Removing numpy requirement from all files in examples/pytorch/domain_templates (#19947)

Author: Bhavay-2001

examples/pytorch/domain_templates/generative_adversarial_net.py

@@ -19,9 +19,9 @@
 
 """
 
+import math
 from argparse import ArgumentParser, Namespace
 
-import numpy as np
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
@@ -59,7 +59,7 @@ def block(in_feat, out_feat, normalize=True):
             *block(128, 256),
             *block(256, 512),
             *block(512, 1024),
-            nn.Linear(1024, int(np.prod(img_shape))),
+            nn.Linear(1024, int(math.prod(img_shape))),
             nn.Tanh(),
         )
 
@@ -80,7 +80,7 @@ def __init__(self, img_shape):
         super().__init__()
 
         self.model = nn.Sequential(
-            nn.Linear(int(np.prod(img_shape)), 512),
+            nn.Linear(int(math.prod(img_shape)), 512),
             nn.LeakyReLU(0.2, inplace=True),
             nn.Linear(512, 256),
             nn.LeakyReLU(0.2, inplace=True),

examples/pytorch/domain_templates/reinforce_learn_Qnet.py

@@ -33,11 +33,11 @@
 """
 
 import argparse
+import random
 from collections import OrderedDict, deque, namedtuple
 from typing import Iterator, List, Tuple
 
 import gym
-import numpy as np
 import torch
 import torch.nn as nn
 import torch.optim as optim
@@ -103,15 +103,15 @@ def append(self, experience: Experience) -> None:
         self.buffer.append(experience)
 
     def sample(self, batch_size: int) -> Tuple:
-        indices = np.random.choice(len(self.buffer), batch_size, replace=False)
+        indices = random.sample(range(len(self.buffer)), batch_size)
         states, actions, rewards, dones, next_states = zip(*(self.buffer[idx] for idx in indices))
 
         return (
-            np.array(states),
-            np.array(actions),
-            np.array(rewards, dtype=np.float32),
-            np.array(dones, dtype=np.bool),
-            np.array(next_states),
+            torch.tensor(states),
+            torch.tensor(actions),
+            torch.tensor(rewards, dtype=torch.float32),
+            torch.tensor(dones, dtype=torch.bool),
+            torch.tensor(next_states),
         )
 
 
@@ -175,7 +175,7 @@ def get_action(self, net: nn.Module, epsilon: float, device: str) -> int:
             action
 
         """
-        if np.random.random() < epsilon:
+        if random.random() < epsilon:
             action = self.env.action_space.sample()
         else:
             state = torch.tensor([self.state])

examples/pytorch/domain_templates/semantic_segmentation.py

@@ -16,7 +16,6 @@
 import random
 from argparse import ArgumentParser, Namespace
 
-import numpy as np
 import torch
 import torch.nn.functional as F
 import torchvision.transforms as transforms
@@ -107,11 +106,11 @@ def __len__(self):
     def __getitem__(self, idx):
         img = Image.open(self.img_list[idx])
         img = img.resize(self.img_size)
-        img = np.array(img)
+        img = torch.tensor(img)
 
         mask = Image.open(self.mask_list[idx]).convert("L")
         mask = mask.resize(self.img_size)
-        mask = np.array(mask)
+        mask = torch.tensor(mask)
         mask = self.encode_segmap(mask)
 
         if self.transform: