Merge pull request HongwenZhang#3 from HongwenZhang/py3.7

add support for python==3.7 torch==1.4.0

lib/modeling/danet.py

@@ -109,7 +109,7 @@ def infer_net(self, image):
             part_iuv_map = None
             part_index_map = None
 
-        if uv_return_dict.has_key('part_featmaps'):
+        if 'part_featmaps' in uv_return_dict:
             part_feat_map = uv_return_dict['part_featmaps']
         else:
             part_feat_map = None
@@ -360,7 +360,7 @@ def __init__(self):
 
         if cfg.DANET.USE_LEARNED_RATIO:
             with open(os.path.join('./data/pretrained_model', 'learned_ratio.pkl'), 'rb') as f:
-                pretrain_ratio = pickle.load(f)
+                pretrain_ratio = pickle.load(f, encoding='iso-8859-1')
 
             if cfg.DANET.INPUT_MODE in ['iuv_gt']:
                 self.learned_ratio = nn.Parameter(torch.FloatTensor(pretrain_ratio['ratio']))

lib/modeling/hr_module.py

@@ -6,7 +6,7 @@
 import logging
 
 from lib.core.config import cfg
-from res_module import BasicBlock, Bottleneck, IUV_predict_layer
+from .res_module import BasicBlock, Bottleneck, IUV_predict_layer
 
 BN_MOMENTUM = 0.1
 logger = logging.getLogger(__name__)

lib/modeling/smpl_model.py

@@ -35,7 +35,7 @@ def __init__(self, model_type=None, joint_type='cocoplus', obj_saveable=False, m
             smpl_model_path = './data/SMPL_data/basicModel_f_lbs_10_207_0_v1.0.0.pkl'
 
         with open(smpl_model_path, 'rb') as f:
-            model = pickle.load(f)
+            model = pickle.load(f, encoding='iso-8859-1')
 
         if obj_saveable:
             self.faces = model['f']
@@ -62,7 +62,7 @@ def __init__(self, model_type=None, joint_type='cocoplus', obj_saveable=False, m
         self.parents = model['kintree_table'][0].astype(np.int32)
 
         with open(os.path.join('./data/pretrained_model', 'joint_regressor.pkl'), 'rb') as f:
-            np_joint_regressor = pickle.load(f)
+            np_joint_regressor = pickle.load(f, encoding='iso-8859-1')
 
         if joint_type == 'lsp':
             self.register_buffer('joint_regressor', torch.from_numpy(np_joint_regressor[:, :14]).float())

lib/nn/parallel/scatter_gather.py

@@ -5,8 +5,18 @@
 from torch.autograd import Variable
 from ._functions import Scatter, Gather
 from torch._six import string_classes, int_classes
-from torch.utils.data.dataloader import numpy_type_map
+# from torch.utils.data.dataloader import numpy_type_map
 
+numpy_type_map = {
+     'float64': torch.DoubleTensor,
+     'float32': torch.FloatTensor,
+     'float16': torch.HalfTensor,
+     'int64': torch.LongTensor,
+     'int32': torch.IntTensor,
+     'int16': torch.ShortTensor,
+     'int8': torch.CharTensor,
+     'uint8': torch.ByteTensor,
+ }
 
 def scatter(inputs, target_gpus, dim=0):
     r"""

lib/utils/opendr_render.py

@@ -8,7 +8,7 @@
 from opendr.renderer import ColoredRenderer
 from opendr.lighting import LambertianPointLight
 from opendr.camera import ProjectPoints
-from smpl_webuser.serialization import load_model
+from .smpl_webuser.serialization import load_model
 from skimage.transform import resize
 
 

requirements.txt

@@ -1,9 +1,9 @@
-torch==1.0.1
-torchvision==0.2.2
+torch==1.4.0
+torchvision==0.5.0
 easydict==1.9
 neural_renderer_pytorch==1.1.3
-packaging==20.1
-PyYAML==5.3
-h5py==2.10.0
-opencv-python==4.1.2.30
-chumpy==0.69
+packaging==20.3
+PyYAML==5.3.1
+h5py==2.8.0
+opencv==3.4.2
+chumpy==0.69