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diff --git a/core/leras/layers/Conv2D.py b/core/leras/layers/Conv2D.py
index ae37c50..7d4d444 100644
--- a/core/leras/layers/Conv2D.py
+++ b/core/leras/layers/Conv2D.py
@@ -23,28 +23,13 @@ class Conv2D(nn.LayerBase):
if padding == "SAME":
padding = ( (kernel_size - 1) * dilations + 1 ) // 2
elif padding == "VALID":
- padding = 0
+ padding = None
else:
raise ValueError ("Wrong padding type. Should be VALID SAME or INT or 4x INTs")
-
- if isinstance(padding, int):
- if padding != 0:
- if nn.data_format == "NHWC":
- padding = [ [0,0], [padding,padding], [padding,padding], [0,0] ]
- else:
- padding = [ [0,0], [0,0], [padding,padding], [padding,padding] ]
- else:
- padding = None
-
- if nn.data_format == "NHWC":
- strides = [1,strides,strides,1]
else:
- strides = [1,1,strides,strides]
-
- if nn.data_format == "NHWC":
- dilations = [1,dilations,dilations,1]
- else:
- dilations = [1,1,dilations,dilations]
+ padding = int(padding)
+
+
self.in_ch = in_ch
self.out_ch = out_ch
@@ -93,10 +78,27 @@ class Conv2D(nn.LayerBase):
if self.use_wscale:
weight = weight * self.wscale
- if self.padding is not None:
- x = tf.pad (x, self.padding, mode='CONSTANT')
+ padding = self.padding
+ if padding is not None:
+ if nn.data_format == "NHWC":
+ padding = [ [0,0], [padding,padding], [padding,padding], [0,0] ]
+ else:
+ padding = [ [0,0], [0,0], [padding,padding], [padding,padding] ]
+ x = tf.pad (x, padding, mode='CONSTANT')
+
+ strides = self.strides
+ if nn.data_format == "NHWC":
+ strides = [1,strides,strides,1]
+ else:
+ strides = [1,1,strides,strides]
- x = tf.nn.conv2d(x, weight, self.strides, 'VALID', dilations=self.dilations, data_format=nn.data_format)
+ dilations = self.dilations
+ if nn.data_format == "NHWC":
+ dilations = [1,dilations,dilations,1]
+ else:
+ dilations = [1,1,dilations,dilations]
+
+ x = tf.nn.conv2d(x, weight, strides, 'VALID', dilations=dilations, data_format=nn.data_format)
if self.use_bias:
if nn.data_format == "NHWC":
bias = tf.reshape (self.bias, (1,1,1,self.out_ch) )
diff --git a/merger/MergeMasked.py b/merger/MergeMasked.py
index 4e94160..7d2caa4 100644
--- a/merger/MergeMasked.py
+++ b/merger/MergeMasked.py
@@ -57,7 +57,9 @@ def MergeMaskedFace (predictor_func, predictor_input_shape,
prd_face_mask_a_0 = cv2.resize (prd_face_mask_a_0, (output_size, output_size), interpolation=cv2.INTER_CUBIC)
prd_face_dst_mask_a_0 = cv2.resize (prd_face_dst_mask_a_0, (output_size, output_size), interpolation=cv2.INTER_CUBIC)
- if cfg.mask_mode == 1: #dst
+ if cfg.mask_mode == 0: #full
+ wrk_face_mask_a_0 = np.ones_like(dst_face_mask_a_0)
+ elif cfg.mask_mode == 1: #dst
wrk_face_mask_a_0 = cv2.resize (dst_face_mask_a_0, (output_size,output_size), interpolation=cv2.INTER_CUBIC)
elif cfg.mask_mode == 2: #learned-prd
wrk_face_mask_a_0 = prd_face_mask_a_0
diff --git a/merger/MergerConfig.py b/merger/MergerConfig.py
index 432bdf1..eba1493 100644
--- a/merger/MergerConfig.py
+++ b/merger/MergerConfig.py
@@ -81,7 +81,8 @@ mode_dict = {0:'original',
mode_str_dict = { mode_dict[key] : key for key in mode_dict.keys() }
-mask_mode_dict = {1:'dst',
+mask_mode_dict = {0:'full',
+ 1:'dst',
2:'learned-prd',
3:'learned-dst',
4:'learned-prd*learned-dst',
diff --git a/models/Model_AMP/Model.py b/models/Model_AMP/Model.py
index ad66365..9347944 100644
--- a/models/Model_AMP/Model.py
+++ b/models/Model_AMP/Model.py
@@ -586,7 +586,7 @@ class AMPModel(ModelBase):
gpu_src_dst_code = tf.concat( ( tf.slice(gpu_dst_inter_src_code, [0,0,0,0], [-1, ae_dims_slice , lowest_dense_res, lowest_dense_res]),
tf.slice(gpu_dst_inter_dst_code, [0,ae_dims_slice,0,0], [-1,ae_dims-ae_dims_slice, lowest_dense_res,lowest_dense_res]) ), 1 )
- gpu_pred_src_dst, gpu_pred_src_dstm = self.decoder(gpu_dst_inter_src_code)
+ gpu_pred_src_dst, gpu_pred_src_dstm = self.decoder(gpu_src_dst_code)
_, gpu_pred_dst_dstm = self.decoder(gpu_dst_inter_dst_code)
def AE_merge(warped_dst, morph_value):
diff --git a/samplelib/SampleLoader.py b/samplelib/SampleLoader.py
index edb3775..2989354 100644
--- a/samplelib/SampleLoader.py
+++ b/samplelib/SampleLoader.py
@@ -23,7 +23,7 @@ class SampleLoader:
try:
samples = samplelib.PackedFaceset.load(samples_path)
except:
- io.log_err(f"Error occured while loading samplelib.PackedFaceset.load {str(samples_dat_path)}, {traceback.format_exc()}")
+ io.log_err(f"Error occured while loading samplelib.PackedFaceset.load {str(samples_path)}, {traceback.format_exc()}")
if samples is None:
raise ValueError("packed faceset not found.")
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