nn.GRUCell(
+                input_size=128 * 6, hidden_size=rnn_hidden_size),
+            #            param_attr=para_attr,
+            #            bias_attr=bias_attr,
+            #            candidate_activation='relu'),
+            is_reverse=False,
+            time_major=False)
+        self.gru_bwd = nn.RNN(
+            cell=nn.GRUCell(
+                input_size=128 * 6, hidden_size=rnn_hidden_size),
+            #            param_attr=para_attr,
+            #            bias_attr=bias_attr,
+            #            candidate_activation='relu'),
+            is_reverse=True,
+            time_major=False)
+        self.encoded_proj_fc = nn.Linear(
             rnn_hidden_size * 2, decoder_size, bias_attr=False)
 
     def forward(self, inputs):
         conv_features = self.backbone(inputs)
-        conv_features = fluid.layers.transpose(
-            conv_features, perm=[0, 3, 1, 2])
+        conv_features = paddle.transpose(conv_features, perm=[0, 3, 1, 2])
 
         n, w, c, h = conv_features.shape
-        seq_feature = fluid.layers.reshape(conv_features, [0, -1, c * h])
+        seq_feature = paddle.reshape(conv_features, [0, -1, c * h])
 
         gru_fwd, _ = self.gru_fwd(seq_feature)
         gru_bwd, _ = self.gru_bwd(seq_feature)
 
-        encoded_vector = fluid.layers.concat(input=[gru_fwd, gru_bwd], axis=2)
+        encoded_vector = paddle.concat([gru_fwd, gru_bwd], axis=2)
         encoded_proj = self.encoded_proj_fc(encoded_vector)
         return gru_bwd, encoded_vector, encoded_proj
 
@@ -194,39 +189,37 @@ class Attention(paddle.nn.Layer):
 
     def __init__(self, decoder_size):
         super(Attention, self).__init__()
-        self.fc1 = fluid.dygraph.Linear(
-            decoder_size, decoder_size, bias_attr=False)
-        self.fc2 = fluid.dygraph.Linear(decoder_size, 1, bias_attr=False)
+        self.fc1 = nn.Linear(decoder_size, decoder_size, bias_attr=False)
+        self.fc2 = nn.Linear(decoder_size, 1, bias_attr=False)
 
     def forward(self, encoder_vec, encoder_proj, decoder_state):
         # alignment model, single-layer multilayer perceptron
         decoder_state = self.fc1(decoder_state)
-        decoder_state = fluid.layers.unsqueeze(decoder_state, [1])
+        decoder_state = paddle.unsqueeze(decoder_state, [1])
 
-        e = fluid.layers.elementwise_add(encoder_proj, decoder_state)
-        e = fluid.layers.tanh(e)
+        e = paddle.add(encoder_proj, decoder_state)
+        e = paddle.tanh(e)
 
         att_scores = self.fc2(e)
-        att_scores = fluid.layers.squeeze(att_scores, [2])
-        att_scores = fluid.layers.softmax(att_scores)
+        att_scores = paddle.squeeze(att_scores, [2])
+        att_scores = F.softmax(att_scores)
 
-        context = fluid.layers.elementwise_mul(
-            x=encoder_vec, y=att_scores, axis=0)
-        context = fluid.layers.reduce_sum(context, dim=1)
+        context = paddle.multiply(encoder_vec, att_scores, axis=0)
+        context = paddle.reduce_sum(context, dim=1)
         return context
 
 
-class DecoderCell(RNNCell):
+class DecoderCell(nn.RNNCellBase):
     def __init__(self, encoder_size=200, decoder_size=128):
         super(DecoderCell, self).__init__()
         self.attention = Attention(decoder_size)
-        self.gru_cell = GRUCell(
+        self.gru_cell = nn.GRUCell(
             input_size=encoder_size * 2 + decoder_size,
             hidden_size=decoder_size)
 
     def forward(self, current_word, states, encoder_vec, encoder_proj):
         context = self.attention(encoder_vec, encoder_proj, states)
-        decoder_inputs = fluid.layers.concat([current_word, context], axis=1)
+        decoder_inputs = paddle.concat([current_word, context], axis=1)
         hidden, _ = self.gru_cell(decoder_inputs, states)
         return hidden, hidden
 
