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[TIPC]update tipc scripts and rm fluid api #11098

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merged 3 commits into from
Oct 19, 2023
Merged

[TIPC]update tipc scripts and rm fluid api #11098

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845b806
[TIPC]update xpu tipc script
USTCKAY Aug 16, 2023
90562d6
Merge branch 'release/2.7' of https://github.com/PaddlePaddle/PaddleO…
USTCKAY Oct 18, 2023
8871fd4
update tipc scripts and remove fluid api
USTCKAY Oct 18, 2023
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120 changes: 64 additions & 56 deletions ppocr/modeling/heads/rec_robustscanner_head.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,7 +11,6 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""
This code is refer from:
https://github.com/open-mmlab/mmocr/blob/main/mmocr/models/textrecog/encoders/channel_reduction_encoder.py
Expand All @@ -28,6 +27,7 @@
import paddle.nn as nn
import paddle.nn.functional as F


class BaseDecoder(nn.Layer):
def __init__(self, **kwargs):
super().__init__()
Expand All @@ -42,15 +42,17 @@ def forward(self,
feat,
out_enc,
label=None,
valid_ratios=None,
valid_ratios=None,
word_positions=None,
train_mode=True):
self.train_mode = train_mode

if train_mode:
return self.forward_train(feat, out_enc, label, valid_ratios, word_positions)
return self.forward_train(feat, out_enc, label, valid_ratios,
word_positions)
return self.forward_test(feat, out_enc, valid_ratios, word_positions)


class ChannelReductionEncoder(nn.Layer):
"""Change the channel number with a one by one convoluational layer.

Expand All @@ -59,14 +61,16 @@ class ChannelReductionEncoder(nn.Layer):
out_channels (int): Number of output channels.
"""

def __init__(self,
in_channels,
out_channels,
**kwargs):
def __init__(self, in_channels, out_channels, **kwargs):
super(ChannelReductionEncoder, self).__init__()

self.layer = nn.Conv2D(
in_channels, out_channels, kernel_size=1, stride=1, padding=0, weight_attr=nn.initializer.XavierNormal())
in_channels,
out_channels,
kernel_size=1,
stride=1,
padding=0,
weight_attr=nn.initializer.XavierNormal())

def forward(self, feat):
"""
Expand All @@ -84,8 +88,8 @@ def masked_fill(x, mask, value):
y = paddle.full(x.shape, value, x.dtype)
return paddle.where(mask, y, x)

class DotProductAttentionLayer(nn.Layer):

class DotProductAttentionLayer(nn.Layer):
def __init__(self, dim_model=None):
super().__init__()

Expand All @@ -99,7 +103,7 @@ def forward(self, query, key, value, h, w, valid_ratios=None):
logits = paddle.reshape(logits, [n, c, h, w])
if valid_ratios is not None:
# cal mask of attention weight
with paddle.fluid.framework._stride_in_no_check_dy2st_diff():
with paddle.base.framework._stride_in_no_check_dy2st_diff():
for i, valid_ratio in enumerate(valid_ratios):
valid_width = min(w, int(w * valid_ratio + 0.5))
if valid_width < w:
Expand All @@ -113,6 +117,7 @@ def forward(self, query, key, value, h, w, valid_ratios=None):
glimpse = paddle.transpose(glimpse, (0, 2, 1))
return glimpse


class SequenceAttentionDecoder(BaseDecoder):
"""Sequence attention decoder for RobustScanner.

Expand Down Expand Up @@ -181,8 +186,8 @@ def __init__(self,
self.prediction = None
if not self.return_feature:
pred_num_classes = num_classes - 1
self.prediction = nn.Linear(
dim_model if encode_value else dim_input, pred_num_classes)
self.prediction = nn.Linear(dim_model if encode_value else
dim_input, pred_num_classes)

def forward_train(self, feat, out_enc, targets, valid_ratios):
"""
Expand Down Expand Up @@ -243,12 +248,13 @@ def forward_test(self, feat, out_enc, valid_ratios):
seq_len = self.max_seq_len
batch_size = feat.shape[0]

decode_sequence = (paddle.ones((batch_size, seq_len), dtype='int64') * self.start_idx)
decode_sequence = (paddle.ones(
(batch_size, seq_len), dtype='int64') * self.start_idx)

outputs = []
for i in range(seq_len):
step_out = self.forward_test_step(feat, out_enc, decode_sequence,
i, valid_ratios)
step_out = self.forward_test_step(feat, out_enc, decode_sequence, i,
valid_ratios)
outputs.append(step_out)
max_idx = paddle.argmax(step_out, axis=1, keepdim=False)
if i < seq_len - 1:
Expand All @@ -274,7 +280,7 @@ def forward_test_step(self, feat, out_enc, decode_sequence, current_step,
Tensor: Shape :math:`(N, C-1)`. The logit tensor of predicted
tokens at current time step.
"""

embed = self.embedding(decode_sequence)

n, c_enc, h, w = out_enc.shape
Expand Down Expand Up @@ -306,7 +312,6 @@ def forward_test_step(self, feat, out_enc, decode_sequence, current_step,


class PositionAwareLayer(nn.Layer):

def __init__(self, dim_model, rnn_layers=2):
super().__init__()

Expand Down Expand Up @@ -384,16 +389,16 @@ def __init__(self,

self.embedding = nn.Embedding(self.max_seq_len + 1, self.dim_model)

self.position_aware_module = PositionAwareLayer(
self.dim_model, rnn_layers)
self.position_aware_module = PositionAwareLayer(self.dim_model,
rnn_layers)

self.attention_layer = DotProductAttentionLayer()

self.prediction = None
if not self.return_feature:
pred_num_classes = num_classes - 1
self.prediction = nn.Linear(
dim_model if encode_value else dim_input, pred_num_classes)
self.prediction = nn.Linear(dim_model if encode_value else
dim_input, pred_num_classes)

def _get_position_index(self, length, batch_size):
position_index_list = []
Expand All @@ -403,7 +408,8 @@ def _get_position_index(self, length, batch_size):
batch_position_index = paddle.stack(position_index_list, axis=0)
return batch_position_index

def forward_train(self, feat, out_enc, targets, valid_ratios, position_index):
def forward_train(self, feat, out_enc, targets, valid_ratios,
position_index):
"""
Args:
feat (Tensor): Tensor of shape :math:`(N, D_i, H, W)`.
Expand All @@ -427,16 +433,16 @@ def forward_train(self, feat, out_enc, targets, valid_ratios, position_index):
assert c_feat == self.dim_input
_, len_q = targets.shape
assert len_q <= self.max_seq_len

position_out_enc = self.position_aware_module(out_enc)

query = self.embedding(position_index)
query = paddle.transpose(query, (0, 2, 1))
key = paddle.reshape(position_out_enc, (n, c_enc, h * w))
if self.encode_value:
value = paddle.reshape(out_enc,(n, c_enc, h * w))
value = paddle.reshape(out_enc, (n, c_enc, h * w))
else:
value = paddle.reshape(feat,(n, c_feat, h * w))
value = paddle.reshape(feat, (n, c_feat, h * w))

attn_out = self.attention_layer(query, key, value, h, w, valid_ratios)
attn_out = paddle.transpose(attn_out, (0, 2, 1)) # [n, len_q, dim_v]
Expand Down Expand Up @@ -467,14 +473,14 @@ def forward_test(self, feat, out_enc, valid_ratios, position_index):
assert c_feat == self.dim_input

position_out_enc = self.position_aware_module(out_enc)

query = self.embedding(position_index)
query = paddle.transpose(query, (0, 2, 1))
key = paddle.reshape(position_out_enc, (n, c_enc, h * w))
if self.encode_value:
value = paddle.reshape(out_enc,(n, c_enc, h * w))
value = paddle.reshape(out_enc, (n, c_enc, h * w))
else:
value = paddle.reshape(feat,(n, c_feat, h * w))
value = paddle.reshape(feat, (n, c_feat, h * w))

attn_out = self.attention_layer(query, key, value, h, w, valid_ratios)
attn_out = paddle.transpose(attn_out, (0, 2, 1)) # [n, len_q, dim_v]
Expand All @@ -484,8 +490,8 @@ def forward_test(self, feat, out_enc, valid_ratios, position_index):

return self.prediction(attn_out)

class RobustScannerFusionLayer(nn.Layer):

class RobustScannerFusionLayer(nn.Layer):
def __init__(self, dim_model, dim=-1):
super(RobustScannerFusionLayer, self).__init__()

Expand All @@ -500,6 +506,7 @@ def forward(self, x0, x1):
output = F.glu(output, self.dim)
return output


class RobustScannerDecoder(BaseDecoder):
"""Decoder for RobustScanner.

Expand Down Expand Up @@ -561,8 +568,7 @@ def __init__(self,
padding_idx=padding_idx,
dropout=hybrid_decoder_dropout,
encode_value=encode_value,
return_feature=True
)
return_feature=True)

# init position decoder
self.position_decoder = PositionAttentionDecoder(
Expand All @@ -573,9 +579,7 @@ def __init__(self,
max_seq_len=max_seq_len,
mask=mask,
encode_value=encode_value,
return_feature=True
)

return_feature=True)

self.fusion_module = RobustScannerFusionLayer(
self.dim_model if encode_value else dim_input)
Expand All @@ -584,7 +588,8 @@ def __init__(self,
self.prediction = nn.Linear(dim_model if encode_value else dim_input,
pred_num_classes)

def forward_train(self, feat, out_enc, target, valid_ratios, word_positions):
def forward_train(self, feat, out_enc, target, valid_ratios,
word_positions):
"""
Args:
feat (Tensor): Tensor of shape :math:`(N, D_i, H, W)`.
Expand All @@ -599,8 +604,8 @@ def forward_train(self, feat, out_enc, target, valid_ratios, word_positions):
Returns:
Tensor: A raw logit tensor of shape :math:`(N, T, C-1)`.
"""
hybrid_glimpse = self.hybrid_decoder.forward_train(
feat, out_enc, target, valid_ratios)
hybrid_glimpse = self.hybrid_decoder.forward_train(feat, out_enc,
target, valid_ratios)
position_glimpse = self.position_decoder.forward_train(
feat, out_enc, target, valid_ratios, word_positions)

Expand All @@ -625,7 +630,8 @@ def forward_test(self, feat, out_enc, valid_ratios, word_positions):
seq_len = self.max_seq_len
batch_size = feat.shape[0]

decode_sequence = (paddle.ones((batch_size, seq_len), dtype='int64') * self.start_idx)
decode_sequence = (paddle.ones(
(batch_size, seq_len), dtype='int64') * self.start_idx)

position_glimpse = self.position_decoder.forward_test(
feat, out_enc, valid_ratios, word_positions)
Expand All @@ -649,28 +655,30 @@ def forward_test(self, feat, out_enc, valid_ratios, word_positions):

return outputs


class RobustScannerHead(nn.Layer):
def __init__(self,
out_channels, # 90 + unknown + start + padding
in_channels,
enc_outchannles=128,
hybrid_dec_rnn_layers=2,
hybrid_dec_dropout=0,
position_dec_rnn_layers=2,
start_idx=0,
max_text_length=40,
mask=True,
padding_idx=None,
encode_value=False,
**kwargs):
def __init__(
self,
out_channels, # 90 + unknown + start + padding
in_channels,
enc_outchannles=128,
hybrid_dec_rnn_layers=2,
hybrid_dec_dropout=0,
position_dec_rnn_layers=2,
start_idx=0,
max_text_length=40,
mask=True,
padding_idx=None,
encode_value=False,
**kwargs):
super(RobustScannerHead, self).__init__()

# encoder module
self.encoder = ChannelReductionEncoder(
in_channels=in_channels, out_channels=enc_outchannles)

# decoder module
self.decoder =RobustScannerDecoder(
self.decoder = RobustScannerDecoder(
num_classes=out_channels,
dim_input=in_channels,
dim_model=enc_outchannles,
Expand All @@ -693,18 +701,18 @@ def forward(self, inputs, targets=None):

if len(targets) > 1:
valid_ratios = targets[-2]

if self.training:
label = targets[0] # label
label = paddle.to_tensor(label, dtype='int64')
final_out = self.decoder(
inputs, out_enc, label, valid_ratios, word_positions)
final_out = self.decoder(inputs, out_enc, label, valid_ratios,
word_positions)
if not self.training:
final_out = self.decoder(
inputs,
out_enc,
label=None,
valid_ratios=valid_ratios,
valid_ratios=valid_ratios,
word_positions=word_positions,
train_mode=False)
return final_out
4 changes: 4 additions & 0 deletions test_tipc/test_train_inference_python_npu.sh
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Original file line number Diff line number Diff line change
Expand Up @@ -43,6 +43,10 @@ if [ $modelname == "rec_r31_sar" ] || [ $modelname == "rec_mtb_nrtr" ]; then
sed -i "s/gpu_list:0|0,1/gpu_list:0,1/g" $FILENAME
sed -i "s/Global.use_npu:True|True/Global.use_npu:True/g" $FILENAME
fi
if [ $modelname == "ch_ppocr_mobile_v2_0_rec_FPGM" ]; then
sed -i '18s/$/ -o Global.use_gpu=False/' $FILENAME
sed -i '32s/$/ Global.use_gpu=False/' $FILENAME
fi

# replace training config file
grep -n 'tools/.*yml' $FILENAME | cut -d ":" -f 1 \
Expand Down
4 changes: 4 additions & 0 deletions test_tipc/test_train_inference_python_xpu.sh
View file
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Original file line number Diff line number Diff line change
Expand Up @@ -43,6 +43,10 @@ if [ $modelname == "rec_r31_sar" ] || [ $modelname == "rec_mtb_nrtr" ]; then
sed -i "s/gpu_list:0|0,1/gpu_list:0,1/g" $FILENAME
sed -i "s/Global.use_xpu:True|True/Global.use_xpu:True/g" $FILENAME
fi
if [ $modelname == "ch_ppocr_mobile_v2_0_rec_FPGM" ]; then
sed -i '18s/$/ -o Global.use_gpu=False/' $FILENAME
sed -i '32s/$/ Global.use_gpu=False/' $FILENAME
fi

# replace training config file
grep -n 'tools/.*yml' $FILENAME | cut -d ":" -f 1 \
Expand Down