@@ -234,9 +227,9 @@ class DecoderCell(RNNCell):
 class Decoder(paddle.nn.Layer):
     def __init__(self, num_classes, emb_dim, encoder_size, decoder_size):
         super(Decoder, self).__init__()
-        self.decoder_attention = RNN(DecoderCell(encoder_size, decoder_size))
-        self.fc = fluid.dygraph.Linear(
-            decoder_size, num_classes + 2, act='softmax')
+        self.decoder_attention = nn.RNN(
+            DecoderCell(encoder_size, decoder_size))
+        self.fc = nn.Linear(decoder_size, num_classes + 2)
 
     def forward(self, target, initial_states, encoder_vec, encoder_proj):
         out, _ = self.decoder_attention(
@@ -258,13 +251,10 @@ class Seq2SeqAttModel(paddle.nn.Layer):
             num_classes=None, ):
         super(Seq2SeqAttModel, self).__init__()
         self.encoder = Encoder(in_channle, encoder_size, decoder_size)
-        self.fc = fluid.dygraph.Linear(
-            input_dim=encoder_size,
-            output_dim=decoder_size,
-            bias_attr=False,
-            act='relu')
-        self.embedding = fluid.dygraph.Embedding(
-            [num_classes + 2, emb_dim], dtype='float32')
+        self.fc = nn.Sequential(
+            nn.Linear(
+                encoder_size, decoder_size, bias_attr=False), nn.ReLU())
+        self.embedding = nn.Embedding(num_classes + 2, emb_dim)
         self.decoder = Decoder(num_classes, emb_dim, encoder_size,
                                decoder_size)
 
@@ -326,7 +316,10 @@ class WeightCrossEntropy(paddle.nn.Layer):
         super(WeightCrossEntropy, self).__init__()
 
     def forward(self, predict, label, mask):
-        loss = layers.cross_entropy(predict, label=label)
-        loss = layers.elementwise_mul(loss, mask, axis=0)
-        loss = layers.reduce_sum(loss)
+        predict = paddle.flatten(predict, start_axis=0, stop_axis=1)
+        label = paddle.reshape(label, shape=[-1, 1])
+        mask = paddle.reshape(mask, shape=[-1, 1])
+        loss = F.cross_entropy(predict, label=label)
+        loss = paddle.multiply(loss, mask, axis=0)
+        loss = paddle.sum(loss)
         return loss

ocr/train.py

@@ -59,7 +59,7 @@ add_arg('dynamic',           bool,  False,      "Whether to use dygraph.")
 
 def main(FLAGS):
     device = paddle.set_device("gpu" if FLAGS.use_gpu else "cpu")
-    fluid.enable_dygraph(device) if FLAGS.dynamic else None
+    paddle.disable_static(device) if FLAGS.dynamic else None
 
     # yapf: disable
     inputs = [
@@ -100,7 +100,7 @@ def main(FLAGS):
         [data.Resize(), data.Normalize(), data.PadTarget()])
     train_sampler = data.BatchSampler(
         train_dataset, batch_size=FLAGS.batch_size, shuffle=True)
-    train_loader = fluid.io.DataLoader(
+    train_loader = paddle.io.DataLoader(
         train_dataset,
         batch_sampler=train_sampler,
         places=device,
@@ -115,7 +115,7 @@ def main(FLAGS):
         batch_size=FLAGS.batch_size,
         drop_last=False,
         shuffle=False)
-    test_loader = fluid.io.DataLoader(
+    test_loader = paddle.io.DataLoader(
         test_dataset,
         batch_sampler=test_sampler,
         places=device,

ocr/utility.py

@@ -21,7 +21,6 @@ import numpy as np
 import six
 
 import paddle
-import paddle.fluid as fluid
 
 from paddle.metric import Metric
 
@@ -74,8 +73,8 @@ class SeqAccuracy(Metric):
         self.reset()
 
     def compute(self, output, label, mask, *args, **kwargs):
-        pred = fluid.layers.flatten(output, axis=2)
-        score, topk = fluid.layers.topk(pred, 1)
+        pred = paddle.flatten(output, start_axis=2)
+        score, topk = paddle.topk(pred, 1)
         return topk, label, mask
 
     def update(self, topk, label, mask, *args, **kwargs):